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Code Packages Releases Wiki Activity

removed contrib, legacy and softcsscade modules; removed latentsvm and datamatrix detector from objdetect. removed haartraining and sft apps.

Browse Source 
some of the stuff will be moved to opencv_contrib module.
in order to make this PR pass buildbot, please, comment off opencv_legacy, opencv_contrib and opencv_softcascade test runs.
This commit is contained in:
Vadim Pisarevsky
2014-06-24 19:18:51 +04:00
parent 6d4c4dcd94
commit 3858f2291d
398 changed files with 12 additions and 150885 deletions
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modules/contrib/CMakeLists.txt
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ocv_define_module(contrib opencv_imgproc opencv_calib3d opencv_ml opencv_video opencv_objdetect OPTIONAL opencv_highgui opencv_nonfree)
modules/contrib/doc/contrib.rst
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***************************************
contrib. Contributed/Experimental Stuff
***************************************
The module contains some recently added functionality that has not been stabilized, or functionality that is considered optional.
.. toctree::
:maxdepth: 2
stereo
FaceRecognizer Documentation <facerec/index>
openfabmap
modules/contrib/doc/facerec/colormaps.rst
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ColorMaps in OpenCV
===================
applyColorMap
---------------------
Applies a GNU Octave/MATLAB equivalent colormap on a given image.
.. ocv:function:: void applyColorMap(InputArray src, OutputArray dst, int colormap)
:param src: The source image, grayscale or colored does not matter.
:param dst: The result is the colormapped source image. Note: :ocv:func:`Mat::create` is called on dst.
:param colormap: The colormap to apply, see the list of available colormaps below.
Currently the following GNU Octave/MATLAB equivalent colormaps are implemented:
.. code-block:: cpp
enum
{
COLORMAP_AUTUMN = 0,
COLORMAP_BONE = 1,
COLORMAP_JET = 2,
COLORMAP_WINTER = 3,
COLORMAP_RAINBOW = 4,
COLORMAP_OCEAN = 5,
COLORMAP_SUMMER = 6,
COLORMAP_SPRING = 7,
COLORMAP_COOL = 8,
COLORMAP_HSV = 9,
COLORMAP_PINK = 10,
COLORMAP_HOT = 11
}
Description
-----------
The human perception isn't built for observing fine changes in grayscale images. Human eyes are more sensitive to observing changes between colors, so you often need to recolor your grayscale images to get a clue about them. OpenCV now comes with various colormaps to enhance the visualization in your computer vision application.
In OpenCV 2.4 you only need :ocv:func:`applyColorMap` to apply a colormap on a given image. The following sample code reads the path to an image from command line, applies a Jet colormap on it and shows the result:
.. code-block:: cpp
#include <opencv2/contrib.hpp>
#include <opencv2/core.hpp>
#include <opencv2/highgui.hpp>
using namespace cv;
int main(int argc, const char *argv[]) {
// Get the path to the image, if it was given
// if no arguments were given.
String filename;
if (argc > 1) {
filename = String(argv[1]);
}
// The following lines show how to apply a colormap on a given image
// and show it with cv::imshow example with an image. An exception is
// thrown if the path to the image is invalid.
if(!filename.empty()) {
Mat img0 = imread(filename);
// Throw an exception, if the image can't be read:
if(img0.empty()) {
CV_Error(CV_StsBadArg, "Sample image is empty. Please adjust your path, so it points to a valid input image!");
}
// Holds the colormap version of the image:
Mat cm_img0;
// Apply the colormap:
applyColorMap(img0, cm_img0, COLORMAP_JET);
// Show the result:
imshow("cm_img0", cm_img0);
waitKey(0);
}
return 0;
}
And here are the color scales for each of the available colormaps:
+-----------------------+---------------------------------------------------+
| Class | Scale |
+=======================+===================================================+
| COLORMAP_AUTUMN | .. image:: img/colormaps/colorscale_autumn.jpg |
+-----------------------+---------------------------------------------------+
| COLORMAP_BONE | .. image:: img/colormaps/colorscale_bone.jpg |
+-----------------------+---------------------------------------------------+
| COLORMAP_COOL | .. image:: img/colormaps/colorscale_cool.jpg |
+-----------------------+---------------------------------------------------+
| COLORMAP_HOT | .. image:: img/colormaps/colorscale_hot.jpg |
+-----------------------+---------------------------------------------------+
| COLORMAP_HSV | .. image:: img/colormaps/colorscale_hsv.jpg |
+-----------------------+---------------------------------------------------+
| COLORMAP_JET | .. image:: img/colormaps/colorscale_jet.jpg |
+-----------------------+---------------------------------------------------+
| COLORMAP_OCEAN | .. image:: img/colormaps/colorscale_ocean.jpg |
+-----------------------+---------------------------------------------------+
| COLORMAP_PINK | .. image:: img/colormaps/colorscale_pink.jpg |
+-----------------------+---------------------------------------------------+
| COLORMAP_RAINBOW | .. image:: img/colormaps/colorscale_rainbow.jpg |
+-----------------------+---------------------------------------------------+
| COLORMAP_SPRING | .. image:: img/colormaps/colorscale_spring.jpg |
+-----------------------+---------------------------------------------------+
| COLORMAP_SUMMER | .. image:: img/colormaps/colorscale_summer.jpg |
+-----------------------+---------------------------------------------------+
| COLORMAP_WINTER | .. image:: img/colormaps/colorscale_winter.jpg |
+-----------------------+---------------------------------------------------+
modules/contrib/doc/facerec/etc/at.txt
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modules/contrib/doc/facerec/facerec_api.rst
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FaceRecognizer
==============
.. highlight:: cpp
.. Sample code::
* An example using the FaceRecognizer class can be found at opencv_source_code/samples/cpp/facerec_demo.cpp
* (Python) An example using the FaceRecognizer class can be found at opencv_source_code/samples/python2/facerec_demo.py
FaceRecognizer
--------------
.. ocv:class:: FaceRecognizer : public Algorithm
All face recognition models in OpenCV are derived from the abstract base class :ocv:class:`FaceRecognizer`, which provides
a unified access to all face recongition algorithms in OpenCV. ::
class FaceRecognizer : public Algorithm
{
public:
//! virtual destructor
virtual ~FaceRecognizer() {}
// Trains a FaceRecognizer.
virtual void train(InputArray src, InputArray labels) = 0;
// Updates a FaceRecognizer.
virtual void update(InputArrayOfArrays src, InputArray labels);
// Gets a prediction from a FaceRecognizer.
virtual int predict(InputArray src) const = 0;
// Predicts the label and confidence for a given sample.
virtual void predict(InputArray src, int &label, double &confidence) const = 0;
// Serializes this object to a given filename.
virtual void save(const String& filename) const;
// Deserializes this object from a given filename.
virtual void load(const String& filename);
// Serializes this object to a given cv::FileStorage.
virtual void save(FileStorage& fs) const = 0;
// Deserializes this object from a given cv::FileStorage.
virtual void load(const FileStorage& fs) = 0;
};
Description
+++++++++++
I'll go a bit more into detail explaining :ocv:class:`FaceRecognizer`, because it doesn't look like a powerful interface at first sight. But: Every :ocv:class:`FaceRecognizer` is an :ocv:class:`Algorithm`, so you can easily get/set all model internals (if allowed by the implementation). :ocv:class:`Algorithm` is a relatively new OpenCV concept, which is available since the 2.4 release. I suggest you take a look at its description.
:ocv:class:`Algorithm` provides the following features for all derived classes:
* So called “virtual constructor”. That is, each Algorithm derivative is registered at program start and you can get the list of registered algorithms and create instance of a particular algorithm by its name (see :ocv:func:`Algorithm::create`). If you plan to add your own algorithms, it is good practice to add a unique prefix to your algorithms to distinguish them from other algorithms.
* Setting/Retrieving algorithm parameters by name. If you used video capturing functionality from OpenCV highgui module, you are probably familar with :ocv:cfunc:`cvSetCaptureProperty`, :ocv:cfunc:`cvGetCaptureProperty`, :ocv:func:`VideoCapture::set` and :ocv:func:`VideoCapture::get`. :ocv:class:`Algorithm` provides similar method where instead of integer id's you specify the parameter names as text Strings. See :ocv:func:`Algorithm::set` and :ocv:func:`Algorithm::get` for details.
* Reading and writing parameters from/to XML or YAML files. Every Algorithm derivative can store all its parameters and then read them back. There is no need to re-implement it each time.
Moreover every :ocv:class:`FaceRecognizer` supports the:
* **Training** of a :ocv:class:`FaceRecognizer` with :ocv:func:`FaceRecognizer::train` on a given set of images (your face database!).
* **Prediction** of a given sample image, that means a face. The image is given as a :ocv:class:`Mat`.
* **Loading/Saving** the model state from/to a given XML or YAML.
.. note:: When using the FaceRecognizer interface in combination with Python, please stick to Python 2. Some underlying scripts like create_csv will not work in other versions, like Python 3.
Setting the Thresholds
+++++++++++++++++++++++
Sometimes you run into the situation, when you want to apply a threshold on the prediction. A common scenario in face recognition is to tell, whether a face belongs to the training dataset or if it is unknown. You might wonder, why there's no public API in :ocv:class:`FaceRecognizer` to set the threshold for the prediction, but rest assured: It's supported. It just means there's no generic way in an abstract class to provide an interface for setting/getting the thresholds of *every possible* :ocv:class:`FaceRecognizer` algorithm. The appropriate place to set the thresholds is in the constructor of the specific :ocv:class:`FaceRecognizer` and since every :ocv:class:`FaceRecognizer` is a :ocv:class:`Algorithm` (see above), you can get/set the thresholds at runtime!
Here is an example of setting a threshold for the Eigenfaces method, when creating the model:
.. code-block:: cpp
// Let's say we want to keep 10 Eigenfaces and have a threshold value of 10.0
int num_components = 10;
double threshold = 10.0;
// Then if you want to have a cv::FaceRecognizer with a confidence threshold,
// create the concrete implementation with the appropiate parameters:
Ptr<FaceRecognizer> model = createEigenFaceRecognizer(num_components, threshold);
Sometimes it's impossible to train the model, just to experiment with threshold values. Thanks to :ocv:class:`Algorithm` it's possible to set internal model thresholds during runtime. Let's see how we would set/get the prediction for the Eigenface model, we've created above:
.. code-block:: cpp
// The following line reads the threshold from the Eigenfaces model:
double current_threshold = model->getDouble("threshold");
// And this line sets the threshold to 0.0:
model->set("threshold", 0.0);
If you've set the threshold to ``0.0`` as we did above, then:
.. code-block:: cpp
//
Mat img = imread("person1/3.jpg", CV_LOAD_IMAGE_GRAYSCALE);
// Get a prediction from the model. Note: We've set a threshold of 0.0 above,
// since the distance is almost always larger than 0.0, you'll get -1 as
// label, which indicates, this face is unknown
int predicted_label = model->predict(img);
// ...
is going to yield ``-1`` as predicted label, which states this face is unknown.
Getting the name of a FaceRecognizer
+++++++++++++++++++++++++++++++++++++
Since every :ocv:class:`FaceRecognizer` is a :ocv:class:`Algorithm`, you can use :ocv:func:`Algorithm::name` to get the name of a :ocv:class:`FaceRecognizer`:
.. code-block:: cpp
// Create a FaceRecognizer:
Ptr<FaceRecognizer> model = createEigenFaceRecognizer();
// And here's how to get its name:
String name = model->name();
FaceRecognizer::train
---------------------
Trains a FaceRecognizer with given data and associated labels.
.. ocv:function:: void FaceRecognizer::train( InputArrayOfArrays src, InputArray labels ) = 0
:param src: The training images, that means the faces you want to learn. The data has to be given as a ``vector<Mat>``.
:param labels: The labels corresponding to the images have to be given either as a ``vector<int>`` or a
The following source code snippet shows you how to learn a Fisherfaces model on a given set of images. The images are read with :ocv:func:`imread` and pushed into a ``std::vector<Mat>``. The labels of each image are stored within a ``std::vector<int>`` (you could also use a :ocv:class:`Mat` of type `CV_32SC1`). Think of the label as the subject (the person) this image belongs to, so same subjects (persons) should have the same label. For the available :ocv:class:`FaceRecognizer` you don't have to pay any attention to the order of the labels, just make sure same persons have the same label:
.. code-block:: cpp
// holds images and labels
vector<Mat> images;
vector<int> labels;
// images for first person
images.push_back(imread("person0/0.jpg", CV_LOAD_IMAGE_GRAYSCALE)); labels.push_back(0);
images.push_back(imread("person0/1.jpg", CV_LOAD_IMAGE_GRAYSCALE)); labels.push_back(0);
images.push_back(imread("person0/2.jpg", CV_LOAD_IMAGE_GRAYSCALE)); labels.push_back(0);
// images for second person
images.push_back(imread("person1/0.jpg", CV_LOAD_IMAGE_GRAYSCALE)); labels.push_back(1);
images.push_back(imread("person1/1.jpg", CV_LOAD_IMAGE_GRAYSCALE)); labels.push_back(1);
images.push_back(imread("person1/2.jpg", CV_LOAD_IMAGE_GRAYSCALE)); labels.push_back(1);
Now that you have read some images, we can create a new :ocv:class:`FaceRecognizer`. In this example I'll create a Fisherfaces model and decide to keep all of the possible Fisherfaces:
.. code-block:: cpp
// Create a new Fisherfaces model and retain all available Fisherfaces,
// this is the most common usage of this specific FaceRecognizer:
//
Ptr<FaceRecognizer> model = createFisherFaceRecognizer();
And finally train it on the given dataset (the face images and labels):
.. code-block:: cpp
// This is the common interface to train all of the available cv::FaceRecognizer
// implementations:
//
model->train(images, labels);
FaceRecognizer::update
----------------------
Updates a FaceRecognizer with given data and associated labels.
.. ocv:function:: void FaceRecognizer::update( InputArrayOfArrays src, InputArray labels )
:param src: The training images, that means the faces you want to learn. The data has to be given as a ``vector<Mat>``.
:param labels: The labels corresponding to the images have to be given either as a ``vector<int>`` or a
This method updates a (probably trained) :ocv:class:`FaceRecognizer`, but only if the algorithm supports it. The Local Binary Patterns Histograms (LBPH) recognizer (see :ocv:func:`createLBPHFaceRecognizer`) can be updated. For the Eigenfaces and Fisherfaces method, this is algorithmically not possible and you have to re-estimate the model with :ocv:func:`FaceRecognizer::train`. In any case, a call to train empties the existing model and learns a new model, while update does not delete any model data.
.. code-block:: cpp
// Create a new LBPH model (it can be updated) and use the default parameters,
// this is the most common usage of this specific FaceRecognizer:
//
Ptr<FaceRecognizer> model = createLBPHFaceRecognizer();
// This is the common interface to train all of the available cv::FaceRecognizer
// implementations:
//
model->train(images, labels);
// Some containers to hold new image:
vector<Mat> newImages;
vector<int> newLabels;
// You should add some images to the containers:
//
// ...
//
// Now updating the model is as easy as calling:
model->update(newImages,newLabels);
// This will preserve the old model data and extend the existing model
// with the new features extracted from newImages!
Calling update on an Eigenfaces model (see :ocv:func:`createEigenFaceRecognizer`), which doesn't support updating, will throw an error similar to:
.. code-block:: none
OpenCV Error: The function/feature is not implemented (This FaceRecognizer (FaceRecognizer.Eigenfaces) does not support updating, you have to use FaceRecognizer::train to update it.) in update, file /home/philipp/git/opencv/modules/contrib/src/facerec.cpp, line 305
terminate called after throwing an instance of 'cv::Exception'
Please note: The :ocv:class:`FaceRecognizer` does not store your training images, because this would be very memory intense and it's not the responsibility of te :ocv:class:`FaceRecognizer` to do so. The caller is responsible for maintaining the dataset, he want to work with.
FaceRecognizer::predict
-----------------------
.. ocv:function:: int FaceRecognizer::predict( InputArray src ) const = 0
.. ocv:function:: void FaceRecognizer::predict( InputArray src, int & label, double & confidence ) const = 0
Predicts a label and associated confidence (e.g. distance) for a given input image.
:param src: Sample image to get a prediction from.
:param label: The predicted label for the given image.
:param confidence: Associated confidence (e.g. distance) for the predicted label.
The suffix ``const`` means that prediction does not affect the internal model
state, so the method can be safely called from within different threads.
The following example shows how to get a prediction from a trained model:
.. code-block:: cpp
using namespace cv;
// Do your initialization here (create the cv::FaceRecognizer model) ...
// ...
// Read in a sample image:
Mat img = imread("person1/3.jpg", CV_LOAD_IMAGE_GRAYSCALE);
// And get a prediction from the cv::FaceRecognizer:
int predicted = model->predict(img);
Or to get a prediction and the associated confidence (e.g. distance):
.. code-block:: cpp
using namespace cv;
// Do your initialization here (create the cv::FaceRecognizer model) ...
// ...
Mat img = imread("person1/3.jpg", CV_LOAD_IMAGE_GRAYSCALE);
// Some variables for the predicted label and associated confidence (e.g. distance):
int predicted_label = -1;
double predicted_confidence = 0.0;
// Get the prediction and associated confidence from the model
model->predict(img, predicted_label, predicted_confidence);
FaceRecognizer::save
--------------------
Saves a :ocv:class:`FaceRecognizer` and its model state.
.. ocv:function:: void FaceRecognizer::save(const String& filename) const
Saves this model to a given filename, either as XML or YAML.
:param filename: The filename to store this :ocv:class:`FaceRecognizer` to (either XML/YAML).
.. ocv:function:: void FaceRecognizer::save(FileStorage& fs) const
Saves this model to a given :ocv:class:`FileStorage`.
:param fs: The :ocv:class:`FileStorage` to store this :ocv:class:`FaceRecognizer` to.
Every :ocv:class:`FaceRecognizer` overwrites ``FaceRecognizer::save(FileStorage& fs)``
to save the internal model state. ``FaceRecognizer::save(const String& filename)`` saves
the state of a model to the given filename.
The suffix ``const`` means that prediction does not affect the internal model
state, so the method can be safely called from within different threads.
FaceRecognizer::load
--------------------
Loads a :ocv:class:`FaceRecognizer` and its model state.
.. ocv:function:: void FaceRecognizer::load( const String& filename )
.. ocv:function:: void FaceRecognizer::load( const FileStorage& fs ) = 0
Loads a persisted model and state from a given XML or YAML file . Every
:ocv:class:`FaceRecognizer` has to overwrite ``FaceRecognizer::load(FileStorage& fs)``
to enable loading the model state. ``FaceRecognizer::load(FileStorage& fs)`` in
turn gets called by ``FaceRecognizer::load(const String& filename)``, to ease
saving a model.
createEigenFaceRecognizer
-------------------------
.. ocv:function:: Ptr<FaceRecognizer> createEigenFaceRecognizer(int num_components = 0, double threshold = DBL_MAX)
:param num_components: The number of components (read: Eigenfaces) kept for this Prinicpal Component Analysis. As a hint: There's no rule how many components (read: Eigenfaces) should be kept for good reconstruction capabilities. It is based on your input data, so experiment with the number. Keeping 80 components should almost always be sufficient.
:param threshold: The threshold applied in the prediciton.
Notes:
++++++
* Training and prediction must be done on grayscale images, use :ocv:func:`cvtColor` to convert between the color spaces.
* **THE EIGENFACES METHOD MAKES THE ASSUMPTION, THAT THE TRAINING AND TEST IMAGES ARE OF EQUAL SIZE.** (caps-lock, because I got so many mails asking for this). You have to make sure your input data has the correct shape, else a meaningful exception is thrown. Use :ocv:func:`resize` to resize the images.
* This model does not support updating.
Model internal data:
++++++++++++++++++++
* ``num_components`` see :ocv:func:`createEigenFaceRecognizer`.
* ``threshold`` see :ocv:func:`createEigenFaceRecognizer`.
* ``eigenvalues`` The eigenvalues for this Principal Component Analysis (ordered descending).
* ``eigenvectors`` The eigenvectors for this Principal Component Analysis (ordered by their eigenvalue).
* ``mean`` The sample mean calculated from the training data.
* ``projections`` The projections of the training data.
* ``labels`` The threshold applied in the prediction. If the distance to the nearest neighbor is larger than the threshold, this method returns -1.
createFisherFaceRecognizer
--------------------------
.. ocv:function:: Ptr<FaceRecognizer> createFisherFaceRecognizer(int num_components = 0, double threshold = DBL_MAX)
:param num_components: The number of components (read: Fisherfaces) kept for this Linear Discriminant Analysis with the Fisherfaces criterion. It's useful to keep all components, that means the number of your classes ``c`` (read: subjects, persons you want to recognize). If you leave this at the default (``0``) or set it to a value less-equal ``0`` or greater ``(c-1)``, it will be set to the correct number ``(c-1)`` automatically.
:param threshold: The threshold applied in the prediction. If the distance to the nearest neighbor is larger than the threshold, this method returns -1.
Notes:
++++++
* Training and prediction must be done on grayscale images, use :ocv:func:`cvtColor` to convert between the color spaces.
* **THE FISHERFACES METHOD MAKES THE ASSUMPTION, THAT THE TRAINING AND TEST IMAGES ARE OF EQUAL SIZE.** (caps-lock, because I got so many mails asking for this). You have to make sure your input data has the correct shape, else a meaningful exception is thrown. Use :ocv:func:`resize` to resize the images.
* This model does not support updating.
Model internal data:
++++++++++++++++++++
* ``num_components`` see :ocv:func:`createFisherFaceRecognizer`.
* ``threshold`` see :ocv:func:`createFisherFaceRecognizer`.
* ``eigenvalues`` The eigenvalues for this Linear Discriminant Analysis (ordered descending).
* ``eigenvectors`` The eigenvectors for this Linear Discriminant Analysis (ordered by their eigenvalue).
* ``mean`` The sample mean calculated from the training data.
* ``projections`` The projections of the training data.
* ``labels`` The labels corresponding to the projections.
createLBPHFaceRecognizer
-------------------------
.. ocv:function:: Ptr<FaceRecognizer> createLBPHFaceRecognizer(int radius=1, int neighbors=8, int grid_x=8, int grid_y=8, double threshold = DBL_MAX)
:param radius: The radius used for building the Circular Local Binary Pattern. The greater the radius, the
:param neighbors: The number of sample points to build a Circular Local Binary Pattern from. An appropriate value is to use `` 8`` sample points. Keep in mind: the more sample points you include, the higher the computational cost.
:param grid_x: The number of cells in the horizontal direction, ``8`` is a common value used in publications. The more cells, the finer the grid, the higher the dimensionality of the resulting feature vector.
:param grid_y: The number of cells in the vertical direction, ``8`` is a common value used in publications. The more cells, the finer the grid, the higher the dimensionality of the resulting feature vector.
:param threshold: The threshold applied in the prediction. If the distance to the nearest neighbor is larger than the threshold, this method returns -1.
Notes:
++++++
* The Circular Local Binary Patterns (used in training and prediction) expect the data given as grayscale images, use :ocv:func:`cvtColor` to convert between the color spaces.
* This model supports updating.
Model internal data:
++++++++++++++++++++
* ``radius`` see :ocv:func:`createLBPHFaceRecognizer`.
* ``neighbors`` see :ocv:func:`createLBPHFaceRecognizer`.
* ``grid_x`` see :ocv:func:`createLBPHFaceRecognizer`.
* ``grid_y`` see :ocv:func:`createLBPHFaceRecognizer`.
* ``threshold`` see :ocv:func:`createLBPHFaceRecognizer`.
* ``histograms`` Local Binary Patterns Histograms calculated from the given training data (empty if none was given).
* ``labels`` Labels corresponding to the calculated Local Binary Patterns Histograms.
modules/contrib/doc/facerec/facerec_changelog.rst
-86
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@@ -1,86 +0,0 @@
Changelog
=========
Release 0.05
------------
This library is now included in the official OpenCV distribution (from 2.4 on).
The :ocv:class`FaceRecognizer` is now an :ocv:class:`Algorithm`, which better fits into the overall
OpenCV API.
To reduce the confusion on user side and minimize my work, libfacerec and OpenCV
have been synchronized and are now based on the same interfaces and implementation.
The library now has an extensive documentation:
* The API is explained in detail and with a lot of code examples.
* The face recognition guide I had written for Python and GNU Octave/MATLAB has been adapted to the new OpenCV C++ ``cv::FaceRecognizer``.
* A tutorial for gender classification with Fisherfaces.
* A tutorial for face recognition in videos (e.g. webcam).
Release highlights
++++++++++++++++++
* There are no single highlights to pick from, this release is a highlight itself.
Release 0.04
------------
This version is fully Windows-compatible and works with OpenCV 2.3.1. Several
bugfixes, but none influenced the recognition rate.
Release highlights
++++++++++++++++++
* A whole lot of exceptions with meaningful error messages.
* A tutorial for Windows users: `http://bytefish.de/blog/opencv_visual_studio_and_libfacerec <http://bytefish.de/blog/opencv_visual_studio_and_libfacerec>`_
Release 0.03
------------
Reworked the library to provide separate implementations in cpp files, because
it's the preferred way of contributing OpenCV libraries. This means the library
is not header-only anymore. Slight API changes were done, please see the
documentation for details.
Release highlights
++++++++++++++++++
* New Unit Tests (for LBP Histograms) make the library more robust.
* Added more documentation.
Release 0.02
------------
Reworked the library to provide separate implementations in cpp files, because
it's the preferred way of contributing OpenCV libraries. This means the library
is not header-only anymore. Slight API changes were done, please see the
documentation for details.
Release highlights
++++++++++++++++++
* New Unit Tests (for LBP Histograms) make the library more robust.
* Added a documentation and changelog in reStructuredText.
Release 0.01
------------
Initial release as header-only library.
Release highlights
++++++++++++++++++
* Colormaps for OpenCV to enhance the visualization.
* Face Recognition algorithms implemented:
* Eigenfaces [TP91]_
* Fisherfaces [BHK97]_
* Local Binary Patterns Histograms [AHP04]_
* Added persistence facilities to store the models with a common API.
* Unit Tests (using `gtest <http://code.google.com/p/googletest/>`_).
* Providing a CMakeLists.txt to enable easy cross-platform building.
modules/contrib/doc/facerec/facerec_tutorial.rst
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Face Recognition with OpenCV
############################
.. contents:: Table of Contents
:depth: 3
Introduction
============
`OpenCV (Open Source Computer Vision) <http://opencv.org>`_ is a popular computer vision library started by `Intel <http://www.intel.com>`_ in 1999. The cross-platform library sets its focus on real-time image processing and includes patent-free implementations of the latest computer vision algorithms. In 2008 `Willow Garage <http://www.willowgarage.com>`_ took over support and OpenCV 2.3.1 now comes with a programming interface to C, C++, `Python <http://www.python.org>`_ and `Android <http://www.android.com>`_. OpenCV is released under a BSD license so it is used in academic projects and commercial products alike.
OpenCV 2.4 now comes with the very new :ocv:class:`FaceRecognizer` class for face recognition, so you can start experimenting with face recognition right away. This document is the guide I've wished for, when I was working myself into face recognition. It shows you how to perform face recognition with :ocv:class:`FaceRecognizer` in OpenCV (with full source code listings) and gives you an introduction into the algorithms behind. I'll also show how to create the visualizations you can find in many publications, because a lot of people asked for.
The currently available algorithms are:
* Eigenfaces (see :ocv:func:`createEigenFaceRecognizer`)
* Fisherfaces (see :ocv:func:`createFisherFaceRecognizer`)
* Local Binary Patterns Histograms (see :ocv:func:`createLBPHFaceRecognizer`)
You don't need to copy and paste the source code examples from this page, because they are available in the ``src`` folder coming with this documentation. If you have built OpenCV with the samples turned on, chances are good you have them compiled already! Although it might be interesting for very advanced users, I've decided to leave the implementation details out as I am afraid they confuse new users.
All code in this document is released under the `BSD license <http://www.opensource.org/licenses/bsd-license>`_, so feel free to use it for your projects.
Face Recognition
================
Face recognition is an easy task for humans. Experiments in [Tu06]_ have shown, that even one to three day old babies are able to distinguish between known faces. So how hard could it be for a computer? It turns out we know little about human recognition to date. Are inner features (eyes, nose, mouth) or outer features (head shape, hairline) used for a successful face recognition? How do we analyze an image and how does the brain encode it? It was shown by `David Hubel <http://en.wikipedia.org/wiki/David_H._Hubel>`_ and `Torsten Wiesel <http://en.wikipedia.org/wiki/Torsten_Wiesel>`_, that our brain has specialized nerve cells responding to specific local features of a scene, such as lines, edges, angles or movement. Since we don't see the world as scattered pieces, our visual cortex must somehow combine the different sources of information into useful patterns. Automatic face recognition is all about extracting those meaningful features from an image, putting them into a useful representation and performing some kind of classification on them.
Face recognition based on the geometric features of a face is probably the most intuitive approach to face recognition. One of the first automated face recognition systems was described in [Kanade73]_: marker points (position of eyes, ears, nose, ...) were used to build a feature vector (distance between the points, angle between them, ...). The recognition was performed by calculating the euclidean distance between feature vectors of a probe and reference image. Such a method is robust against changes in illumination by its nature, but has a huge drawback: the accurate registration of the marker points is complicated, even with state of the art algorithms. Some of the latest work on geometric face recognition was carried out in [Bru92]_. A 22-dimensional feature vector was used and experiments on large datasets have shown, that geometrical features alone my not carry enough information for face recognition.
The Eigenfaces method described in [TP91]_ took a holistic approach to face recognition: A facial image is a point from a high-dimensional image space and a lower-dimensional representation is found, where classification becomes easy. The lower-dimensional subspace is found with Principal Component Analysis, which identifies the axes with maximum variance. While this kind of transformation is optimal from a reconstruction standpoint, it doesn't take any class labels into account. Imagine a situation where the variance is generated from external sources, let it be light. The axes with maximum variance do not necessarily contain any discriminative information at all, hence a classification becomes impossible. So a class-specific projection with a Linear Discriminant Analysis was applied to face recognition in [BHK97]_. The basic idea is to minimize the variance within a class, while maximizing the variance between the classes at the same time.
Recently various methods for a local feature extraction emerged. To avoid the high-dimensionality of the input data only local regions of an image are described, the extracted features are (hopefully) more robust against partial occlusion, illumation and small sample size. Algorithms used for a local feature extraction are Gabor Wavelets ([Wiskott97]_), Discrete Cosinus Transform ([Messer06]_) and Local Binary Patterns ([AHP04]_). It's still an open research question what's the best way to preserve spatial information when applying a local feature extraction, because spatial information is potentially useful information.
Face Database
==============
Let's get some data to experiment with first. I don't want to do a toy example here. We are doing face recognition, so you'll need some face images! You can either create your own dataset or start with one of the available face databases, `http://face-rec.org/databases/ <http://face-rec.org/databases>`_ gives you an up-to-date overview. Three interesting databases are (parts of the description are quoted from `http://face-rec.org <http://face-rec.org>`_):
* `AT&T Facedatabase <http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html>`_ The AT&T Facedatabase, sometimes also referred to as *ORL Database of Faces*, contains ten different images of each of 40 distinct subjects. For some subjects, the images were taken at different times, varying the lighting, facial expressions (open / closed eyes, smiling / not smiling) and facial details (glasses / no glasses). All the images were taken against a dark homogeneous background with the subjects in an upright, frontal position (with tolerance for some side movement).
* `Yale Facedatabase A <http://vision.ucsd.edu/content/yale-face-database>`_, also known as Yalefaces. The AT&T Facedatabase is good for initial tests, but it's a fairly easy database. The Eigenfaces method already has a 97% recognition rate on it, so you won't see any great improvements with other algorithms. The Yale Facedatabase A (also known as Yalefaces) is a more appropriate dataset for initial experiments, because the recognition problem is harder. The database consists of 15 people (14 male, 1 female) each with 11 grayscale images sized :math:`320 \times 243` pixel. There are changes in the light conditions (center light, left light, right light), facial expressions (happy, normal, sad, sleepy, surprised, wink) and glasses (glasses, no-glasses).
The original images are not cropped and aligned. Please look into the :ref:`appendixft` for a Python script, that does the job for you.
* `Extended Yale Facedatabase B <http://vision.ucsd.edu/~leekc/ExtYaleDatabase/ExtYaleB.html>`_ The Extended Yale Facedatabase B contains 2414 images of 38 different people in its cropped version. The focus of this database is set on extracting features that are robust to illumination, the images have almost no variation in emotion/occlusion/... . I personally think, that this dataset is too large for the experiments I perform in this document. You better use the `AT&T Facedatabase <http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html>`_ for intial testing. A first version of the Yale Facedatabase B was used in [BHK97]_ to see how the Eigenfaces and Fisherfaces method perform under heavy illumination changes. [Lee05]_ used the same setup to take 16128 images of 28 people. The Extended Yale Facedatabase B is the merge of the two databases, which is now known as Extended Yalefacedatabase B.
Preparing the data
-------------------
Once we have acquired some data, we'll need to read it in our program. In the demo applications I have decided to read the images from a very simple CSV file. Why? Because it's the simplest platform-independent approach I can think of. However, if you know a simpler solution please ping me about it. Basically all the CSV file needs to contain are lines composed of a ``filename`` followed by a ``;`` followed by the ``label`` (as *integer number*), making up a line like this:
.. code-block:: none
/path/to/image.ext;0
Let's dissect the line. ``/path/to/image.ext`` is the path to an image, probably something like this if you are in Windows: ``C:/faces/person0/image0.jpg``. Then there is the separator ``;`` and finally we assign the label ``0`` to the image. Think of the label as the subject (the person) this image belongs to, so same subjects (persons) should have the same label.
Download the AT&T Facedatabase from AT&T Facedatabase and the corresponding CSV file from at.txt, which looks like this (file is without ... of course):
.. code-block:: none
./at/s1/1.pgm;0
./at/s1/2.pgm;0
...
./at/s2/1.pgm;1
./at/s2/2.pgm;1
...
./at/s40/1.pgm;39
./at/s40/2.pgm;39
Imagine I have extracted the files to ``D:/data/at`` and have downloaded the CSV file to ``D:/data/at.txt``. Then you would simply need to Search & Replace ``./`` with ``D:/data/``. You can do that in an editor of your choice, every sufficiently advanced editor can do this. Once you have a CSV file with valid filenames and labels, you can run any of the demos by passing the path to the CSV file as parameter:
.. code-block:: none
facerec_demo.exe D:/data/at.txt
Creating the CSV File
+++++++++++++++++++++
You don't really want to create the CSV file by hand. I have prepared you a little Python script ``create_csv.py`` (you find it at ``src/create_csv.py`` coming with this tutorial) that automatically creates you a CSV file. If you have your images in hierarchie like this (``/basepath/<subject>/<image.ext>``):
.. code-block:: none
philipp@mango:~/facerec/data/at$ tree
.
|-- s1
| |-- 1.pgm
| |-- ...
| |-- 10.pgm
|-- s2
| |-- 1.pgm
| |-- ...
| |-- 10.pgm
...
|-- s40
| |-- 1.pgm
| |-- ...
| |-- 10.pgm
Then simply call create_csv.py with the path to the folder, just like this and you could save the output:
.. code-block:: none
philipp@mango:~/facerec/data$ python create_csv.py
at/s13/2.pgm;0
at/s13/7.pgm;0
at/s13/6.pgm;0
at/s13/9.pgm;0
at/s13/5.pgm;0
at/s13/3.pgm;0
at/s13/4.pgm;0
at/s13/10.pgm;0
at/s13/8.pgm;0
at/s13/1.pgm;0
at/s17/2.pgm;1
at/s17/7.pgm;1
at/s17/6.pgm;1
at/s17/9.pgm;1
at/s17/5.pgm;1
at/s17/3.pgm;1
[...]
Please see the :ref:`appendixft` for additional informations.
Eigenfaces
==========
The problem with the image representation we are given is its high dimensionality. Two-dimensional :math:`p \times q` grayscale images span a :math:`m = pq`-dimensional vector space, so an image with :math:`100 \times 100` pixels lies in a :math:`10,000`-dimensional image space already. The question is: Are all dimensions equally useful for us? We can only make a decision if there's any variance in data, so what we are looking for are the components that account for most of the information. The Principal Component Analysis (PCA) was independently proposed by `Karl Pearson <http://en.wikipedia.org/wiki/Karl_Pearson>`_ (1901) and `Harold Hotelling <http://en.wikipedia.org/wiki/Harold_Hotelling>`_ (1933) to turn a set of possibly correlated variables into a smaller set of uncorrelated variables. The idea is, that a high-dimensional dataset is often described by correlated variables and therefore only a few meaningful dimensions account for most of the information. The PCA method finds the directions with the greatest variance in the data, called principal components.
Algorithmic Description
-----------------------
Let :math:`X = \{ x_{1}, x_{2}, \ldots, x_{n} \}` be a random vector with observations :math:`x_i \in R^{d}`.
1. Compute the mean :math:`\mu`
.. math::
\mu = \frac{1}{n} \sum_{i=1}^{n} x_{i}
2. Compute the the Covariance Matrix `S`
.. math::
S = \frac{1}{n} \sum_{i=1}^{n} (x_{i} - \mu) (x_{i} - \mu)^{T}`
3. Compute the eigenvalues :math:`\lambda_{i}` and eigenvectors :math:`v_{i}` of :math:`S`
.. math::
S v_{i} = \lambda_{i} v_{i}, i=1,2,\ldots,n
4. Order the eigenvectors descending by their eigenvalue. The :math:`k` principal components are the eigenvectors corresponding to the :math:`k` largest eigenvalues.
The :math:`k` principal components of the observed vector :math:`x` are then given by:
.. math::
y = W^{T} (x - \mu)
where :math:`W = (v_{1}, v_{2}, \ldots, v_{k})`.
The reconstruction from the PCA basis is given by:
.. math::
x = W y + \mu
where :math:`W = (v_{1}, v_{2}, \ldots, v_{k})`.
The Eigenfaces method then performs face recognition by:
* Projecting all training samples into the PCA subspace.
* Projecting the query image into the PCA subspace.
* Finding the nearest neighbor between the projected training images and the projected query image.
Still there's one problem left to solve. Imagine we are given :math:`400` images sized :math:`100 \times 100` pixel. The Principal Component Analysis solves the covariance matrix :math:`S = X X^{T}`, where :math:`{size}(X) = 10000 \times 400` in our example. You would end up with a :math:`10000 \times 10000` matrix, roughly :math:`0.8 GB`. Solving this problem isn't feasible, so we'll need to apply a trick. From your linear algebra lessons you know that a :math:`M \times N` matrix with :math:`M > N` can only have :math:`N - 1` non-zero eigenvalues. So it's possible to take the eigenvalue decomposition :math:`S = X^{T} X` of size :math:`N \times N` instead:
.. math::
X^{T} X v_{i} = \lambda_{i} v{i}
and get the original eigenvectors of :math:`S = X X^{T}` with a left multiplication of the data matrix:
.. math::
X X^{T} (X v_{i}) = \lambda_{i} (X v_{i})
The resulting eigenvectors are orthogonal, to get orthonormal eigenvectors they need to be normalized to unit length. I don't want to turn this into a publication, so please look into [Duda01]_ for the derivation and proof of the equations.
Eigenfaces in OpenCV
--------------------
For the first source code example, I'll go through it with you. I am first giving you the whole source code listing, and after this we'll look at the most important lines in detail. Please note: every source code listing is commented in detail, so you should have no problems following it.
.. literalinclude:: src/facerec_eigenfaces.cpp
:language: cpp
:linenos:
The source code for this demo application is also available in the ``src`` folder coming with this documentation:
* :download:`src/facerec_eigenfaces.cpp <src/facerec_eigenfaces.cpp>`
I've used the jet colormap, so you can see how the grayscale values are distributed within the specific Eigenfaces. You can see, that the Eigenfaces do not only encode facial features, but also the illumination in the images (see the left light in Eigenface \#4, right light in Eigenfaces \#5):
.. image:: img/eigenfaces_opencv.png
:align: center
We've already seen, that we can reconstruct a face from its lower dimensional approximation. So let's see how many Eigenfaces are needed for a good reconstruction. I'll do a subplot with :math:`10,30,\ldots,310` Eigenfaces:
.. code-block:: cpp
// Display or save the image reconstruction at some predefined steps:
for(int num_components = 10; num_components < 300; num_components+=15) {
// slice the eigenvectors from the model
Mat evs = Mat(W, Range::all(), Range(0, num_components));
Mat projection = subspaceProject(evs, mean, images[0].reshape(1,1));
Mat reconstruction = subspaceReconstruct(evs, mean, projection);
// Normalize the result:
reconstruction = norm_0_255(reconstruction.reshape(1, images[0].rows));
// Display or save:
if(argc == 2) {
imshow(format("eigenface_reconstruction_%d", num_components), reconstruction);
} else {
imwrite(format("%s/eigenface_reconstruction_%d.png", output_folder.c_str(), num_components), reconstruction);
}
}
10 Eigenvectors are obviously not sufficient for a good image reconstruction, 50 Eigenvectors may already be sufficient to encode important facial features. You'll get a good reconstruction with approximately 300 Eigenvectors for the AT&T Facedatabase. There are rule of thumbs how many Eigenfaces you should choose for a successful face recognition, but it heavily depends on the input data. [Zhao03]_ is the perfect point to start researching for this:
.. image:: img/eigenface_reconstruction_opencv.png
:align: center
Fisherfaces
============
The Principal Component Analysis (PCA), which is the core of the Eigenfaces method, finds a linear combination of features that maximizes the total variance in data. While this is clearly a powerful way to represent data, it doesn't consider any classes and so a lot of discriminative information *may* be lost when throwing components away. Imagine a situation where the variance in your data is generated by an external source, let it be the light. The components identified by a PCA do not necessarily contain any discriminative information at all, so the projected samples are smeared together and a classification becomes impossible (see `http://www.bytefish.de/wiki/pca_lda_with_gnu_octave <http://www.bytefish.de/wiki/pca_lda_with_gnu_octave>`_ for an example).
The Linear Discriminant Analysis performs a class-specific dimensionality reduction and was invented by the great statistician `Sir R. A. Fisher <http://en.wikipedia.org/wiki/Ronald_Fisher>`_. He successfully used it for classifying flowers in his 1936 paper *The use of multiple measurements in taxonomic problems* [Fisher36]_. In order to find the combination of features that separates best between classes the Linear Discriminant Analysis maximizes the ratio of between-classes to within-classes scatter, instead of maximizing the overall scatter. The idea is simple: same classes should cluster tightly together, while different classes are as far away as possible from each other in the lower-dimensional representation. This was also recognized by `Belhumeur <http://www.cs.columbia.edu/~belhumeur/>`_, `Hespanha <http://www.ece.ucsb.edu/~hespanha/>`_ and `Kriegman <http://cseweb.ucsd.edu/~kriegman/>`_ and so they applied a Discriminant Analysis to face recognition in [BHK97]_.
Algorithmic Description
-----------------------
Let :math:`X` be a random vector with samples drawn from :math:`c` classes:
.. math::
:nowrap:
\begin{align*}
X & = & \{X_1,X_2,\ldots,X_c\} \\
X_i & = & \{x_1, x_2, \ldots, x_n\}
\end{align*}
The scatter matrices :math:`S_{B}` and `S_{W}` are calculated as:
.. math::
:nowrap:
\begin{align*}
S_{B} & = & \sum_{i=1}^{c} N_{i} (\mu_i - \mu)(\mu_i - \mu)^{T} \\
S_{W} & = & \sum_{i=1}^{c} \sum_{x_{j} \in X_{i}} (x_j - \mu_i)(x_j - \mu_i)^{T}
\end{align*}
, where :math:`\mu` is the total mean:
.. math::
\mu = \frac{1}{N} \sum_{i=1}^{N} x_i
And :math:`\mu_i` is the mean of class :math:`i \in \{1,\ldots,c\}`:
.. math::
\mu_i = \frac{1}{|X_i|} \sum_{x_j \in X_i} x_j
Fisher's classic algorithm now looks for a projection :math:`W`, that maximizes the class separability criterion:
.. math::
W_{opt} = \operatorname{arg\,max}_{W} \frac{|W^T S_B W|}{|W^T S_W W|}
Following [BHK97]_, a solution for this optimization problem is given by solving the General Eigenvalue Problem:
.. math::
:nowrap:
\begin{align*}
S_{B} v_{i} & = & \lambda_{i} S_w v_{i} \nonumber \\
S_{W}^{-1} S_{B} v_{i} & = & \lambda_{i} v_{i}
\end{align*}
There's one problem left to solve: The rank of :math:`S_{W}` is at most :math:`(N-c)`, with :math:`N` samples and :math:`c` classes. In pattern recognition problems the number of samples :math:`N` is almost always samller than the dimension of the input data (the number of pixels), so the scatter matrix :math:`S_{W}` becomes singular (see [RJ91]_). In [BHK97]_ this was solved by performing a Principal Component Analysis on the data and projecting the samples into the :math:`(N-c)`-dimensional space. A Linear Discriminant Analysis was then performed on the reduced data, because :math:`S_{W}` isn't singular anymore.
The optimization problem can then be rewritten as:
.. math::
:nowrap:
\begin{align*}
W_{pca} & = & \operatorname{arg\,max}_{W} |W^T S_T W| \\
W_{fld} & = & \operatorname{arg\,max}_{W} \frac{|W^T W_{pca}^T S_{B} W_{pca} W|}{|W^T W_{pca}^T S_{W} W_{pca} W|}
\end{align*}
The transformation matrix :math:`W`, that projects a sample into the :math:`(c-1)`-dimensional space is then given by:
.. math::
W = W_{fld}^{T} W_{pca}^{T}
Fisherfaces in OpenCV
---------------------
.. literalinclude:: src/facerec_fisherfaces.cpp
:language: cpp
:linenos:
The source code for this demo application is also available in the ``src`` folder coming with this documentation:
* :download:`src/facerec_fisherfaces.cpp <src/facerec_fisherfaces.cpp>`
For this example I am going to use the Yale Facedatabase A, just because the plots are nicer. Each Fisherface has the same length as an original image, thus it can be displayed as an image. The demo shows (or saves) the first, at most 16 Fisherfaces:
.. image:: img/fisherfaces_opencv.png
:align: center
The Fisherfaces method learns a class-specific transformation matrix, so the they do not capture illumination as obviously as the Eigenfaces method. The Discriminant Analysis instead finds the facial features to discriminate between the persons. It's important to mention, that the performance of the Fisherfaces heavily depends on the input data as well. Practically said: if you learn the Fisherfaces for well-illuminated pictures only and you try to recognize faces in bad-illuminated scenes, then method is likely to find the wrong components (just because those features may not be predominant on bad illuminated images). This is somewhat logical, since the method had no chance to learn the illumination.
The Fisherfaces allow a reconstruction of the projected image, just like the Eigenfaces did. But since we only identified the features to distinguish between subjects, you can't expect a nice reconstruction of the original image. For the Fisherfaces method we'll project the sample image onto each of the Fisherfaces instead. So you'll have a nice visualization, which feature each of the Fisherfaces describes:
.. code-block:: cpp
// Display or save the image reconstruction at some predefined steps:
for(int num_component = 0; num_component < min(16, W.cols); num_component++) {
// Slice the Fisherface from the model:
Mat ev = W.col(num_component);
Mat projection = subspaceProject(ev, mean, images[0].reshape(1,1));
Mat reconstruction = subspaceReconstruct(ev, mean, projection);
// Normalize the result:
reconstruction = norm_0_255(reconstruction.reshape(1, images[0].rows));
// Display or save:
if(argc == 2) {
imshow(format("fisherface_reconstruction_%d", num_component), reconstruction);
} else {
imwrite(format("%s/fisherface_reconstruction_%d.png", output_folder.c_str(), num_component), reconstruction);
}
}
The differences may be subtle for the human eyes, but you should be able to see some differences:
.. image:: img/fisherface_reconstruction_opencv.png
:align: center
Local Binary Patterns Histograms
================================
Eigenfaces and Fisherfaces take a somewhat holistic approach to face recognition. You treat your data as a vector somewhere in a high-dimensional image space. We all know high-dimensionality is bad, so a lower-dimensional subspace is identified, where (probably) useful information is preserved. The Eigenfaces approach maximizes the total scatter, which can lead to problems if the variance is generated by an external source, because components with a maximum variance over all classes aren't necessarily useful for classification (see `http://www.bytefish.de/wiki/pca_lda_with_gnu_octave <http://www.bytefish.de/wiki/pca_lda_with_gnu_octave>`_). So to preserve some discriminative information we applied a Linear Discriminant Analysis and optimized as described in the Fisherfaces method. The Fisherfaces method worked great... at least for the constrained scenario we've assumed in our model.
Now real life isn't perfect. You simply can't guarantee perfect light settings in your images or 10 different images of a person. So what if there's only one image for each person? Our covariance estimates for the subspace *may* be horribly wrong, so will the recognition. Remember the Eigenfaces method had a 96% recognition rate on the AT&T Facedatabase? How many images do we actually need to get such useful estimates? Here are the Rank-1 recognition rates of the Eigenfaces and Fisherfaces method on the AT&T Facedatabase, which is a fairly easy image database:
.. image:: img/at_database_small_sample_size.png
:scale: 60%
:align: center
So in order to get good recognition rates you'll need at least 8(+-1) images for each person and the Fisherfaces method doesn't really help here. The above experiment is a 10-fold cross validated result carried out with the facerec framework at: `https://github.com/bytefish/facerec <https://github.com/bytefish/facerec>`_. This is not a publication, so I won't back these figures with a deep mathematical analysis. Please have a look into [KM01]_ for a detailed analysis of both methods, when it comes to small training datasets.
So some research concentrated on extracting local features from images. The idea is to not look at the whole image as a high-dimensional vector, but describe only local features of an object. The features you extract this way will have a low-dimensionality implicitly. A fine idea! But you'll soon observe the image representation we are given doesn't only suffer from illumination variations. Think of things like scale, translation or rotation in images - your local description has to be at least a bit robust against those things. Just like :ocv:class:`SIFT`, the Local Binary Patterns methodology has its roots in 2D texture analysis. The basic idea of Local Binary Patterns is to summarize the local structure in an image by comparing each pixel with its neighborhood. Take a pixel as center and threshold its neighbors against. If the intensity of the center pixel is greater-equal its neighbor, then denote it with 1 and 0 if not. You'll end up with a binary number for each pixel, just like 11001111. So with 8 surrounding pixels you'll end up with 2^8 possible combinations, called *Local Binary Patterns* or sometimes referred to as *LBP codes*. The first LBP operator described in literature actually used a fixed 3 x 3 neighborhood just like this:
.. image:: img/lbp/lbp.png
:scale: 80%
:align: center
Algorithmic Description
-----------------------
A more formal description of the LBP operator can be given as:
.. math::
LBP(x_c, y_c) = \sum_{p=0}^{P-1} 2^p s(i_p - i_c)
, with :math:`(x_c, y_c)` as central pixel with intensity :math:`i_c`; and :math:`i_n` being the intensity of the the neighbor pixel. :math:`s` is the sign function defined as:
.. math::
:nowrap:
\begin{equation}
s(x) =
\begin{cases}
1 & \text{if $x \geq 0$}\\
0 & \text{else}
\end{cases}
\end{equation}
This description enables you to capture very fine grained details in images. In fact the authors were able to compete with state of the art results for texture classification. Soon after the operator was published it was noted, that a fixed neighborhood fails to encode details differing in scale. So the operator was extended to use a variable neighborhood in [AHP04]_. The idea is to align an abritrary number of neighbors on a circle with a variable radius, which enables to capture the following neighborhoods:
.. image:: img/lbp/patterns.png
:scale: 80%
:align: center
For a given Point :math:`(x_c,y_c)` the position of the neighbor :math:`(x_p,y_p), p \in P` can be calculated by:
.. math::
:nowrap:
\begin{align*}
x_{p} & = & x_c + R \cos({\frac{2\pi p}{P}})\\
y_{p} & = & y_c - R \sin({\frac{2\pi p}{P}})
\end{align*}
Where :math:`R` is the radius of the circle and :math:`P` is the number of sample points.
The operator is an extension to the original LBP codes, so it's sometimes called *Extended LBP* (also referred to as *Circular LBP*) . If a points coordinate on the circle doesn't correspond to image coordinates, the point get's interpolated. Computer science has a bunch of clever interpolation schemes, the OpenCV implementation does a bilinear interpolation:
.. math::
:nowrap:
\begin{align*}
f(x,y) \approx \begin{bmatrix}
1-x & x \end{bmatrix} \begin{bmatrix}
f(0,0) & f(0,1) \\
f(1,0) & f(1,1) \end{bmatrix} \begin{bmatrix}
1-y \\
y \end{bmatrix}.
\end{align*}
By definition the LBP operator is robust against monotonic gray scale transformations. We can easily verify this by looking at the LBP image of an artificially modified image (so you see what an LBP image looks like!):
.. image:: img/lbp/lbp_yale.jpg
:scale: 60%
:align: center
So what's left to do is how to incorporate the spatial information in the face recognition model. The representation proposed by Ahonen et. al [AHP04]_ is to divide the LBP image into :math:`m` local regions and extract a histogram from each. The spatially enhanced feature vector is then obtained by concatenating the local histograms (**not merging them**). These histograms are called *Local Binary Patterns Histograms*.
Local Binary Patterns Histograms in OpenCV
------------------------------------------
.. literalinclude:: src/facerec_lbph.cpp
:language: cpp
:linenos:
The source code for this demo application is also available in the ``src`` folder coming with this documentation:
* :download:`src/facerec_lbph.cpp <src/facerec_lbph.cpp>`
Conclusion
==========
You've learned how to use the new :ocv:class:`FaceRecognizer` in real applications. After reading the document you also know how the algorithms work, so now it's time for you to experiment with the available algorithms. Use them, improve them and let the OpenCV community participate!
Credits
=======
This document wouldn't be possible without the kind permission to use the face images of the *AT&T Database of Faces* and the *Yale Facedatabase A/B*.
The Database of Faces
---------------------
** Important: when using these images, please give credit to "AT&T Laboratories, Cambridge." **
The Database of Faces, formerly *The ORL Database of Faces*, contains a set of face images taken between April 1992 and April 1994. The database was used in the context of a face recognition project carried out in collaboration with the Speech, Vision and Robotics Group of the Cambridge University Engineering Department.
There are ten different images of each of 40 distinct subjects. For some subjects, the images were taken at different times, varying the lighting, facial expressions (open / closed eyes, smiling / not smiling) and facial details (glasses / no glasses). All the images were taken against a dark homogeneous background with the subjects in an upright, frontal position (with tolerance for some side movement).
The files are in PGM format. The size of each image is 92x112 pixels, with 256 grey levels per pixel. The images are organised in 40 directories (one for each subject), which have names of the form sX, where X indicates the subject number (between 1 and 40). In each of these directories, there are ten different images of that subject, which have names of the form Y.pgm, where Y is the image number for that subject (between 1 and 10).
A copy of the database can be retrieved from: `http://www.cl.cam.ac.uk/research/dtg/attarchive/pub/data/att_faces.zip <http://www.cl.cam.ac.uk/research/dtg/attarchive/pub/data/att_faces.zip>`_.
Yale Facedatabase A
-------------------
*With the permission of the authors I am allowed to show a small number of images (say subject 1 and all the variations) and all images such as Fisherfaces and Eigenfaces from either Yale Facedatabase A or the Yale Facedatabase B.*
The Yale Face Database A (size 6.4MB) contains 165 grayscale images in GIF format of 15 individuals. There are 11 images per subject, one per different facial expression or configuration: center-light, w/glasses, happy, left-light, w/no glasses, normal, right-light, sad, sleepy, surprised, and wink. (Source: `http://cvc.yale.edu/projects/yalefaces/yalefaces.html <http://cvc.yale.edu/projects/yalefaces/yalefaces.html>`_)
Yale Facedatabase B
--------------------
*With the permission of the authors I am allowed to show a small number of images (say subject 1 and all the variations) and all images such as Fisherfaces and Eigenfaces from either Yale Facedatabase A or the Yale Facedatabase B.*
The extended Yale Face Database B contains 16128 images of 28 human subjects under 9 poses and 64 illumination conditions. The data format of this database is the same as the Yale Face Database B. Please refer to the homepage of the Yale Face Database B (or one copy of this page) for more detailed information of the data format.
You are free to use the extended Yale Face Database B for research purposes. All publications which use this database should acknowledge the use of "the Exteded Yale Face Database B" and reference Athinodoros Georghiades, Peter Belhumeur, and David Kriegman's paper, "From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose", PAMI, 2001, `[bibtex] <http://vision.ucsd.edu/~leekc/ExtYaleDatabase/athosref.html>`_.
The extended database as opposed to the original Yale Face Database B with 10 subjects was first reported by Kuang-Chih Lee, Jeffrey Ho, and David Kriegman in "Acquiring Linear Subspaces for Face Recognition under Variable Lighting, PAMI, May, 2005 `[pdf] <http://vision.ucsd.edu/~leekc/papers/9pltsIEEE.pdf>`_." All test image data used in the experiments are manually aligned, cropped, and then re-sized to 168x192 images. If you publish your experimental results with the cropped images, please reference the PAMI2005 paper as well. (Source: `http://vision.ucsd.edu/~leekc/ExtYaleDatabase/ExtYaleB.html <http://vision.ucsd.edu/~leekc/ExtYaleDatabase/ExtYaleB.html>`_)
Literature
==========
.. [AHP04] Ahonen, T., Hadid, A., and Pietikainen, M. *Face Recognition with Local Binary Patterns.* Computer Vision - ECCV 2004 (2004), 469–481.
.. [BHK97] Belhumeur, P. N., Hespanha, J., and Kriegman, D. *Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection.* IEEE Transactions on Pattern Analysis and Machine Intelligence 19, 7 (1997), 711–720.
.. [Bru92] Brunelli, R., Poggio, T. *Face Recognition through Geometrical Features.* European Conference on Computer Vision (ECCV) 1992, S. 792–800.
.. [Duda01] Duda, Richard O. and Hart, Peter E. and Stork, David G., *Pattern Classification* (2nd Edition) 2001.
.. [Fisher36] Fisher, R. A. *The use of multiple measurements in taxonomic problems.* Annals Eugen. 7 (1936), 179–188.
.. [GBK01] Georghiades, A.S. and Belhumeur, P.N. and Kriegman, D.J., *From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose* IEEE Transactions on Pattern Analysis and Machine Intelligence 23, 6 (2001), 643-660.
.. [Kanade73] Kanade, T. *Picture processing system by computer complex and recognition of human faces.* PhD thesis, Kyoto University, November 1973
.. [KM01] Martinez, A and Kak, A. *PCA versus LDA* IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 23, No.2, pp. 228-233, 2001.
.. [Lee05] Lee, K., Ho, J., Kriegman, D. *Acquiring Linear Subspaces for Face Recognition under Variable Lighting.* In: IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI) 27 (2005), Nr. 5
.. [Messer06] Messer, K. et al. *Performance Characterisation of Face Recognition Algorithms and Their Sensitivity to Severe Illumination Changes.* In: In: ICB, 2006, S. 1–11.
.. [RJ91] S. Raudys and A.K. Jain. *Small sample size effects in statistical pattern recognition: Recommendations for practitioneers.* - IEEE Transactions on Pattern Analysis and Machine Intelligence 13, 3 (1991), 252-264.
.. [Tan10] Tan, X., and Triggs, B. *Enhanced local texture feature sets for face recognition under difficult lighting conditions.* IEEE Transactions on Image Processing 19 (2010), 1635–650.
.. [TP91] Turk, M., and Pentland, A. *Eigenfaces for recognition.* Journal of Cognitive Neuroscience 3 (1991), 71–86.
.. [Tu06] Chiara Turati, Viola Macchi Cassia, F. S., and Leo, I. *Newborns face recognition: Role of inner and outer facial features. Child Development* 77, 2 (2006), 297–311.
.. [Wiskott97] Wiskott, L., Fellous, J., Krüger, N., Malsburg, C. *Face Recognition By Elastic Bunch Graph Matching.* IEEE Transactions on Pattern Analysis and Machine Intelligence 19 (1997), S. 775–779
.. [Zhao03] Zhao, W., Chellappa, R., Phillips, P., and Rosenfeld, A. Face recognition: A literature survey. ACM Computing Surveys (CSUR) 35, 4 (2003), 399–458.
.. _appendixft:
Appendix
========
Creating the CSV File
---------------------
You don't really want to create the CSV file by hand. I have prepared you a little Python script ``create_csv.py`` (you find it at ``/src/create_csv.py`` coming with this tutorial) that automatically creates you a CSV file. If you have your images in hierarchie like this (``/basepath/<subject>/<image.ext>``):
.. code-block:: none
philipp@mango:~/facerec/data/at$ tree
.
|-- s1
| |-- 1.pgm
| |-- ...
| |-- 10.pgm
|-- s2
| |-- 1.pgm
| |-- ...
| |-- 10.pgm
...
|-- s40
| |-- 1.pgm
| |-- ...
| |-- 10.pgm
Then simply call ``create_csv.py`` with the path to the folder, just like this and you could save the output:
.. code-block:: none
philipp@mango:~/facerec/data$ python create_csv.py
at/s13/2.pgm;0
at/s13/7.pgm;0
at/s13/6.pgm;0
at/s13/9.pgm;0
at/s13/5.pgm;0
at/s13/3.pgm;0
at/s13/4.pgm;0
at/s13/10.pgm;0
at/s13/8.pgm;0
at/s13/1.pgm;0
at/s17/2.pgm;1
at/s17/7.pgm;1
at/s17/6.pgm;1
at/s17/9.pgm;1
at/s17/5.pgm;1
at/s17/3.pgm;1
[...]
Here is the script, if you can't find it:
.. literalinclude:: ./src/create_csv.py
:language: python
:linenos:
Aligning Face Images
---------------------
An accurate alignment of your image data is especially important in tasks like emotion detection, were you need as much detail as possible. Believe me... You don't want to do this by hand. So I've prepared you a tiny Python script. The code is really easy to use. To scale, rotate and crop the face image you just need to call *CropFace(image, eye_left, eye_right, offset_pct, dest_sz)*, where:
* *eye_left* is the position of the left eye
* *eye_right* is the position of the right eye
* *offset_pct* is the percent of the image you want to keep next to the eyes (horizontal, vertical direction)
* *dest_sz* is the size of the output image
If you are using the same *offset_pct* and *dest_sz* for your images, they are all aligned at the eyes.
.. literalinclude:: ./src/crop_face.py
:language: python
:linenos:
Imagine we are given `this photo of Arnold Schwarzenegger <http://en.wikipedia.org/wiki/File:Arnold_Schwarzenegger_edit%28ws%29.jpg>`_, which is under a Public Domain license. The (x,y)-position of the eyes is approximately *(252,364)* for the left and *(420,366)* for the right eye. Now you only need to define the horizontal offset, vertical offset and the size your scaled, rotated & cropped face should have.
Here are some examples:
+---------------------------------+----------------------------------------------------------------------------+
| Configuration | Cropped, Scaled, Rotated Face |
+=================================+============================================================================+
| 0.1 (10%), 0.1 (10%), (200,200) | .. image:: ./img/tutorial/gender_classification/arnie_10_10_200_200.jpg |
+---------------------------------+----------------------------------------------------------------------------+
| 0.2 (20%), 0.2 (20%), (200,200) | .. image:: ./img/tutorial/gender_classification/arnie_20_20_200_200.jpg |
+---------------------------------+----------------------------------------------------------------------------+
| 0.3 (30%), 0.3 (30%), (200,200) | .. image:: ./img/tutorial/gender_classification/arnie_30_30_200_200.jpg |
+---------------------------------+----------------------------------------------------------------------------+
| 0.2 (20%), 0.2 (20%), (70,70) | .. image:: ./img/tutorial/gender_classification/arnie_20_20_70_70.jpg |
+---------------------------------+----------------------------------------------------------------------------+
CSV for the AT&T Facedatabase
------------------------------
.. literalinclude:: etc/at.txt
:language: none
:linenos:
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FaceRecognizer - Face Recognition with OpenCV
##############################################
OpenCV 2.4 now comes with the very new :ocv:class:`FaceRecognizer` class for face recognition. This documentation is going to explain you :doc:`the API <facerec_api>` in detail and it will give you a lot of help to get started (full source code examples). :doc:`Face Recognition with OpenCV <facerec_tutorial>` is the definite guide to the new :ocv:class:`FaceRecognizer`. There's also a :doc:`tutorial on gender classification <tutorial/facerec_gender_classification>`, a :doc:`tutorial for face recognition in videos <tutorial/facerec_video_recognition>` and it's shown :doc:`how to load & save your results <tutorial/facerec_save_load>`.
These documents are the help I have wished for, when I was working myself into face recognition. I hope you also think the new :ocv:class:`FaceRecognizer` is a useful addition to OpenCV.
Please issue any feature requests and/or bugs on the official OpenCV bug tracker at:
* http://code.opencv.org/projects/opencv/issues
Contents
========
.. toctree::
:maxdepth: 1
FaceRecognizer API <facerec_api>
Guide to Face Recognition with OpenCV <facerec_tutorial>
Tutorial on Gender Classification <tutorial/facerec_gender_classification>
Tutorial on Face Recognition in Videos <tutorial/facerec_video_recognition>
Tutorial On Saving & Loading a FaceRecognizer <tutorial/facerec_save_load>
How to use Colormaps in OpenCV <colormaps>
Changelog <facerec_changelog>
Indices and tables
==================
* :ref:`genindex`
* :ref:`modindex`
* :ref:`search`
modules/contrib/doc/facerec/src/CMakeLists.txt
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CMAKE_MINIMUM_REQUIRED(VERSION 2.6)
set(name "facerec")
project(facerec_cpp_samples)
#SET(OpenCV_DIR /path/to/your/opencv/installation)
# packages
find_package(OpenCV REQUIRED) # http://opencv.org
# probably you should loop through the sample files here
add_executable(facerec_demo facerec_demo.cpp)
target_link_libraries(facerec_demo opencv_core opencv_contrib opencv_imgproc opencv_highgui)
add_executable(facerec_video facerec_video.cpp)
target_link_libraries(facerec_video opencv_contrib opencv_core opencv_imgproc opencv_highgui opencv_objdetect opencv_imgproc)
add_executable(facerec_eigenfaces facerec_eigenfaces.cpp)
target_link_libraries(facerec_eigenfaces opencv_contrib opencv_core opencv_imgproc opencv_highgui)
add_executable(facerec_fisherfaces facerec_fisherfaces.cpp)
target_link_libraries(facerec_fisherfaces opencv_contrib opencv_core opencv_imgproc opencv_highgui)
add_executable(facerec_lbph facerec_lbph.cpp)
target_link_libraries(facerec_lbph opencv_contrib opencv_core opencv_imgproc opencv_highgui)
modules/contrib/doc/facerec/src/create_csv.py
-43
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@@ -1,43 +0,0 @@
#!/usr/bin/env python
import sys
import os.path
# This is a tiny script to help you creating a CSV file from a face
# database with a similar hierarchie:
#
# philipp@mango:~/facerec/data/at$ tree
# .
# |-- README
# |-- s1
# | |-- 1.pgm
# | |-- ...
# | |-- 10.pgm
# |-- s2
# | |-- 1.pgm
# | |-- ...
# | |-- 10.pgm
# ...
# |-- s40
# | |-- 1.pgm
# | |-- ...
# | |-- 10.pgm
#
if __name__ == "__main__":
if len(sys.argv) != 2:
print "usage: create_csv <base_path>"
sys.exit(1)
BASE_PATH=sys.argv[1]
SEPARATOR=";"
label = 0
for dirname, dirnames, filenames in os.walk(BASE_PATH):
for subdirname in dirnames:
subject_path = os.path.join(dirname, subdirname)
for filename in os.listdir(subject_path):
abs_path = "%s/%s" % (subject_path, filename)
print "%s%s%d" % (abs_path, SEPARATOR, label)
label = label + 1
modules/contrib/doc/facerec/src/crop_face.py
-112
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#!/usr/bin/env python
# Software License Agreement (BSD License)
#
# Copyright (c) 2012, Philipp Wagner
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
#
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above
# copyright notice, this list of conditions and the following
# disclaimer in the documentation and/or other materials provided
# with the distribution.
# * Neither the name of the author nor the names of its
# contributors may be used to endorse or promote products derived
# from this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
# FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
# COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
# BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
# LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
# ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.
import sys, math, Image
def Distance(p1,p2):
dx = p2[0] - p1[0]
dy = p2[1] - p1[1]
return math.sqrt(dx*dx+dy*dy)
def ScaleRotateTranslate(image, angle, center = None, new_center = None, scale = None, resample=Image.BICUBIC):
if (scale is None) and (center is None):
return image.rotate(angle=angle, resample=resample)
nx,ny = x,y = center
sx=sy=1.0
if new_center:
(nx,ny) = new_center
if scale:
(sx,sy) = (scale, scale)
cosine = math.cos(angle)
sine = math.sin(angle)
a = cosine/sx
b = sine/sx
c = x-nx*a-ny*b
d = -sine/sy
e = cosine/sy
f = y-nx*d-ny*e
return image.transform(image.size, Image.AFFINE, (a,b,c,d,e,f), resample=resample)
def CropFace(image, eye_left=(0,0), eye_right=(0,0), offset_pct=(0.2,0.2), dest_sz = (70,70)):
# calculate offsets in original image
offset_h = math.floor(float(offset_pct[0])*dest_sz[0])
offset_v = math.floor(float(offset_pct[1])*dest_sz[1])
# get the direction
eye_direction = (eye_right[0] - eye_left[0], eye_right[1] - eye_left[1])
# calc rotation angle in radians
rotation = -math.atan2(float(eye_direction[1]),float(eye_direction[0]))
# distance between them
dist = Distance(eye_left, eye_right)
# calculate the reference eye-width
reference = dest_sz[0] - 2.0*offset_h
# scale factor
scale = float(dist)/float(reference)
# rotate original around the left eye
image = ScaleRotateTranslate(image, center=eye_left, angle=rotation)
# crop the rotated image
crop_xy = (eye_left[0] - scale*offset_h, eye_left[1] - scale*offset_v)
crop_size = (dest_sz[0]*scale, dest_sz[1]*scale)
image = image.crop((int(crop_xy[0]), int(crop_xy[1]), int(crop_xy[0]+crop_size[0]), int(crop_xy[1]+crop_size[1])))
# resize it
image = image.resize(dest_sz, Image.ANTIALIAS)
return image
def readFileNames():
try:
inFile = open('path_to_created_csv_file.csv')
except:
raise IOError('There is no file named path_to_created_csv_file.csv in current directory.')
return False
picPath = []
picIndex = []
for line in inFile.readlines():
if line != '':
fields = line.rstrip().split(';')
picPath.append(fields[0])
picIndex.append(int(fields[1]))
return (picPath, picIndex)
if __name__ == "__main__":
[images, indexes]=readFileNames()
if not os.path.exists("modified"):
os.makedirs("modified")
for img in images:
image = Image.open(img)
CropFace(image, eye_left=(252,364), eye_right=(420,366), offset_pct=(0.1,0.1), dest_sz=(200,200)).save("modified/"+img.rstrip().split('/')[1]+"_10_10_200_200.jpg")
CropFace(image, eye_left=(252,364), eye_right=(420,366), offset_pct=(0.2,0.2), dest_sz=(200,200)).save("modified/"+img.rstrip().split('/')[1]+"_20_20_200_200.jpg")
CropFace(image, eye_left=(252,364), eye_right=(420,366), offset_pct=(0.3,0.3), dest_sz=(200,200)).save("modified/"+img.rstrip().split('/')[1]+"_30_30_200_200.jpg")
CropFace(image, eye_left=(252,364), eye_right=(420,366), offset_pct=(0.2,0.2)).save("modified/"+img.rstrip().split('/')[1]+"_20_20_70_70.jpg")
modules/contrib/doc/facerec/src/facerec_demo.cpp
-169
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@@ -1,169 +0,0 @@
/*
* Copyright (c) 2011. Philipp Wagner <bytefish[at]gmx[dot]de>.
* Released to public domain under terms of the BSD Simplified license.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions are met:
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in the
* documentation and/or other materials provided with the distribution.
* * Neither the name of the organization nor the names of its contributors
* may be used to endorse or promote products derived from this software
* without specific prior written permission.
*
* See <http://www.opensource.org/licenses/bsd-license>
*/
#include "opencv2/core.hpp"
#include "opencv2/contrib.hpp"
#include "opencv2/highgui.hpp"
#include <iostream>
#include <fstream>
#include <sstream>
using namespace cv;
using namespace std;
static Mat norm_0_255(InputArray _src) {
Mat src = _src.getMat();
// Create and return normalized image:
Mat dst;
switch(src.channels()) {
case 1:
cv::normalize(_src, dst, 0, 255, NORM_MINMAX, CV_8UC1);
break;
case 3:
cv::normalize(_src, dst, 0, 255, NORM_MINMAX, CV_8UC3);
break;
default:
src.copyTo(dst);
break;
}
return dst;
}
static void read_csv(const string& filename, vector<Mat>& images, vector<int>& labels, char separator = ';') {
std::ifstream file(filename.c_str(), ifstream::in);
if (!file) {
string error_message = "No valid input file was given, please check the given filename.";
CV_Error(CV_StsBadArg, error_message);
}
string line, path, classlabel;
while (getline(file, line)) {
stringstream liness(line);
getline(liness, path, separator);
getline(liness, classlabel);
if(!path.empty() && !classlabel.empty()) {
images.push_back(imread(path, 0));
labels.push_back(atoi(classlabel.c_str()));
}
}
}
int main(int argc, const char *argv[]) {
// Check for valid command line arguments, print usage
// if no arguments were given.
if (argc != 2) {
cout << "usage: " << argv[0] << " <csv.ext>" << endl;
exit(1);
}
// Get the path to your CSV.
string fn_csv = string(argv[1]);
// These vectors hold the images and corresponding labels.
vector<Mat> images;
vector<int> labels;
// Read in the data. This can fail if no valid
// input filename is given.
try {
read_csv(fn_csv, images, labels);
} catch (cv::Exception& e) {
cerr << "Error opening file \"" << fn_csv << "\". Reason: " << e.msg << endl;
// nothing more we can do
exit(1);
}
// Quit if there are not enough images for this demo.
if(images.size() <= 1) {
string error_message = "This demo needs at least 2 images to work. Please add more images to your data set!";
CV_Error(CV_StsError, error_message);
}
// Get the height from the first image. We'll need this
// later in code to reshape the images to their original
// size:
int height = images[0].rows;
// The following lines simply get the last images from
// your dataset and remove it from the vector. This is
// done, so that the training data (which we learn the
// cv::FaceRecognizer on) and the test data we test
// the model with, do not overlap.
Mat testSample = images[images.size() - 1];
int testLabel = labels[labels.size() - 1];
images.pop_back();
labels.pop_back();
// The following lines create an Eigenfaces model for
// face recognition and train it with the images and
// labels read from the given CSV file.
// This here is a full PCA, if you just want to keep
// 10 principal components (read Eigenfaces), then call
// the factory method like this:
//
// cv::createEigenFaceRecognizer(10);
//
// If you want to create a FaceRecognizer with a
// confidennce threshold, call it with:
//
// cv::createEigenFaceRecognizer(10, 123.0);
//
Ptr<FaceRecognizer> model = createFisherFaceRecognizer();
model->train(images, labels);
// The following line predicts the label of a given
// test image:
int predictedLabel = model->predict(testSample);
//
// To get the confidence of a prediction call the model with:
//
// int predictedLabel = -1;
// double confidence = 0.0;
// model->predict(testSample, predictedLabel, confidence);
//
string result_message = format("Predicted class = %d / Actual class = %d.", predictedLabel, testLabel);
cout << result_message << endl;
// Sometimes you'll need to get/set internal model data,
// which isn't exposed by the public cv::FaceRecognizer.
// Since each cv::FaceRecognizer is derived from a
// cv::Algorithm, you can query the data.
//
// First we'll use it to set the threshold of the FaceRecognizer
// to 0.0 without retraining the model. This can be useful if
// you are evaluating the model:
//
model->set("threshold", 0.0);
// Now the threshold of this model is set to 0.0. A prediction
// now returns -1, as it's impossible to have a distance below
// it
predictedLabel = model->predict(testSample);
cout << "Predicted class = " << predictedLabel << endl;
// Here is how to get the eigenvalues of this Eigenfaces model:
Mat eigenvalues = model->getMat("eigenvalues");
// And we can do the same to display the Eigenvectors (read Eigenfaces):
Mat W = model->getMat("eigenvectors");
// From this we will display the (at most) first 10 Eigenfaces:
for (int i = 0; i < min(10, W.cols); i++) {
string msg = format("Eigenvalue #%d = %.5f", i, eigenvalues.at<double>(i));
cout << msg << endl;
// get eigenvector #i
Mat ev = W.col(i).clone();
// Reshape to original size & normalize to [0...255] for imshow.
Mat grayscale = norm_0_255(ev.reshape(1, height));
// Show the image & apply a Jet colormap for better sensing.
Mat cgrayscale;
applyColorMap(grayscale, cgrayscale, COLORMAP_JET);
imshow(format("%d", i), cgrayscale);
}
waitKey(0);
return 0;
}
modules/contrib/doc/facerec/src/facerec_eigenfaces.cpp
-193
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@@ -1,193 +0,0 @@
/*
* Copyright (c) 2011. Philipp Wagner <bytefish[at]gmx[dot]de>.
* Released to public domain under terms of the BSD Simplified license.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions are met:
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in the
* documentation and/or other materials provided with the distribution.
* * Neither the name of the organization nor the names of its contributors
* may be used to endorse or promote products derived from this software
* without specific prior written permission.
*
* See <http://www.opensource.org/licenses/bsd-license>
*/
#include "opencv2/core.hpp"
#include "opencv2/contrib.hpp"
#include "opencv2/highgui.hpp"
#include <iostream>
#include <fstream>
#include <sstream>
using namespace cv;
using namespace std;
static Mat norm_0_255(InputArray _src) {
Mat src = _src.getMat();
// Create and return normalized image:
Mat dst;
switch(src.channels()) {
case 1:
cv::normalize(_src, dst, 0, 255, NORM_MINMAX, CV_8UC1);
break;
case 3:
cv::normalize(_src, dst, 0, 255, NORM_MINMAX, CV_8UC3);
break;
default:
src.copyTo(dst);
break;
}
return dst;
}
static void read_csv(const string& filename, vector<Mat>& images, vector<int>& labels, char separator = ';') {
std::ifstream file(filename.c_str(), ifstream::in);
if (!file) {
string error_message = "No valid input file was given, please check the given filename.";
CV_Error(CV_StsBadArg, error_message);
}
string line, path, classlabel;
while (getline(file, line)) {
stringstream liness(line);
getline(liness, path, separator);
getline(liness, classlabel);
if(!path.empty() && !classlabel.empty()) {
images.push_back(imread(path, 0));
labels.push_back(atoi(classlabel.c_str()));
}
}
}
int main(int argc, const char *argv[]) {
// Check for valid command line arguments, print usage
// if no arguments were given.
if (argc < 2) {
cout << "usage: " << argv[0] << " <csv.ext> <output_folder> " << endl;
exit(1);
}
string output_folder = ".";
if (argc == 3) {
output_folder = string(argv[2]);
}
// Get the path to your CSV.
string fn_csv = string(argv[1]);
// These vectors hold the images and corresponding labels.
vector<Mat> images;
vector<int> labels;
// Read in the data. This can fail if no valid
// input filename is given.
try {
read_csv(fn_csv, images, labels);
} catch (cv::Exception& e) {
cerr << "Error opening file \"" << fn_csv << "\". Reason: " << e.msg << endl;
// nothing more we can do
exit(1);
}
// Quit if there are not enough images for this demo.
if(images.size() <= 1) {
string error_message = "This demo needs at least 2 images to work. Please add more images to your data set!";
CV_Error(CV_StsError, error_message);
}
// Get the height from the first image. We'll need this
// later in code to reshape the images to their original
// size:
int height = images[0].rows;
// The following lines simply get the last images from
// your dataset and remove it from the vector. This is
// done, so that the training data (which we learn the
// cv::FaceRecognizer on) and the test data we test
// the model with, do not overlap.
Mat testSample = images[images.size() - 1];
int testLabel = labels[labels.size() - 1];
images.pop_back();
labels.pop_back();
// The following lines create an Eigenfaces model for
// face recognition and train it with the images and
// labels read from the given CSV file.
// This here is a full PCA, if you just want to keep
// 10 principal components (read Eigenfaces), then call
// the factory method like this:
//
// cv::createEigenFaceRecognizer(10);
//
// If you want to create a FaceRecognizer with a
// confidence threshold (e.g. 123.0), call it with:
//
// cv::createEigenFaceRecognizer(10, 123.0);
//
// If you want to use _all_ Eigenfaces and have a threshold,
// then call the method like this:
//
// cv::createEigenFaceRecognizer(0, 123.0);
//
Ptr<FaceRecognizer> model = createEigenFaceRecognizer();
model->train(images, labels);
// The following line predicts the label of a given
// test image:
int predictedLabel = model->predict(testSample);
//
// To get the confidence of a prediction call the model with:
//
// int predictedLabel = -1;
// double confidence = 0.0;
// model->predict(testSample, predictedLabel, confidence);
//
string result_message = format("Predicted class = %d / Actual class = %d.", predictedLabel, testLabel);
cout << result_message << endl;
// Here is how to get the eigenvalues of this Eigenfaces model:
Mat eigenvalues = model->getMat("eigenvalues");
// And we can do the same to display the Eigenvectors (read Eigenfaces):
Mat W = model->getMat("eigenvectors");
// Get the sample mean from the training data
Mat mean = model->getMat("mean");
// Display or save:
if(argc == 2) {
imshow("mean", norm_0_255(mean.reshape(1, images[0].rows)));
} else {
imwrite(format("%s/mean.png", output_folder.c_str()), norm_0_255(mean.reshape(1, images[0].rows)));
}
// Display or save the Eigenfaces:
for (int i = 0; i < min(10, W.cols); i++) {
string msg = format("Eigenvalue #%d = %.5f", i, eigenvalues.at<double>(i));
cout << msg << endl;
// get eigenvector #i
Mat ev = W.col(i).clone();
// Reshape to original size & normalize to [0...255] for imshow.
Mat grayscale = norm_0_255(ev.reshape(1, height));
// Show the image & apply a Jet colormap for better sensing.
Mat cgrayscale;
applyColorMap(grayscale, cgrayscale, COLORMAP_JET);
// Display or save:
if(argc == 2) {
imshow(format("eigenface_%d", i), cgrayscale);
} else {
imwrite(format("%s/eigenface_%d.png", output_folder.c_str(), i), norm_0_255(cgrayscale));
}
}
// Display or save the image reconstruction at some predefined steps:
for(int num_components = min(W.cols, 10); num_components < min(W.cols, 300); num_components+=15) {
// slice the eigenvectors from the model
Mat evs = Mat(W, Range::all(), Range(0, num_components));
Mat projection = subspaceProject(evs, mean, images[0].reshape(1,1));
Mat reconstruction = subspaceReconstruct(evs, mean, projection);
// Normalize the result:
reconstruction = norm_0_255(reconstruction.reshape(1, images[0].rows));
// Display or save:
if(argc == 2) {
imshow(format("eigenface_reconstruction_%d", num_components), reconstruction);
} else {
imwrite(format("%s/eigenface_reconstruction_%d.png", output_folder.c_str(), num_components), reconstruction);
}
}
// Display if we are not writing to an output folder:
if(argc == 2) {
waitKey(0);
}
return 0;
}
modules/contrib/doc/facerec/src/facerec_fisherfaces.cpp
-191
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@@ -1,191 +0,0 @@
/*
* Copyright (c) 2011. Philipp Wagner <bytefish[at]gmx[dot]de>.
* Released to public domain under terms of the BSD Simplified license.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions are met:
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in the
* documentation and/or other materials provided with the distribution.
* * Neither the name of the organization nor the names of its contributors
* may be used to endorse or promote products derived from this software
* without specific prior written permission.
*
* See <http://www.opensource.org/licenses/bsd-license>
*/
#include "opencv2/core.hpp"
#include "opencv2/contrib.hpp"
#include "opencv2/highgui.hpp"
#include <iostream>
#include <fstream>
#include <sstream>
using namespace cv;
using namespace std;
static Mat norm_0_255(InputArray _src) {
Mat src = _src.getMat();
// Create and return normalized image:
Mat dst;
switch(src.channels()) {
case 1:
cv::normalize(_src, dst, 0, 255, NORM_MINMAX, CV_8UC1);
break;
case 3:
cv::normalize(_src, dst, 0, 255, NORM_MINMAX, CV_8UC3);
break;
default:
src.copyTo(dst);
break;
}
return dst;
}
static void read_csv(const string& filename, vector<Mat>& images, vector<int>& labels, char separator = ';') {
std::ifstream file(filename.c_str(), ifstream::in);
if (!file) {
string error_message = "No valid input file was given, please check the given filename.";
CV_Error(CV_StsBadArg, error_message);
}
string line, path, classlabel;
while (getline(file, line)) {
stringstream liness(line);
getline(liness, path, separator);
getline(liness, classlabel);
if(!path.empty() && !classlabel.empty()) {
images.push_back(imread(path, 0));
labels.push_back(atoi(classlabel.c_str()));
}
}
}
int main(int argc, const char *argv[]) {
// Check for valid command line arguments, print usage
// if no arguments were given.
if (argc < 2) {
cout << "usage: " << argv[0] << " <csv.ext> <output_folder> " << endl;
exit(1);
}
string output_folder = ".";
if (argc == 3) {
output_folder = string(argv[2]);
}
// Get the path to your CSV.
string fn_csv = string(argv[1]);
// These vectors hold the images and corresponding labels.
vector<Mat> images;
vector<int> labels;
// Read in the data. This can fail if no valid
// input filename is given.
try {
read_csv(fn_csv, images, labels);
} catch (cv::Exception& e) {
cerr << "Error opening file \"" << fn_csv << "\". Reason: " << e.msg << endl;
// nothing more we can do
exit(1);
}
// Quit if there are not enough images for this demo.
if(images.size() <= 1) {
string error_message = "This demo needs at least 2 images to work. Please add more images to your data set!";
CV_Error(CV_StsError, error_message);
}
// Get the height from the first image. We'll need this
// later in code to reshape the images to their original
// size:
int height = images[0].rows;
// The following lines simply get the last images from
// your dataset and remove it from the vector. This is
// done, so that the training data (which we learn the
// cv::FaceRecognizer on) and the test data we test
// the model with, do not overlap.
Mat testSample = images[images.size() - 1];
int testLabel = labels[labels.size() - 1];
images.pop_back();
labels.pop_back();
// The following lines create an Fisherfaces model for
// face recognition and train it with the images and
// labels read from the given CSV file.
// If you just want to keep 10 Fisherfaces, then call
// the factory method like this:
//
// cv::createFisherFaceRecognizer(10);
//
// However it is not useful to discard Fisherfaces! Please
// always try to use _all_ available Fisherfaces for
// classification.
//
// If you want to create a FaceRecognizer with a
// confidence threshold (e.g. 123.0) and use _all_
// Fisherfaces, then call it with:
//
// cv::createFisherFaceRecognizer(0, 123.0);
//
Ptr<FaceRecognizer> model = createFisherFaceRecognizer();
model->train(images, labels);
// The following line predicts the label of a given
// test image:
int predictedLabel = model->predict(testSample);
//
// To get the confidence of a prediction call the model with:
//
// int predictedLabel = -1;
// double confidence = 0.0;
// model->predict(testSample, predictedLabel, confidence);
//
string result_message = format("Predicted class = %d / Actual class = %d.", predictedLabel, testLabel);
cout << result_message << endl;
// Here is how to get the eigenvalues of this Eigenfaces model:
Mat eigenvalues = model->getMat("eigenvalues");
// And we can do the same to display the Eigenvectors (read Eigenfaces):
Mat W = model->getMat("eigenvectors");
// Get the sample mean from the training data
Mat mean = model->getMat("mean");
// Display or save:
if(argc == 2) {
imshow("mean", norm_0_255(mean.reshape(1, images[0].rows)));
} else {
imwrite(format("%s/mean.png", output_folder.c_str()), norm_0_255(mean.reshape(1, images[0].rows)));
}
// Display or save the first, at most 16 Fisherfaces:
for (int i = 0; i < min(16, W.cols); i++) {
string msg = format("Eigenvalue #%d = %.5f", i, eigenvalues.at<double>(i));
cout << msg << endl;
// get eigenvector #i
Mat ev = W.col(i).clone();
// Reshape to original size & normalize to [0...255] for imshow.
Mat grayscale = norm_0_255(ev.reshape(1, height));
// Show the image & apply a Bone colormap for better sensing.
Mat cgrayscale;
applyColorMap(grayscale, cgrayscale, COLORMAP_BONE);
// Display or save:
if(argc == 2) {
imshow(format("fisherface_%d", i), cgrayscale);
} else {
imwrite(format("%s/fisherface_%d.png", output_folder.c_str(), i), norm_0_255(cgrayscale));
}
}
// Display or save the image reconstruction at some predefined steps:
for(int num_component = 0; num_component < min(16, W.cols); num_component++) {
// Slice the Fisherface from the model:
Mat ev = W.col(num_component);
Mat projection = subspaceProject(ev, mean, images[0].reshape(1,1));
Mat reconstruction = subspaceReconstruct(ev, mean, projection);
// Normalize the result:
reconstruction = norm_0_255(reconstruction.reshape(1, images[0].rows));
// Display or save:
if(argc == 2) {
imshow(format("fisherface_reconstruction_%d", num_component), reconstruction);
} else {
imwrite(format("%s/fisherface_reconstruction_%d.png", output_folder.c_str(), num_component), reconstruction);
}
}
// Display if we are not writing to an output folder:
if(argc == 2) {
waitKey(0);
}
return 0;
}
modules/contrib/doc/facerec/src/facerec_lbph.cpp
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@@ -1,155 +0,0 @@
/*
* Copyright (c) 2011. Philipp Wagner <bytefish[at]gmx[dot]de>.
* Released to public domain under terms of the BSD Simplified license.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions are met:
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in the
* documentation and/or other materials provided with the distribution.
* * Neither the name of the organization nor the names of its contributors
* may be used to endorse or promote products derived from this software
* without specific prior written permission.
*
* See <http://www.opensource.org/licenses/bsd-license>
*/
#include "opencv2/core.hpp"
#include "opencv2/contrib.hpp"
#include "opencv2/highgui.hpp"
#include <iostream>
#include <fstream>
#include <sstream>
using namespace cv;
using namespace std;
static void read_csv(const string& filename, vector<Mat>& images, vector<int>& labels, char separator = ';') {
std::ifstream file(filename.c_str(), ifstream::in);
if (!file) {
string error_message = "No valid input file was given, please check the given filename.";
CV_Error(CV_StsBadArg, error_message);
}
string line, path, classlabel;
while (getline(file, line)) {
stringstream liness(line);
getline(liness, path, separator);
getline(liness, classlabel);
if(!path.empty() && !classlabel.empty()) {
images.push_back(imread(path, 0));
labels.push_back(atoi(classlabel.c_str()));
}
}
}
int main(int argc, const char *argv[]) {
// Check for valid command line arguments, print usage
// if no arguments were given.
if (argc != 2) {
cout << "usage: " << argv[0] << " <csv.ext>" << endl;
exit(1);
}
// Get the path to your CSV.
string fn_csv = string(argv[1]);
// These vectors hold the images and corresponding labels.
vector<Mat> images;
vector<int> labels;
// Read in the data. This can fail if no valid
// input filename is given.
try {
read_csv(fn_csv, images, labels);
} catch (cv::Exception& e) {
cerr << "Error opening file \"" << fn_csv << "\". Reason: " << e.msg << endl;
// nothing more we can do
exit(1);
}
// Quit if there are not enough images for this demo.
if(images.size() <= 1) {
string error_message = "This demo needs at least 2 images to work. Please add more images to your data set!";
CV_Error(CV_StsError, error_message);
}
// Get the height from the first image. We'll need this
// later in code to reshape the images to their original
// size:
int height = images[0].rows;
// The following lines simply get the last images from
// your dataset and remove it from the vector. This is
// done, so that the training data (which we learn the
// cv::FaceRecognizer on) and the test data we test
// the model with, do not overlap.
Mat testSample = images[images.size() - 1];
int testLabel = labels[labels.size() - 1];
images.pop_back();
labels.pop_back();
// The following lines create an LBPH model for
// face recognition and train it with the images and
// labels read from the given CSV file.
//
// The LBPHFaceRecognizer uses Extended Local Binary Patterns
// (it's probably configurable with other operators at a later
// point), and has the following default values
//
// radius = 1
// neighbors = 8
// grid_x = 8
// grid_y = 8
//
// So if you want a LBPH FaceRecognizer using a radius of
// 2 and 16 neighbors, call the factory method with:
//
// cv::createLBPHFaceRecognizer(2, 16);
//
// And if you want a threshold (e.g. 123.0) call it with its default values:
//
// cv::createLBPHFaceRecognizer(1,8,8,8,123.0)
//
Ptr<FaceRecognizer> model = createLBPHFaceRecognizer();
model->train(images, labels);
// The following line predicts the label of a given
// test image:
int predictedLabel = model->predict(testSample);
//
// To get the confidence of a prediction call the model with:
//
// int predictedLabel = -1;
// double confidence = 0.0;
// model->predict(testSample, predictedLabel, confidence);
//
string result_message = format("Predicted class = %d / Actual class = %d.", predictedLabel, testLabel);
cout << result_message << endl;
// Sometimes you'll need to get/set internal model data,
// which isn't exposed by the public cv::FaceRecognizer.
// Since each cv::FaceRecognizer is derived from a
// cv::Algorithm, you can query the data.
//
// First we'll use it to set the threshold of the FaceRecognizer
// to 0.0 without retraining the model. This can be useful if
// you are evaluating the model:
//
model->set("threshold", 0.0);
// Now the threshold of this model is set to 0.0. A prediction
// now returns -1, as it's impossible to have a distance below
// it
predictedLabel = model->predict(testSample);
cout << "Predicted class = " << predictedLabel << endl;
// Show some informations about the model, as there's no cool
// Model data to display as in Eigenfaces/Fisherfaces.
// Due to efficiency reasons the LBP images are not stored
// within the model:
cout << "Model Information:" << endl;
string model_info = format("\tLBPH(radius=%i, neighbors=%i, grid_x=%i, grid_y=%i, threshold=%.2f)",
model->getInt("radius"),
model->getInt("neighbors"),
model->getInt("grid_x"),
model->getInt("grid_y"),
model->getDouble("threshold"));
cout << model_info << endl;
// We could get the histograms for example:
vector<Mat> histograms = model->getMatVector("histograms");
// But should I really visualize it? Probably the length is interesting:
cout << "Size of the histograms: " << histograms[0].total() << endl;
return 0;
}
modules/contrib/doc/facerec/src/facerec_save_load.cpp
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/*
* Copyright (c) 2011. Philipp Wagner <bytefish[at]gmx[dot]de>.
* Released to public domain under terms of the BSD Simplified license.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions are met:
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in the
* documentation and/or other materials provided with the distribution.
* * Neither the name of the organization nor the names of its contributors
* may be used to endorse or promote products derived from this software
* without specific prior written permission.
*
* See <http://www.opensource.org/licenses/bsd-license>
*/
#include "opencv2/contrib.hpp"
#include "opencv2/core.hpp"
#include "opencv2/highgui.hpp"
#include <iostream>
#include <fstream>
#include <sstream>
using namespace cv;
using namespace std;
static Mat norm_0_255(InputArray _src) {
Mat src = _src.getMat();
// Create and return normalized image:
Mat dst;
switch(src.channels()) {
case 1:
cv::normalize(_src, dst, 0, 255, NORM_MINMAX, CV_8UC1);
break;
case 3:
cv::normalize(_src, dst, 0, 255, NORM_MINMAX, CV_8UC3);
break;
default:
src.copyTo(dst);
break;
}
return dst;
}
static void read_csv(const string& filename, vector<Mat>& images, vector<int>& labels, char separator = ';') {
std::ifstream file(filename.c_str(), ifstream::in);
if (!file) {
string error_message = "No valid input file was given, please check the given filename.";
CV_Error(CV_StsBadArg, error_message);
}
string line, path, classlabel;
while (getline(file, line)) {
stringstream liness(line);
getline(liness, path, separator);
getline(liness, classlabel);
if(!path.empty() && !classlabel.empty()) {
images.push_back(imread(path, 0));
labels.push_back(atoi(classlabel.c_str()));
}
}
}
int main(int argc, const char *argv[]) {
// Check for valid command line arguments, print usage
// if no arguments were given.
if (argc < 2) {
cout << "usage: " << argv[0] << " <csv.ext> <output_folder> " << endl;
exit(1);
}
string output_folder = ".";
if (argc == 3) {
output_folder = string(argv[2]);
}
// Get the path to your CSV.
string fn_csv = string(argv[1]);
// These vectors hold the images and corresponding labels.
vector<Mat> images;
vector<int> labels;
// Read in the data. This can fail if no valid
// input filename is given.
try {
read_csv(fn_csv, images, labels);
} catch (cv::Exception& e) {
cerr << "Error opening file \"" << fn_csv << "\". Reason: " << e.msg << endl;
// nothing more we can do
exit(1);
}
// Quit if there are not enough images for this demo.
if(images.size() <= 1) {
string error_message = "This demo needs at least 2 images to work. Please add more images to your data set!";
CV_Error(CV_StsError, error_message);
}
// Get the height from the first image. We'll need this
// later in code to reshape the images to their original
// size:
int height = images[0].rows;
// The following lines simply get the last images from
// your dataset and remove it from the vector. This is
// done, so that the training data (which we learn the
// cv::FaceRecognizer on) and the test data we test
// the model with, do not overlap.
Mat testSample = images[images.size() - 1];
int testLabel = labels[labels.size() - 1];
images.pop_back();
labels.pop_back();
// The following lines create an Eigenfaces model for
// face recognition and train it with the images and
// labels read from the given CSV file.
// This here is a full PCA, if you just want to keep
// 10 principal components (read Eigenfaces), then call
// the factory method like this:
//
// cv::createEigenFaceRecognizer(10);
//
// If you want to create a FaceRecognizer with a
// confidence threshold (e.g. 123.0), call it with:
//
// cv::createEigenFaceRecognizer(10, 123.0);
//
// If you want to use _all_ Eigenfaces and have a threshold,
// then call the method like this:
//
// cv::createEigenFaceRecognizer(0, 123.0);
//
Ptr<FaceRecognizer> model0 = createEigenFaceRecognizer();
model0->train(images, labels);
// save the model to eigenfaces_at.yaml
model0->save("eigenfaces_at.yml");
//
//
// Now create a new Eigenfaces Recognizer
//
Ptr<FaceRecognizer> model1 = createEigenFaceRecognizer();
model1->load("eigenfaces_at.yml");
// The following line predicts the label of a given
// test image:
int predictedLabel = model1->predict(testSample);
//
// To get the confidence of a prediction call the model with:
//
// int predictedLabel = -1;
// double confidence = 0.0;
// model->predict(testSample, predictedLabel, confidence);
//
string result_message = format("Predicted class = %d / Actual class = %d.", predictedLabel, testLabel);
cout << result_message << endl;
// Here is how to get the eigenvalues of this Eigenfaces model:
Mat eigenvalues = model1->getMat("eigenvalues");
// And we can do the same to display the Eigenvectors (read Eigenfaces):
Mat W = model1->getMat("eigenvectors");
// Get the sample mean from the training data
Mat mean = model1->getMat("mean");
// Display or save:
if(argc == 2) {
imshow("mean", norm_0_255(mean.reshape(1, images[0].rows)));
} else {
imwrite(format("%s/mean.png", output_folder.c_str()), norm_0_255(mean.reshape(1, images[0].rows)));
}
// Display or save the Eigenfaces:
for (int i = 0; i < min(10, W.cols); i++) {
string msg = format("Eigenvalue #%d = %.5f", i, eigenvalues.at<double>(i));
cout << msg << endl;
// get eigenvector #i
Mat ev = W.col(i).clone();
// Reshape to original size & normalize to [0...255] for imshow.
Mat grayscale = norm_0_255(ev.reshape(1, height));
// Show the image & apply a Jet colormap for better sensing.
Mat cgrayscale;
applyColorMap(grayscale, cgrayscale, COLORMAP_JET);
// Display or save:
if(argc == 2) {
imshow(format("eigenface_%d", i), cgrayscale);
} else {
imwrite(format("%s/eigenface_%d.png", output_folder.c_str(), i), norm_0_255(cgrayscale));
}
}
// Display or save the image reconstruction at some predefined steps:
for(int num_components = 10; num_components < 300; num_components+=15) {
// slice the eigenvectors from the model
Mat evs = Mat(W, Range::all(), Range(0, num_components));
Mat projection = subspaceProject(evs, mean, images[0].reshape(1,1));
Mat reconstruction = subspaceReconstruct(evs, mean, projection);
// Normalize the result:
reconstruction = norm_0_255(reconstruction.reshape(1, images[0].rows));
// Display or save:
if(argc == 2) {
imshow(format("eigenface_reconstruction_%d", num_components), reconstruction);
} else {
imwrite(format("%s/eigenface_reconstruction_%d.png", output_folder.c_str(), num_components), reconstruction);
}
}
// Display if we are not writing to an output folder:
if(argc == 2) {
waitKey(0);
}
return 0;
}
modules/contrib/doc/facerec/src/facerec_video.cpp
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/*
* Copyright (c) 2011. Philipp Wagner <bytefish[at]gmx[dot]de>.
* Released to public domain under terms of the BSD Simplified license.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions are met:
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in the
* documentation and/or other materials provided with the distribution.
* * Neither the name of the organization nor the names of its contributors
* may be used to endorse or promote products derived from this software
* without specific prior written permission.
*
* See <http://www.opensource.org/licenses/bsd-license>
*/
#include "opencv2/core.hpp"
#include "opencv2/contrib.hpp"
#include "opencv2/highgui.hpp"
#include "opencv2/imgproc.hpp"
#include "opencv2/objdetect.hpp"
#include <iostream>
#include <fstream>
#include <sstream>
using namespace cv;
using namespace std;
static void read_csv(const string& filename, vector<Mat>& images, vector<int>& labels, char separator = ';') {
std::ifstream file(filename.c_str(), ifstream::in);
if (!file) {
string error_message = "No valid input file was given, please check the given filename.";
CV_Error(CV_StsBadArg, error_message);
}
string line, path, classlabel;
while (getline(file, line)) {
stringstream liness(line);
getline(liness, path, separator);
getline(liness, classlabel);
if(!path.empty() && !classlabel.empty()) {
images.push_back(imread(path, 0));
labels.push_back(atoi(classlabel.c_str()));
}
}
}
int main(int argc, const char *argv[]) {
// Check for valid command line arguments, print usage
// if no arguments were given.
if (argc != 4) {
cout << "usage: " << argv[0] << " </path/to/haar_cascade> </path/to/csv.ext> </path/to/device id>" << endl;
cout << "\t </path/to/haar_cascade> -- Path to the Haar Cascade for face detection." << endl;
cout << "\t </path/to/csv.ext> -- Path to the CSV file with the face database." << endl;
cout << "\t <device id> -- The webcam device id to grab frames from." << endl;
exit(1);
}
// Get the path to your CSV:
string fn_haar = string(argv[1]);
string fn_csv = string(argv[2]);
int deviceId = atoi(argv[3]);
// These vectors hold the images and corresponding labels:
vector<Mat> images;
vector<int> labels;
// Read in the data (fails if no valid input filename is given, but you'll get an error message):
try {
read_csv(fn_csv, images, labels);
} catch (cv::Exception& e) {
cerr << "Error opening file \"" << fn_csv << "\". Reason: " << e.msg << endl;
// nothing more we can do
exit(1);
}
// Get the height from the first image. We'll need this
// later in code to reshape the images to their original
// size AND we need to reshape incoming faces to this size:
int im_width = images[0].cols;
int im_height = images[0].rows;
// Create a FaceRecognizer and train it on the given images:
Ptr<FaceRecognizer> model = createFisherFaceRecognizer();
model->train(images, labels);
// That's it for learning the Face Recognition model. You now
// need to create the classifier for the task of Face Detection.
// We are going to use the haar cascade you have specified in the
// command line arguments:
//
CascadeClassifier haar_cascade;
haar_cascade.load(fn_haar);
// Get a handle to the Video device:
VideoCapture cap(deviceId);
// Check if we can use this device at all:
if(!cap.isOpened()) {
cerr << "Capture Device ID " << deviceId << "cannot be opened." << endl;
return -1;
}
// Holds the current frame from the Video device:
Mat frame;
for(;;) {
cap >> frame;
// Clone the current frame:
Mat original = frame.clone();
// Convert the current frame to grayscale:
Mat gray;
cvtColor(original, gray, CV_BGR2GRAY);
// Find the faces in the frame:
vector< Rect_<int> > faces;
haar_cascade.detectMultiScale(gray, faces);
// At this point you have the position of the faces in
// faces. Now we'll get the faces, make a prediction and
// annotate it in the video. Cool or what?
for(int i = 0; i < faces.size(); i++) {
// Process face by face:
Rect face_i = faces[i];
// Crop the face from the image. So simple with OpenCV C++:
Mat face = gray(face_i);
// Resizing the face is necessary for Eigenfaces and Fisherfaces. You can easily
// verify this, by reading through the face recognition tutorial coming with OpenCV.
// Resizing IS NOT NEEDED for Local Binary Patterns Histograms, so preparing the
// input data really depends on the algorithm used.
//
// I strongly encourage you to play around with the algorithms. See which work best
// in your scenario, LBPH should always be a contender for robust face recognition.
//
// Since I am showing the Fisherfaces algorithm here, I also show how to resize the
// face you have just found:
Mat face_resized;
cv::resize(face, face_resized, Size(im_width, im_height), 1.0, 1.0, INTER_CUBIC);
// Now perform the prediction, see how easy that is:
int prediction = model->predict(face_resized);
// And finally write all we've found out to the original image!
// First of all draw a green rectangle around the detected face:
rectangle(original, face_i, CV_RGB(0, 255,0), 1);
// Create the text we will annotate the box with:
string box_text = format("Prediction = %d", prediction);
// Calculate the position for annotated text (make sure we don't
// put illegal values in there):
int pos_x = std::max(face_i.tl().x - 10, 0);
int pos_y = std::max(face_i.tl().y - 10, 0);
// And now put it into the image:
putText(original, box_text, Point(pos_x, pos_y), FONT_HERSHEY_PLAIN, 1.0, CV_RGB(0,255,0), 2.0);
}
// Show the result:
imshow("face_recognizer", original);
// And display it:
char key = (char) waitKey(20);
// Exit this loop on escape:
if(key == 27)
break;
}
return 0;
}
modules/contrib/doc/facerec/tutorial/facerec_gender_classification.rst
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Gender Classification with OpenCV
=================================
.. contents:: Table of Contents
:depth: 3
Introduction
------------
A lot of people interested in face recognition, also want to know how to perform image classification tasks like:
* Gender Classification (Gender Detection)
* Emotion Classification (Emotion Detection)
* Glasses Classification (Glasses Detection)
* ...
This is has become very, very easy with the new :ocv:class:`FaceRecognizer` class. In this tutorial I'll show you how to perform gender classification with OpenCV on a set of face images. You'll also learn how to align your images to enhance the recognition results. If you want to do emotion classification instead of gender classification, all you need to do is to update is your training data and the configuration you pass to the demo.
Prerequisites
--------------
For gender classification of faces, you'll need some images of male and female faces first. I've decided to search faces of celebrities using `Google Images <http://www.google.com/images>`_ with the faces filter turned on (my god, they have great algorithms at `Google <http://www.google.com>`_!). My database has 8 male and 5 female subjects, each with 10 images. Here are the names, if you don't know who to search:
* Angelina Jolie
* Arnold Schwarzenegger
* Brad Pitt
* Emma Watson
* George Clooney
* Jennifer Lopez
* Johnny Depp
* Justin Timberlake
* Katy Perry
* Keanu Reeves
* Naomi Watts
* Patrick Stewart
* Tom Cruise
Once you have acquired some images, you'll need to read them. In the demo application I have decided to read the images from a very simple CSV file. Why? Because it's the simplest platform-independent approach I can think of. However, if you know a simpler solution please ping me about it. Basically all the CSV file needs to contain are lines composed of a ``filename`` followed by a ``;`` followed by the ``label`` (as *integer number*), making up a line like this:
.. code-block:: none
/path/to/image.ext;0
Let's dissect the line. ``/path/to/image.ext`` is the path to an image, probably something like this if you are in Windows: ``C:/faces/person0/image0.jpg``. Then there is the separator ``;`` and finally we assign a label ``0`` to the image. Think of the label as the subject (the person, the gender or whatever comes to your mind). In the gender classification scenario, the label is the gender the person has. I'll give the label ``0`` to *male* persons and the label ``1`` is for *female* subjects. So my CSV file looks like this:
.. code-block:: none
/home/philipp/facerec/data/gender/male/keanu_reeves/keanu_reeves_01.jpg;0
/home/philipp/facerec/data/gender/male/keanu_reeves/keanu_reeves_02.jpg;0
/home/philipp/facerec/data/gender/male/keanu_reeves/keanu_reeves_03.jpg;0
...
/home/philipp/facerec/data/gender/female/katy_perry/katy_perry_01.jpg;1
/home/philipp/facerec/data/gender/female/katy_perry/katy_perry_02.jpg;1
/home/philipp/facerec/data/gender/female/katy_perry/katy_perry_03.jpg;1
...
/home/philipp/facerec/data/gender/male/brad_pitt/brad_pitt_01.jpg;0
/home/philipp/facerec/data/gender/male/brad_pitt/brad_pitt_02.jpg;0
/home/philipp/facerec/data/gender/male/brad_pitt/brad_pitt_03.jpg;0
...
/home/philipp/facerec/data/gender/female/emma_watson/emma_watson_08.jpg;1
/home/philipp/facerec/data/gender/female/emma_watson/emma_watson_02.jpg;1
/home/philipp/facerec/data/gender/female/emma_watson/emma_watson_03.jpg;1
All images for this example were chosen to have a frontal face perspective. They have been cropped, scaled and rotated to be aligned at the eyes, just like this set of George Clooney images:
.. image:: ../img/tutorial/gender_classification/clooney_set.png
:align: center
You really don't want to create the CSV file by hand. And you really don't want scale, rotate & translate the images manually. I have prepared you two Python scripts ``create_csv.py`` and ``crop_face.py``, you can find them in the ``src`` folder coming with this documentation. You'll see how to use them in the :ref:`appendixfgc`.
Fisherfaces for Gender Classification
--------------------------------------
If you want to decide whether a person is *male* or *female*, you have to learn the discriminative features of both classes. The Eigenfaces method is based on the Principal Component Analysis, which is an unsupervised statistical model and not suitable for this task. Please see the Face Recognition tutorial for insights into the algorithms. The Fisherfaces instead yields a class-specific linear projection, so it is much better suited for the gender classification task. `http://www.bytefish.de/blog/gender_classification <http://www.bytefish.de/blog/gender_classification>`_ shows the recognition rate of the Fisherfaces method for gender classification.
The Fisherfaces method achieves a 98% recognition rate in a subject-independent cross-validation. A subject-independent cross-validation means *images of the person under test are never used for learning the model*. And could you believe it: you can simply use the facerec_fisherfaces demo, that's inlcuded in OpenCV.
Fisherfaces in OpenCV
---------------------
The source code for this demo application is also available in the ``src`` folder coming with this documentation:
* :download:`src/facerec_fisherfaces.cpp <../src/facerec_fisherfaces.cpp>`
.. literalinclude:: ../src/facerec_fisherfaces.cpp
:language: cpp
:linenos:
Running the Demo
----------------
If you are in Windows, then simply start the demo by running (from command line):
.. code-block:: none
facerec_fisherfaces.exe C:/path/to/your/csv.ext
If you are in Linux, then simply start the demo by running:
.. code-block:: none
./facerec_fisherfaces /path/to/your/csv.ext
If you don't want to display the images, but save them, then pass the desired path to the demo. It works like this in Windows:
.. code-block:: none
facerec_fisherfaces.exe C:/path/to/your/csv.ext C:/path/to/store/results/at
And in Linux:
.. code-block:: none
./facerec_fisherfaces /path/to/your/csv.ext /path/to/store/results/at
Results
-------
If you run the program with your CSV file as parameter, you'll see the Fisherface that separates between male and female images. I've decided to apply a Jet colormap in this demo, so you can see which features the method identifies:
.. image:: ../img/tutorial/gender_classification/fisherface_0.png
The demo also shows the average face of the male and female training images you have passed:
.. image:: ../img/tutorial/gender_classification/mean.png
Moreover it the demo should yield the prediction for the correct gender:
.. code-block:: none
Predicted class = 1 / Actual class = 1.
And for advanced users I have also shown the Eigenvalue for the Fisherface:
.. code-block:: none
Eigenvalue #0 = 152.49493
And the Fisherfaces reconstruction:
.. image:: ../img/tutorial/gender_classification/fisherface_reconstruction_0.png
I hope this gives you an idea how to approach gender classification and the other image classification tasks.
.. _appendixfgc:
Appendix
--------
Creating the CSV File
+++++++++++++++++++++
You don't really want to create the CSV file by hand. I have prepared you a little Python script ``create_csv.py`` (you find it at ``/src/create_csv.py`` coming with this tutorial) that automatically creates you a CSV file. If you have your images in hierarchie like this (``/basepath/<subject>/<image.ext>``):
.. code-block:: none
philipp@mango:~/facerec/data/at$ tree
.
|-- s1
| |-- 1.pgm
| |-- ...
| |-- 10.pgm
|-- s2
| |-- 1.pgm
| |-- ...
| |-- 10.pgm
...
|-- s40
| |-- 1.pgm
| |-- ...
| |-- 10.pgm
Then simply call ``create_csv.py`` with the path to the folder, just like this and you could save the output:
.. code-block:: none
philipp@mango:~/facerec/data$ python create_csv.py
at/s13/2.pgm;0
at/s13/7.pgm;0
at/s13/6.pgm;0
at/s13/9.pgm;0
at/s13/5.pgm;0
at/s13/3.pgm;0
at/s13/4.pgm;0
at/s13/10.pgm;0
at/s13/8.pgm;0
at/s13/1.pgm;0
at/s17/2.pgm;1
at/s17/7.pgm;1
at/s17/6.pgm;1
at/s17/9.pgm;1
at/s17/5.pgm;1
at/s17/3.pgm;1
[...]
Here is the script, if you can't find it:
.. literalinclude:: ../src/create_csv.py
:language: python
:linenos:
Aligning Face Images
++++++++++++++++++++
An accurate alignment of your image data is especially important in tasks like emotion detection, were you need as much detail as possible. Believe me... You don't want to do this by hand. So I've prepared you a tiny Python script. The code is really easy to use. To scale, rotate and crop the face image you just need to call *CropFace(image, eye_left, eye_right, offset_pct, dest_sz)*, where:
* *eye_left* is the position of the left eye
* *eye_right* is the position of the right eye
* *offset_pct* is the percent of the image you want to keep next to the eyes (horizontal, vertical direction)
* *dest_sz* is the size of the output image
If you are using the same *offset_pct* and *dest_sz* for your images, they are all aligned at the eyes.
.. literalinclude:: ../src/crop_face.py
:language: python
:linenos:
Imagine we are given `this photo of Arnold Schwarzenegger <http://en.wikipedia.org/wiki/File:Arnold_Schwarzenegger_edit%28ws%29.jpg>`_, which is under a Public Domain license. The (x,y)-position of the eyes is approximately *(252,364)* for the left and *(420,366)* for the right eye. Now you only need to define the horizontal offset, vertical offset and the size your scaled, rotated & cropped face should have.
Here are some examples:
+---------------------------------+----------------------------------------------------------------------------+
| Configuration | Cropped, Scaled, Rotated Face |
+=================================+============================================================================+
| 0.1 (10%), 0.1 (10%), (200,200) | .. image:: ../img/tutorial/gender_classification/arnie_10_10_200_200.jpg |
+---------------------------------+----------------------------------------------------------------------------+
| 0.2 (20%), 0.2 (20%), (200,200) | .. image:: ../img/tutorial/gender_classification/arnie_20_20_200_200.jpg |
+---------------------------------+----------------------------------------------------------------------------+
| 0.3 (30%), 0.3 (30%), (200,200) | .. image:: ../img/tutorial/gender_classification/arnie_30_30_200_200.jpg |
+---------------------------------+----------------------------------------------------------------------------+
| 0.2 (20%), 0.2 (20%), (70,70) | .. image:: ../img/tutorial/gender_classification/arnie_20_20_70_70.jpg |
+---------------------------------+----------------------------------------------------------------------------+
modules/contrib/doc/facerec/tutorial/facerec_save_load.rst
-46
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@@ -1,46 +0,0 @@
Saving and Loading a FaceRecognizer
===================================
Introduction
------------
Saving and loading a :ocv:class:`FaceRecognizer` is very important. Training a FaceRecognizer can be a very time-intense task, plus it's often impossible to ship the whole face database to the user of your product. The task of saving and loading a FaceRecognizer is easy with :ocv:class:`FaceRecognizer`. You only have to call :ocv:func:`FaceRecognizer::load` for loading and :ocv:func:`FaceRecognizer::save` for saving a :ocv:class:`FaceRecognizer`.
I'll adapt the Eigenfaces example from the :doc:`../facerec_tutorial`: Imagine we want to learn the Eigenfaces of the `AT&T Facedatabase <http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html>`_, store the model to a YAML file and then load it again.
From the loaded model, we'll get a prediction, show the mean, Eigenfaces and the image reconstruction.
Using FaceRecognizer::save and FaceRecognizer::load
-----------------------------------------------------
The source code for this demo application is also available in the ``src`` folder coming with this documentation:
* :download:`src/facerec_save_load.cpp <../src/facerec_save_load.cpp>`
.. literalinclude:: ../src/facerec_save_load.cpp
:language: cpp
:linenos:
Results
-------
``eigenfaces_at.yml`` then contains the model state, we'll simply look at the first 10 lines with ``head eigenfaces_at.yml``:
.. code-block:: none
philipp@mango:~/github/libfacerec-build$ head eigenfaces_at.yml
%YAML:1.0
num_components: 399
mean: !!opencv-matrix
rows: 1
cols: 10304
dt: d
data: [ 8.5558897243107765e+01, 8.5511278195488714e+01,
8.5854636591478695e+01, 8.5796992481203006e+01,
8.5952380952380949e+01, 8.6162907268170414e+01,
8.6082706766917283e+01, 8.5776942355889716e+01,
And here is the Reconstruction, which is the same as the original:
.. image:: ../img/eigenface_reconstruction_opencv.png
:align: center
modules/contrib/doc/facerec/tutorial/facerec_video_recognition.rst
-207
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@@ -1,207 +0,0 @@
Face Recognition in Videos with OpenCV
=======================================
.. contents:: Table of Contents
:depth: 3
Introduction
------------
Whenever you hear the term *face recognition*, you instantly think of surveillance in videos. So performing face recognition in videos (e.g. webcam) is one of the most requested features I have got. I have heard your cries, so here it is. An application, that shows you how to do face recognition in videos! For the face detection part we'll use the awesome :ocv:class:`CascadeClassifier` and we'll use :ocv:class:`FaceRecognizer` for face recognition. This example uses the Fisherfaces method for face recognition, because it is robust against large changes in illumination.
Here is what the final application looks like. As you can see I am only writing the id of the recognized person above the detected face (by the way this id is Arnold Schwarzenegger for my data set):
.. image:: ../img/tutorial/facerec_video/facerec_video.png
:align: center
:scale: 70%
This demo is a basis for your research and it shows you how to implement face recognition in videos. You probably want to extend the application and make it more sophisticated: You could combine the id with the name, then show the confidence of the prediction, recognize the emotion... and and and. But before you send mails, asking what these Haar-Cascade thing is or what a CSV is: Make sure you have read the entire tutorial. It's all explained in here. If you just want to scroll down to the code, please note:
* The available Haar-Cascades for face detection are located in the ``data`` folder of your OpenCV installation! One of the available Haar-Cascades for face detection is for example ``/path/to/opencv/data/haarcascades/haarcascade_frontalface_default.xml``.
I encourage you to experiment with the application. Play around with the available :ocv:class:`FaceRecognizer` implementations, try the available cascades in OpenCV and see if you can improve your results!
Prerequisites
--------------
You want to do face recognition, so you need some face images to learn a :ocv:class:`FaceRecognizer` on. I have decided to reuse the images from the gender classification example: :doc:`facerec_gender_classification`.
I have the following celebrities in my training data set:
* Angelina Jolie
* Arnold Schwarzenegger
* Brad Pitt
* George Clooney
* Johnny Depp
* Justin Timberlake
* Katy Perry
* Keanu Reeves
* Patrick Stewart
* Tom Cruise
In the demo I have decided to read the images from a very simple CSV file. Why? Because it's the simplest platform-independent approach I can think of. However, if you know a simpler solution please ping me about it. Basically all the CSV file needs to contain are lines composed of a ``filename`` followed by a ``;`` followed by the ``label`` (as *integer number*), making up a line like this:
.. code-block:: none
/path/to/image.ext;0
Let's dissect the line. ``/path/to/image.ext`` is the path to an image, probably something like this if you are in Windows: ``C:/faces/person0/image0.jpg``. Then there is the separator ``;`` and finally we assign a label ``0`` to the image. Think of the label as the subject (the person, the gender or whatever comes to your mind). In the face recognition scenario, the label is the person this image belongs to. In the gender classification scenario, the label is the gender the person has. So my CSV file looks like this:
.. code-block:: none
/home/philipp/facerec/data/c/keanu_reeves/keanu_reeves_01.jpg;0
/home/philipp/facerec/data/c/keanu_reeves/keanu_reeves_02.jpg;0
/home/philipp/facerec/data/c/keanu_reeves/keanu_reeves_03.jpg;0
...
/home/philipp/facerec/data/c/katy_perry/katy_perry_01.jpg;1
/home/philipp/facerec/data/c/katy_perry/katy_perry_02.jpg;1
/home/philipp/facerec/data/c/katy_perry/katy_perry_03.jpg;1
...
/home/philipp/facerec/data/c/brad_pitt/brad_pitt_01.jpg;2
/home/philipp/facerec/data/c/brad_pitt/brad_pitt_02.jpg;2
/home/philipp/facerec/data/c/brad_pitt/brad_pitt_03.jpg;2
...
/home/philipp/facerec/data/c1/crop_arnold_schwarzenegger/crop_08.jpg;6
/home/philipp/facerec/data/c1/crop_arnold_schwarzenegger/crop_05.jpg;6
/home/philipp/facerec/data/c1/crop_arnold_schwarzenegger/crop_02.jpg;6
/home/philipp/facerec/data/c1/crop_arnold_schwarzenegger/crop_03.jpg;6
All images for this example were chosen to have a frontal face perspective. They have been cropped, scaled and rotated to be aligned at the eyes, just like this set of George Clooney images:
.. image:: ../img/tutorial/gender_classification/clooney_set.png
:align: center
Face Recongition from Videos
-----------------------------
The source code for the demo is available in the ``src`` folder coming with this documentation:
* :download:`src/facerec_video.cpp <../src/facerec_video.cpp>`
This demo uses the :ocv:class:`CascadeClassifier`:
.. literalinclude:: ../src/facerec_video.cpp
:language: cpp
:linenos:
Running the Demo
----------------
You'll need:
* The path to a valid Haar-Cascade for detecting a face with a :ocv:class:`CascadeClassifier`.
* The path to a valid CSV File for learning a :ocv:class:`FaceRecognizer`.
* A webcam and its device id (you don't know the device id? Simply start from 0 on and see what happens).
If you are in Windows, then simply start the demo by running (from command line):
.. code-block:: none
facerec_video.exe <C:/path/to/your/haar_cascade.xml> <C:/path/to/your/csv.ext> <video device>
If you are in Linux, then simply start the demo by running:
.. code-block:: none
./facerec_video </path/to/your/haar_cascade.xml> </path/to/your/csv.ext> <video device>
An example. If the haar-cascade is at ``C:/opencv/data/haarcascades/haarcascade_frontalface_default.xml``, the CSV file is at ``C:/facerec/data/celebrities.txt`` and I have a webcam with deviceId ``1``, then I would call the demo with:
.. code-block:: none
facerec_video.exe C:/opencv/data/haarcascades/haarcascade_frontalface_default.xml C:/facerec/data/celebrities.txt 1
That's it.
Results
-------
Enjoy!
Appendix
--------
Creating the CSV File
+++++++++++++++++++++
You don't really want to create the CSV file by hand. I have prepared you a little Python script ``create_csv.py`` (you find it at ``/src/create_csv.py`` coming with this tutorial) that automatically creates you a CSV file. If you have your images in hierarchie like this (``/basepath/<subject>/<image.ext>``):
.. code-block:: none
philipp@mango:~/facerec/data/at$ tree
.
|-- s1
| |-- 1.pgm
| |-- ...
| |-- 10.pgm
|-- s2
| |-- 1.pgm
| |-- ...
| |-- 10.pgm
...
|-- s40
| |-- 1.pgm
| |-- ...
| |-- 10.pgm
Then simply call ``create_csv.py`` with the path to the folder, just like this and you could save the output:
.. code-block:: none
philipp@mango:~/facerec/data$ python create_csv.py
at/s13/2.pgm;0
at/s13/7.pgm;0
at/s13/6.pgm;0
at/s13/9.pgm;0
at/s13/5.pgm;0
at/s13/3.pgm;0
at/s13/4.pgm;0
at/s13/10.pgm;0
at/s13/8.pgm;0
at/s13/1.pgm;0
at/s17/2.pgm;1
at/s17/7.pgm;1
at/s17/6.pgm;1
at/s17/9.pgm;1
at/s17/5.pgm;1
at/s17/3.pgm;1
[...]
Here is the script, if you can't find it:
.. literalinclude:: ../src/create_csv.py
:language: python
:linenos:
Aligning Face Images
++++++++++++++++++++
An accurate alignment of your image data is especially important in tasks like emotion detection, were you need as much detail as possible. Believe me... You don't want to do this by hand. So I've prepared you a tiny Python script. The code is really easy to use. To scale, rotate and crop the face image you just need to call *CropFace(image, eye_left, eye_right, offset_pct, dest_sz)*, where:
* *eye_left* is the position of the left eye
* *eye_right* is the position of the right eye
* *offset_pct* is the percent of the image you want to keep next to the eyes (horizontal, vertical direction)
* *dest_sz* is the size of the output image
If you are using the same *offset_pct* and *dest_sz* for your images, they are all aligned at the eyes.
.. literalinclude:: ../src/crop_face.py
:language: python
:linenos:
Imagine we are given `this photo of Arnold Schwarzenegger <http://en.wikipedia.org/wiki/File:Arnold_Schwarzenegger_edit%28ws%29.jpg>`_, which is under a Public Domain license. The (x,y)-position of the eyes is approximately *(252,364)* for the left and *(420,366)* for the right eye. Now you only need to define the horizontal offset, vertical offset and the size your scaled, rotated & cropped face should have.
Here are some examples:
+---------------------------------+----------------------------------------------------------------------------+
| Configuration | Cropped, Scaled, Rotated Face |
+=================================+============================================================================+
| 0.1 (10%), 0.1 (10%), (200,200) | .. image:: ../img/tutorial/gender_classification/arnie_10_10_200_200.jpg |
+---------------------------------+----------------------------------------------------------------------------+
| 0.2 (20%), 0.2 (20%), (200,200) | .. image:: ../img/tutorial/gender_classification/arnie_20_20_200_200.jpg |
+---------------------------------+----------------------------------------------------------------------------+
| 0.3 (30%), 0.3 (30%), (200,200) | .. image:: ../img/tutorial/gender_classification/arnie_30_30_200_200.jpg |
+---------------------------------+----------------------------------------------------------------------------+
| 0.2 (20%), 0.2 (20%), (70,70) | .. image:: ../img/tutorial/gender_classification/arnie_20_20_70_70.jpg |
+---------------------------------+----------------------------------------------------------------------------+
modules/contrib/doc/openfabmap.rst
-232
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@@ -1,232 +0,0 @@
OpenFABMAP
========================================
.. highlight:: cpp
The openFABMAP package has been integrated into OpenCV from the openFABMAP <http://code.google.com/p/openfabmap/> project [ICRA2011]_. OpenFABMAP is an open and modifiable code-source which implements the Fast Appearance-based Mapping algorithm (FAB-MAP) developed by Mark Cummins and Paul Newman. The algorithms used in openFABMAP were developed using only the relevant FAB-MAP publications.
FAB-MAP is an approach to appearance-based place recognition. FAB-MAP compares images of locations that have been visited and determines the probability of re-visiting a location, as well as providing a measure of the probability of being at a new, previously unvisited location. Camera images form the sole input to the system, from which visual bag-of-words models are formed through the extraction of appearance-based (e.g. SURF) features.
openFABMAP requires training data (e.g. a collection of images from a similar but not identical environment) to construct a visual vocabulary for the visual bag-of-words model, along with a Chow-Liu tree representation of feature likelihood and for use in the Sampled new place method (see below).
.. note::
* An example using the openFABMAP package can be found at opencv_source_code/samples/cpp/fabmap_sample.cpp
of2::FabMap
--------------------
.. ocv:class:: of2::FabMap
The main FabMap class performs the comparison between visual bags-of-words extracted from one or more images. The FabMap class is instantiated as one of the four inherited FabMap classes (FabMap1, FabMapLUT, FabMapFBO, FabMap2). Each inherited class performs the comparison differently based on algorithm iterations as published (see each class below for specifics). A Chow-Liu tree, detector model parameters and some option flags are common to all Fabmap variants and are supplied on class creation. Training data (visual bag-of-words) is supplied to the class if using the SAMPLED new place method. Test data (visual bag-of-words) is supplied as images to which query bag-of-words are compared against. The common flags are listed below: ::
enum {
MEAN_FIELD,
SAMPLED,
NAIVE_BAYES,
CHOW_LIU,
MOTION_MODEL
};
#. MEAN_FIELD: Use the Mean Field approximation to determine the new place likelihood (cannot be used for FabMap2).
#. SAMPLED: Use the Sampled approximation to determine the new place likelihood. Requires training data (see below).
#. NAIVE_BAYES: Assume a naive Bayes approximation to feature distribution (i.e. all features are independent). Note that a Chow-Liu tree is still required but only the absolute word probabilities are used, feature co-occurrance information is discarded.
#. CHOW_LIU: Use the full Chow-Liu tree to approximate feature distribution.
#. MOTION_MODEL: Update the location distribution using the previous distribution as a (weak) prior. Used for matching in sequences (i.e. successive video frames).
Training Data
++++++++++++++++++++
Training data is required to use the SAMPLED new place method. The SAMPLED method was shown to have improved performance over the alternative MEAN_FIELD method. Training data can be added singularly or as a batch.
.. ocv:function:: virtual void addTraining(const Mat& queryImgDescriptor)
:param queryImgDescriptor: bag-of-words image descriptors stored as rows in a Mat
.. ocv:function:: virtual void addTraining(const vector<Mat>& queryImgDescriptors)
:param queryImgDescriptors: a vector containing multiple bag-of-words image descriptors
.. ocv:function:: const vector<Mat>& getTrainingImgDescriptors() const
Returns a vector containing multiple bag-of-words image descriptors
Test Data
++++++++++++++++++++
Test Data is the database of images represented using bag-of-words models. When a compare function is called, each query point is compared to the test data.
.. ocv:function:: virtual void add(const Mat& queryImgDescriptor)
:param queryImgDescriptor: bag-of-words image descriptors stored as rows in a Mat
.. ocv:function:: virtual void add(const vector<Mat>& queryImgDescriptors)
:param queryImgDescriptors: a vector containing multiple bag-of-words image descriptors
.. ocv:function:: const vector<Mat>& getTestImgDescriptors() const
Returns a vector containing multiple bag-of-words image descriptors
Image Comparison
++++++++++++++++++++
Image matching is performed calling the compare function. Query bag-of-words image descriptors are provided and compared to test data added to the FabMap class. Alternatively test data can be provided with the call to compare to which the comparison is performed. Results are written to the 'matches' argument.
.. ocv:function:: void compare(const Mat& queryImgDescriptor, vector<IMatch>& matches, bool addQuery = false, const Mat& mask = Mat())
:param queryImgDescriptor: bag-of-words image descriptors stored as rows in a Mat
:param matches: a vector of image match probabilities
:param addQuery: if true the queryImg Descriptor is added to the test data after the comparison is performed.
:param mask: *not implemented*
.. ocv:function:: void compare(const Mat& queryImgDescriptor, const Mat& testImgDescriptors, vector<IMatch>& matches, const Mat& mask = Mat())
:param testImgDescriptors: bag-of-words image descriptors stored as rows in a Mat
.. ocv:function:: void compare(const Mat& queryImgDescriptor, const vector<Mat>& testImgDescriptors, vector<IMatch>& matches, const Mat& mask = Mat())
:param testImgDescriptors: a vector of multiple bag-of-words image descriptors
.. ocv:function:: void compare(const vector<Mat>& queryImgDescriptors, vector<IMatch>& matches, bool addQuery = false, const Mat& mask = Mat())
:param queryImgDescriptors: a vector of multiple bag-of-words image descriptors
.. ocv:function:: void compare(const vector<Mat>& queryImgDescriptors, const vector<Mat>& testImgDescriptors, vector<IMatch>& matches, const Mat& mask = Mat())
FabMap classes
++++++++++++++++++++
.. ocv:class:: FabMap1 : public FabMap
The original FAB-MAP algorithm without any computational improvements as published in [IJRR2008]_
.. ocv:function:: FabMap1::FabMap1(const Mat& clTree, double PzGe, double PzGNe, int flags, int numSamples = 0)
:param clTree: a Chow-Liu tree class
:param PzGe: the dector model recall. The probability of the feature detector extracting a feature from an object given it is in the scene. This is used to account for detector noise.
:param PzGNe: the dector model precision. The probability of the feature detector falsing extracting a feature representing an object that is not in the scene.
:param numSamples: the number of samples to use for the SAMPLED new place calculation
.. ocv:class:: FabMapLUT : public FabMap
The original FAB-MAP algorithm implemented as a look-up table for speed enhancements [ICRA2011]_
.. ocv:function:: FabMapLUT::FabMapLUT(const Mat& clTree, double PzGe, double PzGNe, int flags, int numSamples = 0, int precision = 6)
:param precision: the precision with which to store the pre-computed likelihoods
.. ocv:class:: FabMapFBO : public FabMap
The accelerated FAB-MAP using a 'fast bail-out' approach as in [TRO2010]_
.. ocv:function:: FabMapFBO::FabMapFBO(const Mat& clTree, double PzGe, double PzGNe, int flags, int numSamples = 0, double rejectionThreshold = 1e-8, double PsGd = 1e-8, int bisectionStart = 512, int bisectionIts = 9)
:param rejectionThreshold: images are not considered a match when the likelihood falls below the Bennett bound by the amount given by the rejectionThreshold. The threshold provides a speed/accuracy trade-off. A lower bound will be more accurate
:param PsGd: used to calculate the Bennett bound. Provides a speed/accuracy trade-off. A lower bound will be more accurate
:param bisectionStart: Used to estimate the bound using the bisection method. Must be larger than the largest expected difference between maximum and minimum image likelihoods
:param bisectionIts: The number of iterations for which to perform the bisection method
.. ocv:class:: FabMap2 : public FabMap
The inverted index FAB-MAP as in [IJRR2010]_. This version of FAB-MAP is the fastest without any loss of accuracy.
.. ocv:function:: FabMap2::FabMap2(const Mat& clTree, double PzGe, double PzGNe, int flags)
.. [IJRR2008] M. Cummins and P. Newman, "FAB-MAP: Probabilistic Localization and Mapping in the Space of Appearance," The International Journal of Robotics Research, vol. 27(6), pp. 647-665, 2008
.. [TRO2010] M. Cummins and P. Newman, "Accelerating FAB-MAP with concentration inequalities," IEEE Transactions on Robotics, vol. 26(6), pp. 1042-1050, 2010
.. [IJRR2010] M. Cummins and P. Newman, "Appearance-only SLAM at large scale with FAB-MAP 2.0," The International Journal of Robotics Research, vol. 30(9), pp. 1100-1123, 2010
.. [ICRA2011] A. Glover, et al., "OpenFABMAP: An Open Source Toolbox for Appearance-based Loop Closure Detection," in IEEE International Conference on Robotics and Automation, St Paul, Minnesota, 2011
of2::IMatch
--------------------
.. ocv:struct:: of2::IMatch
FAB-MAP comparison results are stored in a vector of IMatch structs. Each IMatch structure provides the index of the provided query bag-of-words, the index of the test bag-of-words, the raw log-likelihood of the match (independent of other comparisons), and the match probability (normalised over other comparison likelihoods).
::
struct IMatch {
IMatch() :
queryIdx(-1), imgIdx(-1), likelihood(-DBL_MAX), match(-DBL_MAX) {
}
IMatch(int _queryIdx, int _imgIdx, double _likelihood, double _match) :
queryIdx(_queryIdx), imgIdx(_imgIdx), likelihood(_likelihood), match(
_match) {
}
int queryIdx; //query index
int imgIdx; //test index
double likelihood; //raw loglikelihood
double match; //normalised probability
bool operator<(const IMatch& m) const {
return match < m.match;
}
};
of2::ChowLiuTree
--------------------
.. ocv:class:: of2::ChowLiuTree
The Chow-Liu tree is a probabilistic model of the environment in terms of feature occurance and co-occurance. The Chow-Liu tree is a form of Bayesian network. FAB-MAP uses the model when calculating bag-of-words similarity by taking into account feature saliency. Training data is provided to the ChowLiuTree class in the form of bag-of-words image descriptors. The make function produces a cv::Mat that encodes the tree structure.
.. ocv:function:: of2::ChowLiuTree::ChowLiuTree()
.. ocv:function:: void of2::ChowLiuTree::add(const Mat& imgDescriptor)
:param imgDescriptor: bag-of-words image descriptors stored as rows in a Mat
.. ocv:function:: void of2::ChowLiuTree::add(const vector<Mat>& imgDescriptors)
:param imgDescriptors: a vector containing multiple bag-of-words image descriptors
.. ocv:function:: const vector<Mat>& of2::ChowLiuTree::getImgDescriptors() const
Returns a vector containing multiple bag-of-words image descriptors
.. ocv:function:: Mat of2::ChowLiuTree::make(double infoThreshold = 0.0)
:param infoThreshold: a threshold can be set to reduce the amount of memory used when making the Chow-Liu tree, which can occur with large vocabulary sizes. This function can fail if the threshold is set too high. If memory is an issue the value must be set by trial and error (~0.0005)
of2::BOWMSCTrainer
--------------------
.. ocv:class:: of2::BOWMSCTrainer : public of2::BOWTrainer
BOWMSCTrainer is a custom clustering algorithm used to produce the feature vocabulary required to create bag-of-words representations. The algorithm is an implementation of [AVC2007]_. Arguments against using K-means for the FAB-MAP algorithm are discussed in [IJRR2010]_. The BOWMSCTrainer inherits from the cv::BOWTrainer class, overwriting the cluster function.
.. ocv:function:: of2::BOWMSCTrainer::BOWMSCTrainer(double clusterSize = 0.4)
:param clusterSize: the specificity of the vocabulary produced. A smaller cluster size will instigate a larger vocabulary.
.. ocv:function:: virtual Mat of2::BOWMSCTrainer::cluster() const
Cluster using features added to the class
.. ocv:function:: virtual Mat of2::BOWMSCTrainer::cluster(const Mat& descriptors) const
:param descriptors: feature descriptors provided as rows of the Mat.
.. [AVC2007] Alexandra Teynor and Hans Burkhardt, "Fast Codebook Generation by Sequential Data Analysis for Object Classification", in Advances in Visual Computing, pp. 610-620, 2007
modules/contrib/doc/stereo.rst
-115
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@@ -1,115 +0,0 @@
Stereo Correspondence
========================================
.. highlight:: cpp
StereoVar
----------
.. ocv:class:: StereoVar
Class for computing stereo correspondence using the variational matching algorithm ::
class StereoVar
{
StereoVar();
StereoVar( int levels, double pyrScale,
int nIt, int minDisp, int maxDisp,
int poly_n, double poly_sigma, float fi,
float lambda, int penalization, int cycle,
int flags);
virtual ~StereoVar();
virtual void operator()(InputArray left, InputArray right, OutputArray disp);
int levels;
double pyrScale;
int nIt;
int minDisp;
int maxDisp;
int poly_n;
double poly_sigma;
float fi;
float lambda;
int penalization;
int cycle;
int flags;
...
};
The class implements the modified S. G. Kosov algorithm [Publication] that differs from the original one as follows:
* The automatic initialization of method's parameters is added.
* The method of Smart Iteration Distribution (SID) is implemented.
* The support of Multi-Level Adaptation Technique (MLAT) is not included.
* The method of dynamic adaptation of method's parameters is not included.
StereoVar::StereoVar
--------------------------
.. ocv:function:: StereoVar::StereoVar()
.. ocv:function:: StereoVar::StereoVar( int levels, double pyrScale, int nIt, int minDisp, int maxDisp, int poly_n, double poly_sigma, float fi, float lambda, int penalization, int cycle, int flags )
The constructor
:param levels: The number of pyramid layers, including the initial image. levels=1 means that no extra layers are created and only the original images are used. This parameter is ignored if flag USE_AUTO_PARAMS is set.
:param pyrScale: Specifies the image scale (<1) to build the pyramids for each image. pyrScale=0.5 means the classical pyramid, where each next layer is twice smaller than the previous. (This parameter is ignored if flag USE_AUTO_PARAMS is set).
:param nIt: The number of iterations the algorithm does at each pyramid level. (If the flag USE_SMART_ID is set, the number of iterations will be redistributed in such a way, that more iterations will be done on more coarser levels.)
:param minDisp: Minimum possible disparity value. Could be negative in case the left and right input images change places.
:param maxDisp: Maximum possible disparity value.
:param poly_n: Size of the pixel neighbourhood used to find polynomial expansion in each pixel. The larger values mean that the image will be approximated with smoother surfaces, yielding more robust algorithm and more blurred motion field. Typically, poly_n = 3, 5 or 7
:param poly_sigma: Standard deviation of the Gaussian that is used to smooth derivatives that are used as a basis for the polynomial expansion. For poly_n=5 you can set poly_sigma=1.1 , for poly_n=7 a good value would be poly_sigma=1.5
:param fi: The smoothness parameter, ot the weight coefficient for the smoothness term.
:param lambda: The threshold parameter for edge-preserving smoothness. (This parameter is ignored if PENALIZATION_CHARBONNIER or PENALIZATION_PERONA_MALIK is used.)
:param penalization: Possible values: PENALIZATION_TICHONOV - linear smoothness; PENALIZATION_CHARBONNIER - non-linear edge preserving smoothness; PENALIZATION_PERONA_MALIK - non-linear edge-enhancing smoothness. (This parameter is ignored if flag USE_AUTO_PARAMS is set).
:param cycle: Type of the multigrid cycle. Possible values: CYCLE_O and CYCLE_V for null- and v-cycles respectively. (This parameter is ignored if flag USE_AUTO_PARAMS is set).
:param flags: The operation flags; can be a combination of the following:
* USE_INITIAL_DISPARITY: Use the input flow as the initial flow approximation.
* USE_EQUALIZE_HIST: Use the histogram equalization in the pre-processing phase.
* USE_SMART_ID: Use the smart iteration distribution (SID).
* USE_AUTO_PARAMS: Allow the method to initialize the main parameters.
* USE_MEDIAN_FILTERING: Use the median filer of the solution in the post processing phase.
The first constructor initializes ``StereoVar`` with all the default parameters. So, you only have to set ``StereoVar::maxDisp`` and / or ``StereoVar::minDisp`` at minimum. The second constructor enables you to set each parameter to a custom value.
StereoVar::operator ()
-----------------------
.. ocv:function:: void StereoVar::operator()( const Mat& left, const Mat& right, Mat& disp )
Computes disparity using the variational algorithm for a rectified stereo pair.
:param left: Left 8-bit single-channel or 3-channel image.
:param right: Right image of the same size and the same type as the left one.
:param disp: Output disparity map. It is a 8-bit signed single-channel image of the same size as the input image.
The method executes the variational algorithm on a rectified stereo pair. See ``stereo_match.cpp`` OpenCV sample on how to prepare images and call the method.
**Note**:
The method is not constant, so you should not use the same ``StereoVar`` instance from different threads simultaneously.
modules/contrib/include/opencv2/contrib.hpp
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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifndef __OPENCV_CONTRIB_HPP__
#define __OPENCV_CONTRIB_HPP__
#include "opencv2/core.hpp"
#include "opencv2/imgproc.hpp"
#include "opencv2/features2d.hpp"
#include "opencv2/objdetect.hpp"
namespace cv
{
class CV_EXPORTS Octree
{
public:
struct Node
{
Node() { memset(this, 0, sizeof(Node)); }
int begin, end;
float x_min, x_max, y_min, y_max, z_min, z_max;
int maxLevels;
bool isLeaf;
int children[8];
};
Octree();
Octree( const std::vector<Point3f>& points, int maxLevels = 10, int minPoints = 20 );
virtual ~Octree();
virtual void buildTree( const std::vector<Point3f>& points, int maxLevels = 10, int minPoints = 20 );
virtual void getPointsWithinSphere( const Point3f& center, float radius,
std::vector<Point3f>& points ) const;
const std::vector<Node>& getNodes() const { return nodes; }
private:
int minPoints;
std::vector<Point3f> points;
std::vector<Node> nodes;
virtual void buildNext(size_t node_ind);
};
class CV_EXPORTS Mesh3D
{
public:
struct EmptyMeshException {};
Mesh3D();
Mesh3D(const std::vector<Point3f>& vtx);
~Mesh3D();
void buildOctree();
void clearOctree();
float estimateResolution(float tryRatio = 0.1f);
void computeNormals(float normalRadius, int minNeighbors = 20);
void computeNormals(const std::vector<int>& subset, float normalRadius, int minNeighbors = 20);
void writeAsVrml(const String& file, const std::vector<Scalar>& colors = std::vector<Scalar>()) const;
std::vector<Point3f> vtx;
std::vector<Point3f> normals;
float resolution;
Octree octree;
const static Point3f allzero;
};
class CV_EXPORTS SpinImageModel
{
public:
/* model parameters, leave unset for default or auto estimate */
float normalRadius;
int minNeighbors;
float binSize;
int imageWidth;
float lambda;
float gamma;
float T_GeometriccConsistency;
float T_GroupingCorespondances;
/* public interface */
SpinImageModel();
explicit SpinImageModel(const Mesh3D& mesh);
~SpinImageModel();
void selectRandomSubset(float ratio);
void setSubset(const std::vector<int>& subset);
void compute();
void match(const SpinImageModel& scene, std::vector< std::vector<Vec2i> >& result);
Mat packRandomScaledSpins(bool separateScale = false, size_t xCount = 10, size_t yCount = 10) const;
size_t getSpinCount() const { return spinImages.rows; }
Mat getSpinImage(size_t index) const { return spinImages.row((int)index); }
const Point3f& getSpinVertex(size_t index) const { return mesh.vtx[subset[index]]; }
const Point3f& getSpinNormal(size_t index) const { return mesh.normals[subset[index]]; }
const Mesh3D& getMesh() const { return mesh; }
Mesh3D& getMesh() { return mesh; }
/* static utility functions */
static bool spinCorrelation(const Mat& spin1, const Mat& spin2, float lambda, float& result);
static Point2f calcSpinMapCoo(const Point3f& point, const Point3f& vertex, const Point3f& normal);
static float geometricConsistency(const Point3f& pointScene1, const Point3f& normalScene1,
const Point3f& pointModel1, const Point3f& normalModel1,
const Point3f& pointScene2, const Point3f& normalScene2,
const Point3f& pointModel2, const Point3f& normalModel2);
static float groupingCreteria(const Point3f& pointScene1, const Point3f& normalScene1,
const Point3f& pointModel1, const Point3f& normalModel1,
const Point3f& pointScene2, const Point3f& normalScene2,
const Point3f& pointModel2, const Point3f& normalModel2,
float gamma);
protected:
void defaultParams();
void matchSpinToModel(const Mat& spin, std::vector<int>& indeces,
std::vector<float>& corrCoeffs, bool useExtremeOutliers = true) const;
void repackSpinImages(const std::vector<uchar>& mask, Mat& spinImages, bool reAlloc = true) const;
std::vector<int> subset;
Mesh3D mesh;
Mat spinImages;
};
class CV_EXPORTS TickMeter
{
public:
TickMeter();
void start();
void stop();
int64 getTimeTicks() const;
double getTimeMicro() const;
double getTimeMilli() const;
double getTimeSec() const;
int64 getCounter() const;
void reset();
private:
int64 counter;
int64 sumTime;
int64 startTime;
};
//CV_EXPORTS std::ostream& operator<<(std::ostream& out, const TickMeter& tm);
class CV_EXPORTS SelfSimDescriptor
{
public:
SelfSimDescriptor();
SelfSimDescriptor(int _ssize, int _lsize,
int _startDistanceBucket=DEFAULT_START_DISTANCE_BUCKET,
int _numberOfDistanceBuckets=DEFAULT_NUM_DISTANCE_BUCKETS,
int _nangles=DEFAULT_NUM_ANGLES);
SelfSimDescriptor(const SelfSimDescriptor& ss);
virtual ~SelfSimDescriptor();
SelfSimDescriptor& operator = (const SelfSimDescriptor& ss);
size_t getDescriptorSize() const;
Size getGridSize( Size imgsize, Size winStride ) const;
virtual void compute(const Mat& img, std::vector<float>& descriptors, Size winStride=Size(),
const std::vector<Point>& locations=std::vector<Point>()) const;
virtual void computeLogPolarMapping(Mat& mappingMask) const;
virtual void SSD(const Mat& img, Point pt, Mat& ssd) const;
int smallSize;
int largeSize;
int startDistanceBucket;
int numberOfDistanceBuckets;
int numberOfAngles;
enum { DEFAULT_SMALL_SIZE = 5, DEFAULT_LARGE_SIZE = 41,
DEFAULT_NUM_ANGLES = 20, DEFAULT_START_DISTANCE_BUCKET = 3,
DEFAULT_NUM_DISTANCE_BUCKETS = 7 };
};
CV_EXPORTS_W int chamerMatching( Mat& img, Mat& templ,
CV_OUT std::vector<std::vector<Point> >& results, CV_OUT std::vector<float>& cost,
double templScale=1, int maxMatches = 20,
double minMatchDistance = 1.0, int padX = 3,
int padY = 3, int scales = 5, double minScale = 0.6, double maxScale = 1.6,
double orientationWeight = 0.5, double truncate = 20);
class CV_EXPORTS_W StereoVar
{
public:
// Flags
enum {USE_INITIAL_DISPARITY = 1, USE_EQUALIZE_HIST = 2, USE_SMART_ID = 4, USE_AUTO_PARAMS = 8, USE_MEDIAN_FILTERING = 16};
enum {CYCLE_O, CYCLE_V};
enum {PENALIZATION_TICHONOV, PENALIZATION_CHARBONNIER, PENALIZATION_PERONA_MALIK};
//! the default constructor
CV_WRAP StereoVar();
//! the full constructor taking all the necessary algorithm parameters
CV_WRAP StereoVar(int levels, double pyrScale, int nIt, int minDisp, int maxDisp, int poly_n, double poly_sigma, float fi, float lambda, int penalization, int cycle, int flags);
//! the destructor
virtual ~StereoVar();
//! the stereo correspondence operator that computes disparity map for the specified rectified stereo pair
CV_WRAP_AS(compute) virtual void operator()(const Mat& left, const Mat& right, CV_OUT Mat& disp);
CV_PROP_RW int levels;
CV_PROP_RW double pyrScale;
CV_PROP_RW int nIt;
CV_PROP_RW int minDisp;
CV_PROP_RW int maxDisp;
CV_PROP_RW int poly_n;
CV_PROP_RW double poly_sigma;
CV_PROP_RW float fi;
CV_PROP_RW float lambda;
CV_PROP_RW int penalization;
CV_PROP_RW int cycle;
CV_PROP_RW int flags;
private:
void autoParams();
void FMG(Mat &I1, Mat &I2, Mat &I2x, Mat &u, int level);
void VCycle_MyFAS(Mat &I1_h, Mat &I2_h, Mat &I2x_h, Mat &u_h, int level);
void VariationalSolver(Mat &I1_h, Mat &I2_h, Mat &I2x_h, Mat &u_h, int level);
};
CV_EXPORTS void polyfit(const Mat& srcx, const Mat& srcy, Mat& dst, int order);
class CV_EXPORTS Directory
{
public:
static std::vector<String> GetListFiles ( const String& path, const String & exten = "*", bool addPath = true );
static std::vector<String> GetListFilesR ( const String& path, const String & exten = "*", bool addPath = true );
static std::vector<String> GetListFolders( const String& path, const String & exten = "*", bool addPath = true );
};
/*
* Generation of a set of different colors by the following way:
* 1) generate more then need colors (in "factor" times) in RGB,
* 2) convert them to Lab,
* 3) choose the needed count of colors from the set that are more different from
* each other,
* 4) convert the colors back to RGB
*/
CV_EXPORTS void generateColors( std::vector<Scalar>& colors, size_t count, size_t factor=100 );
/*
* Estimate the rigid body motion from frame0 to frame1. The method is based on the paper
* "Real-Time Visual Odometry from Dense RGB-D Images", F. Steinbucker, J. Strum, D. Cremers, ICCV, 2011.
*/
enum { ROTATION = 1,
TRANSLATION = 2,
RIGID_BODY_MOTION = 4
};
CV_EXPORTS bool RGBDOdometry( Mat& Rt, const Mat& initRt,
const Mat& image0, const Mat& depth0, const Mat& mask0,
const Mat& image1, const Mat& depth1, const Mat& mask1,
const Mat& cameraMatrix, float minDepth=0.f, float maxDepth=4.f, float maxDepthDiff=0.07f,
const std::vector<int>& iterCounts=std::vector<int>(),
const std::vector<float>& minGradientMagnitudes=std::vector<float>(),
int transformType=RIGID_BODY_MOTION );
/**
*Bilinear interpolation technique.
*
*The value of a desired cortical pixel is obtained through a bilinear interpolation of the values
*of the four nearest neighbouring Cartesian pixels to the center of the RF.
*The same principle is applied to the inverse transformation.
*
*More details can be found in http://dx.doi.org/10.1007/978-3-642-23968-7_5
*/
class CV_EXPORTS LogPolar_Interp
{
public:
LogPolar_Interp() {}
/**
*Constructor
*\param w the width of the input image
*\param h the height of the input image
*\param center the transformation center: where the output precision is maximal
*\param R the number of rings of the cortical image (default value 70 pixel)
*\param ro0 the radius of the blind spot (default value 3 pixel)
*\param full \a 1 (default value) means that the retinal image (the inverse transform) is computed within the circumscribing circle.
* \a 0 means that the retinal image is computed within the inscribed circle.
*\param S the number of sectors of the cortical image (default value 70 pixel).
* Its value is usually internally computed to obtain a pixel aspect ratio equals to 1.
*\param sp \a 1 (default value) means that the parameter \a S is internally computed.
* \a 0 means that the parameter \a S is provided by the user.
*/
LogPolar_Interp(int w, int h, Point2i center, int R=70, double ro0=3.0,
int interp=INTER_LINEAR, int full=1, int S=117, int sp=1);
/**
*Transformation from Cartesian image to cortical (log-polar) image.
*\param source the Cartesian image
*\return the transformed image (cortical image)
*/
const Mat to_cortical(const Mat &source);
/**
*Transformation from cortical image to retinal (inverse log-polar) image.
*\param source the cortical image
*\return the transformed image (retinal image)
*/
const Mat to_cartesian(const Mat &source);
/**
*Destructor
*/
~LogPolar_Interp();
protected:
Mat Rsri;
Mat Csri;
int S, R, M, N;
int top, bottom,left,right;
double ro0, romax, a, q;
int interp;
Mat ETAyx;
Mat CSIyx;
void create_map(int M, int N, int R, int S, double ro0);
};
/**
*Overlapping circular receptive fields technique
*
*The Cartesian plane is divided in two regions: the fovea and the periphery.
*The fovea (oversampling) is handled by using the bilinear interpolation technique described above, whereas in
*the periphery we use the overlapping Gaussian circular RFs.
*
*More details can be found in http://dx.doi.org/10.1007/978-3-642-23968-7_5
*/
class CV_EXPORTS LogPolar_Overlapping
{
public:
LogPolar_Overlapping() {}
/**
*Constructor
*\param w the width of the input image
*\param h the height of the input image
*\param center the transformation center: where the output precision is maximal
*\param R the number of rings of the cortical image (default value 70 pixel)
*\param ro0 the radius of the blind spot (default value 3 pixel)
*\param full \a 1 (default value) means that the retinal image (the inverse transform) is computed within the circumscribing circle.
* \a 0 means that the retinal image is computed within the inscribed circle.
*\param S the number of sectors of the cortical image (default value 70 pixel).
* Its value is usually internally computed to obtain a pixel aspect ratio equals to 1.
*\param sp \a 1 (default value) means that the parameter \a S is internally computed.
* \a 0 means that the parameter \a S is provided by the user.
*/
LogPolar_Overlapping(int w, int h, Point2i center, int R=70,
double ro0=3.0, int full=1, int S=117, int sp=1);
/**
*Transformation from Cartesian image to cortical (log-polar) image.
*\param source the Cartesian image
*\return the transformed image (cortical image)
*/
const Mat to_cortical(const Mat &source);
/**
*Transformation from cortical image to retinal (inverse log-polar) image.
*\param source the cortical image
*\return the transformed image (retinal image)
*/
const Mat to_cartesian(const Mat &source);
/**
*Destructor
*/
~LogPolar_Overlapping();
protected:
Mat Rsri;
Mat Csri;
std::vector<int> Rsr;
std::vector<int> Csr;
std::vector<double> Wsr;
int S, R, M, N, ind1;
int top, bottom,left,right;
double ro0, romax, a, q;
struct kernel
{
kernel() { w = 0; }
std::vector<double> weights;
int w;
};
Mat ETAyx;
Mat CSIyx;
std::vector<kernel> w_ker_2D;
void create_map(int M, int N, int R, int S, double ro0);
};
/**
* Adjacent receptive fields technique
*
*All the Cartesian pixels, whose coordinates in the cortical domain share the same integer part, are assigned to the same RF.
*The precision of the boundaries of the RF can be improved by breaking each pixel into subpixels and assigning each of them to the correct RF.
*This technique is implemented from: Traver, V., Pla, F.: Log-polar mapping template design: From task-level requirements
*to geometry parameters. Image Vision Comput. 26(10) (2008) 1354-1370
*
*More details can be found in http://dx.doi.org/10.1007/978-3-642-23968-7_5
*/
class CV_EXPORTS LogPolar_Adjacent
{
public:
LogPolar_Adjacent() {}
/**
*Constructor
*\param w the width of the input image
*\param h the height of the input image
*\param center the transformation center: where the output precision is maximal
*\param R the number of rings of the cortical image (default value 70 pixel)
*\param ro0 the radius of the blind spot (default value 3 pixel)
*\param smin the size of the subpixel (default value 0.25 pixel)
*\param full \a 1 (default value) means that the retinal image (the inverse transform) is computed within the circumscribing circle.
* \a 0 means that the retinal image is computed within the inscribed circle.
*\param S the number of sectors of the cortical image (default value 70 pixel).
* Its value is usually internally computed to obtain a pixel aspect ratio equals to 1.
*\param sp \a 1 (default value) means that the parameter \a S is internally computed.
* \a 0 means that the parameter \a S is provided by the user.
*/
LogPolar_Adjacent(int w, int h, Point2i center, int R=70, double ro0=3.0, double smin=0.25, int full=1, int S=117, int sp=1);
/**
*Transformation from Cartesian image to cortical (log-polar) image.
*\param source the Cartesian image
*\return the transformed image (cortical image)
*/
const Mat to_cortical(const Mat &source);
/**
*Transformation from cortical image to retinal (inverse log-polar) image.
*\param source the cortical image
*\return the transformed image (retinal image)
*/
const Mat to_cartesian(const Mat &source);
/**
*Destructor
*/
~LogPolar_Adjacent();
protected:
struct pixel
{
pixel() { u = v = 0; a = 0.; }
int u;
int v;
double a;
};
int S, R, M, N;
int top, bottom,left,right;
double ro0, romax, a, q;
std::vector<std::vector<pixel> > L;
std::vector<double> A;
void subdivide_recursively(double x, double y, int i, int j, double length, double smin);
bool get_uv(double x, double y, int&u, int&v);
void create_map(int M, int N, int R, int S, double ro0, double smin);
};
CV_EXPORTS Mat subspaceProject(InputArray W, InputArray mean, InputArray src);
CV_EXPORTS Mat subspaceReconstruct(InputArray W, InputArray mean, InputArray src);
class CV_EXPORTS LDA
{
public:
// Initializes a LDA with num_components (default 0) and specifies how
// samples are aligned (default dataAsRow=true).
LDA(int num_components = 0) :
_num_components(num_components) { }
// Initializes and performs a Discriminant Analysis with Fisher's
// Optimization Criterion on given data in src and corresponding labels
// in labels. If 0 (or less) number of components are given, they are
// automatically determined for given data in computation.
LDA(InputArrayOfArrays src, InputArray labels,
int num_components = 0) :
_num_components(num_components)
{
this->compute(src, labels); //! compute eigenvectors and eigenvalues
}
// Serializes this object to a given filename.
void save(const String& filename) const;
// Deserializes this object from a given filename.
void load(const String& filename);
// Serializes this object to a given cv::FileStorage.
void save(FileStorage& fs) const;
// Deserializes this object from a given cv::FileStorage.
void load(const FileStorage& node);
// Destructor.
~LDA() {}
//! Compute the discriminants for data in src and labels.
void compute(InputArrayOfArrays src, InputArray labels);
// Projects samples into the LDA subspace.
Mat project(InputArray src);
// Reconstructs projections from the LDA subspace.
Mat reconstruct(InputArray src);
// Returns the eigenvectors of this LDA.
Mat eigenvectors() const { return _eigenvectors; }
// Returns the eigenvalues of this LDA.
Mat eigenvalues() const { return _eigenvalues; }
protected:
bool _dataAsRow;
int _num_components;
Mat _eigenvectors;
Mat _eigenvalues;
void lda(InputArrayOfArrays src, InputArray labels);
};
class CV_EXPORTS_W FaceRecognizer : public Algorithm
{
public:
//! virtual destructor
virtual ~FaceRecognizer() {}
// Trains a FaceRecognizer.
CV_WRAP virtual void train(InputArrayOfArrays src, InputArray labels) = 0;
// Updates a FaceRecognizer.
CV_WRAP virtual void update(InputArrayOfArrays src, InputArray labels);
// Gets a prediction from a FaceRecognizer.
virtual int predict(InputArray src) const = 0;
// Predicts the label and confidence for a given sample.
CV_WRAP virtual void predict(InputArray src, CV_OUT int &label, CV_OUT double &confidence) const = 0;
// Serializes this object to a given filename.
CV_WRAP virtual void save(const String& filename) const;
// Deserializes this object from a given filename.
CV_WRAP virtual void load(const String& filename);
// Serializes this object to a given cv::FileStorage.
virtual void save(FileStorage& fs) const = 0;
// Deserializes this object from a given cv::FileStorage.
virtual void load(const FileStorage& fs) = 0;
};
CV_EXPORTS_W Ptr<FaceRecognizer> createEigenFaceRecognizer(int num_components = 0, double threshold = DBL_MAX);
CV_EXPORTS_W Ptr<FaceRecognizer> createFisherFaceRecognizer(int num_components = 0, double threshold = DBL_MAX);
CV_EXPORTS_W Ptr<FaceRecognizer> createLBPHFaceRecognizer(int radius=1, int neighbors=8,
int grid_x=8, int grid_y=8, double threshold = DBL_MAX);
enum
{
COLORMAP_AUTUMN = 0,
COLORMAP_BONE = 1,
COLORMAP_JET = 2,
COLORMAP_WINTER = 3,
COLORMAP_RAINBOW = 4,
COLORMAP_OCEAN = 5,
COLORMAP_SUMMER = 6,
COLORMAP_SPRING = 7,
COLORMAP_COOL = 8,
COLORMAP_HSV = 9,
COLORMAP_PINK = 10,
COLORMAP_HOT = 11
};
CV_EXPORTS_W void applyColorMap(InputArray src, OutputArray dst, int colormap);
CV_EXPORTS bool initModule_contrib();
}
#include "opencv2/contrib/openfabmap.hpp"
#endif
modules/contrib/include/opencv2/contrib/compat.hpp
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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009-2011, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifndef __OPENCV_CONTRIB_COMPAT_HPP__
#define __OPENCV_CONTRIB_COMPAT_HPP__
#include "opencv2/core/core_c.h"
#ifdef __cplusplus
/****************************************************************************************\
* Adaptive Skin Detector *
\****************************************************************************************/
class CV_EXPORTS CvAdaptiveSkinDetector
{
private:
enum {
GSD_HUE_LT = 3,
GSD_HUE_UT = 33,
GSD_INTENSITY_LT = 15,
GSD_INTENSITY_UT = 250
};
class CV_EXPORTS Histogram
{
private:
enum {
HistogramSize = (GSD_HUE_UT - GSD_HUE_LT + 1)
};
protected:
int findCoverageIndex(double surfaceToCover, int defaultValue = 0);
public:
CvHistogram *fHistogram;
Histogram();
virtual ~Histogram();
void findCurveThresholds(int &x1, int &x2, double percent = 0.05);
void mergeWith(Histogram *source, double weight);
};
int nStartCounter, nFrameCount, nSkinHueLowerBound, nSkinHueUpperBound, nMorphingMethod, nSamplingDivider;
double fHistogramMergeFactor, fHuePercentCovered;
Histogram histogramHueMotion, skinHueHistogram;
IplImage *imgHueFrame, *imgSaturationFrame, *imgLastGrayFrame, *imgMotionFrame, *imgFilteredFrame;
IplImage *imgShrinked, *imgTemp, *imgGrayFrame, *imgHSVFrame;
protected:
void initData(IplImage *src, int widthDivider, int heightDivider);
void adaptiveFilter();
public:
enum {
MORPHING_METHOD_NONE = 0,
MORPHING_METHOD_ERODE = 1,
MORPHING_METHOD_ERODE_ERODE = 2,
MORPHING_METHOD_ERODE_DILATE = 3
};
CvAdaptiveSkinDetector(int samplingDivider = 1, int morphingMethod = MORPHING_METHOD_NONE);
virtual ~CvAdaptiveSkinDetector();
virtual void process(IplImage *inputBGRImage, IplImage *outputHueMask);
};
/****************************************************************************************\
* Fuzzy MeanShift Tracker *
\****************************************************************************************/
class CV_EXPORTS CvFuzzyPoint {
public:
double x, y, value;
CvFuzzyPoint(double _x, double _y);
};
class CV_EXPORTS CvFuzzyCurve {
private:
std::vector<CvFuzzyPoint> points;
double value, centre;
bool between(double x, double x1, double x2);
public:
CvFuzzyCurve();
~CvFuzzyCurve();
void setCentre(double _centre);
double getCentre();
void clear();
void addPoint(double x, double y);
double calcValue(double param);
double getValue();
void setValue(double _value);
};
class CV_EXPORTS CvFuzzyFunction {
public:
std::vector<CvFuzzyCurve> curves;
CvFuzzyFunction();
~CvFuzzyFunction();
void addCurve(CvFuzzyCurve *curve, double value = 0);
void resetValues();
double calcValue();
CvFuzzyCurve *newCurve();
};
class CV_EXPORTS CvFuzzyRule {
private:
CvFuzzyCurve *fuzzyInput1, *fuzzyInput2;
CvFuzzyCurve *fuzzyOutput;
public:
CvFuzzyRule();
~CvFuzzyRule();
void setRule(CvFuzzyCurve *c1, CvFuzzyCurve *c2, CvFuzzyCurve *o1);
double calcValue(double param1, double param2);
CvFuzzyCurve *getOutputCurve();
};
class CV_EXPORTS CvFuzzyController {
private:
std::vector<CvFuzzyRule*> rules;
public:
CvFuzzyController();
~CvFuzzyController();
void addRule(CvFuzzyCurve *c1, CvFuzzyCurve *c2, CvFuzzyCurve *o1);
double calcOutput(double param1, double param2);
};
class CV_EXPORTS CvFuzzyMeanShiftTracker
{
private:
class FuzzyResizer
{
private:
CvFuzzyFunction iInput, iOutput;
CvFuzzyController fuzzyController;
public:
FuzzyResizer();
int calcOutput(double edgeDensity, double density);
};
class SearchWindow
{
public:
FuzzyResizer *fuzzyResizer;
int x, y;
int width, height, maxWidth, maxHeight, ellipseHeight, ellipseWidth;
int ldx, ldy, ldw, ldh, numShifts, numIters;
int xGc, yGc;
long m00, m01, m10, m11, m02, m20;
double ellipseAngle;
double density;
unsigned int depthLow, depthHigh;
int verticalEdgeLeft, verticalEdgeRight, horizontalEdgeTop, horizontalEdgeBottom;
SearchWindow();
~SearchWindow();
void setSize(int _x, int _y, int _width, int _height);
void initDepthValues(IplImage *maskImage, IplImage *depthMap);
bool shift();
void extractInfo(IplImage *maskImage, IplImage *depthMap, bool initDepth);
void getResizeAttribsEdgeDensityLinear(int &resizeDx, int &resizeDy, int &resizeDw, int &resizeDh);
void getResizeAttribsInnerDensity(int &resizeDx, int &resizeDy, int &resizeDw, int &resizeDh);
void getResizeAttribsEdgeDensityFuzzy(int &resizeDx, int &resizeDy, int &resizeDw, int &resizeDh);
bool meanShift(IplImage *maskImage, IplImage *depthMap, int maxIteration, bool initDepth);
};
public:
enum TrackingState
{
tsNone = 0,
tsSearching = 1,
tsTracking = 2,
tsSetWindow = 3,
tsDisabled = 10
};
enum ResizeMethod {
rmEdgeDensityLinear = 0,
rmEdgeDensityFuzzy = 1,
rmInnerDensity = 2
};
enum {
MinKernelMass = 1000
};
SearchWindow kernel;
int searchMode;
private:
enum
{
MaxMeanShiftIteration = 5,
MaxSetSizeIteration = 5
};
void findOptimumSearchWindow(SearchWindow &searchWindow, IplImage *maskImage, IplImage *depthMap, int maxIteration, int resizeMethod, bool initDepth);
public:
CvFuzzyMeanShiftTracker();
~CvFuzzyMeanShiftTracker();
void track(IplImage *maskImage, IplImage *depthMap, int resizeMethod, bool resetSearch, int minKernelMass = MinKernelMass);
};
namespace cv
{
typedef bool (*BundleAdjustCallback)(int iteration, double norm_error, void* user_data);
class CV_EXPORTS LevMarqSparse {
public:
LevMarqSparse();
LevMarqSparse(int npoints, // number of points
int ncameras, // number of cameras
int nPointParams, // number of params per one point (3 in case of 3D points)
int nCameraParams, // number of parameters per one camera
int nErrParams, // number of parameters in measurement vector
// for 1 point at one camera (2 in case of 2D projections)
Mat& visibility, // visibility matrix. rows correspond to points, columns correspond to cameras
// 1 - point is visible for the camera, 0 - invisible
Mat& P0, // starting vector of parameters, first cameras then points
Mat& X, // measurements, in order of visibility. non visible cases are skipped
TermCriteria criteria, // termination criteria
// callback for estimation of Jacobian matrices
void (*fjac)(int i, int j, Mat& point_params,
Mat& cam_params, Mat& A, Mat& B, void* data),
// callback for estimation of backprojection errors
void (*func)(int i, int j, Mat& point_params,
Mat& cam_params, Mat& estim, void* data),
void* data, // user-specific data passed to the callbacks
BundleAdjustCallback cb, void* user_data
);
virtual ~LevMarqSparse();
virtual void run( int npoints, // number of points
int ncameras, // number of cameras
int nPointParams, // number of params per one point (3 in case of 3D points)
int nCameraParams, // number of parameters per one camera
int nErrParams, // number of parameters in measurement vector
// for 1 point at one camera (2 in case of 2D projections)
Mat& visibility, // visibility matrix. rows correspond to points, columns correspond to cameras
// 1 - point is visible for the camera, 0 - invisible
Mat& P0, // starting vector of parameters, first cameras then points
Mat& X, // measurements, in order of visibility. non visible cases are skipped
TermCriteria criteria, // termination criteria
// callback for estimation of Jacobian matrices
void (CV_CDECL * fjac)(int i, int j, Mat& point_params,
Mat& cam_params, Mat& A, Mat& B, void* data),
// callback for estimation of backprojection errors
void (CV_CDECL * func)(int i, int j, Mat& point_params,
Mat& cam_params, Mat& estim, void* data),
void* data // user-specific data passed to the callbacks
);
virtual void clear();
// useful function to do simple bundle adjustment tasks
static void bundleAdjust(std::vector<Point3d>& points, // positions of points in global coordinate system (input and output)
const std::vector<std::vector<Point2d> >& imagePoints, // projections of 3d points for every camera
const std::vector<std::vector<int> >& visibility, // visibility of 3d points for every camera
std::vector<Mat>& cameraMatrix, // intrinsic matrices of all cameras (input and output)
std::vector<Mat>& R, // rotation matrices of all cameras (input and output)
std::vector<Mat>& T, // translation vector of all cameras (input and output)
std::vector<Mat>& distCoeffs, // distortion coefficients of all cameras (input and output)
const TermCriteria& criteria=
TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 30, DBL_EPSILON),
BundleAdjustCallback cb = 0, void* user_data = 0);
public:
virtual void optimize(CvMat &_vis); //main function that runs minimization
//iteratively asks for measurement for visible camera-point pairs
void ask_for_proj(CvMat &_vis,bool once=false);
//iteratively asks for Jacobians for every camera_point pair
void ask_for_projac(CvMat &_vis);
CvMat* err; //error X-hX
double prevErrNorm, errNorm;
double lambda;
CvTermCriteria criteria;
int iters;
CvMat** U; //size of array is equal to number of cameras
CvMat** V; //size of array is equal to number of points
CvMat** inv_V_star; //inverse of V*
CvMat** A;
CvMat** B;
CvMat** W;
CvMat* X; //measurement
CvMat* hX; //current measurement extimation given new parameter vector
CvMat* prevP; //current already accepted parameter.
CvMat* P; // parameters used to evaluate function with new params
// this parameters may be rejected
CvMat* deltaP; //computed increase of parameters (result of normal system solution )
CvMat** ea; // sum_i AijT * e_ij , used as right part of normal equation
// length of array is j = number of cameras
CvMat** eb; // sum_j BijT * e_ij , used as right part of normal equation
// length of array is i = number of points
CvMat** Yj; //length of array is i = num_points
CvMat* S; //big matrix of block Sjk , each block has size num_cam_params x num_cam_params
CvMat* JtJ_diag; //diagonal of JtJ, used to backup diagonal elements before augmentation
CvMat* Vis_index; // matrix which element is index of measurement for point i and camera j
int num_cams;
int num_points;
int num_err_param;
int num_cam_param;
int num_point_param;
//target function and jacobian pointers, which needs to be initialized
void (*fjac)(int i, int j, Mat& point_params, Mat& cam_params, Mat& A, Mat& B, void* data);
void (*func)(int i, int j, Mat& point_params, Mat& cam_params, Mat& estim, void* data);
void* data;
BundleAdjustCallback cb;
void* user_data;
};
} // cv
#endif /* __cplusplus */
#endif /* __OPENCV_CONTRIB_COMPAT_HPP__ */
modules/contrib/include/opencv2/contrib/contrib.hpp
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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifdef __OPENCV_BUILD
#error this is a compatibility header which should not be used inside the OpenCV library
#endif
#include "opencv2/contrib.hpp"
modules/contrib/include/opencv2/contrib/detection_based_tracker.hpp
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#pragma once
#if defined(__linux__) || defined(LINUX) || defined(__APPLE__) || defined(__ANDROID__)
#include <opencv2/core.hpp>
#include <opencv2/objdetect.hpp>
#include <vector>
namespace cv
{
class CV_EXPORTS DetectionBasedTracker
{
public:
struct Parameters
{
int maxTrackLifetime;
int minDetectionPeriod; //the minimal time between run of the big object detector (on the whole frame) in ms (1000 mean 1 sec), default=0
Parameters();
};
class IDetector
{
public:
IDetector():
minObjSize(96, 96),
maxObjSize(INT_MAX, INT_MAX),
minNeighbours(2),
scaleFactor(1.1f)
{}
virtual void detect(const cv::Mat& image, std::vector<cv::Rect>& objects) = 0;
void setMinObjectSize(const cv::Size& min)
{
minObjSize = min;
}
void setMaxObjectSize(const cv::Size& max)
{
maxObjSize = max;
}
cv::Size getMinObjectSize() const
{
return minObjSize;
}
cv::Size getMaxObjectSize() const
{
return maxObjSize;
}
float getScaleFactor()
{
return scaleFactor;
}
void setScaleFactor(float value)
{
scaleFactor = value;
}
int getMinNeighbours()
{
return minNeighbours;
}
void setMinNeighbours(int value)
{
minNeighbours = value;
}
virtual ~IDetector() {}
protected:
cv::Size minObjSize;
cv::Size maxObjSize;
int minNeighbours;
float scaleFactor;
};
DetectionBasedTracker(cv::Ptr<IDetector> mainDetector, cv::Ptr<IDetector> trackingDetector, const Parameters& params);
virtual ~DetectionBasedTracker();
virtual bool run();
virtual void stop();
virtual void resetTracking();
virtual void process(const cv::Mat& imageGray);
bool setParameters(const Parameters& params);
const Parameters& getParameters() const;
typedef std::pair<cv::Rect, int> Object;
virtual void getObjects(std::vector<cv::Rect>& result) const;
virtual void getObjects(std::vector<Object>& result) const;
enum ObjectStatus
{
DETECTED_NOT_SHOWN_YET,
DETECTED,
DETECTED_TEMPORARY_LOST,
WRONG_OBJECT
};
struct ExtObject
{
int id;
cv::Rect location;
ObjectStatus status;
ExtObject(int _id, cv::Rect _location, ObjectStatus _status)
:id(_id), location(_location), status(_status)
{
}
};
virtual void getObjects(std::vector<ExtObject>& result) const;
virtual int addObject(const cv::Rect& location); //returns id of the new object
protected:
class SeparateDetectionWork;
cv::Ptr<SeparateDetectionWork> separateDetectionWork;
friend void* workcycleObjectDetectorFunction(void* p);
struct InnerParameters
{
int numLastPositionsToTrack;
int numStepsToWaitBeforeFirstShow;
int numStepsToTrackWithoutDetectingIfObjectHasNotBeenShown;
int numStepsToShowWithoutDetecting;
float coeffTrackingWindowSize;
float coeffObjectSizeToTrack;
float coeffObjectSpeedUsingInPrediction;
InnerParameters();
};
Parameters parameters;
InnerParameters innerParameters;
struct TrackedObject
{
typedef std::vector<cv::Rect> PositionsVector;
PositionsVector lastPositions;
int numDetectedFrames;
int numFramesNotDetected;
int id;
TrackedObject(const cv::Rect& rect):numDetectedFrames(1), numFramesNotDetected(0)
{
lastPositions.push_back(rect);
id=getNextId();
};
static int getNextId()
{
static int _id=0;
return _id++;
}
};
int numTrackedSteps;
std::vector<TrackedObject> trackedObjects;
std::vector<float> weightsPositionsSmoothing;
std::vector<float> weightsSizesSmoothing;
cv::Ptr<IDetector> cascadeForTracking;
void updateTrackedObjects(const std::vector<cv::Rect>& detectedObjects);
cv::Rect calcTrackedObjectPositionToShow(int i) const;
cv::Rect calcTrackedObjectPositionToShow(int i, ObjectStatus& status) const;
void detectInRegion(const cv::Mat& img, const cv::Rect& r, std::vector<cv::Rect>& detectedObjectsInRegions);
};
} //end of cv namespace
#endif
modules/contrib/include/opencv2/contrib/hybridtracker.hpp
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//*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2008-2011, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifndef __OPENCV_HYBRIDTRACKER_H_
#define __OPENCV_HYBRIDTRACKER_H_
#include "opencv2/core.hpp"
#include "opencv2/imgproc.hpp"
#include "opencv2/features2d.hpp"
#include "opencv2/video/tracking.hpp"
#include "opencv2/ml.hpp"
#ifdef __cplusplus
namespace cv
{
// Motion model for tracking algorithm. Currently supports objects that do not move much.
// To add Kalman filter
struct CV_EXPORTS CvMotionModel
{
enum {LOW_PASS_FILTER = 0, KALMAN_FILTER = 1, EM = 2};
CvMotionModel()
{
}
float low_pass_gain; // low pass gain
};
// Mean Shift Tracker parameters for specifying use of HSV channel and CamShift parameters.
struct CV_EXPORTS CvMeanShiftTrackerParams
{
enum { H = 0, HS = 1, HSV = 2 };
CvMeanShiftTrackerParams(int tracking_type = CvMeanShiftTrackerParams::HS,
CvTermCriteria term_crit = CvTermCriteria());
int tracking_type;
std::vector<float> h_range;
std::vector<float> s_range;
std::vector<float> v_range;
CvTermCriteria term_crit;
};
// Feature tracking parameters
struct CV_EXPORTS CvFeatureTrackerParams
{
enum { SIFT = 0, SURF = 1, OPTICAL_FLOW = 2 };
CvFeatureTrackerParams(int featureType = 0, int windowSize = 0)
{
feature_type = featureType;
window_size = windowSize;
}
int feature_type; // Feature type to use
int window_size; // Window size in pixels around which to search for new window
};
// Hybrid Tracking parameters for specifying weights of individual trackers and motion model.
struct CV_EXPORTS CvHybridTrackerParams
{
CvHybridTrackerParams(float ft_tracker_weight = 0.5, float ms_tracker_weight = 0.5,
CvFeatureTrackerParams ft_params = CvFeatureTrackerParams(),
CvMeanShiftTrackerParams ms_params = CvMeanShiftTrackerParams(),
CvMotionModel model = CvMotionModel());
float ft_tracker_weight;
float ms_tracker_weight;
CvFeatureTrackerParams ft_params;
CvMeanShiftTrackerParams ms_params;
int motion_model;
float low_pass_gain;
};
// Performs Camshift using parameters from MeanShiftTrackerParams
class CV_EXPORTS CvMeanShiftTracker
{
private:
Mat hsv, hue;
Mat backproj;
Mat mask, maskroi;
MatND hist;
Rect prev_trackwindow;
RotatedRect prev_trackbox;
Point2f prev_center;
public:
CvMeanShiftTrackerParams params;
CvMeanShiftTracker();
explicit CvMeanShiftTracker(CvMeanShiftTrackerParams _params);
~CvMeanShiftTracker();
void newTrackingWindow(Mat image, Rect selection);
RotatedRect updateTrackingWindow(Mat image);
Mat getHistogramProjection(int type);
void setTrackingWindow(Rect _window);
Rect getTrackingWindow();
RotatedRect getTrackingEllipse();
Point2f getTrackingCenter();
};
// Performs SIFT/SURF feature tracking using parameters from FeatureTrackerParams
class CV_EXPORTS CvFeatureTracker
{
private:
Ptr<Feature2D> dd;
Ptr<DescriptorMatcher> matcher;
std::vector<DMatch> matches;
Mat prev_image;
Mat prev_image_bw;
Rect prev_trackwindow;
Point2d prev_center;
int ittr;
std::vector<Point2f> features[2];
public:
Mat disp_matches;
CvFeatureTrackerParams params;
CvFeatureTracker();
explicit CvFeatureTracker(CvFeatureTrackerParams params);
~CvFeatureTracker();
void newTrackingWindow(Mat image, Rect selection);
Rect updateTrackingWindow(Mat image);
Rect updateTrackingWindowWithSIFT(Mat image);
Rect updateTrackingWindowWithFlow(Mat image);
void setTrackingWindow(Rect _window);
Rect getTrackingWindow();
Point2f getTrackingCenter();
};
// Performs Hybrid Tracking and combines individual trackers using EM or filters
class CV_EXPORTS CvHybridTracker
{
private:
CvMeanShiftTracker* mstracker;
CvFeatureTracker* fttracker;
CvMat* samples;
CvMat* labels;
Rect prev_window;
Point2f prev_center;
Mat prev_proj;
RotatedRect trackbox;
int ittr;
Point2f curr_center;
inline float getL2Norm(Point2f p1, Point2f p2);
Mat getDistanceProjection(Mat image, Point2f center);
Mat getGaussianProjection(Mat image, int ksize, double sigma, Point2f center);
void updateTrackerWithEM(Mat image);
void updateTrackerWithLowPassFilter(Mat image);
public:
CvHybridTrackerParams params;
CvHybridTracker();
explicit CvHybridTracker(CvHybridTrackerParams params);
~CvHybridTracker();
void newTracker(Mat image, Rect selection);
void updateTracker(Mat image);
Rect getTrackingWindow();
};
typedef CvMotionModel MotionModel;
typedef CvMeanShiftTrackerParams MeanShiftTrackerParams;
typedef CvFeatureTrackerParams FeatureTrackerParams;
typedef CvHybridTrackerParams HybridTrackerParams;
typedef CvMeanShiftTracker MeanShiftTracker;
typedef CvFeatureTracker FeatureTracker;
typedef CvHybridTracker HybridTracker;
}
#endif
#endif
modules/contrib/include/opencv2/contrib/openfabmap.hpp
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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
// This file originates from the openFABMAP project:
// [http://code.google.com/p/openfabmap/]
//
// For published work which uses all or part of OpenFABMAP, please cite:
// [http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6224843]
//
// Original Algorithm by Mark Cummins and Paul Newman:
// [http://ijr.sagepub.com/content/27/6/647.short]
// [http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=5613942]
// [http://ijr.sagepub.com/content/30/9/1100.abstract]
//
// License Agreement
//
// Copyright (C) 2012 Arren Glover [aj.glover@qut.edu.au] and
// Will Maddern [w.maddern@qut.edu.au], all rights reserved.
//
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifndef __OPENCV_OPENFABMAP_H_
#define __OPENCV_OPENFABMAP_H_
#include "opencv2/core.hpp"
#include "opencv2/features2d.hpp"
#include <vector>
#include <list>
#include <map>
#include <set>
#include <valarray>
namespace cv {
namespace of2 {
/*
Return data format of a FABMAP compare call
*/
struct CV_EXPORTS IMatch {
IMatch() :
queryIdx(-1), imgIdx(-1), likelihood(-DBL_MAX), match(-DBL_MAX) {
}
IMatch(int _queryIdx, int _imgIdx, double _likelihood, double _match) :
queryIdx(_queryIdx), imgIdx(_imgIdx), likelihood(_likelihood), match(
_match) {
}
int queryIdx; //query index
int imgIdx; //test index
double likelihood; //raw loglikelihood
double match; //normalised probability
bool operator<(const IMatch& m) const {
return match < m.match;
}
};
/*
Base FabMap class. Each FabMap method inherits from this class.
*/
class CV_EXPORTS FabMap {
public:
//FabMap options
enum {
MEAN_FIELD = 1,
SAMPLED = 2,
NAIVE_BAYES = 4,
CHOW_LIU = 8,
MOTION_MODEL = 16
};
FabMap(const Mat& clTree, double PzGe, double PzGNe, int flags,
int numSamples = 0);
virtual ~FabMap();
//methods to add training data for sampling method
virtual void addTraining(const Mat& queryImgDescriptor);
virtual void addTraining(const std::vector<Mat>& queryImgDescriptors);
//methods to add to the test data
virtual void add(const Mat& queryImgDescriptor);
virtual void add(const std::vector<Mat>& queryImgDescriptors);
//accessors
const std::vector<Mat>& getTrainingImgDescriptors() const;
const std::vector<Mat>& getTestImgDescriptors() const;
//Main FabMap image comparison
void compare(const Mat& queryImgDescriptor,
std::vector<IMatch>& matches, bool addQuery = false,
const Mat& mask = Mat());
void compare(const Mat& queryImgDescriptor,
const Mat& testImgDescriptors, std::vector<IMatch>& matches,
const Mat& mask = Mat());
void compare(const Mat& queryImgDescriptor,
const std::vector<Mat>& testImgDescriptors,
std::vector<IMatch>& matches, const Mat& mask = Mat());
void compare(const std::vector<Mat>& queryImgDescriptors, std::vector<
IMatch>& matches, bool addQuery = false, const Mat& mask =
Mat());
void compare(const std::vector<Mat>& queryImgDescriptors,
const std::vector<Mat>& testImgDescriptors,
std::vector<IMatch>& matches, const Mat& mask = Mat());
protected:
void compareImgDescriptor(const Mat& queryImgDescriptor,
int queryIndex, const std::vector<Mat>& testImgDescriptors,
std::vector<IMatch>& matches);
void addImgDescriptor(const Mat& queryImgDescriptor);
//the getLikelihoods method is overwritten for each different FabMap
//method.
virtual void getLikelihoods(const Mat& queryImgDescriptor,
const std::vector<Mat>& testImgDescriptors,
std::vector<IMatch>& matches);
virtual double getNewPlaceLikelihood(const Mat& queryImgDescriptor);
//turn likelihoods into probabilities (also add in motion model if used)
void normaliseDistribution(std::vector<IMatch>& matches);
//Chow-Liu Tree
int pq(int q);
double Pzq(int q, bool zq);
double PzqGzpq(int q, bool zq, bool zpq);
//FAB-MAP Core
double PzqGeq(bool zq, bool eq);
double PeqGL(int q, bool Lzq, bool eq);
double PzqGL(int q, bool zq, bool zpq, bool Lzq);
double PzqGzpqL(int q, bool zq, bool zpq, bool Lzq);
double (FabMap::*PzGL)(int q, bool zq, bool zpq, bool Lzq);
//data
Mat clTree;
std::vector<Mat> trainingImgDescriptors;
std::vector<Mat> testImgDescriptors;
std::vector<IMatch> priorMatches;
//parameters
double PzGe;
double PzGNe;
double Pnew;
double mBias;
double sFactor;
int flags;
int numSamples;
};
/*
The original FAB-MAP algorithm, developed based on:
http://ijr.sagepub.com/content/27/6/647.short
*/
class CV_EXPORTS FabMap1: public FabMap {
public:
FabMap1(const Mat& clTree, double PzGe, double PzGNe, int flags,
int numSamples = 0);
virtual ~FabMap1();
protected:
//FabMap1 implementation of likelihood comparison
void getLikelihoods(const Mat& queryImgDescriptor, const std::vector<
Mat>& testImgDescriptors, std::vector<IMatch>& matches);
};
/*
A computationally faster version of the original FAB-MAP algorithm. A look-
up-table is used to precompute many of the reoccuring calculations
*/
class CV_EXPORTS FabMapLUT: public FabMap {
public:
FabMapLUT(const Mat& clTree, double PzGe, double PzGNe,
int flags, int numSamples = 0, int precision = 6);
virtual ~FabMapLUT();
protected:
//FabMap look-up-table implementation of the likelihood comparison
void getLikelihoods(const Mat& queryImgDescriptor, const std::vector<
Mat>& testImgDescriptors, std::vector<IMatch>& matches);
//precomputed data
int (*table)[8];
//data precision
int precision;
};
/*
The Accelerated FAB-MAP algorithm, developed based on:
http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=5613942
*/
class CV_EXPORTS FabMapFBO: public FabMap {
public:
FabMapFBO(const Mat& clTree, double PzGe, double PzGNe, int flags,
int numSamples = 0, double rejectionThreshold = 1e-8, double PsGd =
1e-8, int bisectionStart = 512, int bisectionIts = 9);
virtual ~FabMapFBO();
protected:
//FabMap Fast Bail-out implementation of the likelihood comparison
void getLikelihoods(const Mat& queryImgDescriptor, const std::vector<
Mat>& testImgDescriptors, std::vector<IMatch>& matches);
//stucture used to determine word comparison order
struct WordStats {
WordStats() :
q(0), info(0), V(0), M(0) {
}
WordStats(int _q, double _info) :
q(_q), info(_info), V(0), M(0) {
}
int q;
double info;
mutable double V;
mutable double M;
bool operator<(const WordStats& w) const {
return info < w.info;
}
};
//private fast bail-out necessary functions
void setWordStatistics(const Mat& queryImgDescriptor, std::multiset<WordStats>& wordData);
double limitbisection(double v, double m);
double bennettInequality(double v, double m, double delta);
static bool compInfo(const WordStats& first, const WordStats& second);
//parameters
double PsGd;
double rejectionThreshold;
int bisectionStart;
int bisectionIts;
};
/*
The FAB-MAP2.0 algorithm, developed based on:
http://ijr.sagepub.com/content/30/9/1100.abstract
*/
class CV_EXPORTS FabMap2: public FabMap {
public:
FabMap2(const Mat& clTree, double PzGe, double PzGNe, int flags);
virtual ~FabMap2();
//FabMap2 builds the inverted index and requires an additional training/test
//add function
void addTraining(const Mat& queryImgDescriptors) {
FabMap::addTraining(queryImgDescriptors);
}
void addTraining(const std::vector<Mat>& queryImgDescriptors);
void add(const Mat& queryImgDescriptors) {
FabMap::add(queryImgDescriptors);
}
void add(const std::vector<Mat>& queryImgDescriptors);
protected:
//FabMap2 implementation of the likelihood comparison
void getLikelihoods(const Mat& queryImgDescriptor, const std::vector<
Mat>& testImgDescriptors, std::vector<IMatch>& matches);
double getNewPlaceLikelihood(const Mat& queryImgDescriptor);
//the likelihood function using the inverted index
void getIndexLikelihoods(const Mat& queryImgDescriptor, std::vector<
double>& defaults, std::map<int, std::vector<int> >& invertedMap,
std::vector<IMatch>& matches);
void addToIndex(const Mat& queryImgDescriptor,
std::vector<double>& defaults,
std::map<int, std::vector<int> >& invertedMap);
//data
std::vector<double> d1, d2, d3, d4;
std::vector<std::vector<int> > children;
// TODO: inverted map a vector?
std::vector<double> trainingDefaults;
std::map<int, std::vector<int> > trainingInvertedMap;
std::vector<double> testDefaults;
std::map<int, std::vector<int> > testInvertedMap;
};
/*
A Chow-Liu tree is required by FAB-MAP. The Chow-Liu tree provides an
estimate of the full distribution of visual words using a minimum spanning
tree. The tree is generated through training data.
*/
class CV_EXPORTS ChowLiuTree {
public:
ChowLiuTree();
virtual ~ChowLiuTree();
//add data to the chow-liu tree before calling make
void add(const Mat& imgDescriptor);
void add(const std::vector<Mat>& imgDescriptors);
const std::vector<Mat>& getImgDescriptors() const;
Mat make(double infoThreshold = 0.0);
private:
std::vector<Mat> imgDescriptors;
Mat mergedImgDescriptors;
typedef struct info {
float score;
short word1;
short word2;
} info;
//probabilities extracted from mergedImgDescriptors
double P(int a, bool za);
double JP(int a, bool za, int b, bool zb); //a & b
double CP(int a, bool za, int b, bool zb); // a | b
//calculating mutual information of all edges
void createBaseEdges(std::list<info>& edges, double infoThreshold);
double calcMutInfo(int word1, int word2);
static bool sortInfoScores(const info& first, const info& second);
//selecting minimum spanning egdges with maximum information
bool reduceEdgesToMinSpan(std::list<info>& edges);
//building the tree sctructure
Mat buildTree(int root_word, std::list<info> &edges);
void recAddToTree(Mat &cltree, int q, int pq,
std::list<info> &remaining_edges);
std::vector<int> extractChildren(std::list<info> &remaining_edges, int q);
};
/*
A custom vocabulary training method based on:
http://www.springerlink.com/content/d1h6j8x552532003/
*/
class CV_EXPORTS BOWMSCTrainer: public BOWTrainer {
public:
BOWMSCTrainer(double clusterSize = 0.4);
virtual ~BOWMSCTrainer();
// Returns trained vocabulary (i.e. cluster centers).
virtual Mat cluster() const;
virtual Mat cluster(const Mat& descriptors) const;
protected:
double clusterSize;
};
}
}
#endif /* OPENFABMAP_H_ */
modules/contrib/src/adaptiveskindetector.cpp
-288
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@@ -1,288 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install, copy or use the software.
//
// Copyright (C) 2009, Farhad Dadgostar
// Intel Corporation and third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "precomp.hpp"
#include "opencv2/imgproc/imgproc_c.h"
#include "opencv2/contrib/compat.hpp"
#define ASD_INTENSITY_SET_PIXEL(pointer, qq) {(*pointer) = (unsigned char)qq;}
#define ASD_IS_IN_MOTION(pointer, v, threshold) ((abs((*(pointer)) - (v)) > (threshold)) ? true : false)
void CvAdaptiveSkinDetector::initData(IplImage *src, int widthDivider, int heightDivider)
{
CvSize imageSize = cvSize(src->width/widthDivider, src->height/heightDivider);
imgHueFrame = cvCreateImage(imageSize, IPL_DEPTH_8U, 1);
imgShrinked = cvCreateImage(imageSize, IPL_DEPTH_8U, src->nChannels);
imgSaturationFrame = cvCreateImage(imageSize, IPL_DEPTH_8U, 1);
imgMotionFrame = cvCreateImage(imageSize, IPL_DEPTH_8U, 1);
imgTemp = cvCreateImage(imageSize, IPL_DEPTH_8U, 1);
imgFilteredFrame = cvCreateImage(imageSize, IPL_DEPTH_8U, 1);
imgGrayFrame = cvCreateImage(imageSize, IPL_DEPTH_8U, 1);
imgLastGrayFrame = cvCreateImage(imageSize, IPL_DEPTH_8U, 1);
imgHSVFrame = cvCreateImage(imageSize, IPL_DEPTH_8U, 3);
}
CvAdaptiveSkinDetector::CvAdaptiveSkinDetector(int samplingDivider, int morphingMethod)
{
nSkinHueLowerBound = GSD_HUE_LT;
nSkinHueUpperBound = GSD_HUE_UT;
fHistogramMergeFactor = 0.05; // empirical result
fHuePercentCovered = 0.95; // empirical result
nMorphingMethod = morphingMethod;
nSamplingDivider = samplingDivider;
nFrameCount = 0;
nStartCounter = 0;
imgHueFrame = NULL;
imgMotionFrame = NULL;
imgTemp = NULL;
imgFilteredFrame = NULL;
imgShrinked = NULL;
imgGrayFrame = NULL;
imgLastGrayFrame = NULL;
imgSaturationFrame = NULL;
imgHSVFrame = NULL;
}
CvAdaptiveSkinDetector::~CvAdaptiveSkinDetector()
{
cvReleaseImage(&imgHueFrame);
cvReleaseImage(&imgSaturationFrame);
cvReleaseImage(&imgMotionFrame);
cvReleaseImage(&imgTemp);
cvReleaseImage(&imgFilteredFrame);
cvReleaseImage(&imgShrinked);
cvReleaseImage(&imgGrayFrame);
cvReleaseImage(&imgLastGrayFrame);
cvReleaseImage(&imgHSVFrame);
}
void CvAdaptiveSkinDetector::process(IplImage *inputBGRImage, IplImage *outputHueMask)
{
IplImage *src = inputBGRImage;
int h, v, i, l;
bool isInit = false;
nFrameCount++;
if (imgHueFrame == NULL)
{
isInit = true;
initData(src, nSamplingDivider, nSamplingDivider);
}
unsigned char *pShrinked, *pHueFrame, *pMotionFrame, *pLastGrayFrame, *pFilteredFrame, *pGrayFrame;
pShrinked = (unsigned char *)imgShrinked->imageData;
pHueFrame = (unsigned char *)imgHueFrame->imageData;
pMotionFrame = (unsigned char *)imgMotionFrame->imageData;
pLastGrayFrame = (unsigned char *)imgLastGrayFrame->imageData;
pFilteredFrame = (unsigned char *)imgFilteredFrame->imageData;
pGrayFrame = (unsigned char *)imgGrayFrame->imageData;
if ((src->width != imgHueFrame->width) || (src->height != imgHueFrame->height))
{
cvResize(src, imgShrinked);
cvCvtColor(imgShrinked, imgHSVFrame, CV_BGR2HSV);
}
else
{
cvCvtColor(src, imgHSVFrame, CV_BGR2HSV);
}
cvSplit(imgHSVFrame, imgHueFrame, imgSaturationFrame, imgGrayFrame, 0);
cvSetZero(imgMotionFrame);
cvSetZero(imgFilteredFrame);
l = imgHueFrame->height * imgHueFrame->width;
for (i = 0; i < l; i++)
{
v = (*pGrayFrame);
if ((v >= GSD_INTENSITY_LT) && (v <= GSD_INTENSITY_UT))
{
h = (*pHueFrame);
if ((h >= GSD_HUE_LT) && (h <= GSD_HUE_UT))
{
if ((h >= nSkinHueLowerBound) && (h <= nSkinHueUpperBound))
ASD_INTENSITY_SET_PIXEL(pFilteredFrame, h);
if (ASD_IS_IN_MOTION(pLastGrayFrame, v, 7))
ASD_INTENSITY_SET_PIXEL(pMotionFrame, h);
}
}
pShrinked += 3;
pGrayFrame++;
pLastGrayFrame++;
pMotionFrame++;
pHueFrame++;
pFilteredFrame++;
}
if (isInit)
cvCalcHist(&imgHueFrame, skinHueHistogram.fHistogram);
cvCopy(imgGrayFrame, imgLastGrayFrame);
cvErode(imgMotionFrame, imgTemp); // eliminate disperse pixels, which occur because of the camera noise
cvDilate(imgTemp, imgMotionFrame);
cvCalcHist(&imgMotionFrame, histogramHueMotion.fHistogram);
skinHueHistogram.mergeWith(&histogramHueMotion, fHistogramMergeFactor);
skinHueHistogram.findCurveThresholds(nSkinHueLowerBound, nSkinHueUpperBound, 1 - fHuePercentCovered);
switch (nMorphingMethod)
{
case MORPHING_METHOD_ERODE :
cvErode(imgFilteredFrame, imgTemp);
cvCopy(imgTemp, imgFilteredFrame);
break;
case MORPHING_METHOD_ERODE_ERODE :
cvErode(imgFilteredFrame, imgTemp);
cvErode(imgTemp, imgFilteredFrame);
break;
case MORPHING_METHOD_ERODE_DILATE :
cvErode(imgFilteredFrame, imgTemp);
cvDilate(imgTemp, imgFilteredFrame);
break;
}
if (outputHueMask != NULL)
cvCopy(imgFilteredFrame, outputHueMask);
}
//------------------------- Histogram for Adaptive Skin Detector -------------------------//
CvAdaptiveSkinDetector::Histogram::Histogram()
{
int histogramSize[] = { HistogramSize };
float range[] = { GSD_HUE_LT, GSD_HUE_UT };
float *ranges[] = { range };
fHistogram = cvCreateHist(1, histogramSize, CV_HIST_ARRAY, ranges, 1);
cvClearHist(fHistogram);
}
CvAdaptiveSkinDetector::Histogram::~Histogram()
{
cvReleaseHist(&fHistogram);
}
int CvAdaptiveSkinDetector::Histogram::findCoverageIndex(double surfaceToCover, int defaultValue)
{
double s = 0;
for (int i = 0; i < HistogramSize; i++)
{
s += cvGetReal1D( fHistogram->bins, i );
if (s >= surfaceToCover)
{
return i;
}
}
return defaultValue;
}
void CvAdaptiveSkinDetector::Histogram::findCurveThresholds(int &x1, int &x2, double percent)
{
double sum = 0;
for (int i = 0; i < HistogramSize; i++)
{
sum += cvGetReal1D( fHistogram->bins, i );
}
x1 = findCoverageIndex(sum * percent, -1);
x2 = findCoverageIndex(sum * (1-percent), -1);
if (x1 == -1)
x1 = GSD_HUE_LT;
else
x1 += GSD_HUE_LT;
if (x2 == -1)
x2 = GSD_HUE_UT;
else
x2 += GSD_HUE_LT;
}
void CvAdaptiveSkinDetector::Histogram::mergeWith(CvAdaptiveSkinDetector::Histogram *source, double weight)
{
float myweight = (float)(1-weight);
float maxVal1 = 0, maxVal2 = 0, *f1, *f2, ff1, ff2;
cvGetMinMaxHistValue(source->fHistogram, NULL, &maxVal2);
if (maxVal2 > 0 )
{
cvGetMinMaxHistValue(fHistogram, NULL, &maxVal1);
if (maxVal1 <= 0)
{
for (int i = 0; i < HistogramSize; i++)
{
f1 = (float*)cvPtr1D(fHistogram->bins, i);
f2 = (float*)cvPtr1D(source->fHistogram->bins, i);
(*f1) = (*f2);
}
}
else
{
for (int i = 0; i < HistogramSize; i++)
{
f1 = (float*)cvPtr1D(fHistogram->bins, i);
f2 = (float*)cvPtr1D(source->fHistogram->bins, i);
ff1 = ((*f1)/maxVal1)*myweight;
if (ff1 < 0)
ff1 = -ff1;
ff2 = (float)(((*f2)/maxVal2)*weight);
if (ff2 < 0)
ff2 = -ff2;
(*f1) = (ff1 + ff2);
}
}
}
}
modules/contrib/src/ba.cpp
-1128
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modules/contrib/src/bowmsctrainer.cpp
-138
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@@ -1,138 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
// This file originates from the openFABMAP project:
// [http://code.google.com/p/openfabmap/]
//
// For published work which uses all or part of OpenFABMAP, please cite:
// [http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6224843]
//
// Original Algorithm by Mark Cummins and Paul Newman:
// [http://ijr.sagepub.com/content/27/6/647.short]
// [http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=5613942]
// [http://ijr.sagepub.com/content/30/9/1100.abstract]
//
// License Agreement
//
// Copyright (C) 2012 Arren Glover [aj.glover@qut.edu.au] and
// Will Maddern [w.maddern@qut.edu.au], all rights reserved.
//
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "precomp.hpp"
#include "opencv2/contrib/openfabmap.hpp"
namespace cv {
namespace of2 {
BOWMSCTrainer::BOWMSCTrainer(double _clusterSize) :
clusterSize(_clusterSize) {
}
BOWMSCTrainer::~BOWMSCTrainer() {
}
Mat BOWMSCTrainer::cluster() const {
CV_Assert(!descriptors.empty());
int descCount = 0;
for(size_t i = 0; i < descriptors.size(); i++)
descCount += descriptors[i].rows;
Mat mergedDescriptors(descCount, descriptors[0].cols,
descriptors[0].type());
for(size_t i = 0, start = 0; i < descriptors.size(); i++)
{
Mat submut = mergedDescriptors.rowRange((int)start,
(int)(start + descriptors[i].rows));
descriptors[i].copyTo(submut);
start += descriptors[i].rows;
}
return cluster(mergedDescriptors);
}
Mat BOWMSCTrainer::cluster(const Mat& _descriptors) const {
CV_Assert(!_descriptors.empty());
// TODO: sort the descriptors before clustering.
Mat icovar = Mat::eye(_descriptors.cols,_descriptors.cols,_descriptors.type());
std::vector<Mat> initialCentres;
initialCentres.push_back(_descriptors.row(0));
for (int i = 1; i < _descriptors.rows; i++) {
double minDist = DBL_MAX;
for (size_t j = 0; j < initialCentres.size(); j++) {
minDist = std::min(minDist,
cv::Mahalanobis(_descriptors.row(i),initialCentres[j],
icovar));
}
if (minDist > clusterSize)
initialCentres.push_back(_descriptors.row(i));
}
std::vector<std::list<cv::Mat> > clusters;
clusters.resize(initialCentres.size());
for (int i = 0; i < _descriptors.rows; i++) {
int index = 0; double dist = 0, minDist = DBL_MAX;
for (size_t j = 0; j < initialCentres.size(); j++) {
dist = cv::Mahalanobis(_descriptors.row(i),initialCentres[j],icovar);
if (dist < minDist) {
minDist = dist;
index = (int)j;
}
}
clusters[index].push_back(_descriptors.row(i));
}
// TODO: throw away small clusters.
Mat vocabulary;
Mat centre = Mat::zeros(1,_descriptors.cols,_descriptors.type());
for (size_t i = 0; i < clusters.size(); i++) {
centre.setTo(0);
for (std::list<cv::Mat>::iterator Ci = clusters[i].begin(); Ci != clusters[i].end(); Ci++) {
centre += *Ci;
}
centre /= (double)clusters[i].size();
vocabulary.push_back(centre);
}
return vocabulary;
}
}
}
modules/contrib/src/chamfermatching.cpp
-1411
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modules/contrib/src/chowliutree.cpp
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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
// This file originates from the openFABMAP project:
// [http://code.google.com/p/openfabmap/]
//
// For published work which uses all or part of OpenFABMAP, please cite:
// [http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6224843]
//
// Original Algorithm by Mark Cummins and Paul Newman:
// [http://ijr.sagepub.com/content/27/6/647.short]
// [http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=5613942]
// [http://ijr.sagepub.com/content/30/9/1100.abstract]
//
// License Agreement
//
// Copyright (C) 2012 Arren Glover [aj.glover@qut.edu.au] and
// Will Maddern [w.maddern@qut.edu.au], all rights reserved.
//
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "precomp.hpp"
#include "opencv2/contrib/openfabmap.hpp"
namespace cv {
namespace of2 {
ChowLiuTree::ChowLiuTree() {
}
ChowLiuTree::~ChowLiuTree() {
}
void ChowLiuTree::add(const Mat& imgDescriptor) {
CV_Assert(!imgDescriptor.empty());
if (!imgDescriptors.empty()) {
CV_Assert(imgDescriptors[0].cols == imgDescriptor.cols);
CV_Assert(imgDescriptors[0].type() == imgDescriptor.type());
}
imgDescriptors.push_back(imgDescriptor);
}
void ChowLiuTree::add(const std::vector<Mat>& _imgDescriptors) {
for (size_t i = 0; i < _imgDescriptors.size(); i++) {
add(_imgDescriptors[i]);
}
}
const std::vector<cv::Mat>& ChowLiuTree::getImgDescriptors() const {
return imgDescriptors;
}
Mat ChowLiuTree::make(double infoThreshold) {
CV_Assert(!imgDescriptors.empty());
unsigned int descCount = 0;
for (size_t i = 0; i < imgDescriptors.size(); i++)
descCount += imgDescriptors[i].rows;
mergedImgDescriptors = cv::Mat(descCount, imgDescriptors[0].cols,
imgDescriptors[0].type());
for (size_t i = 0, start = 0; i < imgDescriptors.size(); i++)
{
Mat submut = mergedImgDescriptors.rowRange((int)start,
(int)(start + imgDescriptors[i].rows));
imgDescriptors[i].copyTo(submut);
start += imgDescriptors[i].rows;
}
std::list<info> edges;
createBaseEdges(edges, infoThreshold);
// TODO: if it cv_asserts here they really won't know why.
CV_Assert(reduceEdgesToMinSpan(edges));
return buildTree(edges.front().word1, edges);
}
double ChowLiuTree::P(int a, bool za) {
if(za) {
return (0.98 * cv::countNonZero(mergedImgDescriptors.col(a)) /
mergedImgDescriptors.rows) + 0.01;
} else {
return 1 - ((0.98 * cv::countNonZero(mergedImgDescriptors.col(a)) /
mergedImgDescriptors.rows) + 0.01);
}
}
double ChowLiuTree::JP(int a, bool za, int b, bool zb) {
double count = 0;
for(int i = 0; i < mergedImgDescriptors.rows; i++) {
if((mergedImgDescriptors.at<float>(i,a) > 0) == za &&
(mergedImgDescriptors.at<float>(i,b) > 0) == zb) {
count++;
}
}
return count / mergedImgDescriptors.rows;
}
double ChowLiuTree::CP(int a, bool za, int b, bool zb){
int count = 0, total = 0;
for(int i = 0; i < mergedImgDescriptors.rows; i++) {
if((mergedImgDescriptors.at<float>(i,b) > 0) == zb) {
total++;
if((mergedImgDescriptors.at<float>(i,a) > 0) == za) {
count++;
}
}
}
if(total) {
return (double)(0.98 * count)/total + 0.01;
} else {
return (za) ? 0.01 : 0.99;
}
}
cv::Mat ChowLiuTree::buildTree(int root_word, std::list<info> &edges) {
int q = root_word;
cv::Mat cltree(4, (int)edges.size()+1, CV_64F);
cltree.at<double>(0, q) = q;
cltree.at<double>(1, q) = P(q, true);
cltree.at<double>(2, q) = P(q, true);
cltree.at<double>(3, q) = P(q, true);
//setting P(zq|zpq) to P(zq) gives the root node of the chow-liu
//independence from a parent node.
//find all children and do the same
std::vector<int> nextqs = extractChildren(edges, q);
int pq = q;
std::vector<int>::iterator nextq;
for(nextq = nextqs.begin(); nextq != nextqs.end(); nextq++) {
recAddToTree(cltree, *nextq, pq, edges);
}
return cltree;
}
void ChowLiuTree::recAddToTree(cv::Mat &cltree, int q, int pq,
std::list<info>& remaining_edges) {
cltree.at<double>(0, q) = pq;
cltree.at<double>(1, q) = P(q, true);
cltree.at<double>(2, q) = CP(q, true, pq, true);
cltree.at<double>(3, q) = CP(q, true, pq, false);
//find all children and do the same
std::vector<int> nextqs = extractChildren(remaining_edges, q);
pq = q;
std::vector<int>::iterator nextq;
for(nextq = nextqs.begin(); nextq != nextqs.end(); nextq++) {
recAddToTree(cltree, *nextq, pq, remaining_edges);
}
}
std::vector<int> ChowLiuTree::extractChildren(std::list<info> &remaining_edges, int q) {
std::vector<int> children;
std::list<info>::iterator edge = remaining_edges.begin();
while(edge != remaining_edges.end()) {
if(edge->word1 == q) {
children.push_back(edge->word2);
edge = remaining_edges.erase(edge);
continue;
}
if(edge->word2 == q) {
children.push_back(edge->word1);
edge = remaining_edges.erase(edge);
continue;
}
edge++;
}
return children;
}
bool ChowLiuTree::sortInfoScores(const info& first, const info& second) {
return first.score > second.score;
}
double ChowLiuTree::calcMutInfo(int word1, int word2) {
double accumulation = 0;
double P00 = JP(word1, false, word2, false);
if(P00) accumulation += P00 * std::log(P00 / (P(word1, false)*P(word2, false)));
double P01 = JP(word1, false, word2, true);
if(P01) accumulation += P01 * std::log(P01 / (P(word1, false)*P(word2, true)));
double P10 = JP(word1, true, word2, false);
if(P10) accumulation += P10 * std::log(P10 / (P(word1, true)*P(word2, false)));
double P11 = JP(word1, true, word2, true);
if(P11) accumulation += P11 * std::log(P11 / (P(word1, true)*P(word2, true)));
return accumulation;
}
void ChowLiuTree::createBaseEdges(std::list<info>& edges, double infoThreshold) {
int nWords = imgDescriptors[0].cols;
info mutInfo;
for(int word1 = 0; word1 < nWords; word1++) {
for(int word2 = word1 + 1; word2 < nWords; word2++) {
mutInfo.word1 = (short)word1;
mutInfo.word2 = (short)word2;
mutInfo.score = (float)calcMutInfo(word1, word2);
if(mutInfo.score >= infoThreshold)
edges.push_back(mutInfo);
}
}
edges.sort(sortInfoScores);
}
bool ChowLiuTree::reduceEdgesToMinSpan(std::list<info>& edges) {
std::map<int, int> groups;
std::map<int, int>::iterator groupIt;
for(int i = 0; i < imgDescriptors[0].cols; i++) groups[i] = i;
int group1, group2;
std::list<info>::iterator edge = edges.begin();
while(edge != edges.end()) {
if(groups[edge->word1] != groups[edge->word2]) {
group1 = groups[edge->word1];
group2 = groups[edge->word2];
for(groupIt = groups.begin(); groupIt != groups.end(); groupIt++)
if(groupIt->second == group2) groupIt->second = group1;
edge++;
} else {
edge = edges.erase(edge);
}
}
if(edges.size() != (unsigned int)imgDescriptors[0].cols - 1) {
return false;
} else {
return true;
}
}
}
}
modules/contrib/src/colormap.cpp
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/*
* Copyright (c) 2011. Philipp Wagner <bytefish[at]gmx[dot]de>.
* Released to public domain under terms of the BSD Simplified license.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions are met:
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in the
* documentation and/or other materials provided with the distribution.
* * Neither the name of the organization nor the names of its contributors
* may be used to endorse or promote products derived from this software
* without specific prior written permission.
*
* See <http://www.opensource.org/licenses/bsd-license>
*/
#include "precomp.hpp"
#include <iostream>
#ifdef _MSC_VER
#pragma warning( disable: 4305 )
#endif
namespace cv
{
static Mat linspace(float x0, float x1, int n)
{
Mat pts(n, 1, CV_32FC1);
float step = (x1-x0)/(n-1);
for(int i = 0; i < n; i++)
pts.at<float>(i,0) = x0+i*step;
return pts;
}
//------------------------------------------------------------------------------
// cv::sortMatrixRowsByIndices
//------------------------------------------------------------------------------
static void sortMatrixRowsByIndices(InputArray _src, InputArray _indices, OutputArray _dst)
{
if(_indices.getMat().type() != CV_32SC1)
CV_Error(Error::StsUnsupportedFormat, "cv::sortRowsByIndices only works on integer indices!");
Mat src = _src.getMat();
std::vector<int> indices = _indices.getMat();
_dst.create(src.rows, src.cols, src.type());
Mat dst = _dst.getMat();
for(size_t idx = 0; idx < indices.size(); idx++) {
Mat originalRow = src.row(indices[idx]);
Mat sortedRow = dst.row((int)idx);
originalRow.copyTo(sortedRow);
}
}
static Mat sortMatrixRowsByIndices(InputArray src, InputArray indices)
{
Mat dst;
sortMatrixRowsByIndices(src, indices, dst);
return dst;
}
static Mat argsort(InputArray _src, bool ascending=true)
{
Mat src = _src.getMat();
if (src.rows != 1 && src.cols != 1)
CV_Error(Error::StsBadArg, "cv::argsort only sorts 1D matrices.");
int flags = SORT_EVERY_ROW | (ascending ? SORT_ASCENDING : SORT_DESCENDING);
Mat sorted_indices;
sortIdx(src.reshape(1,1),sorted_indices,flags);
return sorted_indices;
}
template <typename _Tp> static
Mat interp1_(const Mat& X_, const Mat& Y_, const Mat& XI)
{
int n = XI.rows;
// sort input table
std::vector<int> sort_indices = argsort(X_);
Mat X = sortMatrixRowsByIndices(X_,sort_indices);
Mat Y = sortMatrixRowsByIndices(Y_,sort_indices);
// interpolated values
Mat yi = Mat::zeros(XI.size(), XI.type());
for(int i = 0; i < n; i++) {
int c = 0;
int low = 0;
int high = X.rows - 1;
// set bounds
if(XI.at<_Tp>(i,0) < X.at<_Tp>(low, 0))
high = 1;
if(XI.at<_Tp>(i,0) > X.at<_Tp>(high, 0))
low = high - 1;
// binary search
while((high-low)>1) {
c = low + ((high - low) >> 1);
if(XI.at<_Tp>(i,0) > X.at<_Tp>(c,0)) {
low = c;
} else {
high = c;
}
}
// linear interpolation
yi.at<_Tp>(i,0) += Y.at<_Tp>(low,0)
+ (XI.at<_Tp>(i,0) - X.at<_Tp>(low,0))
* (Y.at<_Tp>(high,0) - Y.at<_Tp>(low,0))
/ (X.at<_Tp>(high,0) - X.at<_Tp>(low,0));
}
return yi;
}
static Mat interp1(InputArray _x, InputArray _Y, InputArray _xi)
{
// get matrices
Mat x = _x.getMat();
Mat Y = _Y.getMat();
Mat xi = _xi.getMat();
// check types & alignment
CV_Assert((x.type() == Y.type()) && (Y.type() == xi.type()));
CV_Assert((x.cols == 1) && (x.rows == Y.rows) && (x.cols == Y.cols));
// call templated interp1
switch(x.type()) {
case CV_8SC1: return interp1_<char>(x,Y,xi); break;
case CV_8UC1: return interp1_<unsigned char>(x,Y,xi); break;
case CV_16SC1: return interp1_<short>(x,Y,xi); break;
case CV_16UC1: return interp1_<unsigned short>(x,Y,xi); break;
case CV_32SC1: return interp1_<int>(x,Y,xi); break;
case CV_32FC1: return interp1_<float>(x,Y,xi); break;
case CV_64FC1: return interp1_<double>(x,Y,xi); break;
default: CV_Error(Error::StsUnsupportedFormat, ""); break;
}
return Mat();
}
namespace colormap
{
class ColorMap {
protected:
Mat _lut;
public:
virtual ~ColorMap() {}
// Applies the colormap on a given image.
//
// This function expects BGR-aligned data of type CV_8UC1 or
// CV_8UC3. If the wrong image type is given, the original image
// will be returned.
//
// Throws an error for wrong-aligned lookup table, which must be
// of size 256 in the latest OpenCV release (2.3.1).
void operator()(InputArray src, OutputArray dst) const;
// Setup base map to interpolate from.
virtual void init(int n) = 0;
// Interpolates from a base colormap.
static Mat linear_colormap(InputArray X,
InputArray r, InputArray g, InputArray b,
int n) {
return linear_colormap(X,r,g,b,linspace(0,1,n));
}
// Interpolates from a base colormap.
static Mat linear_colormap(InputArray X,
InputArray r, InputArray g, InputArray b,
float begin, float end, float n) {
return linear_colormap(X,r,g,b,linspace(begin,end, cvRound(n)));
}
// Interpolates from a base colormap.
static Mat linear_colormap(InputArray X,
InputArray r, InputArray g, InputArray b,
InputArray xi);
};
// Equals the GNU Octave colormap "autumn".
class Autumn : public ColorMap {
public:
Autumn() : ColorMap() {
init(256);
}
Autumn(int n) : ColorMap() {
init(n);
}
void init(int n) {
float r[] = { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1};
float g[] = { 0, 0.01587301587301587, 0.03174603174603174, 0.04761904761904762, 0.06349206349206349, 0.07936507936507936, 0.09523809523809523, 0.1111111111111111, 0.126984126984127, 0.1428571428571428, 0.1587301587301587, 0.1746031746031746, 0.1904761904761905, 0.2063492063492063, 0.2222222222222222, 0.2380952380952381, 0.253968253968254, 0.2698412698412698, 0.2857142857142857, 0.3015873015873016, 0.3174603174603174, 0.3333333333333333, 0.3492063492063492, 0.3650793650793651, 0.3809523809523809, 0.3968253968253968, 0.4126984126984127, 0.4285714285714285, 0.4444444444444444, 0.4603174603174603, 0.4761904761904762, 0.492063492063492, 0.5079365079365079, 0.5238095238095238, 0.5396825396825397, 0.5555555555555556, 0.5714285714285714, 0.5873015873015873, 0.6031746031746031, 0.6190476190476191, 0.6349206349206349, 0.6507936507936508, 0.6666666666666666, 0.6825396825396826, 0.6984126984126984, 0.7142857142857143, 0.7301587301587301, 0.746031746031746, 0.7619047619047619, 0.7777777777777778, 0.7936507936507936, 0.8095238095238095, 0.8253968253968254, 0.8412698412698413, 0.8571428571428571, 0.873015873015873, 0.8888888888888888, 0.9047619047619048, 0.9206349206349206, 0.9365079365079365, 0.9523809523809523, 0.9682539682539683, 0.9841269841269841, 1};
float b[] = { 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
Mat X = linspace(0,1,64);
this->_lut = ColorMap::linear_colormap(X,
Mat(64,1, CV_32FC1, r).clone(), // red
Mat(64,1, CV_32FC1, g).clone(), // green
Mat(64,1, CV_32FC1, b).clone(), // blue
n); // number of sample points
}
};
// Equals the GNU Octave colormap "bone".
class Bone : public ColorMap {
public:
Bone() : ColorMap() {
init(256);
}
Bone(int n) : ColorMap() {
init(n);
}
void init(int n) {
float r[] = { 0, 0.01388888888888889, 0.02777777777777778, 0.04166666666666666, 0.05555555555555555, 0.06944444444444445, 0.08333333333333333, 0.09722222222222221, 0.1111111111111111, 0.125, 0.1388888888888889, 0.1527777777777778, 0.1666666666666667, 0.1805555555555556, 0.1944444444444444, 0.2083333333333333, 0.2222222222222222, 0.2361111111111111, 0.25, 0.2638888888888889, 0.2777777777777778, 0.2916666666666666, 0.3055555555555555, 0.3194444444444444, 0.3333333333333333, 0.3472222222222222, 0.3611111111111111, 0.375, 0.3888888888888888, 0.4027777777777777, 0.4166666666666666, 0.4305555555555555, 0.4444444444444444, 0.4583333333333333, 0.4722222222222222, 0.4861111111111112, 0.5, 0.5138888888888888, 0.5277777777777778, 0.5416666666666667, 0.5555555555555556, 0.5694444444444444, 0.5833333333333333, 0.5972222222222222, 0.611111111111111, 0.6249999999999999, 0.6388888888888888, 0.6527777777777778, 0.6726190476190474, 0.6944444444444442, 0.7162698412698412, 0.7380952380952381, 0.7599206349206349, 0.7817460317460316, 0.8035714285714286, 0.8253968253968254, 0.8472222222222221, 0.8690476190476188, 0.8908730158730158, 0.9126984126984128, 0.9345238095238095, 0.9563492063492063, 0.978174603174603, 1};
float g[] = { 0, 0.01388888888888889, 0.02777777777777778, 0.04166666666666666, 0.05555555555555555, 0.06944444444444445, 0.08333333333333333, 0.09722222222222221, 0.1111111111111111, 0.125, 0.1388888888888889, 0.1527777777777778, 0.1666666666666667, 0.1805555555555556, 0.1944444444444444, 0.2083333333333333, 0.2222222222222222, 0.2361111111111111, 0.25, 0.2638888888888889, 0.2777777777777778, 0.2916666666666666, 0.3055555555555555, 0.3194444444444444, 0.3353174603174602, 0.3544973544973544, 0.3736772486772486, 0.3928571428571428, 0.412037037037037, 0.4312169312169312, 0.4503968253968254, 0.4695767195767195, 0.4887566137566137, 0.5079365079365078, 0.5271164021164021, 0.5462962962962963, 0.5654761904761904, 0.5846560846560845, 0.6038359788359787, 0.623015873015873, 0.6421957671957671, 0.6613756613756612, 0.6805555555555555, 0.6997354497354497, 0.7189153439153438, 0.7380952380952379, 0.7572751322751322, 0.7764550264550264, 0.7916666666666666, 0.8055555555555555, 0.8194444444444444, 0.8333333333333334, 0.8472222222222222, 0.861111111111111, 0.875, 0.8888888888888888, 0.9027777777777777, 0.9166666666666665, 0.9305555555555555, 0.9444444444444444, 0.9583333333333333, 0.9722222222222221, 0.986111111111111, 1};
float b[] = { 0, 0.01917989417989418, 0.03835978835978836, 0.05753968253968253, 0.07671957671957672, 0.09589947089947089, 0.1150793650793651, 0.1342592592592592, 0.1534391534391534, 0.1726190476190476, 0.1917989417989418, 0.210978835978836, 0.2301587301587301, 0.2493386243386243, 0.2685185185185185, 0.2876984126984127, 0.3068783068783069, 0.326058201058201, 0.3452380952380952, 0.3644179894179894, 0.3835978835978835, 0.4027777777777777, 0.4219576719576719, 0.4411375661375661, 0.4583333333333333, 0.4722222222222222, 0.4861111111111111, 0.5, 0.5138888888888888, 0.5277777777777777, 0.5416666666666666, 0.5555555555555556, 0.5694444444444444, 0.5833333333333333, 0.5972222222222222, 0.6111111111111112, 0.625, 0.6388888888888888, 0.6527777777777778, 0.6666666666666667, 0.6805555555555556, 0.6944444444444444, 0.7083333333333333, 0.7222222222222222, 0.736111111111111, 0.7499999999999999, 0.7638888888888888, 0.7777777777777778, 0.7916666666666666, 0.8055555555555555, 0.8194444444444444, 0.8333333333333334, 0.8472222222222222, 0.861111111111111, 0.875, 0.8888888888888888, 0.9027777777777777, 0.9166666666666665, 0.9305555555555555, 0.9444444444444444, 0.9583333333333333, 0.9722222222222221, 0.986111111111111, 1};
Mat X = linspace(0,1,64);
this->_lut = ColorMap::linear_colormap(X,
Mat(64,1, CV_32FC1, r).clone(), // red
Mat(64,1, CV_32FC1, g).clone(), // green
Mat(64,1, CV_32FC1, b).clone(), // blue
n); // number of sample points
}
};
// Equals the GNU Octave colormap "jet".
class Jet : public ColorMap {
public:
Jet() {
init(256);
}
Jet(int n) : ColorMap() {
init(n);
}
void init(int n) {
// breakpoints
Mat X = linspace(0,1,256);
// define the basemap
float r[] = {0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.00588235294117645,0.02156862745098032,0.03725490196078418,0.05294117647058827,0.06862745098039214,0.084313725490196,0.1000000000000001,0.115686274509804,0.1313725490196078,0.1470588235294117,0.1627450980392156,0.1784313725490196,0.1941176470588235,0.2098039215686274,0.2254901960784315,0.2411764705882353,0.2568627450980392,0.2725490196078431,0.2882352941176469,0.303921568627451,0.3196078431372549,0.3352941176470587,0.3509803921568628,0.3666666666666667,0.3823529411764706,0.3980392156862744,0.4137254901960783,0.4294117647058824,0.4450980392156862,0.4607843137254901,0.4764705882352942,0.4921568627450981,0.5078431372549019,0.5235294117647058,0.5392156862745097,0.5549019607843135,0.5705882352941174,0.5862745098039217,0.6019607843137256,0.6176470588235294,0.6333333333333333,0.6490196078431372,0.664705882352941,0.6803921568627449,0.6960784313725492,0.7117647058823531,0.7274509803921569,0.7431372549019608,0.7588235294117647,0.7745098039215685,0.7901960784313724,0.8058823529411763,0.8215686274509801,0.8372549019607844,0.8529411764705883,0.8686274509803922,0.884313725490196,0.8999999999999999,0.9156862745098038,0.9313725490196076,0.947058823529412,0.9627450980392158,0.9784313725490197,0.9941176470588236,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.9862745098039216,0.9705882352941178,0.9549019607843139,0.93921568627451,0.9235294117647062,0.9078431372549018,0.892156862745098,0.8764705882352941,0.8607843137254902,0.8450980392156864,0.8294117647058825,0.8137254901960786,0.7980392156862743,0.7823529411764705,0.7666666666666666,0.7509803921568627,0.7352941176470589,0.719607843137255,0.7039215686274511,0.6882352941176473,0.6725490196078434,0.6568627450980391,0.6411764705882352,0.6254901960784314,0.6098039215686275,0.5941176470588236,0.5784313725490198,0.5627450980392159,0.5470588235294116,0.5313725490196077,0.5156862745098039,0.5};
float g[] = {0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.001960784313725483,0.01764705882352935,0.03333333333333333,0.0490196078431373,0.06470588235294117,0.08039215686274503,0.09607843137254901,0.111764705882353,0.1274509803921569,0.1431372549019607,0.1588235294117647,0.1745098039215687,0.1901960784313725,0.2058823529411764,0.2215686274509804,0.2372549019607844,0.2529411764705882,0.2686274509803921,0.2843137254901961,0.3,0.3156862745098039,0.3313725490196078,0.3470588235294118,0.3627450980392157,0.3784313725490196,0.3941176470588235,0.4098039215686274,0.4254901960784314,0.4411764705882353,0.4568627450980391,0.4725490196078431,0.4882352941176471,0.503921568627451,0.5196078431372548,0.5352941176470587,0.5509803921568628,0.5666666666666667,0.5823529411764705,0.5980392156862746,0.6137254901960785,0.6294117647058823,0.6450980392156862,0.6607843137254901,0.6764705882352942,0.692156862745098,0.7078431372549019,0.723529411764706,0.7392156862745098,0.7549019607843137,0.7705882352941176,0.7862745098039214,0.8019607843137255,0.8176470588235294,0.8333333333333333,0.8490196078431373,0.8647058823529412,0.8803921568627451,0.8960784313725489,0.9117647058823528,0.9274509803921569,0.9431372549019608,0.9588235294117646,0.9745098039215687,0.9901960784313726,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.9901960784313726,0.9745098039215687,0.9588235294117649,0.943137254901961,0.9274509803921571,0.9117647058823528,0.8960784313725489,0.8803921568627451,0.8647058823529412,0.8490196078431373,0.8333333333333335,0.8176470588235296,0.8019607843137253,0.7862745098039214,0.7705882352941176,0.7549019607843137,0.7392156862745098,0.723529411764706,0.7078431372549021,0.6921568627450982,0.6764705882352944,0.6607843137254901,0.6450980392156862,0.6294117647058823,0.6137254901960785,0.5980392156862746,0.5823529411764707,0.5666666666666669,0.5509803921568626,0.5352941176470587,0.5196078431372548,0.503921568627451,0.4882352941176471,0.4725490196078432,0.4568627450980394,0.4411764705882355,0.4254901960784316,0.4098039215686273,0.3941176470588235,0.3784313725490196,0.3627450980392157,0.3470588235294119,0.331372549019608,0.3156862745098041,0.2999999999999998,0.284313725490196,0.2686274509803921,0.2529411764705882,0.2372549019607844,0.2215686274509805,0.2058823529411766,0.1901960784313728,0.1745098039215689,0.1588235294117646,0.1431372549019607,0.1274509803921569,0.111764705882353,0.09607843137254912,0.08039215686274526,0.06470588235294139,0.04901960784313708,0.03333333333333321,0.01764705882352935,0.001960784313725483,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0};
float b[] = {0.5,0.5156862745098039,0.5313725490196078,0.5470588235294118,0.5627450980392157,0.5784313725490196,0.5941176470588235,0.6098039215686275,0.6254901960784314,0.6411764705882352,0.6568627450980392,0.6725490196078432,0.6882352941176471,0.7039215686274509,0.7196078431372549,0.7352941176470589,0.7509803921568627,0.7666666666666666,0.7823529411764706,0.7980392156862746,0.8137254901960784,0.8294117647058823,0.8450980392156863,0.8607843137254902,0.8764705882352941,0.892156862745098,0.907843137254902,0.9235294117647059,0.9392156862745098,0.9549019607843137,0.9705882352941176,0.9862745098039216,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.9941176470588236,0.9784313725490197,0.9627450980392158,0.9470588235294117,0.9313725490196079,0.915686274509804,0.8999999999999999,0.884313725490196,0.8686274509803922,0.8529411764705883,0.8372549019607844,0.8215686274509804,0.8058823529411765,0.7901960784313726,0.7745098039215685,0.7588235294117647,0.7431372549019608,0.7274509803921569,0.7117647058823531,0.696078431372549,0.6803921568627451,0.6647058823529413,0.6490196078431372,0.6333333333333333,0.6176470588235294,0.6019607843137256,0.5862745098039217,0.5705882352941176,0.5549019607843138,0.5392156862745099,0.5235294117647058,0.5078431372549019,0.4921568627450981,0.4764705882352942,0.4607843137254903,0.4450980392156865,0.4294117647058826,0.4137254901960783,0.3980392156862744,0.3823529411764706,0.3666666666666667,0.3509803921568628,0.335294117647059,0.3196078431372551,0.3039215686274508,0.2882352941176469,0.2725490196078431,0.2568627450980392,0.2411764705882353,0.2254901960784315,0.2098039215686276,0.1941176470588237,0.1784313725490199,0.1627450980392156,0.1470588235294117,0.1313725490196078,0.115686274509804,0.1000000000000001,0.08431372549019622,0.06862745098039236,0.05294117647058805,0.03725490196078418,0.02156862745098032,0.00588235294117645,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0};
// now build lookup table
this->_lut = ColorMap::linear_colormap(X,
Mat(256,1, CV_32FC1, r).clone(), // red
Mat(256,1, CV_32FC1, g).clone(), // green
Mat(256,1, CV_32FC1, b).clone(), // blue
n);
}
};
// Equals the GNU Octave colormap "winter".
class Winter : public ColorMap {
public:
Winter() : ColorMap() {
init(256);
}
Winter(int n) : ColorMap() {
init(n);
}
void init(int n) {
float r[] = {0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0};
float g[] = {0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0};
float b[] = {1.0, 0.95, 0.9, 0.85, 0.8, 0.75, 0.7, 0.65, 0.6, 0.55, 0.5};
Mat X = linspace(0,1,11);
this->_lut = ColorMap::linear_colormap(X,
Mat(11,1, CV_32FC1, r).clone(), // red
Mat(11,1, CV_32FC1, g).clone(), // green
Mat(11,1, CV_32FC1, b).clone(), // blue
n); // number of sample points
}
};
// Equals the GNU Octave colormap "rainbow".
class Rainbow : public ColorMap {
public:
Rainbow() : ColorMap() {
init(256);
}
Rainbow(int n) : ColorMap() {
init(n);
}
void init(int n) {
float r[] = { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.9365079365079367, 0.8571428571428572, 0.7777777777777777, 0.6984126984126986, 0.6190476190476191, 0.53968253968254, 0.4603174603174605, 0.3809523809523814, 0.3015873015873018, 0.2222222222222223, 0.1428571428571432, 0.06349206349206415, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.03174603174603208, 0.08465608465608465, 0.1375661375661377, 0.1904761904761907, 0.2433862433862437, 0.2962962962962963, 0.3492063492063493, 0.4021164021164023, 0.4550264550264553, 0.5079365079365079, 0.5608465608465609, 0.6137566137566139, 0.666666666666667};
float g[] = { 0, 0.03968253968253968, 0.07936507936507936, 0.119047619047619, 0.1587301587301587, 0.1984126984126984, 0.2380952380952381, 0.2777777777777778, 0.3174603174603174, 0.3571428571428571, 0.3968253968253968, 0.4365079365079365, 0.4761904761904762, 0.5158730158730158, 0.5555555555555556, 0.5952380952380952, 0.6349206349206349, 0.6746031746031745, 0.7142857142857142, 0.753968253968254, 0.7936507936507936, 0.8333333333333333, 0.873015873015873, 0.9126984126984127, 0.9523809523809523, 0.992063492063492, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.9841269841269842, 0.9047619047619047, 0.8253968253968256, 0.7460317460317465, 0.666666666666667, 0.587301587301587, 0.5079365079365079, 0.4285714285714288, 0.3492063492063493, 0.2698412698412698, 0.1904761904761907, 0.1111111111111116, 0.03174603174603208, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
float b[] = { 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.01587301587301582, 0.09523809523809534, 0.1746031746031744, 0.2539682539682535, 0.333333333333333, 0.412698412698413, 0.4920634920634921, 0.5714285714285712, 0.6507936507936507, 0.7301587301587302, 0.8095238095238093, 0.8888888888888884, 0.9682539682539679, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1};
Mat X = linspace(0,1,64);
this->_lut = ColorMap::linear_colormap(X,
Mat(64,1, CV_32FC1, r).clone(), // red
Mat(64,1, CV_32FC1, g).clone(), // green
Mat(64,1, CV_32FC1, b).clone(), // blue
n); // number of sample points
}
};
// Equals the GNU Octave colormap "ocean".
class Ocean : public ColorMap {
public:
Ocean() : ColorMap() {
init(256);
}
Ocean(int n) : ColorMap() {
init(n);
}
void init(int n) {
float r[] = { 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.04761904761904762, 0.09523809523809523, 0.1428571428571428, 0.1904761904761905, 0.2380952380952381, 0.2857142857142857, 0.3333333333333333, 0.3809523809523809, 0.4285714285714285, 0.4761904761904762, 0.5238095238095238, 0.5714285714285714, 0.6190476190476191, 0.6666666666666666, 0.7142857142857143, 0.7619047619047619, 0.8095238095238095, 0.8571428571428571, 0.9047619047619048, 0.9523809523809523, 1};
float g[] = { 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.02380952380952381, 0.04761904761904762, 0.07142857142857142, 0.09523809523809523, 0.119047619047619, 0.1428571428571428, 0.1666666666666667, 0.1904761904761905, 0.2142857142857143, 0.2380952380952381, 0.2619047619047619, 0.2857142857142857, 0.3095238095238095, 0.3333333333333333, 0.3571428571428572, 0.3809523809523809, 0.4047619047619048, 0.4285714285714285, 0.4523809523809524, 0.4761904761904762, 0.5, 0.5238095238095238, 0.5476190476190477, 0.5714285714285714, 0.5952380952380952, 0.6190476190476191, 0.6428571428571429, 0.6666666666666666, 0.6904761904761905, 0.7142857142857143, 0.7380952380952381, 0.7619047619047619, 0.7857142857142857, 0.8095238095238095, 0.8333333333333334, 0.8571428571428571, 0.8809523809523809, 0.9047619047619048, 0.9285714285714286, 0.9523809523809523, 0.9761904761904762, 1};
float b[] = { 0, 0.01587301587301587, 0.03174603174603174, 0.04761904761904762, 0.06349206349206349, 0.07936507936507936, 0.09523809523809523, 0.1111111111111111, 0.126984126984127, 0.1428571428571428, 0.1587301587301587, 0.1746031746031746, 0.1904761904761905, 0.2063492063492063, 0.2222222222222222, 0.2380952380952381, 0.253968253968254, 0.2698412698412698, 0.2857142857142857, 0.3015873015873016, 0.3174603174603174, 0.3333333333333333, 0.3492063492063492, 0.3650793650793651, 0.3809523809523809, 0.3968253968253968, 0.4126984126984127, 0.4285714285714285, 0.4444444444444444, 0.4603174603174603, 0.4761904761904762, 0.492063492063492, 0.5079365079365079, 0.5238095238095238, 0.5396825396825397, 0.5555555555555556, 0.5714285714285714, 0.5873015873015873, 0.6031746031746031, 0.6190476190476191, 0.6349206349206349, 0.6507936507936508, 0.6666666666666666, 0.6825396825396826, 0.6984126984126984, 0.7142857142857143, 0.7301587301587301, 0.746031746031746, 0.7619047619047619, 0.7777777777777778, 0.7936507936507936, 0.8095238095238095, 0.8253968253968254, 0.8412698412698413, 0.8571428571428571, 0.873015873015873, 0.8888888888888888, 0.9047619047619048, 0.9206349206349206, 0.9365079365079365, 0.9523809523809523, 0.9682539682539683, 0.9841269841269841, 1};
Mat X = linspace(0,1,64);
this->_lut = ColorMap::linear_colormap(X,
Mat(64,1, CV_32FC1, r).clone(), // red
Mat(64,1, CV_32FC1, g).clone(), // green
Mat(64,1, CV_32FC1, b).clone(), // blue
n); // number of sample points
}
};
// Equals the GNU Octave colormap "summer".
class Summer : public ColorMap {
public:
Summer() : ColorMap() {
init(256);
}
Summer(int n) : ColorMap() {
init(n);
}
void init(int n) {
float r[] = { 0, 0.01587301587301587, 0.03174603174603174, 0.04761904761904762, 0.06349206349206349, 0.07936507936507936, 0.09523809523809523, 0.1111111111111111, 0.126984126984127, 0.1428571428571428, 0.1587301587301587, 0.1746031746031746, 0.1904761904761905, 0.2063492063492063, 0.2222222222222222, 0.2380952380952381, 0.253968253968254, 0.2698412698412698, 0.2857142857142857, 0.3015873015873016, 0.3174603174603174, 0.3333333333333333, 0.3492063492063492, 0.3650793650793651, 0.3809523809523809, 0.3968253968253968, 0.4126984126984127, 0.4285714285714285, 0.4444444444444444, 0.4603174603174603, 0.4761904761904762, 0.492063492063492, 0.5079365079365079, 0.5238095238095238, 0.5396825396825397, 0.5555555555555556, 0.5714285714285714, 0.5873015873015873, 0.6031746031746031, 0.6190476190476191, 0.6349206349206349, 0.6507936507936508, 0.6666666666666666, 0.6825396825396826, 0.6984126984126984, 0.7142857142857143, 0.7301587301587301, 0.746031746031746, 0.7619047619047619, 0.7777777777777778, 0.7936507936507936, 0.8095238095238095, 0.8253968253968254, 0.8412698412698413, 0.8571428571428571, 0.873015873015873, 0.8888888888888888, 0.9047619047619048, 0.9206349206349206, 0.9365079365079365, 0.9523809523809523, 0.9682539682539683, 0.9841269841269841, 1};
float g[] = { 0.5, 0.5079365079365079, 0.5158730158730158, 0.5238095238095238, 0.5317460317460317, 0.5396825396825397, 0.5476190476190477, 0.5555555555555556, 0.5634920634920635, 0.5714285714285714, 0.5793650793650793, 0.5873015873015873, 0.5952380952380952, 0.6031746031746031, 0.6111111111111112, 0.6190476190476191, 0.626984126984127, 0.6349206349206349, 0.6428571428571428, 0.6507936507936508, 0.6587301587301587, 0.6666666666666666, 0.6746031746031746, 0.6825396825396826, 0.6904761904761905, 0.6984126984126984, 0.7063492063492063, 0.7142857142857143, 0.7222222222222222, 0.7301587301587301, 0.7380952380952381, 0.746031746031746, 0.753968253968254, 0.7619047619047619, 0.7698412698412698, 0.7777777777777778, 0.7857142857142857, 0.7936507936507937, 0.8015873015873016, 0.8095238095238095, 0.8174603174603174, 0.8253968253968254, 0.8333333333333333, 0.8412698412698413, 0.8492063492063492, 0.8571428571428572, 0.8650793650793651, 0.873015873015873, 0.8809523809523809, 0.8888888888888888, 0.8968253968253967, 0.9047619047619048, 0.9126984126984127, 0.9206349206349207, 0.9285714285714286, 0.9365079365079365, 0.9444444444444444, 0.9523809523809523, 0.9603174603174602, 0.9682539682539683, 0.9761904761904762, 0.9841269841269842, 0.9920634920634921, 1};
float b[] = { 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4};
Mat X = linspace(0,1,64);
this->_lut = ColorMap::linear_colormap(X,
Mat(64,1, CV_32FC1, r).clone(), // red
Mat(64,1, CV_32FC1, g).clone(), // green
Mat(64,1, CV_32FC1, b).clone(), // blue
n); // number of sample points
}
};
// Equals the GNU Octave colormap "spring".
class Spring : public ColorMap {
public:
Spring() : ColorMap() {
init(256);
}
Spring(int n) : ColorMap() {
init(n);
}
void init(int n) {
float r[] = { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1};
float g[] = { 0, 0.01587301587301587, 0.03174603174603174, 0.04761904761904762, 0.06349206349206349, 0.07936507936507936, 0.09523809523809523, 0.1111111111111111, 0.126984126984127, 0.1428571428571428, 0.1587301587301587, 0.1746031746031746, 0.1904761904761905, 0.2063492063492063, 0.2222222222222222, 0.2380952380952381, 0.253968253968254, 0.2698412698412698, 0.2857142857142857, 0.3015873015873016, 0.3174603174603174, 0.3333333333333333, 0.3492063492063492, 0.3650793650793651, 0.3809523809523809, 0.3968253968253968, 0.4126984126984127, 0.4285714285714285, 0.4444444444444444, 0.4603174603174603, 0.4761904761904762, 0.492063492063492, 0.5079365079365079, 0.5238095238095238, 0.5396825396825397, 0.5555555555555556, 0.5714285714285714, 0.5873015873015873, 0.6031746031746031, 0.6190476190476191, 0.6349206349206349, 0.6507936507936508, 0.6666666666666666, 0.6825396825396826, 0.6984126984126984, 0.7142857142857143, 0.7301587301587301, 0.746031746031746, 0.7619047619047619, 0.7777777777777778, 0.7936507936507936, 0.8095238095238095, 0.8253968253968254, 0.8412698412698413, 0.8571428571428571, 0.873015873015873, 0.8888888888888888, 0.9047619047619048, 0.9206349206349206, 0.9365079365079365, 0.9523809523809523, 0.9682539682539683, 0.9841269841269841, 1};
float b[] = { 1, 0.9841269841269842, 0.9682539682539683, 0.9523809523809523, 0.9365079365079365, 0.9206349206349207, 0.9047619047619048, 0.8888888888888888, 0.873015873015873, 0.8571428571428572, 0.8412698412698413, 0.8253968253968254, 0.8095238095238095, 0.7936507936507937, 0.7777777777777778, 0.7619047619047619, 0.746031746031746, 0.7301587301587302, 0.7142857142857143, 0.6984126984126984, 0.6825396825396826, 0.6666666666666667, 0.6507936507936508, 0.6349206349206349, 0.6190476190476191, 0.6031746031746033, 0.5873015873015873, 0.5714285714285714, 0.5555555555555556, 0.5396825396825398, 0.5238095238095238, 0.5079365079365079, 0.4920634920634921, 0.4761904761904762, 0.4603174603174603, 0.4444444444444444, 0.4285714285714286, 0.4126984126984127, 0.3968253968253969, 0.3809523809523809, 0.3650793650793651, 0.3492063492063492, 0.3333333333333334, 0.3174603174603174, 0.3015873015873016, 0.2857142857142857, 0.2698412698412699, 0.253968253968254, 0.2380952380952381, 0.2222222222222222, 0.2063492063492064, 0.1904761904761905, 0.1746031746031746, 0.1587301587301587, 0.1428571428571429, 0.126984126984127, 0.1111111111111112, 0.09523809523809523, 0.07936507936507942, 0.06349206349206349, 0.04761904761904767, 0.03174603174603174, 0.01587301587301593, 0};
Mat X = linspace(0,1,64);
this->_lut = ColorMap::linear_colormap(X,
Mat(64,1, CV_32FC1, r).clone(), // red
Mat(64,1, CV_32FC1, g).clone(), // green
Mat(64,1, CV_32FC1, b).clone(), // blue
n); // number of sample points
}
};
// Equals the GNU Octave colormap "cool".
class Cool : public ColorMap {
public:
Cool() : ColorMap() {
init(256);
}
Cool(int n) : ColorMap() {
init(n);
}
void init(int n) {
float r[] = { 0, 0.01587301587301587, 0.03174603174603174, 0.04761904761904762, 0.06349206349206349, 0.07936507936507936, 0.09523809523809523, 0.1111111111111111, 0.126984126984127, 0.1428571428571428, 0.1587301587301587, 0.1746031746031746, 0.1904761904761905, 0.2063492063492063, 0.2222222222222222, 0.2380952380952381, 0.253968253968254, 0.2698412698412698, 0.2857142857142857, 0.3015873015873016, 0.3174603174603174, 0.3333333333333333, 0.3492063492063492, 0.3650793650793651, 0.3809523809523809, 0.3968253968253968, 0.4126984126984127, 0.4285714285714285, 0.4444444444444444, 0.4603174603174603, 0.4761904761904762, 0.492063492063492, 0.5079365079365079, 0.5238095238095238, 0.5396825396825397, 0.5555555555555556, 0.5714285714285714, 0.5873015873015873, 0.6031746031746031, 0.6190476190476191, 0.6349206349206349, 0.6507936507936508, 0.6666666666666666, 0.6825396825396826, 0.6984126984126984, 0.7142857142857143, 0.7301587301587301, 0.746031746031746, 0.7619047619047619, 0.7777777777777778, 0.7936507936507936, 0.8095238095238095, 0.8253968253968254, 0.8412698412698413, 0.8571428571428571, 0.873015873015873, 0.8888888888888888, 0.9047619047619048, 0.9206349206349206, 0.9365079365079365, 0.9523809523809523, 0.9682539682539683, 0.9841269841269841, 1};
float g[] = { 1, 0.9841269841269842, 0.9682539682539683, 0.9523809523809523, 0.9365079365079365, 0.9206349206349207, 0.9047619047619048, 0.8888888888888888, 0.873015873015873, 0.8571428571428572, 0.8412698412698413, 0.8253968253968254, 0.8095238095238095, 0.7936507936507937, 0.7777777777777778, 0.7619047619047619, 0.746031746031746, 0.7301587301587302, 0.7142857142857143, 0.6984126984126984, 0.6825396825396826, 0.6666666666666667, 0.6507936507936508, 0.6349206349206349, 0.6190476190476191, 0.6031746031746033, 0.5873015873015873, 0.5714285714285714, 0.5555555555555556, 0.5396825396825398, 0.5238095238095238, 0.5079365079365079, 0.4920634920634921, 0.4761904761904762, 0.4603174603174603, 0.4444444444444444, 0.4285714285714286, 0.4126984126984127, 0.3968253968253969, 0.3809523809523809, 0.3650793650793651, 0.3492063492063492, 0.3333333333333334, 0.3174603174603174, 0.3015873015873016, 0.2857142857142857, 0.2698412698412699, 0.253968253968254, 0.2380952380952381, 0.2222222222222222, 0.2063492063492064, 0.1904761904761905, 0.1746031746031746, 0.1587301587301587, 0.1428571428571429, 0.126984126984127, 0.1111111111111112, 0.09523809523809523, 0.07936507936507942, 0.06349206349206349, 0.04761904761904767, 0.03174603174603174, 0.01587301587301593, 0};
float b[] = { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1};
Mat X = linspace(0,1,64);
this->_lut = ColorMap::linear_colormap(X,
Mat(64,1, CV_32FC1, r).clone(), // red
Mat(64,1, CV_32FC1, g).clone(), // green
Mat(64,1, CV_32FC1, b).clone(), // blue
n); // number of sample points
}
};
// Equals the GNU Octave colormap "hsv".
class HSV : public ColorMap {
public:
HSV() : ColorMap() {
init(256);
}
HSV(int n) : ColorMap() {
init(n);
}
void init(int n) {
float r[] = { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.9523809523809526, 0.8571428571428568, 0.7619047619047614, 0.6666666666666665, 0.5714285714285716, 0.4761904761904763, 0.3809523809523805, 0.2857142857142856, 0.1904761904761907, 0.0952380952380949, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.09523809523809557, 0.1904761904761905, 0.2857142857142854, 0.3809523809523809, 0.4761904761904765, 0.5714285714285714, 0.6666666666666663, 0.7619047619047619, 0.8571428571428574, 0.9523809523809523, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1};
float g[] = { 0, 0.09523809523809523, 0.1904761904761905, 0.2857142857142857, 0.3809523809523809, 0.4761904761904762, 0.5714285714285714, 0.6666666666666666, 0.7619047619047619, 0.8571428571428571, 0.9523809523809523, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.9523809523809526, 0.8571428571428577, 0.7619047619047619, 0.6666666666666665, 0.5714285714285716, 0.4761904761904767, 0.3809523809523814, 0.2857142857142856, 0.1904761904761907, 0.09523809523809579, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
float b[] = { 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.09523809523809523, 0.1904761904761905, 0.2857142857142857, 0.3809523809523809, 0.4761904761904762, 0.5714285714285714, 0.6666666666666666, 0.7619047619047619, 0.8571428571428571, 0.9523809523809523, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.9523809523809526, 0.8571428571428577, 0.7619047619047614, 0.6666666666666665, 0.5714285714285716, 0.4761904761904767, 0.3809523809523805, 0.2857142857142856, 0.1904761904761907, 0.09523809523809579, 0};
Mat X = linspace(0,1,64);
this->_lut = ColorMap::linear_colormap(X,
Mat(64,1, CV_32FC1, r).clone(), // red
Mat(64,1, CV_32FC1, g).clone(), // green
Mat(64,1, CV_32FC1, b).clone(), // blue
n); // number of sample points
}
};
// Equals the GNU Octave colormap "pink".
class Pink : public ColorMap {
public:
Pink() : ColorMap() {
init(256);
}
Pink(int n) : ColorMap() {
init(n);
}
void init(int n) {
float r[] = { 0, 0.1571348402636772, 0.2222222222222222, 0.2721655269759087, 0.3142696805273544, 0.3513641844631533, 0.3849001794597505, 0.415739709641549, 0.4444444444444444, 0.4714045207910317, 0.4969039949999532, 0.5211573066470477, 0.5443310539518174, 0.5665577237325317, 0.5879447357921312, 0.6085806194501846, 0.6285393610547089, 0.6478835438717, 0.6666666666666666, 0.6849348892187751, 0.7027283689263065, 0.7200822998230956, 0.7370277311900888, 0.753592220347252, 0.7663560447348133, 0.7732293307186413, 0.7800420555749596, 0.7867957924694432, 0.7934920476158722, 0.8001322641986387, 0.8067178260046388, 0.8132500607904444, 0.8197302434079591, 0.8261595987094034, 0.8325393042503717, 0.8388704928078611, 0.8451542547285166, 0.8513916401208816, 0.8575836609041332, 0.8637312927246217, 0.8698354767504924, 0.8758971213537393, 0.8819171036881968, 0.8878962711712378, 0.8938354428762595, 0.8997354108424372, 0.9055969413076769, 0.9114207758701963, 0.9172076325837248, 0.9229582069908971, 0.9286731730990523, 0.9343531843023135, 0.9399988742535192, 0.9456108576893002, 0.9511897312113418, 0.9567360740266436, 0.9622504486493763, 0.9677334015667416, 0.9731854638710686, 0.9786071518602129, 0.9839989676081821, 0.9893613995077727, 0.9946949227868761, 1};
float g[] = { 0, 0.1028688999747279, 0.1454785934906616, 0.1781741612749496, 0.2057377999494559, 0.2300218531141181, 0.2519763153394848, 0.2721655269759087, 0.2909571869813232, 0.3086066999241838, 0.3253000243161777, 0.3411775438127727, 0.3563483225498992, 0.3708990935094579, 0.3849001794597505, 0.3984095364447979, 0.4114755998989117, 0.4241393401869012, 0.4364357804719847, 0.4483951394230328, 0.4600437062282361, 0.4714045207910317, 0.4824979096371639, 0.4933419132673033, 0.5091750772173156, 0.5328701692569688, 0.5555555555555556, 0.5773502691896257, 0.5983516452371671, 0.6186404847588913, 0.6382847385042254, 0.6573421981221795, 0.6758625033664688, 0.6938886664887108, 0.7114582486036499, 0.7286042804780002, 0.7453559924999299, 0.7617394000445604, 0.7777777777777778, 0.7934920476158723, 0.8089010988089465, 0.8240220541217402, 0.8388704928078611, 0.8534606386520677, 0.8678055195451838, 0.8819171036881968, 0.8958064164776166, 0.9094836413191612, 0.9172076325837248, 0.9229582069908971, 0.9286731730990523, 0.9343531843023135, 0.9399988742535192, 0.9456108576893002, 0.9511897312113418, 0.9567360740266436, 0.9622504486493763, 0.9677334015667416, 0.9731854638710686, 0.9786071518602129, 0.9839989676081821, 0.9893613995077727, 0.9946949227868761, 1};
float b[] = { 0, 0.1028688999747279, 0.1454785934906616, 0.1781741612749496, 0.2057377999494559, 0.2300218531141181, 0.2519763153394848, 0.2721655269759087, 0.2909571869813232, 0.3086066999241838, 0.3253000243161777, 0.3411775438127727, 0.3563483225498992, 0.3708990935094579, 0.3849001794597505, 0.3984095364447979, 0.4114755998989117, 0.4241393401869012, 0.4364357804719847, 0.4483951394230328, 0.4600437062282361, 0.4714045207910317, 0.4824979096371639, 0.4933419132673033, 0.5039526306789697, 0.5143444998736397, 0.5245305283129621, 0.5345224838248488, 0.5443310539518174, 0.5539659798925444, 0.563436169819011, 0.5727497953228163, 0.5819143739626463, 0.5909368402852788, 0.5998236072282915, 0.6085806194501846, 0.6172133998483676, 0.6257270902992705, 0.6341264874742278, 0.642416074439621, 0.6506000486323554, 0.6586823467062358, 0.6666666666666666, 0.6745564876468501, 0.6823550876255453, 0.6900655593423541, 0.6976908246297114, 0.7052336473499384, 0.7237468644557459, 0.7453559924999298, 0.7663560447348133, 0.7867957924694432, 0.8067178260046388, 0.8261595987094034, 0.8451542547285166, 0.8637312927246217, 0.8819171036881968, 0.8997354108424372, 0.9172076325837248, 0.9343531843023135, 0.9511897312113418, 0.9677334015667416, 0.9839989676081821, 1};
Mat X = linspace(0,1,64);
this->_lut = ColorMap::linear_colormap(X,
Mat(64,1, CV_32FC1, r).clone(), // red
Mat(64,1, CV_32FC1, g).clone(), // green
Mat(64,1, CV_32FC1, b).clone(), // blue
n); // number of sample points
}
};
// Equals the GNU Octave colormap "hot".
class Hot : public ColorMap {
public:
Hot() : ColorMap() {
init(256);
}
Hot(int n) : ColorMap() {
init(n);
}
void init(int n) {
float r[] = { 0, 0.03968253968253968, 0.07936507936507936, 0.119047619047619, 0.1587301587301587, 0.1984126984126984, 0.2380952380952381, 0.2777777777777778, 0.3174603174603174, 0.3571428571428571, 0.3968253968253968, 0.4365079365079365, 0.4761904761904762, 0.5158730158730158, 0.5555555555555556, 0.5952380952380952, 0.6349206349206349, 0.6746031746031745, 0.7142857142857142, 0.753968253968254, 0.7936507936507936, 0.8333333333333333, 0.873015873015873, 0.9126984126984127, 0.9523809523809523, 0.992063492063492, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1};
float g[] = { 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.03174603174603163, 0.0714285714285714, 0.1111111111111112, 0.1507936507936507, 0.1904761904761905, 0.23015873015873, 0.2698412698412698, 0.3095238095238093, 0.3492063492063491, 0.3888888888888888, 0.4285714285714284, 0.4682539682539679, 0.5079365079365079, 0.5476190476190477, 0.5873015873015872, 0.6269841269841268, 0.6666666666666665, 0.7063492063492065, 0.746031746031746, 0.7857142857142856, 0.8253968253968254, 0.8650793650793651, 0.9047619047619047, 0.9444444444444442, 0.984126984126984, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1};
float b[] = { 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.04761904761904745, 0.1269841269841265, 0.2063492063492056, 0.2857142857142856, 0.3650793650793656, 0.4444444444444446, 0.5238095238095237, 0.6031746031746028, 0.6825396825396828, 0.7619047619047619, 0.8412698412698409, 0.92063492063492, 1};
Mat X = linspace(0,1,64);
this->_lut = ColorMap::linear_colormap(X,
Mat(64,1, CV_32FC1, r).clone(), // red
Mat(64,1, CV_32FC1, g).clone(), // green
Mat(64,1, CV_32FC1, b).clone(), // blue
n); // number of sample points
}
};
void ColorMap::operator()(InputArray _src, OutputArray _dst) const
{
if(_lut.total() != 256)
CV_Error(Error::StsAssert, "cv::LUT only supports tables of size 256.");
Mat src = _src.getMat();
// Return original matrix if wrong type is given (is fail loud better here?)
if(src.type() != CV_8UC1 && src.type() != CV_8UC3)
{
src.copyTo(_dst);
return;
}
// Turn into a BGR matrix into its grayscale representation.
if(src.type() == CV_8UC3)
cvtColor(src.clone(), src, COLOR_BGR2GRAY);
cvtColor(src.clone(), src, COLOR_GRAY2BGR);
// Apply the ColorMap.
LUT(src, _lut, _dst);
}
Mat ColorMap::linear_colormap(InputArray X,
InputArray r, InputArray g, InputArray b,
InputArray xi) {
Mat lut, lut8;
Mat planes[] = {
interp1(X, b, xi),
interp1(X, g, xi),
interp1(X, r, xi)};
merge(planes, 3, lut);
lut.convertTo(lut8, CV_8U, 255.);
return lut8;
}
}
void applyColorMap(InputArray src, OutputArray dst, int colormap)
{
colormap::ColorMap* cm =
colormap == COLORMAP_AUTUMN ? (colormap::ColorMap*)(new colormap::Autumn) :
colormap == COLORMAP_BONE ? (colormap::ColorMap*)(new colormap::Bone) :
colormap == COLORMAP_COOL ? (colormap::ColorMap*)(new colormap::Cool) :
colormap == COLORMAP_HOT ? (colormap::ColorMap*)(new colormap::Hot) :
colormap == COLORMAP_HSV ? (colormap::ColorMap*)(new colormap::HSV) :
colormap == COLORMAP_JET ? (colormap::ColorMap*)(new colormap::Jet) :
colormap == COLORMAP_OCEAN ? (colormap::ColorMap*)(new colormap::Ocean) :
colormap == COLORMAP_PINK ? (colormap::ColorMap*)(new colormap::Pink) :
colormap == COLORMAP_RAINBOW ? (colormap::ColorMap*)(new colormap::Rainbow) :
colormap == COLORMAP_SPRING ? (colormap::ColorMap*)(new colormap::Spring) :
colormap == COLORMAP_SUMMER ? (colormap::ColorMap*)(new colormap::Summer) :
colormap == COLORMAP_WINTER ? (colormap::ColorMap*)(new colormap::Winter) : 0;
if( !cm )
CV_Error( Error::StsBadArg, "Unknown colormap id; use one of COLORMAP_*");
(*cm)(src, dst);
delete cm;
}
}
modules/contrib/src/colortracker.cpp
-134
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@@ -1,134 +0,0 @@
//*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2008-2011, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "precomp.hpp"
#include "opencv2/contrib/hybridtracker.hpp"
using namespace cv;
CvMeanShiftTracker::CvMeanShiftTracker(CvMeanShiftTrackerParams _params) : params(_params)
{
}
CvMeanShiftTracker::~CvMeanShiftTracker()
{
}
void CvMeanShiftTracker::newTrackingWindow(Mat image, Rect selection)
{
hist.release();
int channels[] = { 0, 0 , 1, 1};
float hrange[] = { 0, 180 };
float srange[] = { 0, 1 };
const float* ranges[] = {hrange, srange};
cvtColor(image, hsv, COLOR_BGR2HSV);
inRange(hsv, Scalar(0, 30, MIN(10, 256)), Scalar(180, 256, MAX(10, 256)), mask);
hue.create(hsv.size(), CV_8UC2);
mixChannels(&hsv, 1, &hue, 1, channels, 2);
Mat roi(hue, selection);
Mat mskroi(mask, selection);
int ch[] = {0, 1};
int chsize[] = {32, 32};
calcHist(&roi, 1, ch, mskroi, hist, 1, chsize, ranges);
normalize(hist, hist, 0, 255, CV_MINMAX);
prev_trackwindow = selection;
}
RotatedRect CvMeanShiftTracker::updateTrackingWindow(Mat image)
{
int channels[] = { 0, 0 , 1, 1};
float hrange[] = { 0, 180 };
float srange[] = { 0, 1 };
const float* ranges[] = {hrange, srange};
cvtColor(image, hsv, COLOR_BGR2HSV);
inRange(hsv, Scalar(0, 30, MIN(10, 256)), Scalar(180, 256, MAX(10, 256)), mask);
hue.create(hsv.size(), CV_8UC2);
mixChannels(&hsv, 1, &hue, 1, channels, 2);
int ch[] = {0, 1};
calcBackProject(&hue, 1, ch, hist, backproj, ranges);
backproj &= mask;
prev_trackbox = CamShift(backproj, prev_trackwindow, TermCriteria(CV_TERMCRIT_EPS | CV_TERMCRIT_ITER, 10, 1));
int cols = backproj.cols, rows = backproj.rows, r = (MIN(cols, rows) + 5) / 6;
prev_trackwindow = Rect(prev_trackwindow.x - r, prev_trackwindow.y - r, prev_trackwindow.x + r,
prev_trackwindow.y + r) & Rect(0, 0, cols, rows);
prev_center.x = (float)(prev_trackwindow.x + prev_trackwindow.width / 2);
prev_center.y = (float)(prev_trackwindow.y + prev_trackwindow.height / 2);
#ifdef DEBUG_HYTRACKER
ellipse(image, prev_trackbox, Scalar(0, 0, 255), 1, CV_AA);
#endif
return prev_trackbox;
}
Mat CvMeanShiftTracker::getHistogramProjection(int type)
{
Mat ms_backproj_f(backproj.size(), type);
backproj.convertTo(ms_backproj_f, type);
return ms_backproj_f;
}
void CvMeanShiftTracker::setTrackingWindow(Rect window)
{
prev_trackwindow = window;
}
Rect CvMeanShiftTracker::getTrackingWindow()
{
return prev_trackwindow;
}
RotatedRect CvMeanShiftTracker::getTrackingEllipse()
{
return prev_trackbox;
}
Point2f CvMeanShiftTracker::getTrackingCenter()
{
return prev_center;
}
modules/contrib/src/contrib_init.cpp
-43
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@@ -1,43 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "precomp.hpp"
modules/contrib/src/detection_based_tracker.cpp
-877
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@@ -1,877 +0,0 @@
#if defined(__linux__) || defined(LINUX) || defined(__APPLE__) || defined(ANDROID)
#include "opencv2/contrib/detection_based_tracker.hpp"
#include "opencv2/core/utility.hpp"
#include <pthread.h>
#if defined(DEBUG) || defined(_DEBUG)
#undef DEBUGLOGS
#define DEBUGLOGS 1
#endif
#ifndef DEBUGLOGS
#define DEBUGLOGS 0
#endif
#ifdef ANDROID
#include <android/log.h>
#define LOG_TAG "OBJECT_DETECTOR"
#define LOGD0(...) ((void)__android_log_print(ANDROID_LOG_DEBUG, LOG_TAG, __VA_ARGS__))
#define LOGI0(...) ((void)__android_log_print(ANDROID_LOG_INFO, LOG_TAG, __VA_ARGS__))
#define LOGW0(...) ((void)__android_log_print(ANDROID_LOG_WARN, LOG_TAG, __VA_ARGS__))
#define LOGE0(...) ((void)__android_log_print(ANDROID_LOG_ERROR, LOG_TAG, __VA_ARGS__))
#else
#include <stdio.h>
#define LOGD0(_str, ...) do{printf(_str , ## __VA_ARGS__); printf("\n");fflush(stdout);} while(0)
#define LOGI0(_str, ...) do{printf(_str , ## __VA_ARGS__); printf("\n");fflush(stdout);} while(0)
#define LOGW0(_str, ...) do{printf(_str , ## __VA_ARGS__); printf("\n");fflush(stdout);} while(0)
#define LOGE0(_str, ...) do{printf(_str , ## __VA_ARGS__); printf("\n");fflush(stdout);} while(0)
#endif
#if DEBUGLOGS
#define LOGD(_str, ...) LOGD0(_str , ## __VA_ARGS__)
#define LOGI(_str, ...) LOGI0(_str , ## __VA_ARGS__)
#define LOGW(_str, ...) LOGW0(_str , ## __VA_ARGS__)
#define LOGE(_str, ...) LOGE0(_str , ## __VA_ARGS__)
#else
#define LOGD(...) do{} while(0)
#define LOGI(...) do{} while(0)
#define LOGW(...) do{} while(0)
#define LOGE(...) do{} while(0)
#endif
using namespace cv;
static inline cv::Point2f centerRect(const cv::Rect& r)
{
return cv::Point2f(r.x+((float)r.width)/2, r.y+((float)r.height)/2);
}
static inline cv::Rect scale_rect(const cv::Rect& r, float scale)
{
cv::Point2f m=centerRect(r);
float width = r.width * scale;
float height = r.height * scale;
int x=cvRound(m.x - width/2);
int y=cvRound(m.y - height/2);
return cv::Rect(x, y, cvRound(width), cvRound(height));
}
namespace cv
{
void* workcycleObjectDetectorFunction(void* p);
}
class cv::DetectionBasedTracker::SeparateDetectionWork
{
public:
SeparateDetectionWork(cv::DetectionBasedTracker& _detectionBasedTracker, cv::Ptr<DetectionBasedTracker::IDetector> _detector);
virtual ~SeparateDetectionWork();
bool communicateWithDetectingThread(const Mat& imageGray, std::vector<Rect>& rectsWhereRegions);
bool run();
void stop();
void resetTracking();
inline bool isWorking()
{
return (stateThread==STATE_THREAD_WORKING_SLEEPING) || (stateThread==STATE_THREAD_WORKING_WITH_IMAGE);
}
inline void lock()
{
pthread_mutex_lock(&mutex);
}
inline void unlock()
{
pthread_mutex_unlock(&mutex);
}
protected:
DetectionBasedTracker& detectionBasedTracker;
cv::Ptr<DetectionBasedTracker::IDetector> cascadeInThread;
pthread_t second_workthread;
pthread_mutex_t mutex;
pthread_cond_t objectDetectorRun;
pthread_cond_t objectDetectorThreadStartStop;
std::vector<cv::Rect> resultDetect;
volatile bool isObjectDetectingReady;
volatile bool shouldObjectDetectingResultsBeForgot;
enum StateSeparatedThread {
STATE_THREAD_STOPPED=0,
STATE_THREAD_WORKING_SLEEPING,
STATE_THREAD_WORKING_WITH_IMAGE,
STATE_THREAD_WORKING,
STATE_THREAD_STOPPING
};
volatile StateSeparatedThread stateThread;
cv::Mat imageSeparateDetecting;
void workcycleObjectDetector();
friend void* workcycleObjectDetectorFunction(void* p);
long long timeWhenDetectingThreadStartedWork;
};
cv::DetectionBasedTracker::SeparateDetectionWork::SeparateDetectionWork(DetectionBasedTracker& _detectionBasedTracker, cv::Ptr<DetectionBasedTracker::IDetector> _detector)
:detectionBasedTracker(_detectionBasedTracker),
cascadeInThread(),
isObjectDetectingReady(false),
shouldObjectDetectingResultsBeForgot(false),
stateThread(STATE_THREAD_STOPPED),
timeWhenDetectingThreadStartedWork(-1)
{
CV_Assert(_detector);
cascadeInThread = _detector;
int res=0;
res=pthread_mutex_init(&mutex, NULL);//TODO: should be attributes?
if (res) {
LOGE("ERROR in DetectionBasedTracker::SeparateDetectionWork::SeparateDetectionWork in pthread_mutex_init(&mutex, NULL) is %d", res);
throw(std::exception());
}
res=pthread_cond_init (&objectDetectorRun, NULL);
if (res) {
LOGE("ERROR in DetectionBasedTracker::SeparateDetectionWork::SeparateDetectionWork in pthread_cond_init(&objectDetectorRun,, NULL) is %d", res);
pthread_mutex_destroy(&mutex);
throw(std::exception());
}
res=pthread_cond_init (&objectDetectorThreadStartStop, NULL);
if (res) {
LOGE("ERROR in DetectionBasedTracker::SeparateDetectionWork::SeparateDetectionWork in pthread_cond_init(&objectDetectorThreadStartStop,, NULL) is %d", res);
pthread_cond_destroy(&objectDetectorRun);
pthread_mutex_destroy(&mutex);
throw(std::exception());
}
}
cv::DetectionBasedTracker::SeparateDetectionWork::~SeparateDetectionWork()
{
if(stateThread!=STATE_THREAD_STOPPED) {
LOGE("\n\n\nATTENTION!!! dangerous algorithm error: destructor DetectionBasedTracker::DetectionBasedTracker::~SeparateDetectionWork is called before stopping the workthread");
}
pthread_cond_destroy(&objectDetectorThreadStartStop);
pthread_cond_destroy(&objectDetectorRun);
pthread_mutex_destroy(&mutex);
}
bool cv::DetectionBasedTracker::SeparateDetectionWork::run()
{
LOGD("DetectionBasedTracker::SeparateDetectionWork::run() --- start");
pthread_mutex_lock(&mutex);
if (stateThread != STATE_THREAD_STOPPED) {
LOGE("DetectionBasedTracker::SeparateDetectionWork::run is called while the previous run is not stopped");
pthread_mutex_unlock(&mutex);
return false;
}
stateThread=STATE_THREAD_WORKING_SLEEPING;
pthread_create(&second_workthread, NULL, workcycleObjectDetectorFunction, (void*)this); //TODO: add attributes?
pthread_cond_wait(&objectDetectorThreadStartStop, &mutex);
pthread_mutex_unlock(&mutex);
LOGD("DetectionBasedTracker::SeparateDetectionWork::run --- end");
return true;
}
#define CATCH_ALL_AND_LOG(_block) \
do { \
try { \
_block; \
break; \
} \
catch(cv::Exception& e) { \
LOGE0("\n %s: ERROR: OpenCV Exception caught: \n'%s'\n\n", CV_Func, e.what()); \
} catch(std::exception& e) { \
LOGE0("\n %s: ERROR: Exception caught: \n'%s'\n\n", CV_Func, e.what()); \
} catch(...) { \
LOGE0("\n %s: ERROR: UNKNOWN Exception caught\n\n", CV_Func); \
} \
} while(0)
void* cv::workcycleObjectDetectorFunction(void* p)
{
CATCH_ALL_AND_LOG({ ((cv::DetectionBasedTracker::SeparateDetectionWork*)p)->workcycleObjectDetector(); });
try{
((cv::DetectionBasedTracker::SeparateDetectionWork*)p)->stateThread = cv::DetectionBasedTracker::SeparateDetectionWork::STATE_THREAD_STOPPED;
} catch(...) {
LOGE0("DetectionBasedTracker: workcycleObjectDetectorFunction: ERROR concerning pointer, received as the function parameter");
}
return NULL;
}
void cv::DetectionBasedTracker::SeparateDetectionWork::workcycleObjectDetector()
{
static double freq = getTickFrequency();
LOGD("DetectionBasedTracker::SeparateDetectionWork::workcycleObjectDetector() --- start");
std::vector<Rect> objects;
CV_Assert(stateThread==STATE_THREAD_WORKING_SLEEPING);
pthread_mutex_lock(&mutex);
{
pthread_cond_signal(&objectDetectorThreadStartStop);
LOGD("DetectionBasedTracker::SeparateDetectionWork::workcycleObjectDetector() --- before waiting");
CV_Assert(stateThread==STATE_THREAD_WORKING_SLEEPING);
pthread_cond_wait(&objectDetectorRun, &mutex);
if (isWorking()) {
stateThread=STATE_THREAD_WORKING_WITH_IMAGE;
}
LOGD("DetectionBasedTracker::SeparateDetectionWork::workcycleObjectDetector() --- after waiting");
}
pthread_mutex_unlock(&mutex);
bool isFirstStep=true;
isObjectDetectingReady=false;
while(isWorking())
{
LOGD("DetectionBasedTracker::SeparateDetectionWork::workcycleObjectDetector() --- next step");
if (! isFirstStep) {
LOGD("DetectionBasedTracker::SeparateDetectionWork::workcycleObjectDetector() --- before waiting");
CV_Assert(stateThread==STATE_THREAD_WORKING_SLEEPING);
pthread_mutex_lock(&mutex);
if (!isWorking()) {//it is a rare case, but may cause a crash
LOGD("DetectionBasedTracker::SeparateDetectionWork::workcycleObjectDetector() --- go out from the workcycle from inner part of lock just before waiting");
pthread_mutex_unlock(&mutex);
break;
}
CV_Assert(stateThread==STATE_THREAD_WORKING_SLEEPING);
pthread_cond_wait(&objectDetectorRun, &mutex);
if (isWorking()) {
stateThread=STATE_THREAD_WORKING_WITH_IMAGE;
}
pthread_mutex_unlock(&mutex);
LOGD("DetectionBasedTracker::SeparateDetectionWork::workcycleObjectDetector() --- after waiting");
} else {
isFirstStep=false;
}
if (!isWorking()) {
LOGD("DetectionBasedTracker::SeparateDetectionWork::workcycleObjectDetector() --- go out from the workcycle just after waiting");
break;
}
if (imageSeparateDetecting.empty()) {
LOGD("DetectionBasedTracker::SeparateDetectionWork::workcycleObjectDetector() --- imageSeparateDetecting is empty, continue");
continue;
}
LOGD("DetectionBasedTracker::SeparateDetectionWork::workcycleObjectDetector() --- start handling imageSeparateDetecting, img.size=%dx%d, img.data=0x%p",
imageSeparateDetecting.size().width, imageSeparateDetecting.size().height, (void*)imageSeparateDetecting.data);
int64 t1_detect=getTickCount();
cascadeInThread->detect(imageSeparateDetecting, objects);
/*cascadeInThread.detectMultiScale( imageSeparateDetecting, objects,
detectionBasedTracker.parameters.scaleFactor, detectionBasedTracker.parameters.minNeighbors, 0
|CV_HAAR_SCALE_IMAGE
,
min_objectSize,
max_objectSize
);
*/
LOGD("DetectionBasedTracker::SeparateDetectionWork::workcycleObjectDetector() --- end handling imageSeparateDetecting");
if (!isWorking()) {
LOGD("DetectionBasedTracker::SeparateDetectionWork::workcycleObjectDetector() --- go out from the workcycle just after detecting");
break;
}
int64 t2_detect = getTickCount();
int64 dt_detect = t2_detect-t1_detect;
double dt_detect_ms=((double)dt_detect)/freq * 1000.0;
(void)(dt_detect_ms);
LOGI("DetectionBasedTracker::SeparateDetectionWork::workcycleObjectDetector() --- objects num==%d, t_ms=%.4f", (int)objects.size(), dt_detect_ms);
pthread_mutex_lock(&mutex);
if (!shouldObjectDetectingResultsBeForgot) {
resultDetect=objects;
isObjectDetectingReady=true;
} else { //shouldObjectDetectingResultsBeForgot==true
resultDetect.clear();
isObjectDetectingReady=false;
shouldObjectDetectingResultsBeForgot=false;
}
if(isWorking()) {
stateThread=STATE_THREAD_WORKING_SLEEPING;
}
pthread_mutex_unlock(&mutex);
objects.clear();
}// while(isWorking())
pthread_mutex_lock(&mutex);
stateThread=STATE_THREAD_STOPPED;
isObjectDetectingReady=false;
shouldObjectDetectingResultsBeForgot=false;
pthread_cond_signal(&objectDetectorThreadStartStop);
pthread_mutex_unlock(&mutex);
LOGI("DetectionBasedTracker::SeparateDetectionWork::workcycleObjectDetector: Returning");
}
void cv::DetectionBasedTracker::SeparateDetectionWork::stop()
{
//FIXME: TODO: should add quickStop functionality
pthread_mutex_lock(&mutex);
if (!isWorking()) {
pthread_mutex_unlock(&mutex);
LOGE("SimpleHighguiDemoCore::stop is called but the SimpleHighguiDemoCore pthread is not active");
return;
}
stateThread=STATE_THREAD_STOPPING;
LOGD("DetectionBasedTracker::SeparateDetectionWork::stop: before going to sleep to wait for the signal from the workthread");
pthread_cond_signal(&objectDetectorRun);
pthread_cond_wait(&objectDetectorThreadStartStop, &mutex);
LOGD("DetectionBasedTracker::SeparateDetectionWork::stop: after receiving the signal from the workthread, stateThread=%d", (int)stateThread);
pthread_mutex_unlock(&mutex);
}
void cv::DetectionBasedTracker::SeparateDetectionWork::resetTracking()
{
LOGD("DetectionBasedTracker::SeparateDetectionWork::resetTracking");
pthread_mutex_lock(&mutex);
if (stateThread == STATE_THREAD_WORKING_WITH_IMAGE) {
LOGD("DetectionBasedTracker::SeparateDetectionWork::resetTracking: since workthread is detecting objects at the moment, we should make cascadeInThread stop detecting and forget the detecting results");
shouldObjectDetectingResultsBeForgot=true;
//cascadeInThread.setStopFlag();//FIXME: TODO: this feature also should be contributed to OpenCV
} else {
LOGD("DetectionBasedTracker::SeparateDetectionWork::resetTracking: since workthread is NOT detecting objects at the moment, we should NOT make any additional actions");
}
resultDetect.clear();
isObjectDetectingReady=false;
pthread_mutex_unlock(&mutex);
}
bool cv::DetectionBasedTracker::SeparateDetectionWork::communicateWithDetectingThread(const Mat& imageGray, std::vector<Rect>& rectsWhereRegions)
{
static double freq = getTickFrequency();
bool shouldCommunicateWithDetectingThread = (stateThread==STATE_THREAD_WORKING_SLEEPING);
LOGD("DetectionBasedTracker::SeparateDetectionWork::communicateWithDetectingThread: shouldCommunicateWithDetectingThread=%d", (shouldCommunicateWithDetectingThread?1:0));
if (!shouldCommunicateWithDetectingThread) {
return false;
}
bool shouldHandleResult = false;
pthread_mutex_lock(&mutex);
if (isObjectDetectingReady) {
shouldHandleResult=true;
rectsWhereRegions = resultDetect;
isObjectDetectingReady=false;
double lastBigDetectionDuration = 1000.0 * (((double)(getTickCount() - timeWhenDetectingThreadStartedWork )) / freq);
(void)(lastBigDetectionDuration);
LOGD("DetectionBasedTracker::SeparateDetectionWork::communicateWithDetectingThread: lastBigDetectionDuration=%f ms", (double)lastBigDetectionDuration);
}
bool shouldSendNewDataToWorkThread = true;
if (timeWhenDetectingThreadStartedWork > 0) {
double time_from_previous_launch_in_ms=1000.0 * (((double)(getTickCount() - timeWhenDetectingThreadStartedWork )) / freq); //the same formula as for lastBigDetectionDuration
shouldSendNewDataToWorkThread = (time_from_previous_launch_in_ms >= detectionBasedTracker.parameters.minDetectionPeriod);
LOGD("DetectionBasedTracker::SeparateDetectionWork::communicateWithDetectingThread: shouldSendNewDataToWorkThread was 1, now it is %d, since time_from_previous_launch_in_ms=%.2f, minDetectionPeriod=%d",
(shouldSendNewDataToWorkThread?1:0), time_from_previous_launch_in_ms, detectionBasedTracker.parameters.minDetectionPeriod);
}
if (shouldSendNewDataToWorkThread) {
imageSeparateDetecting.create(imageGray.size(), CV_8UC1);
imageGray.copyTo(imageSeparateDetecting);//may change imageSeparateDetecting ptr. But should not.
timeWhenDetectingThreadStartedWork = getTickCount() ;
pthread_cond_signal(&objectDetectorRun);
}
pthread_mutex_unlock(&mutex);
LOGD("DetectionBasedTracker::SeparateDetectionWork::communicateWithDetectingThread: result: shouldHandleResult=%d", (shouldHandleResult?1:0));
return shouldHandleResult;
}
cv::DetectionBasedTracker::Parameters::Parameters()
{
maxTrackLifetime=5;
minDetectionPeriod=0;
}
cv::DetectionBasedTracker::InnerParameters::InnerParameters()
{
numLastPositionsToTrack=4;
numStepsToWaitBeforeFirstShow=6;
numStepsToTrackWithoutDetectingIfObjectHasNotBeenShown=3;
numStepsToShowWithoutDetecting=3;
coeffTrackingWindowSize=2.0;
coeffObjectSizeToTrack=0.85;
coeffObjectSpeedUsingInPrediction=0.8;
}
cv::DetectionBasedTracker::DetectionBasedTracker(cv::Ptr<IDetector> mainDetector, cv::Ptr<IDetector> trackingDetector, const Parameters& params)
:separateDetectionWork(),
parameters(params),
innerParameters(),
numTrackedSteps(0),
cascadeForTracking(trackingDetector)
{
CV_Assert( (params.maxTrackLifetime >= 0)
// && mainDetector
&& trackingDetector );
if (mainDetector) {
separateDetectionWork.reset(new SeparateDetectionWork(*this, mainDetector));
}
weightsPositionsSmoothing.push_back(1);
weightsSizesSmoothing.push_back(0.5);
weightsSizesSmoothing.push_back(0.3);
weightsSizesSmoothing.push_back(0.2);
}
cv::DetectionBasedTracker::~DetectionBasedTracker()
{
}
void DetectionBasedTracker::process(const Mat& imageGray)
{
CV_Assert(imageGray.type()==CV_8UC1);
if ( separateDetectionWork && !separateDetectionWork->isWorking() ) {
separateDetectionWork->run();
}
static double freq = getTickFrequency();
static long long time_when_last_call_started=getTickCount();
{
double delta_time_from_prev_call=1000.0 * (((double)(getTickCount() - time_when_last_call_started)) / freq);
(void)(delta_time_from_prev_call);
LOGD("DetectionBasedTracker::process: time from the previous call is %f ms", (double)delta_time_from_prev_call);
time_when_last_call_started=getTickCount();
}
Mat imageDetect=imageGray;
std::vector<Rect> rectsWhereRegions;
bool shouldHandleResult=false;
if (separateDetectionWork) {
shouldHandleResult = separateDetectionWork->communicateWithDetectingThread(imageGray, rectsWhereRegions);
}
if (shouldHandleResult) {
LOGD("DetectionBasedTracker::process: get _rectsWhereRegions were got from resultDetect");
} else {
LOGD("DetectionBasedTracker::process: get _rectsWhereRegions from previous positions");
for(size_t i = 0; i < trackedObjects.size(); i++) {
int n = trackedObjects[i].lastPositions.size();
CV_Assert(n > 0);
Rect r = trackedObjects[i].lastPositions[n-1];
if(r.area() == 0) {
LOGE("DetectionBasedTracker::process: ERROR: ATTENTION: strange algorithm's behavior: trackedObjects[i].rect() is empty");
continue;
}
//correction by speed of rectangle
if (n > 1) {
Point2f center = centerRect(r);
Point2f center_prev = centerRect(trackedObjects[i].lastPositions[n-2]);
Point2f shift = (center - center_prev) * innerParameters.coeffObjectSpeedUsingInPrediction;
r.x += cvRound(shift.x);
r.y += cvRound(shift.y);
}
rectsWhereRegions.push_back(r);
}
}
LOGI("DetectionBasedTracker::process: tracked objects num==%d", (int)trackedObjects.size());
std::vector<Rect> detectedObjectsInRegions;
LOGD("DetectionBasedTracker::process: rectsWhereRegions.size()=%d", (int)rectsWhereRegions.size());
for(size_t i=0; i < rectsWhereRegions.size(); i++) {
Rect r = rectsWhereRegions[i];
detectInRegion(imageDetect, r, detectedObjectsInRegions);
}
LOGD("DetectionBasedTracker::process: detectedObjectsInRegions.size()=%d", (int)detectedObjectsInRegions.size());
updateTrackedObjects(detectedObjectsInRegions);
}
void cv::DetectionBasedTracker::getObjects(std::vector<cv::Rect>& result) const
{
result.clear();
for(size_t i=0; i < trackedObjects.size(); i++) {
Rect r=calcTrackedObjectPositionToShow(i);
if (r.area()==0) {
continue;
}
result.push_back(r);
LOGD("DetectionBasedTracker::process: found a object with SIZE %d x %d, rect={%d, %d, %d x %d}", r.width, r.height, r.x, r.y, r.width, r.height);
}
}
void cv::DetectionBasedTracker::getObjects(std::vector<Object>& result) const
{
result.clear();
for(size_t i=0; i < trackedObjects.size(); i++) {
Rect r=calcTrackedObjectPositionToShow(i);
if (r.area()==0) {
continue;
}
result.push_back(Object(r, trackedObjects[i].id));
LOGD("DetectionBasedTracker::process: found a object with SIZE %d x %d, rect={%d, %d, %d x %d}", r.width, r.height, r.x, r.y, r.width, r.height);
}
}
void cv::DetectionBasedTracker::getObjects(std::vector<ExtObject>& result) const
{
result.clear();
for(size_t i=0; i < trackedObjects.size(); i++) {
ObjectStatus status;
Rect r=calcTrackedObjectPositionToShow(i, status);
result.push_back(ExtObject(trackedObjects[i].id, r, status));
LOGD("DetectionBasedTracker::process: found a object with SIZE %d x %d, rect={%d, %d, %d x %d}, status = %d", r.width, r.height, r.x, r.y, r.width, r.height, (int)status);
}
}
bool cv::DetectionBasedTracker::run()
{
if (separateDetectionWork) {
return separateDetectionWork->run();
}
return false;
}
void cv::DetectionBasedTracker::stop()
{
if (separateDetectionWork) {
separateDetectionWork->stop();
}
}
void cv::DetectionBasedTracker::resetTracking()
{
if (separateDetectionWork) {
separateDetectionWork->resetTracking();
}
trackedObjects.clear();
}
void cv::DetectionBasedTracker::updateTrackedObjects(const std::vector<Rect>& detectedObjects)
{
enum {
NEW_RECTANGLE=-1,
INTERSECTED_RECTANGLE=-2
};
int N1=trackedObjects.size();
int N2=detectedObjects.size();
LOGD("DetectionBasedTracker::updateTrackedObjects: N1=%d, N2=%d", N1, N2);
for(int i=0; i < N1; i++) {
trackedObjects[i].numDetectedFrames++;
}
std::vector<int> correspondence(detectedObjects.size(), NEW_RECTANGLE);
correspondence.clear();
correspondence.resize(detectedObjects.size(), NEW_RECTANGLE);
for(int i=0; i < N1; i++) {
LOGD("DetectionBasedTracker::updateTrackedObjects: i=%d", i);
TrackedObject& curObject=trackedObjects[i];
int bestIndex=-1;
int bestArea=-1;
int numpositions=curObject.lastPositions.size();
CV_Assert(numpositions > 0);
Rect prevRect=curObject.lastPositions[numpositions-1];
LOGD("DetectionBasedTracker::updateTrackedObjects: prevRect[%d]={%d, %d, %d x %d}", i, prevRect.x, prevRect.y, prevRect.width, prevRect.height);
for(int j=0; j < N2; j++) {
LOGD("DetectionBasedTracker::updateTrackedObjects: j=%d", j);
if (correspondence[j] >= 0) {
LOGD("DetectionBasedTracker::updateTrackedObjects: j=%d is rejected, because it has correspondence=%d", j, correspondence[j]);
continue;
}
if (correspondence[j] !=NEW_RECTANGLE) {
LOGD("DetectionBasedTracker::updateTrackedObjects: j=%d is rejected, because it is intersected with another rectangle", j);
continue;
}
LOGD("DetectionBasedTracker::updateTrackedObjects: detectedObjects[%d]={%d, %d, %d x %d}",
j, detectedObjects[j].x, detectedObjects[j].y, detectedObjects[j].width, detectedObjects[j].height);
Rect r=prevRect & detectedObjects[j];
if ( (r.width > 0) && (r.height > 0) ) {
LOGD("DetectionBasedTracker::updateTrackedObjects: There is intersection between prevRect and detectedRect, r={%d, %d, %d x %d}",
r.x, r.y, r.width, r.height);
correspondence[j]=INTERSECTED_RECTANGLE;
if ( r.area() > bestArea) {
LOGD("DetectionBasedTracker::updateTrackedObjects: The area of intersection is %d, it is better than bestArea=%d", r.area(), bestArea);
bestIndex=j;
bestArea=r.area();
}
}
}
if (bestIndex >= 0) {
LOGD("DetectionBasedTracker::updateTrackedObjects: The best correspondence for i=%d is j=%d", i, bestIndex);
correspondence[bestIndex]=i;
for(int j=0; j < N2; j++) {
if (correspondence[j] >= 0)
continue;
Rect r=detectedObjects[j] & detectedObjects[bestIndex];
if ( (r.width > 0) && (r.height > 0) ) {
LOGD("DetectionBasedTracker::updateTrackedObjects: Found intersection between "
"rectangles j=%d and bestIndex=%d, rectangle j=%d is marked as intersected", j, bestIndex, j);
correspondence[j]=INTERSECTED_RECTANGLE;
}
}
} else {
LOGD("DetectionBasedTracker::updateTrackedObjects: There is no correspondence for i=%d ", i);
curObject.numFramesNotDetected++;
}
}
LOGD("DetectionBasedTracker::updateTrackedObjects: start second cycle");
for(int j=0; j < N2; j++) {
LOGD("DetectionBasedTracker::updateTrackedObjects: j=%d", j);
int i=correspondence[j];
if (i >= 0) {//add position
LOGD("DetectionBasedTracker::updateTrackedObjects: add position");
trackedObjects[i].lastPositions.push_back(detectedObjects[j]);
while ((int)trackedObjects[i].lastPositions.size() > (int) innerParameters.numLastPositionsToTrack) {
trackedObjects[i].lastPositions.erase(trackedObjects[i].lastPositions.begin());
}
trackedObjects[i].numFramesNotDetected=0;
} else if (i==NEW_RECTANGLE){ //new object
LOGD("DetectionBasedTracker::updateTrackedObjects: new object");
trackedObjects.push_back(detectedObjects[j]);
} else {
LOGD("DetectionBasedTracker::updateTrackedObjects: was auxiliary intersection");
}
}
std::vector<TrackedObject>::iterator it=trackedObjects.begin();
while( it != trackedObjects.end() ) {
if ( (it->numFramesNotDetected > parameters.maxTrackLifetime)
||
(
(it->numDetectedFrames <= innerParameters.numStepsToWaitBeforeFirstShow)
&&
(it->numFramesNotDetected > innerParameters.numStepsToTrackWithoutDetectingIfObjectHasNotBeenShown)
)
)
{
int numpos=it->lastPositions.size();
CV_Assert(numpos > 0);
Rect r = it->lastPositions[numpos-1];
(void)(r);
LOGD("DetectionBasedTracker::updateTrackedObjects: deleted object {%d, %d, %d x %d}",
r.x, r.y, r.width, r.height);
it=trackedObjects.erase(it);
} else {
it++;
}
}
}
int cv::DetectionBasedTracker::addObject(const Rect& location)
{
LOGD("DetectionBasedTracker::addObject: new object {%d, %d %dx%d}",location.x, location.y, location.width, location.height);
trackedObjects.push_back(TrackedObject(location));
int newId = trackedObjects.back().id;
LOGD("DetectionBasedTracker::addObject: newId = %d", newId);
return newId;
}
Rect cv::DetectionBasedTracker::calcTrackedObjectPositionToShow(int i) const
{
ObjectStatus status;
return calcTrackedObjectPositionToShow(i, status);
}
Rect cv::DetectionBasedTracker::calcTrackedObjectPositionToShow(int i, ObjectStatus& status) const
{
if ( (i < 0) || (i >= (int)trackedObjects.size()) ) {
LOGE("DetectionBasedTracker::calcTrackedObjectPositionToShow: ERROR: wrong i=%d", i);
status = WRONG_OBJECT;
return Rect();
}
if (trackedObjects[i].numDetectedFrames <= innerParameters.numStepsToWaitBeforeFirstShow){
LOGI("DetectionBasedTracker::calcTrackedObjectPositionToShow: trackedObjects[%d].numDetectedFrames=%d <= numStepsToWaitBeforeFirstShow=%d --- return empty Rect()",
i, trackedObjects[i].numDetectedFrames, innerParameters.numStepsToWaitBeforeFirstShow);
status = DETECTED_NOT_SHOWN_YET;
return Rect();
}
if (trackedObjects[i].numFramesNotDetected > innerParameters.numStepsToShowWithoutDetecting) {
status = DETECTED_TEMPORARY_LOST;
return Rect();
}
const TrackedObject::PositionsVector& lastPositions=trackedObjects[i].lastPositions;
int N=lastPositions.size();
if (N<=0) {
LOGE("DetectionBasedTracker::calcTrackedObjectPositionToShow: ERROR: no positions for i=%d", i);
status = WRONG_OBJECT;
return Rect();
}
int Nsize=std::min(N, (int)weightsSizesSmoothing.size());
int Ncenter= std::min(N, (int)weightsPositionsSmoothing.size());
Point2f center;
double w=0, h=0;
if (Nsize > 0) {
double sum=0;
for(int j=0; j < Nsize; j++) {
int k=N-j-1;
w += lastPositions[k].width * weightsSizesSmoothing[j];
h += lastPositions[k].height * weightsSizesSmoothing[j];
sum+=weightsSizesSmoothing[j];
}
w /= sum;
h /= sum;
} else {
w=lastPositions[N-1].width;
h=lastPositions[N-1].height;
}
if (Ncenter > 0) {
double sum=0;
for(int j=0; j < Ncenter; j++) {
int k=N-j-1;
Point tl(lastPositions[k].tl());
Point br(lastPositions[k].br());
Point2f c1;
c1=tl;
c1=c1* 0.5f;
Point2f c2;
c2=br;
c2=c2*0.5f;
c1=c1+c2;
center=center+ (c1 * weightsPositionsSmoothing[j]);
sum+=weightsPositionsSmoothing[j];
}
center *= (float)(1 / sum);
} else {
int k=N-1;
Point tl(lastPositions[k].tl());
Point br(lastPositions[k].br());
Point2f c1;
c1=tl;
c1=c1* 0.5f;
Point2f c2;
c2=br;
c2=c2*0.5f;
center=c1+c2;
}
Point2f tl=center-(Point2f(w,h)*0.5);
Rect res(cvRound(tl.x), cvRound(tl.y), cvRound(w), cvRound(h));
LOGD("DetectionBasedTracker::calcTrackedObjectPositionToShow: Result for i=%d: {%d, %d, %d x %d}", i, res.x, res.y, res.width, res.height);
status = DETECTED;
return res;
}
void cv::DetectionBasedTracker::detectInRegion(const Mat& img, const Rect& r, std::vector<Rect>& detectedObjectsInRegions)
{
Rect r0(Point(), img.size());
Rect r1 = scale_rect(r, innerParameters.coeffTrackingWindowSize);
r1 = r1 & r0;
if ( (r1.width <=0) || (r1.height <= 0) ) {
LOGD("DetectionBasedTracker::detectInRegion: Empty intersection");
return;
}
int d = cvRound(std::min(r.width, r.height) * innerParameters.coeffObjectSizeToTrack);
std::vector<Rect> tmpobjects;
Mat img1(img, r1);//subimage for rectangle -- without data copying
LOGD("DetectionBasedTracker::detectInRegion: img1.size()=%d x %d, d=%d",
img1.size().width, img1.size().height, d);
cascadeForTracking->setMinObjectSize(Size(d, d));
cascadeForTracking->detect(img1, tmpobjects);
/*
detectMultiScale( img1, tmpobjects,
parameters.scaleFactor, parameters.minNeighbors, 0
|CV_HAAR_FIND_BIGGEST_OBJECT
|CV_HAAR_SCALE_IMAGE
,
Size(d,d),
max_objectSize
);*/
for(size_t i=0; i < tmpobjects.size(); i++) {
Rect curres(tmpobjects[i].tl() + r1.tl(), tmpobjects[i].size());
detectedObjectsInRegions.push_back(curres);
}
}
bool cv::DetectionBasedTracker::setParameters(const Parameters& params)
{
if ( params.maxTrackLifetime < 0 )
{
LOGE("DetectionBasedTracker::setParameters: ERROR: wrong parameters value");
return false;
}
if (separateDetectionWork) {
separateDetectionWork->lock();
}
parameters=params;
if (separateDetectionWork) {
separateDetectionWork->unlock();
}
return true;
}
const cv::DetectionBasedTracker::Parameters& DetectionBasedTracker::getParameters() const
{
return parameters;
}
#endif
modules/contrib/src/facerec.cpp
-901
View File
@@ -1,901 +0,0 @@
/*
* Copyright (c) 2011,2012. Philipp Wagner <bytefish[at]gmx[dot]de>.
* Released to public domain under terms of the BSD Simplified license.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions are met:
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in the
* documentation and/or other materials provided with the distribution.
* * Neither the name of the organization nor the names of its contributors
* may be used to endorse or promote products derived from this software
* without specific prior written permission.
*
* See <http://www.opensource.org/licenses/bsd-license>
*/
#include "precomp.hpp"
#include <set>
#include <limits>
namespace cv
{
// Reads a sequence from a FileNode::SEQ with type _Tp into a result vector.
template<typename _Tp>
inline void readFileNodeList(const FileNode& fn, std::vector<_Tp>& result) {
if (fn.type() == FileNode::SEQ) {
for (FileNodeIterator it = fn.begin(); it != fn.end();) {
_Tp item;
it >> item;
result.push_back(item);
}
}
}
// Writes the a list of given items to a cv::FileStorage.
template<typename _Tp>
inline void writeFileNodeList(FileStorage& fs, const String& name,
const std::vector<_Tp>& items) {
// typedefs
typedef typename std::vector<_Tp>::const_iterator constVecIterator;
// write the elements in item to fs
fs << name << "[";
for (constVecIterator it = items.begin(); it != items.end(); ++it) {
fs << *it;
}
fs << "]";
}
static Mat asRowMatrix(InputArrayOfArrays src, int rtype, double alpha=1, double beta=0) {
// make sure the input data is a vector of matrices or vector of vector
if(src.kind() != _InputArray::STD_VECTOR_MAT && src.kind() != _InputArray::STD_VECTOR_VECTOR) {
String error_message = "The data is expected as InputArray::STD_VECTOR_MAT (a std::vector<Mat>) or _InputArray::STD_VECTOR_VECTOR (a std::vector< std::vector<...> >).";
CV_Error(Error::StsBadArg, error_message);
}
// number of samples
size_t n = src.total();
// return empty matrix if no matrices given
if(n == 0)
return Mat();
// dimensionality of (reshaped) samples
size_t d = src.getMat(0).total();
// create data matrix
Mat data((int)n, (int)d, rtype);
// now copy data
for(unsigned int i = 0; i < n; i++) {
// make sure data can be reshaped, throw exception if not!
if(src.getMat(i).total() != d) {
String error_message = format("Wrong number of elements in matrix #%d! Expected %d was %d.", i, d, src.getMat(i).total());
CV_Error(Error::StsBadArg, error_message);
}
// get a hold of the current row
Mat xi = data.row(i);
// make reshape happy by cloning for non-continuous matrices
if(src.getMat(i).isContinuous()) {
src.getMat(i).reshape(1, 1).convertTo(xi, rtype, alpha, beta);
} else {
src.getMat(i).clone().reshape(1, 1).convertTo(xi, rtype, alpha, beta);
}
}
return data;
}
// Removes duplicate elements in a given vector.
template<typename _Tp>
inline std::vector<_Tp> remove_dups(const std::vector<_Tp>& src) {
typedef typename std::set<_Tp>::const_iterator constSetIterator;
typedef typename std::vector<_Tp>::const_iterator constVecIterator;
std::set<_Tp> set_elems;
for (constVecIterator it = src.begin(); it != src.end(); ++it)
set_elems.insert(*it);
std::vector<_Tp> elems;
for (constSetIterator it = set_elems.begin(); it != set_elems.end(); ++it)
elems.push_back(*it);
return elems;
}
// Turk, M., and Pentland, A. "Eigenfaces for recognition.". Journal of
// Cognitive Neuroscience 3 (1991), 71–86.
class Eigenfaces : public FaceRecognizer
{
private:
int _num_components;
double _threshold;
std::vector<Mat> _projections;
Mat _labels;
Mat _eigenvectors;
Mat _eigenvalues;
Mat _mean;
public:
using FaceRecognizer::save;
using FaceRecognizer::load;
// Initializes an empty Eigenfaces model.
Eigenfaces(int num_components = 0, double threshold = DBL_MAX) :
_num_components(num_components),
_threshold(threshold) {}
// Initializes and computes an Eigenfaces model with images in src and
// corresponding labels in labels. num_components will be kept for
// classification.
Eigenfaces(InputArrayOfArrays src, InputArray labels,
int num_components = 0, double threshold = DBL_MAX) :
_num_components(num_components),
_threshold(threshold) {
train(src, labels);
}
// Computes an Eigenfaces model with images in src and corresponding labels
// in labels.
void train(InputArrayOfArrays src, InputArray labels);
// Predicts the label of a query image in src.
int predict(InputArray src) const;
// Predicts the label and confidence for a given sample.
void predict(InputArray _src, int &label, double &dist) const;
// See FaceRecognizer::load.
void load(const FileStorage& fs);
// See FaceRecognizer::save.
void save(FileStorage& fs) const;
AlgorithmInfo* info() const;
};
// Belhumeur, P. N., Hespanha, J., and Kriegman, D. "Eigenfaces vs. Fisher-
// faces: Recognition using class specific linear projection.". IEEE
// Transactions on Pattern Analysis and Machine Intelligence 19, 7 (1997),
// 711–720.
class Fisherfaces: public FaceRecognizer
{
private:
int _num_components;
double _threshold;
Mat _eigenvectors;
Mat _eigenvalues;
Mat _mean;
std::vector<Mat> _projections;
Mat _labels;
public:
using FaceRecognizer::save;
using FaceRecognizer::load;
// Initializes an empty Fisherfaces model.
Fisherfaces(int num_components = 0, double threshold = DBL_MAX) :
_num_components(num_components),
_threshold(threshold) {}
// Initializes and computes a Fisherfaces model with images in src and
// corresponding labels in labels. num_components will be kept for
// classification.
Fisherfaces(InputArrayOfArrays src, InputArray labels,
int num_components = 0, double threshold = DBL_MAX) :
_num_components(num_components),
_threshold(threshold) {
train(src, labels);
}
~Fisherfaces() {}
// Computes a Fisherfaces model with images in src and corresponding labels
// in labels.
void train(InputArrayOfArrays src, InputArray labels);
// Predicts the label of a query image in src.
int predict(InputArray src) const;
// Predicts the label and confidence for a given sample.
void predict(InputArray _src, int &label, double &dist) const;
// See FaceRecognizer::load.
void load(const FileStorage& fs);
// See FaceRecognizer::save.
void save(FileStorage& fs) const;
AlgorithmInfo* info() const;
};
// Face Recognition based on Local Binary Patterns.
//
// Ahonen T, Hadid A. and Pietikäinen M. "Face description with local binary
// patterns: Application to face recognition." IEEE Transactions on Pattern
// Analysis and Machine Intelligence, 28(12):2037-2041.
//
class LBPH : public FaceRecognizer
{
private:
int _grid_x;
int _grid_y;
int _radius;
int _neighbors;
double _threshold;
std::vector<Mat> _histograms;
Mat _labels;
// Computes a LBPH model with images in src and
// corresponding labels in labels, possibly preserving
// old model data.
void train(InputArrayOfArrays src, InputArray labels, bool preserveData);
public:
using FaceRecognizer::save;
using FaceRecognizer::load;
// Initializes this LBPH Model. The current implementation is rather fixed
// as it uses the Extended Local Binary Patterns per default.
//
// radius, neighbors are used in the local binary patterns creation.
// grid_x, grid_y control the grid size of the spatial histograms.
LBPH(int radius_=1, int neighbors_=8,
int gridx=8, int gridy=8,
double threshold = DBL_MAX) :
_grid_x(gridx),
_grid_y(gridy),
_radius(radius_),
_neighbors(neighbors_),
_threshold(threshold) {}
// Initializes and computes this LBPH Model. The current implementation is
// rather fixed as it uses the Extended Local Binary Patterns per default.
//
// (radius=1), (neighbors=8) are used in the local binary patterns creation.
// (grid_x=8), (grid_y=8) controls the grid size of the spatial histograms.
LBPH(InputArrayOfArrays src,
InputArray labels,
int radius_=1, int neighbors_=8,
int gridx=8, int gridy=8,
double threshold = DBL_MAX) :
_grid_x(gridx),
_grid_y(gridy),
_radius(radius_),
_neighbors(neighbors_),
_threshold(threshold) {
train(src, labels);
}
~LBPH() { }
// Computes a LBPH model with images in src and
// corresponding labels in labels.
void train(InputArrayOfArrays src, InputArray labels);
// Updates this LBPH model with images in src and
// corresponding labels in labels.
void update(InputArrayOfArrays src, InputArray labels);
// Predicts the label of a query image in src.
int predict(InputArray src) const;
// Predicts the label and confidence for a given sample.
void predict(InputArray _src, int &label, double &dist) const;
// See FaceRecognizer::load.
void load(const FileStorage& fs);
// See FaceRecognizer::save.
void save(FileStorage& fs) const;
// Getter functions.
int neighbors() const { return _neighbors; }
int radius() const { return _radius; }
int grid_x() const { return _grid_x; }
int grid_y() const { return _grid_y; }
AlgorithmInfo* info() const;
};
//------------------------------------------------------------------------------
// FaceRecognizer
//------------------------------------------------------------------------------
void FaceRecognizer::update(InputArrayOfArrays src, InputArray labels ) {
if( dynamic_cast<LBPH*>(this) != 0 )
{
dynamic_cast<LBPH*>(this)->update( src, labels );
return;
}
String error_msg = format("This FaceRecognizer (%s) does not support updating, you have to use FaceRecognizer::train to update it.", this->name().c_str());
CV_Error(Error::StsNotImplemented, error_msg);
}
void FaceRecognizer::save(const String& filename) const {
FileStorage fs(filename, FileStorage::WRITE);
if (!fs.isOpened())
CV_Error(Error::StsError, "File can't be opened for writing!");
this->save(fs);
fs.release();
}
void FaceRecognizer::load(const String& filename) {
FileStorage fs(filename, FileStorage::READ);
if (!fs.isOpened())
CV_Error(Error::StsError, "File can't be opened for writing!");
this->load(fs);
fs.release();
}
//------------------------------------------------------------------------------
// Eigenfaces
//------------------------------------------------------------------------------
void Eigenfaces::train(InputArrayOfArrays _src, InputArray _local_labels) {
if(_src.total() == 0) {
String error_message = format("Empty training data was given. You'll need more than one sample to learn a model.");
CV_Error(Error::StsBadArg, error_message);
} else if(_local_labels.getMat().type() != CV_32SC1) {
String error_message = format("Labels must be given as integer (CV_32SC1). Expected %d, but was %d.", CV_32SC1, _local_labels.type());
CV_Error(Error::StsBadArg, error_message);
}
// make sure data has correct size
if(_src.total() > 1) {
for(int i = 1; i < static_cast<int>(_src.total()); i++) {
if(_src.getMat(i-1).total() != _src.getMat(i).total()) {
String error_message = format("In the Eigenfaces method all input samples (training images) must be of equal size! Expected %d pixels, but was %d pixels.", _src.getMat(i-1).total(), _src.getMat(i).total());
CV_Error(Error::StsUnsupportedFormat, error_message);
}
}
}
// get labels
Mat labels = _local_labels.getMat();
// observations in row
Mat data = asRowMatrix(_src, CV_64FC1);
// number of samples
int n = data.rows;
// assert there are as much samples as labels
if(static_cast<int>(labels.total()) != n) {
String error_message = format("The number of samples (src) must equal the number of labels (labels)! len(src)=%d, len(labels)=%d.", n, labels.total());
CV_Error(Error::StsBadArg, error_message);
}
// clear existing model data
_labels.release();
_projections.clear();
// clip number of components to be valid
if((_num_components <= 0) || (_num_components > n))
_num_components = n;
// perform the PCA
PCA pca(data, Mat(), PCA::DATA_AS_ROW, _num_components);
// copy the PCA results
_mean = pca.mean.reshape(1,1); // store the mean vector
_eigenvalues = pca.eigenvalues.clone(); // eigenvalues by row
transpose(pca.eigenvectors, _eigenvectors); // eigenvectors by column
// store labels for prediction
_labels = labels.clone();
// save projections
for(int sampleIdx = 0; sampleIdx < data.rows; sampleIdx++) {
Mat p = subspaceProject(_eigenvectors, _mean, data.row(sampleIdx));
_projections.push_back(p);
}
}
void Eigenfaces::predict(InputArray _src, int &minClass, double &minDist) const {
// get data
Mat src = _src.getMat();
// make sure the user is passing correct data
if(_projections.empty()) {
// throw error if no data (or simply return -1?)
String error_message = "This Eigenfaces model is not computed yet. Did you call Eigenfaces::train?";
CV_Error(Error::StsError, error_message);
} else if(_eigenvectors.rows != static_cast<int>(src.total())) {
// check data alignment just for clearer exception messages
String error_message = format("Wrong input image size. Reason: Training and Test images must be of equal size! Expected an image with %d elements, but got %d.", _eigenvectors.rows, src.total());
CV_Error(Error::StsBadArg, error_message);
}
// project into PCA subspace
Mat q = subspaceProject(_eigenvectors, _mean, src.reshape(1,1));
minDist = DBL_MAX;
minClass = -1;
for(size_t sampleIdx = 0; sampleIdx < _projections.size(); sampleIdx++) {
double dist = norm(_projections[sampleIdx], q, NORM_L2);
if((dist < minDist) && (dist < _threshold)) {
minDist = dist;
minClass = _labels.at<int>((int)sampleIdx);
}
}
}
int Eigenfaces::predict(InputArray _src) const {
int label;
double dummy;
predict(_src, label, dummy);
return label;
}
void Eigenfaces::load(const FileStorage& fs) {
//read matrices
fs["num_components"] >> _num_components;
fs["mean"] >> _mean;
fs["eigenvalues"] >> _eigenvalues;
fs["eigenvectors"] >> _eigenvectors;
// read sequences
readFileNodeList(fs["projections"], _projections);
fs["labels"] >> _labels;
}
void Eigenfaces::save(FileStorage& fs) const {
// write matrices
fs << "num_components" << _num_components;
fs << "mean" << _mean;
fs << "eigenvalues" << _eigenvalues;
fs << "eigenvectors" << _eigenvectors;
// write sequences
writeFileNodeList(fs, "projections", _projections);
fs << "labels" << _labels;
}
//------------------------------------------------------------------------------
// Fisherfaces
//------------------------------------------------------------------------------
void Fisherfaces::train(InputArrayOfArrays src, InputArray _lbls) {
if(src.total() == 0) {
String error_message = format("Empty training data was given. You'll need more than one sample to learn a model.");
CV_Error(Error::StsBadArg, error_message);
} else if(_lbls.getMat().type() != CV_32SC1) {
String error_message = format("Labels must be given as integer (CV_32SC1). Expected %d, but was %d.", CV_32SC1, _lbls.type());
CV_Error(Error::StsBadArg, error_message);
}
// make sure data has correct size
if(src.total() > 1) {
for(int i = 1; i < static_cast<int>(src.total()); i++) {
if(src.getMat(i-1).total() != src.getMat(i).total()) {
String error_message = format("In the Fisherfaces method all input samples (training images) must be of equal size! Expected %d pixels, but was %d pixels.", src.getMat(i-1).total(), src.getMat(i).total());
CV_Error(Error::StsUnsupportedFormat, error_message);
}
}
}
// get data
Mat labels = _lbls.getMat();
Mat data = asRowMatrix(src, CV_64FC1);
// number of samples
int N = data.rows;
// make sure labels are passed in correct shape
if(labels.total() != (size_t) N) {
String error_message = format("The number of samples (src) must equal the number of labels (labels)! len(src)=%d, len(labels)=%d.", N, labels.total());
CV_Error(Error::StsBadArg, error_message);
} else if(labels.rows != 1 && labels.cols != 1) {
String error_message = format("Expected the labels in a matrix with one row or column! Given dimensions are rows=%s, cols=%d.", labels.rows, labels.cols);
CV_Error(Error::StsBadArg, error_message);
}
// clear existing model data
_labels.release();
_projections.clear();
// safely copy from cv::Mat to std::vector
std::vector<int> ll;
for(unsigned int i = 0; i < labels.total(); i++) {
ll.push_back(labels.at<int>(i));
}
// get the number of unique classes
int C = (int) remove_dups(ll).size();
// clip number of components to be a valid number
if((_num_components <= 0) || (_num_components > (C-1)))
_num_components = (C-1);
// perform a PCA and keep (N-C) components
PCA pca(data, Mat(), PCA::DATA_AS_ROW, (N-C));
// project the data and perform a LDA on it
LDA lda(pca.project(data),labels, _num_components);
// store the total mean vector
_mean = pca.mean.reshape(1,1);
// store labels
_labels = labels.clone();
// store the eigenvalues of the discriminants
lda.eigenvalues().convertTo(_eigenvalues, CV_64FC1);
// Now calculate the projection matrix as pca.eigenvectors * lda.eigenvectors.
// Note: OpenCV stores the eigenvectors by row, so we need to transpose it!
gemm(pca.eigenvectors, lda.eigenvectors(), 1.0, Mat(), 0.0, _eigenvectors, GEMM_1_T);
// store the projections of the original data
for(int sampleIdx = 0; sampleIdx < data.rows; sampleIdx++) {
Mat p = subspaceProject(_eigenvectors, _mean, data.row(sampleIdx));
_projections.push_back(p);
}
}
void Fisherfaces::predict(InputArray _src, int &minClass, double &minDist) const {
Mat src = _src.getMat();
// check data alignment just for clearer exception messages
if(_projections.empty()) {
// throw error if no data (or simply return -1?)
String error_message = "This Fisherfaces model is not computed yet. Did you call Fisherfaces::train?";
CV_Error(Error::StsBadArg, error_message);
} else if(src.total() != (size_t) _eigenvectors.rows) {
String error_message = format("Wrong input image size. Reason: Training and Test images must be of equal size! Expected an image with %d elements, but got %d.", _eigenvectors.rows, src.total());
CV_Error(Error::StsBadArg, error_message);
}
// project into LDA subspace
Mat q = subspaceProject(_eigenvectors, _mean, src.reshape(1,1));
// find 1-nearest neighbor
minDist = DBL_MAX;
minClass = -1;
for(size_t sampleIdx = 0; sampleIdx < _projections.size(); sampleIdx++) {
double dist = norm(_projections[sampleIdx], q, NORM_L2);
if((dist < minDist) && (dist < _threshold)) {
minDist = dist;
minClass = _labels.at<int>((int)sampleIdx);
}
}
}
int Fisherfaces::predict(InputArray _src) const {
int label;
double dummy;
predict(_src, label, dummy);
return label;
}
// See FaceRecognizer::load.
void Fisherfaces::load(const FileStorage& fs) {
//read matrices
fs["num_components"] >> _num_components;
fs["mean"] >> _mean;
fs["eigenvalues"] >> _eigenvalues;
fs["eigenvectors"] >> _eigenvectors;
// read sequences
readFileNodeList(fs["projections"], _projections);
fs["labels"] >> _labels;
}
// See FaceRecognizer::save.
void Fisherfaces::save(FileStorage& fs) const {
// write matrices
fs << "num_components" << _num_components;
fs << "mean" << _mean;
fs << "eigenvalues" << _eigenvalues;
fs << "eigenvectors" << _eigenvectors;
// write sequences
writeFileNodeList(fs, "projections", _projections);
fs << "labels" << _labels;
}
//------------------------------------------------------------------------------
// LBPH
//------------------------------------------------------------------------------
template <typename _Tp> static
void olbp_(InputArray _src, OutputArray _dst) {
// get matrices
Mat src = _src.getMat();
// allocate memory for result
_dst.create(src.rows-2, src.cols-2, CV_8UC1);
Mat dst = _dst.getMat();
// zero the result matrix
dst.setTo(0);
// calculate patterns
for(int i=1;i<src.rows-1;i++) {
for(int j=1;j<src.cols-1;j++) {
_Tp center = src.at<_Tp>(i,j);
unsigned char code = 0;
code |= (src.at<_Tp>(i-1,j-1) >= center) << 7;
code |= (src.at<_Tp>(i-1,j) >= center) << 6;
code |= (src.at<_Tp>(i-1,j+1) >= center) << 5;
code |= (src.at<_Tp>(i,j+1) >= center) << 4;
code |= (src.at<_Tp>(i+1,j+1) >= center) << 3;
code |= (src.at<_Tp>(i+1,j) >= center) << 2;
code |= (src.at<_Tp>(i+1,j-1) >= center) << 1;
code |= (src.at<_Tp>(i,j-1) >= center) << 0;
dst.at<unsigned char>(i-1,j-1) = code;
}
}
}
//------------------------------------------------------------------------------
// cv::elbp
//------------------------------------------------------------------------------
template <typename _Tp> static
inline void elbp_(InputArray _src, OutputArray _dst, int radius, int neighbors) {
//get matrices
Mat src = _src.getMat();
// allocate memory for result
_dst.create(src.rows-2*radius, src.cols-2*radius, CV_32SC1);
Mat dst = _dst.getMat();
// zero
dst.setTo(0);
for(int n=0; n<neighbors; n++) {
// sample points
float x = static_cast<float>(radius * cos(2.0*CV_PI*n/static_cast<float>(neighbors)));
float y = static_cast<float>(-radius * sin(2.0*CV_PI*n/static_cast<float>(neighbors)));
// relative indices
int fx = static_cast<int>(floor(x));
int fy = static_cast<int>(floor(y));
int cx = static_cast<int>(ceil(x));
int cy = static_cast<int>(ceil(y));
// fractional part
float ty = y - fy;
float tx = x - fx;
// set interpolation weights
float w1 = (1 - tx) * (1 - ty);
float w2 = tx * (1 - ty);
float w3 = (1 - tx) * ty;
float w4 = tx * ty;
// iterate through your data
for(int i=radius; i < src.rows-radius;i++) {
for(int j=radius;j < src.cols-radius;j++) {
// calculate interpolated value
float t = static_cast<float>(w1*src.at<_Tp>(i+fy,j+fx) + w2*src.at<_Tp>(i+fy,j+cx) + w3*src.at<_Tp>(i+cy,j+fx) + w4*src.at<_Tp>(i+cy,j+cx));
// floating point precision, so check some machine-dependent epsilon
dst.at<int>(i-radius,j-radius) += ((t > src.at<_Tp>(i,j)) || (std::abs(t-src.at<_Tp>(i,j)) < std::numeric_limits<float>::epsilon())) << n;
}
}
}
}
static void elbp(InputArray src, OutputArray dst, int radius, int neighbors)
{
int type = src.type();
switch (type) {
case CV_8SC1: elbp_<char>(src,dst, radius, neighbors); break;
case CV_8UC1: elbp_<unsigned char>(src, dst, radius, neighbors); break;
case CV_16SC1: elbp_<short>(src,dst, radius, neighbors); break;
case CV_16UC1: elbp_<unsigned short>(src,dst, radius, neighbors); break;
case CV_32SC1: elbp_<int>(src,dst, radius, neighbors); break;
case CV_32FC1: elbp_<float>(src,dst, radius, neighbors); break;
case CV_64FC1: elbp_<double>(src,dst, radius, neighbors); break;
default:
String error_msg = format("Using Original Local Binary Patterns for feature extraction only works on single-channel images (given %d). Please pass the image data as a grayscale image!", type);
CV_Error(Error::StsNotImplemented, error_msg);
break;
}
}
static Mat
histc_(const Mat& src, int minVal=0, int maxVal=255, bool normed=false)
{
Mat result;
// Establish the number of bins.
int histSize = maxVal-minVal+1;
// Set the ranges.
float range[] = { static_cast<float>(minVal), static_cast<float>(maxVal+1) };
const float* histRange = { range };
// calc histogram
calcHist(&src, 1, 0, Mat(), result, 1, &histSize, &histRange, true, false);
// normalize
if(normed) {
result /= (int)src.total();
}
return result.reshape(1,1);
}
static Mat histc(InputArray _src, int minVal, int maxVal, bool normed)
{
Mat src = _src.getMat();
switch (src.type()) {
case CV_8SC1:
return histc_(Mat_<float>(src), minVal, maxVal, normed);
break;
case CV_8UC1:
return histc_(src, minVal, maxVal, normed);
break;
case CV_16SC1:
return histc_(Mat_<float>(src), minVal, maxVal, normed);
break;
case CV_16UC1:
return histc_(src, minVal, maxVal, normed);
break;
case CV_32SC1:
return histc_(Mat_<float>(src), minVal, maxVal, normed);
break;
case CV_32FC1:
return histc_(src, minVal, maxVal, normed);
break;
default:
CV_Error(Error::StsUnmatchedFormats, "This type is not implemented yet."); break;
}
return Mat();
}
static Mat spatial_histogram(InputArray _src, int numPatterns,
int grid_x, int grid_y, bool /*normed*/)
{
Mat src = _src.getMat();
// calculate LBP patch size
int width = src.cols/grid_x;
int height = src.rows/grid_y;
// allocate memory for the spatial histogram
Mat result = Mat::zeros(grid_x * grid_y, numPatterns, CV_32FC1);
// return matrix with zeros if no data was given
if(src.empty())
return result.reshape(1,1);
// initial result_row
int resultRowIdx = 0;
// iterate through grid
for(int i = 0; i < grid_y; i++) {
for(int j = 0; j < grid_x; j++) {
Mat src_cell = Mat(src, Range(i*height,(i+1)*height), Range(j*width,(j+1)*width));
Mat cell_hist = histc(src_cell, 0, (numPatterns-1), true);
// copy to the result matrix
Mat result_row = result.row(resultRowIdx);
cell_hist.reshape(1,1).convertTo(result_row, CV_32FC1);
// increase row count in result matrix
resultRowIdx++;
}
}
// return result as reshaped feature vector
return result.reshape(1,1);
}
//------------------------------------------------------------------------------
// wrapper to cv::elbp (extended local binary patterns)
//------------------------------------------------------------------------------
static Mat elbp(InputArray src, int radius, int neighbors) {
Mat dst;
elbp(src, dst, radius, neighbors);
return dst;
}
void LBPH::load(const FileStorage& fs) {
fs["radius"] >> _radius;
fs["neighbors"] >> _neighbors;
fs["grid_x"] >> _grid_x;
fs["grid_y"] >> _grid_y;
//read matrices
readFileNodeList(fs["histograms"], _histograms);
fs["labels"] >> _labels;
}
// See FaceRecognizer::save.
void LBPH::save(FileStorage& fs) const {
fs << "radius" << _radius;
fs << "neighbors" << _neighbors;
fs << "grid_x" << _grid_x;
fs << "grid_y" << _grid_y;
// write matrices
writeFileNodeList(fs, "histograms", _histograms);
fs << "labels" << _labels;
}
void LBPH::train(InputArrayOfArrays _in_src, InputArray _in_labels) {
this->train(_in_src, _in_labels, false);
}
void LBPH::update(InputArrayOfArrays _in_src, InputArray _in_labels) {
// got no data, just return
if(_in_src.total() == 0)
return;
this->train(_in_src, _in_labels, true);
}
void LBPH::train(InputArrayOfArrays _in_src, InputArray _in_labels, bool preserveData) {
if(_in_src.kind() != _InputArray::STD_VECTOR_MAT && _in_src.kind() != _InputArray::STD_VECTOR_VECTOR) {
String error_message = "The images are expected as InputArray::STD_VECTOR_MAT (a std::vector<Mat>) or _InputArray::STD_VECTOR_VECTOR (a std::vector< std::vector<...> >).";
CV_Error(Error::StsBadArg, error_message);
}
if(_in_src.total() == 0) {
String error_message = format("Empty training data was given. You'll need more than one sample to learn a model.");
CV_Error(Error::StsUnsupportedFormat, error_message);
} else if(_in_labels.getMat().type() != CV_32SC1) {
String error_message = format("Labels must be given as integer (CV_32SC1). Expected %d, but was %d.", CV_32SC1, _in_labels.type());
CV_Error(Error::StsUnsupportedFormat, error_message);
}
// get the vector of matrices
std::vector<Mat> src;
_in_src.getMatVector(src);
// get the label matrix
Mat labels = _in_labels.getMat();
// check if data is well- aligned
if(labels.total() != src.size()) {
String error_message = format("The number of samples (src) must equal the number of labels (labels). Was len(samples)=%d, len(labels)=%d.", src.size(), _labels.total());
CV_Error(Error::StsBadArg, error_message);
}
// if this model should be trained without preserving old data, delete old model data
if(!preserveData) {
_labels.release();
_histograms.clear();
}
// append labels to _labels matrix
for(size_t labelIdx = 0; labelIdx < labels.total(); labelIdx++) {
_labels.push_back(labels.at<int>((int)labelIdx));
}
// store the spatial histograms of the original data
for(size_t sampleIdx = 0; sampleIdx < src.size(); sampleIdx++) {
// calculate lbp image
Mat lbp_image = elbp(src[sampleIdx], _radius, _neighbors);
// get spatial histogram from this lbp image
Mat p = spatial_histogram(
lbp_image, /* lbp_image */
static_cast<int>(std::pow(2.0, static_cast<double>(_neighbors))), /* number of possible patterns */
_grid_x, /* grid size x */
_grid_y, /* grid size y */
true);
// add to templates
_histograms.push_back(p);
}
}
void LBPH::predict(InputArray _src, int &minClass, double &minDist) const {
if(_histograms.empty()) {
// throw error if no data (or simply return -1?)
String error_message = "This LBPH model is not computed yet. Did you call the train method?";
CV_Error(Error::StsBadArg, error_message);
}
Mat src = _src.getMat();
// get the spatial histogram from input image
Mat lbp_image = elbp(src, _radius, _neighbors);
Mat query = spatial_histogram(
lbp_image, /* lbp_image */
static_cast<int>(std::pow(2.0, static_cast<double>(_neighbors))), /* number of possible patterns */
_grid_x, /* grid size x */
_grid_y, /* grid size y */
true /* normed histograms */);
// find 1-nearest neighbor
minDist = DBL_MAX;
minClass = -1;
for(size_t sampleIdx = 0; sampleIdx < _histograms.size(); sampleIdx++) {
double dist = compareHist(_histograms[sampleIdx], query, HISTCMP_CHISQR_ALT);
if((dist < minDist) && (dist < _threshold)) {
minDist = dist;
minClass = _labels.at<int>((int) sampleIdx);
}
}
}
int LBPH::predict(InputArray _src) const {
int label;
double dummy;
predict(_src, label, dummy);
return label;
}
Ptr<FaceRecognizer> createEigenFaceRecognizer(int num_components, double threshold)
{
return makePtr<Eigenfaces>(num_components, threshold);
}
Ptr<FaceRecognizer> createFisherFaceRecognizer(int num_components, double threshold)
{
return makePtr<Fisherfaces>(num_components, threshold);
}
Ptr<FaceRecognizer> createLBPHFaceRecognizer(int radius, int neighbors,
int grid_x, int grid_y, double threshold)
{
return makePtr<LBPH>(radius, neighbors, grid_x, grid_y, threshold);
}
CV_INIT_ALGORITHM(Eigenfaces, "FaceRecognizer.Eigenfaces",
obj.info()->addParam(obj, "ncomponents", obj._num_components);
obj.info()->addParam(obj, "threshold", obj._threshold);
obj.info()->addParam(obj, "projections", obj._projections, true);
obj.info()->addParam(obj, "labels", obj._labels, true);
obj.info()->addParam(obj, "eigenvectors", obj._eigenvectors, true);
obj.info()->addParam(obj, "eigenvalues", obj._eigenvalues, true);
obj.info()->addParam(obj, "mean", obj._mean, true))
CV_INIT_ALGORITHM(Fisherfaces, "FaceRecognizer.Fisherfaces",
obj.info()->addParam(obj, "ncomponents", obj._num_components);
obj.info()->addParam(obj, "threshold", obj._threshold);
obj.info()->addParam(obj, "projections", obj._projections, true);
obj.info()->addParam(obj, "labels", obj._labels, true);
obj.info()->addParam(obj, "eigenvectors", obj._eigenvectors, true);
obj.info()->addParam(obj, "eigenvalues", obj._eigenvalues, true);
obj.info()->addParam(obj, "mean", obj._mean, true))
CV_INIT_ALGORITHM(LBPH, "FaceRecognizer.LBPH",
obj.info()->addParam(obj, "radius", obj._radius);
obj.info()->addParam(obj, "neighbors", obj._neighbors);
obj.info()->addParam(obj, "grid_x", obj._grid_x);
obj.info()->addParam(obj, "grid_y", obj._grid_y);
obj.info()->addParam(obj, "threshold", obj._threshold);
obj.info()->addParam(obj, "histograms", obj._histograms, true);
obj.info()->addParam(obj, "labels", obj._labels, true))
bool initModule_contrib()
{
Ptr<Algorithm> efaces = createEigenfaces_ptr_hidden(), ffaces = createFisherfaces_ptr_hidden(), lbph = createLBPH_ptr_hidden();
return efaces->info() != 0 && ffaces->info() != 0 && lbph->info() != 0;
}
}
modules/contrib/src/featuretracker.cpp
-229
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@@ -1,229 +0,0 @@
//*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2008-2011, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "precomp.hpp"
#include <stdio.h>
#include <iostream>
#include "opencv2/calib3d.hpp"
#include "opencv2/contrib/hybridtracker.hpp"
#ifdef HAVE_OPENCV_NONFREE
#include "opencv2/nonfree/nonfree.hpp"
static bool makeUseOfNonfree = initModule_nonfree();
#endif
using namespace cv;
CvFeatureTracker::CvFeatureTracker(CvFeatureTrackerParams _params) :
params(_params)
{
switch (params.feature_type)
{
case CvFeatureTrackerParams::SIFT:
dd = Algorithm::create<Feature2D>("Feature2D.SIFT");
if( !dd )
CV_Error(CV_StsNotImplemented, "OpenCV has been compiled without SIFT support");
dd->set("nOctaveLayers", 5);
dd->set("contrastThreshold", 0.04);
dd->set("edgeThreshold", 10.7);
break;
case CvFeatureTrackerParams::SURF:
dd = Algorithm::create<Feature2D>("Feature2D.SURF");
if( !dd )
CV_Error(CV_StsNotImplemented, "OpenCV has been compiled without SURF support");
dd->set("hessianThreshold", 400);
dd->set("nOctaves", 3);
dd->set("nOctaveLayers", 4);
break;
default:
CV_Error(CV_StsBadArg, "Unknown feature type");
break;
}
matcher = makePtr<BFMatcher>(int(NORM_L2));
}
CvFeatureTracker::~CvFeatureTracker()
{
}
void CvFeatureTracker::newTrackingWindow(Mat image, Rect selection)
{
image.copyTo(prev_image);
cvtColor(prev_image, prev_image_bw, COLOR_BGR2GRAY);
prev_trackwindow = selection;
prev_center.x = selection.x;
prev_center.y = selection.y;
ittr = 0;
}
Rect CvFeatureTracker::updateTrackingWindow(Mat image)
{
if(params.feature_type == CvFeatureTrackerParams::OPTICAL_FLOW)
return updateTrackingWindowWithFlow(image);
else
return updateTrackingWindowWithSIFT(image);
}
Rect CvFeatureTracker::updateTrackingWindowWithSIFT(Mat image)
{
ittr++;
std::vector<KeyPoint> prev_keypoints, curr_keypoints;
std::vector<Point2f> prev_keys, curr_keys;
Mat prev_desc, curr_desc;
Rect window = prev_trackwindow;
Mat mask = Mat::zeros(image.size(), CV_8UC1);
rectangle(mask, Point(window.x, window.y), Point(window.x + window.width,
window.y + window.height), Scalar(255), CV_FILLED);
dd->operator()(prev_image, mask, prev_keypoints, prev_desc);
window.x -= params.window_size;
window.y -= params.window_size;
window.width += params.window_size;
window.height += params.window_size;
rectangle(mask, Point(window.x, window.y), Point(window.x + window.width,
window.y + window.height), Scalar(255), CV_FILLED);
dd->operator()(image, mask, curr_keypoints, curr_desc);
if (prev_keypoints.size() > 4 && curr_keypoints.size() > 4)
{
//descriptor->compute(prev_image, prev_keypoints, prev_desc);
//descriptor->compute(image, curr_keypoints, curr_desc);
matcher->match(prev_desc, curr_desc, matches);
for (int i = 0; i < (int)matches.size(); i++)
{
prev_keys.push_back(prev_keypoints[matches[i].queryIdx].pt);
curr_keys.push_back(curr_keypoints[matches[i].trainIdx].pt);
}
Mat T = findHomography(prev_keys, curr_keys, LMEDS);
prev_trackwindow.x += cvRound(T.at<double> (0, 2));
prev_trackwindow.y += cvRound(T.at<double> (1, 2));
}
prev_center.x = prev_trackwindow.x;
prev_center.y = prev_trackwindow.y;
prev_image = image;
return prev_trackwindow;
}
Rect CvFeatureTracker::updateTrackingWindowWithFlow(Mat image)
{
ittr++;
Size subPixWinSize(10,10), winSize(31,31);
Mat image_bw;
TermCriteria termcrit(TermCriteria::COUNT | TermCriteria::EPS, 20, 0.03);
std::vector<uchar> status;
std::vector<float> err;
cvtColor(image, image_bw, COLOR_BGR2GRAY);
cvtColor(prev_image, prev_image_bw, COLOR_BGR2GRAY);
if (ittr == 1)
{
Mat mask = Mat::zeros(image.size(), CV_8UC1);
rectangle(mask, Point(prev_trackwindow.x, prev_trackwindow.y), Point(
prev_trackwindow.x + prev_trackwindow.width, prev_trackwindow.y
+ prev_trackwindow.height), Scalar(255), CV_FILLED);
goodFeaturesToTrack(image_bw, features[1], 500, 0.01, 20, mask, 3, 0, 0.04);
cornerSubPix(image_bw, features[1], subPixWinSize, Size(-1, -1), termcrit);
}
else
{
calcOpticalFlowPyrLK(prev_image_bw, image_bw, features[0], features[1],
status, err, winSize, 3, termcrit);
Point2f feature0_center(0, 0);
Point2f feature1_center(0, 0);
int goodtracks = 0;
for (int i = 0; i < (int)features[1].size(); i++)
{
if (status[i] == 1)
{
feature0_center.x += features[0][i].x;
feature0_center.y += features[0][i].y;
feature1_center.x += features[1][i].x;
feature1_center.y += features[1][i].y;
goodtracks++;
}
}
feature0_center.x /= goodtracks;
feature0_center.y /= goodtracks;
feature1_center.x /= goodtracks;
feature1_center.y /= goodtracks;
prev_center.x += (feature1_center.x - feature0_center.x);
prev_center.y += (feature1_center.y - feature0_center.y);
prev_trackwindow.x = (int)prev_center.x;
prev_trackwindow.y = (int)prev_center.y;
}
swap(features[0], features[1]);
image.copyTo(prev_image);
return prev_trackwindow;
}
void CvFeatureTracker::setTrackingWindow(Rect _window)
{
prev_trackwindow = _window;
}
Rect CvFeatureTracker::getTrackingWindow()
{
return prev_trackwindow;
}
Point2f CvFeatureTracker::getTrackingCenter()
{
Point2f center(0, 0);
center.x = (float)(prev_center.x + prev_trackwindow.width/2.0);
center.y = (float)(prev_center.y + prev_trackwindow.height/2.0);
return center;
}
modules/contrib/src/fuzzymeanshifttracker.cpp
-721
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@@ -1,721 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install, copy or use the software.
//
// Copyright (C) 2009, Farhad Dadgostar
// Intel Corporation and third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "precomp.hpp"
#include "opencv2/contrib/compat.hpp"
CvFuzzyPoint::CvFuzzyPoint(double _x, double _y)
{
x = _x;
y = _y;
}
bool CvFuzzyCurve::between(double x, double x1, double x2)
{
if ((x >= x1) && (x <= x2))
return true;
else if ((x >= x2) && (x <= x1))
return true;
return false;
}
CvFuzzyCurve::CvFuzzyCurve()
{
value = 0;
}
CvFuzzyCurve::~CvFuzzyCurve()
{
// nothing to do
}
void CvFuzzyCurve::setCentre(double _centre)
{
centre = _centre;
}
double CvFuzzyCurve::getCentre()
{
return centre;
}
void CvFuzzyCurve::clear()
{
points.clear();
}
void CvFuzzyCurve::addPoint(double x, double y)
{
points.push_back(CvFuzzyPoint(x, y));
}
double CvFuzzyCurve::calcValue(double param)
{
int size = (int)points.size();
double x1, y1, x2, y2, m, y;
for (int i = 1; i < size; i++)
{
x1 = points[i-1].x;
x2 = points[i].x;
if (between(param, x1, x2)) {
y1 = points[i-1].y;
y2 = points[i].y;
if (x2 == x1)
return y2;
m = (y2-y1)/(x2-x1);
y = m*(param-x1)+y1;
return y;
}
}
return 0;
}
double CvFuzzyCurve::getValue()
{
return value;
}
void CvFuzzyCurve::setValue(double _value)
{
value = _value;
}
CvFuzzyFunction::CvFuzzyFunction()
{
// nothing to do
}
CvFuzzyFunction::~CvFuzzyFunction()
{
curves.clear();
}
void CvFuzzyFunction::addCurve(CvFuzzyCurve *curve, double value)
{
curves.push_back(*curve);
curve->setValue(value);
}
void CvFuzzyFunction::resetValues()
{
int numCurves = (int)curves.size();
for (int i = 0; i < numCurves; i++)
curves[i].setValue(0);
}
double CvFuzzyFunction::calcValue()
{
double s1 = 0, s2 = 0, v;
int numCurves = (int)curves.size();
for (int i = 0; i < numCurves; i++)
{
v = curves[i].getValue();
s1 += curves[i].getCentre() * v;
s2 += v;
}
if (s2 != 0)
return s1/s2;
else
return 0;
}
CvFuzzyCurve *CvFuzzyFunction::newCurve()
{
CvFuzzyCurve *c;
c = new CvFuzzyCurve();
addCurve(c);
return c;
}
CvFuzzyRule::CvFuzzyRule()
{
fuzzyInput1 = NULL;
fuzzyInput2 = NULL;
fuzzyOutput = NULL;
}
CvFuzzyRule::~CvFuzzyRule()
{
if (fuzzyInput1 != NULL)
delete fuzzyInput1;
if (fuzzyInput2 != NULL)
delete fuzzyInput2;
if (fuzzyOutput != NULL)
delete fuzzyOutput;
}
void CvFuzzyRule::setRule(CvFuzzyCurve *c1, CvFuzzyCurve *c2, CvFuzzyCurve *o1)
{
fuzzyInput1 = c1;
fuzzyInput2 = c2;
fuzzyOutput = o1;
}
double CvFuzzyRule::calcValue(double param1, double param2)
{
double v1, v2;
v1 = fuzzyInput1->calcValue(param1);
if (fuzzyInput2 != NULL)
{
v2 = fuzzyInput2->calcValue(param2);
if (v1 < v2)
return v1;
else
return v2;
}
else
return v1;
}
CvFuzzyCurve *CvFuzzyRule::getOutputCurve()
{
return fuzzyOutput;
}
CvFuzzyController::CvFuzzyController()
{
// nothing to do
}
CvFuzzyController::~CvFuzzyController()
{
int size = (int)rules.size();
for(int i = 0; i < size; i++)
delete rules[i];
}
void CvFuzzyController::addRule(CvFuzzyCurve *c1, CvFuzzyCurve *c2, CvFuzzyCurve *o1)
{
CvFuzzyRule *f = new CvFuzzyRule();
rules.push_back(f);
f->setRule(c1, c2, o1);
}
double CvFuzzyController::calcOutput(double param1, double param2)
{
double v;
CvFuzzyFunction list;
int size = (int)rules.size();
for(int i = 0; i < size; i++)
{
v = rules[i]->calcValue(param1, param2);
if (v != 0)
list.addCurve(rules[i]->getOutputCurve(), v);
}
v = list.calcValue();
return v;
}
CvFuzzyMeanShiftTracker::FuzzyResizer::FuzzyResizer()
{
CvFuzzyCurve *i1L, *i1M, *i1H;
CvFuzzyCurve *oS, *oZE, *oE;
CvFuzzyCurve *c;
double MedStart = 0.1, MedWidth = 0.15;
c = iInput.newCurve();
c->addPoint(0, 1);
c->addPoint(0.1, 0);
c->setCentre(0);
i1L = c;
c = iInput.newCurve();
c->addPoint(0.05, 0);
c->addPoint(MedStart, 1);
c->addPoint(MedStart+MedWidth, 1);
c->addPoint(MedStart+MedWidth+0.05, 0);
c->setCentre(MedStart+(MedWidth/2));
i1M = c;
c = iInput.newCurve();
c->addPoint(MedStart+MedWidth, 0);
c->addPoint(1, 1);
c->addPoint(1000, 1);
c->setCentre(1);
i1H = c;
c = iOutput.newCurve();
c->addPoint(-10000, 1);
c->addPoint(-5, 1);
c->addPoint(-0.5, 0);
c->setCentre(-5);
oS = c;
c = iOutput.newCurve();
c->addPoint(-1, 0);
c->addPoint(-0.05, 1);
c->addPoint(0.05, 1);
c->addPoint(1, 0);
c->setCentre(0);
oZE = c;
c = iOutput.newCurve();
c->addPoint(-0.5, 0);
c->addPoint(5, 1);
c->addPoint(1000, 1);
c->setCentre(5);
oE = c;
fuzzyController.addRule(i1L, NULL, oS);
fuzzyController.addRule(i1M, NULL, oZE);
fuzzyController.addRule(i1H, NULL, oE);
}
int CvFuzzyMeanShiftTracker::FuzzyResizer::calcOutput(double edgeDensity, double density)
{
return (int)fuzzyController.calcOutput(edgeDensity, density);
}
CvFuzzyMeanShiftTracker::SearchWindow::SearchWindow()
{
x = 0;
y = 0;
width = 0;
height = 0;
maxWidth = 0;
maxHeight = 0;
xGc = 0;
yGc = 0;
m00 = 0;
m01 = 0;
m10 = 0;
m11 = 0;
m02 = 0;
m20 = 0;
ellipseHeight = 0;
ellipseWidth = 0;
ellipseAngle = 0;
density = 0;
depthLow = 0;
depthHigh = 0;
fuzzyResizer = NULL;
}
CvFuzzyMeanShiftTracker::SearchWindow::~SearchWindow()
{
if (fuzzyResizer != NULL)
delete fuzzyResizer;
}
void CvFuzzyMeanShiftTracker::SearchWindow::setSize(int _x, int _y, int _width, int _height)
{
x = _x;
y = _y;
width = _width;
height = _height;
if (x < 0)
x = 0;
if (y < 0)
y = 0;
if (x + width > maxWidth)
width = maxWidth - x;
if (y + height > maxHeight)
height = maxHeight - y;
}
void CvFuzzyMeanShiftTracker::SearchWindow::initDepthValues(IplImage *maskImage, IplImage *depthMap)
{
unsigned int d=0, mind = 0xFFFF, maxd = 0, m0 = 0, m1 = 0, mc, dd;
unsigned char *data = NULL;
unsigned short *depthData = NULL;
for (int j = 0; j < height; j++)
{
data = (unsigned char *)(maskImage->imageData + (maskImage->widthStep * (j + y)) + x);
if (depthMap)
depthData = (unsigned short *)(depthMap->imageData + (depthMap->widthStep * (j + y)) + x);
for (int i = 0; i < width; i++)
{
if (*data)
{
m0 += 1;
if (depthData)
{
if (*depthData)
{
d = *depthData;
m1 += d;
if (d < mind)
mind = d;
if (d > maxd)
maxd = d;
}
depthData++;
}
}
data++;
}
}
if (m0 > 0)
{
mc = m1/m0;
if ((mc - mind) > (maxd - mc))
dd = maxd - mc;
else
dd = mc - mind;
dd = dd - dd/10;
depthHigh = mc + dd;
depthLow = mc - dd;
}
else
{
depthHigh = 32000;
depthLow = 0;
}
}
bool CvFuzzyMeanShiftTracker::SearchWindow::shift()
{
if ((xGc != (width/2)) || (yGc != (height/2)))
{
setSize(x + (xGc-(width/2)), y + (yGc-(height/2)), width, height);
return true;
}
else
{
return false;
}
}
void CvFuzzyMeanShiftTracker::SearchWindow::extractInfo(IplImage *maskImage, IplImage *depthMap, bool initDepth)
{
m00 = 0;
m10 = 0;
m01 = 0;
m11 = 0;
density = 0;
m02 = 0;
m20 = 0;
ellipseHeight = 0;
ellipseWidth = 0;
maxWidth = maskImage->width;
maxHeight = maskImage->height;
if (initDepth)
initDepthValues(maskImage, depthMap);
unsigned char *maskData = NULL;
unsigned short *depthData = NULL, depth;
bool isOk;
unsigned long count;
verticalEdgeLeft = 0;
verticalEdgeRight = 0;
horizontalEdgeTop = 0;
horizontalEdgeBottom = 0;
for (int j = 0; j < height; j++)
{
maskData = (unsigned char *)(maskImage->imageData + (maskImage->widthStep * (j + y)) + x);
if (depthMap)
depthData = (unsigned short *)(depthMap->imageData + (depthMap->widthStep * (j + y)) + x);
count = 0;
for (int i = 0; i < width; i++)
{
if (*maskData)
{
isOk = true;
if (depthData)
{
depth = (*depthData);
if ((depth > depthHigh) || (depth < depthLow))
isOk = false;
depthData++;
}
if (isOk)
{
m00++;
m01 += j;
m10 += i;
m02 += (j * j);
m20 += (i * i);
m11 += (j * i);
if (i == 0)
verticalEdgeLeft++;
else if (i == width-1)
verticalEdgeRight++;
else if (j == 0)
horizontalEdgeTop++;
else if (j == height-1)
horizontalEdgeBottom++;
count++;
}
}
maskData++;
}
}
if (m00 > 0)
{
xGc = (m10 / m00);
yGc = (m01 / m00);
double a, b, c, e1, e2, e3;
a = ((double)m20/(double)m00)-(xGc * xGc);
b = 2*(((double)m11/(double)m00)-(xGc * yGc));
c = ((double)m02/(double)m00)-(yGc * yGc);
e1 = a+c;
e3 = a-c;
e2 = sqrt((b*b)+(e3*e3));
ellipseHeight = int(sqrt(0.5*(e1+e2)));
ellipseWidth = int(sqrt(0.5*(e1-e2)));
if (e3 == 0)
ellipseAngle = 0;
else
ellipseAngle = 0.5*atan(b/e3);
density = (double)m00/(double)(width * height);
}
else
{
xGc = width / 2;
yGc = height / 2;
ellipseHeight = 0;
ellipseWidth = 0;
ellipseAngle = 0;
density = 0;
}
}
void CvFuzzyMeanShiftTracker::SearchWindow::getResizeAttribsEdgeDensityLinear(int &resizeDx, int &resizeDy, int &resizeDw, int &resizeDh) {
int x1 = horizontalEdgeTop;
int x2 = horizontalEdgeBottom;
int y1 = verticalEdgeLeft;
int y2 = verticalEdgeRight;
int gx = (width*2)/5;
int gy = (height*2)/5;
int lx = width/10;
int ly = height/10;
resizeDy = 0;
resizeDh = 0;
resizeDx = 0;
resizeDw = 0;
if (x1 > gx) {
resizeDy = -1;
} else if (x1 < lx) {
resizeDy = +1;
}
if (x2 > gx) {
resizeDh = resizeDy + 1;
} else if (x2 < lx) {
resizeDh = - (resizeDy + 1);
} else {
resizeDh = - resizeDy;
}
if (y1 > gy) {
resizeDx = -1;
} else if (y1 < ly) {
resizeDx = +1;
}
if (y2 > gy) {
resizeDw = resizeDx + 1;
} else if (y2 < ly) {
resizeDw = - (resizeDx + 1);
} else {
resizeDw = - resizeDx;
}
}
void CvFuzzyMeanShiftTracker::SearchWindow::getResizeAttribsInnerDensity(int &resizeDx, int &resizeDy, int &resizeDw, int &resizeDh)
{
int newWidth, newHeight, dx, dy;
double px, py;
newWidth = int(sqrt(double(m00)*1.3));
newHeight = int(newWidth*1.2);
dx = (newWidth - width);
dy = (newHeight - height);
px = (double)xGc/(double)width;
py = (double)yGc/(double)height;
resizeDx = (int)(px*dx);
resizeDy = (int)(py*dy);
resizeDw = (int)((1-px)*dx);
resizeDh = (int)((1-py)*dy);
}
void CvFuzzyMeanShiftTracker::SearchWindow::getResizeAttribsEdgeDensityFuzzy(int &resizeDx, int &resizeDy, int &resizeDw, int &resizeDh)
{
double dx1=0, dx2, dy1, dy2;
resizeDy = 0;
resizeDh = 0;
resizeDx = 0;
resizeDw = 0;
if (fuzzyResizer == NULL)
fuzzyResizer = new FuzzyResizer();
dx2 = fuzzyResizer->calcOutput(double(verticalEdgeRight)/double(height), density);
if (dx1 == dx2)
{
resizeDx = int(-dx1);
resizeDw = int(dx1+dx2);
}
dy1 = fuzzyResizer->calcOutput(double(horizontalEdgeTop)/double(width), density);
dy2 = fuzzyResizer->calcOutput(double(horizontalEdgeBottom)/double(width), density);
dx1 = fuzzyResizer->calcOutput(double(verticalEdgeLeft)/double(height), density);
dx2 = fuzzyResizer->calcOutput(double(verticalEdgeRight)/double(height), density);
//if (dx1 == dx2)
{
resizeDx = int(-dx1);
resizeDw = int(dx1+dx2);
}
dy1 = fuzzyResizer->calcOutput(double(horizontalEdgeTop)/double(width), density);
dy2 = fuzzyResizer->calcOutput(double(horizontalEdgeBottom)/double(width), density);
//if (dy1 == dy2)
{
resizeDy = int(-dy1);
resizeDh = int(dy1+dy2);
}
}
bool CvFuzzyMeanShiftTracker::SearchWindow::meanShift(IplImage *maskImage, IplImage *depthMap, int maxIteration, bool initDepth)
{
numShifts = 0;
do
{
extractInfo(maskImage, depthMap, initDepth);
if (! shift())
return true;
} while (++numShifts < maxIteration);
return false;
}
void CvFuzzyMeanShiftTracker::findOptimumSearchWindow(SearchWindow &searchWindow, IplImage *maskImage, IplImage *depthMap, int maxIteration, int resizeMethod, bool initDepth)
{
int resizeDx, resizeDy, resizeDw, resizeDh;
resizeDx = 0;
resizeDy = 0;
resizeDw = 0;
resizeDh = 0;
searchWindow.numIters = 0;
for (int i = 0; i < maxIteration; i++)
{
searchWindow.numIters++;
searchWindow.meanShift(maskImage, depthMap, MaxMeanShiftIteration, initDepth);
switch (resizeMethod)
{
case rmEdgeDensityLinear :
searchWindow.getResizeAttribsEdgeDensityLinear(resizeDx, resizeDy, resizeDw, resizeDh);
break;
case rmEdgeDensityFuzzy :
//searchWindow.getResizeAttribsEdgeDensityLinear(resizeDx, resizeDy, resizeDw, resizeDh);
searchWindow.getResizeAttribsEdgeDensityFuzzy(resizeDx, resizeDy, resizeDw, resizeDh);
break;
case rmInnerDensity :
searchWindow.getResizeAttribsInnerDensity(resizeDx, resizeDy, resizeDw, resizeDh);
break;
default:
searchWindow.getResizeAttribsEdgeDensityLinear(resizeDx, resizeDy, resizeDw, resizeDh);
}
searchWindow.ldx = resizeDx;
searchWindow.ldy = resizeDy;
searchWindow.ldw = resizeDw;
searchWindow.ldh = resizeDh;
if ((resizeDx == 0) && (resizeDy == 0) && (resizeDw == 0) && (resizeDh == 0))
break;
searchWindow.setSize(searchWindow.x + resizeDx, searchWindow.y + resizeDy, searchWindow.width + resizeDw, searchWindow.height + resizeDh);
}
}
CvFuzzyMeanShiftTracker::CvFuzzyMeanShiftTracker()
{
searchMode = tsSetWindow;
}
CvFuzzyMeanShiftTracker::~CvFuzzyMeanShiftTracker()
{
// nothing to do
}
void CvFuzzyMeanShiftTracker::track(IplImage *maskImage, IplImage *depthMap, int resizeMethod, bool resetSearch, int minKernelMass)
{
bool initDepth = false;
if (resetSearch)
searchMode = tsSetWindow;
switch (searchMode)
{
case tsDisabled:
return;
case tsSearching:
return;
case tsSetWindow:
kernel.maxWidth = maskImage->width;
kernel.maxHeight = maskImage->height;
kernel.setSize(0, 0, maskImage->width, maskImage->height);
initDepth = true;
case tsTracking:
searchMode = tsSearching;
findOptimumSearchWindow(kernel, maskImage, depthMap, MaxSetSizeIteration, resizeMethod, initDepth);
if ((kernel.density == 0) || (kernel.m00 < minKernelMass))
searchMode = tsSetWindow;
else
searchMode = tsTracking;
}
}
modules/contrib/src/gencolors.cpp
-139
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@@ -1,139 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "precomp.hpp"
#include <iostream>
using namespace cv;
static void downsamplePoints( const Mat& src, Mat& dst, size_t count )
{
CV_Assert( count >= 2 );
CV_Assert( src.cols == 1 || src.rows == 1 );
CV_Assert( src.total() >= count );
CV_Assert( src.type() == CV_8UC3);
dst.create( 1, (int)count, CV_8UC3 );
//TODO: optimize by exploiting symmetry in the distance matrix
Mat dists( (int)src.total(), (int)src.total(), CV_32FC1, Scalar(0) );
if( dists.empty() )
std::cerr << "Such big matrix cann't be created." << std::endl;
for( int i = 0; i < dists.rows; i++ )
{
for( int j = i; j < dists.cols; j++ )
{
float dist = (float)norm(src.at<Point3_<uchar> >(i) - src.at<Point3_<uchar> >(j));
dists.at<float>(j, i) = dists.at<float>(i, j) = dist;
}
}
double maxVal;
Point maxLoc;
minMaxLoc(dists, 0, &maxVal, 0, &maxLoc);
dst.at<Point3_<uchar> >(0) = src.at<Point3_<uchar> >(maxLoc.x);
dst.at<Point3_<uchar> >(1) = src.at<Point3_<uchar> >(maxLoc.y);
Mat activedDists( 0, dists.cols, dists.type() );
Mat candidatePointsMask( 1, dists.cols, CV_8UC1, Scalar(255) );
activedDists.push_back( dists.row(maxLoc.y) );
candidatePointsMask.at<uchar>(0, maxLoc.y) = 0;
for( size_t i = 2; i < count; i++ )
{
activedDists.push_back(dists.row(maxLoc.x));
candidatePointsMask.at<uchar>(0, maxLoc.x) = 0;
Mat minDists;
reduce( activedDists, minDists, 0, REDUCE_MIN );
minMaxLoc( minDists, 0, &maxVal, 0, &maxLoc, candidatePointsMask );
dst.at<Point3_<uchar> >((int)i) = src.at<Point3_<uchar> >(maxLoc.x);
}
}
void cv::generateColors( std::vector<Scalar>& colors, size_t count, size_t factor )
{
if( count < 1 )
return;
colors.resize(count);
if( count == 1 )
{
colors[0] = Scalar(0,0,255); // red
return;
}
if( count == 2 )
{
colors[0] = Scalar(0,0,255); // red
colors[1] = Scalar(0,255,0); // green
return;
}
// Generate a set of colors in RGB space. A size of the set is severel times (=factor) larger then
// the needed count of colors.
Mat bgr( 1, (int)(count*factor), CV_8UC3 );
randu( bgr, 0, 256 );
// Convert the colors set to Lab space.
// Distances between colors in this space correspond a human perception.
Mat lab;
cvtColor( bgr, lab, COLOR_BGR2Lab);
// Subsample colors from the generated set so that
// to maximize the minimum distances between each other.
// Douglas-Peucker algorithm is used for this.
Mat lab_subset;
downsamplePoints( lab, lab_subset, count );
// Convert subsampled colors back to RGB
Mat bgr_subset;
cvtColor( lab_subset, bgr_subset, COLOR_Lab2BGR );
CV_Assert( bgr_subset.total() == count );
for( size_t i = 0; i < count; i++ )
{
Point3_<uchar> c = bgr_subset.at<Point3_<uchar> >((int)i);
colors[i] = Scalar(c.x, c.y, c.z);
}
}
modules/contrib/src/hybridtracker.cpp
-235
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@@ -1,235 +0,0 @@
//*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2008-2011, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "precomp.hpp"
#include "opencv2/contrib/hybridtracker.hpp"
using namespace cv;
CvHybridTrackerParams::CvHybridTrackerParams(float _ft_tracker_weight, float _ms_tracker_weight,
CvFeatureTrackerParams _ft_params,
CvMeanShiftTrackerParams _ms_params,
CvMotionModel)
{
ft_tracker_weight = _ft_tracker_weight;
ms_tracker_weight = _ms_tracker_weight;
ft_params = _ft_params;
ms_params = _ms_params;
}
CvMeanShiftTrackerParams::CvMeanShiftTrackerParams(int _tracking_type, CvTermCriteria _term_crit)
{
tracking_type = _tracking_type;
term_crit = _term_crit;
}
CvHybridTracker::CvHybridTracker() {
}
CvHybridTracker::CvHybridTracker(HybridTrackerParams _params) :
params(_params) {
params.ft_params.feature_type = CvFeatureTrackerParams::SIFT;
mstracker = new CvMeanShiftTracker(params.ms_params);
fttracker = new CvFeatureTracker(params.ft_params);
}
CvHybridTracker::~CvHybridTracker() {
if (mstracker != NULL)
delete mstracker;
if (fttracker != NULL)
delete fttracker;
}
inline float CvHybridTracker::getL2Norm(Point2f p1, Point2f p2) {
float distance = (p1.x - p2.x) * (p1.x - p2.x) + (p1.y - p2.y) * (p1.y
- p2.y);
return std::sqrt(distance);
}
Mat CvHybridTracker::getDistanceProjection(Mat image, Point2f center) {
Mat hist(image.size(), CV_64F);
double lu = getL2Norm(Point(0, 0), center);
double ru = getL2Norm(Point(0, image.size().width), center);
double rd = getL2Norm(Point(image.size().height, image.size().width),
center);
double ld = getL2Norm(Point(image.size().height, 0), center);
double max = (lu < ru) ? lu : ru;
max = (max < rd) ? max : rd;
max = (max < ld) ? max : ld;
for (int i = 0; i < hist.rows; i++)
for (int j = 0; j < hist.cols; j++)
hist.at<double> (i, j) = 1.0 - (getL2Norm(Point(i, j), center)
/ max);
return hist;
}
Mat CvHybridTracker::getGaussianProjection(Mat image, int ksize, double sigma,
Point2f center) {
Mat kernel = getGaussianKernel(ksize, sigma, CV_64F);
double max = kernel.at<double> (ksize / 2);
Mat hist(image.size(), CV_64F);
for (int i = 0; i < hist.rows; i++)
for (int j = 0; j < hist.cols; j++) {
int pos = cvRound(getL2Norm(Point(i, j), center));
if (pos < ksize / 2.0)
hist.at<double> (i, j) = 1.0 - (kernel.at<double> (pos) / max);
}
return hist;
}
void CvHybridTracker::newTracker(Mat image, Rect selection) {
prev_proj = Mat::zeros(image.size(), CV_64FC1);
prev_center = Point2f(selection.x + selection.width / 2.0f, selection.y
+ selection.height / 2.0f);
prev_window = selection;
mstracker->newTrackingWindow(image, selection);
fttracker->newTrackingWindow(image, selection);
samples = cvCreateMat(2, 1, CV_32FC1);
labels = cvCreateMat(2, 1, CV_32SC1);
ittr = 0;
}
void CvHybridTracker::updateTracker(Mat image) {
ittr++;
//copy over clean images: TODO
mstracker->updateTrackingWindow(image);
fttracker->updateTrackingWindowWithFlow(image);
if (params.motion_model == CvMotionModel::EM)
updateTrackerWithEM(image);
else
updateTrackerWithLowPassFilter(image);
// Regression to find new weights
Point2f ms_center = mstracker->getTrackingEllipse().center;
Point2f ft_center = fttracker->getTrackingCenter();
#ifdef DEBUG_HYTRACKER
circle(image, ms_center, 3, Scalar(0, 0, 255), -1, 8);
circle(image, ft_center, 3, Scalar(255, 0, 0), -1, 8);
putText(image, "ms", Point(ms_center.x+2, ms_center.y), FONT_HERSHEY_PLAIN, 0.75, Scalar(255, 255, 255));
putText(image, "ft", Point(ft_center.x+2, ft_center.y), FONT_HERSHEY_PLAIN, 0.75, Scalar(255, 255, 255));
#endif
double ms_len = getL2Norm(ms_center, curr_center);
double ft_len = getL2Norm(ft_center, curr_center);
double total_len = ms_len + ft_len;
params.ms_tracker_weight *= (ittr - 1);
params.ms_tracker_weight += (float)((ms_len / total_len));
params.ms_tracker_weight /= ittr;
params.ft_tracker_weight *= (ittr - 1);
params.ft_tracker_weight += (float)((ft_len / total_len));
params.ft_tracker_weight /= ittr;
circle(image, prev_center, 3, Scalar(0, 0, 0), -1, 8);
circle(image, curr_center, 3, Scalar(255, 255, 255), -1, 8);
prev_center = curr_center;
prev_window.x = (int)(curr_center.x-prev_window.width/2.0);
prev_window.y = (int)(curr_center.y-prev_window.height/2.0);
mstracker->setTrackingWindow(prev_window);
fttracker->setTrackingWindow(prev_window);
}
void CvHybridTracker::updateTrackerWithEM(Mat image) {
Mat ms_backproj = mstracker->getHistogramProjection(CV_64F);
Mat ms_distproj = getDistanceProjection(image, mstracker->getTrackingCenter());
Mat ms_proj = ms_backproj.mul(ms_distproj);
float dist_err = getL2Norm(mstracker->getTrackingCenter(), fttracker->getTrackingCenter());
Mat ft_gaussproj = getGaussianProjection(image, cvRound(dist_err), -1, fttracker->getTrackingCenter());
Mat ft_distproj = getDistanceProjection(image, fttracker->getTrackingCenter());
Mat ft_proj = ft_gaussproj.mul(ft_distproj);
Mat proj = params.ms_tracker_weight * ms_proj + params.ft_tracker_weight * ft_proj + prev_proj;
int sample_count = countNonZero(proj);
cvReleaseMat(&samples);
cvReleaseMat(&labels);
samples = cvCreateMat(sample_count, 2, CV_32FC1);
labels = cvCreateMat(sample_count, 1, CV_32SC1);
int count = 0;
for (int i = 0; i < proj.rows; i++)
for (int j = 0; j < proj.cols; j++)
if (proj.at<double> (i, j) > 0) {
samples->data.fl[count * 2] = (float)i;
samples->data.fl[count * 2 + 1] = (float)j;
count++;
}
cv::Mat lbls;
EM em_model(1, EM::COV_MAT_SPHERICAL, TermCriteria(TermCriteria::COUNT + TermCriteria::EPS, 10000, 0.001));
em_model.train(cvarrToMat(samples), noArray(), lbls);
if(labels)
lbls.copyTo(cvarrToMat(labels));
Mat em_means = em_model.get<Mat>("means");
curr_center.x = (float)em_means.at<float>(0, 0);
curr_center.y = (float)em_means.at<float>(0, 1);
}
void CvHybridTracker::updateTrackerWithLowPassFilter(Mat) {
RotatedRect ms_track = mstracker->getTrackingEllipse();
Point2f ft_center = fttracker->getTrackingCenter();
float a = params.low_pass_gain;
curr_center.x = (1 - a) * prev_center.x + a * (params.ms_tracker_weight * ms_track.center.x + params.ft_tracker_weight * ft_center.x);
curr_center.y = (1 - a) * prev_center.y + a * (params.ms_tracker_weight * ms_track.center.y + params.ft_tracker_weight * ft_center.y);
}
Rect CvHybridTracker::getTrackingWindow() {
return prev_window;
}
modules/contrib/src/inputoutput.cpp
-204
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@@ -1,204 +0,0 @@
#include "opencv2/contrib.hpp"
#include "cvconfig.h"
#if defined(WIN32) || defined(_WIN32)
#include <windows.h>
#include <tchar.h>
#else
#include <dirent.h>
#endif
namespace cv
{
std::vector<String> Directory::GetListFiles( const String& path, const String & exten, bool addPath )
{
std::vector<String> list;
list.clear();
String path_f = path + "/" + exten;
#ifdef WIN32
#ifdef HAVE_WINRT
WIN32_FIND_DATAW FindFileData;
#else
WIN32_FIND_DATAA FindFileData;
#endif
HANDLE hFind;
#ifdef HAVE_WINRT
wchar_t wpath[MAX_PATH];
size_t copied = mbstowcs(wpath, path_f.c_str(), MAX_PATH);
CV_Assert((copied != MAX_PATH) && (copied != (size_t)-1));
hFind = FindFirstFileExW(wpath, FindExInfoStandard, &FindFileData, FindExSearchNameMatch, NULL, 0);
#else
hFind = FindFirstFileA((LPCSTR)path_f.c_str(), &FindFileData);
#endif
if (hFind == INVALID_HANDLE_VALUE)
{
return list;
}
else
{
do
{
if (FindFileData.dwFileAttributes == FILE_ATTRIBUTE_NORMAL ||
FindFileData.dwFileAttributes == FILE_ATTRIBUTE_ARCHIVE ||
FindFileData.dwFileAttributes == FILE_ATTRIBUTE_HIDDEN ||
FindFileData.dwFileAttributes == FILE_ATTRIBUTE_SYSTEM ||
FindFileData.dwFileAttributes == FILE_ATTRIBUTE_READONLY)
{
char* fname;
#ifdef HAVE_WINRT
char fname_tmp[MAX_PATH] = {0};
size_t copied = wcstombs(fname_tmp, FindFileData.cFileName, MAX_PATH);
CV_Assert((copied != MAX_PATH) && (copied != (size_t)-1));
fname = fname_tmp;
#else
fname = FindFileData.cFileName;
#endif
if (addPath)
list.push_back(path + "/" + String(fname));
else
list.push_back(String(fname));
}
}
#ifdef HAVE_WINRT
while(FindNextFileW(hFind, &FindFileData));
#else
while(FindNextFileA(hFind, &FindFileData));
#endif
FindClose(hFind);
}
#else
(void)addPath;
DIR *dp;
struct dirent *dirp;
if((dp = opendir(path.c_str())) == NULL)
{
return list;
}
while ((dirp = readdir(dp)) != NULL)
{
if (dirp->d_type == DT_REG)
{
if (exten.compare("*") == 0)
list.push_back(static_cast<String>(dirp->d_name));
else
if (String(dirp->d_name).find(exten) != String::npos)
list.push_back(static_cast<String>(dirp->d_name));
}
}
closedir(dp);
#endif
return list;
}
std::vector<String> Directory::GetListFolders( const String& path, const String & exten, bool addPath )
{
std::vector<String> list;
String path_f = path + "/" + exten;
list.clear();
#ifdef WIN32
#ifdef HAVE_WINRT
WIN32_FIND_DATAW FindFileData;
#else
WIN32_FIND_DATAA FindFileData;
#endif
HANDLE hFind;
#ifdef HAVE_WINRT
wchar_t wpath [MAX_PATH];
size_t copied = mbstowcs(wpath, path_f.c_str(), path_f.size());
CV_Assert((copied != MAX_PATH) && (copied != (size_t)-1));
hFind = FindFirstFileExW(wpath, FindExInfoStandard, &FindFileData, FindExSearchNameMatch, NULL, 0);
#else
hFind = FindFirstFileA((LPCSTR)path_f.c_str(), &FindFileData);
#endif
if (hFind == INVALID_HANDLE_VALUE)
{
return list;
}
else
{
do
{
#ifdef HAVE_WINRT
if (FindFileData.dwFileAttributes == FILE_ATTRIBUTE_DIRECTORY &&
wcscmp(FindFileData.cFileName, L".") != 0 &&
wcscmp(FindFileData.cFileName, L"..") != 0)
#else
if (FindFileData.dwFileAttributes == FILE_ATTRIBUTE_DIRECTORY &&
strcmp(FindFileData.cFileName, ".") != 0 &&
strcmp(FindFileData.cFileName, "..") != 0)
#endif
{
char* fname;
#ifdef HAVE_WINRT
char fname_tmp[MAX_PATH];
size_t copied = wcstombs(fname_tmp, FindFileData.cFileName, MAX_PATH);
CV_Assert((copied != MAX_PATH) && (copied != (size_t)-1));
fname = fname_tmp;
#else
fname = FindFileData.cFileName;
#endif
if (addPath)
list.push_back(path + "/" + String(fname));
else
list.push_back(String(fname));
}
}
#ifdef HAVE_WINRT
while(FindNextFileW(hFind, &FindFileData));
#else
while(FindNextFileA(hFind, &FindFileData));
#endif
FindClose(hFind);
}
#else
(void)addPath;
DIR *dp;
struct dirent *dirp;
if((dp = opendir(path_f.c_str())) == NULL)
{
return list;
}
while ((dirp = readdir(dp)) != NULL)
{
if (dirp->d_type == DT_DIR &&
strcmp(dirp->d_name, ".") != 0 &&
strcmp(dirp->d_name, "..") != 0 )
{
if (exten.compare("*") == 0)
list.push_back(static_cast<String>(dirp->d_name));
else
if (String(dirp->d_name).find(exten) != String::npos)
list.push_back(static_cast<String>(dirp->d_name));
}
}
closedir(dp);
#endif
return list;
}
std::vector<String> Directory::GetListFilesR ( const String& path, const String & exten, bool addPath )
{
std::vector<String> list = Directory::GetListFiles(path, exten, addPath);
std::vector<String> dirs = Directory::GetListFolders(path, exten, addPath);
std::vector<String>::const_iterator it;
for (it = dirs.begin(); it != dirs.end(); ++it)
{
std::vector<String> cl = Directory::GetListFiles(*it, exten, addPath);
list.insert(list.end(), cl.begin(), cl.end());
}
return list;
}
}
modules/contrib/src/lda.cpp
-1108
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File diff suppressed because it is too large Load Diff
modules/contrib/src/logpolar_bsm.cpp
-651
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@@ -1,651 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2012, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The names of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
/*******************************************************************************************
The LogPolar Blind Spot Model code has been contributed by Fabio Solari and Manuela Chessa.
More details can be found in:
M. Chessa, S. P. Sabatini, F. Solari and F. Tatti (2011)
A Quantitative Comparison of Speed and Reliability for Log-Polar Mapping Techniques,
Computer Vision Systems - 8th International Conference,
ICVS 2011, Sophia Antipolis, France, September 20-22, 2011
(http://dx.doi.org/10.1007/978-3-642-23968-7_5)
********************************************************************************************/
#include "precomp.hpp"
#include <cmath>
#include <vector>
namespace cv
{
//------------------------------------interp-------------------------------------------
LogPolar_Interp::LogPolar_Interp(int w, int h, Point2i center, int _R, double _ro0, int _interp, int full, int _s, int sp)
{
if ( (center.x!=w/2 || center.y!=h/2) && full==0) full=1;
if (center.x<0) center.x=0;
if (center.y<0) center.y=0;
if (center.x>=w) center.x=w-1;
if (center.y>=h) center.y=h-1;
if (full){
int rtmp;
if (center.x<=w/2 && center.y>=h/2)
rtmp=(int)std::sqrt((float)center.y*center.y + (float)(w-center.x)*(w-center.x));
else if (center.x>=w/2 && center.y>=h/2)
rtmp=(int)std::sqrt((float)center.y*center.y + (float)center.x*center.x);
else if (center.x>=w/2 && center.y<=h/2)
rtmp=(int)std::sqrt((float)(h-center.y)*(h-center.y) + (float)center.x*center.x);
else //if (center.x<=w/2 && center.y<=h/2)
rtmp=(int)std::sqrt((float)(h-center.y)*(h-center.y) + (float)(w-center.x)*(w-center.x));
M=2*rtmp; N=2*rtmp;
top = M/2 - center.y;
bottom = M/2 - (h-center.y);
left = M/2 - center.x;
right = M/2 - (w - center.x);
}else{
top=bottom=left=right=0;
M=w; N=h;
}
if (sp){
int jc=M/2-1, ic=N/2-1;
int _romax=std::min(ic, jc);
double _a=std::exp(std::log((double)(_romax/2-1)/(double)ro0)/(double)R);
S=(int) floor(2*CV_PI/(_a-1)+0.5);
}
interp=_interp;
create_map(M, N, _R, _s, _ro0);
}
void LogPolar_Interp::create_map(int _M, int _n, int _R, int _s, double _ro0)
{
M=_M;
N=_n;
R=_R;
S=_s;
ro0=_ro0;
int jc=N/2-1, ic=M/2-1;
romax=std::min(ic, jc);
a=std::exp(std::log((double)romax/(double)ro0)/(double)R);
q=((double)S)/(2*CV_PI);
Rsri = Mat::zeros(S,R,CV_32FC1);
Csri = Mat::zeros(S,R,CV_32FC1);
ETAyx = Mat::zeros(N,M,CV_32FC1);
CSIyx = Mat::zeros(N,M,CV_32FC1);
for(int v=0; v<S; v++)
{
for(int u=0; u<R; u++)
{
Rsri.at<float>(v,u)=(float)(ro0*std::pow(a,u)*sin(v/q)+jc);
Csri.at<float>(v,u)=(float)(ro0*std::pow(a,u)*cos(v/q)+ic);
}
}
for(int j=0; j<N; j++)
{
for(int i=0; i<M; i++)
{
double theta;
if(i>=ic)
theta=atan((double)(j-jc)/(double)(i-ic));
else
theta=atan((double)(j-jc)/(double)(i-ic))+CV_PI;
if(theta<0)
theta+=2*CV_PI;
ETAyx.at<float>(j,i)=(float)(q*theta);
double ro2=(j-jc)*(j-jc)+(i-ic)*(i-ic);
CSIyx.at<float>(j,i)=(float)(0.5*std::log(ro2/(ro0*ro0))/std::log(a));
}
}
}
const Mat LogPolar_Interp::to_cortical(const Mat &source)
{
Mat out(S,R,CV_8UC1,Scalar(0));
Mat source_border;
copyMakeBorder(source,source_border,top,bottom,left,right,BORDER_CONSTANT,Scalar(0));
remap(source_border,out,Csri,Rsri,interp);
return out;
}
const Mat LogPolar_Interp::to_cartesian(const Mat &source)
{
Mat out(N,M,CV_8UC1,Scalar(0));
Mat source_border;
if (interp==INTER_NEAREST || interp==INTER_LINEAR){
copyMakeBorder(source,source_border,0,1,0,0,BORDER_CONSTANT,Scalar(0));
Mat rowS0 = source_border.row(S);
source_border.row(0).copyTo(rowS0);
} else if (interp==INTER_CUBIC){
copyMakeBorder(source,source_border,0,2,0,0,BORDER_CONSTANT,Scalar(0));
Mat rowS0 = source_border.row(S);
Mat rowS1 = source_border.row(S+1);
source_border.row(0).copyTo(rowS0);
source_border.row(1).copyTo(rowS1);
} else if (interp==INTER_LANCZOS4){
copyMakeBorder(source,source_border,0,4,0,0,BORDER_CONSTANT,Scalar(0));
Mat rowS0 = source_border.row(S);
Mat rowS1 = source_border.row(S+1);
Mat rowS2 = source_border.row(S+2);
Mat rowS3 = source_border.row(S+3);
source_border.row(0).copyTo(rowS0);
source_border.row(1).copyTo(rowS1);
source_border.row(2).copyTo(rowS2);
source_border.row(3).copyTo(rowS3);
}
remap(source_border,out,CSIyx,ETAyx,interp);
Mat out_cropped=out(Range(top,N-1-bottom),Range(left,M-1-right));
return out_cropped;
}
LogPolar_Interp::~LogPolar_Interp()
{
}
//------------------------------------overlapping----------------------------------
LogPolar_Overlapping::LogPolar_Overlapping(int w, int h, Point2i center, int _R, double _ro0, int full, int _s, int sp)
{
if ( (center.x!=w/2 || center.y!=h/2) && full==0) full=1;
if (center.x<0) center.x=0;
if (center.y<0) center.y=0;
if (center.x>=w) center.x=w-1;
if (center.y>=h) center.y=h-1;
if (full){
int rtmp;
if (center.x<=w/2 && center.y>=h/2)
rtmp=(int)std::sqrt((float)center.y*center.y + (float)(w-center.x)*(w-center.x));
else if (center.x>=w/2 && center.y>=h/2)
rtmp=(int)std::sqrt((float)center.y*center.y + (float)center.x*center.x);
else if (center.x>=w/2 && center.y<=h/2)
rtmp=(int)std::sqrt((float)(h-center.y)*(h-center.y) + (float)center.x*center.x);
else //if (center.x<=w/2 && center.y<=h/2)
rtmp=(int)std::sqrt((float)(h-center.y)*(h-center.y) + (float)(w-center.x)*(w-center.x));
M=2*rtmp; N=2*rtmp;
top = M/2 - center.y;
bottom = M/2 - (h-center.y);
left = M/2 - center.x;
right = M/2 - (w - center.x);
}else{
top=bottom=left=right=0;
M=w; N=h;
}
if (sp){
int jc=M/2-1, ic=N/2-1;
int _romax=std::min(ic, jc);
double _a=std::exp(std::log((double)(_romax/2-1)/(double)ro0)/(double)R);
S=(int) floor(2*CV_PI/(_a-1)+0.5);
}
create_map(M, N, _R, _s, _ro0);
}
void LogPolar_Overlapping::create_map(int _M, int _n, int _R, int _s, double _ro0)
{
M=_M;
N=_n;
R=_R;
S=_s;
ro0=_ro0;
int jc=N/2-1, ic=M/2-1;
romax=std::min(ic, jc);
a=std::exp(std::log((double)romax/(double)ro0)/(double)R);
q=((double)S)/(2*CV_PI);
ind1=0;
Rsri=Mat::zeros(S,R,CV_32FC1);
Csri=Mat::zeros(S,R,CV_32FC1);
ETAyx=Mat::zeros(N,M,CV_32FC1);
CSIyx=Mat::zeros(N,M,CV_32FC1);
Rsr.resize(R*S);
Csr.resize(R*S);
Wsr.resize(R);
w_ker_2D.resize(R*S);
for(int v=0; v<S; v++)
{
for(int u=0; u<R; u++)
{
Rsri.at<float>(v,u)=(float)(ro0*std::pow(a,u)*sin(v/q)+jc);
Csri.at<float>(v,u)=(float)(ro0*std::pow(a,u)*cos(v/q)+ic);
Rsr[v*R+u]=(int)floor(Rsri.at<float>(v,u));
Csr[v*R+u]=(int)floor(Csri.at<float>(v,u));
}
}
bool done=false;
for(int i=0; i<R; i++)
{
Wsr[i]=ro0*(a-1)*std::pow(a,i-1);
if((Wsr[i]>1)&&(done==false))
{
ind1=i;
done =true;
}
}
for(int j=0; j<N; j++)
{
for(int i=0; i<M; i++)//mdf
{
double theta;
if(i>=ic)
theta=atan((double)(j-jc)/(double)(i-ic));
else
theta=atan((double)(j-jc)/(double)(i-ic))+CV_PI;
if(theta<0)
theta+=2*CV_PI;
ETAyx.at<float>(j,i)=(float)(q*theta);
double ro2=(j-jc)*(j-jc)+(i-ic)*(i-ic);
CSIyx.at<float>(j,i)=(float)(0.5*std::log(ro2/(ro0*ro0))/std::log(a));
}
}
for(int v=0; v<S; v++)
for(int u=ind1; u<R; u++)
{
//double sigma=Wsr[u]/2.0;
double sigma=Wsr[u]/3.0;//modf
int w=(int) floor(3*sigma+0.5);
w_ker_2D[v*R+u].w=w;
w_ker_2D[v*R+u].weights.resize((2*w+1)*(2*w+1));
double dx=Csri.at<float>(v,u)-Csr[v*R+u];
double dy=Rsri.at<float>(v,u)-Rsr[v*R+u];
double tot=0;
for(int j=0; j<2*w+1; j++)
for(int i=0; i<2*w+1; i++)
{
(w_ker_2D[v*R+u].weights)[j*(2*w+1)+i]=std::exp(-(std::pow(i-w-dx, 2)+std::pow(j-w-dy, 2))/(2*sigma*sigma));
tot+=(w_ker_2D[v*R+u].weights)[j*(2*w+1)+i];
}
for(int j=0; j<(2*w+1); j++)
for(int i=0; i<(2*w+1); i++)
(w_ker_2D[v*R+u].weights)[j*(2*w+1)+i]/=tot;
}
}
const Mat LogPolar_Overlapping::to_cortical(const Mat &source)
{
Mat out(S,R,CV_8UC1,Scalar(0));
Mat source_border;
copyMakeBorder(source,source_border,top,bottom,left,right,BORDER_CONSTANT,Scalar(0));
remap(source_border,out,Csri,Rsri,INTER_LINEAR);
int wm=w_ker_2D[R-1].w;
std::vector<int> IMG((M+2*wm+1)*(N+2*wm+1), 0);
for(int j=0; j<N; j++)
for(int i=0; i<M; i++)
IMG[(M+2*wm+1)*(j+wm)+i+wm]=source_border.at<uchar>(j,i);
for(int v=0; v<S; v++)
for(int u=ind1; u<R; u++)
{
int w=w_ker_2D[v*R+u].w;
double tmp=0;
for(int rf=0; rf<(2*w+1); rf++)
{
for(int cf=0; cf<(2*w+1); cf++)
{
double weight=(w_ker_2D[v*R+u]).weights[rf*(2*w+1)+cf];
tmp+=IMG[(M+2*wm+1)*((rf-w)+Rsr[v*R+u]+wm)+((cf-w)+Csr[v*R+u]+wm)]*weight;
}
}
out.at<uchar>(v,u)=(uchar) floor(tmp+0.5);
}
return out;
}
const Mat LogPolar_Overlapping::to_cartesian(const Mat &source)
{
Mat out(N,M,CV_8UC1,Scalar(0));
Mat source_border;
copyMakeBorder(source,source_border,0,1,0,0,BORDER_CONSTANT,Scalar(0));
Mat rowS = source_border.row(S);
source_border.row(0).copyTo(rowS);
remap(source_border,out,CSIyx,ETAyx,INTER_LINEAR);
int wm=w_ker_2D[R-1].w;
std::vector<double> IMG((N+2*wm+1)*(M+2*wm+1), 0.);
std::vector<double> NOR((N+2*wm+1)*(M+2*wm+1), 0.);
for(int v=0; v<S; v++)
for(int u=ind1; u<R; u++)
{
int w=w_ker_2D[v*R+u].w;
for(int j=0; j<(2*w+1); j++)
{
for(int i=0; i<(2*w+1); i++)
{
int ind=(M+2*wm+1)*((j-w)+Rsr[v*R+u]+wm)+(i-w)+Csr[v*R+u]+wm;
IMG[ind]+=((w_ker_2D[v*R+u]).weights[j*(2*w+1)+i])*source.at<uchar>(v, u);
NOR[ind]+=((w_ker_2D[v*R+u]).weights[j*(2*w+1)+i]);
}
}
}
for(int i=0; i<((N+2*wm+1)*(M+2*wm+1)); i++)
IMG[i]/=NOR[i];
//int xc=M/2-1, yc=N/2-1;
for(int j=wm; j<N+wm; j++)
for(int i=wm; i<M+wm; i++)
{
/*if(NOR[(M+2*wm+1)*j+i]>0)
ret[M*(j-wm)+i-wm]=(int) floor(IMG[(M+2*wm+1)*j+i]+0.5);*/
//int ro=(int)floor(std::sqrt((double)((j-wm-yc)*(j-wm-yc)+(i-wm-xc)*(i-wm-xc))));
int csi=(int) floor(CSIyx.at<float>(j-wm,i-wm));
if((csi>=(ind1-(w_ker_2D[ind1]).w))&&(csi<R))
out.at<uchar>(j-wm,i-wm)=(uchar) floor(IMG[(M+2*wm+1)*j+i]+0.5);
}
Mat out_cropped=out(Range(top,N-1-bottom),Range(left,M-1-right));
return out_cropped;
}
LogPolar_Overlapping::~LogPolar_Overlapping()
{
}
//----------------------------------------adjacent---------------------------------------
LogPolar_Adjacent::LogPolar_Adjacent(int w, int h, Point2i center, int _R, double _ro0, double smin, int full, int _s, int sp)
{
if ( (center.x!=w/2 || center.y!=h/2) && full==0) full=1;
if (center.x<0) center.x=0;
if (center.y<0) center.y=0;
if (center.x>=w) center.x=w-1;
if (center.y>=h) center.y=h-1;
if (full){
int rtmp;
if (center.x<=w/2 && center.y>=h/2)
rtmp=(int)std::sqrt((float)center.y*center.y + (float)(w-center.x)*(w-center.x));
else if (center.x>=w/2 && center.y>=h/2)
rtmp=(int)std::sqrt((float)center.y*center.y + (float)center.x*center.x);
else if (center.x>=w/2 && center.y<=h/2)
rtmp=(int)std::sqrt((float)(h-center.y)*(h-center.y) + (float)center.x*center.x);
else //if (center.x<=w/2 && center.y<=h/2)
rtmp=(int)std::sqrt((float)(h-center.y)*(h-center.y) + (float)(w-center.x)*(w-center.x));
M=2*rtmp; N=2*rtmp;
top = M/2 - center.y;
bottom = M/2 - (h-center.y);
left = M/2 - center.x;
right = M/2 - (w - center.x);
}else{
top=bottom=left=right=0;
M=w; N=h;
}
if (sp){
int jc=M/2-1, ic=N/2-1;
int _romax=std::min(ic, jc);
double _a=std::exp(std::log((double)(_romax/2-1)/(double)ro0)/(double)R);
S=(int) floor(2*CV_PI/(_a-1)+0.5);
}
create_map(M, N, _R, _s, _ro0, smin);
}
void LogPolar_Adjacent::create_map(int _M, int _n, int _R, int _s, double _ro0, double smin)
{
M=_M;
N=_n;
R=_R;
S=_s;
ro0=_ro0;
romax=std::min(M/2.0, N/2.0);
a=std::exp(std::log(romax/ro0)/(double)R);
q=S/(2*CV_PI);
A.resize(R*S);
L.resize(M*N);
for(int i=0; i<R*S; i++)
A[i]=0;
double xc=M/2.0, yc=N/2.0;
for(int j=0; j<N; j++)
for(int i=0; i<M; i++)
{
double x=i+0.5-xc, y=j+0.5-yc;
subdivide_recursively(x, y, i, j, 1, smin);
}
}
void LogPolar_Adjacent::subdivide_recursively(double x, double y, int i, int j, double length, double smin)
{
if(length<=smin)
{
int u, v;
if(get_uv(x, y, u, v))
{
pixel p;
p.u=u;
p.v=v;
p.a=length*length;
L[M*j+i].push_back(p);
A[v*R+u]+=length*length;
}
}
if(length>smin)
{
double xs[4], ys[4];
int us[4], vs[4];
xs[0]=xs[3]=x+length/4.0;
xs[1]=xs[2]=x-length/4.0;
ys[1]=ys[0]=y+length/4.0;
ys[2]=ys[3]=y-length/4.0;
for(int z=0; z<4; z++)
get_uv(xs[z], ys[z], us[z], vs[z]);
bool c=true;
for(int w=1; w<4; w++)
{
if(us[w]!=us[w-1])
c=false;
if(vs[w]!=vs[w-1])
c=false;
}
if(c)
{
if(us[0]!=-1)
{
pixel p;
p.u=us[0];
p.v=vs[0];
p.a=length*length;
L[M*j+i].push_back(p);
A[vs[0]*R+us[0]]+=length*length;
}
}
else
{
for(int z=0; z<4; z++)
if(us[z]!=-1)
subdivide_recursively(xs[z], ys[z], i, j, length/2.0, smin);
}
}
}
const Mat LogPolar_Adjacent::to_cortical(const Mat &source)
{
Mat source_border;
copyMakeBorder(source,source_border,top,bottom,left,right,BORDER_CONSTANT,Scalar(0));
std::vector<double> map(R*S, 0.);
for(int j=0; j<N; j++)
for(int i=0; i<M; i++)
{
for(size_t z=0; z<(L[M*j+i]).size(); z++)
{
map[R*((L[M*j+i])[z].v)+((L[M*j+i])[z].u)]+=((L[M*j+i])[z].a)*(source_border.at<uchar>(j,i));
}
}
for(int i=0; i<R*S; i++)
map[i]/=A[i];
Mat out(S,R,CV_8UC1,Scalar(0));
for(int i=0; i<S; i++)
for(int j=0;j<R;j++)
out.at<uchar>(i,j)=(uchar) floor(map[i*R+j]+0.5);
return out;
}
const Mat LogPolar_Adjacent::to_cartesian(const Mat &source)
{
std::vector<double> map(M*N, 0.);
for(int j=0; j<N; j++)
for(int i=0; i<M; i++)
{
for(size_t z=0; z<(L[M*j+i]).size(); z++)
{
map[M*j+i]+=(L[M*j+i])[z].a*source.at<uchar>((L[M*j+i])[z].v,(L[M*j+i])[z].u);
}
}
Mat out(N,M,CV_8UC1,Scalar(0));
for(int i=0; i<N; i++)
for(int j=0; j<M; j++)
out.at<uchar>(i,j)=(uchar) floor(map[i*M+j]+0.5);
Mat out_cropped=out(Range(top,N-1-bottom),Range(left,M-1-right));
return out_cropped;
}
bool LogPolar_Adjacent::get_uv(double x, double y, int&u, int&v)
{
double ro=std::sqrt(x*x+y*y), theta;
if(x>0)
theta=atan(y/x);
else
theta=atan(y/x)+CV_PI;
if(ro<ro0||ro>romax)
{
u=-1;
v=-1;
return false;
}
else
{
u= (int) floor(std::log(ro/ro0)/std::log(a));
if(theta>=0)
v= (int) floor(q*theta);
else
v= (int) floor(q*(theta+2*CV_PI));
return true;
}
}
LogPolar_Adjacent::~LogPolar_Adjacent()
{
}
}
modules/contrib/src/octree.cpp
-344
View File
@@ -1,344 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "precomp.hpp"
#include <limits>
namespace
{
using namespace cv;
const size_t MAX_STACK_SIZE = 255;
const size_t MAX_LEAFS = 8;
bool checkIfNodeOutsideSphere(const Octree::Node& node, const Point3f& c, float r)
{
if (node.x_max < (c.x - r) || node.y_max < (c.y - r) || node.z_max < (c.z - r))
return true;
if ((c.x + r) < node.x_min || (c.y + r) < node.y_min || (c.z + r) < node.z_min)
return true;
return false;
}
bool checkIfNodeInsideSphere(const Octree::Node& node, const Point3f& c, float r)
{
r *= r;
float d2_xmin = (node.x_min - c.x) * (node.x_min - c.x);
float d2_ymin = (node.y_min - c.y) * (node.y_min - c.y);
float d2_zmin = (node.z_min - c.z) * (node.z_min - c.z);
if (d2_xmin + d2_ymin + d2_zmin > r)
return false;
float d2_zmax = (node.z_max - c.z) * (node.z_max - c.z);
if (d2_xmin + d2_ymin + d2_zmax > r)
return false;
float d2_ymax = (node.y_max - c.y) * (node.y_max - c.y);
if (d2_xmin + d2_ymax + d2_zmin > r)
return false;
if (d2_xmin + d2_ymax + d2_zmax > r)
return false;
float d2_xmax = (node.x_max - c.x) * (node.x_max - c.x);
if (d2_xmax + d2_ymin + d2_zmin > r)
return false;
if (d2_xmax + d2_ymin + d2_zmax > r)
return false;
if (d2_xmax + d2_ymax + d2_zmin > r)
return false;
if (d2_xmax + d2_ymax + d2_zmax > r)
return false;
return true;
}
void fillMinMax(const std::vector<Point3f>& points, Octree::Node& node)
{
node.x_max = node.y_max = node.z_max = std::numeric_limits<float>::min();
node.x_min = node.y_min = node.z_min = std::numeric_limits<float>::max();
for (size_t i = 0; i < points.size(); ++i)
{
const Point3f& point = points[i];
if (node.x_max < point.x)
node.x_max = point.x;
if (node.y_max < point.y)
node.y_max = point.y;
if (node.z_max < point.z)
node.z_max = point.z;
if (node.x_min > point.x)
node.x_min = point.x;
if (node.y_min > point.y)
node.y_min = point.y;
if (node.z_min > point.z)
node.z_min = point.z;
}
}
size_t findSubboxForPoint(const Point3f& point, const Octree::Node& node)
{
size_t ind_x = point.x < (node.x_max + node.x_min) / 2 ? 0 : 1;
size_t ind_y = point.y < (node.y_max + node.y_min) / 2 ? 0 : 1;
size_t ind_z = point.z < (node.z_max + node.z_min) / 2 ? 0 : 1;
return (ind_x << 2) + (ind_y << 1) + (ind_z << 0);
}
void initChildBox(const Octree::Node& parent, size_t boxIndex, Octree::Node& child)
{
child.x_min = child.x_max = (parent.x_max + parent.x_min) / 2;
child.y_min = child.y_max = (parent.y_max + parent.y_min) / 2;
child.z_min = child.z_max = (parent.z_max + parent.z_min) / 2;
if ((boxIndex >> 0) & 1)
child.z_max = parent.z_max;
else
child.z_min = parent.z_min;
if ((boxIndex >> 1) & 1)
child.y_max = parent.y_max;
else
child.y_min = parent.y_min;
if ((boxIndex >> 2) & 1)
child.x_max = parent.x_max;
else
child.x_min = parent.x_min;
}
}//namespace
////////////////////////////////////////////////////////////////////////////////////////
/////////////////////////// Octree //////////////////////////////////////
////////////////////////////////////////////////////////////////////////////////////////
namespace cv
{
Octree::Octree()
{
}
Octree::Octree(const std::vector<Point3f>& points3d, int maxLevels, int _minPoints)
{
buildTree(points3d, maxLevels, _minPoints);
}
Octree::~Octree()
{
}
void Octree::getPointsWithinSphere(const Point3f& center, float radius, std::vector<Point3f>& out) const
{
out.clear();
if (nodes.empty())
return;
int stack[MAX_STACK_SIZE];
int pos = 0;
stack[pos] = 0;
while (pos >= 0)
{
const Node& cur = nodes[stack[pos--]];
if (checkIfNodeOutsideSphere(cur, center, radius))
continue;
if (checkIfNodeInsideSphere(cur, center, radius))
{
size_t sz = out.size();
out.resize(sz + cur.end - cur.begin);
for (int i = cur.begin; i < cur.end; ++i)
out[sz++] = points[i];
continue;
}
if (cur.isLeaf)
{
double r2 = radius * radius;
size_t sz = out.size();
out.resize(sz + (cur.end - cur.begin));
for (int i = cur.begin; i < cur.end; ++i)
{
const Point3f& point = points[i];
double dx = (point.x - center.x);
double dy = (point.y - center.y);
double dz = (point.z - center.z);
double dist2 = dx * dx + dy * dy + dz * dz;
if (dist2 < r2)
out[sz++] = point;
};
out.resize(sz);
continue;
}
if (cur.children[0])
stack[++pos] = cur.children[0];
if (cur.children[1])
stack[++pos] = cur.children[1];
if (cur.children[2])
stack[++pos] = cur.children[2];
if (cur.children[3])
stack[++pos] = cur.children[3];
if (cur.children[4])
stack[++pos] = cur.children[4];
if (cur.children[5])
stack[++pos] = cur.children[5];
if (cur.children[6])
stack[++pos] = cur.children[6];
if (cur.children[7])
stack[++pos] = cur.children[7];
}
}
void Octree::buildTree(const std::vector<Point3f>& points3d, int maxLevels, int _minPoints)
{
CV_Assert((size_t)maxLevels * 8 < MAX_STACK_SIZE);
points.resize(points3d.size());
std::copy(points3d.begin(), points3d.end(), points.begin());
minPoints = _minPoints;
nodes.clear();
nodes.push_back(Node());
Node& root = nodes[0];
fillMinMax(points, root);
root.isLeaf = true;
root.maxLevels = maxLevels;
root.begin = 0;
root.end = (int)points.size();
for (size_t i = 0; i < MAX_LEAFS; i++)
root.children[i] = 0;
if (maxLevels != 1 && (root.end - root.begin) > _minPoints)
{
root.isLeaf = false;
buildNext(0);
}
}
void Octree::buildNext(size_t nodeInd)
{
size_t size = nodes[nodeInd].end - nodes[nodeInd].begin;
std::vector<size_t> boxBorders(MAX_LEAFS+1, 0);
std::vector<size_t> boxIndices(size);
std::vector<Point3f> tempPoints(size);
for (int i = nodes[nodeInd].begin, j = 0; i < nodes[nodeInd].end; ++i, ++j)
{
const Point3f& p = points[i];
size_t subboxInd = findSubboxForPoint(p, nodes[nodeInd]);
boxBorders[subboxInd+1]++;
boxIndices[j] = subboxInd;
tempPoints[j] = p;
}
for (size_t i = 1; i < boxBorders.size(); ++i)
boxBorders[i] += boxBorders[i-1];
std::vector<size_t> writeInds(boxBorders.begin(), boxBorders.end());
for (size_t i = 0; i < size; ++i)
{
size_t boxIndex = boxIndices[i];
Point3f& curPoint = tempPoints[i];
size_t copyTo = nodes[nodeInd].begin + writeInds[boxIndex]++;
points[copyTo] = curPoint;
}
for (size_t i = 0; i < MAX_LEAFS; ++i)
{
if (boxBorders[i] == boxBorders[i+1])
continue;
nodes.push_back(Node());
Node& child = nodes.back();
initChildBox(nodes[nodeInd], i, child);
child.isLeaf = true;
child.maxLevels = nodes[nodeInd].maxLevels - 1;
child.begin = nodes[nodeInd].begin + (int)boxBorders[i+0];
child.end = nodes[nodeInd].begin + (int)boxBorders[i+1];
for (size_t k = 0; k < MAX_LEAFS; k++)
child.children[k] = 0;
nodes[nodeInd].children[i] = (int)(nodes.size() - 1);
if (child.maxLevels != 1 && (child.end - child.begin) > minPoints)
{
child.isLeaf = false;
buildNext(nodes.size() - 1);
}
}
}
}
modules/contrib/src/openfabmap.cpp
-778
View File
@@ -1,778 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
// This file originates from the openFABMAP project:
// [http://code.google.com/p/openfabmap/]
//
// For published work which uses all or part of OpenFABMAP, please cite:
// [http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6224843]
//
// Original Algorithm by Mark Cummins and Paul Newman:
// [http://ijr.sagepub.com/content/27/6/647.short]
// [http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=5613942]
// [http://ijr.sagepub.com/content/30/9/1100.abstract]
//
// License Agreement
//
// Copyright (C) 2012 Arren Glover [aj.glover@qut.edu.au] and
// Will Maddern [w.maddern@qut.edu.au], all rights reserved.
//
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "precomp.hpp"
#include "opencv2/contrib/openfabmap.hpp"
/*
Calculate the sum of two log likelihoods
*/
namespace cv {
namespace of2 {
static double logsumexp(double a, double b) {
return a > b ? std::log(1 + std::exp(b - a)) + a : std::log(1 + std::exp(a - b)) + b;
}
FabMap::FabMap(const Mat& _clTree, double _PzGe,
double _PzGNe, int _flags, int _numSamples) :
clTree(_clTree), PzGe(_PzGe), PzGNe(_PzGNe), flags(
_flags), numSamples(_numSamples) {
CV_Assert(flags & MEAN_FIELD || flags & SAMPLED);
CV_Assert(flags & NAIVE_BAYES || flags & CHOW_LIU);
if (flags & NAIVE_BAYES) {
PzGL = &FabMap::PzqGL;
} else {
PzGL = &FabMap::PzqGzpqL;
}
//check for a valid Chow-Liu tree
CV_Assert(clTree.type() == CV_64FC1);
cv::checkRange(clTree.row(0), false, NULL, 0, clTree.cols);
cv::checkRange(clTree.row(1), false, NULL, DBL_MIN, 1);
cv::checkRange(clTree.row(2), false, NULL, DBL_MIN, 1);
cv::checkRange(clTree.row(3), false, NULL, DBL_MIN, 1);
// TODO: Add default values for member variables
Pnew = 0.9;
sFactor = 0.99;
mBias = 0.5;
}
FabMap::~FabMap() {
}
const std::vector<cv::Mat>& FabMap::getTrainingImgDescriptors() const {
return trainingImgDescriptors;
}
const std::vector<cv::Mat>& FabMap::getTestImgDescriptors() const {
return testImgDescriptors;
}
void FabMap::addTraining(const Mat& queryImgDescriptor) {
CV_Assert(!queryImgDescriptor.empty());
std::vector<Mat> queryImgDescriptors;
for (int i = 0; i < queryImgDescriptor.rows; i++) {
queryImgDescriptors.push_back(queryImgDescriptor.row(i));
}
addTraining(queryImgDescriptors);
}
void FabMap::addTraining(const std::vector<Mat>& queryImgDescriptors) {
for (size_t i = 0; i < queryImgDescriptors.size(); i++) {
CV_Assert(!queryImgDescriptors[i].empty());
CV_Assert(queryImgDescriptors[i].rows == 1);
CV_Assert(queryImgDescriptors[i].cols == clTree.cols);
CV_Assert(queryImgDescriptors[i].type() == CV_32F);
trainingImgDescriptors.push_back(queryImgDescriptors[i]);
}
}
void FabMap::add(const cv::Mat& queryImgDescriptor) {
CV_Assert(!queryImgDescriptor.empty());
std::vector<Mat> queryImgDescriptors;
for (int i = 0; i < queryImgDescriptor.rows; i++) {
queryImgDescriptors.push_back(queryImgDescriptor.row(i));
}
add(queryImgDescriptors);
}
void FabMap::add(const std::vector<cv::Mat>& queryImgDescriptors) {
for (size_t i = 0; i < queryImgDescriptors.size(); i++) {
CV_Assert(!queryImgDescriptors[i].empty());
CV_Assert(queryImgDescriptors[i].rows == 1);
CV_Assert(queryImgDescriptors[i].cols == clTree.cols);
CV_Assert(queryImgDescriptors[i].type() == CV_32F);
testImgDescriptors.push_back(queryImgDescriptors[i]);
}
}
void FabMap::compare(const Mat& queryImgDescriptor,
std::vector<IMatch>& matches, bool addQuery,
const Mat& mask) {
CV_Assert(!queryImgDescriptor.empty());
std::vector<Mat> queryImgDescriptors;
for (int i = 0; i < queryImgDescriptor.rows; i++) {
queryImgDescriptors.push_back(queryImgDescriptor.row(i));
}
compare(queryImgDescriptors,matches,addQuery,mask);
}
void FabMap::compare(const Mat& queryImgDescriptor,
const Mat& testImgDescriptor, std::vector<IMatch>& matches,
const Mat& mask) {
CV_Assert(!queryImgDescriptor.empty());
std::vector<Mat> queryImgDescriptors;
for (int i = 0; i < queryImgDescriptor.rows; i++) {
queryImgDescriptors.push_back(queryImgDescriptor.row(i));
}
CV_Assert(!testImgDescriptor.empty());
std::vector<Mat> _testImgDescriptors;
for (int i = 0; i < testImgDescriptor.rows; i++) {
_testImgDescriptors.push_back(testImgDescriptor.row(i));
}
compare(queryImgDescriptors,_testImgDescriptors,matches,mask);
}
void FabMap::compare(const Mat& queryImgDescriptor,
const std::vector<Mat>& _testImgDescriptors,
std::vector<IMatch>& matches, const Mat& mask) {
CV_Assert(!queryImgDescriptor.empty());
std::vector<Mat> queryImgDescriptors;
for (int i = 0; i < queryImgDescriptor.rows; i++) {
queryImgDescriptors.push_back(queryImgDescriptor.row(i));
}
compare(queryImgDescriptors,_testImgDescriptors,matches,mask);
}
void FabMap::compare(const std::vector<Mat>& queryImgDescriptors,
std::vector<IMatch>& matches, bool addQuery, const Mat& /*mask*/) {
// TODO: add first query if empty (is this necessary)
for (size_t i = 0; i < queryImgDescriptors.size(); i++) {
CV_Assert(!queryImgDescriptors[i].empty());
CV_Assert(queryImgDescriptors[i].rows == 1);
CV_Assert(queryImgDescriptors[i].cols == clTree.cols);
CV_Assert(queryImgDescriptors[i].type() == CV_32F);
// TODO: add mask
compareImgDescriptor(queryImgDescriptors[i],
(int)i, testImgDescriptors, matches);
if (addQuery)
add(queryImgDescriptors[i]);
}
}
void FabMap::compare(const std::vector<Mat>& queryImgDescriptors,
const std::vector<Mat>& _testImgDescriptors,
std::vector<IMatch>& matches, const Mat& /*mask*/) {
CV_Assert(!(flags & MOTION_MODEL));
for (size_t i = 0; i < _testImgDescriptors.size(); i++) {
CV_Assert(!_testImgDescriptors[i].empty());
CV_Assert(_testImgDescriptors[i].rows == 1);
CV_Assert(_testImgDescriptors[i].cols == clTree.cols);
CV_Assert(_testImgDescriptors[i].type() == CV_32F);
}
for (size_t i = 0; i < queryImgDescriptors.size(); i++) {
CV_Assert(!queryImgDescriptors[i].empty());
CV_Assert(queryImgDescriptors[i].rows == 1);
CV_Assert(queryImgDescriptors[i].cols == clTree.cols);
CV_Assert(queryImgDescriptors[i].type() == CV_32F);
// TODO: add mask
compareImgDescriptor(queryImgDescriptors[i],
(int)i, _testImgDescriptors, matches);
}
}
void FabMap::compareImgDescriptor(const Mat& queryImgDescriptor,
int queryIndex, const std::vector<Mat>& _testImgDescriptors,
std::vector<IMatch>& matches) {
std::vector<IMatch> queryMatches;
queryMatches.push_back(IMatch(queryIndex,-1,
getNewPlaceLikelihood(queryImgDescriptor),0));
getLikelihoods(queryImgDescriptor,_testImgDescriptors,queryMatches);
normaliseDistribution(queryMatches);
for (size_t j = 1; j < queryMatches.size(); j++) {
queryMatches[j].queryIdx = queryIndex;
}
matches.insert(matches.end(), queryMatches.begin(), queryMatches.end());
}
void FabMap::getLikelihoods(const Mat& /*queryImgDescriptor*/,
const std::vector<Mat>& /*testImgDescriptors*/, std::vector<IMatch>& /*matches*/) {
}
double FabMap::getNewPlaceLikelihood(const Mat& queryImgDescriptor) {
if (flags & MEAN_FIELD) {
double logP = 0;
bool zq, zpq;
if(flags & NAIVE_BAYES) {
for (int q = 0; q < clTree.cols; q++) {
zq = queryImgDescriptor.at<float>(0,q) > 0;
logP += std::log(Pzq(q, false) * PzqGeq(zq, false) +
Pzq(q, true) * PzqGeq(zq, true));
}
} else {
for (int q = 0; q < clTree.cols; q++) {
zq = queryImgDescriptor.at<float>(0,q) > 0;
zpq = queryImgDescriptor.at<float>(0,pq(q)) > 0;
double alpha, beta, p;
alpha = Pzq(q, zq) * PzqGeq(!zq, false) * PzqGzpq(q, !zq, zpq);
beta = Pzq(q, !zq) * PzqGeq(zq, false) * PzqGzpq(q, zq, zpq);
p = Pzq(q, false) * beta / (alpha + beta);
alpha = Pzq(q, zq) * PzqGeq(!zq, true) * PzqGzpq(q, !zq, zpq);
beta = Pzq(q, !zq) * PzqGeq(zq, true) * PzqGzpq(q, zq, zpq);
p += Pzq(q, true) * beta / (alpha + beta);
logP += std::log(p);
}
}
return logP;
}
if (flags & SAMPLED) {
CV_Assert(!trainingImgDescriptors.empty());
CV_Assert(numSamples > 0);
std::vector<Mat> sampledImgDescriptors;
// TODO: this method can result in the same sample being added
// multiple times. Is this desired?
for (int i = 0; i < numSamples; i++) {
int index = rand() % trainingImgDescriptors.size();
sampledImgDescriptors.push_back(trainingImgDescriptors[index]);
}
std::vector<IMatch> matches;
getLikelihoods(queryImgDescriptor,sampledImgDescriptors,matches);
double averageLogLikelihood = -DBL_MAX + matches.front().likelihood + 1;
for (int i = 0; i < numSamples; i++) {
averageLogLikelihood =
logsumexp(matches[i].likelihood, averageLogLikelihood);
}
return averageLogLikelihood - std::log((double)numSamples);
}
return 0;
}
void FabMap::normaliseDistribution(std::vector<IMatch>& matches) {
CV_Assert(!matches.empty());
if (flags & MOTION_MODEL) {
matches[0].match = matches[0].likelihood + std::log(Pnew);
if (priorMatches.size() > 2) {
matches[1].match = matches[1].likelihood;
matches[1].match += std::log(
(2 * (1-mBias) * priorMatches[1].match +
priorMatches[1].match +
2 * mBias * priorMatches[2].match) / 3);
for (size_t i = 2; i < priorMatches.size()-1; i++) {
matches[i].match = matches[i].likelihood;
matches[i].match += std::log(
(2 * (1-mBias) * priorMatches[i-1].match +
priorMatches[i].match +
2 * mBias * priorMatches[i+1].match)/3);
}
matches[priorMatches.size()-1].match =
matches[priorMatches.size()-1].likelihood;
matches[priorMatches.size()-1].match += std::log(
(2 * (1-mBias) * priorMatches[priorMatches.size()-2].match +
priorMatches[priorMatches.size()-1].match +
2 * mBias * priorMatches[priorMatches.size()-1].match)/3);
for(size_t i = priorMatches.size(); i < matches.size(); i++) {
matches[i].match = matches[i].likelihood;
}
} else {
for(size_t i = 1; i < matches.size(); i++) {
matches[i].match = matches[i].likelihood;
}
}
double logsum = -DBL_MAX + matches.front().match + 1;
//calculate the normalising constant
for (size_t i = 0; i < matches.size(); i++) {
logsum = logsumexp(logsum, matches[i].match);
}
//normalise
for (size_t i = 0; i < matches.size(); i++) {
matches[i].match = std::exp(matches[i].match - logsum);
}
//smooth final probabilities
for (size_t i = 0; i < matches.size(); i++) {
matches[i].match = sFactor*matches[i].match +
(1 - sFactor)/matches.size();
}
//update our location priors
priorMatches = matches;
} else {
double logsum = -DBL_MAX + matches.front().likelihood + 1;
for (size_t i = 0; i < matches.size(); i++) {
logsum = logsumexp(logsum, matches[i].likelihood);
}
for (size_t i = 0; i < matches.size(); i++) {
matches[i].match = std::exp(matches[i].likelihood - logsum);
}
for (size_t i = 0; i < matches.size(); i++) {
matches[i].match = sFactor*matches[i].match +
(1 - sFactor)/matches.size();
}
}
}
int FabMap::pq(int q) {
return (int)clTree.at<double>(0,q);
}
double FabMap::Pzq(int q, bool zq) {
return (zq) ? clTree.at<double>(1,q) : 1 - clTree.at<double>(1,q);
}
double FabMap::PzqGzpq(int q, bool zq, bool zpq) {
if (zpq) {
return (zq) ? clTree.at<double>(2,q) : 1 - clTree.at<double>(2,q);
} else {
return (zq) ? clTree.at<double>(3,q) : 1 - clTree.at<double>(3,q);
}
}
double FabMap::PzqGeq(bool zq, bool eq) {
if (eq) {
return (zq) ? PzGe : 1 - PzGe;
} else {
return (zq) ? PzGNe : 1 - PzGNe;
}
}
double FabMap::PeqGL(int q, bool Lzq, bool eq) {
double alpha, beta;
alpha = PzqGeq(Lzq, true) * Pzq(q, true);
beta = PzqGeq(Lzq, false) * Pzq(q, false);
if (eq) {
return alpha / (alpha + beta);
} else {
return 1 - (alpha / (alpha + beta));
}
}
double FabMap::PzqGL(int q, bool zq, bool /*zpq*/, bool Lzq) {
return PeqGL(q, Lzq, false) * PzqGeq(zq, false) +
PeqGL(q, Lzq, true) * PzqGeq(zq, true);
}
double FabMap::PzqGzpqL(int q, bool zq, bool zpq, bool Lzq) {
double p;
double alpha, beta;
alpha = Pzq(q, zq) * PzqGeq(!zq, false) * PzqGzpq(q, !zq, zpq);
beta = Pzq(q, !zq) * PzqGeq( zq, false) * PzqGzpq(q, zq, zpq);
p = PeqGL(q, Lzq, false) * beta / (alpha + beta);
alpha = Pzq(q, zq) * PzqGeq(!zq, true) * PzqGzpq(q, !zq, zpq);
beta = Pzq(q, !zq) * PzqGeq( zq, true) * PzqGzpq(q, zq, zpq);
p += PeqGL(q, Lzq, true) * beta / (alpha + beta);
return p;
}
FabMap1::FabMap1(const Mat& _clTree, double _PzGe, double _PzGNe, int _flags,
int _numSamples) : FabMap(_clTree, _PzGe, _PzGNe, _flags,
_numSamples) {
}
FabMap1::~FabMap1() {
}
void FabMap1::getLikelihoods(const Mat& queryImgDescriptor,
const std::vector<Mat>& testImageDescriptors, std::vector<IMatch>& matches) {
for (size_t i = 0; i < testImageDescriptors.size(); i++) {
bool zq, zpq, Lzq;
double logP = 0;
for (int q = 0; q < clTree.cols; q++) {
zq = queryImgDescriptor.at<float>(0,q) > 0;
zpq = queryImgDescriptor.at<float>(0,pq(q)) > 0;
Lzq = testImageDescriptors[i].at<float>(0,q) > 0;
logP += std::log((this->*PzGL)(q, zq, zpq, Lzq));
}
matches.push_back(IMatch(0,(int)i,logP,0));
}
}
FabMapLUT::FabMapLUT(const Mat& _clTree, double _PzGe, double _PzGNe,
int _flags, int _numSamples, int _precision) :
FabMap(_clTree, _PzGe, _PzGNe, _flags, _numSamples), precision(_precision) {
int nWords = clTree.cols;
double precFactor = (double)std::pow(10.0, precision);
table = new int[nWords][8];
for (int q = 0; q < nWords; q++) {
for (unsigned char i = 0; i < 8; i++) {
bool Lzq = (bool) ((i >> 2) & 0x01);
bool zq = (bool) ((i >> 1) & 0x01);
bool zpq = (bool) (i & 1);
table[q][i] = -(int)(std::log((this->*PzGL)(q, zq, zpq, Lzq))
* precFactor);
}
}
}
FabMapLUT::~FabMapLUT() {
delete[] table;
}
void FabMapLUT::getLikelihoods(const Mat& queryImgDescriptor,
const std::vector<Mat>& testImageDescriptors, std::vector<IMatch>& matches) {
double precFactor = (double)std::pow(10.0, -precision);
for (size_t i = 0; i < testImageDescriptors.size(); i++) {
unsigned long long int logP = 0;
for (int q = 0; q < clTree.cols; q++) {
logP += table[q][(queryImgDescriptor.at<float>(0,pq(q)) > 0) +
((queryImgDescriptor.at<float>(0, q) > 0) << 1) +
((testImageDescriptors[i].at<float>(0,q) > 0) << 2)];
}
matches.push_back(IMatch(0,(int)i,-precFactor*(double)logP,0));
}
}
FabMapFBO::FabMapFBO(const Mat& _clTree, double _PzGe, double _PzGNe,
int _flags, int _numSamples, double _rejectionThreshold,
double _PsGd, int _bisectionStart, int _bisectionIts) :
FabMap(_clTree, _PzGe, _PzGNe, _flags, _numSamples), PsGd(_PsGd),
rejectionThreshold(_rejectionThreshold), bisectionStart(_bisectionStart),
bisectionIts(_bisectionIts) {
}
FabMapFBO::~FabMapFBO() {
}
void FabMapFBO::getLikelihoods(const Mat& queryImgDescriptor,
const std::vector<Mat>& testImageDescriptors, std::vector<IMatch>& matches) {
std::multiset<WordStats> wordData;
setWordStatistics(queryImgDescriptor, wordData);
std::vector<int> matchIndices;
std::vector<IMatch> queryMatches;
for (size_t i = 0; i < testImageDescriptors.size(); i++) {
queryMatches.push_back(IMatch(0,(int)i,0,0));
matchIndices.push_back((int)i);
}
double currBest = -DBL_MAX;
double bailedOut = DBL_MAX;
for (std::multiset<WordStats>::iterator wordIter = wordData.begin();
wordIter != wordData.end(); wordIter++) {
bool zq = queryImgDescriptor.at<float>(0,wordIter->q) > 0;
bool zpq = queryImgDescriptor.at<float>(0,pq(wordIter->q)) > 0;
currBest = -DBL_MAX;
for (size_t i = 0; i < matchIndices.size(); i++) {
bool Lzq =
testImageDescriptors[matchIndices[i]].at<float>(0,wordIter->q) > 0;
queryMatches[matchIndices[i]].likelihood +=
std::log((this->*PzGL)(wordIter->q,zq,zpq,Lzq));
currBest =
std::max(queryMatches[matchIndices[i]].likelihood, currBest);
}
if (matchIndices.size() == 1)
continue;
double delta = std::max(limitbisection(wordIter->V, wordIter->M),
-std::log(rejectionThreshold));
std::vector<int>::iterator matchIter = matchIndices.begin();
while (matchIter != matchIndices.end()) {
if (currBest - queryMatches[*matchIter].likelihood > delta) {
queryMatches[*matchIter].likelihood = bailedOut;
matchIter = matchIndices.erase(matchIter);
} else {
matchIter++;
}
}
}
for (size_t i = 0; i < queryMatches.size(); i++) {
if (queryMatches[i].likelihood == bailedOut) {
queryMatches[i].likelihood = currBest + std::log(rejectionThreshold);
}
}
matches.insert(matches.end(), queryMatches.begin(), queryMatches.end());
}
void FabMapFBO::setWordStatistics(const Mat& queryImgDescriptor,
std::multiset<WordStats>& wordData) {
//words are sorted according to information = -ln(P(zq|zpq))
//in non-log format this is lowest probability first
for (int q = 0; q < clTree.cols; q++) {
wordData.insert(WordStats(q,PzqGzpq(q,
queryImgDescriptor.at<float>(0,q) > 0,
queryImgDescriptor.at<float>(0,pq(q)) > 0)));
}
double d = 0, V = 0, M = 0;
bool zq, zpq;
for (std::multiset<WordStats>::reverse_iterator wordIter =
wordData.rbegin();
wordIter != wordData.rend(); wordIter++) {
zq = queryImgDescriptor.at<float>(0,wordIter->q) > 0;
zpq = queryImgDescriptor.at<float>(0,pq(wordIter->q)) > 0;
d = std::log((this->*PzGL)(wordIter->q, zq, zpq, true)) -
std::log((this->*PzGL)(wordIter->q, zq, zpq, false));
V += std::pow(d, 2.0) * 2 *
(Pzq(wordIter->q, true) - std::pow(Pzq(wordIter->q, true), 2.0));
M = std::max(M, fabs(d));
wordIter->V = V;
wordIter->M = M;
}
}
double FabMapFBO::limitbisection(double v, double m) {
double midpoint, left_val, mid_val;
double left = 0, right = bisectionStart;
left_val = bennettInequality(v, m, left) - PsGd;
for(int i = 0; i < bisectionIts; i++) {
midpoint = (left + right)*0.5;
mid_val = bennettInequality(v, m, midpoint)- PsGd;
if(left_val * mid_val > 0) {
left = midpoint;
left_val = mid_val;
} else {
right = midpoint;
}
}
return (right + left) * 0.5;
}
double FabMapFBO::bennettInequality(double v, double m, double delta) {
double DMonV = delta * m / v;
double f_delta = std::log(DMonV + std::sqrt(std::pow(DMonV, 2.0) + 1));
return std::exp((v / std::pow(m, 2.0))*(cosh(f_delta) - 1 - DMonV * f_delta));
}
bool FabMapFBO::compInfo(const WordStats& first, const WordStats& second) {
return first.info < second.info;
}
FabMap2::FabMap2(const Mat& _clTree, double _PzGe, double _PzGNe,
int _flags) :
FabMap(_clTree, _PzGe, _PzGNe, _flags) {
CV_Assert(flags & SAMPLED);
children.resize(clTree.cols);
for (int q = 0; q < clTree.cols; q++) {
d1.push_back(std::log((this->*PzGL)(q, false, false, true) /
(this->*PzGL)(q, false, false, false)));
d2.push_back(std::log((this->*PzGL)(q, false, true, true) /
(this->*PzGL)(q, false, true, false)) - d1[q]);
d3.push_back(std::log((this->*PzGL)(q, true, false, true) /
(this->*PzGL)(q, true, false, false))- d1[q]);
d4.push_back(std::log((this->*PzGL)(q, true, true, true) /
(this->*PzGL)(q, true, true, false))- d1[q]);
children[pq(q)].push_back(q);
}
}
FabMap2::~FabMap2() {
}
void FabMap2::addTraining(const std::vector<Mat>& queryImgDescriptors) {
for (size_t i = 0; i < queryImgDescriptors.size(); i++) {
CV_Assert(!queryImgDescriptors[i].empty());
CV_Assert(queryImgDescriptors[i].rows == 1);
CV_Assert(queryImgDescriptors[i].cols == clTree.cols);
CV_Assert(queryImgDescriptors[i].type() == CV_32F);
trainingImgDescriptors.push_back(queryImgDescriptors[i]);
addToIndex(queryImgDescriptors[i], trainingDefaults, trainingInvertedMap);
}
}
void FabMap2::add(const std::vector<Mat>& queryImgDescriptors) {
for (size_t i = 0; i < queryImgDescriptors.size(); i++) {
CV_Assert(!queryImgDescriptors[i].empty());
CV_Assert(queryImgDescriptors[i].rows == 1);
CV_Assert(queryImgDescriptors[i].cols == clTree.cols);
CV_Assert(queryImgDescriptors[i].type() == CV_32F);
testImgDescriptors.push_back(queryImgDescriptors[i]);
addToIndex(queryImgDescriptors[i], testDefaults, testInvertedMap);
}
}
void FabMap2::getLikelihoods(const Mat& queryImgDescriptor,
const std::vector<Mat>& testImageDescriptors, std::vector<IMatch>& matches) {
if (&testImageDescriptors == &testImgDescriptors) {
getIndexLikelihoods(queryImgDescriptor, testDefaults, testInvertedMap,
matches);
} else {
CV_Assert(!(flags & MOTION_MODEL));
std::vector<double> defaults;
std::map<int, std::vector<int> > invertedMap;
for (size_t i = 0; i < testImageDescriptors.size(); i++) {
addToIndex(testImageDescriptors[i],defaults,invertedMap);
}
getIndexLikelihoods(queryImgDescriptor, defaults, invertedMap, matches);
}
}
double FabMap2::getNewPlaceLikelihood(const Mat& queryImgDescriptor) {
CV_Assert(!trainingImgDescriptors.empty());
std::vector<IMatch> matches;
getIndexLikelihoods(queryImgDescriptor, trainingDefaults,
trainingInvertedMap, matches);
double averageLogLikelihood = -DBL_MAX + matches.front().likelihood + 1;
for (size_t i = 0; i < matches.size(); i++) {
averageLogLikelihood =
logsumexp(matches[i].likelihood, averageLogLikelihood);
}
return averageLogLikelihood - std::log((double)trainingDefaults.size());
}
void FabMap2::addToIndex(const Mat& queryImgDescriptor,
std::vector<double>& defaults,
std::map<int, std::vector<int> >& invertedMap) {
defaults.push_back(0);
for (int q = 0; q < clTree.cols; q++) {
if (queryImgDescriptor.at<float>(0,q) > 0) {
defaults.back() += d1[q];
invertedMap[q].push_back((int)defaults.size()-1);
}
}
}
void FabMap2::getIndexLikelihoods(const Mat& queryImgDescriptor,
std::vector<double>& defaults,
std::map<int, std::vector<int> >& invertedMap,
std::vector<IMatch>& matches) {
std::vector<int>::iterator LwithI, child;
std::vector<double> likelihoods = defaults;
for (int q = 0; q < clTree.cols; q++) {
if (queryImgDescriptor.at<float>(0,q) > 0) {
for (LwithI = invertedMap[q].begin();
LwithI != invertedMap[q].end(); LwithI++) {
if (queryImgDescriptor.at<float>(0,pq(q)) > 0) {
likelihoods[*LwithI] += d4[q];
} else {
likelihoods[*LwithI] += d3[q];
}
}
for (child = children[q].begin(); child != children[q].end();
child++) {
if (queryImgDescriptor.at<float>(0,*child) == 0) {
for (LwithI = invertedMap[*child].begin();
LwithI != invertedMap[*child].end(); LwithI++) {
likelihoods[*LwithI] += d2[*child];
}
}
}
}
}
for (size_t i = 0; i < likelihoods.size(); i++) {
matches.push_back(IMatch(0,(int)i,likelihoods[i],0));
}
}
}
}
modules/contrib/src/polyfit.cpp
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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
// This original code was written by
// Onkar Raut
// Graduate Student,
// University of North Carolina at Charlotte
#include "precomp.hpp"
typedef double polyfit_type;
void cv::polyfit(const Mat& src_x, const Mat& src_y, Mat& dst, int order)
{
const int wdepth = DataType<polyfit_type>::depth;
int npoints = src_x.checkVector(1);
int nypoints = src_y.checkVector(1);
CV_Assert(npoints == nypoints && npoints >= order+1);
Mat srcX = Mat_<polyfit_type>(src_x), srcY = Mat_<polyfit_type>(src_y);
Mat X = Mat::zeros(order + 1, npoints, wdepth);
polyfit_type* pSrcX = (polyfit_type*)srcX.data;
polyfit_type* pXData = (polyfit_type*)X.data;
int stepX = (int)(X.step/X.elemSize1());
for (int y = 0; y < order + 1; ++y)
{
for (int x = 0; x < npoints; ++x)
{
if (y == 0)
pXData[x] = 1;
else if (y == 1)
pXData[x + stepX] = pSrcX[x];
else pXData[x + y*stepX] = pSrcX[x]* pXData[x + (y-1)*stepX];
}
}
Mat A, b, w;
mulTransposed(X, A, false);
b = X*srcY;
solve(A, b, w, DECOMP_SVD);
w.convertTo(dst, std::max(std::max(src_x.depth(), src_y.depth()), CV_32F));
}
modules/contrib/src/precomp.hpp
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@@ -1,64 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifndef __OPENCV_PRECOMP_H__
#define __OPENCV_PRECOMP_H__
#include "opencv2/contrib.hpp"
#include "opencv2/features2d.hpp"
#include "opencv2/objdetect.hpp"
#include "opencv2/imgproc.hpp"
#include "opencv2/core/utility.hpp"
#include "opencv2/core/private.hpp"
namespace cv
{
// special function to get pointer to constant valarray elements, since
// simple &arr[0] does not compile on VS2005/VS2008.
template<typename T> inline const T* get_data(const std::valarray<T>& arr)
{ return &((std::valarray<T>&)arr)[0]; }
}
#endif
modules/contrib/src/rgbdodometry.cpp
-635
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@@ -1,635 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "precomp.hpp"
#define SHOW_DEBUG_IMAGES 0
#include "opencv2/core.hpp"
#include "opencv2/calib3d.hpp"
#if SHOW_DEBUG_IMAGES
# include "opencv2/highgui.hpp"
#endif
#include <iostream>
#include <limits>
#if defined(HAVE_EIGEN) && EIGEN_WORLD_VERSION == 3
# ifdef ANDROID
template <typename Scalar> Scalar log2(Scalar v) { return std::log(v)/std::log(Scalar(2)); }
# endif
# if defined __GNUC__ && defined __APPLE__
# pragma GCC diagnostic ignored "-Wshadow"
# endif
# include <unsupported/Eigen/MatrixFunctions>
# include <Eigen/Dense>
#endif
using namespace cv;
inline static
void computeC_RigidBodyMotion( double* C, double dIdx, double dIdy, const Point3f& p3d, double fx, double fy )
{
double invz = 1. / p3d.z,
v0 = dIdx * fx * invz,
v1 = dIdy * fy * invz,
v2 = -(v0 * p3d.x + v1 * p3d.y) * invz;
C[0] = -p3d.z * v1 + p3d.y * v2;
C[1] = p3d.z * v0 - p3d.x * v2;
C[2] = -p3d.y * v0 + p3d.x * v1;
C[3] = v0;
C[4] = v1;
C[5] = v2;
}
inline static
void computeC_Rotation( double* C, double dIdx, double dIdy, const Point3f& p3d, double fx, double fy )
{
double invz = 1. / p3d.z,
v0 = dIdx * fx * invz,
v1 = dIdy * fy * invz,
v2 = -(v0 * p3d.x + v1 * p3d.y) * invz;
C[0] = -p3d.z * v1 + p3d.y * v2;
C[1] = p3d.z * v0 - p3d.x * v2;
C[2] = -p3d.y * v0 + p3d.x * v1;
}
inline static
void computeC_Translation( double* C, double dIdx, double dIdy, const Point3f& p3d, double fx, double fy )
{
double invz = 1. / p3d.z,
v0 = dIdx * fx * invz,
v1 = dIdy * fy * invz,
v2 = -(v0 * p3d.x + v1 * p3d.y) * invz;
C[0] = v0;
C[1] = v1;
C[2] = v2;
}
inline static
void computeProjectiveMatrix( const Mat& ksi, Mat& Rt )
{
CV_Assert( ksi.size() == Size(1,6) && ksi.type() == CV_64FC1 );
#if defined(HAVE_EIGEN) && EIGEN_WORLD_VERSION == 3 && (!defined _MSC_VER || !defined _M_X64 || _MSC_VER > 1500)
const double* ksi_ptr = reinterpret_cast<const double*>(ksi.ptr(0));
Eigen::Matrix<double,4,4> twist, g;
twist << 0., -ksi_ptr[2], ksi_ptr[1], ksi_ptr[3],
ksi_ptr[2], 0., -ksi_ptr[0], ksi_ptr[4],
-ksi_ptr[1], ksi_ptr[0], 0, ksi_ptr[5],
0., 0., 0., 0.;
g = twist.exp();
eigen2cv(g, Rt);
#else
// for infinitesimal transformation
Rt = Mat::eye(4, 4, CV_64FC1);
Mat R = Rt(Rect(0,0,3,3));
Mat rvec = ksi.rowRange(0,3);
Rodrigues( rvec, R );
Rt.at<double>(0,3) = ksi.at<double>(3);
Rt.at<double>(1,3) = ksi.at<double>(4);
Rt.at<double>(2,3) = ksi.at<double>(5);
#endif
}
static
void cvtDepth2Cloud( const Mat& depth, Mat& cloud, const Mat& cameraMatrix )
{
CV_Assert( cameraMatrix.type() == CV_64FC1 );
const double inv_fx = 1.f/cameraMatrix.at<double>(0,0);
const double inv_fy = 1.f/cameraMatrix.at<double>(1,1);
const double ox = cameraMatrix.at<double>(0,2);
const double oy = cameraMatrix.at<double>(1,2);
cloud.create( depth.size(), CV_32FC3 );
for( int y = 0; y < cloud.rows; y++ )
{
Point3f* cloud_ptr = reinterpret_cast<Point3f*>(cloud.ptr(y));
const float* depth_prt = reinterpret_cast<const float*>(depth.ptr(y));
for( int x = 0; x < cloud.cols; x++ )
{
float z = depth_prt[x];
cloud_ptr[x].x = (float)((x - ox) * z * inv_fx);
cloud_ptr[x].y = (float)((y - oy) * z * inv_fy);
cloud_ptr[x].z = z;
}
}
}
#if SHOW_DEBUG_IMAGES
template<class ImageElemType>
static void warpImage( const Mat& image, const Mat& depth,
const Mat& Rt, const Mat& cameraMatrix, const Mat& distCoeff,
Mat& warpedImage )
{
const Rect rect = Rect(0, 0, image.cols, image.rows);
std::vector<Point2f> points2d;
Mat cloud, transformedCloud;
cvtDepth2Cloud( depth, cloud, cameraMatrix );
perspectiveTransform( cloud, transformedCloud, Rt );
projectPoints( transformedCloud.reshape(3,1), Mat::eye(3,3,CV_64FC1), Mat::zeros(3,1,CV_64FC1), cameraMatrix, distCoeff, points2d );
Mat pointsPositions( points2d );
pointsPositions = pointsPositions.reshape( 2, image.rows );
warpedImage.create( image.size(), image.type() );
warpedImage = Scalar::all(0);
Mat zBuffer( image.size(), CV_32FC1, FLT_MAX );
for( int y = 0; y < image.rows; y++ )
{
for( int x = 0; x < image.cols; x++ )
{
const Point3f p3d = transformedCloud.at<Point3f>(y,x);
const Point p2d = pointsPositions.at<Point2f>(y,x);
if( !cvIsNaN(cloud.at<Point3f>(y,x).z) && cloud.at<Point3f>(y,x).z > 0 &&
rect.contains(p2d) && zBuffer.at<float>(p2d) > p3d.z )
{
warpedImage.at<ImageElemType>(p2d) = image.at<ImageElemType>(y,x);
zBuffer.at<float>(p2d) = p3d.z;
}
}
}
}
#endif
static inline
void set2shorts( int& dst, int short_v1, int short_v2 )
{
unsigned short* ptr = reinterpret_cast<unsigned short*>(&dst);
ptr[0] = static_cast<unsigned short>(short_v1);
ptr[1] = static_cast<unsigned short>(short_v2);
}
static inline
void get2shorts( int src, int& short_v1, int& short_v2 )
{
typedef union { int vint32; unsigned short vuint16[2]; } s32tou16;
const unsigned short* ptr = (reinterpret_cast<s32tou16*>(&src))->vuint16;
short_v1 = ptr[0];
short_v2 = ptr[1];
}
static
int computeCorresp( const Mat& K, const Mat& K_inv, const Mat& Rt,
const Mat& depth0, const Mat& depth1, const Mat& texturedMask1, float maxDepthDiff,
Mat& corresps )
{
CV_Assert( K.type() == CV_64FC1 );
CV_Assert( K_inv.type() == CV_64FC1 );
CV_Assert( Rt.type() == CV_64FC1 );
corresps.create( depth1.size(), CV_32SC1 );
Mat R = Rt(Rect(0,0,3,3)).clone();
Mat KRK_inv = K * R * K_inv;
const double * KRK_inv_ptr = reinterpret_cast<const double *>(KRK_inv.ptr());
Mat Kt = Rt(Rect(3,0,1,3)).clone();
Kt = K * Kt;
const double * Kt_ptr = reinterpret_cast<const double *>(Kt.ptr());
Rect r(0, 0, depth1.cols, depth1.rows);
corresps = Scalar(-1);
int correspCount = 0;
for( int v1 = 0; v1 < depth1.rows; v1++ )
{
for( int u1 = 0; u1 < depth1.cols; u1++ )
{
float d1 = depth1.at<float>(v1,u1);
if( !cvIsNaN(d1) && texturedMask1.at<uchar>(v1,u1) )
{
float transformed_d1 = (float)(d1 * (KRK_inv_ptr[6] * u1 + KRK_inv_ptr[7] * v1 + KRK_inv_ptr[8]) + Kt_ptr[2]);
int u0 = cvRound((d1 * (KRK_inv_ptr[0] * u1 + KRK_inv_ptr[1] * v1 + KRK_inv_ptr[2]) + Kt_ptr[0]) / transformed_d1);
int v0 = cvRound((d1 * (KRK_inv_ptr[3] * u1 + KRK_inv_ptr[4] * v1 + KRK_inv_ptr[5]) + Kt_ptr[1]) / transformed_d1);
if( r.contains(Point(u0,v0)) )
{
float d0 = depth0.at<float>(v0,u0);
if( !cvIsNaN(d0) && std::abs(transformed_d1 - d0) <= maxDepthDiff )
{
int c = corresps.at<int>(v0,u0);
if( c != -1 )
{
int exist_u1, exist_v1;
get2shorts( c, exist_u1, exist_v1);
float exist_d1 = (float)(depth1.at<float>(exist_v1,exist_u1) * (KRK_inv_ptr[6] * exist_u1 + KRK_inv_ptr[7] * exist_v1 + KRK_inv_ptr[8]) + Kt_ptr[2]);
if( transformed_d1 > exist_d1 )
continue;
}
else
correspCount++;
set2shorts( corresps.at<int>(v0,u0), u1, v1 );
}
}
}
}
}
return correspCount;
}
static inline
void preprocessDepth( Mat depth0, Mat depth1,
const Mat& validMask0, const Mat& validMask1,
float minDepth, float maxDepth )
{
CV_DbgAssert( depth0.size() == depth1.size() );
for( int y = 0; y < depth0.rows; y++ )
{
for( int x = 0; x < depth0.cols; x++ )
{
float& d0 = depth0.at<float>(y,x);
if( !cvIsNaN(d0) && (d0 > maxDepth || d0 < minDepth || d0 <= 0 || (!validMask0.empty() && !validMask0.at<uchar>(y,x))) )
d0 = std::numeric_limits<float>::quiet_NaN();
float& d1 = depth1.at<float>(y,x);
if( !cvIsNaN(d1) && (d1 > maxDepth || d1 < minDepth || d1 <= 0 || (!validMask1.empty() && !validMask1.at<uchar>(y,x))) )
d1 = std::numeric_limits<float>::quiet_NaN();
}
}
}
static
void buildPyramids( const Mat& image0, const Mat& image1,
const Mat& depth0, const Mat& depth1,
const Mat& cameraMatrix, int sobelSize, double sobelScale,
const std::vector<float>& minGradMagnitudes,
std::vector<Mat>& pyramidImage0, std::vector<Mat>& pyramidDepth0,
std::vector<Mat>& pyramidImage1, std::vector<Mat>& pyramidDepth1,
std::vector<Mat>& pyramid_dI_dx1, std::vector<Mat>& pyramid_dI_dy1,
std::vector<Mat>& pyramidTexturedMask1, std::vector<Mat>& pyramidCameraMatrix )
{
const int pyramidMaxLevel = (int)minGradMagnitudes.size() - 1;
buildPyramid( image0, pyramidImage0, pyramidMaxLevel );
buildPyramid( image1, pyramidImage1, pyramidMaxLevel );
pyramid_dI_dx1.resize( pyramidImage1.size() );
pyramid_dI_dy1.resize( pyramidImage1.size() );
pyramidTexturedMask1.resize( pyramidImage1.size() );
pyramidCameraMatrix.reserve( pyramidImage1.size() );
Mat cameraMatrix_dbl;
cameraMatrix.convertTo( cameraMatrix_dbl, CV_64FC1 );
for( size_t i = 0; i < pyramidImage1.size(); i++ )
{
Sobel( pyramidImage1[i], pyramid_dI_dx1[i], CV_16S, 1, 0, sobelSize );
Sobel( pyramidImage1[i], pyramid_dI_dy1[i], CV_16S, 0, 1, sobelSize );
const Mat& dx = pyramid_dI_dx1[i];
const Mat& dy = pyramid_dI_dy1[i];
Mat texturedMask( dx.size(), CV_8UC1, Scalar(0) );
const float minScalesGradMagnitude2 = (float)((minGradMagnitudes[i] * minGradMagnitudes[i]) / (sobelScale * sobelScale));
for( int y = 0; y < dx.rows; y++ )
{
for( int x = 0; x < dx.cols; x++ )
{
float m2 = (float)(dx.at<short>(y,x)*dx.at<short>(y,x) + dy.at<short>(y,x)*dy.at<short>(y,x));
if( m2 >= minScalesGradMagnitude2 )
texturedMask.at<uchar>(y,x) = 255;
}
}
pyramidTexturedMask1[i] = texturedMask;
Mat levelCameraMatrix = i == 0 ? cameraMatrix_dbl : 0.5f * pyramidCameraMatrix[i-1];
levelCameraMatrix.at<double>(2,2) = 1.;
pyramidCameraMatrix.push_back( levelCameraMatrix );
}
buildPyramid( depth0, pyramidDepth0, pyramidMaxLevel );
buildPyramid( depth1, pyramidDepth1, pyramidMaxLevel );
}
static
bool solveSystem( const Mat& C, const Mat& dI_dt, double detThreshold, Mat& ksi )
{
#if defined(HAVE_EIGEN) && EIGEN_WORLD_VERSION == 3
Eigen::Matrix<double, Eigen::Dynamic, Eigen::Dynamic> eC, eCt, edI_dt;
cv2eigen(C, eC);
cv2eigen(dI_dt, edI_dt);
eCt = eC.transpose();
Eigen::Matrix<double, Eigen::Dynamic, Eigen::Dynamic> A, B, eksi;
A = eCt * eC;
double det = A.determinant();
if( fabs (det) < detThreshold || cvIsNaN(det) || cvIsInf(det) )
return false;
B = -eCt * edI_dt;
eksi = A.ldlt().solve(B);
eigen2cv( eksi, ksi );
#else
Mat A = C.t() * C;
double det = cv::determinant(A);
if( fabs (det) < detThreshold || cvIsNaN(det) || cvIsInf(det) )
return false;
Mat B = -C.t() * dI_dt;
cv::solve( A, B, ksi, DECOMP_CHOLESKY );
#endif
return true;
}
typedef void (*ComputeCFuncPtr)( double* C, double dIdx, double dIdy, const Point3f& p3d, double fx, double fy );
static
bool computeKsi( int transformType,
const Mat& image0, const Mat& cloud0,
const Mat& image1, const Mat& dI_dx1, const Mat& dI_dy1,
const Mat& corresps, int correspsCount,
double fx, double fy, double sobelScale, double determinantThreshold,
Mat& ksi )
{
int Cwidth = -1;
ComputeCFuncPtr computeCFuncPtr = 0;
if( transformType == RIGID_BODY_MOTION )
{
Cwidth = 6;
computeCFuncPtr = computeC_RigidBodyMotion;
}
else if( transformType == ROTATION )
{
Cwidth = 3;
computeCFuncPtr = computeC_Rotation;
}
else if( transformType == TRANSLATION )
{
Cwidth = 3;
computeCFuncPtr = computeC_Translation;
}
else
CV_Error(Error::StsBadFlag, "Unsupported value of transformation type flag.");
Mat C( correspsCount, Cwidth, CV_64FC1 );
Mat dI_dt( correspsCount, 1, CV_64FC1 );
double sigma = 0;
int pointCount = 0;
for( int v0 = 0; v0 < corresps.rows; v0++ )
{
for( int u0 = 0; u0 < corresps.cols; u0++ )
{
if( corresps.at<int>(v0,u0) != -1 )
{
int u1, v1;
get2shorts( corresps.at<int>(v0,u0), u1, v1 );
double diff = static_cast<double>(image1.at<uchar>(v1,u1)) -
static_cast<double>(image0.at<uchar>(v0,u0));
sigma += diff * diff;
pointCount++;
}
}
}
sigma = std::sqrt(sigma/pointCount);
pointCount = 0;
for( int v0 = 0; v0 < corresps.rows; v0++ )
{
for( int u0 = 0; u0 < corresps.cols; u0++ )
{
if( corresps.at<int>(v0,u0) != -1 )
{
int u1, v1;
get2shorts( corresps.at<int>(v0,u0), u1, v1 );
double diff = static_cast<double>(image1.at<uchar>(v1,u1)) -
static_cast<double>(image0.at<uchar>(v0,u0));
double w = sigma + std::abs(diff);
w = w > DBL_EPSILON ? 1./w : 1.;
(*computeCFuncPtr)( (double*)C.ptr(pointCount),
w * sobelScale * dI_dx1.at<short int>(v1,u1),
w * sobelScale * dI_dy1.at<short int>(v1,u1),
cloud0.at<Point3f>(v0,u0), fx, fy);
dI_dt.at<double>(pointCount) = w * diff;
pointCount++;
}
}
}
Mat sln;
bool solutionExist = solveSystem( C, dI_dt, determinantThreshold, sln );
if( solutionExist )
{
ksi.create(6,1,CV_64FC1);
ksi = Scalar(0);
Mat subksi;
if( transformType == RIGID_BODY_MOTION )
{
subksi = ksi;
}
else if( transformType == ROTATION )
{
subksi = ksi.rowRange(0,3);
}
else if( transformType == TRANSLATION )
{
subksi = ksi.rowRange(3,6);
}
sln.copyTo( subksi );
}
return solutionExist;
}
bool cv::RGBDOdometry( cv::Mat& Rt, const Mat& initRt,
const cv::Mat& image0, const cv::Mat& _depth0, const cv::Mat& validMask0,
const cv::Mat& image1, const cv::Mat& _depth1, const cv::Mat& validMask1,
const cv::Mat& cameraMatrix, float minDepth, float maxDepth, float maxDepthDiff,
const std::vector<int>& iterCounts, const std::vector<float>& minGradientMagnitudes,
int transformType )
{
const int sobelSize = 3;
const double sobelScale = 1./8;
Mat depth0 = _depth0.clone(),
depth1 = _depth1.clone();
// check RGB-D input data
CV_Assert( !image0.empty() );
CV_Assert( image0.type() == CV_8UC1 );
CV_Assert( depth0.type() == CV_32FC1 && depth0.size() == image0.size() );
CV_Assert( image1.size() == image0.size() );
CV_Assert( image1.type() == CV_8UC1 );
CV_Assert( depth1.type() == CV_32FC1 && depth1.size() == image0.size() );
// check masks
CV_Assert( validMask0.empty() || (validMask0.type() == CV_8UC1 && validMask0.size() == image0.size()) );
CV_Assert( validMask1.empty() || (validMask1.type() == CV_8UC1 && validMask1.size() == image0.size()) );
// check camera params
CV_Assert( cameraMatrix.type() == CV_32FC1 && cameraMatrix.size() == Size(3,3) );
// other checks
CV_Assert( iterCounts.empty() || minGradientMagnitudes.empty() ||
minGradientMagnitudes.size() == iterCounts.size() );
CV_Assert( initRt.empty() || (initRt.type()==CV_64FC1 && initRt.size()==Size(4,4) ) );
std::vector<int> defaultIterCounts;
std::vector<float> defaultMinGradMagnitudes;
std::vector<int> const* iterCountsPtr = &iterCounts;
std::vector<float> const* minGradientMagnitudesPtr = &minGradientMagnitudes;
if( iterCounts.empty() || minGradientMagnitudes.empty() )
{
defaultIterCounts.resize(4);
defaultIterCounts[0] = 7;
defaultIterCounts[1] = 7;
defaultIterCounts[2] = 7;
defaultIterCounts[3] = 10;
defaultMinGradMagnitudes.resize(4);
defaultMinGradMagnitudes[0] = 12;
defaultMinGradMagnitudes[1] = 5;
defaultMinGradMagnitudes[2] = 3;
defaultMinGradMagnitudes[3] = 1;
iterCountsPtr = &defaultIterCounts;
minGradientMagnitudesPtr = &defaultMinGradMagnitudes;
}
preprocessDepth( depth0, depth1, validMask0, validMask1, minDepth, maxDepth );
std::vector<Mat> pyramidImage0, pyramidDepth0,
pyramidImage1, pyramidDepth1, pyramid_dI_dx1, pyramid_dI_dy1, pyramidTexturedMask1,
pyramidCameraMatrix;
buildPyramids( image0, image1, depth0, depth1, cameraMatrix, sobelSize, sobelScale, *minGradientMagnitudesPtr,
pyramidImage0, pyramidDepth0, pyramidImage1, pyramidDepth1,
pyramid_dI_dx1, pyramid_dI_dy1, pyramidTexturedMask1, pyramidCameraMatrix );
Mat resultRt = initRt.empty() ? Mat::eye(4,4,CV_64FC1) : initRt.clone();
Mat currRt, ksi;
for( int level = (int)iterCountsPtr->size() - 1; level >= 0; level-- )
{
const Mat& levelCameraMatrix = pyramidCameraMatrix[level];
const Mat& levelImage0 = pyramidImage0[level];
const Mat& levelDepth0 = pyramidDepth0[level];
Mat levelCloud0;
cvtDepth2Cloud( pyramidDepth0[level], levelCloud0, levelCameraMatrix );
const Mat& levelImage1 = pyramidImage1[level];
const Mat& levelDepth1 = pyramidDepth1[level];
const Mat& level_dI_dx1 = pyramid_dI_dx1[level];
const Mat& level_dI_dy1 = pyramid_dI_dy1[level];
CV_Assert( level_dI_dx1.type() == CV_16S );
CV_Assert( level_dI_dy1.type() == CV_16S );
const double fx = levelCameraMatrix.at<double>(0,0);
const double fy = levelCameraMatrix.at<double>(1,1);
const double determinantThreshold = 1e-6;
Mat corresps( levelImage0.size(), levelImage0.type() );
// Run transformation search on current level iteratively.
for( int iter = 0; iter < (*iterCountsPtr)[level]; iter ++ )
{
int correspsCount = computeCorresp( levelCameraMatrix, levelCameraMatrix.inv(), resultRt.inv(DECOMP_SVD),
levelDepth0, levelDepth1, pyramidTexturedMask1[level], maxDepthDiff,
corresps );
if( correspsCount == 0 )
break;
bool solutionExist = computeKsi( transformType,
levelImage0, levelCloud0,
levelImage1, level_dI_dx1, level_dI_dy1,
corresps, correspsCount,
fx, fy, sobelScale, determinantThreshold,
ksi );
if( !solutionExist )
break;
computeProjectiveMatrix( ksi, currRt );
resultRt = currRt * resultRt;
#if SHOW_DEBUG_IMAGES
std::cout << "currRt " << currRt << std::endl;
Mat warpedImage0;
const Mat distCoeff(1,5,CV_32FC1,Scalar(0));
warpImage<uchar>( levelImage0, levelDepth0, resultRt, levelCameraMatrix, distCoeff, warpedImage0 );
imshow( "im0", levelImage0 );
imshow( "wim0", warpedImage0 );
imshow( "im1", levelImage1 );
waitKey();
#endif
}
}
Rt = resultRt;
return !Rt.empty();
}
modules/contrib/src/selfsimilarity.cpp
-259
View File
@@ -1,259 +0,0 @@
// This is based on Rainer Lienhart contribution. Below is the original copyright:
//
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// University of Augsburg License Agreement
// For Open Source MultiMedia Computing (MMC) Library
//
// Copyright (C) 2007, University of Augsburg, Germany, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of University of Augsburg, Germany may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the University of Augsburg, Germany or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
// * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *
// Author: Rainer Lienhart
// email: Rainer.Lienhart@informatik.uni-augsburg.de
// * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *
// Please cite the following two papers:
// 1. Shechtman, E., Irani, M.:
// Matching local self-similarities across images and videos.
// CVPR, (2007)
// 2. Eva Horster, Thomas Greif, Rainer Lienhart, Malcolm Slaney.
// Comparing Local Feature Descriptors in pLSA-Based Image Models.
// 30th Annual Symposium of the German Association for
// Pattern Recognition (DAGM) 2008, Munich, Germany, June 2008.
#include "precomp.hpp"
namespace cv
{
SelfSimDescriptor::SelfSimDescriptor()
{
smallSize = DEFAULT_SMALL_SIZE;
largeSize = DEFAULT_LARGE_SIZE;
numberOfAngles = DEFAULT_NUM_ANGLES;
startDistanceBucket = DEFAULT_START_DISTANCE_BUCKET;
numberOfDistanceBuckets = DEFAULT_NUM_DISTANCE_BUCKETS;
}
SelfSimDescriptor::SelfSimDescriptor(int _ssize, int _lsize,
int _startDistanceBucket,
int _numberOfDistanceBuckets, int _numberOfAngles)
{
smallSize = _ssize;
largeSize = _lsize;
startDistanceBucket = _startDistanceBucket;
numberOfDistanceBuckets = _numberOfDistanceBuckets;
numberOfAngles = _numberOfAngles;
}
SelfSimDescriptor::SelfSimDescriptor(const SelfSimDescriptor& ss)
{
smallSize = ss.smallSize;
largeSize = ss.largeSize;
startDistanceBucket = ss.startDistanceBucket;
numberOfDistanceBuckets = ss.numberOfDistanceBuckets;
numberOfAngles = ss.numberOfAngles;
}
SelfSimDescriptor::~SelfSimDescriptor()
{
}
SelfSimDescriptor& SelfSimDescriptor::operator = (const SelfSimDescriptor& ss)
{
if( this != &ss )
{
smallSize = ss.smallSize;
largeSize = ss.largeSize;
startDistanceBucket = ss.startDistanceBucket;
numberOfDistanceBuckets = ss.numberOfDistanceBuckets;
numberOfAngles = ss.numberOfAngles;
}
return *this;
}
size_t SelfSimDescriptor::getDescriptorSize() const
{
return numberOfAngles*(numberOfDistanceBuckets - startDistanceBucket);
}
Size SelfSimDescriptor::getGridSize( Size imgSize, Size winStride ) const
{
winStride.width = std::max(winStride.width, 1);
winStride.height = std::max(winStride.height, 1);
int border = largeSize/2 + smallSize/2;
return Size(std::max(imgSize.width - border*2 + winStride.width - 1, 0)/winStride.width,
std::max(imgSize.height - border*2 + winStride.height - 1, 0)/winStride.height);
}
// TODO: optimized with SSE2
void SelfSimDescriptor::SSD(const Mat& img, Point pt, Mat& ssd) const
{
int x, y, dx, dy, r0 = largeSize/2, r1 = smallSize/2;
int step = (int)img.step;
for( y = -r0; y <= r0; y++ )
{
float* sptr = ssd.ptr<float>(y+r0) + r0;
for( x = -r0; x <= r0; x++ )
{
int sum = 0;
const uchar* src0 = img.ptr<uchar>(y + pt.y - r1) + x + pt.x;
const uchar* src1 = img.ptr<uchar>(pt.y - r1) + pt.x;
for( dy = -r1; dy <= r1; dy++, src0 += step, src1 += step )
for( dx = -r1; dx <= r1; dx++ )
{
int t = src0[dx] - src1[dx];
sum += t*t;
}
sptr[x] = (float)sum;
}
}
}
void SelfSimDescriptor::compute(const Mat& img, std::vector<float>& descriptors, Size winStride,
const std::vector<Point>& locations) const
{
CV_Assert( img.depth() == CV_8U );
winStride.width = std::max(winStride.width, 1);
winStride.height = std::max(winStride.height, 1);
Size gridSize = getGridSize(img.size(), winStride);
int i, nwindows = locations.empty() ? gridSize.width*gridSize.height : (int)locations.size();
int border = largeSize/2 + smallSize/2;
int fsize = (int)getDescriptorSize();
std::vector<float> tempFeature(fsize+1);
descriptors.resize(fsize*nwindows + 1);
Mat ssd(largeSize, largeSize, CV_32F), mappingMask;
computeLogPolarMapping(mappingMask);
#if 0 //def _OPENMP
int nthreads = cvGetNumThreads();
#pragma omp parallel for num_threads(nthreads)
#endif
for( i = 0; i < nwindows; i++ )
{
Point pt;
float* feature0 = &descriptors[fsize*i];
float* feature = &tempFeature[0];
int x, y, j;
if( !locations.empty() )
{
pt = locations[i];
if( pt.x < border || pt.x >= img.cols - border ||
pt.y < border || pt.y >= img.rows - border )
{
for( j = 0; j < fsize; j++ )
feature0[j] = 0.f;
continue;
}
}
else
pt = Point((i % gridSize.width)*winStride.width + border,
(i / gridSize.width)*winStride.height + border);
SSD(img, pt, ssd);
// Determine in the local neighborhood the largest difference and use for normalization
float var_noise = 1000.f;
for( y = -1; y <= 1 ; y++ )
for( x = -1 ; x <= 1 ; x++ )
var_noise = std::max(var_noise, ssd.at<float>(largeSize/2+y, largeSize/2+x));
for( j = 0; j <= fsize; j++ )
feature[j] = FLT_MAX;
// Derive feature vector before exp(-x) computation
// Idea: for all x,a >= 0, a=const. we have:
// max [ exp( -x / a) ] = exp ( -min(x) / a )
// Thus, determine min(ssd) and store in feature[...]
for( y = 0; y < ssd.rows; y++ )
{
const schar *mappingMaskPtr = mappingMask.ptr<schar>(y);
const float *ssdPtr = ssd.ptr<float>(y);
for( x = 0 ; x < ssd.cols; x++ )
{
int index = mappingMaskPtr[x];
feature[index] = std::min(feature[index], ssdPtr[x]);
}
}
var_noise = -1.f/var_noise;
for( j = 0; j < fsize; j++ )
feature0[j] = feature[j]*var_noise;
Mat _f(1, fsize, CV_32F, feature0);
cv::exp(_f, _f);
}
}
void SelfSimDescriptor::computeLogPolarMapping(Mat& mappingMask) const
{
mappingMask.create(largeSize, largeSize, CV_8S);
// What we want is
// log_m (radius) = numberOfDistanceBuckets
// <==> log_10 (radius) / log_10 (m) = numberOfDistanceBuckets
// <==> log_10 (radius) / numberOfDistanceBuckets = log_10 (m)
// <==> m = 10 ^ log_10(m) = 10 ^ [log_10 (radius) / numberOfDistanceBuckets]
//
int radius = largeSize/2, angleBucketSize = 360 / numberOfAngles;
int fsize = (int)getDescriptorSize();
double inv_log10m = (double)numberOfDistanceBuckets/log10((double)radius);
for (int y=-radius ; y<=radius ; y++)
{
schar* mrow = mappingMask.ptr<schar>(y+radius);
for (int x=-radius ; x<=radius ; x++)
{
int index = fsize;
float dist = (float)std::sqrt((float)x*x + (float)y*y);
int distNo = dist > 0 ? cvRound(log10(dist)*inv_log10m) : 0;
if( startDistanceBucket <= distNo && distNo < numberOfDistanceBuckets )
{
float angle = std::atan2( (float)y, (float)x ) / (float)CV_PI * 180.0f;
if (angle < 0) angle += 360.0f;
int angleInt = (cvRound(angle) + angleBucketSize/2) % 360;
int angleIndex = angleInt / angleBucketSize;
index = (distNo-startDistanceBucket)*numberOfAngles + angleIndex;
}
mrow[x + radius] = saturate_cast<schar>(index);
}
}
}
}
modules/contrib/src/spinimages.cpp
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modules/contrib/src/stereovar.cpp
-411
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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
/*
This is a modification of the variational stereo correspondence algorithm, described in:
S. Kosov, T. Thormaehlen, H.-P. Seidel "Accurate Real-Time Disparity Estimation with Variational Methods"
Proceedings of the 5th International Symposium on Visual Computing, Vegas, USA
This code is written by Sergey G. Kosov for "Visir PX" application as part of Project X (www.project-10.de)
*/
#include "precomp.hpp"
#include <limits.h>
namespace cv
{
StereoVar::StereoVar() : levels(3), pyrScale(0.5), nIt(5), minDisp(0), maxDisp(16), poly_n(3), poly_sigma(0), fi(25.0f), lambda(0.03f), penalization(PENALIZATION_TICHONOV), cycle(CYCLE_V), flags(USE_SMART_ID | USE_AUTO_PARAMS)
{
}
StereoVar::StereoVar(int _levels, double _pyrScale, int _nIt, int _minDisp, int _maxDisp, int _poly_n, double _poly_sigma, float _fi, float _lambda, int _penalization, int _cycle, int _flags) : levels(_levels), pyrScale(_pyrScale), nIt(_nIt), minDisp(_minDisp), maxDisp(_maxDisp), poly_n(_poly_n), poly_sigma(_poly_sigma), fi(_fi), lambda(_lambda), penalization(_penalization), cycle(_cycle), flags(_flags)
{ // No Parameters check, since they are all public
}
StereoVar::~StereoVar()
{
}
static Mat diffX(Mat &src)
{
int cols = src.cols - 1;
Mat dst(src.size(), src.type());
for(int y = 0; y < src.rows; y++){
const float* pSrc = src.ptr<float>(y);
float* pDst = dst.ptr<float>(y);
int x = 0;
#if CV_SSE2
for (x = 0; x <= cols - 8; x += 8) {
__m128 a0 = _mm_loadu_ps(pSrc + x);
__m128 b0 = _mm_loadu_ps(pSrc + x + 1);
__m128 a1 = _mm_loadu_ps(pSrc + x + 4);
__m128 b1 = _mm_loadu_ps(pSrc + x + 5);
b0 = _mm_sub_ps(b0, a0);
b1 = _mm_sub_ps(b1, a1);
_mm_storeu_ps(pDst + x, b0);
_mm_storeu_ps(pDst + x + 4, b1);
}
#endif
for( ; x < cols; x++) pDst[x] = pSrc[x+1] - pSrc[x];
pDst[cols] = 0.f;
}
return dst;
}
static Mat getGradient(Mat &src)
{
register int x, y;
Mat dst(src.size(), src.type());
dst.setTo(0);
for (y = 0; y < src.rows - 1; y++) {
float *pSrc = src.ptr<float>(y);
float *pSrcF = src.ptr<float>(y + 1);
float *pDst = dst.ptr<float>(y);
for (x = 0; x < src.cols - 1; x++)
pDst[x] = fabs(pSrc[x + 1] - pSrc[x]) + fabs(pSrcF[x] - pSrc[x]);
}
return dst;
}
static Mat getG_c(Mat &src, float l)
{
Mat dst(src.size(), src.type());
for (register int y = 0; y < src.rows; y++) {
float *pSrc = src.ptr<float>(y);
float *pDst = dst.ptr<float>(y);
for (register int x = 0; x < src.cols; x++)
pDst[x] = 0.5f*l / sqrtf(l*l + pSrc[x]*pSrc[x]);
}
return dst;
}
static Mat getG_p(Mat &src, float l)
{
Mat dst(src.size(), src.type());
for (register int y = 0; y < src.rows; y++) {
float *pSrc = src.ptr<float>(y);
float *pDst = dst.ptr<float>(y);
for (register int x = 0; x < src.cols; x++)
pDst[x] = 0.5f*l*l / (l*l + pSrc[x]*pSrc[x]);
}
return dst;
}
void StereoVar::VariationalSolver(Mat &I1, Mat &I2, Mat &I2x, Mat &u, int level)
{
register int n, x, y;
float gl = 1, gr = 1, gu = 1, gd = 1, gc = 4;
Mat g_c, g_p;
Mat U;
u.copyTo(U);
int N = nIt;
float l = lambda;
float Fi = fi;
if (flags & USE_SMART_ID) {
double scale = std::pow(pyrScale, (double) level) * (1 + pyrScale);
N = (int) (N / scale);
}
double scale = std::pow(pyrScale, (double) level);
Fi /= (float) scale;
l *= (float) scale;
int width = u.cols - 1;
int height = u.rows - 1;
for (n = 0; n < N; n++) {
if (penalization != PENALIZATION_TICHONOV) {
Mat gradient = getGradient(U);
switch (penalization) {
case PENALIZATION_CHARBONNIER: g_c = getG_c(gradient, l); break;
case PENALIZATION_PERONA_MALIK: g_p = getG_p(gradient, l); break;
}
gradient.release();
}
for (y = 1 ; y < height; y++) {
float *pU = U.ptr<float>(y);
float *pUu = U.ptr<float>(y + 1);
float *pUd = U.ptr<float>(y - 1);
float *pu = u.ptr<float>(y);
float *pI1 = I1.ptr<float>(y);
float *pI2 = I2.ptr<float>(y);
float *pI2x = I2x.ptr<float>(y);
float *pG_c = NULL, *pG_cu = NULL, *pG_cd = NULL;
float *pG_p = NULL, *pG_pu = NULL, *pG_pd = NULL;
switch (penalization) {
case PENALIZATION_CHARBONNIER:
pG_c = g_c.ptr<float>(y);
pG_cu = g_c.ptr<float>(y + 1);
pG_cd = g_c.ptr<float>(y - 1);
break;
case PENALIZATION_PERONA_MALIK:
pG_p = g_p.ptr<float>(y);
pG_pu = g_p.ptr<float>(y + 1);
pG_pd = g_p.ptr<float>(y - 1);
break;
}
for (x = 1; x < width; x++) {
switch (penalization) {
case PENALIZATION_CHARBONNIER:
gc = pG_c[x];
gl = gc + pG_c[x - 1];
gr = gc + pG_c[x + 1];
gu = gc + pG_cu[x];
gd = gc + pG_cd[x];
gc = gl + gr + gu + gd;
break;
case PENALIZATION_PERONA_MALIK:
gc = pG_p[x];
gl = gc + pG_p[x - 1];
gr = gc + pG_p[x + 1];
gu = gc + pG_pu[x];
gd = gc + pG_pd[x];
gc = gl + gr + gu + gd;
break;
}
float _fi = Fi;
if (maxDisp > minDisp) {
if (pU[x] > maxDisp * scale) {_fi *= 1000; pU[x] = static_cast<float>(maxDisp * scale);}
if (pU[x] < minDisp * scale) {_fi *= 1000; pU[x] = static_cast<float>(minDisp * scale);}
}
int A = static_cast<int>(pU[x]);
int neg = 0; if (pU[x] <= 0) neg = -1;
if (x + A > width)
pu[x] = pU[width - A];
else if (x + A + neg < 0)
pu[x] = pU[- A + 2];
else {
pu[x] = A + (pI2x[x + A + neg] * (pI1[x] - pI2[x + A])
+ _fi * (gr * pU[x + 1] + gl * pU[x - 1] + gu * pUu[x] + gd * pUd[x] - gc * A))
/ (pI2x[x + A + neg] * pI2x[x + A + neg] + gc * _fi) ;
}
}// x
pu[0] = pu[1];
pu[width] = pu[width - 1];
}// y
for (x = 0; x <= width; x++) {
u.at<float>(0, x) = u.at<float>(1, x);
u.at<float>(height, x) = u.at<float>(height - 1, x);
}
u.copyTo(U);
if (!g_c.empty()) g_c.release();
if (!g_p.empty()) g_p.release();
}//n
}
void StereoVar::VCycle_MyFAS(Mat &I1, Mat &I2, Mat &I2x, Mat &_u, int level)
{
Size imgSize = _u.size();
Size frmSize = Size((int) (imgSize.width * pyrScale + 0.5), (int) (imgSize.height * pyrScale + 0.5));
Mat I1_h, I2_h, I2x_h, u_h, U, U_h;
//PRE relaxation
VariationalSolver(I1, I2, I2x, _u, level);
if (level >= levels - 1) return;
level ++;
//scaling DOWN
resize(I1, I1_h, frmSize, 0, 0, INTER_AREA);
resize(I2, I2_h, frmSize, 0, 0, INTER_AREA);
resize(_u, u_h, frmSize, 0, 0, INTER_AREA);
u_h.convertTo(u_h, u_h.type(), pyrScale);
I2x_h = diffX(I2_h);
//Next level
U_h = u_h.clone();
VCycle_MyFAS(I1_h, I2_h, I2x_h, U_h, level);
subtract(U_h, u_h, U_h);
U_h.convertTo(U_h, U_h.type(), 1.0 / pyrScale);
//scaling UP
resize(U_h, U, imgSize);
//correcting the solution
add(_u, U, _u);
//POST relaxation
VariationalSolver(I1, I2, I2x, _u, level - 1);
if (flags & USE_MEDIAN_FILTERING) medianBlur(_u, _u, 3);
I1_h.release();
I2_h.release();
I2x_h.release();
u_h.release();
U.release();
U_h.release();
}
void StereoVar::FMG(Mat &I1, Mat &I2, Mat &I2x, Mat &u, int level)
{
double scale = std::pow(pyrScale, (double) level);
Size frmSize = Size((int) (u.cols * scale + 0.5), (int) (u.rows * scale + 0.5));
Mat I1_h, I2_h, I2x_h, u_h;
//scaling DOWN
resize(I1, I1_h, frmSize, 0, 0, INTER_AREA);
resize(I2, I2_h, frmSize, 0, 0, INTER_AREA);
resize(u, u_h, frmSize, 0, 0, INTER_AREA);
u_h.convertTo(u_h, u_h.type(), scale);
I2x_h = diffX(I2_h);
switch (cycle) {
case CYCLE_O:
VariationalSolver(I1_h, I2_h, I2x_h, u_h, level);
break;
case CYCLE_V:
VCycle_MyFAS(I1_h, I2_h, I2x_h, u_h, level);
break;
}
u_h.convertTo(u_h, u_h.type(), 1.0 / scale);
//scaling UP
resize(u_h, u, u.size(), 0, 0, INTER_CUBIC);
I1_h.release();
I2_h.release();
I2x_h.release();
u_h.release();
level--;
if ((flags & USE_AUTO_PARAMS) && (level < levels / 3)) {
penalization = PENALIZATION_PERONA_MALIK;
fi *= 100;
flags -= USE_AUTO_PARAMS;
autoParams();
}
if (flags & USE_MEDIAN_FILTERING) medianBlur(u, u, 3);
if (level >= 0) FMG(I1, I2, I2x, u, level);
}
void StereoVar::autoParams()
{
int maxD = MAX(labs(maxDisp), labs(minDisp));
if (!maxD) pyrScale = 0.85;
else if (maxD < 8) pyrScale = 0.5;
else if (maxD < 64) pyrScale = 0.5 + static_cast<double>(maxD - 8) * 0.00625;
else pyrScale = 0.85;
if (maxD) {
levels = 0;
while ( std::pow(pyrScale, levels) * maxD > 1.5) levels ++;
levels++;
}
switch(penalization) {
case PENALIZATION_TICHONOV: cycle = CYCLE_V; break;
case PENALIZATION_CHARBONNIER: cycle = CYCLE_O; break;
case PENALIZATION_PERONA_MALIK: cycle = CYCLE_O; break;
}
}
void StereoVar::operator ()( const Mat& left, const Mat& right, Mat& disp )
{
CV_Assert(left.size() == right.size() && left.type() == right.type());
Size imgSize = left.size();
int MaxD = MAX(labs(minDisp), labs(maxDisp));
int SignD = 1; if (MIN(minDisp, maxDisp) < 0) SignD = -1;
if (minDisp >= maxDisp) {MaxD = 256; SignD = 1;}
Mat u;
if ((flags & USE_INITIAL_DISPARITY) && (!disp.empty())) {
CV_Assert(disp.size() == left.size() && disp.type() == CV_8UC1);
disp.convertTo(u, CV_32FC1, static_cast<double>(SignD * MaxD) / 256);
} else {
u.create(imgSize, CV_32FC1);
u.setTo(0);
}
// Preprocessing
Mat leftgray, rightgray;
if (left.type() != CV_8UC1) {
cvtColor(left, leftgray, COLOR_BGR2GRAY);
cvtColor(right, rightgray, COLOR_BGR2GRAY);
} else {
left.copyTo(leftgray);
right.copyTo(rightgray);
}
if (flags & USE_EQUALIZE_HIST) {
equalizeHist(leftgray, leftgray);
equalizeHist(rightgray, rightgray);
}
if (poly_sigma > 0.0001) {
GaussianBlur(leftgray, leftgray, Size(poly_n, poly_n), poly_sigma);
GaussianBlur(rightgray, rightgray, Size(poly_n, poly_n), poly_sigma);
}
if (flags & USE_AUTO_PARAMS) {
penalization = PENALIZATION_TICHONOV;
autoParams();
}
Mat I1, I2;
leftgray.convertTo(I1, CV_32FC1);
rightgray.convertTo(I2, CV_32FC1);
leftgray.release();
rightgray.release();
Mat I2x = diffX(I2);
FMG(I1, I2, I2x, u, levels - 1);
I1.release();
I2.release();
I2x.release();
disp.create( left.size(), CV_8UC1 );
u = abs(u);
u.convertTo(disp, disp.type(), 256 / MaxD, 0);
u.release();
}
} // namespace
modules/contrib/test/test_main.cpp
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#include "test_precomp.hpp"
CV_TEST_MAIN("cv")
modules/contrib/test/test_precomp.hpp
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#ifdef __GNUC__
# pragma GCC diagnostic ignored "-Wmissing-declarations"
# if defined __clang__ || defined __APPLE__
# pragma GCC diagnostic ignored "-Wmissing-prototypes"
# pragma GCC diagnostic ignored "-Wextra"
# endif
#endif
#ifndef __OPENCV_TEST_PRECOMP_HPP__
#define __OPENCV_TEST_PRECOMP_HPP__
#include "opencv2/ts.hpp"
#include "opencv2/contrib.hpp"
#include <iostream>
#endif
modules/legacy/CMakeLists.txt
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ocv_define_module(legacy opencv_calib3d opencv_video opencv_ml OPTIONAL opencv_highgui)
modules/legacy/doc/common_interfaces_of_descriptor_extractors.rst
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Common Interfaces of Descriptor Extractors
==========================================
.. highlight:: cpp
Extractors of keypoint descriptors in OpenCV have wrappers with a common interface that enables you to easily switch
between different algorithms solving the same problem. This section is devoted to computing descriptors
represented as vectors in a multidimensional space. All objects that implement the ``vector``
descriptor extractors inherit the
:ocv:class:`DescriptorExtractor` interface.
CalonderDescriptorExtractor
---------------------------
.. ocv:class:: CalonderDescriptorExtractor : public DescriptorExtractor
Wrapping class for computing descriptors by using the
:ocv:class:`RTreeClassifier` class. ::
template<typename T>
class CalonderDescriptorExtractor : public DescriptorExtractor
{
public:
CalonderDescriptorExtractor( const String& classifierFile );
virtual void read( const FileNode &fn );
virtual void write( FileStorage &fs ) const;
virtual int descriptorSize() const;
virtual int descriptorType() const;
virtual int defaultNorm() const;
protected:
...
}
modules/legacy/doc/common_interfaces_of_generic_descriptor_matchers.rst
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Common Interfaces of Generic Descriptor Matchers
================================================
.. highlight:: cpp
OneWayDescriptorBase
--------------------
.. ocv:class:: OneWayDescriptorBase
Class encapsulates functionality for training/loading a set of one way descriptors
and finding the nearest closest descriptor to an input feature. ::
class CV_EXPORTS OneWayDescriptorBase
{
public:
// creates an instance of OneWayDescriptor from a set of training files
// - patch_size: size of the input (large) patch
// - pose_count: the number of poses to generate for each descriptor
// - train_path: path to training files
// - pca_config: the name of the file that contains PCA for small patches (2 times smaller
// than patch_size each dimension
// - pca_hr_config: the name of the file that contains PCA for large patches (of patch_size size)
// - pca_desc_config: the name of the file that contains descriptors of PCA components
OneWayDescriptorBase(CvSize patch_size, int pose_count, const char* train_path = 0, const char* pca_config = 0,
const char* pca_hr_config = 0, const char* pca_desc_config = 0, int pyr_levels = 1,
int pca_dim_high = 100, int pca_dim_low = 100);
OneWayDescriptorBase(CvSize patch_size, int pose_count, const String &pca_filename, const String &train_path = String(), const String &images_list = String(),
float _scale_min = 0.7f, float _scale_max=1.5f, float _scale_step=1.2f, int pyr_levels = 1,
int pca_dim_high = 100, int pca_dim_low = 100);
virtual ~OneWayDescriptorBase();
void clear ();
// Allocate: allocates memory for a given number of descriptors
void Allocate(int train_feature_count);
// AllocatePCADescriptors: allocates memory for pca descriptors
void AllocatePCADescriptors();
// returns patch size
CvSize GetPatchSize() const {return m_patch_size;};
// returns the number of poses for each descriptor
int GetPoseCount() const {return m_pose_count;};
// returns the number of pyramid levels
int GetPyrLevels() const {return m_pyr_levels;};
// returns the number of descriptors
int GetDescriptorCount() const {return m_train_feature_count;};
// CreateDescriptorsFromImage: creates descriptors for each of the input features
// - src: input image
// - features: input features
// - pyr_levels: the number of pyramid levels
void CreateDescriptorsFromImage(IplImage* src, const vector<KeyPoint>& features);
// CreatePCADescriptors: generates descriptors for PCA components, needed for fast generation of feature descriptors
void CreatePCADescriptors();
// returns a feature descriptor by feature index
const OneWayDescriptor* GetDescriptor(int desc_idx) const {return &m_descriptors[desc_idx];};
// FindDescriptor: finds the closest descriptor
// - patch: input image patch
// - desc_idx: output index of the closest descriptor to the input patch
// - pose_idx: output index of the closest pose of the closest descriptor to the input patch
// - distance: distance from the input patch to the closest feature pose
// - _scales: scales of the input patch for each descriptor
// - scale_ranges: input scales variation (float[2])
void FindDescriptor(IplImage* patch, int& desc_idx, int& pose_idx, float& distance, float* _scale = 0, float* scale_ranges = 0) const;
// - patch: input image patch
// - n: number of the closest indexes
// - desc_idxs: output indexes of the closest descriptor to the input patch (n)
// - pose_idx: output indexes of the closest pose of the closest descriptor to the input patch (n)
// - distances: distance from the input patch to the closest feature pose (n)
// - _scales: scales of the input patch
// - scale_ranges: input scales variation (float[2])
void FindDescriptor(IplImage* patch, int n, vector<int>& desc_idxs, vector<int>& pose_idxs,
vector<float>& distances, vector<float>& _scales, float* scale_ranges = 0) const;
// FindDescriptor: finds the closest descriptor
// - src: input image
// - pt: center of the feature
// - desc_idx: output index of the closest descriptor to the input patch
// - pose_idx: output index of the closest pose of the closest descriptor to the input patch
// - distance: distance from the input patch to the closest feature pose
void FindDescriptor(IplImage* src, cv::Point2f pt, int& desc_idx, int& pose_idx, float& distance) const;
// InitializePoses: generates random poses
void InitializePoses();
// InitializeTransformsFromPoses: generates 2x3 affine matrices from poses (initializes m_transforms)
void InitializeTransformsFromPoses();
// InitializePoseTransforms: subsequently calls InitializePoses and InitializeTransformsFromPoses
void InitializePoseTransforms();
// InitializeDescriptor: initializes a descriptor
// - desc_idx: descriptor index
// - train_image: image patch (ROI is supported)
// - feature_label: feature textual label
void InitializeDescriptor(int desc_idx, IplImage* train_image, const char* feature_label);
void InitializeDescriptor(int desc_idx, IplImage* train_image, const KeyPoint& keypoint, const char* feature_label);
// InitializeDescriptors: load features from an image and create descriptors for each of them
void InitializeDescriptors(IplImage* train_image, const vector<KeyPoint>& features,
const char* feature_label = "", int desc_start_idx = 0);
// Write: writes this object to a file storage
// - fs: output filestorage
void Write (FileStorage &fs) const;
// Read: reads OneWayDescriptorBase object from a file node
// - fn: input file node
void Read (const FileNode &fn);
// LoadPCADescriptors: loads PCA descriptors from a file
// - filename: input filename
int LoadPCADescriptors(const char* filename);
// LoadPCADescriptors: loads PCA descriptors from a file node
// - fn: input file node
int LoadPCADescriptors(const FileNode &fn);
// SavePCADescriptors: saves PCA descriptors to a file
// - filename: output filename
void SavePCADescriptors(const char* filename);
// SavePCADescriptors: saves PCA descriptors to a file storage
// - fs: output file storage
void SavePCADescriptors(CvFileStorage* fs) const;
// GeneratePCA: calculate and save PCA components and descriptors
// - img_path: path to training PCA images directory
// - images_list: filename with filenames of training PCA images
void GeneratePCA(const char* img_path, const char* images_list, int pose_count=500);
// SetPCAHigh: sets the high resolution pca matrices (copied to internal structures)
void SetPCAHigh(CvMat* avg, CvMat* eigenvectors);
// SetPCALow: sets the low resolution pca matrices (copied to internal structures)
void SetPCALow(CvMat* avg, CvMat* eigenvectors);
int GetLowPCA(CvMat** avg, CvMat** eigenvectors)
{
*avg = m_pca_avg;
*eigenvectors = m_pca_eigenvectors;
return m_pca_dim_low;
};
int GetPCADimLow() const {return m_pca_dim_low;};
int GetPCADimHigh() const {return m_pca_dim_high;};
void ConvertDescriptorsArrayToTree(); // Converting pca_descriptors array to KD tree
// GetPCAFilename: get default PCA filename
static String GetPCAFilename () { return "pca.yml"; }
virtual bool empty() const { return m_train_feature_count <= 0 ? true : false; }
protected:
...
};
OneWayDescriptorMatcher
-----------------------
.. ocv:class:: OneWayDescriptorMatcher : public GenericDescriptorMatcher
Wrapping class for computing, matching, and classifying descriptors using the
:ocv:class:`OneWayDescriptorBase` class. ::
class OneWayDescriptorMatcher : public GenericDescriptorMatcher
{
public:
class Params
{
public:
static const int POSE_COUNT = 500;
static const int PATCH_WIDTH = 24;
static const int PATCH_HEIGHT = 24;
static float GET_MIN_SCALE() { return 0.7f; }
static float GET_MAX_SCALE() { return 1.5f; }
static float GET_STEP_SCALE() { return 1.2f; }
Params( int poseCount = POSE_COUNT,
Size patchSize = Size(PATCH_WIDTH, PATCH_HEIGHT),
String pcaFilename = String(),
String trainPath = String(), String trainImagesList = String(),
float minScale = GET_MIN_SCALE(), float maxScale = GET_MAX_SCALE(),
float stepScale = GET_STEP_SCALE() );
int poseCount;
Size patchSize;
String pcaFilename;
String trainPath;
String trainImagesList;
float minScale, maxScale, stepScale;
};
OneWayDescriptorMatcher( const Params& params=Params() );
virtual ~OneWayDescriptorMatcher();
void initialize( const Params& params, const Ptr<OneWayDescriptorBase>& base=Ptr<OneWayDescriptorBase>() );
// Clears keypoints stored in collection and OneWayDescriptorBase
virtual void clear();
virtual void train();
virtual bool isMaskSupported();
virtual void read( const FileNode &fn );
virtual void write( FileStorage& fs ) const;
virtual Ptr<GenericDescriptorMatcher> clone( bool emptyTrainData=false ) const;
protected:
...
};
FernClassifier
--------------
.. ocv:class:: FernClassifier
::
class CV_EXPORTS FernClassifier
{
public:
FernClassifier();
FernClassifier(const FileNode& node);
FernClassifier(const vector<vector<Point2f> >& points,
const vector<Mat>& refimgs,
const vector<vector<int> >& labels=vector<vector<int> >(),
int _nclasses=0, int _patchSize=PATCH_SIZE,
int _signatureSize=DEFAULT_SIGNATURE_SIZE,
int _nstructs=DEFAULT_STRUCTS,
int _structSize=DEFAULT_STRUCT_SIZE,
int _nviews=DEFAULT_VIEWS,
int _compressionMethod=COMPRESSION_NONE,
const PatchGenerator& patchGenerator=PatchGenerator());
virtual ~FernClassifier();
virtual void read(const FileNode& n);
virtual void write(FileStorage& fs, const String& name=String()) const;
virtual void trainFromSingleView(const Mat& image,
const vector<KeyPoint>& keypoints,
int _patchSize=PATCH_SIZE,
int _signatureSize=DEFAULT_SIGNATURE_SIZE,
int _nstructs=DEFAULT_STRUCTS,
int _structSize=DEFAULT_STRUCT_SIZE,
int _nviews=DEFAULT_VIEWS,
int _compressionMethod=COMPRESSION_NONE,
const PatchGenerator& patchGenerator=PatchGenerator());
virtual void train(const vector<vector<Point2f> >& points,
const vector<Mat>& refimgs,
const vector<vector<int> >& labels=vector<vector<int> >(),
int _nclasses=0, int _patchSize=PATCH_SIZE,
int _signatureSize=DEFAULT_SIGNATURE_SIZE,
int _nstructs=DEFAULT_STRUCTS,
int _structSize=DEFAULT_STRUCT_SIZE,
int _nviews=DEFAULT_VIEWS,
int _compressionMethod=COMPRESSION_NONE,
const PatchGenerator& patchGenerator=PatchGenerator());
virtual int operator()(const Mat& img, Point2f kpt, vector<float>& signature) const;
virtual int operator()(const Mat& patch, vector<float>& signature) const;
virtual void clear();
virtual bool empty() const;
void setVerbose(bool verbose);
int getClassCount() const;
int getStructCount() const;
int getStructSize() const;
int getSignatureSize() const;
int getCompressionMethod() const;
Size getPatchSize() const;
struct Feature
{
uchar x1, y1, x2, y2;
Feature() : x1(0), y1(0), x2(0), y2(0) {}
Feature(int _x1, int _y1, int _x2, int _y2)
: x1((uchar)_x1), y1((uchar)_y1), x2((uchar)_x2), y2((uchar)_y2)
{}
template<typename _Tp> bool operator ()(const Mat_<_Tp>& patch) const
{ return patch(y1,x1) > patch(y2, x2); }
};
enum
{
PATCH_SIZE = 31,
DEFAULT_STRUCTS = 50,
DEFAULT_STRUCT_SIZE = 9,
DEFAULT_VIEWS = 5000,
DEFAULT_SIGNATURE_SIZE = 176,
COMPRESSION_NONE = 0,
COMPRESSION_RANDOM_PROJ = 1,
COMPRESSION_PCA = 2,
DEFAULT_COMPRESSION_METHOD = COMPRESSION_NONE
};
protected:
...
};
FernDescriptorMatcher
---------------------
.. ocv:class:: FernDescriptorMatcher : public GenericDescriptorMatcher
Wrapping class for computing, matching, and classifying descriptors using the
:ocv:class:`FernClassifier` class. ::
class FernDescriptorMatcher : public GenericDescriptorMatcher
{
public:
class Params
{
public:
Params( int nclasses=0,
int patchSize=FernClassifier::PATCH_SIZE,
int signatureSize=FernClassifier::DEFAULT_SIGNATURE_SIZE,
int nstructs=FernClassifier::DEFAULT_STRUCTS,
int structSize=FernClassifier::DEFAULT_STRUCT_SIZE,
int nviews=FernClassifier::DEFAULT_VIEWS,
int compressionMethod=FernClassifier::COMPRESSION_NONE,
const PatchGenerator& patchGenerator=PatchGenerator() );
Params( const String& filename );
int nclasses;
int patchSize;
int signatureSize;
int nstructs;
int structSize;
int nviews;
int compressionMethod;
PatchGenerator patchGenerator;
String filename;
};
FernDescriptorMatcher( const Params& params=Params() );
virtual ~FernDescriptorMatcher();
virtual void clear();
virtual void train();
virtual bool isMaskSupported();
virtual void read( const FileNode &fn );
virtual void write( FileStorage& fs ) const;
virtual Ptr<GenericDescriptorMatcher> clone( bool emptyTrainData=false ) const;
protected:
...
};
modules/legacy/doc/expectation_maximization.rst
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Expectation Maximization
========================
This section describes obsolete ``C`` interface of EM algorithm. Details of the algorithm and its ``C++`` interface can be found in the other section :ref:`ML_Expectation Maximization`.
.. highlight:: cpp
.. note::
* An example on using the Expectation Maximalization algorithm can be found at opencv_source_code/samples/cpp/em.cpp
* (Python) An example using Expectation Maximalization for Gaussian Mixing can be found at opencv_source_code/samples/python2/gaussian_mix.py
CvEMParams
----------
.. ocv:struct:: CvEMParams
Parameters of the EM algorithm. All parameters are public. You can initialize them by a constructor and then override some of them directly if you want.
CvEMParams::CvEMParams
----------------------
The constructors
.. ocv:function:: CvEMParams::CvEMParams()
.. ocv:function:: CvEMParams::CvEMParams( int nclusters, int cov_mat_type=EM::COV_MAT_DIAGONAL, int start_step=EM::START_AUTO_STEP, CvTermCriteria term_crit=cvTermCriteria(CV_TERMCRIT_ITER+CV_TERMCRIT_EPS, 100, FLT_EPSILON), const CvMat* probs=0, const CvMat* weights=0, const CvMat* means=0, const CvMat** covs=0 )
:param nclusters: The number of mixture components in the Gaussian mixture model. Some of EM implementation could determine the optimal number of mixtures within a specified value range, but that is not the case in ML yet.
:param cov_mat_type: Constraint on covariance matrices which defines type of matrices. Possible values are:
* **CvEM::COV_MAT_SPHERICAL** A scaled identity matrix :math:`\mu_k * I`. There is the only parameter :math:`\mu_k` to be estimated for each matrix. The option may be used in special cases, when the constraint is relevant, or as a first step in the optimization (for example in case when the data is preprocessed with PCA). The results of such preliminary estimation may be passed again to the optimization procedure, this time with ``cov_mat_type=CvEM::COV_MAT_DIAGONAL``.
* **CvEM::COV_MAT_DIAGONAL** A diagonal matrix with positive diagonal elements. The number of free parameters is ``d`` for each matrix. This is most commonly used option yielding good estimation results.
* **CvEM::COV_MAT_GENERIC** A symmetric positively defined matrix. The number of free parameters in each matrix is about :math:`d^2/2`. It is not recommended to use this option, unless there is pretty accurate initial estimation of the parameters and/or a huge number of training samples.
:param start_step: The start step of the EM algorithm:
* **CvEM::START_E_STEP** Start with Expectation step. You need to provide means :math:`a_k` of mixture components to use this option. Optionally you can pass weights :math:`\pi_k` and covariance matrices :math:`S_k` of mixture components.
* **CvEM::START_M_STEP** Start with Maximization step. You need to provide initial probabilities :math:`p_{i,k}` to use this option.
* **CvEM::START_AUTO_STEP** Start with Expectation step. You need not provide any parameters because they will be estimated by the kmeans algorithm.
:param term_crit: The termination criteria of the EM algorithm. The EM algorithm can be terminated by the number of iterations ``term_crit.max_iter`` (number of M-steps) or when relative change of likelihood logarithm is less than ``term_crit.epsilon``.
:param probs: Initial probabilities :math:`p_{i,k}` of sample :math:`i` to belong to mixture component :math:`k`. It is a floating-point matrix of :math:`nsamples \times nclusters` size. It is used and must be not NULL only when ``start_step=CvEM::START_M_STEP``.
:param weights: Initial weights :math:`\pi_k` of mixture components. It is a floating-point vector with :math:`nclusters` elements. It is used (if not NULL) only when ``start_step=CvEM::START_E_STEP``.
:param means: Initial means :math:`a_k` of mixture components. It is a floating-point matrix of :math:`nclusters \times dims` size. It is used used and must be not NULL only when ``start_step=CvEM::START_E_STEP``.
:param covs: Initial covariance matrices :math:`S_k` of mixture components. Each of covariance matrices is a valid square floating-point matrix of :math:`dims \times dims` size. It is used (if not NULL) only when ``start_step=CvEM::START_E_STEP``.
The default constructor represents a rough rule-of-the-thumb:
::
CvEMParams() : nclusters(10), cov_mat_type(1/*CvEM::COV_MAT_DIAGONAL*/),
start_step(0/*CvEM::START_AUTO_STEP*/), probs(0), weights(0), means(0), covs(0)
{
term_crit=cvTermCriteria( CV_TERMCRIT_ITER+CV_TERMCRIT_EPS, 100, FLT_EPSILON );
}
With another constructor it is possible to override a variety of parameters from a single number of mixtures (the only essential problem-dependent parameter) to initial values for the mixture parameters.
CvEM
----
.. ocv:class:: CvEM : public CvStatModel
The class implements the EM algorithm as described in the beginning of the section :ref:`ML_Expectation Maximization`.
CvEM::train
-----------
Estimates the Gaussian mixture parameters from a sample set.
.. ocv:function:: bool CvEM::train( const Mat& samples, const Mat& sampleIdx=Mat(), CvEMParams params=CvEMParams(), Mat* labels=0 )
.. ocv:function:: bool CvEM::train( const CvMat* samples, const CvMat* sampleIdx=0, CvEMParams params=CvEMParams(), CvMat* labels=0 )
:param samples: Samples from which the Gaussian mixture model will be estimated.
:param sampleIdx: Mask of samples to use. All samples are used by default.
:param params: Parameters of the EM algorithm.
:param labels: The optional output "class label" for each sample: :math:`\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N` (indices of the most probable mixture component for each sample).
Unlike many of the ML models, EM is an unsupervised learning algorithm and it does not take responses (class labels or function values) as input. Instead, it computes the
*Maximum Likelihood Estimate* of the Gaussian mixture parameters from an input sample set, stores all the parameters inside the structure:
:math:`p_{i,k}` in ``probs``,
:math:`a_k` in ``means`` ,
:math:`S_k` in ``covs[k]``,
:math:`\pi_k` in ``weights`` , and optionally computes the output "class label" for each sample:
:math:`\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N` (indices of the most probable mixture component for each sample).
The trained model can be used further for prediction, just like any other classifier. The trained model is similar to the
:ocv:class:`CvNormalBayesClassifier`.
For an example of clustering random samples of the multi-Gaussian distribution using EM, see ``em.cpp`` sample in the OpenCV distribution.
CvEM::predict
-------------
Returns a mixture component index of a sample.
.. ocv:function:: float CvEM::predict( const Mat& sample, Mat* probs=0 ) const
.. ocv:function:: float CvEM::predict( const CvMat* sample, CvMat* probs ) const
:param sample: A sample for classification.
:param probs: If it is not null then the method will write posterior probabilities of each component given the sample data to this parameter.
CvEM::getNClusters
------------------
Returns the number of mixture components :math:`M` in the Gaussian mixture model.
.. ocv:function:: int CvEM::getNClusters() const
.. ocv:function:: int CvEM::get_nclusters() const
CvEM::getMeans
------------------
Returns mixture means :math:`a_k`.
.. ocv:function:: Mat CvEM::getMeans() const
.. ocv:function:: const CvMat* CvEM::get_means() const
CvEM::getCovs
-------------
Returns mixture covariance matrices :math:`S_k`.
.. ocv:function:: void CvEM::getCovs(std::vector<cv::Mat>& covs) const
.. ocv:function:: const CvMat** CvEM::get_covs() const
CvEM::getWeights
----------------
Returns mixture weights :math:`\pi_k`.
.. ocv:function:: Mat CvEM::getWeights() const
.. ocv:function:: const CvMat* CvEM::get_weights() const
CvEM::getProbs
--------------
Returns vectors of probabilities for each training sample.
.. ocv:function:: Mat CvEM::getProbs() const
.. ocv:function:: const CvMat* CvEM::get_probs() const
For each training sample :math:`i` (that have been passed to the constructor or to :ocv:func:`CvEM::train`) returns probabilities :math:`p_{i,k}` to belong to a mixture component :math:`k`.
CvEM::getLikelihood
-------------------
Returns logarithm of likelihood.
.. ocv:function:: double CvEM::getLikelihood() const
.. ocv:function:: double CvEM::get_log_likelihood() const
CvEM::write
-----------
Writes the trained Gaussian mixture model to the file storage.
.. ocv:function:: void CvEM::write( CvFileStorage* fs, const char* name ) const
:param fs: A file storage where the model will be written.
:param name: A name of the file node where the model data will be written.
CvEM::read
-----------------
Reads the trained Gaussian mixture model from the file storage.
.. ocv:function:: void CvEM::read( CvFileStorage* fs, CvFileNode* node )
:param fs: A file storage with the trained model.
:param node: The parent map. If it is NULL, the function searches a node with parameters in all the top-level nodes (streams), starting with the first one.
modules/legacy/doc/feature_detection_and_description.rst
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Feature Detection and Description
=================================
.. highlight:: cpp
RandomizedTree
--------------
.. ocv:class:: RandomizedTree
Class containing a base structure for ``RTreeClassifier``. ::
class CV_EXPORTS RandomizedTree
{
public:
friend class RTreeClassifier;
RandomizedTree();
~RandomizedTree();
void train(std::vector<BaseKeypoint> const& base_set,
RNG &rng, int depth, int views,
size_t reduced_num_dim, int num_quant_bits);
void train(std::vector<BaseKeypoint> const& base_set,
RNG &rng, PatchGenerator &make_patch, int depth,
int views, size_t reduced_num_dim, int num_quant_bits);
// next two functions are EXPERIMENTAL
//(do not use unless you know exactly what you do)
static void quantizeVector(float *vec, int dim, int N, float bnds[2],
int clamp_mode=0);
static void quantizeVector(float *src, int dim, int N, float bnds[2],
uchar *dst);
// patch_data must be a 32x32 array (no row padding)
float* getPosterior(uchar* patch_data);
const float* getPosterior(uchar* patch_data) const;
uchar* getPosterior2(uchar* patch_data);
void read(const char* file_name, int num_quant_bits);
void read(std::istream &is, int num_quant_bits);
void write(const char* file_name) const;
void write(std::ostream &os) const;
int classes() { return classes_; }
int depth() { return depth_; }
void discardFloatPosteriors() { freePosteriors(1); }
inline void applyQuantization(int num_quant_bits)
{ makePosteriors2(num_quant_bits); }
private:
int classes_;
int depth_;
int num_leaves_;
std::vector<RTreeNode> nodes_;
float **posteriors_; // 16-byte aligned posteriors
uchar **posteriors2_; // 16-byte aligned posteriors
std::vector<int> leaf_counts_;
void createNodes(int num_nodes, RNG &rng);
void allocPosteriorsAligned(int num_leaves, int num_classes);
void freePosteriors(int which);
// which: 1=posteriors_, 2=posteriors2_, 3=both
void init(int classes, int depth, RNG &rng);
void addExample(int class_id, uchar* patch_data);
void finalize(size_t reduced_num_dim, int num_quant_bits);
int getIndex(uchar* patch_data) const;
inline float* getPosteriorByIndex(int index);
inline uchar* getPosteriorByIndex2(int index);
inline const float* getPosteriorByIndex(int index) const;
void convertPosteriorsToChar();
void makePosteriors2(int num_quant_bits);
void compressLeaves(size_t reduced_num_dim);
void estimateQuantPercForPosteriors(float perc[2]);
};
.. note::
* : PYTHON : An example using Randomized Tree training for letter recognition can be found at opencv_source_code/samples/python2/letter_recog.py
RandomizedTree::train
-------------------------
Trains a randomized tree using an input set of keypoints.
.. ocv:function:: void RandomizedTree::train( vector<BaseKeypoint> const& base_set, RNG & rng, int depth, int views, size_t reduced_num_dim, int num_quant_bits )
.. ocv:function:: void RandomizedTree::train( vector<BaseKeypoint> const& base_set, RNG & rng, PatchGenerator & make_patch, int depth, int views, size_t reduced_num_dim, int num_quant_bits )
:param base_set: Vector of the ``BaseKeypoint`` type. It contains image keypoints used for training.
:param rng: Random-number generator used for training.
:param make_patch: Patch generator used for training.
:param depth: Maximum tree depth.
:param views: Number of random views of each keypoint neighborhood to generate.
:param reduced_num_dim: Number of dimensions used in the compressed signature.
:param num_quant_bits: Number of bits used for quantization.
.. note::
* : An example on training a Random Tree Classifier for letter recognition can be found at opencv_source_code\samples\cpp\letter_recog.cpp
RandomizedTree::read
------------------------
Reads a pre-saved randomized tree from a file or stream.
.. ocv:function:: RandomizedTree::read(const char* file_name, int num_quant_bits)
.. ocv:function:: RandomizedTree::read(std::istream &is, int num_quant_bits)
:param file_name: Name of the file that contains randomized tree data.
:param is: Input stream associated with the file that contains randomized tree data.
:param num_quant_bits: Number of bits used for quantization.
RandomizedTree::write
-------------------------
Writes the current randomized tree to a file or stream.
.. ocv:function:: void RandomizedTree::write(const char* file_name) const
.. ocv:function:: void RandomizedTree::write(std::ostream &os) const
:param file_name: Name of the file where randomized tree data is stored.
:param os: Output stream associated with the file where randomized tree data is stored.
RandomizedTree::applyQuantization
-------------------------------------
.. ocv:function:: void RandomizedTree::applyQuantization(int num_quant_bits)
Applies quantization to the current randomized tree.
:param num_quant_bits: Number of bits used for quantization.
RTreeNode
---------
.. ocv:struct:: RTreeNode
Class containing a base structure for ``RandomizedTree``. ::
struct RTreeNode
{
short offset1, offset2;
RTreeNode() {}
RTreeNode(uchar x1, uchar y1, uchar x2, uchar y2)
: offset1(y1*PATCH_SIZE + x1),
offset2(y2*PATCH_SIZE + x2)
{}
//! Left child on 0, right child on 1
inline bool operator() (uchar* patch_data) const
{
return patch_data[offset1] > patch_data[offset2];
}
};
RTreeClassifier
---------------
.. ocv:class:: RTreeClassifier
Class containing ``RTreeClassifier``. It represents the Calonder descriptor originally introduced by Michael Calonder. ::
class CV_EXPORTS RTreeClassifier
{
public:
static const int DEFAULT_TREES = 48;
static const size_t DEFAULT_NUM_QUANT_BITS = 4;
RTreeClassifier();
void train(std::vector<BaseKeypoint> const& base_set,
RNG &rng,
int num_trees = RTreeClassifier::DEFAULT_TREES,
int depth = DEFAULT_DEPTH,
int views = DEFAULT_VIEWS,
size_t reduced_num_dim = DEFAULT_REDUCED_NUM_DIM,
int num_quant_bits = DEFAULT_NUM_QUANT_BITS,
bool print_status = true);
void train(std::vector<BaseKeypoint> const& base_set,
RNG &rng,
PatchGenerator &make_patch,
int num_trees = RTreeClassifier::DEFAULT_TREES,
int depth = DEFAULT_DEPTH,
int views = DEFAULT_VIEWS,
size_t reduced_num_dim = DEFAULT_REDUCED_NUM_DIM,
int num_quant_bits = DEFAULT_NUM_QUANT_BITS,
bool print_status = true);
// sig must point to a memory block of at least
//classes()*sizeof(float|uchar) bytes
void getSignature(IplImage *patch, uchar *sig);
void getSignature(IplImage *patch, float *sig);
void getSparseSignature(IplImage *patch, float *sig,
float thresh);
static int countNonZeroElements(float *vec, int n, double tol=1e-10);
static inline void safeSignatureAlloc(uchar **sig, int num_sig=1,
int sig_len=176);
static inline uchar* safeSignatureAlloc(int num_sig=1,
int sig_len=176);
inline int classes() { return classes_; }
inline int original_num_classes()
{ return original_num_classes_; }
void setQuantization(int num_quant_bits);
void discardFloatPosteriors();
void read(const char* file_name);
void read(std::istream &is);
void write(const char* file_name) const;
void write(std::ostream &os) const;
std::vector<RandomizedTree> trees_;
private:
int classes_;
int num_quant_bits_;
uchar **posteriors_;
ushort *ptemp_;
int original_num_classes_;
bool keep_floats_;
};
RTreeClassifier::train
--------------------------
Trains a randomized tree classifier using an input set of keypoints.
.. ocv:function:: void RTreeClassifier::train( vector<BaseKeypoint> const& base_set, RNG & rng, int num_trees=RTreeClassifier::DEFAULT_TREES, int depth=RandomizedTree::DEFAULT_DEPTH, int views=RandomizedTree::DEFAULT_VIEWS, size_t reduced_num_dim=RandomizedTree::DEFAULT_REDUCED_NUM_DIM, int num_quant_bits=DEFAULT_NUM_QUANT_BITS )
.. ocv:function:: void RTreeClassifier::train( vector<BaseKeypoint> const& base_set, RNG & rng, PatchGenerator & make_patch, int num_trees=RTreeClassifier::DEFAULT_TREES, int depth=RandomizedTree::DEFAULT_DEPTH, int views=RandomizedTree::DEFAULT_VIEWS, size_t reduced_num_dim=RandomizedTree::DEFAULT_REDUCED_NUM_DIM, int num_quant_bits=DEFAULT_NUM_QUANT_BITS )
:param base_set: Vector of the ``BaseKeypoint`` type. It contains image keypoints used for training.
:param rng: Random-number generator used for training.
:param make_patch: Patch generator used for training.
:param num_trees: Number of randomized trees used in ``RTreeClassificator`` .
:param depth: Maximum tree depth.
:param views: Number of random views of each keypoint neighborhood to generate.
:param reduced_num_dim: Number of dimensions used in the compressed signature.
:param num_quant_bits: Number of bits used for quantization.
RTreeClassifier::getSignature
---------------------------------
Returns a signature for an image patch.
.. ocv:function:: void RTreeClassifier::getSignature(IplImage *patch, uchar *sig)
.. ocv:function:: void RTreeClassifier::getSignature(IplImage *patch, float *sig)
:param patch: Image patch to calculate the signature for.
:param sig: Output signature (array dimension is ``reduced_num_dim)`` .
RTreeClassifier::getSparseSignature
---------------------------------------
Returns a sparse signature for an image patch
.. ocv:function:: void RTreeClassifier::getSparseSignature(IplImage *patch, float *sig, float thresh)
:param patch: Image patch to calculate the signature for.
:param sig: Output signature (array dimension is ``reduced_num_dim)`` .
:param thresh: Threshold used for compressing the signature.
Returns a signature for an image patch similarly to ``getSignature`` but uses a threshold for removing all signature elements below the threshold so that the signature is compressed.
RTreeClassifier::countNonZeroElements
-----------------------------------------
Returns the number of non-zero elements in an input array.
.. ocv:function:: static int RTreeClassifier::countNonZeroElements(float *vec, int n, double tol=1e-10)
:param vec: Input vector containing float elements.
:param n: Input vector size.
:param tol: Threshold used for counting elements. All elements less than ``tol`` are considered as zero elements.
RTreeClassifier::read
-------------------------
Reads a pre-saved ``RTreeClassifier`` from a file or stream.
.. ocv:function:: void RTreeClassifier::read(const char* file_name)
.. ocv:function:: void RTreeClassifier::read( std::istream & is )
:param file_name: Name of the file that contains randomized tree data.
:param is: Input stream associated with the file that contains randomized tree data.
RTreeClassifier::write
--------------------------
Writes the current ``RTreeClassifier`` to a file or stream.
.. ocv:function:: void RTreeClassifier::write(const char* file_name) const
.. ocv:function:: void RTreeClassifier::write(std::ostream &os) const
:param file_name: Name of the file where randomized tree data is stored.
:param os: Output stream associated with the file where randomized tree data is stored.
RTreeClassifier::setQuantization
------------------------------------
Applies quantization to the current randomized tree.
.. ocv:function:: void RTreeClassifier::setQuantization(int num_quant_bits)
:param num_quant_bits: Number of bits used for quantization.
The example below demonstrates the usage of ``RTreeClassifier`` for matching the features. The features are extracted from the test and train images with SURF. Output is
:math:`best\_corr` and
:math:`best\_corr\_idx` arrays that keep the best probabilities and corresponding features indices for every train feature. ::
CvMemStorage* storage = cvCreateMemStorage(0);
CvSeq *objectKeypoints = 0, *objectDescriptors = 0;
CvSeq *imageKeypoints = 0, *imageDescriptors = 0;
CvSURFParams params = cvSURFParams(500, 1);
cvExtractSURF( test_image, 0, &imageKeypoints, &imageDescriptors,
storage, params );
cvExtractSURF( train_image, 0, &objectKeypoints, &objectDescriptors,
storage, params );
RTreeClassifier detector;
int patch_width = PATCH_SIZE;
iint patch_height = PATCH_SIZE;
vector<BaseKeypoint> base_set;
int i=0;
CvSURFPoint* point;
for (i=0;i<(n_points > 0 ? n_points : objectKeypoints->total);i++)
{
point=(CvSURFPoint*)cvGetSeqElem(objectKeypoints,i);
base_set.push_back(
BaseKeypoint(point->pt.x,point->pt.y,train_image));
}
//Detector training
RNG rng( cvGetTickCount() );
PatchGenerator gen(0,255,2,false,0.7,1.3,-CV_PI/3,CV_PI/3,
-CV_PI/3,CV_PI/3);
printf("RTree Classifier training...n");
detector.train(base_set,rng,gen,24,DEFAULT_DEPTH,2000,
(int)base_set.size(), detector.DEFAULT_NUM_QUANT_BITS);
printf("Donen");
float* signature = new float[detector.original_num_classes()];
float* best_corr;
int* best_corr_idx;
if (imageKeypoints->total > 0)
{
best_corr = new float[imageKeypoints->total];
best_corr_idx = new int[imageKeypoints->total];
}
for(i=0; i < imageKeypoints->total; i++)
{
point=(CvSURFPoint*)cvGetSeqElem(imageKeypoints,i);
int part_idx = -1;
float prob = 0.0f;
CvRect roi = cvRect((int)(point->pt.x) - patch_width/2,
(int)(point->pt.y) - patch_height/2,
patch_width, patch_height);
cvSetImageROI(test_image, roi);
roi = cvGetImageROI(test_image);
if(roi.width != patch_width || roi.height != patch_height)
{
best_corr_idx[i] = part_idx;
best_corr[i] = prob;
}
else
{
cvSetImageROI(test_image, roi);
IplImage* roi_image =
cvCreateImage(cvSize(roi.width, roi.height),
test_image->depth, test_image->nChannels);
cvCopy(test_image,roi_image);
detector.getSignature(roi_image, signature);
for (int j = 0; j< detector.original_num_classes();j++)
{
if (prob < signature[j])
{
part_idx = j;
prob = signature[j];
}
}
best_corr_idx[i] = part_idx;
best_corr[i] = prob;
if (roi_image)
cvReleaseImage(&roi_image);
}
cvResetImageROI(test_image);
}
..
modules/legacy/doc/histograms.rst
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Histograms
==========
.. highlight:: cpp
CalcPGH
-------
Calculates a pair-wise geometrical histogram for a contour.
.. ocv:cfunction:: void cvCalcPGH( const CvSeq* contour, CvHistogram* hist )
:param contour: Input contour. Currently, only integer point coordinates are allowed.
:param hist: Calculated histogram. It must be two-dimensional.
The function calculates a 2D pair-wise geometrical histogram (PGH), described in [Iivarinen97]_ for the contour. The algorithm considers every pair of contour
edges. The angle between the edges and the minimum/maximum distances
are determined for every pair. To do this, each of the edges in turn
is taken as the base, while the function loops through all the other
edges. When the base edge and any other edge are considered, the minimum
and maximum distances from the points on the non-base edge and line of
the base edge are selected. The angle between the edges defines the row
of the histogram in which all the bins that correspond to the distance
between the calculated minimum and maximum distances are incremented
(that is, the histogram is transposed relatively to the definition in the original paper). The histogram can be used for contour matching.
.. [Iivarinen97] Jukka Iivarinen, Markus Peura, Jaakko Srel, and Ari Visa. *Comparison of Combined Shape Descriptors for Irregular Objects*, 8th British Machine Vision Conference, BMVC'97. http://www.cis.hut.fi/research/IA/paper/publications/bmvc97/bmvc97.html
QueryHistValue*D
----------------
Queries the value of the histogram bin.
.. ocv:cfunction:: float cvQueryHistValue_1D(CvHistogram hist, int idx0)
.. ocv:cfunction:: float cvQueryHistValue_2D(CvHistogram hist, int idx0, int idx1)
.. ocv:cfunction:: float cvQueryHistValue_3D(CvHistogram hist, int idx0, int idx1, int idx2)
.. ocv:cfunction:: float cvQueryHistValue_nD(CvHistogram hist, const int* idx)
:param hist: Histogram.
:param idx0: 0-th index.
:param idx1: 1-st index.
:param idx2: 2-nd index.
:param idx: Array of indices.
The macros return the value of the specified bin of the 1D, 2D, 3D, or N-D histogram. In case of a sparse histogram, the function returns 0. If the bin is not present in the histogram, no new bin is created.
GetHistValue\_?D
----------------
Returns a pointer to the histogram bin.
.. ocv:cfunction:: float cvGetHistValue_1D(CvHistogram hist, int idx0)
.. ocv:cfunction:: float cvGetHistValue_2D(CvHistogram hist, int idx0, int idx1)
.. ocv:cfunction:: float cvGetHistValue_3D(CvHistogram hist, int idx0, int idx1, int idx2)
.. ocv:cfunction:: float cvGetHistValue_nD(CvHistogram hist, int idx)
:param hist: Histogram.
:param idx0: 0-th index.
:param idx1: 1-st index.
:param idx2: 2-nd index.
:param idx: Array of indices.
::
#define cvGetHistValue_1D( hist, idx0 )
((float*)(cvPtr1D( (hist)->bins, (idx0), 0 ))
#define cvGetHistValue_2D( hist, idx0, idx1 )
((float*)(cvPtr2D( (hist)->bins, (idx0), (idx1), 0 )))
#define cvGetHistValue_3D( hist, idx0, idx1, idx2 )
((float*)(cvPtr3D( (hist)->bins, (idx0), (idx1), (idx2), 0 )))
#define cvGetHistValue_nD( hist, idx )
((float*)(cvPtrND( (hist)->bins, (idx), 0 )))
..
The macros ``GetHistValue`` return a pointer to the specified bin of the 1D, 2D, 3D, or N-D histogram. In case of a sparse histogram, the function creates a new bin and sets it to 0, unless it exists already.
modules/legacy/doc/legacy.rst
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********************************
legacy. Deprecated stuff
********************************
.. highlight:: cpp
.. toctree::
:maxdepth: 2
motion_analysis
expectation_maximization
histograms
planar_subdivisions
feature_detection_and_description
common_interfaces_of_descriptor_extractors
common_interfaces_of_generic_descriptor_matchers
modules/legacy/doc/motion_analysis.rst
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Motion Analysis
===============
.. highlight:: cpp
CalcOpticalFlowBM
-----------------
Calculates the optical flow for two images by using the block matching method.
.. ocv:cfunction:: void cvCalcOpticalFlowBM( const CvArr* prev, const CvArr* curr, CvSize block_size, CvSize shift_size, CvSize max_range, int use_previous, CvArr* velx, CvArr* vely )
:param prev: First image, 8-bit, single-channel
:param curr: Second image, 8-bit, single-channel
:param block_size: Size of basic blocks that are compared
:param shift_size: Block coordinate increments
:param max_range: Size of the scanned neighborhood in pixels around the block
:param use_previous: Flag that specifies whether to use the input velocity as initial approximations or not.
:param velx: Horizontal component of the optical flow of
.. math::
\left \lfloor \frac{\texttt{prev->width} - \texttt{block\_size.width}}{\texttt{shift\_size.width}} \right \rfloor \times \left \lfloor \frac{\texttt{prev->height} - \texttt{block\_size.height}}{\texttt{shift\_size.height}} \right \rfloor
size, 32-bit floating-point, single-channel
:param vely: Vertical component of the optical flow of the same size ``velx`` , 32-bit floating-point, single-channel
The function calculates the optical flow for overlapped blocks ``block_size.width x block_size.height`` pixels each, thus the velocity fields are smaller than the original images. For every block in ``prev``
the functions tries to find a similar block in ``curr`` in some neighborhood of the original block or shifted by ``(velx(x0,y0), vely(x0,y0))`` block as has been calculated by previous function call (if ``use_previous=1``)
CalcOpticalFlowHS
-----------------
Calculates the optical flow for two images using Horn-Schunck algorithm.
.. ocv:cfunction:: void cvCalcOpticalFlowHS(const CvArr* prev, const CvArr* curr, int use_previous, CvArr* velx, CvArr* vely, double lambda, CvTermCriteria criteria)
:param prev: First image, 8-bit, single-channel
:param curr: Second image, 8-bit, single-channel
:param use_previous: Flag that specifies whether to use the input velocity as initial approximations or not.
:param velx: Horizontal component of the optical flow of the same size as input images, 32-bit floating-point, single-channel
:param vely: Vertical component of the optical flow of the same size as input images, 32-bit floating-point, single-channel
:param lambda: Smoothness weight. The larger it is, the smoother optical flow map you get.
:param criteria: Criteria of termination of velocity computing
The function computes the flow for every pixel of the first input image using the Horn and Schunck algorithm [Horn81]_. The function is obsolete. To track sparse features, use :ocv:func:`calcOpticalFlowPyrLK`. To track all the pixels, use :ocv:func:`calcOpticalFlowFarneback`.
CalcOpticalFlowLK
-----------------
Calculates the optical flow for two images using Lucas-Kanade algorithm.
.. ocv:cfunction:: void cvCalcOpticalFlowLK( const CvArr* prev, const CvArr* curr, CvSize win_size, CvArr* velx, CvArr* vely )
:param prev: First image, 8-bit, single-channel
:param curr: Second image, 8-bit, single-channel
:param win_size: Size of the averaging window used for grouping pixels
:param velx: Horizontal component of the optical flow of the same size as input images, 32-bit floating-point, single-channel
:param vely: Vertical component of the optical flow of the same size as input images, 32-bit floating-point, single-channel
The function computes the flow for every pixel of the first input image using the Lucas and Kanade algorithm [Lucas81]_. The function is obsolete. To track sparse features, use :ocv:func:`calcOpticalFlowPyrLK`. To track all the pixels, use :ocv:func:`calcOpticalFlowFarneback`.
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modules/legacy/doc/planar_subdivisions.rst
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Planar Subdivisions (C API)
============================
.. highlight:: c
CvSubdiv2D
----------
.. ocv:struct:: CvSubdiv2D
Planar subdivision.
::
#define CV_SUBDIV2D_FIELDS() \
CV_GRAPH_FIELDS() \
int quad_edges; \
int is_geometry_valid; \
CvSubdiv2DEdge recent_edge; \
CvPoint2D32f topleft; \
CvPoint2D32f bottomright;
typedef struct CvSubdiv2D
{
CV_SUBDIV2D_FIELDS()
}
CvSubdiv2D;
..
Planar subdivision is the subdivision of a plane into a set of
non-overlapped regions (facets) that cover the whole plane. The above
structure describes a subdivision built on a 2D point set, where the points
are linked together and form a planar graph, which, together with a few
edges connecting the exterior subdivision points (namely, convex hull points)
with infinity, subdivides a plane into facets by its edges.
For every subdivision, there is a dual subdivision in which facets and
points (subdivision vertices) swap their roles. This means that a facet is
treated as a vertex (called a virtual point below) of the dual subdivision and
the original subdivision vertices become facets. In the figure below, the
original subdivision is marked with solid lines and dual subdivision -
with dotted lines.
.. image:: pics/subdiv.png
OpenCV subdivides a plane into triangles using the Delaunay's
algorithm. Subdivision is built iteratively starting from a dummy
triangle that includes all the subdivision points for sure. In this
case, the dual subdivision is a Voronoi diagram of the input 2D point set. The
subdivisions can be used for the 3D piece-wise transformation of a plane,
morphing, fast location of points on the plane, building special graphs
(such as NNG,RNG), and so forth.
CvQuadEdge2D
------------
.. ocv:struct:: CvQuadEdge2D
Quad-edge of a planar subdivision.
::
/* one of edges within quad-edge, lower 2 bits is index (0..3)
and upper bits are quad-edge pointer */
typedef long CvSubdiv2DEdge;
/* quad-edge structure fields */
#define CV_QUADEDGE2D_FIELDS() \
int flags; \
struct CvSubdiv2DPoint* pt[4]; \
CvSubdiv2DEdge next[4];
typedef struct CvQuadEdge2D
{
CV_QUADEDGE2D_FIELDS()
}
CvQuadEdge2D;
..
Quad-edge is a basic element of a subdivision containing four edges (e, eRot, reversed e, and reversed eRot):
.. image:: pics/quadedge.png
CvSubdiv2DPoint
---------------
.. ocv:struct:: CvSubdiv2DPoint
Point of an original or dual subdivision.
::
#define CV_SUBDIV2D_POINT_FIELDS()\
int flags; \
CvSubdiv2DEdge first; \
CvPoint2D32f pt; \
int id;
#define CV_SUBDIV2D_VIRTUAL_POINT_FLAG (1 << 30)
typedef struct CvSubdiv2DPoint
{
CV_SUBDIV2D_POINT_FIELDS()
}
CvSubdiv2DPoint;
..
* id
This integer can be used to index auxiliary data associated with each vertex of the planar subdivision.
CalcSubdivVoronoi2D
-------------------
Calculates the coordinates of the Voronoi diagram cells.
.. ocv:cfunction:: void cvCalcSubdivVoronoi2D( CvSubdiv2D* subdiv )
:param subdiv: Delaunay subdivision, in which all the points are already added.
The function calculates the coordinates
of virtual points. All virtual points corresponding to a vertex of the
original subdivision form (when connected together) a boundary of the Voronoi
cell at that point.
ClearSubdivVoronoi2D
--------------------
Removes all virtual points.
.. ocv:cfunction:: void cvClearSubdivVoronoi2D( CvSubdiv2D* subdiv )
:param subdiv: Delaunay subdivision.
The function removes all of the virtual points. It
is called internally in
:ocv:cfunc:`CalcSubdivVoronoi2D`
if the subdivision
was modified after the previous call to the function.
CreateSubdivDelaunay2D
----------------------
Creates an empty Delaunay triangulation.
.. ocv:cfunction:: CvSubdiv2D* cvCreateSubdivDelaunay2D( CvRect rect, CvMemStorage* storage )
:param rect: Rectangle that includes all of the 2D points that are to be added to the subdivision.
:param storage: Container for the subdivision.
The function creates an empty Delaunay
subdivision where 2D points can be added using the function
:ocv:cfunc:`SubdivDelaunay2DInsert`
. All of the points to be added must be within
the specified rectangle, otherwise a runtime error is raised.
Note that the triangulation is a single large triangle that covers the given rectangle. Hence the three vertices of this triangle are outside the rectangle
``rect``
.
FindNearestPoint2D
------------------
Finds the subdivision vertex closest to the given point.
.. ocv:cfunction:: CvSubdiv2DPoint* cvFindNearestPoint2D( CvSubdiv2D* subdiv, CvPoint2D32f pt )
:param subdiv: Delaunay or another subdivision.
:param pt: Input point.
The function is another function that
locates the input point within the subdivision. It finds the subdivision vertex that
is the closest to the input point. It is not necessarily one of vertices
of the facet containing the input point, though the facet (located using
:ocv:cfunc:`Subdiv2DLocate`
) is used as a starting
point. The function returns a pointer to the found subdivision vertex.
Subdiv2DEdgeDst
---------------
Returns the edge destination.
.. ocv:cfunction:: CvSubdiv2DPoint* cvSubdiv2DEdgeDst( CvSubdiv2DEdge edge )
:param edge: Subdivision edge (not a quad-edge).
The function returns the edge destination. The
returned pointer may be NULL if the edge is from a dual subdivision and
the virtual point coordinates are not calculated yet. The virtual points
can be calculated using the function
:ocv:cfunc:`CalcSubdivVoronoi2D`.
Subdiv2DGetEdge
---------------
Returns one of the edges related to the given edge.
.. ocv:cfunction:: CvSubdiv2DEdge cvSubdiv2DGetEdge( CvSubdiv2DEdge edge, CvNextEdgeType type )
:param edge: Subdivision edge (not a quad-edge).
:param type: Parameter specifying which of the related edges to return. The following values are possible:
* **CV_NEXT_AROUND_ORG** next around the edge origin ( ``eOnext`` on the picture below if ``e`` is the input edge)
* **CV_NEXT_AROUND_DST** next around the edge vertex ( ``eDnext`` )
* **CV_PREV_AROUND_ORG** previous around the edge origin (reversed ``eRnext`` )
* **CV_PREV_AROUND_DST** previous around the edge destination (reversed ``eLnext`` )
* **CV_NEXT_AROUND_LEFT** next around the left facet ( ``eLnext`` )
* **CV_NEXT_AROUND_RIGHT** next around the right facet ( ``eRnext`` )
* **CV_PREV_AROUND_LEFT** previous around the left facet (reversed ``eOnext`` )
* **CV_PREV_AROUND_RIGHT** previous around the right facet (reversed ``eDnext`` )
.. image:: pics/quadedge.png
The function returns one of the edges related to the input edge.
Subdiv2DNextEdge
----------------
Returns next edge around the edge origin.
.. ocv:cfunction:: CvSubdiv2DEdge cvSubdiv2DNextEdge( CvSubdiv2DEdge edge )
:param edge: Subdivision edge (not a quad-edge).
The function returns the next edge around the edge origin:
``eOnext``
on the picture above if
``e``
is the input edge).
Subdiv2DLocate
--------------
Returns the location of a point within a Delaunay triangulation.
.. ocv:cfunction:: CvSubdiv2DPointLocation cvSubdiv2DLocate( CvSubdiv2D* subdiv, CvPoint2D32f pt, CvSubdiv2DEdge* edge, CvSubdiv2DPoint** vertex=NULL )
:param subdiv: Delaunay or another subdivision.
:param pt: Point to locate.
:param edge: Output edge that the point belongs to or is located to the right of it.
:param vertex: Optional output vertex double pointer the input point coincides with.
The function locates the input point within the subdivision. There are five cases:
*
The point falls into some facet. The function returns
``CV_PTLOC_INSIDE``
and
``*edge``
will contain one of edges of the facet.
*
The point falls onto the edge. The function returns
``CV_PTLOC_ON_EDGE``
and
``*edge``
will contain this edge.
*
The point coincides with one of the subdivision vertices. The function returns
``CV_PTLOC_VERTEX``
and
``*vertex``
will contain a pointer to the vertex.
*
The point is outside the subdivision reference rectangle. The function returns
``CV_PTLOC_OUTSIDE_RECT``
and no pointers are filled.
*
One of input arguments is invalid. A runtime error is raised or, if silent or "parent" error processing mode is selected,
``CV_PTLOC_ERROR``
is returnd.
Subdiv2DRotateEdge
------------------
Returns another edge of the same quad-edge.
.. ocv:cfunction:: CvSubdiv2DEdge cvSubdiv2DRotateEdge( CvSubdiv2DEdge edge, int rotate )
:param edge: Subdivision edge (not a quad-edge).
:param rotate: Parameter specifying which of the edges of the same quad-edge as the input one to return. The following values are possible:
* **0** the input edge ( ``e`` on the picture below if ``e`` is the input edge)
* **1** the rotated edge ( ``eRot`` )
* **2** the reversed edge (reversed ``e`` (in green))
* **3** the reversed rotated edge (reversed ``eRot`` (in green))
The function returns one of the edges of the same quad-edge as the input edge.
SubdivDelaunay2DInsert
----------------------
Inserts a single point into a Delaunay triangulation.
.. ocv:cfunction:: CvSubdiv2DPoint* cvSubdivDelaunay2DInsert( CvSubdiv2D* subdiv, CvPoint2D32f pt)
:param subdiv: Delaunay subdivision created by the function :ocv:cfunc:`CreateSubdivDelaunay2D`.
:param pt: Inserted point.
The function inserts a single point into a subdivision and modifies the subdivision topology appropriately. If a point with the same coordinates exists already, no new point is added. The function returns a pointer to the allocated point. No virtual point coordinates are calculated at this stage.
modules/legacy/include/opencv2/legacy.hpp
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modules/legacy/include/opencv2/legacy/blobtrack.hpp
-948
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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifndef __OPENCV_VIDEOSURVEILLANCE_H__
#define __OPENCV_VIDEOSURVEILLANCE_H__
/* Turn off the functionality until cvaux/src/Makefile.am gets updated: */
//#if _MSC_VER >= 1200
#include "opencv2/core/core_c.h"
#include <stdio.h>
#if (defined _MSC_VER && _MSC_VER >= 1200) || defined __BORLANDC__
#define cv_stricmp stricmp
#define cv_strnicmp strnicmp
#if defined WINCE
#define strdup _strdup
#define stricmp _stricmp
#endif
#elif defined __GNUC__ || defined __sun
#define cv_stricmp strcasecmp
#define cv_strnicmp strncasecmp
#else
#error Do not know how to make case-insensitive string comparison on this platform
#endif
//struct DefParam;
struct CvDefParam
{
struct CvDefParam* next;
char* pName;
char* pComment;
double* pDouble;
double Double;
float* pFloat;
float Float;
int* pInt;
int Int;
char** pStr;
char* Str;
};
class CV_EXPORTS CvVSModule
{
private: /* Internal data: */
CvDefParam* m_pParamList;
char* m_pModuleTypeName;
char* m_pModuleName;
char* m_pNickName;
protected:
int m_Wnd;
public: /* Constructor and destructor: */
CvVSModule();
virtual ~CvVSModule();
private: /* Internal functions: */
void FreeParam(CvDefParam** pp);
CvDefParam* NewParam(const char* name);
CvDefParam* GetParamPtr(int index);
CvDefParam* GetParamPtr(const char* name);
protected: /* INTERNAL INTERFACE */
int IsParam(const char* name);
void AddParam(const char* name, double* pAddr);
void AddParam(const char* name, float* pAddr);
void AddParam(const char* name, int* pAddr);
void AddParam(const char* name, const char** pAddr);
void AddParam(const char* name);
void CommentParam(const char* name, const char* pComment);
void SetTypeName(const char* name);
void SetModuleName(const char* name);
void DelParam(const char* name);
public: /* EXTERNAL INTERFACE */
const char* GetParamName(int index);
const char* GetParamComment(const char* name);
double GetParam(const char* name);
const char* GetParamStr(const char* name);
void SetParam(const char* name, double val);
void SetParamStr(const char* name, const char* str);
void TransferParamsFromChild(CvVSModule* pM, const char* prefix = NULL);
void TransferParamsToChild(CvVSModule* pM, char* prefix = NULL);
virtual void ParamUpdate();
const char* GetTypeName();
int IsModuleTypeName(const char* name);
char* GetModuleName();
int IsModuleName(const char* name);
void SetNickName(const char* pStr);
const char* GetNickName();
virtual void SaveState(CvFileStorage*);
virtual void LoadState(CvFileStorage*, CvFileNode*);
virtual void Release() = 0;
};/* CvVMModule */
CV_EXPORTS void cvWriteStruct(CvFileStorage* fs, const char* name, void* addr, const char* desc, int num=1);
CV_EXPORTS void cvReadStructByName(CvFileStorage* fs, CvFileNode* node, const char* name, void* addr, const char* desc);
/* FOREGROUND DETECTOR INTERFACE */
class CV_EXPORTS CvFGDetector : public CvVSModule
{
public:
CvFGDetector();
virtual IplImage* GetMask() = 0;
/* Process current image: */
virtual void Process(IplImage* pImg) = 0;
/* Release foreground detector: */
virtual void Release() = 0;
};
CV_EXPORTS void cvReleaseFGDetector(CvFGDetector** ppT );
CV_EXPORTS CvFGDetector* cvCreateFGDetectorBase(int type, void *param);
/* BLOB STRUCTURE*/
struct CvBlob
{
float x,y; /* blob position */
float w,h; /* blob sizes */
int ID; /* blob ID */
};
inline CvBlob cvBlob(float x,float y, float w, float h)
{
CvBlob B = {x,y,w,h,0};
return B;
}
#define CV_BLOB_MINW 5
#define CV_BLOB_MINH 5
#define CV_BLOB_ID(pB) (((CvBlob*)(pB))->ID)
#define CV_BLOB_CENTER(pB) cvPoint2D32f(((CvBlob*)(pB))->x,((CvBlob*)(pB))->y)
#define CV_BLOB_X(pB) (((CvBlob*)(pB))->x)
#define CV_BLOB_Y(pB) (((CvBlob*)(pB))->y)
#define CV_BLOB_WX(pB) (((CvBlob*)(pB))->w)
#define CV_BLOB_WY(pB) (((CvBlob*)(pB))->h)
#define CV_BLOB_RX(pB) (0.5f*CV_BLOB_WX(pB))
#define CV_BLOB_RY(pB) (0.5f*CV_BLOB_WY(pB))
#define CV_BLOB_RECT(pB) cvRect(cvRound(((CvBlob*)(pB))->x-CV_BLOB_RX(pB)),cvRound(((CvBlob*)(pB))->y-CV_BLOB_RY(pB)),cvRound(CV_BLOB_WX(pB)),cvRound(CV_BLOB_WY(pB)))
/* END BLOB STRUCTURE*/
/* simple BLOBLIST */
class CV_EXPORTS CvBlobSeq
{
public:
CvBlobSeq(int BlobSize = sizeof(CvBlob))
{
m_pMem = cvCreateMemStorage();
m_pSeq = cvCreateSeq(0,sizeof(CvSeq),BlobSize,m_pMem);
strcpy(m_pElemFormat,"ffffi");
}
virtual ~CvBlobSeq()
{
cvReleaseMemStorage(&m_pMem);
};
virtual CvBlob* GetBlob(int BlobIndex)
{
return (CvBlob*)cvGetSeqElem(m_pSeq,BlobIndex);
};
virtual CvBlob* GetBlobByID(int BlobID)
{
int i;
for(i=0; i<m_pSeq->total; ++i)
if(BlobID == CV_BLOB_ID(GetBlob(i)))
return GetBlob(i);
return NULL;
};
virtual void DelBlob(int BlobIndex)
{
cvSeqRemove(m_pSeq,BlobIndex);
};
virtual void DelBlobByID(int BlobID)
{
int i;
for(i=0; i<m_pSeq->total; ++i)
{
if(BlobID == CV_BLOB_ID(GetBlob(i)))
{
DelBlob(i);
return;
}
}
};
virtual void Clear()
{
cvClearSeq(m_pSeq);
};
virtual void AddBlob(CvBlob* pB)
{
cvSeqPush(m_pSeq,pB);
};
virtual int GetBlobNum()
{
return m_pSeq->total;
};
virtual void Write(CvFileStorage* fs, const char* name)
{
const char* attr[] = {"dt",m_pElemFormat,NULL};
if(fs)
{
cvWrite(fs,name,m_pSeq,cvAttrList(attr,NULL));
}
}
virtual void Load(CvFileStorage* fs, CvFileNode* node)
{
if(fs==NULL) return;
CvSeq* pSeq = (CvSeq*)cvRead(fs, node);
if(pSeq)
{
int i;
cvClearSeq(m_pSeq);
for(i=0;i<pSeq->total;++i)
{
void* pB = cvGetSeqElem( pSeq, i );
cvSeqPush( m_pSeq, pB );
}
}
}
void AddFormat(const char* str){strcat(m_pElemFormat,str);}
protected:
CvMemStorage* m_pMem;
CvSeq* m_pSeq;
char m_pElemFormat[1024];
};
/* simple BLOBLIST */
/* simple TRACKLIST */
struct CvBlobTrack
{
int TrackID;
int StartFrame;
CvBlobSeq* pBlobSeq;
};
class CV_EXPORTS CvBlobTrackSeq
{
public:
CvBlobTrackSeq(int TrackSize = sizeof(CvBlobTrack));
virtual ~CvBlobTrackSeq();
virtual CvBlobTrack* GetBlobTrack(int TrackIndex);
virtual CvBlobTrack* GetBlobTrackByID(int TrackID);
virtual void DelBlobTrack(int TrackIndex);
virtual void DelBlobTrackByID(int TrackID);
virtual void Clear();
virtual void AddBlobTrack(int TrackID, int StartFrame = 0);
virtual int GetBlobTrackNum();
protected:
CvMemStorage* m_pMem;
CvSeq* m_pSeq;
};
/* simple TRACKLIST */
/* BLOB DETECTOR INTERFACE */
class CV_EXPORTS CvBlobDetector: public CvVSModule
{
public:
CvBlobDetector(){SetTypeName("BlobDetector");};
/* Try to detect new blob entrance based on foreground mask. */
/* pFGMask - image of foreground mask */
/* pNewBlob - pointer to CvBlob structure which will be filled if new blob entrance detected */
/* pOldBlobList - pointer to blob list which already exist on image */
virtual int DetectNewBlob(IplImage* pImg, IplImage* pImgFG, CvBlobSeq* pNewBlobList, CvBlobSeq* pOldBlobList) = 0;
/* release blob detector */
virtual void Release()=0;
};
/* Release any blob detector: */
CV_EXPORTS void cvReleaseBlobDetector(CvBlobDetector** ppBD);
/* Declarations of constructors of implemented modules: */
CV_EXPORTS CvBlobDetector* cvCreateBlobDetectorSimple();
CV_EXPORTS CvBlobDetector* cvCreateBlobDetectorCC();
struct CV_EXPORTS CvDetectedBlob : public CvBlob
{
float response;
};
CV_INLINE CvDetectedBlob cvDetectedBlob( float x, float y, float w, float h, int ID = 0, float response = 0.0F )
{
CvDetectedBlob b;
b.x = x; b.y = y; b.w = w; b.h = h; b.ID = ID; b.response = response;
return b;
}
class CV_EXPORTS CvObjectDetector
{
public:
CvObjectDetector( const char* /*detector_file_name*/ = 0 );
~CvObjectDetector();
/*
* Release the current detector and load new detector from file
* (if detector_file_name is not 0)
* Return true on success:
*/
bool Load( const char* /*detector_file_name*/ = 0 );
/* Return min detector window size: */
CvSize GetMinWindowSize() const;
/* Return max border: */
int GetMaxBorderSize() const;
/*
* Detect the object on the image and push the detected
* blobs into <detected_blob_seq> which must be the sequence of <CvDetectedBlob>s
*/
void Detect( const CvArr* /*img*/, /* out */ CvBlobSeq* /*detected_blob_seq*/ = 0 );
protected:
class CvObjectDetectorImpl* impl;
};
CV_INLINE CvRect cvRectIntersection( const CvRect r1, const CvRect r2 )
{
CvRect r = cvRect( MAX(r1.x, r2.x), MAX(r1.y, r2.y), 0, 0 );
r.width = MIN(r1.x + r1.width, r2.x + r2.width) - r.x;
r.height = MIN(r1.y + r1.height, r2.y + r2.height) - r.y;
return r;
}
/*
* CvImageDrawer
*
* Draw on an image the specified ROIs from the source image and
* given blobs as ellipses or rectangles:
*/
struct CvDrawShape
{
enum {RECT, ELLIPSE} shape;
CvScalar color;
};
/*extern const CvDrawShape icv_shape[] =
{
{ CvDrawShape::ELLIPSE, CV_RGB(255,0,0) },
{ CvDrawShape::ELLIPSE, CV_RGB(0,255,0) },
{ CvDrawShape::ELLIPSE, CV_RGB(0,0,255) },
{ CvDrawShape::ELLIPSE, CV_RGB(255,255,0) },
{ CvDrawShape::ELLIPSE, CV_RGB(0,255,255) },
{ CvDrawShape::ELLIPSE, CV_RGB(255,0,255) }
};*/
class CV_EXPORTS CvImageDrawer
{
public:
CvImageDrawer() : m_image(0) {}
~CvImageDrawer() { cvReleaseImage( &m_image ); }
void SetShapes( const CvDrawShape* shapes, int num );
/* <blob_seq> must be the sequence of <CvDetectedBlob>s */
IplImage* Draw( const CvArr* src, CvBlobSeq* blob_seq = 0, const CvSeq* roi_seq = 0 );
IplImage* GetImage() { return m_image; }
protected:
//static const int MAX_SHAPES = sizeof(icv_shape) / sizeof(icv_shape[0]);;
IplImage* m_image;
CvDrawShape m_shape[16];
};
/* Trajectory generation module: */
class CV_EXPORTS CvBlobTrackGen: public CvVSModule
{
public:
CvBlobTrackGen(){SetTypeName("BlobTrackGen");};
virtual void SetFileName(char* pFileName) = 0;
virtual void AddBlob(CvBlob* pBlob) = 0;
virtual void Process(IplImage* pImg = NULL, IplImage* pFG = NULL) = 0;
virtual void Release() = 0;
};
inline void cvReleaseBlobTrackGen(CvBlobTrackGen** pBTGen)
{
if(*pBTGen)(*pBTGen)->Release();
*pBTGen = 0;
}
/* Declarations of constructors of implemented modules: */
CV_EXPORTS CvBlobTrackGen* cvCreateModuleBlobTrackGen1();
CV_EXPORTS CvBlobTrackGen* cvCreateModuleBlobTrackGenYML();
/* BLOB TRACKER INTERFACE */
class CV_EXPORTS CvBlobTracker: public CvVSModule
{
public:
CvBlobTracker();
/* Add new blob to track it and assign to this blob personal ID */
/* pBlob - pointer to structure with blob parameters (ID is ignored)*/
/* pImg - current image */
/* pImgFG - current foreground mask */
/* Return pointer to new added blob: */
virtual CvBlob* AddBlob(CvBlob* pBlob, IplImage* pImg, IplImage* pImgFG = NULL ) = 0;
/* Return number of currently tracked blobs: */
virtual int GetBlobNum() = 0;
/* Return pointer to specified by index blob: */
virtual CvBlob* GetBlob(int BlobIndex) = 0;
/* Delete blob by its index: */
virtual void DelBlob(int BlobIndex) = 0;
/* Process current image and track all existed blobs: */
virtual void Process(IplImage* pImg, IplImage* pImgFG = NULL) = 0;
/* Release blob tracker: */
virtual void Release() = 0;
/* Process one blob (for multi hypothesis tracing): */
virtual void ProcessBlob(int BlobIndex, CvBlob* pBlob, IplImage* /*pImg*/, IplImage* /*pImgFG*/ = NULL);
/* Get confidence/wieght/probability (0-1) for blob: */
virtual double GetConfidence(int /*BlobIndex*/, CvBlob* /*pBlob*/, IplImage* /*pImg*/, IplImage* /*pImgFG*/ = NULL);
virtual double GetConfidenceList(CvBlobSeq* pBlobList, IplImage* pImg, IplImage* pImgFG = NULL);
virtual void UpdateBlob(int /*BlobIndex*/, CvBlob* /*pBlob*/, IplImage* /*pImg*/, IplImage* /*pImgFG*/ = NULL);
/* Update all blob models: */
virtual void Update(IplImage* pImg, IplImage* pImgFG = NULL);
/* Return pointer to blob by its unique ID: */
virtual int GetBlobIndexByID(int BlobID);
/* Return pointer to blob by its unique ID: */
virtual CvBlob* GetBlobByID(int BlobID);
/* Delete blob by its ID: */
virtual void DelBlobByID(int BlobID);
/* Set new parameters for specified (by index) blob: */
virtual void SetBlob(int /*BlobIndex*/, CvBlob* /*pBlob*/);
/* Set new parameters for specified (by ID) blob: */
virtual void SetBlobByID(int BlobID, CvBlob* pBlob);
/* =============== MULTI HYPOTHESIS INTERFACE ================== */
/* Return number of position hyposetis of currently tracked blob: */
virtual int GetBlobHypNum(int /*BlobIdx*/);
/* Return pointer to specified blob hypothesis by index blob: */
virtual CvBlob* GetBlobHyp(int BlobIndex, int /*hypothesis*/);
/* Set new parameters for specified (by index) blob hyp
* (can be called several times for each hyp ):
*/
virtual void SetBlobHyp(int /*BlobIndex*/, CvBlob* /*pBlob*/);
};
CV_EXPORTS void cvReleaseBlobTracker(CvBlobTracker**ppT );
/* BLOB TRACKER INTERFACE */
/*BLOB TRACKER ONE INTERFACE */
class CV_EXPORTS CvBlobTrackerOne : public CvVSModule
{
public:
virtual void Init(CvBlob* pBlobInit, IplImage* pImg, IplImage* pImgFG = NULL) = 0;
virtual CvBlob* Process(CvBlob* pBlobPrev, IplImage* pImg, IplImage* pImgFG = NULL) = 0;
virtual void Release() = 0;
/* Non-required methods: */
virtual void SkipProcess(CvBlob* /*pBlobPrev*/, IplImage* /*pImg*/, IplImage* /*pImgFG*/ = NULL){};
virtual void Update(CvBlob* /*pBlob*/, IplImage* /*pImg*/, IplImage* /*pImgFG*/ = NULL){};
virtual void SetCollision(int /*CollisionFlag*/){}; /* call in case of blob collision situation*/
virtual double GetConfidence(CvBlob* /*pBlob*/, IplImage* /*pImg*/,
IplImage* /*pImgFG*/ = NULL, IplImage* /*pImgUnusedReg*/ = NULL)
{
return 1;
};
};
inline void cvReleaseBlobTrackerOne(CvBlobTrackerOne **ppT )
{
ppT[0]->Release();
ppT[0] = 0;
}
CV_EXPORTS CvBlobTracker* cvCreateBlobTrackerList(CvBlobTrackerOne* (*create)());
/*BLOB TRACKER ONE INTERFACE */
/* Declarations of constructors of implemented modules: */
/* Some declarations for specific MeanShift tracker: */
#define PROFILE_EPANECHNIKOV 0
#define PROFILE_DOG 1
struct CvBlobTrackerParamMS
{
int noOfSigBits;
int appearance_profile;
int meanshift_profile;
float sigma;
};
CV_EXPORTS CvBlobTracker* cvCreateBlobTrackerMS1(CvBlobTrackerParamMS* param);
CV_EXPORTS CvBlobTracker* cvCreateBlobTrackerMS2(CvBlobTrackerParamMS* param);
CV_EXPORTS CvBlobTracker* cvCreateBlobTrackerMS1ByList();
/* Some declarations for specific Likelihood tracker: */
struct CvBlobTrackerParamLH
{
int HistType; /* see Prob.h */
int ScaleAfter;
};
/* Without scale optimization: */
CV_EXPORTS CvBlobTracker* cvCreateBlobTrackerLHR(CvBlobTrackerParamLH* /*param*/ = NULL);
/* With scale optimization: */
CV_EXPORTS CvBlobTracker* cvCreateBlobTrackerLHRS(CvBlobTrackerParamLH* /*param*/ = NULL);
/* Simple blob tracker based on connected component tracking: */
CV_EXPORTS CvBlobTracker* cvCreateBlobTrackerCC();
/* Connected component tracking and mean-shift particle filter collion-resolver: */
CV_EXPORTS CvBlobTracker* cvCreateBlobTrackerCCMSPF();
/* Blob tracker that integrates meanshift and connected components: */
CV_EXPORTS CvBlobTracker* cvCreateBlobTrackerMSFG();
CV_EXPORTS CvBlobTracker* cvCreateBlobTrackerMSFGS();
/* Meanshift without connected-components */
CV_EXPORTS CvBlobTracker* cvCreateBlobTrackerMS();
/* Particle filtering via Bhattacharya coefficient, which */
/* is roughly the dot-product of two probability densities. */
/* See: Real-Time Tracking of Non-Rigid Objects using Mean Shift */
/* Comanicius, Ramesh, Meer, 2000, 8p */
/* http://citeseer.ist.psu.edu/321441.html */
CV_EXPORTS CvBlobTracker* cvCreateBlobTrackerMSPF();
/* =========== tracker integrators trackers =============*/
/* Integrator based on Particle Filtering method: */
//CV_EXPORTS CvBlobTracker* cvCreateBlobTrackerIPF();
/* Rule based integrator: */
//CV_EXPORTS CvBlobTracker* cvCreateBlobTrackerIRB();
/* Integrator based on data fusion using particle filtering: */
//CV_EXPORTS CvBlobTracker* cvCreateBlobTrackerIPFDF();
/* Trajectory postprocessing module: */
class CV_EXPORTS CvBlobTrackPostProc: public CvVSModule
{
public:
CvBlobTrackPostProc(){SetTypeName("BlobTrackPostProc");};
virtual void AddBlob(CvBlob* pBlob) = 0;
virtual void Process() = 0;
virtual int GetBlobNum() = 0;
virtual CvBlob* GetBlob(int index) = 0;
virtual void Release() = 0;
/* Additional functionality: */
virtual CvBlob* GetBlobByID(int BlobID)
{
int i;
for(i=GetBlobNum();i>0;i--)
{
CvBlob* pB=GetBlob(i-1);
if(pB->ID==BlobID) return pB;
}
return NULL;
};
};
inline void cvReleaseBlobTrackPostProc(CvBlobTrackPostProc** pBTPP)
{
if(pBTPP == NULL) return;
if(*pBTPP)(*pBTPP)->Release();
*pBTPP = 0;
}
/* Trajectory generation module: */
class CV_EXPORTS CvBlobTrackPostProcOne: public CvVSModule
{
public:
CvBlobTrackPostProcOne(){SetTypeName("BlobTrackPostOne");};
virtual CvBlob* Process(CvBlob* pBlob) = 0;
virtual void Release() = 0;
};
/* Create blob tracking post processing module based on simle module: */
CV_EXPORTS CvBlobTrackPostProc* cvCreateBlobTrackPostProcList(CvBlobTrackPostProcOne* (*create)());
/* Declarations of constructors of implemented modules: */
CV_EXPORTS CvBlobTrackPostProc* cvCreateModuleBlobTrackPostProcKalman();
CV_EXPORTS CvBlobTrackPostProc* cvCreateModuleBlobTrackPostProcTimeAverRect();
CV_EXPORTS CvBlobTrackPostProc* cvCreateModuleBlobTrackPostProcTimeAverExp();
/* PREDICTORS */
/* blob PREDICTOR */
class CvBlobTrackPredictor: public CvVSModule
{
public:
CvBlobTrackPredictor(){SetTypeName("BlobTrackPredictor");};
virtual CvBlob* Predict() = 0;
virtual void Update(CvBlob* pBlob) = 0;
virtual void Release() = 0;
};
CV_EXPORTS CvBlobTrackPredictor* cvCreateModuleBlobTrackPredictKalman();
/* Trajectory analyser module: */
class CV_EXPORTS CvBlobTrackAnalysis: public CvVSModule
{
public:
CvBlobTrackAnalysis(){SetTypeName("BlobTrackAnalysis");};
virtual void AddBlob(CvBlob* pBlob) = 0;
virtual void Process(IplImage* pImg, IplImage* pFG) = 0;
virtual float GetState(int BlobID) = 0;
/* return 0 if trajectory is normal
return >0 if trajectory abnormal */
virtual const char* GetStateDesc(int /*BlobID*/){return NULL;};
virtual void SetFileName(char* /*DataBaseName*/){};
virtual void Release() = 0;
};
inline void cvReleaseBlobTrackAnalysis(CvBlobTrackAnalysis** pBTPP)
{
if(pBTPP == NULL) return;
if(*pBTPP)(*pBTPP)->Release();
*pBTPP = 0;
}
/* Feature-vector generation module: */
class CV_EXPORTS CvBlobTrackFVGen : public CvVSModule
{
public:
CvBlobTrackFVGen(){SetTypeName("BlobTrackFVGen");};
virtual void AddBlob(CvBlob* pBlob) = 0;
virtual void Process(IplImage* pImg, IplImage* pFG) = 0;
virtual void Release() = 0;
virtual int GetFVSize() = 0;
virtual int GetFVNum() = 0;
virtual float* GetFV(int index, int* pFVID) = 0; /* Returns pointer to FV, if return 0 then FV not created */
virtual float* GetFVVar(){return NULL;}; /* Returns pointer to array of variation of values of FV, if returns 0 then FVVar does not exist. */
virtual float* GetFVMin() = 0; /* Returns pointer to array of minimal values of FV, if returns 0 then FVrange does not exist */
virtual float* GetFVMax() = 0; /* Returns pointer to array of maximal values of FV, if returns 0 then FVrange does not exist */
};
/* Trajectory Analyser module: */
class CV_EXPORTS CvBlobTrackAnalysisOne
{
public:
virtual ~CvBlobTrackAnalysisOne() {};
virtual int Process(CvBlob* pBlob, IplImage* pImg, IplImage* pFG) = 0;
/* return 0 if trajectory is normal
return >0 if trajectory abnormal */
virtual void Release() = 0;
};
/* Create blob tracking post processing module based on simle module: */
CV_EXPORTS CvBlobTrackAnalysis* cvCreateBlobTrackAnalysisList(CvBlobTrackAnalysisOne* (*create)());
/* Declarations of constructors of implemented modules: */
/* Based on histogram analysis of 2D FV (x,y): */
CV_EXPORTS CvBlobTrackAnalysis* cvCreateModuleBlobTrackAnalysisHistP();
/* Based on histogram analysis of 4D FV (x,y,vx,vy): */
CV_EXPORTS CvBlobTrackAnalysis* cvCreateModuleBlobTrackAnalysisHistPV();
/* Based on histogram analysis of 5D FV (x,y,vx,vy,state): */
CV_EXPORTS CvBlobTrackAnalysis* cvCreateModuleBlobTrackAnalysisHistPVS();
/* Based on histogram analysis of 4D FV (startpos,stoppos): */
CV_EXPORTS CvBlobTrackAnalysis* cvCreateModuleBlobTrackAnalysisHistSS();
/* Based on SVM classifier analysis of 2D FV (x,y): */
//CV_EXPORTS CvBlobTrackAnalysis* cvCreateModuleBlobTrackAnalysisSVMP();
/* Based on SVM classifier analysis of 4D FV (x,y,vx,vy): */
//CV_EXPORTS CvBlobTrackAnalysis* cvCreateModuleBlobTrackAnalysisSVMPV();
/* Based on SVM classifier analysis of 5D FV (x,y,vx,vy,state): */
//CV_EXPORTS CvBlobTrackAnalysis* cvCreateModuleBlobTrackAnalysisSVMPVS();
/* Based on SVM classifier analysis of 4D FV (startpos,stoppos): */
//CV_EXPORTS CvBlobTrackAnalysis* cvCreateModuleBlobTrackAnalysisSVMSS();
/* Track analysis based on distance between tracks: */
CV_EXPORTS CvBlobTrackAnalysis* cvCreateModuleBlobTrackAnalysisTrackDist();
/* Analyzer based on reation Road and height map: */
//CV_EXPORTS CvBlobTrackAnalysis* cvCreateModuleBlobTrackAnalysis3DRoadMap();
/* Analyzer that makes OR decision using set of analyzers: */
CV_EXPORTS CvBlobTrackAnalysis* cvCreateModuleBlobTrackAnalysisIOR();
/* Estimator of human height: */
class CV_EXPORTS CvBlobTrackAnalysisHeight: public CvBlobTrackAnalysis
{
public:
virtual double GetHeight(CvBlob* pB) = 0;
};
//CV_EXPORTS CvBlobTrackAnalysisHeight* cvCreateModuleBlobTrackAnalysisHeightScale();
/* AUTO BLOB TRACKER INTERFACE -- pipeline of 3 modules: */
class CV_EXPORTS CvBlobTrackerAuto: public CvVSModule
{
public:
CvBlobTrackerAuto(){SetTypeName("BlobTrackerAuto");};
virtual void Process(IplImage* pImg, IplImage* pMask = NULL) = 0;
virtual CvBlob* GetBlob(int index) = 0;
virtual CvBlob* GetBlobByID(int ID) = 0;
virtual int GetBlobNum() = 0;
virtual IplImage* GetFGMask(){return NULL;};
virtual float GetState(int BlobID) = 0;
virtual const char* GetStateDesc(int BlobID) = 0;
/* return 0 if trajectory is normal;
* return >0 if trajectory abnormal. */
virtual void Release() = 0;
};
inline void cvReleaseBlobTrackerAuto(CvBlobTrackerAuto** ppT)
{
ppT[0]->Release();
ppT[0] = 0;
}
/* END AUTO BLOB TRACKER INTERFACE */
/* Constructor functions and data for specific BlobTRackerAuto modules: */
/* Parameters of blobtracker auto ver1: */
struct CvBlobTrackerAutoParam1
{
int FGTrainFrames; /* Number of frames needed for FG (foreground) detector to train. */
CvFGDetector* pFG; /* FGDetector module. If this field is NULL the Process FG mask is used. */
CvBlobDetector* pBD; /* Selected blob detector module. */
/* If this field is NULL default blobdetector module will be created. */
CvBlobTracker* pBT; /* Selected blob tracking module. */
/* If this field is NULL default blobtracker module will be created. */
CvBlobTrackGen* pBTGen; /* Selected blob trajectory generator. */
/* If this field is NULL no generator is used. */
CvBlobTrackPostProc* pBTPP; /* Selected blob trajectory postprocessing module. */
/* If this field is NULL no postprocessing is done. */
int UsePPData;
CvBlobTrackAnalysis* pBTA; /* Selected blob trajectory analysis module. */
/* If this field is NULL no track analysis is done. */
};
/* Create blob tracker auto ver1: */
CV_EXPORTS CvBlobTrackerAuto* cvCreateBlobTrackerAuto1(CvBlobTrackerAutoParam1* param = NULL);
/* Simple loader for many auto trackers by its type : */
inline CvBlobTrackerAuto* cvCreateBlobTrackerAuto(int type, void* param)
{
if(type == 0) return cvCreateBlobTrackerAuto1((CvBlobTrackerAutoParam1*)param);
return 0;
}
struct CvTracksTimePos
{
int len1,len2;
int beg1,beg2;
int end1,end2;
int comLen; //common length for two tracks
int shift1,shift2;
};
/*CV_EXPORTS int cvCompareTracks( CvBlobTrackSeq *groundTruth,
CvBlobTrackSeq *result,
FILE *file);*/
/* Constructor functions: */
CV_EXPORTS void cvCreateTracks_One(CvBlobTrackSeq *TS);
CV_EXPORTS void cvCreateTracks_Same(CvBlobTrackSeq *TS1, CvBlobTrackSeq *TS2);
CV_EXPORTS void cvCreateTracks_AreaErr(CvBlobTrackSeq *TS1, CvBlobTrackSeq *TS2, int addW, int addH);
/* HIST API */
class CV_EXPORTS CvProb
{
public:
virtual ~CvProb() {};
/* Calculate probability value: */
virtual double Value(int* /*comp*/, int /*x*/ = 0, int /*y*/ = 0){return -1;};
/* Update histograpp Pnew = (1-W)*Pold + W*Padd*/
/* W weight of new added prob */
/* comps - matrix of new fetature vectors used to update prob */
virtual void AddFeature(float W, int* comps, int x =0, int y = 0) = 0;
virtual void Scale(float factor = 0, int x = -1, int y = -1) = 0;
virtual void Release() = 0;
};
inline void cvReleaseProb(CvProb** ppProb){ppProb[0]->Release();ppProb[0]=NULL;}
/* HIST API */
/* Some Prob: */
CV_EXPORTS CvProb* cvCreateProbS(int dim, CvSize size, int sample_num);
CV_EXPORTS CvProb* cvCreateProbMG(int dim, CvSize size, int sample_num);
CV_EXPORTS CvProb* cvCreateProbMG2(int dim, CvSize size, int sample_num);
CV_EXPORTS CvProb* cvCreateProbHist(int dim, CvSize size);
#define CV_BT_HIST_TYPE_S 0
#define CV_BT_HIST_TYPE_MG 1
#define CV_BT_HIST_TYPE_MG2 2
#define CV_BT_HIST_TYPE_H 3
inline CvProb* cvCreateProb(int type, int dim, CvSize size = cvSize(1,1), void* /*param*/ = NULL)
{
if(type == CV_BT_HIST_TYPE_S) return cvCreateProbS(dim, size, -1);
if(type == CV_BT_HIST_TYPE_MG) return cvCreateProbMG(dim, size, -1);
if(type == CV_BT_HIST_TYPE_MG2) return cvCreateProbMG2(dim, size, -1);
if(type == CV_BT_HIST_TYPE_H) return cvCreateProbHist(dim, size);
return NULL;
}
/* Noise type definitions: */
#define CV_NOISE_NONE 0
#define CV_NOISE_GAUSSIAN 1
#define CV_NOISE_UNIFORM 2
#define CV_NOISE_SPECKLE 3
#define CV_NOISE_SALT_AND_PEPPER 4
/* Add some noise to image: */
/* pImg - (input) image without noise */
/* pImg - (output) image with noise */
/* noise_type - type of added noise */
/* CV_NOISE_GAUSSIAN - pImg += n , n - is gaussian noise with Ampl standart deviation */
/* CV_NOISE_UNIFORM - pImg += n , n - is uniform noise with Ampl standart deviation */
/* CV_NOISE_SPECKLE - pImg += n*pImg , n - is gaussian noise with Ampl standart deviation */
/* CV_NOISE_SALT_AND_PAPPER - pImg = pImg with blacked and whited pixels,
Ampl is density of brocken pixels (0-there are not broken pixels, 1 - all pixels are broken)*/
/* Ampl - "amplitude" of noise */
//CV_EXPORTS void cvAddNoise(IplImage* pImg, int noise_type, double Ampl, CvRNG* rnd_state = NULL);
/*================== GENERATOR OF TEST VIDEO SEQUENCE ===================== */
typedef void CvTestSeq;
/* pConfigfile - Name of file (yml or xml) with description of test sequence */
/* videos - array of names of test videos described in "pConfigfile" file */
/* numvideos - size of "videos" array */
CV_EXPORTS CvTestSeq* cvCreateTestSeq(char* pConfigfile, char** videos, int numvideo, float Scale = 1, int noise_type = CV_NOISE_NONE, double noise_ampl = 0);
CV_EXPORTS void cvReleaseTestSeq(CvTestSeq** ppTestSeq);
/* Generate next frame from test video seq and return pointer to it: */
CV_EXPORTS IplImage* cvTestSeqQueryFrame(CvTestSeq* pTestSeq);
/* Return pointer to current foreground mask: */
CV_EXPORTS IplImage* cvTestSeqGetFGMask(CvTestSeq* pTestSeq);
/* Return pointer to current image: */
CV_EXPORTS IplImage* cvTestSeqGetImage(CvTestSeq* pTestSeq);
/* Return frame size of result test video: */
CV_EXPORTS CvSize cvTestSeqGetImageSize(CvTestSeq* pTestSeq);
/* Return number of frames result test video: */
CV_EXPORTS int cvTestSeqFrameNum(CvTestSeq* pTestSeq);
/* Return number of existing objects.
* This is general number of any objects.
* For example number of trajectories may be equal or less than returned value:
*/
CV_EXPORTS int cvTestSeqGetObjectNum(CvTestSeq* pTestSeq);
/* Return 0 if there is not position for current defined on current frame */
/* Return 1 if there is object position and pPos was filled */
CV_EXPORTS int cvTestSeqGetObjectPos(CvTestSeq* pTestSeq, int ObjIndex, CvPoint2D32f* pPos);
CV_EXPORTS int cvTestSeqGetObjectSize(CvTestSeq* pTestSeq, int ObjIndex, CvPoint2D32f* pSize);
/* Add noise to final image: */
CV_EXPORTS void cvTestSeqAddNoise(CvTestSeq* pTestSeq, int noise_type = CV_NOISE_NONE, double noise_ampl = 0);
/* Add Intensity variation: */
CV_EXPORTS void cvTestSeqAddIntensityVariation(CvTestSeq* pTestSeq, float DI_per_frame, float MinI, float MaxI);
CV_EXPORTS void cvTestSeqSetFrame(CvTestSeq* pTestSeq, int n);
#endif
/* End of file. */
modules/legacy/include/opencv2/legacy/compat.hpp
-735
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@@ -1,735 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright( C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
//(including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort(including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifndef __OPENCV_COMPAT_HPP__
#define __OPENCV_COMPAT_HPP__
#include "opencv2/core/core_c.h"
#include "opencv2/imgproc/types_c.h"
#include <math.h>
#include <string.h>
#define CV_NOOP(...)
#ifdef __cplusplus
extern "C" {
#endif
typedef int CvMatType;
typedef int CvDisMaskType;
typedef CvMat CvMatArray;
typedef int CvThreshType;
typedef int CvAdaptiveThreshMethod;
typedef int CvCompareMethod;
typedef int CvFontFace;
typedef int CvPolyApproxMethod;
typedef int CvContoursMatchMethod;
typedef int CvContourTreesMatchMethod;
typedef int CvCoeffType;
typedef int CvRodriguesType;
typedef int CvElementShape;
typedef int CvMorphOp;
typedef int CvTemplMatchMethod;
typedef CvPoint2D64f CvPoint2D64d;
typedef CvPoint3D64f CvPoint3D64d;
enum
{
CV_MAT32F = CV_32FC1,
CV_MAT3x1_32F = CV_32FC1,
CV_MAT4x1_32F = CV_32FC1,
CV_MAT3x3_32F = CV_32FC1,
CV_MAT4x4_32F = CV_32FC1,
CV_MAT64D = CV_64FC1,
CV_MAT3x1_64D = CV_64FC1,
CV_MAT4x1_64D = CV_64FC1,
CV_MAT3x3_64D = CV_64FC1,
CV_MAT4x4_64D = CV_64FC1
};
enum
{
IPL_GAUSSIAN_5x5 = 7
};
typedef CvBox2D CvBox2D32f;
/* allocation/deallocation macros */
#define cvCreateImageData cvCreateData
#define cvReleaseImageData cvReleaseData
#define cvSetImageData cvSetData
#define cvGetImageRawData cvGetRawData
#define cvmAlloc cvCreateData
#define cvmFree cvReleaseData
#define cvmAllocArray cvCreateData
#define cvmFreeArray cvReleaseData
#define cvIntegralImage cvIntegral
#define cvMatchContours cvMatchShapes
CV_EXPORTS CvMat cvMatArray( int rows, int cols, int type,
int count, void* data CV_DEFAULT(0));
#define cvUpdateMHIByTime cvUpdateMotionHistory
#define cvAccMask cvAcc
#define cvSquareAccMask cvSquareAcc
#define cvMultiplyAccMask cvMultiplyAcc
#define cvRunningAvgMask(imgY, imgU, mask, alpha) cvRunningAvg(imgY, imgU, alpha, mask)
#define cvSetHistThresh cvSetHistBinRanges
#define cvCalcHistMask(img, mask, hist, doNotClear) cvCalcHist(img, hist, doNotClear, mask)
CV_EXPORTS double cvMean( const CvArr* image, const CvArr* mask CV_DEFAULT(0));
CV_EXPORTS double cvSumPixels( const CvArr* image );
CV_EXPORTS void cvMean_StdDev( const CvArr* image, double* mean, double* sdv,
const CvArr* mask CV_DEFAULT(0));
CV_EXPORTS void cvmPerspectiveProject( const CvMat* mat, const CvArr* src, CvArr* dst );
CV_EXPORTS void cvFillImage( CvArr* mat, double color );
#define cvCvtPixToPlane cvSplit
#define cvCvtPlaneToPix cvMerge
typedef struct CvRandState
{
CvRNG state; /* RNG state (the current seed and carry)*/
int disttype; /* distribution type */
CvScalar param[2]; /* parameters of RNG */
} CvRandState;
/* Changes RNG range while preserving RNG state */
CV_EXPORTS void cvRandSetRange( CvRandState* state, double param1,
double param2, int index CV_DEFAULT(-1));
CV_EXPORTS void cvRandInit( CvRandState* state, double param1,
double param2, int seed,
int disttype CV_DEFAULT(CV_RAND_UNI));
/* Fills array with random numbers */
CV_EXPORTS void cvRand( CvRandState* state, CvArr* arr );
#define cvRandNext( _state ) cvRandInt( &(_state)->state )
CV_EXPORTS void cvbRand( CvRandState* state, float* dst, int len );
CV_EXPORTS void cvbCartToPolar( const float* y, const float* x,
float* magnitude, float* angle, int len );
CV_EXPORTS void cvbFastArctan( const float* y, const float* x, float* angle, int len );
CV_EXPORTS void cvbSqrt( const float* x, float* y, int len );
CV_EXPORTS void cvbInvSqrt( const float* x, float* y, int len );
CV_EXPORTS void cvbReciprocal( const float* x, float* y, int len );
CV_EXPORTS void cvbFastExp( const float* x, double* y, int len );
CV_EXPORTS void cvbFastLog( const double* x, float* y, int len );
CV_EXPORTS CvRect cvContourBoundingRect( void* point_set, int update CV_DEFAULT(0));
CV_EXPORTS double cvPseudoInverse( const CvArr* src, CvArr* dst );
#define cvPseudoInv cvPseudoInverse
#define cvContourMoments( contour, moments ) cvMoments( contour, moments, 0 )
#define cvGetPtrAt cvPtr2D
#define cvGetAt cvGet2D
#define cvSetAt(arr,val,y,x) cvSet2D((arr),(y),(x),(val))
#define cvMeanMask cvMean
#define cvMean_StdDevMask(img,mask,mean,sdv) cvMean_StdDev(img,mean,sdv,mask)
#define cvNormMask(imgA,imgB,mask,normType) cvNorm(imgA,imgB,normType,mask)
#define cvMinMaxLocMask(img, mask, min_val, max_val, min_loc, max_loc) \
cvMinMaxLoc(img, min_val, max_val, min_loc, max_loc, mask)
#define cvRemoveMemoryManager CV_NOOP
#define cvSetMemoryManager CV_NOOP
#define cvmSetZero( mat ) cvSetZero( mat )
#define cvmSetIdentity( mat ) cvSetIdentity( mat )
#define cvmAdd( src1, src2, dst ) cvAdd( src1, src2, dst, 0 )
#define cvmSub( src1, src2, dst ) cvSub( src1, src2, dst, 0 )
#define cvmCopy( src, dst ) cvCopy( src, dst, 0 )
#define cvmMul( src1, src2, dst ) cvMatMulAdd( src1, src2, 0, dst )
#define cvmTranspose( src, dst ) cvT( src, dst )
#define cvmInvert( src, dst ) cvInv( src, dst )
#define cvmMahalanobis(vec1, vec2, mat) cvMahalanobis( vec1, vec2, mat )
#define cvmDotProduct( vec1, vec2 ) cvDotProduct( vec1, vec2 )
#define cvmCrossProduct(vec1, vec2,dst) cvCrossProduct( vec1, vec2, dst )
#define cvmTrace( mat ) (cvTrace( mat )).val[0]
#define cvmMulTransposed( src, dst, order ) cvMulTransposed( src, dst, order )
#define cvmEigenVV( mat, evec, eval, eps) cvEigenVV( mat, evec, eval, eps )
#define cvmDet( mat ) cvDet( mat )
#define cvmScale( src, dst, scale ) cvScale( src, dst, scale )
#define cvCopyImage( src, dst ) cvCopy( src, dst, 0 )
#define cvReleaseMatHeader cvReleaseMat
/* Calculates exact convex hull of 2d point set */
CV_EXPORTS void cvConvexHull( CvPoint* points, int num_points,
CvRect* bound_rect,
int orientation, int* hull, int* hullsize );
CV_EXPORTS void cvMinAreaRect( CvPoint* points, int n,
int left, int bottom,
int right, int top,
CvPoint2D32f* anchor,
CvPoint2D32f* vect1,
CvPoint2D32f* vect2 );
typedef int CvDisType;
typedef int CvChainApproxMethod;
typedef int CvContourRetrievalMode;
CV_EXPORTS void cvFitLine3D( CvPoint3D32f* points, int count, int dist,
void *param, float reps, float aeps, float* line );
/* Fits a line into set of 2d points in a robust way (M-estimator technique) */
CV_EXPORTS void cvFitLine2D( CvPoint2D32f* points, int count, int dist,
void *param, float reps, float aeps, float* line );
CV_EXPORTS void cvFitEllipse( const CvPoint2D32f* points, int count, CvBox2D* box );
/* Projects 2d points to one of standard coordinate planes
(i.e. removes one of coordinates) */
CV_EXPORTS void cvProject3D( CvPoint3D32f* points3D, int count,
CvPoint2D32f* points2D,
int xIndx CV_DEFAULT(0),
int yIndx CV_DEFAULT(1));
/* Retrieves value of the particular bin
of x-dimensional (x=1,2,3,...) histogram */
#define cvQueryHistValue_1D( hist, idx0 ) \
((float)cvGetReal1D( (hist)->bins, (idx0)))
#define cvQueryHistValue_2D( hist, idx0, idx1 ) \
((float)cvGetReal2D( (hist)->bins, (idx0), (idx1)))
#define cvQueryHistValue_3D( hist, idx0, idx1, idx2 ) \
((float)cvGetReal3D( (hist)->bins, (idx0), (idx1), (idx2)))
#define cvQueryHistValue_nD( hist, idx ) \
((float)cvGetRealND( (hist)->bins, (idx)))
/* Returns pointer to the particular bin of x-dimesional histogram.
For sparse histogram the bin is created if it didn't exist before */
#define cvGetHistValue_1D( hist, idx0 ) \
((float*)cvPtr1D( (hist)->bins, (idx0), 0))
#define cvGetHistValue_2D( hist, idx0, idx1 ) \
((float*)cvPtr2D( (hist)->bins, (idx0), (idx1), 0))
#define cvGetHistValue_3D( hist, idx0, idx1, idx2 ) \
((float*)cvPtr3D( (hist)->bins, (idx0), (idx1), (idx2), 0))
#define cvGetHistValue_nD( hist, idx ) \
((float*)cvPtrND( (hist)->bins, (idx), 0))
#define CV_IS_SET_ELEM_EXISTS CV_IS_SET_ELEM
CV_EXPORTS int cvHoughLines( CvArr* image, double rho,
double theta, int threshold,
float* lines, int linesNumber );
CV_EXPORTS int cvHoughLinesP( CvArr* image, double rho,
double theta, int threshold,
int lineLength, int lineGap,
int* lines, int linesNumber );
CV_EXPORTS int cvHoughLinesSDiv( CvArr* image, double rho, int srn,
double theta, int stn, int threshold,
float* lines, int linesNumber );
CV_EXPORTS float cvCalcEMD( const float* signature1, int size1,
const float* signature2, int size2,
int dims, int dist_type CV_DEFAULT(CV_DIST_L2),
CvDistanceFunction dist_func CV_DEFAULT(0),
float* lower_bound CV_DEFAULT(0),
void* user_param CV_DEFAULT(0));
CV_EXPORTS void cvKMeans( int num_clusters, float** samples,
int num_samples, int vec_size,
CvTermCriteria termcrit, int* cluster_idx );
CV_EXPORTS void cvStartScanGraph( CvGraph* graph, CvGraphScanner* scanner,
CvGraphVtx* vtx CV_DEFAULT(NULL),
int mask CV_DEFAULT(CV_GRAPH_ALL_ITEMS));
CV_EXPORTS void cvEndScanGraph( CvGraphScanner* scanner );
/* old drawing functions */
CV_EXPORTS void cvLineAA( CvArr* img, CvPoint pt1, CvPoint pt2,
double color, int scale CV_DEFAULT(0));
CV_EXPORTS void cvCircleAA( CvArr* img, CvPoint center, int radius,
double color, int scale CV_DEFAULT(0) );
CV_EXPORTS void cvEllipseAA( CvArr* img, CvPoint center, CvSize axes,
double angle, double start_angle,
double end_angle, double color,
int scale CV_DEFAULT(0) );
CV_EXPORTS void cvPolyLineAA( CvArr* img, CvPoint** pts, int* npts, int contours,
int is_closed, double color, int scale CV_DEFAULT(0) );
/****************************************************************************************\
* Pixel Access Macros *
\****************************************************************************************/
typedef struct _CvPixelPosition8u
{
uchar* currline; /* pointer to the start of the current pixel line */
uchar* topline; /* pointer to the start of the top pixel line */
uchar* bottomline; /* pointer to the start of the first line */
/* which is below the image */
int x; /* current x coordinate ( in pixels ) */
int width; /* width of the image ( in pixels ) */
int height; /* height of the image ( in pixels ) */
int step; /* distance between lines ( in elements of single */
/* plane ) */
int step_arr[3]; /* array: ( 0, -step, step ). It is used for */
/* vertical moving */
} CvPixelPosition8u;
/* this structure differs from the above only in data type */
typedef struct _CvPixelPosition8s
{
schar* currline;
schar* topline;
schar* bottomline;
int x;
int width;
int height;
int step;
int step_arr[3];
} CvPixelPosition8s;
/* this structure differs from the CvPixelPosition8u only in data type */
typedef struct _CvPixelPosition32f
{
float* currline;
float* topline;
float* bottomline;
int x;
int width;
int height;
int step;
int step_arr[3];
} CvPixelPosition32f;
/* Initialize one of the CvPixelPosition structures. */
/* pos - initialized structure */
/* origin - pointer to the left-top corner of the ROI */
/* step - width of the whole image in bytes */
/* roi - width & height of the ROI */
/* x, y - initial position */
#define CV_INIT_PIXEL_POS(pos, origin, _step, roi, _x, _y, orientation) \
( \
(pos).step = (_step)/sizeof((pos).currline[0]) * (orientation ? -1 : 1), \
(pos).width = (roi).width, \
(pos).height = (roi).height, \
(pos).bottomline = (origin) + (pos).step*(pos).height, \
(pos).topline = (origin) - (pos).step, \
(pos).step_arr[0] = 0, \
(pos).step_arr[1] = -(pos).step, \
(pos).step_arr[2] = (pos).step, \
(pos).x = (_x), \
(pos).currline = (origin) + (pos).step*(_y) )
/* Move to specified point ( absolute shift ) */
/* pos - position structure */
/* x, y - coordinates of the new position */
/* cs - number of the image channels */
#define CV_MOVE_TO( pos, _x, _y, cs ) \
((pos).currline = (_y) >= 0 && (_y) < (pos).height ? (pos).topline + ((_y)+1)*(pos).step : 0, \
(pos).x = (_x) >= 0 && (_x) < (pos).width ? (_x) : 0, (pos).currline + (_x) * (cs) )
/* Get current coordinates */
/* pos - position structure */
/* x, y - coordinates of the new position */
/* cs - number of the image channels */
#define CV_GET_CURRENT( pos, cs ) ((pos).currline + (pos).x * (cs))
/* Move by one pixel relatively to current position */
/* pos - position structure */
/* cs - number of the image channels */
/* left */
#define CV_MOVE_LEFT( pos, cs ) \
( --(pos).x >= 0 ? (pos).currline + (pos).x*(cs) : 0 )
/* right */
#define CV_MOVE_RIGHT( pos, cs ) \
( ++(pos).x < (pos).width ? (pos).currline + (pos).x*(cs) : 0 )
/* up */
#define CV_MOVE_UP( pos, cs ) \
(((pos).currline -= (pos).step) != (pos).topline ? (pos).currline + (pos).x*(cs) : 0 )
/* down */
#define CV_MOVE_DOWN( pos, cs ) \
(((pos).currline += (pos).step) != (pos).bottomline ? (pos).currline + (pos).x*(cs) : 0 )
/* left up */
#define CV_MOVE_LU( pos, cs ) ( CV_MOVE_LEFT(pos, cs), CV_MOVE_UP(pos, cs))
/* right up */
#define CV_MOVE_RU( pos, cs ) ( CV_MOVE_RIGHT(pos, cs), CV_MOVE_UP(pos, cs))
/* left down */
#define CV_MOVE_LD( pos, cs ) ( CV_MOVE_LEFT(pos, cs), CV_MOVE_DOWN(pos, cs))
/* right down */
#define CV_MOVE_RD( pos, cs ) ( CV_MOVE_RIGHT(pos, cs), CV_MOVE_DOWN(pos, cs))
/* Move by one pixel relatively to current position with wrapping when the position */
/* achieves image boundary */
/* pos - position structure */
/* cs - number of the image channels */
/* left */
#define CV_MOVE_LEFT_WRAP( pos, cs ) \
((pos).currline + ( --(pos).x >= 0 ? (pos).x : ((pos).x = (pos).width-1))*(cs))
/* right */
#define CV_MOVE_RIGHT_WRAP( pos, cs ) \
((pos).currline + ( ++(pos).x < (pos).width ? (pos).x : ((pos).x = 0))*(cs) )
/* up */
#define CV_MOVE_UP_WRAP( pos, cs ) \
((((pos).currline -= (pos).step) != (pos).topline ? \
(pos).currline : ((pos).currline = (pos).bottomline - (pos).step)) + (pos).x*(cs) )
/* down */
#define CV_MOVE_DOWN_WRAP( pos, cs ) \
((((pos).currline += (pos).step) != (pos).bottomline ? \
(pos).currline : ((pos).currline = (pos).topline + (pos).step)) + (pos).x*(cs) )
/* left up */
#define CV_MOVE_LU_WRAP( pos, cs ) ( CV_MOVE_LEFT_WRAP(pos, cs), CV_MOVE_UP_WRAP(pos, cs))
/* right up */
#define CV_MOVE_RU_WRAP( pos, cs ) ( CV_MOVE_RIGHT_WRAP(pos, cs), CV_MOVE_UP_WRAP(pos, cs))
/* left down */
#define CV_MOVE_LD_WRAP( pos, cs ) ( CV_MOVE_LEFT_WRAP(pos, cs), CV_MOVE_DOWN_WRAP(pos, cs))
/* right down */
#define CV_MOVE_RD_WRAP( pos, cs ) ( CV_MOVE_RIGHT_WRAP(pos, cs), CV_MOVE_DOWN_WRAP(pos, cs))
/* Numeric constants which used for moving in arbitrary direction */
enum
{
CV_SHIFT_NONE = 2,
CV_SHIFT_LEFT = 1,
CV_SHIFT_RIGHT = 3,
CV_SHIFT_UP = 6,
CV_SHIFT_DOWN = 10,
CV_SHIFT_LU = 5,
CV_SHIFT_RU = 7,
CV_SHIFT_LD = 9,
CV_SHIFT_RD = 11
};
/* Move by one pixel in specified direction */
/* pos - position structure */
/* shift - direction ( it's value must be one of the CV_SHIFT_Ö constants ) */
/* cs - number of the image channels */
#define CV_MOVE_PARAM( pos, shift, cs ) \
( (pos).currline += (pos).step_arr[(shift)>>2], (pos).x += ((shift)&3)-2, \
((pos).currline != (pos).topline && (pos).currline != (pos).bottomline && \
(pos).x >= 0 && (pos).x < (pos).width) ? (pos).currline + (pos).x*(cs) : 0 )
/* Move by one pixel in specified direction with wrapping when the */
/* position achieves image boundary */
/* pos - position structure */
/* shift - direction ( it's value must be one of the CV_SHIFT_Ö constants ) */
/* cs - number of the image channels */
#define CV_MOVE_PARAM_WRAP( pos, shift, cs ) \
( (pos).currline += (pos).step_arr[(shift)>>2], \
(pos).currline = ((pos).currline == (pos).topline ? \
(pos).bottomline - (pos).step : \
(pos).currline == (pos).bottomline ? \
(pos).topline + (pos).step : (pos).currline), \
\
(pos).x += ((shift)&3)-2, \
(pos).x = ((pos).x < 0 ? (pos).width-1 : (pos).x >= (pos).width ? 0 : (pos).x), \
\
(pos).currline + (pos).x*(cs) )
typedef float* CvVect32f;
typedef float* CvMatr32f;
typedef double* CvVect64d;
typedef double* CvMatr64d;
CV_EXPORTS void cvUnDistortOnce( const CvArr* src, CvArr* dst,
const float* intrinsic_matrix,
const float* distortion_coeffs,
int interpolate );
/* the two functions below have quite hackerish implementations, use with care
(or, which is better, switch to cvUndistortInitMap and cvRemap instead */
CV_EXPORTS void cvUnDistortInit( const CvArr* src,
CvArr* undistortion_map,
const float* A, const float* k,
int interpolate );
CV_EXPORTS void cvUnDistort( const CvArr* src, CvArr* dst,
const CvArr* undistortion_map,
int interpolate );
/* Find fundamental matrix */
CV_EXPORTS void cvFindFundamentalMatrix( int* points1, int* points2,
int numpoints, int method, float* matrix );
CV_EXPORTS int cvFindChessBoardCornerGuesses( const void* arr, void* thresharr,
CvMemStorage* storage,
CvSize pattern_size, CvPoint2D32f * corners,
int *corner_count );
/* Calibrates camera using multiple views of calibration pattern */
CV_EXPORTS void cvCalibrateCamera( int image_count, int* _point_counts,
CvSize image_size, CvPoint2D32f* _image_points, CvPoint3D32f* _object_points,
float* _distortion_coeffs, float* _camera_matrix, float* _translation_vectors,
float* _rotation_matrices, int flags );
CV_EXPORTS void cvCalibrateCamera_64d( int image_count, int* _point_counts,
CvSize image_size, CvPoint2D64f* _image_points, CvPoint3D64f* _object_points,
double* _distortion_coeffs, double* _camera_matrix, double* _translation_vectors,
double* _rotation_matrices, int flags );
/* Find 3d position of object given intrinsic camera parameters,
3d model of the object and projection of the object into view plane */
CV_EXPORTS void cvFindExtrinsicCameraParams( int point_count,
CvSize image_size, CvPoint2D32f* _image_points,
CvPoint3D32f* _object_points, float* focal_length,
CvPoint2D32f principal_point, float* _distortion_coeffs,
float* _rotation_vector, float* _translation_vector );
/* Variant of the previous function that takes double-precision parameters */
CV_EXPORTS void cvFindExtrinsicCameraParams_64d( int point_count,
CvSize image_size, CvPoint2D64f* _image_points,
CvPoint3D64f* _object_points, double* focal_length,
CvPoint2D64f principal_point, double* _distortion_coeffs,
double* _rotation_vector, double* _translation_vector );
/* Rodrigues transform */
enum
{
CV_RODRIGUES_M2V = 0,
CV_RODRIGUES_V2M = 1
};
/* Converts rotation_matrix matrix to rotation_matrix vector or vice versa */
CV_EXPORTS void cvRodrigues( CvMat* rotation_matrix, CvMat* rotation_vector,
CvMat* jacobian, int conv_type );
/* Does reprojection of 3d object points to the view plane */
CV_EXPORTS void cvProjectPoints( int point_count, CvPoint3D64f* _object_points,
double* _rotation_vector, double* _translation_vector,
double* focal_length, CvPoint2D64f principal_point,
double* _distortion, CvPoint2D64f* _image_points,
double* _deriv_points_rotation_matrix,
double* _deriv_points_translation_vect,
double* _deriv_points_focal,
double* _deriv_points_principal_point,
double* _deriv_points_distortion_coeffs );
/* Simpler version of the previous function */
CV_EXPORTS void cvProjectPointsSimple( int point_count, CvPoint3D64f* _object_points,
double* _rotation_matrix, double* _translation_vector,
double* _camera_matrix, double* _distortion, CvPoint2D64f* _image_points );
#define cvMake2DPoints cvConvertPointsHomogeneous
#define cvMake3DPoints cvConvertPointsHomogeneous
#define cvWarpPerspectiveQMatrix cvGetPerspectiveTransform
#define cvConvertPointsHomogenious cvConvertPointsHomogeneous
//////////////////////////////////// feature extractors: obsolete API //////////////////////////////////
typedef struct CvSURFPoint
{
CvPoint2D32f pt;
int laplacian;
int size;
float dir;
float hessian;
} CvSURFPoint;
CV_INLINE CvSURFPoint cvSURFPoint( CvPoint2D32f pt, int laplacian,
int size, float dir CV_DEFAULT(0),
float hessian CV_DEFAULT(0))
{
CvSURFPoint kp;
kp.pt = pt;
kp.laplacian = laplacian;
kp.size = size;
kp.dir = dir;
kp.hessian = hessian;
return kp;
}
typedef struct CvSURFParams
{
int extended;
int upright;
double hessianThreshold;
int nOctaves;
int nOctaveLayers;
} CvSURFParams;
CVAPI(CvSURFParams) cvSURFParams( double hessianThreshold, int extended CV_DEFAULT(0) );
// If useProvidedKeyPts!=0, keypoints are not detected, but descriptors are computed
// at the locations provided in keypoints (a CvSeq of CvSURFPoint).
CVAPI(void) cvExtractSURF( const CvArr* img, const CvArr* mask,
CvSeq** keypoints, CvSeq** descriptors,
CvMemStorage* storage, CvSURFParams params,
int useProvidedKeyPts CV_DEFAULT(0) );
/*!
Maximal Stable Regions Parameters
*/
typedef struct CvMSERParams
{
//! delta, in the code, it compares (size_{i}-size_{i-delta})/size_{i-delta}
int delta;
//! prune the area which bigger than maxArea
int maxArea;
//! prune the area which smaller than minArea
int minArea;
//! prune the area have simliar size to its children
float maxVariation;
//! trace back to cut off mser with diversity < min_diversity
float minDiversity;
/////// the next few params for MSER of color image
//! for color image, the evolution steps
int maxEvolution;
//! the area threshold to cause re-initialize
double areaThreshold;
//! ignore too small margin
double minMargin;
//! the aperture size for edge blur
int edgeBlurSize;
} CvMSERParams;
CVAPI(CvMSERParams) cvMSERParams( int delta CV_DEFAULT(5), int min_area CV_DEFAULT(60),
int max_area CV_DEFAULT(14400), float max_variation CV_DEFAULT(.25f),
float min_diversity CV_DEFAULT(.2f), int max_evolution CV_DEFAULT(200),
double area_threshold CV_DEFAULT(1.01),
double min_margin CV_DEFAULT(.003),
int edge_blur_size CV_DEFAULT(5) );
// Extracts the contours of Maximally Stable Extremal Regions
CVAPI(void) cvExtractMSER( CvArr* _img, CvArr* _mask, CvSeq** contours, CvMemStorage* storage, CvMSERParams params );
typedef struct CvStarKeypoint
{
CvPoint pt;
int size;
float response;
} CvStarKeypoint;
CV_INLINE CvStarKeypoint cvStarKeypoint(CvPoint pt, int size, float response)
{
CvStarKeypoint kpt;
kpt.pt = pt;
kpt.size = size;
kpt.response = response;
return kpt;
}
typedef struct CvStarDetectorParams
{
int maxSize;
int responseThreshold;
int lineThresholdProjected;
int lineThresholdBinarized;
int suppressNonmaxSize;
} CvStarDetectorParams;
CV_INLINE CvStarDetectorParams cvStarDetectorParams(
int maxSize CV_DEFAULT(45),
int responseThreshold CV_DEFAULT(30),
int lineThresholdProjected CV_DEFAULT(10),
int lineThresholdBinarized CV_DEFAULT(8),
int suppressNonmaxSize CV_DEFAULT(5))
{
CvStarDetectorParams params;
params.maxSize = maxSize;
params.responseThreshold = responseThreshold;
params.lineThresholdProjected = lineThresholdProjected;
params.lineThresholdBinarized = lineThresholdBinarized;
params.suppressNonmaxSize = suppressNonmaxSize;
return params;
}
CVAPI(CvSeq*) cvGetStarKeypoints( const CvArr* img, CvMemStorage* storage,
CvStarDetectorParams params CV_DEFAULT(cvStarDetectorParams()));
#ifdef __cplusplus
}
#endif
#endif

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