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Merge branch 4.x
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Alexander Alekhin
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@tableofcontents
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@prev_tutorial{tutorial_dnn_halide_scheduling}
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@prev_tutorial{tutorial_dnn_openvino}
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@next_tutorial{tutorial_dnn_yolo}
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@@ -3,7 +3,7 @@
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@tableofcontents
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@prev_tutorial{tutorial_dnn_halide}
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@next_tutorial{tutorial_dnn_android}
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@next_tutorial{tutorial_dnn_openvino}
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OpenCV usage with OpenVINO {#tutorial_dnn_openvino}
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=====================
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@prev_tutorial{tutorial_dnn_halide_scheduling}
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@next_tutorial{tutorial_dnn_android}
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| Original author | Aleksandr Voron |
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| Compatibility | OpenCV == 4.x |
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This tutorial provides OpenCV installation guidelines how to use OpenCV with OpenVINO.
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Since 2021.1.1 release OpenVINO does not provide pre-built OpenCV.
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The change does not affect you if you are using OpenVINO runtime directly or OpenVINO samples: it does not have a strong dependency to OpenCV.
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However, if you are using Open Model Zoo demos or OpenVINO runtime as OpenCV DNN backend you need to get the OpenCV build.
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There are 2 approaches how to get OpenCV:
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- Install pre-built OpenCV from another sources: system repositories, pip, conda, homebrew. Generic pre-built OpenCV package may have several limitations:
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- OpenCV version may be out-of-date
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- OpenCV may not contain G-API module with enabled OpenVINO support (e.g. some OMZ demos use G-API functionality)
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- OpenCV may not be optimized for modern hardware (default builds need to cover wide range of hardware)
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- OpenCV may not support Intel TBB, Intel Media SDK
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- OpenCV DNN module may not use OpenVINO as an inference backend
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- Build OpenCV from source code against specific version of OpenVINO. This approach solves the limitations mentioned above.
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The instruction how to follow both approaches is provided in [OpenCV wiki](https://github.com/opencv/opencv/wiki/BuildOpenCV4OpenVINO).
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@@ -4,6 +4,7 @@ Deep Neural Networks (dnn module) {#tutorial_table_of_content_dnn}
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- @subpage tutorial_dnn_googlenet
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- @subpage tutorial_dnn_halide
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- @subpage tutorial_dnn_halide_scheduling
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- @subpage tutorial_dnn_openvino
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- @subpage tutorial_dnn_android
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- @subpage tutorial_dnn_yolo
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- @subpage tutorial_dnn_javascript
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@@ -418,10 +418,18 @@ homography from camera displacement:
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The homography matrices are similar. If we compare the image 1 warped using both homography matrices:
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Visually, it is hard to distinguish a difference between the result image from the homography computed from the camera displacement and the one estimated with @ref cv::findHomography function.
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#### Exercise
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This demo shows you how to compute the homography transformation from two camera poses. Try to perform the same operations, but by computing N inter homography this time. Instead of computing one homography to directly warp the source image to the desired camera viewpoint, perform N warping operations to see the different transformations operating.
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You should get something similar to the following:
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### Demo 4: Decompose the homography matrix {#tutorial_homography_Demo4}
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OpenCV 3 contains the function @ref cv::decomposeHomographyMat which allows to decompose the homography matrix to a set of rotations, translations and plane normals.
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Using DepthAI Hardware / OAK depth sensors {#tutorial_gapi_oak_devices}
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=======================================================================
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@tableofcontents
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@prev_tutorial{tutorial_gapi_face_beautification}
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Depth sensors compatible with Luxonis DepthAI library are supported through OpenCV Graph API (or G-API) module. RGB image and some other formats of output can be retrieved by using familiar interface of G-API module.
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In order to use DepthAI sensor with OpenCV you should do the following preliminary steps:
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-# Install Luxonis DepthAI library [depthai-core](https://github.com/luxonis/depthai-core).
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-# Configure OpenCV with DepthAI library support by setting `WITH_OAK` flag in CMake. If DepthAI library is found in install folders OpenCV will be built with depthai-core (see a status `WITH_OAK` in CMake log).
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-# Build OpenCV.
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Source code
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-----------
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You can find source code how to process heterogeneous graphs in the `modules/gapi/samples/oak_basic_infer.cpp` of the OpenCV source code library.
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@add_toggle_cpp
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@include modules/gapi/samples/oak_basic_infer.cpp
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@end_toggle
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In this tutorial we build a complex hybrid Computer Vision/Deep
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Learning video processing pipeline with G-API.
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- @subpage tutorial_gapi_oak_devices
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*Languages:* C++
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*Compatibility:* \> OpenCV 4.6
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*Author:* Alessandro de Oliveira Faria (A.K.A. CABELO)
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In this tutorial we showed how to use the Luxonis DepthAI library with G-API.
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+97
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Object detection with Generalized Ballard and Guil Hough Transform {#tutorial_generalized_hough_ballard_guil}
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==================================================================
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@tableofcontents
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@prev_tutorial{tutorial_hough_circle}
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@next_tutorial{tutorial_remap}
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| Original author | Markus Heck |
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| Compatibility | OpenCV >= 3.4 |
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Goal
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----
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In this tutorial you will learn how to:
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- Use @ref cv::GeneralizedHoughBallard and @ref cv::GeneralizedHoughGuil to detect an object
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Example
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-------
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### What does this program do?
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1. Load the image and template
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2. Instantiate @ref cv::GeneralizedHoughBallard with the help of `createGeneralizedHoughBallard()`
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3. Instantiate @ref cv::GeneralizedHoughGuil with the help of `createGeneralizedHoughGuil()`
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4. Set the required parameters for both GeneralizedHough variants
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5. Detect and show found results
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@note
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- Both variants can't be instantiated directly. Using the create methods is required.
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- Guil Hough is very slow. Calculating the results for the "mini" files used in this tutorial
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takes only a few seconds. With image and template in a higher resolution, as shown below,
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my notebook requires about 5 minutes to calculate a result.
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### Code
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The complete code for this tutorial is shown below.
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@include samples/cpp/tutorial_code/ImgTrans/generalizedHoughTransform.cpp
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Explanation
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-----------
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### Load image, template and setup variables
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@snippet samples/cpp/tutorial_code/ImgTrans/generalizedHoughTransform.cpp generalized-hough-transform-load-and-setup
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The position vectors will contain the matches the detectors will find.
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Every entry contains four floating point values:
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position vector
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- *[0]*: x coordinate of center point
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- *[1]*: y coordinate of center point
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- *[2]*: scale of detected object compared to template
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- *[3]*: rotation of detected object in degree in relation to template
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An example could look as follows: `[200, 100, 0.9, 120]`
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### Setup parameters
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@snippet samples/cpp/tutorial_code/ImgTrans/generalizedHoughTransform.cpp generalized-hough-transform-setup-parameters
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Finding the optimal values can end up in trial and error and depends on many factors, such as the image resolution.
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### Run detection
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@snippet samples/cpp/tutorial_code/ImgTrans/generalizedHoughTransform.cpp generalized-hough-transform-run
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As mentioned above, this step will take some time, especially with larger images and when using Guil.
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### Draw results and show image
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@snippet samples/cpp/tutorial_code/ImgTrans/generalizedHoughTransform.cpp generalized-hough-transform-draw-results
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Result
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------
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The blue rectangle shows the result of @ref cv::GeneralizedHoughBallard and the green rectangles the results of @ref
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cv::GeneralizedHoughGuil.
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Getting perfect results like in this example is unlikely if the parameters are not perfectly adapted to the sample.
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An example with less perfect parameters is shown below.
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For the Ballard variant, only the center of the result is marked as a black dot on this image. The rectangle would be
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the same as on the previous image.
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@@ -4,7 +4,7 @@ Hough Circle Transform {#tutorial_hough_circle}
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@tableofcontents
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@prev_tutorial{tutorial_hough_lines}
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@next_tutorial{tutorial_remap}
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@next_tutorial{tutorial_generalized_hough_ballard_guil}
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@@ -3,7 +3,7 @@ Remapping {#tutorial_remap}
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@tableofcontents
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@prev_tutorial{tutorial_hough_circle}
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@prev_tutorial{tutorial_generalized_hough_ballard_guil}
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@next_tutorial{tutorial_warp_affine}
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@@ -23,6 +23,7 @@ Transformations
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- @subpage tutorial_canny_detector
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- @subpage tutorial_hough_lines
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- @subpage tutorial_hough_circle
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- @subpage tutorial_generalized_hough_ballard_guil
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- @subpage tutorial_remap
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- @subpage tutorial_warp_affine
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@@ -26,7 +26,7 @@ Installation by Using the Pre-built Libraries {#tutorial_windows_install_prebuil
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=============================================
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-# Launch a web browser of choice and go to our [page on
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Sourceforge](http://sourceforge.net/projects/opencvlibrary/files/opencv-win/).
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Sourceforge](http://sourceforge.net/projects/opencvlibrary/files/).
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-# Choose a build you want to use and download it.
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-# Make sure you have admin rights. Unpack the self-extracting archive.
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-# You can check the installation at the chosen path as you can see below.
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@@ -370,18 +370,18 @@ Set the OpenCV environment variable and add it to the systems path {#tutorial_wi
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First, we set an environment variable to make our work easier. This will hold the build directory of
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our OpenCV library that we use in our projects. Start up a command window and enter:
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@code
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setx -m OPENCV_DIR D:\OpenCV\Build\x86\vc11 (suggested for Visual Studio 2012 - 32 bit Windows)
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setx -m OPENCV_DIR D:\OpenCV\Build\x64\vc11 (suggested for Visual Studio 2012 - 64 bit Windows)
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setx OpenCV_DIR D:\OpenCV\build\x64\vc14 (suggested for Visual Studio 2015 - 64 bit Windows)
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setx OpenCV_DIR D:\OpenCV\build\x86\vc14 (suggested for Visual Studio 2015 - 32 bit Windows)
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setx -m OPENCV_DIR D:\OpenCV\Build\x86\vc12 (suggested for Visual Studio 2013 - 32 bit Windows)
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setx -m OPENCV_DIR D:\OpenCV\Build\x64\vc12 (suggested for Visual Studio 2013 - 64 bit Windows)
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setx OpenCV_DIR D:\OpenCV\build\x64\vc15 (suggested for Visual Studio 2017 - 64 bit Windows)
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setx OpenCV_DIR D:\OpenCV\build\x86\vc15 (suggested for Visual Studio 2017 - 32 bit Windows)
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setx -m OPENCV_DIR D:\OpenCV\Build\x64\vc14 (suggested for Visual Studio 2015 - 64 bit Windows)
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setx OpenCV_DIR D:\OpenCV\build\x64\vc16 (suggested for Visual Studio 2019 - 64 bit Windows)
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setx OpenCV_DIR D:\OpenCV\build\x86\vc16 (suggested for Visual Studio 2019 - 32 bit Windows)
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@endcode
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Here the directory is where you have your OpenCV binaries (*extracted* or *built*). You can have
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different platform (e.g. x64 instead of x86) or compiler type, so substitute appropriate value.
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Inside this, you should have two folders called *lib* and *bin*. The -m should be added if you wish
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to make the settings computer wise, instead of user wise.
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Inside this, you should have two folders called *lib* and *bin*.
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If you built static libraries then you are done. Otherwise, you need to add the *bin* folders path
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to the systems path. This is because you will use the OpenCV library in form of *"Dynamic-link
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