Merge pull request #9466 from huningxin:js

GSoC 2017: Improve and Extend the JavaScript Bindings for OpenCV (#9466)

* Initial support for build with emscripten

mkdir build_js
cd build_js
cmake -D CMAKE_TOOLCHAIN_FILE=/path/to/emsdk/emsdk-portable/emscripten/master/cmake/Modules/Platform/Emscripten.cmake -D CMAKE_BUILD_TYPE=Release -D CMAKE_INSTALL_PREFIX=/usr/local ..

* Add js module

The output is build/bin/opencv_js.js

* Fix opencv2/calib3d.hpp not found issue

* Add module name

Usage:
var cv = cv();

* Add total memory as 128MB and allow growth

* Add compilation flags for emscripten

* Use EMSCRIPTEN build target

* Disable js module for non emscripten build

* Bind the preload file path to root

Usage:
face_cascade.load('haarcascade_frontalface_default.xml');

* add test folder

* fix test files

* Copy js module test to bin

* Support to run tests on Node.js

Fix tests to import cv Module when runtime is node.
Add tests.js to use qunit to auto run tests.
Modify umd wrapper to support Module is not defined.

Usage:
node tests.js

* Support UMD and file system

Wrap the opencv_js.js to opencv.js by UMD wrapper

Use emscripten file system API to load files instead of generating data file or
embedding them. It supports both browser and node.js usages.

* Fix incorrect module name in tests

* Add package.json to add dependence of qunit

* Add js_tutorials folder and a intro page of opencv.js

Enable BUILD_DOCS in CMakeLists.txt.
Add new folder of js_tutorials in folder opencv/doc.
Imitate the tutorials of OpenCV-Python to create a intro page of opencv.js and a setup guide

* Import and use binding gen from opencvjs project

* Modify the embindgen.py to pass the build and test

* Add classes and functions white list

* Consolidate hdr_parser.py (#31)

Use hdr_parser.py of python module

Add js flag to support js binding generator.

* Use emscripten::vecFromJSArray for input vector param

Fix part of #23

* Fix test cases after #34

Fix #39

* Expose groupRectangles and CascadeClassifier.empty

* Add js highgui tutorials

add tutorials of imread&imshow and createTrackbar in doc/js_tutorials/js_gui folder
add interactive tutorial webpage for imread&imshow and createTrackbar in doc/js_tutorials/js_interactive_tutorials folder, and some images needed.
change doc/CMakeLists.txt to copy the interactive tutorial webpage and opencv.js to the tutorials' destination folder

* rm useless annotation in doc/CMakeLists.txt

* fix some nonstandard indentation and space

* add check if canvas is valid

* Expose BackgroundSubtractorMOG2

Fix #43

* Fix build of js doc

Limit copy_js_interactive_tutorials for doxygen build
Add dep to opencv.js

Fix #53

* Implement cv.imread & cv.imshow and insert interactive pages in tutorials (#55)

* add helper.js

* delete ALL in add target copy_js_interactive_tutorials to avoid dependence error

* Insert interactive pages in tutorials

insert the old interactive pages in markdown by using \htmlonly and \endhtmlonly command.
delete the useless interactive page
rename js_interactive_tutorials to js_assets to put some images needed in

* fix the depends of the target doxygen

add opencv.js to depends and delete the useless target of copy_js_assets

* change filename helper.js to helpers.js

* disable button or trankbar before opencv.js is ready

* Expose CV_64F

Fix #65

* improve cv.imshow to display different types as native imshow

* add utils.js to reuse functions and update tutorials

* Make doxygen depend on bin/opencv.js

* Fix memory issue of matFromArray

Fix #37

* Merge pull request from ganwenyao/tutorial_18

* Add notes for ganwenyao/tutorial_18

* Modifying for ganwenyao/tutorial_18

* Change Mat constructor with data to 5 parameters

* Mat supports constructor with Scalar

Fix #60

* update cv.imread cause the memory issue of matFromArray has been fixed

* fix canvas name and default input image

* Expose cv::Moments

Fix #85

* Add -Wno-missing-prototypes for emscripten build

* fix canvas name

* add tutorial of video input and output

* Expose enums as emscripten consts

Fix #72

* update the tutorial to use Mat constructor with Scalar and change lena.jpg

* Exclude cv::Mat for vecFromJSArray

Fix #82

* Add unit tests for cv.moments

* Fix the unit tests.

* add checkbox and stop button

* add adapter.js to make sure compatibility fo video tutorials

* Support default parameters with function overloading

* modify enums to constants

* Use https URL for MathJax.js

Fix #109

* Comment out the debug print in embindgen.py

* Expose RotatedRect

Fix #96

* replace enum with constants and improve onload function

* delete some useless paras cause #105 fixed this

* Modify const name

* Modify Contour Properties

* tutorials for imgprc2 and objdec

* Expose more functions for img proc tutorials

Fix #76

* Expose polylines for video analysis tutorial

Fix #121

* Expose constants for default parameters of img proc tutorials

Fix #122

* Fix wrong parameter types of Mat.copyTo

Fix #87

* Support default parameters of mat.convertTo

Fix #123

* Support default parameters for external constructors

Fix #131

* Revert "Expose polylines for video analysis tutorial"

This reverts commit 3ce7615652e510d30e3c0014706ac38c98883189.

Fix #121

* Support cv.minMaxLoc

Fix #127

* Expose cv.minEnclosingCircle

Fix #126

* Add video analysis tutorials

add three video tutorials, Meanshift and Camshift, Optical Flow Background Subtraction
add cup.mp4 and box.mp4 for demo in tutorials

* improve image processing tutorials

* repalce console.warn with throw to throw exception

* add try-catch to throw exception in code demo

* Change mat.size() return value to JS Array object

Fix #140

* add a note about different channels order between canvas and native opencv

* add a note about how to capture video from video files

* Binding cv.Scalar to JS array

Fix #147

* Add JS cv.Scalar object into helpers.js

* Update Install OpenCV-JavaScript tutorial page

Fix #44

* Update the OpenCV-JavaScript introduction page

Fix #44

* add cv.VideoCapture and read() function

* set the size of the hidden canvas same as the video

* Add Using OpenCV-JavaScript tutorial page

Fix #44

* fix some bad code style

* Update tutorials after 8/2 sync meeting

Changes include:
- Use OpenCV.js name instead of OpenCV-JavaScript
- Put using OpenCV.js ahead of build OpenCV.js
- Refine usage and introduction page
- Muted the video in tutorials

* Fix a typo in introduction page

* use cv.VideoCapture and its read() function to read video

* replace OpenCV-JavaScript with OpenCV.js

* Use onload of async script in js_usage tutorial

* add more info about mat.data

* Change Size to value_object

* Integrate Moh and Sajjad's editing into introduction page

* Change Point to value_object

* Change Rect to value_object with helper object

* Add helper objects for Point and Size

* Change RotatedRect to value_object with helpers

* Change MinMaxLoc and Circle to value_object

* Change TermCriteria to value_object

* Fix core_bindings.cpp for MinMaxLoc and Circle

* Remove unused types

* Change meanShift and CamShift to return Rect

* Change methods of RotatedRect to static

* Change mat.data from methods to property

Fix #75 and #77

* support img id and element in cv.imread

* Change mat.size to property and add mat.step

Fix #163

* Add matFromArray and matFromImageData as JS helpers

Fix #79, #78

* Lower camel case for Mat element getters

Fix #81

* Mat.getRoiRect and tests

Fix #86

* Support type for Mat.ptr

Fix #83

* Name changing of Mat element getters

'getUcharAt` -> 'ucharAt'

* fix code style and args names

* Fix helpers.js due to cv.Mat API update

* Fix opencv.js usage tutorial

* Fix a typo of js_setup

* Change Moments to value_object

* Add Range as value_object

Fix #171

* Support Mat.diag and Mat.isContinous

Fix #84 and #89

* Support Mat.setTo

Fix #88

* Apply edits to js_intro

* Apply edits to js_usage

* Apply edits to js_setup

* update tutorials to apply data type change

* Modify tutorials

* add core tutorials

* delete MatVector elements and delete useless set operation

* add tutorials_objdec_camera

* Add instructions for WebAssembly

* apply tech writer's feedbacks into tutorials

* Organize white list by modules

* Change size to method and bind to MatExpr.size()

Fix #177

* improve tutorials

* Modify core tutorials

* add params list and explanations for OpenCV.js functions

* remove face_profile from Face Detection in Video Capture

* Add demos link

* Change Gui to GUI

* Update js_intro based on Moh and Sajjad's edits

* Fixup for 3.3.0 rebase

* Update js_intro per Moh's suggestion

* Update contributors list per Moh's idea

* add adapter.js in video_display tutorial

* Change Mat.getRoiRect to Mat.roi

Fix #194

* Remove unnecessary files for test

Fix #192

* Licenses updated to UC BSD 3-Clause

* Apply OpenCV coding style for C++ files

* Add OpenCV license for python and js files

* Fix coding style issue in helpers.js

* Remove unused test_commons.js

* Fix coding style of test_imgproc.js

* Fix coding style of test_mat.js

* Fix space before semicolon

* Fix coding style of test_objdetect.js

* Fix coding style of tests.js

* Fix coding style of test_utils.js

* Fix coding style of test_video.js

* Fix failures of node.js tests

* Add eslint rule config and fix eslint errors

* Add eslint config for js/src and fix eslint errors

* Clean up the opencv.js dependencies

Fix #186

* Fix build issue for python generator

* Fix doxygen buildbot failure

* delete trailing whitespace, blank line at EOF and replace tab with space

* Fix tutorial_js_root reference issue for non opencv.js build

* replace the file with small size

* Initial commit of build_js.py

* Move the js build configurations to build script

* Add wasm build support

* Update OpenCV.js build tutorial by using script

* Fix global var issue in tests

* Add a README.md for build_js.py

* Copy the haar cascade files from data dir for tutorials

* Not use memory init file

* Disable debug print for modules/js/CMakeLists.txt

* Check files when build done

* Fix image name in js_gradients tutorial

* Fix image load issue in js_trackbar tutorial

* Find the opencv source directory via relative path by default

* Make the cmake args based on build_doc option

* Fix a typo in js_setup.markdown

* Fix make failure issue on config generated by build_js.py

* Eliminate js branch of hdr_parser.py

* Extract examples from js_basic_ops tutorial

* Fix coding style of utils.js

* Improve examples error handling

Handle:
1. opencv.js loading errors
2. script errors (Error)
3. cv::Exception

Fix #217

* Add enable_exception option into build_js.py

* Support print exception for exception catching disabled build

* Extract example from js_usage tutorial

* Avoid copying .eslintrc.json when building doc

Fix #223

* Revert to use onload as opencv.js ready event

* Use 4 spaces indention for js examples

* embed html in tutorials with iframe tag

* Revert to use onload as opencv.js ready event

* Extract examples from js_video_display tutorial

* Implement Utils object

* modify core imgprc and face_detection tutorials

* Fix examples of js_gui tutorials

* Fix coding style of utils.js

* Modify tutorials

* Extract example from js_face_detection_camera tutorial

* Disable new-cap check in eslint

* Extract examples from js_meanshift tutorial

* Extract examples from video tutorials

* Remove new-cap declaration and update grammer in comments

* Change textarea width to 100 to align with eslint config

* Fix printError issue when opencv.js loading fails

* Remove BUILD_opencv_js dependency for doc build

Fix #213

* Expose cv::getBuildInformation

* Dump opencv build info when opencv.js loaded for live examples

* Make the button to stand out in js live examples

Fix #235

* Style for disabled button

* Add js_imgproc_camera.html example

* Fix coding style of imgproc_camera example

* Add js_imgproc_camera tutorial

* Remove link to opencv.js demos

* doc: copy opencv.js on build, use absolute paths for assets

* doc: reuse existed file box.mp4

doc/js_tutorials/js_objdetect/js_face_detection/js_face_detection.markdown

@@ -0,0 +1,107 @@
+Face Detection using Haar Cascades {#tutorial_js_face_detection}
+==================================
+
+Goal
+----
+
+-   learn the basics of face detection using Haar Feature-based Cascade Classifiers
+-   extend the same for eye detection etc.
+
+Basics
+------
+
+Object Detection using Haar feature-based cascade classifiers is an effective method proposed by Paul Viola and Michael Jones in the 2001 paper, "Rapid Object Detection using a
+Boosted Cascade of Simple Features". It is a machine learning based approach in which a cascade
+function is trained from a lot of positive and negative images. It is then used to detect objects in
+other images.
+
+Here we will work with face detection. Initially, the algorithm needs a lot of positive images
+(images of faces) and negative images (images without faces) to train the classifier. Then we need
+to extract features from it. For this, Haar features shown in below image are used. They are just
+like our convolutional kernel. Each feature is a single value obtained by subtracting the sum of pixels
+under the white rectangle from the sum of pixels under the black rectangle.
+
+![image](images/haar_features.jpg)
+
+Now all possible sizes and locations of each kernel are used to calculate plenty of features. For each
+feature calculation, we need to find the sum of the pixels under the white and black rectangles. To solve this,
+they introduced the integral images. It simplifies calculation of the sum of the pixels, how large may be
+the number of pixels, to an operation involving just four pixels.
+
+But among all these features we calculated, most of them are irrelevant. For example, consider the
+image below. Top row shows two good features. The first feature selected seems to focus on the
+property that the region of the eyes is often darker than the region of the nose and cheeks. The
+second feature selected relies on the property that the eyes are darker than the bridge of the nose.
+But the same windows applying on cheeks or any other place is irrelevant. So how do we select the
+best features out of 160000+ features? It is achieved by **Adaboost**.
+
+![image](images/haar.png)
+
+For this, we apply each and every feature on all the training images. For each feature, it finds the
+best threshold which will classify the faces to positive and negative. But obviously, there will be
+errors or misclassifications. We select the features with minimum error rate, which means they are
+the features that best classifies the face and non-face images. (The process is not as simple as
+this. Each image is given an equal weight in the beginning. After each classification, weights of
+misclassified images are increased. Then again same process is done. New error rates are calculated.
+Also new weights. The process is continued until required accuracy or error rate is achieved or
+required number of features are found).
+
+Final classifier is a weighted sum of these weak classifiers. It is called weak because it alone
+can't classify the image, but together with others forms a strong classifier. The paper says even
+200 features provide detection with 95% accuracy. Their final setup had around 6000 features.
+(Imagine a reduction from 160000+ features to 6000 features. That is a big gain).
+
+So now you take an image. Take each 24x24 window. Apply 6000 features to it. Check if it is face or
+not. Wow.. Wow.. Isn't it a little inefficient and time consuming? Yes, it is. Authors have a good
+solution for that.
+
+In an image, most of the image region is non-face region. So it is a better idea to have a simple
+method to check if a window is not a face region. If it is not, discard it in a single shot. Don't
+process it again. Instead focus on region where there can be a face. This way, we can find more time
+to check a possible face region.
+
+For this they introduced the concept of **Cascade of Classifiers**. Instead of applying all the 6000
+features on a window, group the features into different stages of classifiers and apply one-by-one.
+(Normally first few stages will contain very less number of features). If a window fails the first
+stage, discard it. We don't consider remaining features on it. If it passes, apply the second stage
+of features and continue the process. The window which passes all stages is a face region. How is
+the plan !!!
+
+Authors' detector had 6000+ features with 38 stages with 1, 10, 25, 25 and 50 features in first five
+stages. (Two features in the above image is actually obtained as the best two features from
+Adaboost). According to authors, on an average, 10 features out of 6000+ are evaluated per
+sub-window.
+
+So this is a simple intuitive explanation of how Viola-Jones face detection works. Read paper for
+more details.
+
+Haar-cascade Detection in OpenCV
+--------------------------------
+
+Here we will deal with detection. OpenCV already contains many pre-trained classifiers for face,
+eyes, smile etc. Those XML files are stored in opencv/data/haarcascades/ folder. Let's create a face
+and eye detector with OpenCV.
+
+We use the function: **detectMultiScale (image, objects, scaleFactor = 1.1, minNeighbors = 3, flags = 0, minSize = new cv.Size(0, 0), maxSize = new cv.Size(0, 0))**
+
+@param image               matrix of the type CV_8U containing an image where objects are detected.
+@param objects             vector of rectangles where each rectangle contains the detected object. The rectangles may be partially outside the original image.
+@param scaleFactor         parameter specifying how much the image size is reduced at each image scale.
+@param minNeighbors        parameter specifying how many neighbors each candidate rectangle should have to retain it.
+@param flags               parameter with the same meaning for an old cascade as in the function cvHaarDetectObjects. It is not used for a new cascade.
+@param minSize             minimum possible object size. Objects smaller than this are ignored.
+@param maxSize             maximum possible object size. Objects larger than this are ignored. If maxSize == minSize model is evaluated on single scale.
+
+@note Don't forget to delete CascadeClassifier and RectVector!
+
+Try it
+------
+
+Try this demo using the code above. Canvas elements named haarCascadeDetectionCanvasInput and haarCascadeDetectionCanvasOutput have been prepared. Choose an image and
+click `Try it` to see the result. You can change the code in the textbox to investigate more.
+
+\htmlonly
+<iframe src="../../js_face_detection.html" width="100%"
+        onload="this.style.height=this.contentDocument.body.scrollHeight +'px';">
+</iframe>
+\endhtmlonly

doc/js_tutorials/js_objdetect/js_face_detection/js_face_detection_camera.markdown

@@ -0,0 +1,15 @@
+Face Detection in Video Capture {#tutorial_js_face_detection_camera}
+==================================
+
+Goal
+----
+
+-   learn how to detect faces in video capture.
+
+@note  If you don't know how to capture video from camera, please review @ref tutorial_js_video_display.
+
+\htmlonly
+<iframe src="../../js_face_detection_camera.html" width="100%"
+        onload="this.style.height=this.contentDocument.body.scrollHeight +'px';">
+</iframe>
+\endhtmlonly

doc/js_tutorials/js_objdetect/js_table_of_contents_objdetect.markdown

@@ -0,0 +1,11 @@
+Object Detection {#tutorial_js_table_of_contents_objdetect}
+================
+
+-   @subpage tutorial_js_face_detection
+
+    Face detection
+    using haar-cascades
+
+-   @subpage tutorial_js_face_detection_camera
+
+    Face Detection in Video Capture