Merge pull request #4012 from mshabunin:doc-fixes
This commit is contained in:
Vadim Pisarevsky
commit
a87e1c2f90
@ -111,12 +111,11 @@ if(BUILD_DOCS AND DOXYGEN_FOUND)
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set(faqfile "${CMAKE_CURRENT_SOURCE_DIR}/faq.markdown")
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set(tutorial_path "${CMAKE_CURRENT_SOURCE_DIR}/tutorials")
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set(tutorial_py_path "${CMAKE_CURRENT_SOURCE_DIR}/py_tutorials")
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set(user_guide_path "${CMAKE_CURRENT_SOURCE_DIR}/user_guide")
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set(example_path "${CMAKE_SOURCE_DIR}/samples")
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# set export variables
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string(REPLACE ";" " \\\n" CMAKE_DOXYGEN_INPUT_LIST "${rootfile} ; ${faqfile} ; ${paths_include} ; ${paths_doc} ; ${tutorial_path} ; ${tutorial_py_path} ; ${user_guide_path} ; ${paths_tutorial}")
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string(REPLACE ";" " \\\n" CMAKE_DOXYGEN_IMAGE_PATH "${paths_doc} ; ${tutorial_path} ; ${tutorial_py_path} ; ${user_guide_path} ; ${paths_tutorial}")
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string(REPLACE ";" " \\\n" CMAKE_DOXYGEN_INPUT_LIST "${rootfile} ; ${faqfile} ; ${paths_include} ; ${paths_doc} ; ${tutorial_path} ; ${tutorial_py_path} ; ${paths_tutorial}")
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string(REPLACE ";" " \\\n" CMAKE_DOXYGEN_IMAGE_PATH "${paths_doc} ; ${tutorial_path} ; ${tutorial_py_path} ; ${paths_tutorial}")
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# TODO: remove paths_doc from EXAMPLE_PATH after face module tutorials/samples moved to separate folders
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string(REPLACE ";" " \\\n" CMAKE_DOXYGEN_EXAMPLE_PATH "${example_path} ; ${paths_doc} ; ${paths_sample}")
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set(CMAKE_DOXYGEN_LAYOUT "${CMAKE_CURRENT_SOURCE_DIR}/DoxygenLayout.xml")
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@ -22,17 +22,17 @@ red line. All the expected straight lines are bulged out. Visit [Distortion
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This distortion is solved as follows:
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This distortion is represented as follows:
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\f[x_{corrected} = x( 1 + k_1 r^2 + k_2 r^4 + k_3 r^6) \\
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y_{corrected} = y( 1 + k_1 r^2 + k_2 r^4 + k_3 r^6)\f]
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\f[x_{distorted} = x( 1 + k_1 r^2 + k_2 r^4 + k_3 r^6) \\
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y_{distorted} = y( 1 + k_1 r^2 + k_2 r^4 + k_3 r^6)\f]
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Similarly, another distortion is the tangential distortion which occurs because image taking lense
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is not aligned perfectly parallel to the imaging plane. So some areas in image may look nearer than
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expected. It is solved as below:
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expected. It is represented as below:
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\f[x_{corrected} = x + [ 2p_1xy + p_2(r^2+2x^2)] \\
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y_{corrected} = y + [ p_1(r^2+ 2y^2)+ 2p_2xy]\f]
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\f[x_{distorted} = x + [ 2p_1xy + p_2(r^2+2x^2)] \\
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y_{distorted} = y + [ p_1(r^2+ 2y^2)+ 2p_2xy]\f]
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In short, we need to find five parameters, known as distortion coefficients given by:
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@ -89,7 +89,7 @@ just by moving your hand in front of camera and many other funny stuffs.
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How to find HSV values to track?
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--------------------------------
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This is a common question found in [stackoverflow.com](www.stackoverflow.com). It is very simple and
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This is a common question found in [stackoverflow.com](http://www.stackoverflow.com). It is very simple and
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you can use the same function, cv2.cvtColor(). Instead of passing an image, you just pass the BGR
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values you want. For example, to find the HSV value of Green, try following commands in Python
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terminal:
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@ -85,7 +85,7 @@ Haar-cascade Detection in OpenCV
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OpenCV comes with a trainer as well as detector. If you want to train your own classifier for any
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object like car, planes etc. you can use OpenCV to create one. Its full details are given here:
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[Cascade Classifier Training.](http://docs.opencv.org/doc/user_guide/ug_traincascade.html)
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[Cascade Classifier Training](@ref tutorial_traincascade).
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Here we will deal with detection. OpenCV already contains many pre-trained classifiers for face,
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eyes, smile etc. Those XML files are stored in opencv/data/haarcascades/ folder. Let's create face
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@ -4,7 +4,6 @@ OpenCV modules {#mainpage}
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- @ref intro
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- @ref tutorial_root
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- @ref tutorial_py_root
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- @ref tutorial_user_guide
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- @ref faq
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- @ref citelist
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@ -14,18 +14,18 @@ Theory
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For the distortion OpenCV takes into account the radial and tangential factors. For the radial
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factor one uses the following formula:
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\f[x_{corrected} = x( 1 + k_1 r^2 + k_2 r^4 + k_3 r^6) \\
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y_{corrected} = y( 1 + k_1 r^2 + k_2 r^4 + k_3 r^6)\f]
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\f[x_{distorted} = x( 1 + k_1 r^2 + k_2 r^4 + k_3 r^6) \\
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y_{distorted} = y( 1 + k_1 r^2 + k_2 r^4 + k_3 r^6)\f]
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So for an old pixel point at \f$(x,y)\f$ coordinates in the input image, its position on the corrected
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output image will be \f$(x_{corrected} y_{corrected})\f$. The presence of the radial distortion
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manifests in form of the "barrel" or "fish-eye" effect.
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So for an undistorted pixel point at \f$(x,y)\f$ coordinates, its position on the distorted image
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will be \f$(x_{distorted} y_{distorted})\f$. The presence of the radial distortion manifests in form
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of the "barrel" or "fish-eye" effect.
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Tangential distortion occurs because the image taking lenses are not perfectly parallel to the
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imaging plane. It can be corrected via the formulas:
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imaging plane. It can be represented via the formulas:
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\f[x_{corrected} = x + [ 2p_1xy + p_2(r^2+2x^2)] \\
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y_{corrected} = y + [ p_1(r^2+ 2y^2)+ 2p_2xy]\f]
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\f[x_{distorted} = x + [ 2p_1xy + p_2(r^2+2x^2)] \\
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y_{distorted} = y + [ p_1(r^2+ 2y^2)+ 2p_2xy]\f]
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So we have five distortion parameters which in OpenCV are presented as one row matrix with 5
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columns:
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@ -1,4 +1,4 @@
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Operations with images {#tutorial_ug_mat}
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Operations with images {#tutorial_mat_operations}
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======================
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Input/Output
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@ -27,11 +27,6 @@ If you read a jpg file, a 3 channel image is created by default. If you need a g
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@note use imdecode and imencode to read and write image from/to memory rather than a file.
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XML/YAML
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--------
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TBD
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Basic operations with images
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----------------------------
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@ -32,6 +32,9 @@ understanding how to manipulate the images on a pixel level.
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You'll find out how to scan images with neighbor access and use the @ref cv::filter2D
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function to apply kernel filters on images.
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- @subpage tutorial_mat_operations
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Reading/writing images from file, accessing pixels, primitive operations, visualizing images.
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- @subpage tutorial_adding_images
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@ -1,4 +1,4 @@
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Using Creative Senz3D and other Intel Perceptual Computing SDK compatible depth sensors {#tutorial_ug_intelperc}
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Using Creative Senz3D and other Intel Perceptual Computing SDK compatible depth sensors {#tutorial_intelperc}
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=======================================================================================
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Depth sensors compatible with Intel Perceptual Computing SDK are supported through VideoCapture
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@ -78,5 +78,5 @@ there are two flags that should be used to set/get property of the needed genera
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flag value is assumed by default if neither of the two possible values of the property is set.
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For more information please refer to the example of usage
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[intelpercccaptureccpp](https://github.com/Itseez/opencv/tree/master/samples/cpp/intelperc_capture.cpp)
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[intelperc_capture.cpp](https://github.com/Itseez/opencv/tree/master/samples/cpp/intelperc_capture.cpp)
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in opencv/samples/cpp folder.
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@ -1,4 +1,4 @@
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Using Kinect and other OpenNI compatible depth sensors {#tutorial_ug_highgui}
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Using Kinect and other OpenNI compatible depth sensors {#tutorial_kinect_openni}
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======================================================
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Depth sensors compatible with OpenNI (Kinect, XtionPRO, ...) are supported through VideoCapture
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@ -134,5 +134,5 @@ property. The following properties of cameras available through OpenNI interface
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- CAP_OPENNI_DEPTH_GENERATOR_REGISTRATION = CAP_OPENNI_DEPTH_GENERATOR + CAP_PROP_OPENNI_REGISTRATION
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For more information please refer to the example of usage
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[openniccaptureccpp](https://github.com/Itseez/opencv/tree/master/samples/cpp/openni_capture.cpp) in
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[openni_capture.cpp](https://github.com/Itseez/opencv/tree/master/samples/cpp/openni_capture.cpp) in
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opencv/samples/cpp folder.
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@ -37,3 +37,7 @@ use the built-in graphical user interface of the library.
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*Author:* Marvin Smith
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Read common GIS Raster and DEM files to display and manipulate geographic data.
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- @subpage tutorial_kinect_openni
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- @subpage tutorial_intelperc
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@ -77,8 +77,7 @@ Following scheme represents common documentation places for _opencv_ repository:
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<opencv>
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├── doc - doxygen config files, root page (root.markdown.in), BibTeX file (opencv.bib)
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│ ├── tutorials - tutorials hierarchy (pages and images)
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│ ├── py_tutorials - python tutorials hierarchy (pages and images)
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│ └── user_guide - old user guide (pages and images)
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│ └── py_tutorials - python tutorials hierarchy (pages and images)
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├── modules
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│ └── <modulename>
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│ ├── doc - documentation pages and images for module
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@ -10,3 +10,7 @@ Ever wondered how your digital camera detects peoples and faces? Look here to fi
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*Author:* Ana Huamán
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Here we learn how to use *objdetect* to find objects in our images or videos
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- @subpage tutorial_traincascade
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This tutorial describes _opencv_traincascade_ application and its parameters.
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@ -1,4 +1,4 @@
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Cascade Classifier Training {#tutorial_ug_traincascade}
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Cascade Classifier Training {#tutorial_traincascade}
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===========================
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Introduction
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@ -1,110 +0,0 @@
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Features2d {#tutorial_ug_features2d}
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==========
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Detectors
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---------
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Descriptors
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-----------
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Matching keypoints
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------------------
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### The code
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We will start with a short sample \`opencv/samples/cpp/matcher_simple.cpp\`:
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@code{.cpp}
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Mat img1 = imread(argv[1], IMREAD_GRAYSCALE);
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Mat img2 = imread(argv[2], IMREAD_GRAYSCALE);
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if(img1.empty() || img2.empty())
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{
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printf("Can't read one of the images\n");
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return -1;
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}
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// detecting keypoints
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SurfFeatureDetector detector(400);
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vector<KeyPoint> keypoints1, keypoints2;
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detector.detect(img1, keypoints1);
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detector.detect(img2, keypoints2);
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// computing descriptors
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SurfDescriptorExtractor extractor;
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Mat descriptors1, descriptors2;
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extractor.compute(img1, keypoints1, descriptors1);
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extractor.compute(img2, keypoints2, descriptors2);
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// matching descriptors
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BruteForceMatcher<L2<float> > matcher;
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vector<DMatch> matches;
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matcher.match(descriptors1, descriptors2, matches);
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// drawing the results
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namedWindow("matches", 1);
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Mat img_matches;
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drawMatches(img1, keypoints1, img2, keypoints2, matches, img_matches);
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imshow("matches", img_matches);
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waitKey(0);
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@endcode
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### The code explained
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Let us break the code down.
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@code{.cpp}
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Mat img1 = imread(argv[1], IMREAD_GRAYSCALE);
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Mat img2 = imread(argv[2], IMREAD_GRAYSCALE);
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if(img1.empty() || img2.empty())
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{
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printf("Can't read one of the images\n");
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return -1;
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}
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@endcode
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We load two images and check if they are loaded correctly.
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@code{.cpp}
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// detecting keypoints
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Ptr<FeatureDetector> detector = FastFeatureDetector::create(15);
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vector<KeyPoint> keypoints1, keypoints2;
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detector->detect(img1, keypoints1);
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detector->detect(img2, keypoints2);
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@endcode
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First, we create an instance of a keypoint detector. All detectors inherit the abstract
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FeatureDetector interface, but the constructors are algorithm-dependent. The first argument to each
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detector usually controls the balance between the amount of keypoints and their stability. The range
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of values is different for different detectors (For instance, *FAST* threshold has the meaning of
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pixel intensity difference and usually varies in the region *[0,40]*. *SURF* threshold is applied to
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a Hessian of an image and usually takes on values larger than *100*), so use defaults in case of
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doubt.
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@code{.cpp}
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// computing descriptors
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Ptr<SURF> extractor = SURF::create();
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Mat descriptors1, descriptors2;
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extractor->compute(img1, keypoints1, descriptors1);
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extractor->compute(img2, keypoints2, descriptors2);
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@endcode
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We create an instance of descriptor extractor. The most of OpenCV descriptors inherit
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DescriptorExtractor abstract interface. Then we compute descriptors for each of the keypoints. The
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output Mat of the DescriptorExtractor::compute method contains a descriptor in a row *i* for each
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*i*-th keypoint. Note that the method can modify the keypoints vector by removing the keypoints such
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that a descriptor for them is not defined (usually these are the keypoints near image border). The
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method makes sure that the ouptut keypoints and descriptors are consistent with each other (so that
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the number of keypoints is equal to the descriptors row count). :
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@code{.cpp}
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// matching descriptors
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BruteForceMatcher<L2<float> > matcher;
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vector<DMatch> matches;
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matcher.match(descriptors1, descriptors2, matches);
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@endcode
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Now that we have descriptors for both images, we can match them. First, we create a matcher that for
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each descriptor from image 2 does exhaustive search for the nearest descriptor in image 1 using
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Euclidean metric. Manhattan distance is also implemented as well as a Hamming distance for Brief
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descriptor. The output vector matches contains pairs of corresponding points indices. :
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@code{.cpp}
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// drawing the results
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namedWindow("matches", 1);
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Mat img_matches;
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drawMatches(img1, keypoints1, img2, keypoints2, matches, img_matches);
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imshow("matches", img_matches);
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waitKey(0);
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@endcode
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The final part of the sample is about visualizing the matching results.
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@ -1,8 +0,0 @@
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OpenCV User Guide {#tutorial_user_guide}
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=================
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- @subpage tutorial_ug_mat
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- @subpage tutorial_ug_features2d
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- @subpage tutorial_ug_highgui
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- @subpage tutorial_ug_traincascade
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- @subpage tutorial_ug_intelperc
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@ -1282,7 +1282,8 @@ equivalent matrix expressions:
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@endcode
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@param src1 first input array or a scalar; when it is an array, it must have a single channel.
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@param src2 second input array or a scalar; when it is an array, it must have a single channel.
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@param dst output array that has the same size and type as the input arrays.
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@param dst output array of type ref CV_8U that has the same size and the same number of channels as
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the input arrays.
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@param cmpop a flag, that specifies correspondence between the arrays (cv::CmpTypes)
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@sa checkRange, min, max, threshold
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*/
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@ -539,7 +539,7 @@ private:
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virtual void deleteDataInstance(void* data) const { delete (T*)data; }
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};
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/** @brief designed for command line arguments parsing
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/** @brief Designed for command line parsing
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The sample below demonstrates how to use CommandLineParser:
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@code
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@ -569,8 +569,19 @@ The sample below demonstrates how to use CommandLineParser:
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return 0;
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}
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@endcode
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Syntax:
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@code
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### Keys syntax
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The keys parameter is a string containing several blocks, each one is enclosed in curley braces and
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describes one argument. Each argument contains three parts separated by the `|` symbol:
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-# argument names is a space-separated list of option synonyms (to mark argument as positional, prefix it with the `@` symbol)
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-# default value will be used if the argument was not provided (can be empty)
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-# help message (can be empty)
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For example:
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@code{.cpp}
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const String keys =
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"{help h usage ? | | print this message }"
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"{@image1 | | image1 for compare }"
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@ -581,27 +592,89 @@ Syntax:
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"{N count |100 | count of objects }"
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"{ts timestamp | | use time stamp }"
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;
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}
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@endcode
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Use:
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@code
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# ./app -N=200 1.png 2.jpg 19 -ts
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# ./app -fps=aaa
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### Usage
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For the described keys:
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@code{.sh}
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# Good call (3 positional parameters: image1, image2 and repeat; N is 200, ts is true)
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$ ./app -N=200 1.png 2.jpg 19 -ts
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# Bad call
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$ ./app -fps=aaa
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ERRORS:
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Exception: can not convert: [aaa] to [double]
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@endcode
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*/
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class CV_EXPORTS CommandLineParser
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{
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public:
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public:
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/** @brief Constructor
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Initializes command line parser object
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@param argc number of command line arguments (from main())
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@param argv array of command line arguments (from main())
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@param keys string describing acceptable command line parameters (see class description for syntax)
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*/
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CommandLineParser(int argc, const char* const argv[], const String& keys);
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/** @brief Copy constructor */
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CommandLineParser(const CommandLineParser& parser);
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/** @brief Assignment operator */
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CommandLineParser& operator = (const CommandLineParser& parser);
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/** @brief Destructor */
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~CommandLineParser();
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||||
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||||
/** @brief Returns application path
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||||
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||||
This method returns the path to the executable from the command line (`argv[0]`).
|
||||
|
||||
For example, if the application has been started with such command:
|
||||
@code{.sh}
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$ ./bin/my-executable
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||||
@endcode
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||||
this method will return `./bin`.
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||||
*/
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String getPathToApplication() const;
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/** @brief Access arguments by name
|
||||
|
||||
Returns argument converted to selected type. If the argument is not known or can not be
|
||||
converted to selected type, the error flag is set (can be checked with @ref check).
|
||||
|
||||
For example, define:
|
||||
@code{.cpp}
|
||||
String keys = "{N count||}";
|
||||
@endcode
|
||||
|
||||
Call:
|
||||
@code{.sh}
|
||||
$ ./my-app -N=20
|
||||
# or
|
||||
$ ./my-app --count=20
|
||||
@endcode
|
||||
|
||||
Access:
|
||||
@code{.cpp}
|
||||
int N = parser.get<int>("N");
|
||||
@endcode
|
||||
|
||||
@param name name of the argument
|
||||
@param space_delete remove spaces from the left and right of the string
|
||||
@tparam T the argument will be converted to this type if possible
|
||||
|
||||
@note You can access positional arguments by their `@`-prefixed name:
|
||||
@code{.cpp}
|
||||
parser.get<String>("@image");
|
||||
@endcode
|
||||
*/
|
||||
template <typename T>
|
||||
T get(const String& name, bool space_delete = true) const
|
||||
{
|
||||
@ -610,6 +683,30 @@ class CV_EXPORTS CommandLineParser
|
||||
return val;
|
||||
}
|
||||
|
||||
/** @brief Access positional arguments by index
|
||||
|
||||
Returns argument converted to selected type. Indexes are counted from zero.
|
||||
|
||||
For example, define:
|
||||
@code{.cpp}
|
||||
String keys = "{@arg1||}{@arg2||}"
|
||||
@endcode
|
||||
|
||||
Call:
|
||||
@code{.sh}
|
||||
./my-app abc qwe
|
||||
@endcode
|
||||
|
||||
Access arguments:
|
||||
@code{.cpp}
|
||||
String val_1 = parser.get<String>(0); // returns "abc", arg1
|
||||
String val_2 = parser.get<String>(1); // returns "qwe", arg2
|
||||
@endcode
|
||||
|
||||
@param index index of the argument
|
||||
@param space_delete remove spaces from the left and right of the string
|
||||
@tparam T the argument will be converted to this type if possible
|
||||
*/
|
||||
template <typename T>
|
||||
T get(int index, bool space_delete = true) const
|
||||
{
|
||||
@ -618,13 +715,37 @@ class CV_EXPORTS CommandLineParser
|
||||
return val;
|
||||
}
|
||||
|
||||
/** @brief Check if field was provided in the command line
|
||||
|
||||
@param name argument name to check
|
||||
*/
|
||||
bool has(const String& name) const;
|
||||
|
||||
/** @brief Check for parsing errors
|
||||
|
||||
Returns true if error occured while accessing the parameters (bad conversion, missing arguments,
|
||||
etc.). Call @ref printErrors to print error messages list.
|
||||
*/
|
||||
bool check() const;
|
||||
|
||||
/** @brief Set the about message
|
||||
|
||||
The about message will be shown when @ref printMessage is called, right before arguments table.
|
||||
*/
|
||||
void about(const String& message);
|
||||
|
||||
/** @brief Print help message
|
||||
|
||||
This method will print standard help message containing the about message and arguments description.
|
||||
|
||||
@sa about
|
||||
*/
|
||||
void printMessage() const;
|
||||
|
||||
/** @brief Print list of errors occured
|
||||
|
||||
@sa check
|
||||
*/
|
||||
void printErrors() const;
|
||||
|
||||
protected:
|
||||
|
||||
@ -108,7 +108,7 @@ void CommandLineParser::getByName(const String& name, bool space_delete, int typ
|
||||
}
|
||||
}
|
||||
impl->error = true;
|
||||
impl->error_message = impl->error_message + "Unknown parametes " + name + "\n";
|
||||
impl->error_message = impl->error_message + "Unknown parameter " + name + "\n";
|
||||
}
|
||||
catch (std::exception& e)
|
||||
{
|
||||
@ -133,7 +133,7 @@ void CommandLineParser::getByIndex(int index, bool space_delete, int type, void*
|
||||
}
|
||||
}
|
||||
impl->error = true;
|
||||
impl->error_message = impl->error_message + "Unknown parametes #" + format("%d", index) + "\n";
|
||||
impl->error_message = impl->error_message + "Unknown parameter #" + format("%d", index) + "\n";
|
||||
}
|
||||
catch(std::exception & e)
|
||||
{
|
||||
|
||||
@ -78,9 +78,9 @@ scaled to fit the 0 to 1 range.
|
||||
\f[L \leftarrow \frac{V_{max} + V_{min}}{2}\f]
|
||||
\f[S \leftarrow \fork { \frac{V_{max} - V_{min}}{V_{max} + V_{min}} }{if \(L < 0.5\) }
|
||||
{ \frac{V_{max} - V_{min}}{2 - (V_{max} + V_{min})} }{if \(L \ge 0.5\) }\f]
|
||||
\f[H \leftarrow \forkthree {{60(G - B)}/{S}}{if \(V_{max}=R\) }
|
||||
{{120+60(B - R)}/{S}}{if \(V_{max}=G\) }
|
||||
{{240+60(R - G)}/{S}}{if \(V_{max}=B\) }\f]
|
||||
\f[H \leftarrow \forkthree {{60(G - B)}/{(V_{max}-V_{min})}}{if \(V_{max}=R\) }
|
||||
{{120+60(B - R)}/{(V_{max}-V_{min})}}{if \(V_{max}=G\) }
|
||||
{{240+60(R - G)}/{(V_{max}-V_{min})}}{if \(V_{max}=B\) }\f]
|
||||
If \f$H<0\f$ then \f$H \leftarrow H+360\f$ . On output \f$0 \leq L \leq 1\f$, \f$0 \leq S \leq
|
||||
1\f$, \f$0 \leq H \leq 360\f$ .
|
||||
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user