diff --git a/doc/CMakeLists.txt b/doc/CMakeLists.txt index a7f5372bfa..bb17f7fe11 100644 --- a/doc/CMakeLists.txt +++ b/doc/CMakeLists.txt @@ -111,12 +111,11 @@ if(BUILD_DOCS AND DOXYGEN_FOUND) set(faqfile "${CMAKE_CURRENT_SOURCE_DIR}/faq.markdown") set(tutorial_path "${CMAKE_CURRENT_SOURCE_DIR}/tutorials") set(tutorial_py_path "${CMAKE_CURRENT_SOURCE_DIR}/py_tutorials") - set(user_guide_path "${CMAKE_CURRENT_SOURCE_DIR}/user_guide") set(example_path "${CMAKE_SOURCE_DIR}/samples") # set export variables - string(REPLACE ";" " \\\n" CMAKE_DOXYGEN_INPUT_LIST "${rootfile} ; ${faqfile} ; ${paths_include} ; ${paths_doc} ; ${tutorial_path} ; ${tutorial_py_path} ; ${user_guide_path} ; ${paths_tutorial}") - string(REPLACE ";" " \\\n" CMAKE_DOXYGEN_IMAGE_PATH "${paths_doc} ; ${tutorial_path} ; ${tutorial_py_path} ; ${user_guide_path} ; ${paths_tutorial}") + string(REPLACE ";" " \\\n" CMAKE_DOXYGEN_INPUT_LIST "${rootfile} ; ${faqfile} ; ${paths_include} ; ${paths_doc} ; ${tutorial_path} ; ${tutorial_py_path} ; ${paths_tutorial}") + string(REPLACE ";" " \\\n" CMAKE_DOXYGEN_IMAGE_PATH "${paths_doc} ; ${tutorial_path} ; ${tutorial_py_path} ; ${paths_tutorial}") # TODO: remove paths_doc from EXAMPLE_PATH after face module tutorials/samples moved to separate folders string(REPLACE ";" " \\\n" CMAKE_DOXYGEN_EXAMPLE_PATH "${example_path} ; ${paths_doc} ; ${paths_sample}") set(CMAKE_DOXYGEN_LAYOUT "${CMAKE_CURRENT_SOURCE_DIR}/DoxygenLayout.xml") diff --git a/doc/py_tutorials/py_objdetect/py_face_detection/py_face_detection.markdown b/doc/py_tutorials/py_objdetect/py_face_detection/py_face_detection.markdown index 7d45e9d403..31763c9c0a 100644 --- a/doc/py_tutorials/py_objdetect/py_face_detection/py_face_detection.markdown +++ b/doc/py_tutorials/py_objdetect/py_face_detection/py_face_detection.markdown @@ -85,7 +85,7 @@ Haar-cascade Detection in OpenCV OpenCV comes with a trainer as well as detector. If you want to train your own classifier for any object like car, planes etc. you can use OpenCV to create one. Its full details are given here: -[Cascade Classifier Training.](http://docs.opencv.org/doc/user_guide/ug_traincascade.html) +[Cascade Classifier Training](@ref tutorial_traincascade). 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 face diff --git a/doc/root.markdown.in b/doc/root.markdown.in index 3a781a5ede..3e7a33199f 100644 --- a/doc/root.markdown.in +++ b/doc/root.markdown.in @@ -4,7 +4,6 @@ OpenCV modules {#mainpage} - @ref intro - @ref tutorial_root - @ref tutorial_py_root -- @ref tutorial_user_guide - @ref faq - @ref citelist diff --git a/doc/user_guide/ug_mat.markdown b/doc/tutorials/core/mat_operations.markdown similarity index 98% rename from doc/user_guide/ug_mat.markdown rename to doc/tutorials/core/mat_operations.markdown index d3994a8ea3..15a9869018 100644 --- a/doc/user_guide/ug_mat.markdown +++ b/doc/tutorials/core/mat_operations.markdown @@ -1,4 +1,4 @@ -Operations with images {#tutorial_ug_mat} +Operations with images {#tutorial_mat_operations} ====================== Input/Output @@ -27,11 +27,6 @@ If you read a jpg file, a 3 channel image is created by default. If you need a g @note use imdecode and imencode to read and write image from/to memory rather than a file. -XML/YAML --------- - -TBD - Basic operations with images ---------------------------- diff --git a/doc/tutorials/core/table_of_content_core.markdown b/doc/tutorials/core/table_of_content_core.markdown index 42440708f0..99e004819f 100644 --- a/doc/tutorials/core/table_of_content_core.markdown +++ b/doc/tutorials/core/table_of_content_core.markdown @@ -32,6 +32,9 @@ understanding how to manipulate the images on a pixel level. You'll find out how to scan images with neighbor access and use the @ref cv::filter2D function to apply kernel filters on images. +- @subpage tutorial_mat_operations + + Reading/writing images from file, accessing pixels, primitive operations, visualizing images. - @subpage tutorial_adding_images diff --git a/doc/user_guide/ug_intelperc.markdown b/doc/tutorials/highgui/intelperc.markdown similarity index 96% rename from doc/user_guide/ug_intelperc.markdown rename to doc/tutorials/highgui/intelperc.markdown index e5e0ddeb8d..b5f2ed64ed 100644 --- a/doc/user_guide/ug_intelperc.markdown +++ b/doc/tutorials/highgui/intelperc.markdown @@ -1,4 +1,4 @@ -Using Creative Senz3D and other Intel Perceptual Computing SDK compatible depth sensors {#tutorial_ug_intelperc} +Using Creative Senz3D and other Intel Perceptual Computing SDK compatible depth sensors {#tutorial_intelperc} ======================================================================================= Depth sensors compatible with Intel Perceptual Computing SDK are supported through VideoCapture @@ -78,5 +78,5 @@ there are two flags that should be used to set/get property of the needed genera flag value is assumed by default if neither of the two possible values of the property is set. For more information please refer to the example of usage -[intelpercccaptureccpp](https://github.com/Itseez/opencv/tree/master/samples/cpp/intelperc_capture.cpp) +[intelperc_capture.cpp](https://github.com/Itseez/opencv/tree/master/samples/cpp/intelperc_capture.cpp) in opencv/samples/cpp folder. diff --git a/doc/user_guide/ug_highgui.markdown b/doc/tutorials/highgui/kinect_openni.markdown similarity index 98% rename from doc/user_guide/ug_highgui.markdown rename to doc/tutorials/highgui/kinect_openni.markdown index ace4721d75..c9c33a2a05 100644 --- a/doc/user_guide/ug_highgui.markdown +++ b/doc/tutorials/highgui/kinect_openni.markdown @@ -1,4 +1,4 @@ -Using Kinect and other OpenNI compatible depth sensors {#tutorial_ug_highgui} +Using Kinect and other OpenNI compatible depth sensors {#tutorial_kinect_openni} ====================================================== Depth sensors compatible with OpenNI (Kinect, XtionPRO, ...) are supported through VideoCapture @@ -134,5 +134,5 @@ property. The following properties of cameras available through OpenNI interface - CAP_OPENNI_DEPTH_GENERATOR_REGISTRATION = CAP_OPENNI_DEPTH_GENERATOR + CAP_PROP_OPENNI_REGISTRATION For more information please refer to the example of usage -[openniccaptureccpp](https://github.com/Itseez/opencv/tree/master/samples/cpp/openni_capture.cpp) in +[openni_capture.cpp](https://github.com/Itseez/opencv/tree/master/samples/cpp/openni_capture.cpp) in opencv/samples/cpp folder. diff --git a/doc/tutorials/highgui/table_of_content_highgui.markdown b/doc/tutorials/highgui/table_of_content_highgui.markdown index 2b51dcb7b6..3ff0e0322d 100644 --- a/doc/tutorials/highgui/table_of_content_highgui.markdown +++ b/doc/tutorials/highgui/table_of_content_highgui.markdown @@ -37,3 +37,7 @@ use the built-in graphical user interface of the library. *Author:* Marvin Smith Read common GIS Raster and DEM files to display and manipulate geographic data. + +- @subpage tutorial_kinect_openni + +- @subpage tutorial_intelperc diff --git a/doc/tutorials/introduction/documenting_opencv/documentation_tutorial.markdown b/doc/tutorials/introduction/documenting_opencv/documentation_tutorial.markdown index 051651b3dd..f1d9f9cea1 100644 --- a/doc/tutorials/introduction/documenting_opencv/documentation_tutorial.markdown +++ b/doc/tutorials/introduction/documenting_opencv/documentation_tutorial.markdown @@ -77,8 +77,7 @@ Following scheme represents common documentation places for _opencv_ repository: ├── doc - doxygen config files, root page (root.markdown.in), BibTeX file (opencv.bib) │   ├── tutorials - tutorials hierarchy (pages and images) -│   ├── py_tutorials - python tutorials hierarchy (pages and images) -│   └── user_guide - old user guide (pages and images) +│   └── py_tutorials - python tutorials hierarchy (pages and images) ├── modules │   └── │      ├── doc - documentation pages and images for module diff --git a/doc/tutorials/objdetect/table_of_content_objdetect.markdown b/doc/tutorials/objdetect/table_of_content_objdetect.markdown index 0a4c208a8a..e8f4fbc1bf 100644 --- a/doc/tutorials/objdetect/table_of_content_objdetect.markdown +++ b/doc/tutorials/objdetect/table_of_content_objdetect.markdown @@ -10,3 +10,7 @@ Ever wondered how your digital camera detects peoples and faces? Look here to fi *Author:* Ana Huamán Here we learn how to use *objdetect* to find objects in our images or videos + +- @subpage tutorial_traincascade + + This tutorial describes _opencv_traincascade_ application and its parameters. diff --git a/doc/user_guide/ug_traincascade.markdown b/doc/tutorials/objdetect/traincascade.markdown similarity index 99% rename from doc/user_guide/ug_traincascade.markdown rename to doc/tutorials/objdetect/traincascade.markdown index 1bc7ff5f9a..3e7db48284 100644 --- a/doc/user_guide/ug_traincascade.markdown +++ b/doc/tutorials/objdetect/traincascade.markdown @@ -1,4 +1,4 @@ -Cascade Classifier Training {#tutorial_ug_traincascade} +Cascade Classifier Training {#tutorial_traincascade} =========================== Introduction diff --git a/doc/user_guide/ug_features2d.markdown b/doc/user_guide/ug_features2d.markdown deleted file mode 100644 index 25ec20ab66..0000000000 --- a/doc/user_guide/ug_features2d.markdown +++ /dev/null @@ -1,110 +0,0 @@ -Features2d {#tutorial_ug_features2d} -========== - -Detectors ---------- - -Descriptors ------------ - -Matching keypoints ------------------- - -### The code - -We will start with a short sample \`opencv/samples/cpp/matcher_simple.cpp\`: - -@code{.cpp} - Mat img1 = imread(argv[1], IMREAD_GRAYSCALE); - Mat img2 = imread(argv[2], IMREAD_GRAYSCALE); - if(img1.empty() || img2.empty()) - { - printf("Can't read one of the images\n"); - return -1; - } - - // detecting keypoints - SurfFeatureDetector detector(400); - vector keypoints1, keypoints2; - detector.detect(img1, keypoints1); - detector.detect(img2, keypoints2); - - // computing descriptors - SurfDescriptorExtractor extractor; - Mat descriptors1, descriptors2; - extractor.compute(img1, keypoints1, descriptors1); - extractor.compute(img2, keypoints2, descriptors2); - - // matching descriptors - BruteForceMatcher > matcher; - vector matches; - matcher.match(descriptors1, descriptors2, matches); - - // drawing the results - namedWindow("matches", 1); - Mat img_matches; - drawMatches(img1, keypoints1, img2, keypoints2, matches, img_matches); - imshow("matches", img_matches); - waitKey(0); -@endcode - -### The code explained - -Let us break the code down. -@code{.cpp} - Mat img1 = imread(argv[1], IMREAD_GRAYSCALE); - Mat img2 = imread(argv[2], IMREAD_GRAYSCALE); - if(img1.empty() || img2.empty()) - { - printf("Can't read one of the images\n"); - return -1; - } -@endcode -We load two images and check if they are loaded correctly. -@code{.cpp} - // detecting keypoints - Ptr detector = FastFeatureDetector::create(15); - vector keypoints1, keypoints2; - detector->detect(img1, keypoints1); - detector->detect(img2, keypoints2); -@endcode -First, we create an instance of a keypoint detector. All detectors inherit the abstract -FeatureDetector interface, but the constructors are algorithm-dependent. The first argument to each -detector usually controls the balance between the amount of keypoints and their stability. The range -of values is different for different detectors (For instance, *FAST* threshold has the meaning of -pixel intensity difference and usually varies in the region *[0,40]*. *SURF* threshold is applied to -a Hessian of an image and usually takes on values larger than *100*), so use defaults in case of -doubt. -@code{.cpp} - // computing descriptors - Ptr extractor = SURF::create(); - Mat descriptors1, descriptors2; - extractor->compute(img1, keypoints1, descriptors1); - extractor->compute(img2, keypoints2, descriptors2); -@endcode -We create an instance of descriptor extractor. The most of OpenCV descriptors inherit -DescriptorExtractor abstract interface. Then we compute descriptors for each of the keypoints. The -output Mat of the DescriptorExtractor::compute method contains a descriptor in a row *i* for each -*i*-th keypoint. Note that the method can modify the keypoints vector by removing the keypoints such -that a descriptor for them is not defined (usually these are the keypoints near image border). The -method makes sure that the ouptut keypoints and descriptors are consistent with each other (so that -the number of keypoints is equal to the descriptors row count). : -@code{.cpp} - // matching descriptors - BruteForceMatcher > matcher; - vector matches; - matcher.match(descriptors1, descriptors2, matches); -@endcode -Now that we have descriptors for both images, we can match them. First, we create a matcher that for -each descriptor from image 2 does exhaustive search for the nearest descriptor in image 1 using -Euclidean metric. Manhattan distance is also implemented as well as a Hamming distance for Brief -descriptor. The output vector matches contains pairs of corresponding points indices. : -@code{.cpp} - // drawing the results - namedWindow("matches", 1); - Mat img_matches; - drawMatches(img1, keypoints1, img2, keypoints2, matches, img_matches); - imshow("matches", img_matches); - waitKey(0); -@endcode -The final part of the sample is about visualizing the matching results. diff --git a/doc/user_guide/user_guide.markdown b/doc/user_guide/user_guide.markdown deleted file mode 100644 index f940bf866e..0000000000 --- a/doc/user_guide/user_guide.markdown +++ /dev/null @@ -1,8 +0,0 @@ -OpenCV User Guide {#tutorial_user_guide} -================= - -- @subpage tutorial_ug_mat -- @subpage tutorial_ug_features2d -- @subpage tutorial_ug_highgui -- @subpage tutorial_ug_traincascade -- @subpage tutorial_ug_intelperc