Merge pull request #1107 from abidrahmank:master

modules/features2d/doc/common_interfaces_of_descriptor_extractors.rst

@@ -57,6 +57,8 @@ Computes the descriptors for a set of keypoints detected in an image (first vari
 
 .. ocv:function:: void DescriptorExtractor::compute( const vector<Mat>& images, vector<vector<KeyPoint> >& keypoints, vector<Mat>& descriptors ) const
 
+.. ocv:pyfunction:: cv2.DescriptorExtractor_create.compute(image, keypoints[, descriptors]) -> keypoints, descriptors
+
     :param image: Image.
 
     :param images: Image set.
@@ -72,6 +74,8 @@ Creates a descriptor extractor by name.
 
 .. ocv:function:: Ptr<DescriptorExtractor>  DescriptorExtractor::create( const String& descriptorExtractorType )
 
+.. ocv:pyfunction:: cv2.DescriptorExtractor_create(descriptorExtractorType) -> retval
+
     :param descriptorExtractorType: Descriptor extractor type.
 
 The current implementation supports the following types of a descriptor extractor:

modules/features2d/doc/common_interfaces_of_feature_detectors.rst

@@ -44,6 +44,8 @@ Detects keypoints in an image (first variant) or image set (second variant).
 
 .. ocv:function:: void FeatureDetector::detect( const vector<Mat>& images, vector<vector<KeyPoint> >& keypoints, const vector<Mat>& masks=vector<Mat>() ) const
 
+.. ocv:pyfunction:: cv2.FeatureDetector_create.detect(image[, mask]) -> keypoints
+
     :param image: Image.
 
     :param images: Image set.
@@ -60,6 +62,8 @@ Creates a feature detector by its name.
 
 .. ocv:function:: Ptr<FeatureDetector> FeatureDetector::create( const String& detectorType )
 
+.. ocv:pyfunction:: cv2.FeatureDetector_create(detectorType) -> retval
+
     :param detectorType: Feature detector type.
 
 The following detector types are supported:

modules/features2d/doc/drawing_function_of_keypoints_and_matches.rst

@@ -11,6 +11,10 @@ Draws the found matches of keypoints from two images.
 
 .. ocv:function:: void drawMatches( const Mat& img1, const vector<KeyPoint>& keypoints1, const Mat& img2, const vector<KeyPoint>& keypoints2, const vector<vector<DMatch> >& matches1to2, Mat& outImg, const Scalar& matchColor=Scalar::all(-1), const Scalar& singlePointColor=Scalar::all(-1), const vector<vector<char> >& matchesMask=vector<vector<char> >(), int flags=DrawMatchesFlags::DEFAULT )
 
+.. ocv:pyfunction:: cv2.drawMatches(img1, keypoints1, img2, keypoints2, matches1to2[, outImg[, matchColor[, singlePointColor[, matchesMask[, flags]]]]]) -> outImg
+
+.. ocv:pyfunction:: cv2.drawMatchesKnn(img1, keypoints1, img2, keypoints2, matches1to2[, outImg[, matchColor[, singlePointColor[, matchesMask[, flags]]]]]) -> outImg
+
 
     :param img1: First source image.
 
@@ -67,6 +71,8 @@ Draws keypoints.
 
 .. ocv:function:: void drawKeypoints( const Mat& image, const vector<KeyPoint>& keypoints, Mat& outImage, const Scalar& color=Scalar::all(-1), int flags=DrawMatchesFlags::DEFAULT )
 
+.. ocv:pyfunction:: cv2.drawKeypoints(image, keypoints[, outImage[, color[, flags]]]) -> outImage
+
     :param image: Source image.
 
     :param keypoints: Keypoints from the source image.
@@ -77,3 +83,4 @@ Draws keypoints.
 
     :param flags: Flags setting drawing features. Possible  ``flags``  bit values are defined by  ``DrawMatchesFlags``. See details above in  :ocv:func:`drawMatches` .
 
+.. note:: For Python API, flags are modified as `cv2.DRAW_MATCHES_FLAGS_DEFAULT`, `cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS`, `cv2.DRAW_MATCHES_FLAGS_DRAW_OVER_OUTIMG`, `cv2.DRAW_MATCHES_FLAGS_NOT_DRAW_SINGLE_POINTS` 

modules/features2d/doc/feature_detection_and_description.rst

@@ -10,6 +10,11 @@ Detects corners using the FAST algorithm
 .. ocv:function:: void FAST( InputArray image, vector<KeyPoint>& keypoints, int threshold, bool nonmaxSupression=true )
 .. ocv:function:: void FAST( InputArray image, vector<KeyPoint>& keypoints, int threshold, bool nonmaxSupression, int type )
 
+.. ocv:pyfunction:: cv2.FastFeatureDetector([, threshold[, nonmaxSuppression]]) -> <FastFeatureDetector object>
+.. ocv:pyfunction:: cv2.FastFeatureDetector(threshold, nonmaxSuppression, type) -> <FastFeatureDetector object>
+.. ocv:pyfunction:: cv2.FastFeatureDetector.detect(image[, mask]) -> keypoints
+
+
     :param image: grayscale image where keypoints (corners) are detected.
 
     :param keypoints: keypoints detected on the image.
@@ -22,6 +27,9 @@ Detects corners using the FAST algorithm
 
 Detects corners using the FAST algorithm by [Rosten06]_.
 
+..note:: In Python API, types are given as ``cv2.FAST_FEATURE_DETECTOR_TYPE_5_8``, ``cv2.FAST_FEATURE_DETECTOR_TYPE_7_12`` and  ``cv2.FAST_FEATURE_DETECTOR_TYPE_9_16``. For corner detection, use ``cv2.FAST.detect()`` method.
+
+
 .. [Rosten06] E. Rosten. Machine Learning for High-speed Corner Detection, 2006.
 
 
@@ -65,6 +73,9 @@ The ORB constructor
 
 .. ocv:function:: ORB::ORB(int nfeatures = 500, float scaleFactor = 1.2f, int nlevels = 8, int edgeThreshold = 31, int firstLevel = 0, int WTA_K=2, int scoreType=ORB::HARRIS_SCORE, int patchSize=31)
 
+.. ocv:pyfunction:: cv2.ORB([, nfeatures[, scaleFactor[, nlevels[, edgeThreshold[, firstLevel[, WTA_K[, scoreType[, patchSize]]]]]]]]) -> <ORB object>
+
+
     :param nfeatures: The maximum number of features to retain.
 
     :param scaleFactor: Pyramid decimation ratio, greater than 1. ``scaleFactor==2`` means the classical pyramid, where each next level has 4x less pixels than the previous, but such a big scale factor will degrade feature matching scores dramatically. On the other hand, too close to 1 scale factor will mean that to cover certain scale range you will need more pyramid levels and so the speed will suffer.
@@ -87,6 +98,11 @@ Finds keypoints in an image and computes their descriptors
 
 .. ocv:function:: void ORB::operator()(InputArray image, InputArray mask, vector<KeyPoint>& keypoints, OutputArray descriptors, bool useProvidedKeypoints=false ) const
 
+.. ocv:pyfunction:: cv2.ORB.detect(image[, mask]) -> keypoints
+.. ocv:pyfunction:: cv2.ORB.compute(image, keypoints[, descriptors]) -> keypoints, descriptors
+.. ocv:pyfunction:: cv2.ORB.detectAndCompute(image, mask[, descriptors[, useProvidedKeypoints]]) -> keypoints, descriptors
+
+
     :param image: The input 8-bit grayscale image.
 
     :param mask: The operation mask.
@@ -96,6 +112,7 @@ Finds keypoints in an image and computes their descriptors
     :param descriptors: The output descriptors. Pass ``cv::noArray()`` if you do not need it.
 
     :param useProvidedKeypoints: If it is true, then the method will use the provided vector of keypoints instead of detecting them.
+    
 
 BRISK
 -----
@@ -111,6 +128,8 @@ The BRISK constructor
 
 .. ocv:function:: BRISK::BRISK(int thresh=30, int octaves=3, float patternScale=1.0f)
 
+.. ocv:pyfunction:: cv2.BRISK([, thresh[, octaves[, patternScale]]]) -> <BRISK object>
+
     :param thresh: FAST/AGAST detection threshold score.
 
     :param octaves: detection octaves. Use 0 to do single scale.
@@ -123,6 +142,8 @@ The BRISK constructor for a custom pattern
 
 .. ocv:function:: BRISK::BRISK(std::vector<float> &radiusList, std::vector<int> &numberList, float dMax=5.85f, float dMin=8.2f, std::vector<int> indexChange=std::vector<int>())
 
+.. ocv:pyfunction:: cv2.BRISK(radiusList, numberList[, dMax[, dMin[, indexChange]]]) -> <BRISK object>
+
     :param radiusList: defines the radii (in pixels) where the samples around a keypoint are taken (for keypoint scale 1).
 
     :param numberList: defines the number of sampling points on the sampling circle. Must be the same size as radiusList..
@@ -139,6 +160,10 @@ Finds keypoints in an image and computes their descriptors
 
 .. ocv:function:: void BRISK::operator()(InputArray image, InputArray mask, vector<KeyPoint>& keypoints, OutputArray descriptors, bool useProvidedKeypoints=false ) const
 
+.. ocv:pyfunction:: cv2.BRISK.detect(image[, mask]) -> keypoints
+.. ocv:pyfunction:: cv2.BRISK.compute(image, keypoints[, descriptors]) -> keypoints, descriptors
+.. ocv:pyfunction:: cv2.BRISK.detectAndCompute(image, mask[, descriptors[, useProvidedKeypoints]]) -> keypoints, descriptors
+
     :param image: The input 8-bit grayscale image.
 
     :param mask: The operation mask.

modules/features2d/include/opencv2/features2d.hpp

@@ -1404,15 +1404,15 @@ CV_EXPORTS_W void drawKeypoints( const Mat& image, const std::vector<KeyPoint>&
                                const Scalar& color=Scalar::all(-1), int flags=DrawMatchesFlags::DEFAULT );
 
 // Draws matches of keypints from two images on output image.
-CV_EXPORTS void drawMatches( const Mat& img1, const std::vector<KeyPoint>& keypoints1,
+CV_EXPORTS_W void drawMatches( const Mat& img1, const std::vector<KeyPoint>& keypoints1,
                              const Mat& img2, const std::vector<KeyPoint>& keypoints2,
-                             const std::vector<DMatch>& matches1to2, Mat& outImg,
+                             const std::vector<DMatch>& matches1to2, CV_OUT Mat& outImg,
                              const Scalar& matchColor=Scalar::all(-1), const Scalar& singlePointColor=Scalar::all(-1),
                              const std::vector<char>& matchesMask=std::vector<char>(), int flags=DrawMatchesFlags::DEFAULT );
 
-CV_EXPORTS void drawMatches( const Mat& img1, const std::vector<KeyPoint>& keypoints1,
+CV_EXPORTS_AS(drawMatchesKnn) void drawMatches( const Mat& img1, const std::vector<KeyPoint>& keypoints1,
                              const Mat& img2, const std::vector<KeyPoint>& keypoints2,
-                             const std::vector<std::vector<DMatch> >& matches1to2, Mat& outImg,
+                             const std::vector<std::vector<DMatch> >& matches1to2, CV_OUT Mat& outImg,
                              const Scalar& matchColor=Scalar::all(-1), const Scalar& singlePointColor=Scalar::all(-1),
                              const std::vector<std::vector<char> >& matchesMask=std::vector<std::vector<char> >(), int flags=DrawMatchesFlags::DEFAULT );
 

modules/nonfree/doc/feature_detection.rst

@@ -16,6 +16,8 @@ The SIFT constructors.
 
 .. ocv:function:: SIFT::SIFT( int nfeatures=0, int nOctaveLayers=3, double contrastThreshold=0.04, double edgeThreshold=10, double sigma=1.6)
 
+.. ocv:pyfunction:: cv2.SIFT([, nfeatures[, nOctaveLayers[, contrastThreshold[, edgeThreshold[, sigma]]]]]) -> <SIFT object>
+
     :param nfeatures: The number of best features to retain. The features are ranked by their scores (measured in SIFT algorithm as the local contrast)
 
     :param nOctaveLayers: The number of layers in each octave. 3 is the value used in D. Lowe paper. The number of octaves is computed automatically from the image resolution.
@@ -33,6 +35,12 @@ Extract features and computes their descriptors using SIFT algorithm
 
 .. ocv:function:: void SIFT::operator()(InputArray img, InputArray mask, vector<KeyPoint>& keypoints, OutputArray descriptors, bool useProvidedKeypoints=false)
 
+.. ocv:pyfunction:: cv2.SIFT.detect(image[, mask]) -> keypoints
+
+.. ocv:pyfunction:: cv2.SIFT.compute(image, keypoints[, descriptors]) -> keypoints, descriptors
+
+.. ocv:pyfunction:: cv2.SIFT.detectAndCompute(image, mask[, descriptors[, useProvidedKeypoints]]) -> keypoints, descriptors
+
     :param img: Input 8-bit grayscale image
 
     :param mask: Optional input mask that marks the regions where we should detect features.
@@ -43,6 +51,7 @@ Extract features and computes their descriptors using SIFT algorithm
 
     :param useProvidedKeypoints: Boolean flag. If it is true, the keypoint detector is not run. Instead, the provided vector of keypoints is used and the algorithm just computes their descriptors.
 
+.. note:: Python API provides three functions. First one finds keypoints only. Second function computes the descriptors based on the keypoints we provide. Third function detects the keypoints and computes their descriptors. If you want both keypoints and descriptors, directly use third function as ``kp, des = cv2.SIFT.detectAndCompute(image, None)``
 
 SURF
 ----
@@ -105,6 +114,8 @@ Detects keypoints and computes SURF descriptors for them.
 .. ocv:function:: void SURF::operator()(InputArray img, InputArray mask, vector<KeyPoint>& keypoints, OutputArray descriptors, bool useProvidedKeypoints=false)
 
 .. ocv:pyfunction:: cv2.SURF.detect(image[, mask]) -> keypoints
+.. ocv:pyfunction:: cv2.SURF.compute(image, keypoints[, descriptors]) -> keypoints, descriptors
+.. ocv:pyfunction:: cv2.SURF.detectAndCompute(image, mask[, descriptors[, useProvidedKeypoints]]) -> keypoints, descriptors
 
 .. ocv:cfunction:: void cvExtractSURF( const CvArr* image, const CvArr* mask, CvSeq** keypoints, CvSeq** descriptors, CvMemStorage* storage, CvSURFParams params )
 
@@ -325,4 +336,4 @@ The ``descriptors`` matrix is :math:`\texttt{nFeatures} \times \texttt{descripto
 
 The class ``SURF_OCL`` uses some buffers and provides access to it. All buffers can be safely released between function calls.
 
-.. seealso:: :ocv:class:`SURF`
+.. seealso:: :ocv:class:`SURF`

modules/python/src2/cv2.cpp

@@ -97,6 +97,7 @@ using namespace cv;
 typedef cv::softcascade::ChannelFeatureBuilder softcascade_ChannelFeatureBuilder;
 
 typedef std::vector<uchar> vector_uchar;
+typedef std::vector<char> vector_char;
 typedef std::vector<int> vector_int;
 typedef std::vector<float> vector_float;
 typedef std::vector<double> vector_double;
@@ -112,6 +113,8 @@ typedef std::vector<KeyPoint> vector_KeyPoint;
 typedef std::vector<Mat> vector_Mat;
 typedef std::vector<DMatch> vector_DMatch;
 typedef std::vector<String> vector_String;
+
+typedef std::vector<std::vector<char> > vector_vector_char;
 typedef std::vector<std::vector<Point> > vector_vector_Point;
 typedef std::vector<std::vector<Point2f> > vector_vector_Point2f;
 typedef std::vector<std::vector<Point3f> > vector_vector_Point3f;
@@ -830,7 +833,7 @@ template<typename _Tp> struct pyopencvVecConverter
     }
 };
 
-template <typename _Tp>
+template<typename _Tp>
 bool pyopencv_to(PyObject* obj, std::vector<_Tp>& value, const ArgInfo info)
 {
     return pyopencvVecConverter<_Tp>::to(obj, value, info);
@@ -888,9 +891,9 @@ template<typename _Tp> static inline PyObject* pyopencv_from_generic_vec(const s
 
 template<typename _Tp> struct pyopencvVecConverter<std::vector<_Tp> >
 {
-    static bool to(PyObject* obj, std::vector<std::vector<_Tp> >& value, const char* name="<unknown>")
+    static bool to(PyObject* obj, std::vector<std::vector<_Tp> >& value, const ArgInfo info)
     {
-        return pyopencv_to_generic_vec(obj, value, name);
+        return pyopencv_to_generic_vec(obj, value, info);
     }
 
     static PyObject* from(const std::vector<std::vector<_Tp> >& value)