handling Ptr<> arguments and return values in Python wrappers

partially wrapped features2d framework
added feature_homography.py sample

modules/features2d/include/opencv2/features2d/features2d.hpp

@@ -1378,7 +1378,7 @@ protected:
 /*
  * Abstract base class for 2D image feature detectors.
  */
-class CV_EXPORTS FeatureDetector
+class CV_EXPORTS_W FeatureDetector
 {
 public:
     virtual ~FeatureDetector();
@@ -1390,7 +1390,7 @@ public:
      * mask         Mask specifying where to look for keypoints (optional). Must be a char
      *              matrix with non-zero values in the region of interest.
      */
-    void detect( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask=Mat() ) const;
+    CV_WRAP void detect( const Mat& image, CV_OUT vector<KeyPoint>& keypoints, const Mat& mask=Mat() ) const;
     
     /*
      * Detect keypoints in an image set.
@@ -1406,10 +1406,10 @@ public:
     virtual void write( FileStorage& ) const;
 
     // Return true if detector object is empty
-    virtual bool empty() const;
+    CV_WRAP virtual bool empty() const;
 
     // Create feature detector by detector name.
-    static Ptr<FeatureDetector> create( const string& detectorType );
+    CV_WRAP static Ptr<FeatureDetector> create( const string& detectorType );
 
 protected:
     virtual void detectImpl( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask=Mat() ) const = 0;
@@ -1422,10 +1422,10 @@ protected:
     static void removeInvalidPoints( const Mat& mask, vector<KeyPoint>& keypoints );
 };
 
-class CV_EXPORTS FastFeatureDetector : public FeatureDetector
+class CV_EXPORTS_W FastFeatureDetector : public FeatureDetector
 {
 public:
-    FastFeatureDetector( int threshold=10, bool nonmaxSuppression=true );
+    CV_WRAP FastFeatureDetector( int threshold=10, bool nonmaxSuppression=true );
     virtual void read( const FileNode& fn );
     virtual void write( FileStorage& fs ) const;
 
@@ -1673,11 +1673,11 @@ protected:
  * Adapts a detector to detect points over multiple levels of a Gaussian
  * pyramid. Useful for detectors that are not inherently scaled.
  */
-class CV_EXPORTS PyramidAdaptedFeatureDetector : public FeatureDetector
+class CV_EXPORTS_W PyramidAdaptedFeatureDetector : public FeatureDetector
 {
 public:
     // maxLevel - The 0-based index of the last pyramid layer
-    PyramidAdaptedFeatureDetector( const Ptr<FeatureDetector>& detector, int maxLevel=2 );
+    CV_WRAP PyramidAdaptedFeatureDetector( const Ptr<FeatureDetector>& detector, int maxLevel=2 );
     
     // TODO implement read/write
     virtual bool empty() const;
@@ -1835,7 +1835,7 @@ CV_EXPORTS Mat windowedMatchingMask( const vector<KeyPoint>& keypoints1, const v
  * distances between descriptors. Therefore we represent a collection of
  * descriptors as a cv::Mat, where each row is one keypoint descriptor.
  */
-class CV_EXPORTS DescriptorExtractor
+class CV_EXPORTS_W DescriptorExtractor
 {
 public:
     virtual ~DescriptorExtractor();
@@ -1846,7 +1846,7 @@ public:
      * keypoints    The input keypoints. Keypoints for which a descriptor cannot be computed are removed.
      * descriptors  Copmputed descriptors. Row i is the descriptor for keypoint i.
      */
-    void compute( const Mat& image, vector<KeyPoint>& keypoints, Mat& descriptors ) const;
+    CV_WRAP void compute( const Mat& image, CV_IN_OUT vector<KeyPoint>& keypoints, CV_OUT Mat& descriptors ) const;
 
     /*
      * Compute the descriptors for a keypoints collection detected in image collection.
@@ -1860,12 +1860,12 @@ public:
     virtual void read( const FileNode& );
     virtual void write( FileStorage& ) const;
 
-    virtual int descriptorSize() const = 0;
-    virtual int descriptorType() const = 0;
+    CV_WRAP virtual int descriptorSize() const = 0;
+    CV_WRAP virtual int descriptorType() const = 0;
 
-    virtual bool empty() const;
+    CV_WRAP virtual bool empty() const;
 
-    static Ptr<DescriptorExtractor> create( const string& descriptorExtractorType );
+    CV_WRAP static Ptr<DescriptorExtractor> create( const string& descriptorExtractorType );
 
 protected:
     virtual void computeImpl( const Mat& image, vector<KeyPoint>& keypoints, Mat& descriptors ) const = 0;
@@ -2201,19 +2201,19 @@ struct CV_EXPORTS Hamming
 /*
  * Struct for matching: query descriptor index, train descriptor index, train image index and distance between descriptors.
  */
-struct CV_EXPORTS DMatch
+struct CV_EXPORTS_W_SIMPLE DMatch
 {
-    DMatch() : queryIdx(-1), trainIdx(-1), imgIdx(-1), distance(std::numeric_limits<float>::max()) {}
-    DMatch( int _queryIdx, int _trainIdx, float _distance ) :
+    CV_WRAP DMatch() : queryIdx(-1), trainIdx(-1), imgIdx(-1), distance(std::numeric_limits<float>::max()) {}
+    CV_WRAP DMatch( int _queryIdx, int _trainIdx, float _distance ) :
             queryIdx(_queryIdx), trainIdx(_trainIdx), imgIdx(-1), distance(_distance) {}
-    DMatch( int _queryIdx, int _trainIdx, int _imgIdx, float _distance ) :
+    CV_WRAP DMatch( int _queryIdx, int _trainIdx, int _imgIdx, float _distance ) :
             queryIdx(_queryIdx), trainIdx(_trainIdx), imgIdx(_imgIdx), distance(_distance) {}
 
-    int queryIdx; // query descriptor index
-    int trainIdx; // train descriptor index
-    int imgIdx;   // train image index
+    CV_PROP_RW int queryIdx; // query descriptor index
+    CV_PROP_RW int trainIdx; // train descriptor index
+    CV_PROP_RW int imgIdx;   // train image index
 
-    float distance;
+    CV_PROP_RW float distance;
 
     // less is better
     bool operator<( const DMatch &m ) const
@@ -2228,7 +2228,7 @@ struct CV_EXPORTS DMatch
 /*
  * Abstract base class for matching two sets of descriptors.
  */
-class CV_EXPORTS DescriptorMatcher
+class CV_EXPORTS_W DescriptorMatcher
 {
 public:
     virtual ~DescriptorMatcher();
@@ -2273,14 +2273,14 @@ public:
      * Method train() is run in this methods.
      */
     // Find one best match for each query descriptor (if mask is empty).
-    void match( const Mat& queryDescriptors, const Mat& trainDescriptors,
-                vector<DMatch>& matches, const Mat& mask=Mat() ) const;
+    CV_WRAP void match( const Mat& queryDescriptors, const Mat& trainDescriptors,
+                CV_OUT vector<DMatch>& matches, const Mat& mask=Mat() ) const;
     // Find k best matches for each query descriptor (in increasing order of distances).
     // compactResult is used when mask is not empty. If compactResult is false matches
     // vector will have the same size as queryDescriptors rows. If compactResult is true
     // matches vector will not contain matches for fully masked out query descriptors.
-    void knnMatch( const Mat& queryDescriptors, const Mat& trainDescriptors,
-                   vector<vector<DMatch> >& matches, int k,
+    CV_WRAP void knnMatch( const Mat& queryDescriptors, const Mat& trainDescriptors,
+                   CV_OUT vector<vector<DMatch> >& matches, int k,
                    const Mat& mask=Mat(), bool compactResult=false ) const;
     // Find best matches for each query descriptor which have distance less than
     // maxDistance (in increasing order of distances).
@@ -2308,7 +2308,7 @@ public:
     // but with empty train data.
     virtual Ptr<DescriptorMatcher> clone( bool emptyTrainData=false ) const = 0;
 
-    static Ptr<DescriptorMatcher> create( const string& descriptorMatcherType );
+    CV_WRAP static Ptr<DescriptorMatcher> create( const string& descriptorMatcherType );
 protected:
     /*
      * Class to work with descriptors from several images as with one merged matrix.

modules/python/src2/cv2.cpp

@@ -64,9 +64,15 @@ typedef vector<Vec4i> vector_Vec4i;
 typedef vector<Rect> vector_Rect;
 typedef vector<KeyPoint> vector_KeyPoint;
 typedef vector<Mat> vector_Mat;
+typedef vector<DMatch> vector_DMatch;
 typedef vector<vector<Point> > vector_vector_Point;
 typedef vector<vector<Point2f> > vector_vector_Point2f;
 typedef vector<vector<Point3f> > vector_vector_Point3f;
+typedef vector<vector<DMatch> > vector_vector_DMatch;
+
+typedef Ptr<FeatureDetector> Ptr_FeatureDetector;
+typedef Ptr<DescriptorExtractor> Ptr_DescriptorExtractor;
+typedef Ptr<DescriptorMatcher> Ptr_DescriptorMatcher;
 
 static PyObject* failmsgp(const char *fmt, ...)
 {
@@ -312,6 +318,14 @@ static PyObject* pyopencv_from(size_t value)
     return PyLong_FromUnsignedLong((unsigned long)value);
 }
 
+static bool pyopencv_to(PyObject* obj, size_t& value, const char* name = "<unknown>")
+{
+    if(!obj || obj == Py_None)
+        return true;
+    value = (int)PyLong_AsUnsignedLong(obj);
+    return value != -1 || !PyErr_Occurred();
+}
+
 static PyObject* pyopencv_from(int value)
 {
     return PyInt_FromLong(value);
@@ -605,6 +619,7 @@ template<typename _Tp> static inline PyObject* pyopencv_from(const vector<_Tp>&
 }
 
 static PyObject* pyopencv_from(const KeyPoint&);
+static PyObject* pyopencv_from(const DMatch&);
 
 template<typename _Tp> static inline bool pyopencv_to_generic_vec(PyObject* obj, vector<_Tp>& value, const char* name="<unknown>")
 {
@@ -689,6 +704,19 @@ template<> struct pyopencvVecConverter<KeyPoint>
     }
 };
 
+template<> struct pyopencvVecConverter<DMatch>
+{
+    static bool to(PyObject* obj, vector<DMatch>& value, const char* name="<unknown>")
+    {
+        return pyopencv_to_generic_vec(obj, value, name);
+    }
+    
+    static PyObject* from(const vector<DMatch>& value)
+    {
+        return pyopencv_from_generic_vec(value);
+    }
+};
+
 
 static inline bool pyopencv_to(PyObject *obj, CvTermCriteria& dst, const char *name="<unknown>")
 {

modules/python/src2/gen2.py

@@ -7,7 +7,11 @@ gen_template_check_self = Template("""    if(!PyObject_TypeCheck(self, &pyopencv
 """)
 
 gen_template_call_constructor = Template("""self = PyObject_NEW(pyopencv_${name}_t, &pyopencv_${name}_Type);
-        if(self) ERRWRAP2(self->v = $op$cname""")
+        new (&(self->v)) Ptr<$cname>(); // init Ptr with placement new
+        if(self) ERRWRAP2(self->v = new $cname""")
+
+gen_template_simple_call_constructor = Template("""self = PyObject_NEW(pyopencv_${name}_t, &pyopencv_${name}_Type);
+        if(self) ERRWRAP2(self->v = $cname""")
 
 gen_template_parse_args = Template("""const char* keywords[] = { $kw_list, NULL };
     if( PyArg_ParseTupleAndKeywords(args, kw, "$fmtspec", (char**)keywords, $parse_arglist)$code_cvt )""")
@@ -66,7 +70,7 @@ gen_template_type_decl = Template("""
 struct pyopencv_${name}_t
 {
     PyObject_HEAD
-    ${cname}* v;
+    Ptr<${cname}> v;
 };
 
 static PyTypeObject pyopencv_${name}_Type =
@@ -79,9 +83,31 @@ static PyTypeObject pyopencv_${name}_Type =
 
 static void pyopencv_${name}_dealloc(PyObject* self)
 {
-    delete ((pyopencv_${name}_t*)self)->v;
+    ((pyopencv_${name}_t*)self)->v = NULL;
     PyObject_Del(self);
 }
+
+static PyObject* pyopencv_from(const Ptr<${cname}>& r)
+{
+    pyopencv_${name}_t *m = PyObject_NEW(pyopencv_${name}_t, &pyopencv_${name}_Type);
+    new (&(m->v)) Ptr<$cname>(); // init Ptr with placement new
+    m->v = r;
+    return (PyObject*)m;
+}
+
+static bool pyopencv_to(PyObject* src, Ptr<${cname}>& dst, const char* name="<unknown>")
+{
+    if( src == NULL || src == Py_None )
+        return true;
+    if(!PyObject_TypeCheck(src, &pyopencv_${name}_Type))
+    {
+        failmsg("Expected ${cname} for argument '%s'", name);
+        return false;
+    }
+    dst = ((pyopencv_${name}_t*)src)->v;
+    return true;
+}
+
 """)
 
 gen_template_map_type_cvt = Template("""
@@ -490,10 +516,10 @@ class FuncInfo(object):
 
             if self.isconstructor:
                 code_decl += "    pyopencv_%s_t* self = 0;\n" % selfinfo.name
-                op = "new "
+                templ = gen_template_call_constructor
                 if selfinfo.issimple:
-                    op = ""
-                code_fcall = gen_template_call_constructor.substitute(name=selfinfo.name, cname=selfinfo.cname, op=op)
+                    templ = gen_template_simple_call_constructor
+                code_fcall = templ.substitute(name=selfinfo.name, cname=selfinfo.cname)
             else:
                 code_fcall = "ERRWRAP2( "
                 if v.rettype:

samples/python2/feature_homography.py

@@ -0,0 +1,73 @@
+'''
+Feature homography
+==================
+
+Example of using features2d framework for interactive video homography matching.
+
+Keys
+----
+SPACE - set reference frame
+ESC   - exit
+'''
+
+import numpy as np
+import cv2
+import video
+from common import draw_str
+
+if __name__ == '__main__':
+
+    print __doc__
+
+    detector = cv2.FeatureDetector_create('ORB')
+    extractor = cv2.DescriptorExtractor_create('ORB')
+    matcher = cv2.DescriptorMatcher_create('BruteForce-Hamming') # 'BruteForce-Hamming' # FlannBased
+
+    ref_desc = None
+    ref_kp = None
+
+    green, red = (0, 255, 0), (0, 0, 255)
+
+    cap = video.create_capture(0)
+    while True:
+        ret, img = cap.read()
+        vis = img.copy()
+        kp = detector.detect(img)
+
+        for p in kp:
+            x, y = np.int32(p.pt)
+            r = int(0.5*p.size)
+            cv2.circle(vis, (x, y), r, (0, 255, 0))
+        draw_str(vis, (20, 20), 'feature_n: %d' % len(kp))
+        
+        desc = extractor.compute(img, kp)
+        if ref_desc is not None:
+            raw_matches = matcher.knnMatch(desc, ref_desc, 2)
+            eps = 1e-5
+            matches = [(m1.trainIdx, m1.queryIdx) for m1, m2 in raw_matches if (m1.distance+eps) / (m2.distance+eps) < 0.7]
+            match_n = len(matches)
+
+            inliner_n = 0
+            if match_n > 10:
+                p0 = np.float32( [ref_kp[i].pt for i, j in matches] )
+                p1 = np.float32( [kp[j].pt for i, j in matches] )
+
+                H, status = cv2.findHomography(p0, p1, cv2.RANSAC, 10.0)
+                inlier_n = sum(status)
+                if inlier_n > 10:
+                    for (x1, y1), (x2, y2), inlier in zip(np.int32(p0), np.int32(p1), status):
+                        cv2.line(vis, (x1, y1), (x2, y2), (red, green)[inlier])
+
+                    h, w = img.shape[:2]
+                    overlay = cv2.warpPerspective(ref_img, H, (w, h))
+                    vis = cv2.addWeighted(vis, 0.5, overlay, 0.5, 0.0)
+            draw_str(vis, (20, 40), 'matched: %d ( %d outliers )' % (match_n, match_n-inlier_n))
+        
+        cv2.imshow('img', vis)
+        ch = cv2.waitKey(1)
+        if ch == ord(' '):
+            ref_desc = desc
+            ref_kp = kp
+            ref_img = img.copy()
+        if ch == 27:
+            break