handling Ptr<> arguments and return values in Python wrappers

partially wrapped features2d framework added feature_homography.py sample
2011-09-07 09:38:22 +00:00
parent 2ef4e2eeb7
commit d50cc51070
4 changed files with 161 additions and 34 deletions
--- a/samples/python2/feature_homography.py
+++ b/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