lk_homography.py sample added

samples/python2/lk_homography.py

@@ -0,0 +1,112 @@
+'''
+Lucas-Kanade homography tracker
+===============================
+
+Lucas-Kanade sparse optical flow demo. Uses goodFeaturesToTrack
+for track initialization and back-tracking for match verification
+between frames. Finds homography between reference and current views.
+
+Usage
+-----
+lk_homography.py [<video_source>]
+
+
+Keys
+----
+ESC   - exit
+SPACE - start tracking
+r     - toggle RANSAC
+'''
+
+import numpy as np
+import cv2
+import video
+from common import draw_str
+
+lk_params = dict( winSize  = (19, 19), 
+                  maxLevel = 2, 
+                  criteria = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 0.03),
+                  derivLambda = 0.0 )    
+
+feature_params = dict( maxCorners = 1000, 
+                       qualityLevel = 0.01,
+                       minDistance = 8,
+                       blockSize = 19 )
+
+def checkedTrace(img0, img1, p0, back_threshold = 1.0):
+    p1, st, err = cv2.calcOpticalFlowPyrLK(img0, img1, p0, None, **lk_params)
+    p0r, st, err = cv2.calcOpticalFlowPyrLK(img1, img0, p1, None, **lk_params)
+    d = abs(p0-p0r).reshape(-1, 2).max(-1)
+    status = d < back_threshold
+    return p1, status
+
+green = (0, 255, 0)
+red = (0, 0, 255)
+
+class App:
+    def __init__(self, video_src):
+        self.cam = video.create_capture(video_src)
+        self.p0 = None
+        self.use_ransac = True
+
+    def run(self):
+        while True:
+            ret, frame = self.cam.read()
+            frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+            vis = frame.copy()
+            if self.p0 is not None:
+                p2, trace_status = checkedTrace(self.gray1, frame_gray, self.p1)
+
+                self.p1 = p2[trace_status].copy()
+                self.p0 = self.p0[trace_status].copy()
+                self.gray1 = frame_gray
+
+                if len(self.p0) < 4:
+                    self.p0 = None
+                    continue
+                H, status = cv2.findHomography(self.p0, self.p1, (0, cv2.RANSAC)[self.use_ransac], 10.0)
+                h, w = frame.shape[:2]
+                overlay = cv2.warpPerspective(self.frame0, H, (w, h))
+                vis = cv2.addWeighted(vis, 0.5, overlay, 0.5, 0.0)
+                
+                for (x0, y0), (x1, y1), good in zip(self.p0[:,0], self.p1[:,0], status[:,0]):
+                    if good:
+                        cv2.line(vis, (x0, y0), (x1, y1), (0, 128, 0))
+                    cv2.circle(vis, (x1, y1), 2, (red, green)[good], -1)
+                draw_str(vis, (20, 20), 'track count: %d' % len(self.p1))
+                if self.use_ransac:
+                    draw_str(vis, (20, 40), 'RANSAC')
+            else:
+                p = cv2.goodFeaturesToTrack(frame_gray, **feature_params)
+                if p is not None:
+                    for x, y in p[:,0]:
+                        cv2.circle(vis, (x, y), 2, green, -1)
+                    draw_str(vis, (20, 20), 'feature count: %d' % len(p))
+                
+            cv2.imshow('lk_homography', vis)
+
+            ch = cv2.waitKey(1)
+            if ch == 27:
+                break
+            if ch == ord(' '):
+                self.frame0 = frame.copy()
+                self.p0 = cv2.goodFeaturesToTrack(frame_gray, **feature_params)
+                if self.p0 is not None:
+                    self.p1 = self.p0
+                    self.gray0 = frame_gray
+                    self.gray1 = frame_gray
+            if ch == ord('r'):
+                self.use_ransac = not self.use_ransac
+
+
+
+def main():
+    import sys
+    try: video_src = sys.argv[1]
+    except: video_src = 0
+
+    print __doc__
+    App(video_src).run()
+
+if __name__ == '__main__':
+    main()