def runTracker(self):
foregroundPointsNum = 0
while True:
frame = self.render.getNextFrame()
frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
if len(self.tracks) > 0:
img0, img1 = self.prev_gray, frame_gray
p0 = np.float32([tr[-1][0] for tr in self.tracks]).reshape(-1, 1, 2)
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)
good = d < 1
new_tracks = []
for tr, (x, y), good_flag in zip(self.tracks, p1.reshape(-1, 2), good):
if not good_flag:
continue
tr.append([(x, y), self.frame_idx])
if len(tr) > self.track_len:
del tr[0]
new_tracks.append(tr)
self.tracks = new_tracks
if self.frame_idx % self.detect_interval == 0:
goodTracksCount = 0
for tr in self.tracks:
oldRect = self.render.getRectInTime(self.render.timeStep * tr[0][1])
newRect = self.render.getRectInTime(self.render.timeStep * tr[-1][1])
if isPointInRect(tr[0][0], oldRect) and isPointInRect(tr[-1][0], newRect):
goodTracksCount += 1
if self.frame_idx == self.detect_interval:
foregroundPointsNum = goodTracksCount
fgIndex = float(foregroundPointsNum) / (foregroundPointsNum + 1)
fgRate = float(goodTracksCount) / (len(self.tracks) + 1)
if self.frame_idx > 0:
self.assertGreater(fgIndex, 0.9)
self.assertGreater(fgRate, 0.2)
mask = np.zeros_like(frame_gray)
mask[:] = 255
for x, y in [np.int32(tr[-1][0]) for tr in self.tracks]:
cv2.circle(mask, (x, y), 5, 0, -1)
p = cv2.goodFeaturesToTrack(frame_gray, mask = mask, **feature_params)
if p is not None:
for x, y in np.float32(p).reshape(-1, 2):
self.tracks.append([[(x, y), self.frame_idx]])
self.frame_idx += 1
self.prev_gray = frame_gray
if self.frame_idx > 300:
break
def runTracker(self):
foregroundPointsNum = 0
while True:
frame = self.render.getNextFrame()
frame_gray = cv.cvtColor(frame, cv.COLOR_BGR2GRAY)
if len(self.tracks) > 0:
img0, img1 = self.prev_gray, frame_gray
p0 = np.float32([tr[-1][0] for tr in self.tracks]).reshape(-1, 1, 2)
p1, _st, _err = cv.calcOpticalFlowPyrLK(img0, img1, p0, None, **lk_params)
p0r, _st, _err = cv.calcOpticalFlowPyrLK(img1, img0, p1, None, **lk_params)
d = abs(p0-p0r).reshape(-1, 2).max(-1)
good = d < 1
new_tracks = []
for tr, (x, y), good_flag in zip(self.tracks, p1.reshape(-1, 2), good):
if not good_flag:
continue
tr.append([(x, y), self.frame_idx])
if len(tr) > self.track_len:
del tr[0]
new_tracks.append(tr)
self.tracks = new_tracks
if self.frame_idx % self.detect_interval == 0:
goodTracksCount = 0
for tr in self.tracks:
oldRect = self.render.getRectInTime(self.render.timeStep * tr[0][1])
newRect = self.render.getRectInTime(self.render.timeStep * tr[-1][1])
if isPointInRect(tr[0][0], oldRect) and isPointInRect(tr[-1][0], newRect):
goodTracksCount += 1
if self.frame_idx == self.detect_interval:
foregroundPointsNum = goodTracksCount
fgIndex = float(foregroundPointsNum) / (foregroundPointsNum + 1)
fgRate = float(goodTracksCount) / (len(self.tracks) + 1)
if self.frame_idx > 0:
self.assertGreater(fgIndex, 0.9)
self.assertGreater(fgRate, 0.2)
mask = np.zeros_like(frame_gray)
mask[:] = 255
for x, y in [np.int32(tr[-1][0]) for tr in self.tracks]:
cv.circle(mask, (x, y), 5, 0, -1)
p = cv.goodFeaturesToTrack(frame_gray, mask = mask, **feature_params)
if p is not None:
for x, y in np.float32(p).reshape(-1, 2):
self.tracks.append([[(x, y), self.frame_idx]])
self.frame_idx += 1
self.prev_gray = frame_gray
if self.frame_idx > 300:
break
def test_lk_homography(self):
self.render = TestSceneRender(self.get_sample('samples/python2/data/graf1.png'),
self.get_sample('samples/c/box.png'), noise = 0.1, speed = 1.0)
frame = self.render.getNextFrame()
frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
self.frame0 = frame.copy()
self.p0 = cv2.goodFeaturesToTrack(frame_gray, **feature_params)
isForegroundHomographyFound = False
if self.p0 is not None:
self.p1 = self.p0
self.gray0 = frame_gray
self.gray1 = frame_gray
currRect = self.render.getCurrentRect()
for (x,y) in self.p0[:,0]:
if isPointInRect((x,y), currRect):
self.numFeaturesInRectOnStart += 1
while self.framesCounter < 200:
self.framesCounter += 1
frame = self.render.getNextFrame()
frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
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, cv2.RANSAC, 5.0)
goodPointsInRect = 0
goodPointsOutsideRect = 0
for (x0, y0), (x1, y1), good in zip(self.p0[:,0], self.p1[:,0], status[:,0]):
if good:
if isPointInRect((x1,y1), self.render.getCurrentRect()):
goodPointsInRect += 1
else: goodPointsOutsideRect += 1
if goodPointsOutsideRect < goodPointsInRect:
isForegroundHomographyFound = True
self.assertGreater(float(goodPointsInRect) / (self.numFeaturesInRectOnStart + 1), 0.6)
else:
p = cv2.goodFeaturesToTrack(frame_gray, **feature_params)
self.assertEqual(isForegroundHomographyFound, True)
