def track(self, frame):
# Resize for efficiency
frame = cv2.pyrDown(frame)
frame = cv2.cvtColor(frame, cv.CV_BGR2Lab)
self.frame = frame
l,a,b = self.hist
# Convert to float32 so we can represent probability values
frame = frame.astype(np.float32)
lback = cv2.calcBackProject([frame], [0], l, [0,255], 1)
aback = cv2.calcBackProject([frame], [1], a, [0,255], 1)
bback = cv2.calcBackProject([frame], [2], b, [0,255], 1)
# Combine channels and convert to image
self.back = (aback * bback * lback ) * 255
self.back = self.back.astype(np.uint8)
tmp = np.array(self.back)
loc, contours = util.contour(tmp)
self.contours = contours
# Scale loc back up due to pyrDown
if loc != None:
loc = [x*2 for x in loc if loc != None]
return loc
def track(self, frame):
# IDEA: Hugh transform to look for circle shape
#frame = cv2.pyrDown(frame)
thresh = cv2.cvtColor(frame, cv.CV_RGB2GRAY)
self.frame = thresh
_, thresh = cv2.threshold(thresh, self.threshold, 255, cv.CV_THRESH_BINARY)
#contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
#loc = bestcontour(contours, 10)
loc, contours = util.contour(thresh)
self.shape = frame.shape
self.contours = contours
return loc
def track(self, frame):
# Resize for efficiency
frame = cv2.pyrDown(frame)
frame = cv2.cvtColor(frame, cv.CV_BGR2Lab)
self.frame = frame
capture = cv2.inRange(frame, self.minbound, self.maxbound)
#contours, _ = cv2.findContours(capture, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
# Stupid hack to fix some numpy / opencv bug
#contours = [c.astype(np.int32) for c in contours]
loc, contours = util.contour(capture)
#loc = bestcontour(contours, 10)
if loc != None:
loc = [x*2 for x in loc if loc != None]
self.shape = frame.shape
self.contours = contours
return loc
