def track_video(self, videoPath):
n = 0
box_out = np.zeros((1, 4))
cap = cv2.VideoCapture(videoPath)
fourcc = cv2.VideoWriter_fourcc(*'XVID')
out = cv2.VideoWriter(videoPath + 'output.avi', fourcc, 20.0,
(1080, 720))
ret, frame = cap.read()
img_traing = np.zeros((np.shape(frame)[0], np.shape(frame)[1], 3),
dtype=np.uint8)
while (cap.isOpened()):
if np.shape(frame) == ():
break
frame = cv2.resize(frame, (1080, 720))
cv2.imshow('frame', frame)
ret, frame = cap.read()
n = 0
pro = 1.00
if cv2.waitKey(30) & 0xFF == ord('q'):
while (cap.isOpened()):
if np.shape(frame) == ():
cv2.destroyAllWindows()
break
if not np.shape(frame) == ():
inputImage = cv2.resize(frame, (1080, 720))
if n == 0:
box_local = [100, 100, 100, 100]
pro = 1.00
box = cv2.selectROI('roi', inputImage, False,
False)
inputImage = cv2.cvtColor(inputImage,
cv2.COLOR_BGR2RGB)
self.init(inputImage, box)
cv2.destroyAllWindows()
else:
inputImage = cv2.cvtColor(inputImage,
cv2.COLOR_BGR2RGB)
box_out, pro = self.update(inputImage)
n += 1
outimage, box_local = ops.show_image(inputImage,
img_traing,
box_out,
pro=pro)
out.write(outimage)
if (box_local[0] < 5) or (box_local[1] < 5) or (
(1280 - box_local[0])) < ((box_local[2])) or (
(720 - box_local[1]) <
(box_local[3])) or (pro < 1.00):
box_local = [100, 100, 100, 100]
box = cv2.selectROI('roi',
cv2.resize(frame, (1080, 720)),
False, False)
pro = 0.00
self.init(inputImage, box)
cv2.destroyAllWindows()
ret, frame = cap.read()
out.release()
def track(self, img_files, box, visualize=False):
frame_num = len(img_files)
boxes = np.zeros((frame_num, 4))
boxes[0] = box
times = np.zeros(frame_num)
for f, img_file in enumerate(img_files):
img = ops.read_image(img_file)
begin = time.time()
if f == 0:
self.init(img, box)
else:
boxes[f, :] = self.update(img)
times[f] = time.time() - begin
if visualize:
ops.show_image(img, boxes[f, :])
return boxes, times