def track(video_path, use_gpu=False):
video = cv2.VideoCapture(video_path)
ret, frame = video.read()
if ret:
frame = cv2.resize(frame, (input_width, input_height))
if use_gpu:
caffe.set_mode_gpu()
tracker = Sort(max_age=10)
detector = Detector()
classes = detector.get_classes()
while ret:
frame_disp = np.copy(frame)
bounding_boxes, counting = detector.infer(frame)
class_counting = zip(classes, counting)
for pair in class_counting:
print('{:s} {:03d}'.format(*pair))
print('')
if len(bounding_boxes) > 0:
bounding_boxes = np.array(bounding_boxes, np.int32)
# convert (x, y, w, h) to (x1, y1, x2, y2)
bounding_boxes[:, 2:4] += bounding_boxes[:, 0:2]
bounding_boxes[:, 2:4] -= 1
track_results = tracker.update(bounding_boxes)
draw_tracking_results(track_results, frame_disp)
cv2.imshow('tracking', frame_disp)
key = cv2.waitKey(1)
if key == 27:
return
ret, frame = video.read()
if ret:
frame = cv2.resize(frame, (input_width, input_height))
