def main():
print('Connecting to camera')
cap = cv2.VideoCapture(0)
# cap = cv2.VideoCapture('rtsp://*****:*****@[email protected]/H264?ch=1&subtype=0') # - rtsp://admin:comvis@[email protected]:554/H.264
assert cap.isOpened(), 'Unable to connect to camera'
device = 'cuda:0' if torch.cuda.is_available() else 'cpu'
print('Loading models')
detector = Detector('weights/yolov5s.pt',
img_size=(640, 640),
conf_thresh=0.4,
iou_thresh=0.5,
agnostic_nms=False,
device=device)
deepsort = DeepSort('weights/ckpt.t7',
max_dist=0.2,
min_confidence=0.3,
nms_max_overlap=0.5,
max_iou_distance=0.7,
max_age=70,
n_init=3,
nn_budget=100,
device=device)
bboxes_visualizer = BoundingBoxesVisualizer()
fps_estimator = MeanEstimator()
person_cls_id = detector.names.index('person') # get id of 'person' class
width, height = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)), int(
cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
cam_fps = int(cap.get(cv2.CAP_PROP_FPS))
print(f'Starting capture, camera_fps={cam_fps}')
# Start of demo
win_name = 'MICA ReID Demo'
cv2.namedWindow(win_name, cv2.WINDOW_GUI_NORMAL | cv2.WINDOW_FREERATIO)
cv2.resizeWindow(win_name, width, height)
frame_id = 0
while True:
start_it = time.time()
ret, img = cap.read()
if not ret:
print('Unable to read camera')
break
detections = detector.detect([img])[0]
num_people = 0
if detections is not None:
detections = detections[detections[:, -1].eq(
person_cls_id)] # filter person
xywh, confs = parse_detection(detections)
outputs = deepsort.update(xywh, confs, img)
num_people = len(outputs)
bboxes_visualizer.remove([
t.track_id for t in deepsort.tracker.tracks
if t.time_since_update > 3 or t.is_deleted()
])
bboxes_visualizer.update(outputs)
# draw detections
for pid in outputs[:, -1]:
bboxes_visualizer.plot(img,
pid,
label=f'Person {pid}',
line_thickness=5,
trail_trajectory=True,
trail_bbox=False)
# draw counting
overlay = img.copy()
count_str = f'Number of people: {num_people}'
text_size = cv2.getTextSize(count_str, 0, fontScale=0.5,
thickness=1)[0]
cv2.rectangle(overlay, (10, 10 + 10),
(15 + text_size[0], 10 + 20 + text_size[1]),
(255, 255, 255), -1)
img = cv2.addWeighted(overlay, 0.4, img, 0.6, 0)
cv2.putText(img,
count_str, (12, 10 + 15 + text_size[1]),
0,
0.5, (0, 0, 0),
thickness=1,
lineType=cv2.LINE_AA)
# show
cv2.imshow(win_name, img)
key = cv2.waitKey(1)
elapsed_time = time.time() - start_it
fps = fps_estimator.update(1 / elapsed_time)
print(
f'[{frame_id:06d}] num_detections={num_people} fps={fps:.02f} elapsed_time={elapsed_time:.03f}'
)
# check key pressed
if key == ord('q') or key == 27: # q or esc to quit
break
elif key == ord('r'): # r to reset tracking
deepsort.reset()
bboxes_visualizer.clear()
elif key == 32: # space to pause
key = cv2.waitKey(0)
if key == ord('q') or key == 27:
break
frame_id += 1
cv2.destroyAllWindows()
cap.release()
def main():
print('Connecting to camera')
# cap = cv2.VideoCapture(0)
cap = ThreadedVideoCapture(
'rtsp://*****:*****@[email protected]:554/Streaming/Channels/101/')
# cap = ThreadedVideoCapture('rtsp://*****:*****@[email protected]/H264?ch=1&subtype=0')
assert cap.isOpened(), 'Unable to connect to camera'
width, height = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)), int(
cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
cam_fps = int(cap.get(cv2.CAP_PROP_FPS))
device = 'cuda:0' if torch.cuda.is_available() else 'cpu'
print('Loading models')
detector = Detector('weights/yolov5s.pt',
img_size=(640, 640),
conf_thresh=0.5,
iou_thresh=0.5,
agnostic_nms=False,
device=device)
deepsort = DeepSort('weights/ckpt.t7',
max_dist=0.2,
min_confidence=0.3,
nms_max_overlap=0.5,
max_iou_distance=0.7,
max_age=100,
lingering_age=5,
n_init=5,
nn_budget=100,
device=device)
bboxes_visualizer = BBoxVisualizer()
fps_estimator = IncrementalMeanTracker(max_count=cam_fps * 5)
person_cls_id = detector.names.index('person') # get id of 'person' class
print(f'Starting capture, camera_fps={cam_fps}')
# Start
cap.start()
win_name = 'MICA ReID Demo'
cv2.namedWindow(win_name, cv2.WINDOW_GUI_NORMAL | cv2.WINDOW_FREERATIO)
cv2.resizeWindow(win_name, width, height)
frame_id = 0
pbar = tqdm(desc=win_name)
while True:
start_it = time.time()
ret, img = cap.read()
if not ret:
print('Unable to read camera')
break
detections = detector.detect([img])[0]
num_people = 0
if detections is not None:
detections = detections[detections[:, -1].eq(
person_cls_id)] # filter person
xywh, confs = parse_detection(detections)
outputs = deepsort.update(xywh, confs, img)
num_people = len(outputs)
bboxes_visualizer.remove([
t.track_id for t in deepsort.tracker.tracks
if t.time_since_update > 3 or t.is_deleted()
])
bboxes_visualizer.update(outputs)
# draw detections
for pid in outputs[:, -1]:
bboxes_visualizer.box(img,
pid,
label=f'Person {pid}',
line_thickness=5,
trail_trajectory=True,
trail_bbox=False)
# draw counting
count_str = f'Number of people: {num_people}'
img = bboxes_visualizer.text(img,
count_str, (960, 25),
fontScale=0.8,
box_alpha=0.4,
color=(255, 255, 255),
box_color=(0, 0, 0))
# show
cv2.imshow(win_name, img)
key = cv2.waitKey(1)
elapsed_time = time.time() - start_it
fps = fps_estimator.update(1 / elapsed_time)
desc = f'[{frame_id:06d}] num_detections={num_people} fps={fps:.02f} elapsed_time={elapsed_time:.03f}'
pbar.update()
pbar.set_description(desc)
# check key pressed
if key == ord('q') or key == 27: # q or esc to quit
break
elif key == ord('r'): # r to reset tracking
deepsort.reset()
bboxes_visualizer.clear()
elif key == 32: # space to pause
key = cv2.waitKey(0)
if key == ord('q') or key == 27:
break
frame_id += 1
cv2.destroyAllWindows()
cap.release()
