def demo_video(video_file):
import time
facedemo = Face(detector_method=DETECTOR, recognition_method=None)
cap = common.VideoStream(video_file, queueSize=4).start()
time.sleep(1)
total_t, counter = 0, 0
t = common.clock()
while not cap.stopped:
imgcv = cap.read()
if imgcv is not None:
counter += 1
detections = facedemo.detect(imgcv, upsamples=0)
ids = range(len(detections))
# temp = mtracker.update(imgcv, to_cvbox(detections))
# cvboxes, ids = [], []
# for tid,tracker in mtracker.trackers.items():
# if tracker.visible_count > 3 and tracker.consecutive_invisible_count<10:
# cvboxes.append(tracker.bbox)
# ids.append(tid)
# detections = to_bbox(cvboxes)
print(detections)
common.showImage(common.drawObjects(imgcv, detections, ids))
key = cv2.waitKey(1) & 0xFF
if key == 27:
break
t1 = common.clock()
dt = t1 - t
t = t1
total_t += dt
print(counter / total_t)
def demo_video(video_file):
import cv2
# with tf.device(device_id):
cap = common.VideoStream(video_file).start()
dmg = Demography()
def draw_faces(img, detections):
""" Draws bounding boxes of objects detected on given image """
h, w = img.shape[:2]
for face in detections:
# draw rectangle
x1, y1, x2, y2 = face['box']['topleft']['x'], face['box']['topleft']['y'], face['box']['bottomright']['x'], face['box']['bottomright']['y']
cv2.rectangle(img, (x1, y1), (x2, y2), (0,255,0), 2)
# draw class text
text = "%s %s"%(face['classes'][0]['meta']['gender'], face['classes'][0]['meta']['age'])
return common.draw_label(img, text, (x1, y1))
while cap.more():
img = cap.read()
if img is not None:
faces = dmg.run(img)
img = draw_faces(img, faces)
common.showImage(img)
key = cv2.waitKey(1) & 0xff
if key == 27:
break
else:
print('Cannot read frame')
break
def process_frame(frame):
detections = facedemo.detect(frame, upsamples=0)
# frame = draw_faces(frame, detections)
return frame, detections
if __name__ == '__main__':
# threadn = cv2.getNumberOfCPUs()
# print threadn
# from multiprocessing import Pool
# pool = Pool(processes = threadn)
# pending = deque()
multi = common.Multicore(process_frame)
total_t, counter = 0, 0
cap = common.VideoStream(url, queueSize=4).start()
t = common.clock()
while not cap.stopped:
# while len(pending) > 0 and pending[0].ready():
# frame = pending.popleft().get()
# common.showImage(frame)
# if len(pending) < threadn:
# imgcv = cap.read()
# if imgcv is not None:
# counter += 1
# task = pool.apply_async(process_frame, (imgcv.copy(),))
# pending.append(task)
# else:
# print "Cannot read frame"
def demo_video(video_file):
detector = ObjectDetectorYolo(model='tiny-yolo-voc')
mtracker = KalmanTracker(['person'], tracker='deep_sort')
cap = common.VideoStream(video_file, queueSize=4).start()
cv2.waitKey(500)
Outcount, Incount = 0, 0
total_t, counter = 0, 0
while not cap.stopped:
t = common.clock()
imgcv = cap.read()
if imgcv is not None:
counter += 1
detections = detector.run(imgcv)
mtracker.update(imgcv, detections)
cvboxes, ids = [], []
for tid, tracker in mtracker.trackers.iteritems():
if tracker.consecutive_invisible_count < 5:
state_current = get_pos(tracker.bbox)
try:
if state_current != tracker.regionside:
tracker.statechange += 1
print state_current, tracker.regionside, tracker.statechange
if state_current == 'Positive':
if tracker.statechange % 2:
Incount += 1
else:
Outcount -= 1
else:
if tracker.statechange % 2:
Outcount += 1
else:
Incount -= 1
tracker.regionside = state_current
except AttributeError:
tracker.regionside = state_current
tracker.statechange = 0
cvboxes.append(tracker.bbox)
ids.append(tid)
print Incount, Outcount
cv2.line(imgcv, (LINE['x1'], LINE['y1']), (LINE['x2'], LINE['y2']),
(0, 0, 255), 4)
common.drawLabel(imgcv,
"IN:%d OUT:%d" % (Incount, Outcount), (10, 10),
size=1,
color=(0, 0, 255))
common.showImage(draw_boxes(imgcv, cvboxes, ids))
key = cv2.waitKey(1) & 0xFF
if key == 27:
break
t1 = common.clock()
dt = t1 - t
t = t1
total_t += dt
print counter / total_t
def demo_video(video_file):
facedemo = Face(detector_method=DETECTOR, recognition_method=None)
mtracker = MultiTracker(SingleTrackerType=CorrelationTracker)
# mtracker = MultiTracker(SingleTrackerType=CorrelationTracker,
# removalConfig=removalConfig)
# mtracker = MultiTracker(SingleTrackerType = cv2.TrackerKCF_create)
cap = common.VideoStream(video_file, queueSize=4).start()
cv2.waitKey(500)
Outcount, Incount = 0, 0
while not cap.stopped:
t = common.clock()
total_t, counter = 0, 0
imgcv = cap.read()
if imgcv is not None:
counter += 1
detections = facedemo.detect(imgcv, upsamples=0)
mtracker.update(imgcv, common.toCvbox(detections))
cvboxes, ids = [], []
for tid, tracker in mtracker.trackers.items():
if tracker.visible_count > 3 and tracker.consecutive_invisible_count < 10:
state_current = get_pos(tracker.bbox)
try:
if state_current != tracker.regionside:
tracker.statechange += 1
print state_current, tracker.regionside, tracker.statechange
if state_current == 'Positive':
if tracker.statechange % 2:
Incount += 1
else:
Outcount -= 1
else:
if tracker.statechange % 2:
Outcount += 1
else:
Incount -= 1
tracker.regionside = state_current
except AttributeError:
tracker.regionside = state_current
tracker.statechange = 0
cvboxes.append(tracker.bbox)
ids.append(tid)
detections = to_bbox(cvboxes)
print Incount, Outcount
cv2.line(imgcv, (LINE['x1'], LINE['y1']), (LINE['x2'], LINE['y2']),
(0, 0, 255), 4)
imgcv = common.drawLabel(imgcv,
"IN:%d OUT:%d" % (Incount, Outcount),
(10, 10),
color=(0, 0, 255))
common.showImage(common.drawObjects(imgcv, detections, ids))
key = cv2.waitKey(1) & 0xFF
if key == 27:
break
t1 = common.clock()
dt = t1 - t
t = t1
total_t += dt
print counter / total_t