def detection(d):
det = Detector(detector_name='mobilenet_ssd', config_path='./detectors.cfg')
while True:
if(d['tracker_ready'] == True):
results = det.detect_image_frame(d['%d_frame'%d['index']], to_xywh=True)
d['%d_boxes'%d['index']] = np.array([result[1:5] for result in results])
d['%d_scores'%d['index']] = np.array([result[5] for result in results])
d['tracker_ready'] = False
d['detector_ready'] = True
class tracking_by_detection(object):
def __init__(self,
detector_name,
tracker_name,
config_path='./config.cfg'):
self.det = Detector(detector_name, config_path)
self.tra = Tracker_temp(tracker_name, config_path)
def open_with_mkdir(self, path):
try:
os.makedirs(os.path.dirname(path))
except OSError as e:
if e.errno == errno.EEXIST and os.path.isdir(
os.path.dirname(path)):
pass
else:
raise
return open(path, 'w')
def tracking_by_detection(self,
video_stream,
output_file,
show_image=True,
detect_freq=1,
down_sample_ratio=1.0,
is_probability_driven_detect=True,
print_fps=False):
video_capture = cv2.VideoCapture(video_stream)
fps = 0.0
step_counter = 0
counter = 0
first_time_flag = True
start_time = time.time()
total_time = time.time()
result_list = []
frame_index = 1
while True:
ret, frame = video_capture.read()
if ret != True:
break
(h, w) = frame.shape[:2]
frame_resized = cv2.resize(
frame,
(int(w * down_sample_ratio), int(h * down_sample_ratio)))
if ((step_counter % detect_freq == 0) or counter == 0
or (is_probability_driven_detect == True
and self.tra.is_detection_needed() == True)):
results = self.det.detect_image_frame(frame_resized,
to_xywh=True)
boxes = np.array([result[1:5] for result in results])
scores = np.array([result[5] for result in results])
self.tra.set_detecion_needed(False)
tracker, detections = self.tra.start_tracking(
frame_resized, boxes, scores)
# Call the tracker
if (IS_TRACKING_DISPLAY is True):
for track in tracker.tracks:
if track.is_confirmed() and track.time_since_update > 1:
continue
bbox = track.to_tlbr()
cv2.rectangle(frame, (int(bbox[0] / down_sample_ratio),
int(bbox[1] / down_sample_ratio)),
(int(bbox[2] / down_sample_ratio),
int(bbox[3] / down_sample_ratio)),
(255, 255, 255), 2)
cv2.putText(frame, str(track.track_id),
(int(bbox[0] / down_sample_ratio),
int(bbox[1] / down_sample_ratio)), 0,
5e-3 * 200, (0, 255, 0), 2)
bbox = track.to_tlwh()
result_list.append([
frame_index, track.track_id,
bbox[0] / down_sample_ratio,
bbox[1] / down_sample_ratio,
bbox[2] / down_sample_ratio,
bbox[3] / down_sample_ratio
])
if (IS_DETECTION_DISPLAY is True):
for detection in detections:
bbox = detection.to_tlbr()
cv2.rectangle(frame, (int(bbox[0] / down_sample_ratio),
int(bbox[1] / down_sample_ratio)),
(int(bbox[2] / down_sample_ratio),
int(bbox[3] / down_sample_ratio)),
(255, 0, 0), 2)
counter += 1
step_counter += 1
if (step_counter % detect_freq == 0):
fps = step_counter / (time.time() - start_time)
if (print_fps is True):
print(fps)
step_counter = 0
cv2.putText(frame, 'FPS:' + str(round(fps, 1)), (0, 25),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
start_time = time.time()
if (first_time_flag is True):
step_counter = 0
counter = 0
total_time = time.time()
first_time_flag = False
if (show_image == True):
cv2.imshow('image', frame)
frame_index += 1
if cv2.waitKey(1) & 0xFF == ord('q'):
break
fps = counter / (time.time() - total_time)
print('Average FPS:', round(fps, 1))
print('Total eplased:', round(time.time() - total_time, 2))
try:
f = self.open_with_mkdir(output_file)
for result in result_list:
print('%d,%d,%.2f,%.2f,%.2f,%.2f,1,-1,-1,-1' %
(int(result[0]), int(result[1]), float(result[2]),
float(result[3]), float(result[4]), float(result[5])),
file=f)
f.close()
except Exception as e:
print(e)
print('Something went wrong when writing output file!')
video_capture.release()
cv2.destroyAllWindows()
try:
del tracker
del detections
del result_list
del video_capture
del frame
del frame_resized
del results
del boxes
del bbox
del scores
del step_counter
del first_time_flag
del start_time
del total_time
del frame_index
except Exception as e:
print(e)
return fps, counter
