def run_detection(video_source, detection_source):
"""Runs detection with MoVe Extrapolation based on a av.container video source and
a detection source that implements `poll` behaviour."""
mv_filter_method = C.move_config_aggregation_method
acc = Accumulator()
acc.register(extract_mvs)
acc.register(apply_queue_of_mvs)
acc.register(apply_mvs)
run_data = {
"results": [],
"statistics": {
"loop_time_samples": [],
"debugging": acc,
},
}
first_frame = True
curr_mvs = None
curr_bboxes = None
mvs_buffer = queue.Queue()
for i, frame in tqdm(enumerate(video_source.decode(video=0))):
img = unpack_frame_to_img(frame)
loop_last = time.perf_counter()
if C.move_config_bypass:
detections = detection_source.wait(i)
curr_bboxes = detections[0]
else:
curr_mvs = extract_mvs(frame)
mvs_buffer.put(curr_mvs)
detections = detection_source.poll(i)
if first_frame and detections is None:
print(
"Skipping loop iteration until first detections come in.")
continue
elif not first_frame and detections is None:
curr_bboxes = apply_mvs(curr_bboxes, curr_mvs,
mv_filter_method)
else:
first_frame = False
bboxes = detections[0]
bboxes = apply_queue_of_mvs(mvs_buffer, bboxes,
mv_filter_method)
curr_bboxes = bboxes.copy()
loop_now = time.perf_counter()
run_data["statistics"]["loop_time_samples"].append(loop_now -
loop_last)
h, w, _ = img.shape
track_ids = [-1] * len(curr_bboxes)
for j in range(len(curr_bboxes)):
bbox = clamp_bbox_to_frame(curr_bboxes[j], (h, w))
run_data["results"].append([
i,
track_ids[j],
bbox[0],
bbox[1],
bbox[2],
bbox[3],
])
try:
run_data["statistics"] = post_process_statistics(
run_data["statistics"])
except Exception as e:
print("ERROR: ", e)
run_data["statistics"] = None
return run_data
