def remove_duplicates(blobs):
for id_a, blob_a in list(blobs.items()):
for id_b, blob_b in list(blobs.items()):
if blob_a == blob_b:
break
if blob_a.area >= blob_b.area and box_contains_point(
blob_a.bounding_box, blob_b.centroid):
del blobs[id_b]
elif blob_b.area >= blob_a.area and box_contains_point(
blob_b.bounding_box, blob_a.centroid):
del blobs[id_a]
return blobs
def add_new_blobs(boxes, blobs, frame, tracker, current_blob_id, counting_line,
line_position, mcdf):
# add new blobs to existing blobs
matched_blob_ids = []
for box in boxes:
box_centroid = get_centroid(box)
box_area = get_area(box)
match_found = False
for _id, blob in blobs.items():
if blob.counted == False and box_contains_point(
box, blob.centroid):
match_found = True
if _id not in matched_blob_ids:
blob.num_consecutive_detection_failures = 0
matched_blob_ids.append(_id)
temp_blob = create_blob(
box, frame,
tracker) # TODO: update blob w/o creating temp blob
blob.update(temp_blob.bounding_box, temp_blob.tracker)
break
if not match_found and not is_passed_counting_line(
box_centroid, counting_line, line_position):
_blob = create_blob(box, frame, tracker)
blobs[current_blob_id] = _blob
current_blob_id += 1
blobs = remove_stray_blobs(blobs, matched_blob_ids, mcdf)
return blobs, current_blob_id
def add_new_blobs(boxes, blobs, frame, tracker, current_blob_id):
# add new blobs to existing blobs
for box in boxes:
box_centroid = get_centroid(box)
box_area = get_area(box)
match_found = False
for _id, blob in blobs.items():
if (blob.area >= box_area and box_contains_point(blob.bounding_box, box_centroid)) \
or (box_area >= blob.area and box_contains_point(box, blob.centroid)):
match_found = True
temp_blob = create_blob(
box, frame,
tracker) # TODO: update blob w/o creating temp blob
blob.update(temp_blob.bounding_box, temp_blob.tracker)
break
if not match_found:
_blob = create_blob(box, frame, tracker)
blobs[current_blob_id] = _blob
current_blob_id += 1
return blobs, current_blob_id
def add_new_blobs(boxes, blobs, frame, tracker, current_blob_id, counting_line,
line_position, mcdf, detector):
path = 'detectors\yolo'
labelsPath = os.path.join(path, 'coco.names')
LABELS = open(labelsPath).read().strip().split("\n")
# add new blobs to existing blobs
matched_blob_ids = []
if detector == 'yolo':
for box in boxes:
#print(boxes)
box_centroid = get_centroid(box[0])
box_area = get_area(box[0])
match_found = False
for _id, blob in blobs.items():
#print(blobs.items())
if blob[0].counted == False and box_contains_point(
box[0], blob[0].centroid):
#((blob.area >= box_area and box_contains_point(blob.bounding_box, box_centroid)) \
#or (box_area >= blob.area and box_contains_point(box[0], blob.centroid))):
match_found = True
if _id not in matched_blob_ids:
blob[0].num_consecutive_detection_failures = 0
matched_blob_ids.append(_id)
temp_blob = create_blob(
box[0], frame,
tracker) # TODO: update blob w/o creating temp blob
blob[0].update(temp_blob.bounding_box, temp_blob.tracker)
break
if not match_found and not is_passed_counting_line(
box_centroid, counting_line, line_position):
_blob = create_blob(box[0], frame, tracker)
blobs[current_blob_id] = _blob, LABELS[box[1][0]]
current_blob_id += 1
else:
for box in boxes:
#print(boxes)
box_centroid = get_centroid(box)
box_area = get_area(box)
match_found = False
for _id, blob in blobs.items():
#print(blobs.items())
if blob.counted == False and box_contains_point(
box, blob.centroid):
#((blob.area >= box_area and box_contains_point(blob.bounding_box, box_centroid)) \
#or (box_area >= blob.area and box_contains_point(box[0], blob.centroid))):
match_found = True
if _id not in matched_blob_ids:
blob.num_consecutive_detection_failures = 0
matched_blob_ids.append(_id)
temp_blob = create_blob(
box, frame,
tracker) # TODO: update blob w/o creating temp blob
blob.update(temp_blob.bounding_box, temp_blob.tracker)
break
if not match_found and not is_passed_counting_line(
box_centroid, counting_line, line_position):
_blob = create_blob(box, frame, tracker)
blobs[current_blob_id] = _blob
current_blob_id += 1
blobs = remove_stray_blobs(blobs, matched_blob_ids, mcdf)
return blobs, current_blob_id
