def add_new_objects(boxes, classes, confidences, objects, frame, tracker, mcdf,
overlap_threshold):
"""
This function create new objects by the detected boxes or update existing one.
decide to update existing one if the overlap between them is grater than the overlap_threshold
:param boxes: the bounding boxes of the detected objects (the new ones)
:param classes: the classes of the objects
:param confidences: the confidence of the objects
:param objects: list of the objects that exists (that was updated by the tracker)
:param frame: the frame that we detect.
:param tracker: the tracker.
:param mcdf: the maximum detection failures, remove objects that have mcdf greater than this param.
:param overlap_threshold: the maximum overlap between the detected object (boxes) to the tracked object (from objects) in order to update the object and not create new one.
:return: the objects.
"""
matched_object_ids = []
for i, box in enumerate(boxes):
_type = classes[i] if classes is not None else None
_confidence = confidences[i] if confidences is not None else None
_tracker = get_tracker(tracker, box, frame)
match_found = False
for _id, object in objects.items():
if get_overlap(box, object.bounding_box) >= overlap_threshold:
match_found = True
if _id not in matched_object_ids:
object.num_consecutive_detection_failures = 0
matched_object_ids.append(_id)
object.update(box, _type, _confidence, _tracker)
logger.info('Object updated.',
extra={
'meta': {
'cat': 'Object updated',
'people_id': _id,
'bounding_box': object.bounding_box,
'type': object.type,
'type_confidence': object.type_confidence,
},
})
break
if not match_found:
# create new object
_object = Object(box, _type, _confidence, _tracker)
object_id = generate_people_id()
objects[object_id] = _object
logger.info('Object created.',
extra={
'meta': {
'cat': 'Object created',
'people_id': object_id,
'bounding_box': _object.bounding_box,
'type': _object.type,
'type_confidence': _object.type_confidence,
},
})
objects = remove_stray_objects(objects, matched_object_ids, mcdf)
return remove_duplicates(objects, overlap_threshold)
def remove_duplicates(objects, overlap_threshold):
"""
Removes duplicate objects i.e objects that point to an already detected and tracked people.
:param objects: the detected objects.
:param overlap_threshold: the maximum overlap between the objects in order to consider one of them as duplicate.
:return: the objects
"""
for _id, object_a in list(objects.items()):
for _, object_b in list(objects.items()):
if object_a == object_b:
break
overlap = get_overlap(object_a.bounding_box, object_b.bounding_box)
logger.info('overlap info:',
extra={
'meta': {
'cat': 'OBJECT_UPDATE',
'people_id_a': _id,
'bounding_box_a': object_a.bounding_box,
'people_id_b': _,
'bounding_box_b': object_b.bounding_box,
'overlap': overlap
},
})
if overlap >= overlap_threshold and _id in objects:
logger.info('People tracker delete',
extra={
'meta': {
'cat': 'TRACKER_UPDATE',
'people_id': _id,
'bounding_box': object_a.bounding_box,
'overlap': overlap
},
})
del objects[_id]
return objects
def remove_duplicates(blobs):
for blob_id, blob_a in list(blobs.items()):
for _, blob_b in list(blobs.items()):
if blob_a == blob_b:
break
if get_overlap(blob_a.bounding_box,
blob_b.bounding_box) >= 0.6 and blob_id in blobs:
del blobs[blob_id]
return blobs
def remove_duplicates(blobs):
'''
Removes duplicate blobs i.e blobs that point to an already detected and tracked vehicle.
'''
for _id, blob_a in list(blobs.items()):
for _, blob_b in list(blobs.items()):
if blob_a == blob_b:
break
if get_overlap(blob_a.bounding_box, blob_b.bounding_box) >= 0.7 and _id in blobs:
del blobs[_id]
return blobs
def _match_boxes_simple(boxes, classes, confidences, blobs):
'''
old algorithm used to match boxes with blobs
I'm keep it so I can compare results
'''
# Note: in this algorithm multiple boxes can match the same blob
matches = []
for i, box in enumerate(boxes):
for _id, blob in blobs.items():
if get_overlap(box, blob.bounding_box) >= 0.6:
matches.append((i, _id))
return matches
def add_new_blobs(boxes, classes, confidences, blobs, frame, tracker, mcdf):
'''
Add new blobs or updates existing ones.
'''
matched_blob_ids = []
for i, box in enumerate(boxes):
_type = classes[i] if classes is not None else None
_confidence = confidences[i] if confidences is not None else None
_tracker = get_tracker(tracker, box, frame)
match_found = False
for _id, blob in blobs.items():
if get_overlap(box, blob.bounding_box) >= 0.6:
match_found = True
if _id not in matched_blob_ids:
blob.num_consecutive_detection_failures = 0
matched_blob_ids.append(_id)
blob.update(box, _type, _confidence, _tracker)
blob_update_log_meta = {
'label': 'BLOB_UPDATE',
'object_id': _id,
'bounding_box': blob.bounding_box,
'type': blob.type,
'type_confidence': blob.type_confidence,
}
if settings.LOG_IMAGES:
blob_update_log_meta['image'] = get_base64_image(
get_box_image(frame, blob.bounding_box))
logger.debug('Blob updated.',
extra={'meta': blob_update_log_meta})
break
if not match_found:
_blob = Blob(box, _type, _confidence, _tracker)
blob_id = generate_object_id()
blobs[blob_id] = _blob
blog_create_log_meta = {
'label': 'BLOB_CREATE',
'object_id': blob_id,
'bounding_box': _blob.bounding_box,
'type': _blob.type,
'type_confidence': _blob.type_confidence,
}
if settings.LOG_IMAGES:
blog_create_log_meta['image'] = get_base64_image(
get_box_image(frame, _blob.bounding_box))
logger.debug('Blob created.', extra={'meta': blog_create_log_meta})
blobs = _remove_stray_blobs(blobs, matched_blob_ids, mcdf)
return blobs
def add_new_blobs(boxes, classes, confidences, blobs, frame, tracker, counting_line, line_position, mcdf):
'''
Adds new blobs or updates existing ones.
'''
matched_blob_ids = []
for i, box in enumerate(boxes):
_type = classes[i] if classes is not None else None
_confidence = confidences[i] if confidences is not None else None
_tracker = get_tracker(tracker, box, frame)
box_centroid = get_centroid(box)
match_found = False
for _id, blob in blobs.items():
if not blob.counted and get_overlap(box, blob.bounding_box) >= 0.7:
match_found = True
if _id not in matched_blob_ids:
blob.num_consecutive_detection_failures = 0
matched_blob_ids.append(_id)
blob.update(box, _type, _confidence, _tracker)
logger.debug('Blob updated.', extra={
'meta': {
'cat': 'BLOB_UPSERT',
'vehicle_id': _id,
'bounding_box': blob.bounding_box,
'type': blob.type,
'type_confidence': blob.type_confidence,
'image': get_base64_image(get_box_image(frame, blob.bounding_box)),
},
})
break
if not match_found and not is_passed_counting_line(box_centroid, counting_line, line_position):
_blob = Blob(box, _type, _confidence, _tracker)
blob_id = generate_vehicle_id()
blobs[blob_id] = _blob
logger.debug('Blob created.', extra={
'meta': {
'cat': 'BLOB_UPSERT',
'vehicle_id': blob_id,
'bounding_box': _blob.bounding_box,
'type': _blob.type,
'type_confidence': _blob.type_confidence,
'image': get_base64_image(get_box_image(frame, _blob.bounding_box)),
},
})
blobs = remove_stray_blobs(blobs, matched_blob_ids, mcdf)
return blobs
def add_new_blobs(boxes, classes, confidences, blobs, frame, tracker, mcdf):
matched_blob_ids = []
for i, box in enumerate(boxes):
_type = classes[i] if classes is not None else None
_confidence = confidences[i] if confidences is not None else None
_tracker = get_tracker(tracker, box, frame)
match_found = False
for _id, blob in blobs.items():
if get_overlap(box, blob.bounding_box) >= 0.6:
match_found = True
if _id not in matched_blob_ids:
blob.num_consecutive_detection_failures = 0
matched_blob_ids.append(_id)
blob.update(box, _type, _confidence, _tracker)
logger.debug('Blob updated.',
extra={
'meta': {
'label': 'BLOB_UPDATE',
'vehicle_id': _id,
'bounding_box': blob.bounding_box,
'type': blob.type,
'type_confidence': blob.type_confidence,
},
})
break
if not match_found:
_blob = Blob(box, _type, _confidence, _tracker)
blob_id = generate_vehicle_id()
blobs[blob_id] = _blob
logger.debug('Blob created.',
extra={
'meta': {
'label': 'BLOB_CREATE',
'vehicle_id': blob_id,
'bounding_box': _blob.bounding_box,
'type': _blob.type,
'type_confidence': _blob.type_confidence,
},
})
blobs = _remove_stray_blobs(blobs, matched_blob_ids, mcdf)
return blobs