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',
'vehicle_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_vehicle_id()
blobs[blob_id] = _blob
blog_create_log_meta = {
'label': 'BLOB_CREATE',
'vehicle_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 test_generate_vehicle_id():
v_id = generate_vehicle_id()
assert type(v_id) is str, "vehicle id is a string"
assert v_id.startswith('veh_'), "vehicle id starts with 'veh_'"
assert len(v_id) == 4 + 32, "vehicle id is 36 characters in length"
def add_new_blobs(boxes, classes, confidences, blobs, frame, tracker, counting_line, line_position, mcdf):
# add new blobs or update existing ones
matched_blob_ids = []
for i in range(len(boxes)):
_type = classes[i] if classes != None else None
#print (confidences)
if confidences != []:
_confidence = confidences[i]
else:
_confidence=None
box_centroid = get_centroid(boxes[i])
box_area = get_area(boxes[i])
match_found = False
for _id, blob in blobs.items():
#print (_id,blob)
if blob.counted == False and get_iou(boxes[i], blob.bounding_box) > 0.5:
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(boxes[i], _type, _confidence, frame, tracker) # TODO: update blob w/o creating temp blob
blob.update(temp_blob.bounding_box, _type, _confidence, temp_blob.tracker)
#blob.trajectory
# Create a sequence of points to make a contour
#contour=cv2.contourArea([(100,100),(200,200),(500,500)])
#cv2.pointPolygonTest([(100,100),(200,200),(500,500)], box_centroid, false)
#area_of_left_left = np.array([[1, 1], [10, 50], [50, 50]], dtype=np.int32)
# area_of_left_straight=np.array([[1, 1], [10, 50], [50, 50]], dtype=np.int32)
# area_of_left_right=np.array([[1, 1], [10, 50], [50, 50]], dtype=np.int32)
# area_of_left=np.array([[1, 1], [10, 50], [50, 50]], dtype=np.int32)
#left_left = object_in_polygon(frame, area_of_left_left,box_centroid)
#print(box_centroid)
#print(left_left)
log_info('Blob updated.', {
'event': 'BLOB_UPSERT',
'vehicle_id': _id,
'bounding_box': blob.bounding_box,
'type': blob.type,
'type_confidence': blob.type_confidence,
'center': box_centroid,
#'direction': direction
#'trajectory':trajectory.append
#'image': get_base64_image(get_box_image(frame, blob.bounding_box))
})
f = './ProcessRecords/Blobupdated58.txt'
with open(f, "a") as file:
file.write(
'BLOB_UPSERT' + '-' + 'id' + str(_id) + '-' + 'bounding_box'+str(blob.bounding_box)+'-'+'type' + str(blob.type) + '-' + 'type_confidence' + str(
blob.type_confidence) + '-' + 'center' + str(
box_centroid) + "\n")
break
if not match_found and not is_passed_counting_line(box_centroid, counting_line, line_position):
_blob = create_blob(boxes[i], _type, _confidence, frame, tracker)
blob_id = generate_vehicle_id()
blobs[blob_id] = _blob
log_info('Blob created.', {
'event': 'BLOB_UPSERT',
'vehicle_id': blob_id,
'bounding_box': _blob.bounding_box,
'type': _blob.type,
'type_confidence': _blob.type_confidence,
'center': box_centroid
#'image': get_base64_image(get_box_image(frame, _blob.bounding_box))
})
f = './ProcessRecords/Blobcreated58.txt'
with open(f, "a") as file:
file.write(
'BLOB_UPSERT' + '-' + 'id' + str(blob_id) + '-' + 'bounding_box' + str(
_blob.bounding_box) + '-' + 'type' + str(_blob.type) + '-' + 'type_confidence' + str(
_blob.type_confidence) + '-' + 'center' + str(box_centroid) + "\n")
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):
# add new blobs or update existing ones
matched_blob_ids = []
for i in range(len(boxes)):
_type = classes[i] if classes != None else None
_confidence = confidences[i] if confidences != None else None
_tracker = get_tracker(tracker, boxes[i], frame)
box_centroid = get_centroid(boxes[i])
box_area = get_area(boxes[i])
match_found = False
for _id, blob in blobs.items():
if blob.counted == False and get_iou(boxes[i],
blob.bounding_box) > 0.5:
match_found = True
if _id not in matched_blob_ids:
blob.num_consecutive_detection_failures = 0
matched_blob_ids.append(_id)
blob.update(boxes[i], _type, _confidence, _tracker)
log_info(
'Blob updated.', {
'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(boxes[i], _type, _confidence, _tracker)
blob_id = generate_vehicle_id()
blobs[blob_id] = _blob
log_info(
'Blob created.', {
'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, counting_line, line_position, mcdf):
# add new blobs or update existing ones
matched_blob_ids = []
print(classes)
print(boxes)
for i in range(len(boxes)):
if classes != []:
print(classes[i])
_type = classes[i]
else:
_type=None
print (confidences)
if confidences != []:
_confidence = confidences[i]
else:
_confidence=None
box_centroid = get_centroid(boxes[i])
box_area = get_area(boxes[i])
match_found = False
for _id, blob in blobs.items():
#print (_id,blob)
if blob.counted == False and get_iou(boxes[i], blob.bounding_box) > 0.5:
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(boxes[i], _type, _confidence, frame, tracker) # TODO: update blob w/o creating temp blob
blob.update(temp_blob.bounding_box, _type, _confidence, temp_blob.tracker)
#blob.trajectory
# Create a sequence of points to make a contour
#contour=cv2.contourArea([(100,100),(200,200),(500,500)])
#cv2.pointPolygonTest([(100,100),(200,200),(500,500)], box_centroid, false)
log_info('Blob updated.', {
'event': 'BLOB_UPSERT',
'vehicle_id': _id,
'bounding_box': blob.bounding_box,
'type': blob.type,
'type_confidence': blob.type_confidence,
'center': box_centroid,
#'direction': direction
#'trajectory':trajectory.append
#'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 = create_blob(boxes[i], _type, _confidence, frame, tracker)
blob_id = generate_vehicle_id()
blobs[blob_id] = _blob
log_info('Blob created.', {
'event': 'BLOB_UPSERT',
'vehicle_id': blob_id,
'bounding_box': _blob.bounding_box,
'type': _blob.type,
'type_confidence': _blob.type_confidence,
'centere': box_centroid
#'image': get_base64_image(get_box_image(frame, _blob.bounding_box))
})
blobs = remove_stray_blobs(blobs, matched_blob_ids, mcdf)
return blobs
