def add_new_blobs(boxes, classes, confidences, blobs, frame, tracker,
counting_line, line_position, mcdf):
# add new blobs to existing blobs
matched_blob_ids = []
for _idx in range(len(boxes)):
_type = classes[_idx] if classes != None else None
_confidence = confidences[_idx] if confidences != None else None
box_centroid = get_centroid(boxes[_idx])
box_area = get_area(boxes[_idx])
match_found = False
for _id, blob in blobs.items():
if blob.counted == False and get_iou(boxes[_idx],
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[_idx], _type, _confidence, frame,
tracker) # TODO: update blob w/o creating temp blob
blob.update(temp_blob.bounding_box, _type, _confidence,
temp_blob.tracker)
break
if not match_found and not is_passed_counting_line(
box_centroid, counting_line, line_position):
_blob = create_blob(boxes[_idx], _type, _confidence, frame,
tracker)
blob_id = generate_vehicle_id()
blobs[blob_id] = _blob
blobs = remove_stray_blobs(blobs, matched_blob_ids, mcdf)
return blobs
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 get_iou(blob_a.bounding_box, blob_b.bounding_box) > 0.5 and id_a in blobs:
del blobs[id_a]
return blobs
def remove_duplicates(blobs):
for id_a, blob_a in list(blobs.items()):
a=blob_a.bounding_box
if (a[2]>1/4*640 or a[3]>1/4*480 ) and id_a in blobs:
del blobs[id_a]
break
for id_b, blob_b in list(blobs.items()):
if blob_a == blob_b:
break
b=blob_b.bounding_box
if (b[2]>1/4*640 or b[3]>1/4*480 ) and id_b in blobs:
del blobs[id_b]
break
p=get_inside(blob_a.bounding_box, blob_b.bounding_box)
if p==1 and id_b in blobs:
del blobs[id_b]
if p==2 and id_a in blobs:
del blobs[id_a]
if get_iou(blob_a.bounding_box, blob_b.bounding_box) > 0.5 and id_a in blobs:
del blobs[id_a]
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
#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 = []
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