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 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 __init__(self, _bounding_box, _tracker):
self.bounding_box = _bounding_box
self.centroid = get_centroid(_bounding_box)
self.area = get_area(_bounding_box)
self.tracker = _tracker
self.num_consecutive_tracking_failures = 0
self.counted = False
def update(self, _bounding_box, _tracker=None):
self.bounding_box = _bounding_box
self.centroid = get_centroid(_bounding_box)
self.area = get_area(_bounding_box)
self.frames += 1
if _tracker:
self.tracker = _tracker
def update(self, _bounding_box, _tracker=None):
self.bounding_box = _bounding_box
self.centroid = get_centroid(_bounding_box)
self.area = get_area(_bounding_box)
self.classID = get_classID(_bounding_box)
if _tracker:
self.tracker = _tracker
def update(self, _bounding_box, _type=None, _confidence=None, _tracker=None):
self.bounding_box = _bounding_box
self.type = _type if _type != None else self.type
self.type_confidence = _confidence if _confidence !=None else self.type_confidence
self.centroid = get_centroid(_bounding_box)
self.area = get_area(_bounding_box)
if _tracker:
self.tracker = _tracker
def __init__(self, _bounding_box, _tracker, _type):
self.bounding_box = _bounding_box
self.centroid = get_centroid(_bounding_box)
self.area = get_area(_bounding_box)
self.vehicle_type = _type
self.tracker = _tracker
self.num_consecutive_tracking_failures = 0
self.num_consecutive_detection_failures = 0
self.counted = False
def __init__(self, _bounding_box, _type, _confidence, _tracker):
self.bounding_box = _bounding_box
self.type = _type
self.type_confidence = _confidence
self.centroid = get_centroid(_bounding_box)
self.area = get_area(_bounding_box)
self.tracker = _tracker
self.num_consecutive_tracking_failures = 0
self.num_consecutive_detection_failures = 0
self.counted = False
self.position_first_detected = tuple(self.centroid)
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, 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
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 0, 255), 2)
cv2.putText(frame, 'person' + str(_id), (x, y - 2),
cv2.FONT_HERSHEY_DUPLEX, 0.5, (0, 0, 255), 1,
cv2.LINE_AA)
else:
blob.num_consecutive_tracking_failures += 1
if blob.num_consecutive_tracking_failures >= MAX_CONSECUTIVE_TRACKING_FAILURES:
del blobs[_id]
# rerun detection, add new blobs
if frame_counter >= DETECTION_FRAME_RATE:
boxes = get_bounding_boxes(frame)
for box in boxes:
box_centroid = get_centroid(box)
match_found = False
for _id, blob in blobs.items():
dist = np.linalg.norm(
np.array(box_centroid) - np.array(blob.centroid))
if dist <= 5: # 5 pixels
match_found = True
tracker = cv2.TrackerKCF_create()
tracker.init(frame, tuple(box))
blob.update(box, tracker)
break
if not match_found:
blob_id += 1
tracker = cv2.TrackerCSRT_create()
tracker.init(frame, tuple(box))
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
def update(self, _bounding_box, _tracker=None):
self.bounding_box = _bounding_box
self.centroid = get_centroid(_bounding_box)
if _tracker:
self.tracker = _tracker
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
