def __init__(self, initial_frame, detector, tracker, droi, show_droi, mcdf,
mctf, di, cl_position):
self.frame = initial_frame # current frame of video
self.detector = detector
self.tracker = tracker
self.droi = droi # detection region of interest
self.show_droi = show_droi
self.mcdf = mcdf # maximum consecutive detection failures
self.mctf = mctf # maximum consecutive tracking failures
self.di = di # detection interval
self.cl_position = cl_position # counting line position
self.blobs = OrderedDict()
self.blob_id = 1
self.f_height, self.f_width, _ = self.frame.shape
self.frame_count = 0 # number of frames since last detection
self.vehicle_count = 0 # number of vehicles counted
self.counting_line = None if cl_position == None else get_counting_line(
self.cl_position, self.f_width, self.f_height)
# create blobs from initial frame
droi_frame = get_roi_frame(self.frame, self.droi)
initial_bboxes = get_bounding_boxes(droi_frame, self.detector)
for box in initial_bboxes:
_blob = create_blob(box, frame, self.tracker)
self.blobs[self.blob_id] = _blob
self.blob_id += 1
vehicle_count = 0
# create detection ROI
droi = [(0, 0), (f_width, 0), (f_width, f_height), (0, f_height)]
if args.droi:
droi = []
points = args.droi.replace(' ', '').split('|')
for point_str in points:
point = tuple(map(int, point_str.split(',')))
droi.append(point)
# initialize trackers and create new blobs
droi_frame = get_roi_frame(frame, droi)
initial_bboxes = get_bounding_boxes(droi_frame, detector)
for box in initial_bboxes:
_blob = create_blob(box, frame, tracker)
blobs[blob_id] = _blob
blob_id += 1
while True:
k = cv2.waitKey(1)
if args.iscam or cap.get(cv2.CAP_PROP_POS_FRAMES) + 1 < cap.get(
cv2.CAP_PROP_FRAME_COUNT):
_, frame = cap.read()
nframes = cap.get(cv2.CAP_PROP_POS_FRAMES)
frame_count = cap.get(cv2.CAP_PROP_FRAME_COUNT)
if nframes % 10 == 0 or nframes == 1:
print("Processing {} of {} frames".format(nframes, frame_count))
for _id, blob in list(blobs.items()):
def __init__(self, initial_frame, detector, tracker, droi, show_droi, mcdf, mctf, di, cl_position):
ha = os.path.join('detect-and-count-object','detectors')
path = os.path.join(ha,'yolo')
labelsPath = os.path.join(path,'coco.names')
LABELS = open(labelsPath).read().strip().split("\n")
self.frame = initial_frame # current frame of video
self.detector = detector
self.tracker = tracker
self.droi = droi # detection region of interest
self.show_droi = show_droi
self.mcdf = mcdf # maximum consecutive detection failures
self.mctf = mctf # maximum consecutive tracking failures
self.di = di # detection interval
self.cl_position = cl_position # counting line position
self.countCar = 0
self.countTruck = 0
self.countBus = 0
self.countPerson = 0
self.countBicycle = 0
self.countMotorcycle = 0
self.blobs = OrderedDict()
self.blob_id = 1
self.f_height, self.f_width, _ = self.frame.shape
self.frame_count = 0 # number of frames since last detection
self.vehicle_count = 0 # number of vehicles counted
self.counting_line = get_counting_line(self.cl_position, self.f_width, self.f_height)
# create blobs from initial frame
droi_frame = get_roi_frame(self.frame, self.droi)
initial_bboxes = get_bounding_boxes(droi_frame, self.detector)
for box in initial_bboxes:
#print(box)
if self.detector == 'yolo':
_blob = create_blob(box[0], frame, self.tracker)
style = LABELS[box[1][0]]
self.blobs[self.blob_id] = _blob, style
self.blob_id += 1
else:
_blob = create_blob(box, frame, self.tracker)
self.blobs[self.blob_id] = _blob
self.blob_id += 1
