blob_brightness = [20, 30, 150, 254]
max_nb_images = 1
# Bfinder parameters
args = dict()
# loop over the frames of the video
t_start = time.time()
# mask
background = btgen.background()
mask = btgen.mask(background)
# Create a bfinder tracker
bfinder = BlobFinder(mask)
bfinder.erode_iter = 2
bfinder.dilate_iter = 2
bfinder.area_lim = [0, 10000]
bfinder.roundness_lim = [0, 1]
bfinder.background_init = 60
bfinder.threshold = 10
bfinder.gaussian_blur = 1
bfinder.skip_filter_contours = False
# Parameter for croping
xmargin = [0, 0]
ymargin = [0, 0]
# Build up an expected histogram
ninitframes = 50
bin_edges = np.arange(1, 255)
ref_cumsum = np.zeros(bin_edges.shape[0] - 1)
for _ in range(bfinder.background_init):
# grab the current frame and initialize the occupied/unoccupied
frame = btgen.randomblob(background, color=blob_brightness[0])[0]
# otherwise, we are reading from a video file
else:
argcam = args["video"]
if args["tra_file"] is None:
if args["folder"] is None:
raise NameError('target trajectory file is not present')
else:
folder_path = os.path.dirname(args["folder"])
args["tra_file"] = os.path.join(folder_path, 'trajectory_v2.tra')
bmarker = BlobFinder(mask, None)
bmarker.erode_iter = 1
bmarker.dilate_iter = 1
bmarker.area_lim = [0, 2000]
bmarker.roundness_lim = [0, 1]
bmarker.threshold = args["threshold"]
bmarker.gaussian_blur = 1
bmarker.skip_filter_contours = True
# Create image for display
scale = 0.4
refresh_time = 0.5
nframe_bg = 100
camera = cv2.VideoCapture(argcam)
background = get_background(camera, nframe_bg)
background = background[..., 0]
camera.release()
camera = cv2.VideoCapture(argcam)