def __init__(self):
self.birdDetector = BirdDetector(
frameFilter=FrameFilter(),
backgroundSubtractor=BackgroundSubtractor(),
blobDetector=BlobDetector())
self.activeStageIndex = -1 # By default show last stage
self.profiler = FPSProfiler()
def __init__(self,
video_src,
quiet=True,
invisible=False,
draw_contours=True,
bgsub_thresh=64):
self.quiet = quiet
self.invisible = invisible
self.drawContours = draw_contours
self.threshold = bgsub_thresh
self.drawTracks = True
self.drawFrameNum = False
self.areas = []
# Learn the bg
self.operator = BackgroundSubtractor(2000, self.threshold, True)
self.operator.model_bg2(video_src)
self.track_len = 10
self.detect_interval = 5
self.tracks = []
self.cam = cv2.VideoCapture(video_src)
self.frame_idx = 0
self.arrivals = self.departures = 0
def __init__(self, video_src="", quiet=False, invisible=False, draw_contours=False,
bgsub_thresh=64, drawTracks=False, drawFrameNum=False, drawBoundary=False):
self.quiet = quiet
self.invisible = invisible
self.drawContours = draw_contours
self.threshold = bgsub_thresh
self.drawTracks = drawTracks
self.drawFrameNum = drawFrameNum
self.drawBoundary = drawBoundary
self.areas = []
# Learn the bg
self.operator = BackgroundSubtractor(2000, self.threshold, True)
self.operator.model_bg2(video_src)
self.cam = cv2.VideoCapture(video_src)
self.maxTimeInvisible = 0
self.trackAgeThreshold = 4
self.tracks = []
self.lostTracks = []
self.frame_idx = 0
self.arrivals = 0
self.departures = 0
def bgsub(vidfile_basename, threshold, quiet=False, drawBoxes=True):
operator = BackgroundSubtractor(2000, threshold, True)
# Learn the bg
operator.model_bg2(VIDEO_DIR + vidfile_basename)
tp_t = fp_t = fn_t = p_t = n_t = 0
video = cv2.VideoCapture(VIDEO_DIR + vidfile_basename)
ret, frame = video.read()
frame_num = 0
while ret:
mask = operator.apply(frame)
mask = tools.morph_openclose(mask)
mask_binary = (mask == 255).astype(np.uint8)
gt_filename = "{0}/{1}/{2}.jpg.seg.bmp".format(GT_IMG_DIR, vidfile_basename, frame_num)
if os.path.exists(gt_filename):
if not quiet:
cv2.imshow("Ground truth", cv2.imread(gt_filename) * 255)
tp, fp, fn = compare_response_to_truth(mask_binary, gt_filename)
# print("True Pos: {0}\nFalse Pos: {1}".format(tp, fp))
pos_detected, neg_detected = class_counter.count_posneg(mask_binary)
tp_t += tp
fp_t += fp
fn_t += fn
p_t += pos_detected
n_t += neg_detected
# print("Foreground pixels: {0}\nBackground pixels: {1}".format(pos_detected, neg_detected))
if not quiet:
mask = ((mask == 255) * 255).astype(np.uint8)
cv2.imshow("Mask", mask)
if drawBoxes:
blob_detect(mask, frame)
else:
cv2.imshow("Frame", frame)
ret, frame = video.read()
frame_num += 1
if handle_keys() is 1: break
with np.errstate(invalid='ignore'):
precision = np.float64(tp_t) / (tp_t + fp_t)
recall = np.float64(tp_t) / (tp_t + fn_t)
if np.isinf(precision) or np.isnan(precision):
precision = 1
if np.isinf(recall) or np.isnan(recall):
recall = 1
return precision, recall
def __init__(self, args, main_out_vid_name="", with_video_output=True):
super().__init__(args, main_out_vid_name, with_video_output)
args.datapath = self.datapath
self.background_subtractor = BackgroundSubtractor(args)
