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 cascade_detect(vidfile_basename, min_neighbors, quiet=False):
cascade = cv2.CascadeClassifier("../classifier/v2verticaldown/cascade.xml")
cascade2 = cv2.CascadeClassifier("../classifier/v2leftside/cascade.xml")
print(quiet)
tp_t = fp_t = fn_t = 0
video = cv2.VideoCapture(VIDEO_DIR + vidfile_basename)
ret, frame = video.read()
frame_h, frame_w, _ = frame.shape
frame_num = 0
while ret:
gt_filename = "{0}/{1}/{2}.jpg.seg.bmp".format(GT_IMG_DIR, vidfile_basename,
frame_num)
bees = cascade.detectMultiScale(frame, minNeighbors=min_neighbors, scaleFactor=1.025)
mask_binary = np.zeros((frame_h, frame_w))
for x, y, w, h in bees:
cv2.rectangle(frame,(x,y),(x+w,y+h),(0,0,255),2)
mask_binary[y:y+h, x:x+w] = 1
if not quiet:
cv2.imshow("Detections", mask_binary * 255)
cv2.imshow("Frame- {0}".format(min_neighbors), frame)
if os.path.exists(gt_filename):
tp, fp, fn = compare_response_to_truth(mask_binary, gt_filename, cascade=True)
tp_t += tp
fp_t += fp
fn_t += fn
# precision, recall = area_precision_recall(mask_binary, gt_filename)
if not quiet:
cv2.imshow("Ground truth", cv2.imread(gt_filename) * 255)
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