def vis_frame(frame, im_res, opt, format='coco'):
'''
frame: frame image
im_res: im_res of predictions
format: coco or mpii
return rendered image
'''
kp_num = 26
if len(im_res['result']) > 0:
kp_num = len(im_res['result'][0]['keypoints'])
if kp_num == 17:
if format == 'coco':
l_pair = [
(0, 1),
(0, 2),
(1, 3),
(2, 4), # Head
(5, 6),
(5, 7),
(7, 9),
(6, 8),
(8, 10),
(17, 11),
(17, 12), # Body
(11, 13),
(12, 14),
(13, 15),
(14, 16)
]
p_color = [
(0, 255, 255),
(0, 191, 255),
(0, 255, 102),
(0, 77, 255),
(0, 255, 0),
# Nose, LEye, REye, LEar, REar
(77, 255, 255),
(77, 255, 204),
(77, 204, 255),
(191, 255, 77),
(77, 191, 255),
(191, 255, 77),
# LShoulder, RShoulder, LElbow, RElbow, LWrist, RWrist
(204, 77, 255),
(77, 255, 204),
(191, 77, 255),
(77, 255, 191),
(127, 77, 255),
(77, 255, 127),
(0, 255, 255)
] # LHip, RHip, LKnee, Rknee, LAnkle, RAnkle, Neck
line_color = [(0, 215, 255), (0, 255, 204), (0, 134, 255),
(0, 255, 50), (77, 255, 222), (77, 196, 255),
(77, 135, 255), (191, 255, 77), (77, 255, 77),
(77, 222, 255), (255, 156, 127), (0, 127, 255),
(255, 127, 77), (0, 77, 255), (255, 77, 36)]
elif format == 'mpii':
l_pair = [(8, 9), (11, 12), (11, 10), (2, 1), (1, 0), (13, 14),
(14, 15), (3, 4), (4, 5), (8, 7), (7, 6), (6, 2), (6, 3),
(8, 12), (8, 13)]
p_color = [
PURPLE, BLUE, BLUE, RED, RED, BLUE, BLUE, RED, RED, PURPLE,
PURPLE, PURPLE, RED, RED, BLUE, BLUE
]
line_color = [
PURPLE, BLUE, BLUE, RED, RED, BLUE, BLUE, RED, RED, PURPLE,
PURPLE, RED, RED, BLUE, BLUE
]
else:
raise NotImplementedError
elif kp_num == 136:
l_pair = [
(0, 1),
(0, 2),
(1, 3),
(2, 4), # Head
(5, 18),
(6, 18),
(5, 7),
(7, 9),
(6, 8),
(8, 10), # Body
(17, 18),
(18, 19),
(19, 11),
(19, 12),
(11, 13),
(12, 14),
(13, 15),
(14, 16),
(20, 24),
(21, 25),
(23, 25),
(22, 24),
(15, 24),
(16, 25), # Foot
(26, 27),
(27, 28),
(28, 29),
(29, 30),
(30, 31),
(31, 32),
(32, 33),
(33, 34),
(34, 35),
(35, 36),
(36, 37),
(37, 38),
# Face
(38, 39),
(39, 40),
(40, 41),
(41, 42),
(43, 44),
(44, 45),
(45, 46),
(46, 47),
(48, 49),
(49, 50),
(50, 51),
(51, 52),
# Face
(53, 54),
(54, 55),
(55, 56),
(57, 58),
(58, 59),
(59, 60),
(60, 61),
(62, 63),
(63, 64),
(64, 65),
(65, 66),
(66, 67),
# Face
(68, 69),
(69, 70),
(70, 71),
(71, 72),
(72, 73),
(74, 75),
(75, 76),
(76, 77),
(77, 78),
(78, 79),
(79, 80),
(80, 81),
# Face
(81, 82),
(82, 83),
(83, 84),
(84, 85),
(85, 86),
(86, 87),
(87, 88),
(88, 89),
(89, 90),
(90, 91),
(91, 92),
(92, 93),
# Face
(94, 95),
(95, 96),
(96, 97),
(97, 98),
(94, 99),
(99, 100),
(100, 101),
(101, 102),
(94, 103),
(103, 104),
(104, 105),
# LeftHand
(105, 106),
(94, 107),
(107, 108),
(108, 109),
(109, 110),
(94, 111),
(111, 112),
(112, 113),
(113, 114),
# LeftHand
(115, 116),
(116, 117),
(117, 118),
(118, 119),
(115, 120),
(120, 121),
(121, 122),
(122, 123),
(115, 124),
(124, 125),
# RightHand
(125, 126),
(126, 127),
(115, 128),
(128, 129),
(129, 130),
(130, 131),
(115, 132),
(132, 133),
(133, 134),
(134, 135)
# RightHand
]
p_color = [
(0, 255, 255),
(0, 191, 255),
(0, 255, 102),
(0, 77, 255),
(0, 255, 0),
# Nose, LEye, REye, LEar, REar
(77, 255, 255),
(77, 255, 204),
(77, 204, 255),
(191, 255, 77),
(77, 191, 255),
(191, 255, 77),
# LShoulder, RShoulder, LElbow, RElbow, LWrist, RWrist
(204, 77, 255),
(77, 255, 204),
(191, 77, 255),
(77, 255, 191),
(127, 77, 255),
(77, 255, 127),
# LHip, RHip, LKnee, Rknee, LAnkle, RAnkle, Neck
(77, 255, 255),
(0, 255, 255),
(77, 204, 255), # head, neck, shoulder
(0, 255, 255),
(0, 191, 255),
(0, 255, 102),
(0, 77, 255),
(0, 255, 0),
(77, 255, 255)
] # foot
line_color = [(0, 215, 255), (0, 255, 204), (0, 134, 255),
(0, 255, 50), (0, 255, 102), (77, 255, 222),
(77, 196, 255), (77, 135, 255), (191, 255, 77),
(77, 255, 77), (77, 191, 255), (204, 77, 255),
(77, 222, 255), (255, 156, 127), (0, 127, 255),
(255, 127, 77), (0, 77, 255), (255, 77, 36),
(0, 77, 255), (0, 77, 255), (0, 77, 255), (0, 77, 255),
(255, 156, 127), (255, 156, 127)]
elif kp_num == 26:
l_pair = [
(0, 1),
(0, 2),
(1, 3),
(2, 4), # Head
(5, 18),
(6, 18),
(5, 7),
(7, 9),
(6, 8),
(8, 10), # Body
(17, 18),
(18, 19),
(19, 11),
(19, 12),
(11, 13),
(12, 14),
(13, 15),
(14, 16),
(20, 24),
(21, 25),
(23, 25),
(22, 24),
(15, 24),
(16, 25), # Foot
]
p_color = [
(0, 255, 255),
(0, 191, 255),
(0, 255, 102),
(0, 77, 255),
(0, 255, 0),
# Nose, LEye, REye, LEar, REar
(77, 255, 255),
(77, 255, 204),
(77, 204, 255),
(191, 255, 77),
(77, 191, 255),
(191, 255, 77),
# LShoulder, RShoulder, LElbow, RElbow, LWrist, RWrist
(204, 77, 255),
(77, 255, 204),
(191, 77, 255),
(77, 255, 191),
(127, 77, 255),
(77, 255, 127),
# LHip, RHip, LKnee, Rknee, LAnkle, RAnkle, Neck
(77, 255, 255),
(0, 255, 255),
(77, 204, 255), # head, neck, shoulder
(0, 255, 255),
(0, 191, 255),
(0, 255, 102),
(0, 77, 255),
(0, 255, 0),
(77, 255, 255)
] # foot
line_color = [(0, 215, 255), (0, 255, 204), (0, 134, 255),
(0, 255, 50), (0, 255, 102), (77, 255, 222),
(77, 196, 255), (77, 135, 255), (191, 255, 77),
(77, 255, 77), (77, 191, 255), (204, 77, 255),
(77, 222, 255), (255, 156, 127), (0, 127, 255),
(255, 127, 77), (0, 77, 255), (255, 77, 36),
(0, 77, 255), (0, 77, 255), (0, 77, 255), (0, 77, 255),
(255, 156, 127), (255, 156, 127)]
else:
raise NotImplementedError
# im_name = os.path.basename(im_res['imgname'])
img = frame.copy()
height, width = img.shape[:2]
for human in im_res['result']:
part_line = {}
kp_preds = human['keypoints']
kp_scores = human['kp_score']
if kp_num == 17:
kp_preds = torch.cat(
(kp_preds,
torch.unsqueeze((kp_preds[5, :] + kp_preds[6, :]) / 2, 0)))
kp_scores = torch.cat(
(kp_scores,
torch.unsqueeze((kp_scores[5, :] + kp_scores[6, :]) / 2, 0)))
if opt.tracking:
color = get_color_fast(int(abs(human['idx'])))
else:
color = BLUE
# Draw bboxes
if opt.showbox:
if 'box' in human.keys():
bbox = human['box']
bbox = [
bbox[0], bbox[0] + bbox[2], bbox[1], bbox[1] + bbox[3]
] # xmin,xmax,ymin,ymax
else:
from trackers.PoseFlow.poseflow_infer import get_box
keypoints = []
for n in range(kp_scores.shape[0]):
keypoints.append(float(kp_preds[n, 0]))
keypoints.append(float(kp_preds[n, 1]))
keypoints.append(float(kp_scores[n]))
bbox = get_box(keypoints, height, width)
# color = get_color_fast(int(abs(human['idx'][0][0])))
cv2.rectangle(img, (int(bbox[0]), int(bbox[2])),
(int(bbox[1]), int(bbox[3])), color, 1)
if opt.tracking:
cv2.putText(img, str(human['idx']),
(int(bbox[0]), int(
(bbox[2] + 26))), DEFAULT_FONT, 1, BLACK, 2)
# Draw keypoints
for n in range(kp_scores.shape[0]):
if kp_scores[n] <= 0.4:
continue
cor_x, cor_y = int(kp_preds[n, 0]), int(kp_preds[n, 1])
part_line[n] = (int(cor_x), int(cor_y))
bg = img.copy()
if n < len(p_color):
if opt.tracking:
cv2.circle(bg, (int(cor_x), int(cor_y)), 2, color, -1)
else:
cv2.circle(bg, (int(cor_x), int(cor_y)), 2, p_color[n], -1)
else:
cv2.circle(bg, (int(cor_x), int(cor_y)), 1, (255, 255, 255), 2)
# Now create a mask of logo and create its inverse mask also
transparency = float(max(0, min(1, kp_scores[n])))
img = cv2.addWeighted(bg, transparency, img, 1 - transparency, 0)
# Draw limbs
for i, (start_p, end_p) in enumerate(l_pair):
if start_p in part_line and end_p in part_line:
start_xy = part_line[start_p]
end_xy = part_line[end_p]
bg = img.copy()
X = (start_xy[0], end_xy[0])
Y = (start_xy[1], end_xy[1])
mX = np.mean(X)
mY = np.mean(Y)
length = ((Y[0] - Y[1])**2 + (X[0] - X[1])**2)**0.5
angle = math.degrees(math.atan2(Y[0] - Y[1], X[0] - X[1]))
if (start_p, end_p) == (18, 19):
print(Y[0], "--", X[0], "==", angle, " ")
stickwidth = (kp_scores[start_p] + kp_scores[end_p]) + 1
polygon = cv2.ellipse2Poly((int(mX), int(mY)),
(int(length / 2), int(stickwidth)),
int(angle), 0, 360, 1)
if i < len(line_color):
if opt.tracking:
cv2.fillConvexPoly(bg, polygon, color)
else:
cv2.fillConvexPoly(bg, polygon, line_color[i])
else:
cv2.line(bg, start_xy, end_xy, (255, 255, 255), 1)
fontpath = "/home/hyx/STHeiti Medium.ttc"
font = ImageFont.truetype(fontpath, 32)
img_pil = Image.fromarray(bg)
draw = ImageDraw.Draw(img_pil)
if start_p == 17:
if abs(angle) <= 15.0 or abs(angle) >= 150.0:
draw.text((X[0] - 30, Y[0] - 30),
"倒地",
font=font,
fill=(0, 0, 255))
else:
draw.text((X[0] - 30, Y[0] - 30),
"正常",
font=font,
fill=(255, 0, 0))
bg = np.array(img_pil)
transparency = float(
max(
0,
min(
1, 0.5 * (kp_scores[start_p] + kp_scores[end_p]) -
0.1)))
img = cv2.addWeighted(bg, transparency, img, 1 - transparency,
0)
return img
def vis_frame_fast(frame, im_res, opt, format='coco'):
'''
frame: frame image
im_res: im_res of predictions
format: coco or mpii
return rendered image
'''
kp_num = 17
if len(im_res['result']) > 0:
kp_num = len(im_res['result'][0]['keypoints'])
if kp_num == 17:
if format == 'coco':
l_pair = [
(0, 1), (0, 2), (1, 3), (2, 4), # Head
(5, 6), (5, 7), (7, 9), (6, 8), (8, 10),
(17, 11), (17, 12), # Body
(11, 13), (12, 14), (13, 15), (14, 16)
]
p_color = [(0, 255, 255), (0, 191, 255), (0, 255, 102), (0, 77, 255), (0, 255, 0), # Nose, LEye, REye, LEar, REar
(77, 255, 255), (77, 255, 204), (77, 204, 255), (191, 255, 77), (77, 191, 255), (191, 255, 77), # LShoulder, RShoulder, LElbow, RElbow, LWrist, RWrist
(204, 77, 255), (77, 255, 204), (191, 77, 255), (77, 255, 191), (127, 77, 255), (77, 255, 127), (0, 255, 255)] # LHip, RHip, LKnee, Rknee, LAnkle, RAnkle, Neck
line_color = [(0, 215, 255), (0, 255, 204), (0, 134, 255), (0, 255, 50),
(77, 255, 222), (77, 196, 255), (77, 135, 255), (191, 255, 77), (77, 255, 77),
(77, 222, 255), (255, 156, 127),
(0, 127, 255), (255, 127, 77), (0, 77, 255), (255, 77, 36)]
elif format == 'mpii':
l_pair = [
(8, 9), (11, 12), (11, 10), (2, 1), (1, 0),
(13, 14), (14, 15), (3, 4), (4, 5),
(8, 7), (7, 6), (6, 2), (6, 3), (8, 12), (8, 13)
]
p_color = [PURPLE, BLUE, BLUE, RED, RED, BLUE, BLUE, RED, RED, PURPLE, PURPLE, PURPLE, RED, RED, BLUE, BLUE]
else:
raise NotImplementedError
elif kp_num == 136:
l_pair = [
(0, 1), (0, 2), (1, 3), (2, 4), # Head
(5, 18), (6, 18), (5, 7), (7, 9), (6, 8), (8, 10),# Body
(17, 18), (18, 19), (19, 11), (19, 12),
(11, 13), (12, 14), (13, 15), (14, 16),
(20, 24), (21, 25), (23, 25), (22, 24), (15, 24), (16, 25),# Foot
(26, 27),(27, 28),(28, 29),(29, 30),(30, 31),(31, 32),(32, 33),(33, 34),(34, 35),(35, 36),(36, 37),(37, 38),#Face
(38, 39),(39, 40),(40, 41),(41, 42),(43, 44),(44, 45),(45, 46),(46, 47),(48, 49),(49, 50),(50, 51),(51, 52),#Face
(53, 54),(54, 55),(55, 56),(57, 58),(58, 59),(59, 60),(60, 61),(62, 63),(63, 64),(64, 65),(65, 66),(66, 67),#Face
(68, 69),(69, 70),(70, 71),(71, 72),(72, 73),(74, 75),(75, 76),(76, 77),(77, 78),(78, 79),(79, 80),(80, 81),#Face
(81, 82),(82, 83),(83, 84),(84, 85),(85, 86),(86, 87),(87, 88),(88, 89),(89, 90),(90, 91),(91, 92),(92, 93),#Face
(94,95),(95,96),(96,97),(97,98),(94,99),(99,100),(100,101),(101,102),(94,103),(103,104),(104,105),#LeftHand
(105,106),(94,107),(107,108),(108,109),(109,110),(94,111),(111,112),(112,113),(113,114),#LeftHand
(115,116),(116,117),(117,118),(118,119),(115,120),(120,121),(121,122),(122,123),(115,124),(124,125),#RightHand
(125,126),(126,127),(115,128),(128,129),(129,130),(130,131),(115,132),(132,133),(133,134),(134,135)#RightHand
]
p_color = [(0, 255, 255), (0, 191, 255), (0, 255, 102), (0, 77, 255), (0, 255, 0), # Nose, LEye, REye, LEar, REar
(77, 255, 255), (77, 255, 204), (77, 204, 255), (191, 255, 77), (77, 191, 255), (191, 255, 77), # LShoulder, RShoulder, LElbow, RElbow, LWrist, RWrist
(204, 77, 255), (77, 255, 204), (191, 77, 255), (77, 255, 191), (127, 77, 255), (77, 255, 127), # LHip, RHip, LKnee, Rknee, LAnkle, RAnkle, Neck
(77, 255, 255), (0, 255, 255), (77, 204, 255), # head, neck, shoulder
(0, 255, 255), (0, 191, 255), (0, 255, 102), (0, 77, 255), (0, 255, 0), (77, 255, 255)] # foot
line_color = [(0, 215, 255), (0, 255, 204), (0, 134, 255), (0, 255, 50),
(0, 255, 102), (77, 255, 222), (77, 196, 255), (77, 135, 255), (191, 255, 77), (77, 255, 77),
(77, 191, 255), (204, 77, 255), (77, 222, 255), (255, 156, 127),
(0, 127, 255), (255, 127, 77), (0, 77, 255), (255, 77, 36),
(0, 77, 255), (0, 77, 255), (0, 77, 255), (0, 77, 255), (255, 156, 127), (255, 156, 127)]
elif kp_num == 26:
l_pair = [
(0, 1), (0, 2), (1, 3), (2, 4), # Head
(5, 18), (6, 18), (5, 7), (7, 9), (6, 8), (8, 10),# Body
(17, 18), (18, 19), (19, 11), (19, 12),
(11, 13), (12, 14), (13, 15), (14, 16),
(20, 24), (21, 25), (23, 25), (22, 24), (15, 24), (16, 25),# Foot
]
p_color = [(0, 255, 255), (0, 191, 255), (0, 255, 102), (0, 77, 255), (0, 255, 0), # Nose, LEye, REye, LEar, REar
(77, 255, 255), (77, 255, 204), (77, 204, 255), (191, 255, 77), (77, 191, 255), (191, 255, 77), # LShoulder, RShoulder, LElbow, RElbow, LWrist, RWrist
(204, 77, 255), (77, 255, 204), (191, 77, 255), (77, 255, 191), (127, 77, 255), (77, 255, 127), # LHip, RHip, LKnee, Rknee, LAnkle, RAnkle, Neck
(77, 255, 255), (0, 255, 255), (77, 204, 255), # head, neck, shoulder
(0, 255, 255), (0, 191, 255), (0, 255, 102), (0, 77, 255), (0, 255, 0), (77, 255, 255)] # foot
line_color = [(0, 215, 255), (0, 255, 204), (0, 134, 255), (0, 255, 50),
(0, 255, 102), (77, 255, 222), (77, 196, 255), (77, 135, 255), (191, 255, 77), (77, 255, 77),
(77, 191, 255), (204, 77, 255), (77, 222, 255), (255, 156, 127),
(0, 127, 255), (255, 127, 77), (0, 77, 255), (255, 77, 36),
(0, 77, 255), (0, 77, 255), (0, 77, 255), (0, 77, 255), (255, 156, 127), (255, 156, 127)]
else:
raise NotImplementedError
# im_name = os.path.basename(im_res['imgname'])
img = frame.copy()
height, width = img.shape[:2]
for human in im_res['result']:
part_line = {}
kp_preds = human['keypoints']
kp_scores = human['kp_score']
if kp_num == 17:
kp_preds = torch.cat((kp_preds, torch.unsqueeze((kp_preds[5, :] + kp_preds[6, :]) / 2, 0)))
kp_scores = torch.cat((kp_scores, torch.unsqueeze((kp_scores[5, :] + kp_scores[6, :]) / 2, 0)))
if opt.pose_track or opt.tracking:
color = get_color_fast(int(abs(human['idx'])))
else:
color = BLUE
# Draw bboxes
if opt.showbox:
if 'box' in human.keys():
bbox = human['box']
bbox = [bbox[0], bbox[0]+bbox[2], bbox[1], bbox[1]+bbox[3]]#xmin,xmax,ymin,ymax
else:
from trackers.PoseFlow.poseflow_infer import get_box
keypoints = []
for n in range(kp_scores.shape[0]):
keypoints.append(float(kp_preds[n, 0]))
keypoints.append(float(kp_preds[n, 1]))
keypoints.append(float(kp_scores[n]))
bbox = get_box(keypoints, height, width)
cv2.rectangle(img, (int(bbox[0]), int(bbox[2])), (int(bbox[1]), int(bbox[3])), color, 2)
if opt.tracking:
cv2.putText(img, str(human['idx']), (int(bbox[0]), int((bbox[2] + 26))), DEFAULT_FONT, 1, BLACK, 2)
# Draw keypoints
for n in range(kp_scores.shape[0]):
if kp_scores[n] <= 0.4:
continue
cor_x, cor_y = int(kp_preds[n, 0]), int(kp_preds[n, 1])
part_line[n] = (cor_x, cor_y)
if n < len(p_color):
if opt.tracking:
cv2.circle(img, (cor_x, cor_y), 3, color, -1)
else:
cv2.circle(img, (cor_x, cor_y), 3, p_color[n], -1)
else:
cv2.circle(img, (cor_x, cor_y), 1, (255,255,255), 2)
# Draw limbs
for i, (start_p, end_p) in enumerate(l_pair):
if start_p in part_line and end_p in part_line:
start_xy = part_line[start_p]
end_xy = part_line[end_p]
if i < len(line_color):
if opt.tracking:
cv2.line(img, start_xy, end_xy, color, 2 * int(kp_scores[start_p] + kp_scores[end_p]) + 1)
else:
cv2.line(img, start_xy, end_xy, line_color[i], 2 * int(kp_scores[start_p] + kp_scores[end_p]) + 1)
else:
cv2.line(img, start_xy, end_xy, (255,255,255), 1)
return img
def vis_frame_bbox(frame, im_res, opt, name_image, format='coco'):
'''
frame: frame image
im_res: im_res of predictions
format: coco or mpii
return rendered image
'''
print(name_image)
kp_num = 17
if len(im_res['result']) > 0:
kp_num = len(im_res['result'][0]['keypoints'])
img = frame.copy()
height, width = img.shape[:2]
incr = 0
for human in im_res[
'result']: #Incrémentation sur le nombre de résultat donc le nombre de bbox
part_line = {}
kp_preds = human['keypoints']
kp_scores = human['kp_score']
if kp_num == 17:
kp_preds = torch.cat(
(kp_preds,
torch.unsqueeze((kp_preds[5, :] + kp_preds[6, :]) / 2, 0)))
kp_scores = torch.cat(
(kp_scores,
torch.unsqueeze((kp_scores[5, :] + kp_scores[6, :]) / 2, 0)))
if opt.tracking:
color = get_color_fast(int(abs(human['idx'])))
else:
color = BLUE
treshold_value = opt.thr_bbox
if (kp_scores[0] >= treshold_value
and kp_scores[-1] >= treshold_value):
# Draw bboxes
if 'box' in human.keys():
bbox = human['box']
bbox = [
bbox[0], bbox[0] + bbox[2], bbox[1], bbox[1] + bbox[3]
] #xmin,xmax,ymin,ymax
else:
from trackers.PoseFlow.poseflow_infer import get_box
keypoints = []
for n in range(kp_scores.shape[0]):
keypoints.append(float(kp_preds[n, 0]))
keypoints.append(float(kp_preds[n, 1]))
keypoints.append(float(kp_scores[n]))
bbox = get_box(keypoints, height, width)
# color = get_color_fast(int(abs(human['idx'][0][0])))
position = [
int(bbox[0]),
int(bbox[2]),
int(bbox[1]),
int(bbox[3])
] #xmin,ymin,xmax,ymax
for number in range(
len(position)): # check if there is some negative value
if position[number] < 0:
position[number] = 0
len_y, len_x = position[3] - position[1], position[2] - position[0]
# new_image = np.zeros((len_y, len_x, 3), dtype=np.uint8)
# new_image[:, :, :] = img[ position[1]:position[3] , position[0]:position[2] , 0:3]
new_image = []
new_image = img[position[1]:position[3], position[0]:position[2],
0:3]
os.makedirs(opt.outputpath + 'crop/', exist_ok=True)
new_name = name_image[:-4] + '_' + str(incr) + '.png'
## SANS CONDITION SUR LA TAILLE DE L'IMAGE
# cv2.imwrite(opt.outputpath + 'crop/' + new_name,new_image)
# incr+=1
## CONDITION SUR LA TAILLE DE L'IMAGE
# condition sur la hauteur ou la largeur : valeur 30%
if int((new_image.shape[0] * 100) /
img.shape[0]) > opt.size_bbox or int(
(new_image.shape[1] * 100) /
img.shape[1]) > opt.size_bbox:
cv2.imwrite(opt.outputpath + 'crop/' + new_name, new_image)
incr += 1
