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PaddleSeg
未验证 提交 f5eccbd0 编写于 作者: W wuzewu 提交者： GitHub
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add background replace (#271) 

* update train.py

* update post_quantization.py

* add background replace

* update optical flow

* update humanseg_postprocess

* update humanseg_postprocess

* add background replace doc

* Update video_infer.py

* Update bg_replace.py
Co-authored-by: Nwuzewu <wuzewu@baidu.com>
上级 954ef311 64a3468a
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contrib/HumanSeg/README.md
浏览文件 @ f5eccbd0
...@@ -70,10 +70,30 @@ python video_infer.py --model_dir pretrained_weights/humanseg_lite_inference --v ...@@ -70,10 +70,30 @@ python video_infer.py --model_dir pretrained_weights/humanseg_lite_inference --v
<img src="https://paddleseg.bj.bcebos.com/humanseg/data/video_test.gif" width="20%" height="20%"><img src="https://paddleseg.bj.bcebos.com/humanseg/data/result.gif" width="20%" height="20%"> <img src="https://paddleseg.bj.bcebos.com/humanseg/data/video_test.gif" width="20%" height="20%"><img src="https://paddleseg.bj.bcebos.com/humanseg/data/result.gif" width="20%" height="20%">
根据所选背景进行背景替换，背景可以是一张图片，也可以是一段视频。
```bash
# 通过电脑摄像头进行实时背景替换处理, 也可通过'--background_video_path'传入背景视频
python bg_replace.py --model_dir pretrained_weights/humanseg_lite_inference --background_image_path data/background.jpg
# 对人像视频进行背景替换处理, 也可通过'--background_video_path'传入背景视频
python bg_replace.py --model_dir pretrained_weights/humanseg_lite_inference --video_path data/video_test.mp4 --background_image_path data/background.jpg
# 对单张图像进行背景替换
python bg_replace.py --model_dir pretrained_weights/humanseg_lite_inference --image_path data/human_image.jpg --background_image_path data/background.jpg
```
背景替换结果如下：
<img src="https://paddleseg.bj.bcebos.com/humanseg/data/video_test.gif" width="20%" height="20%"><img src="https://paddleseg.bj.bcebos.com/humanseg/data/bg_replace.gif" width="20%" height="20%">
**NOTE**: **NOTE**:
视频分割处理时间需要几分钟，请耐心等待。 视频分割处理时间需要几分钟，请耐心等待。
提供的模型适用于手机摄像头竖屏拍摄场景，宽屏效果会略差一些。
## 训练 ## 训练
使用下述命令基于与训练模型进行Fine-tuning，请确保选用的模型结构`model_type`与模型参数`pretrained_weights`匹配。 使用下述命令基于与训练模型进行Fine-tuning，请确保选用的模型结构`model_type`与模型参数`pretrained_weights`匹配。
```bash ```bash
......
contrib/HumanSeg/bg_replace.py 0 → 100644
浏览文件 @ f5eccbd0
# coding: utf8
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import os
import os.path as osp
import cv2
import numpy as np
from utils.humanseg_postprocess import postprocess, threshold_mask
import models
import transforms
def parse_args():
parser = argparse.ArgumentParser(description='HumanSeg inference for video')
parser.add_argument(
'--model_dir',
dest='model_dir',
help='Model path for inference',
type=str)
parser.add_argument(
'--image_path',
dest='image_path',
help='Image including human',
type=str,
default=None)
parser.add_argument(
'--background_image_path',
dest='background_image_path',
help='Background image for replacing',
type=str,
default=None)
parser.add_argument(
'--video_path',
dest='video_path',
help='Video path for inference',
type=str,
default=None)
parser.add_argument(
'--background_video_path',
dest='background_video_path',
help='Background video path for replacing',
type=str,
default=None)
parser.add_argument(
'--save_dir',
dest='save_dir',
help='The directory for saving the inference results',
type=str,
default='./output')
parser.add_argument(
"--image_shape",
dest="image_shape",
help="The image shape for net inputs.",
nargs=2,
default=[192, 192],
type=int)
return parser.parse_args()
def predict(img, model, test_transforms):
model.arrange_transform(transforms=test_transforms, mode='test')
img, im_info = test_transforms(img)
img = np.expand_dims(img, axis=0)
result = model.exe.run(
model.test_prog,
feed={'image': img},
fetch_list=list(model.test_outputs.values()))
score_map = result[1]
score_map = np.squeeze(score_map, axis=0)
score_map = np.transpose(score_map, (1, 2, 0))
return score_map, im_info
def recover(img, im_info):
keys = list(im_info.keys())
for k in keys[::-1]:
if k == 'shape_before_resize':
h, w = im_info[k][0], im_info[k][1]
img = cv2.resize(img, (w, h), cv2.INTER_LINEAR)
elif k == 'shape_before_padding':
h, w = im_info[k][0], im_info[k][1]
img = img[0:h, 0:w]
return img
def bg_replace(score_map, img, bg):
h, w, _ = img.shape
bg = cv2.resize(bg, (w, h))
score_map = np.repeat(score_map[:, :, np.newaxis], 3, axis=2)
comb = (score_map * img + (1 - score_map) * bg).astype(np.uint8)
return comb
def infer(args):
resize_h = args.image_shape[1]
resize_w = args.image_shape[0]
test_transforms = transforms.Compose(
[transforms.Resize((resize_w, resize_h)),
transforms.Normalize()])
model = models.load_model(args.model_dir)
if not osp.exists(args.save_dir):
os.makedirs(args.save_dir)
# 图像背景替换
if args.image_path is not None:
if not osp.exists(args.image_path):
raise ('The --image_path is not existed: {}'.format(
args.image_path))
if args.background_image_path is None:
raise ('The --background_image_path is not set. Please set it')
else:
if not osp.exists(args.background_image_path):
raise ('The --background_image_path is not existed: {}'.format(
args.background_image_path))
img = cv2.imread(args.image_path)
score_map, im_info = predict(img, model, test_transforms)
score_map = score_map[:, :, 1]
score_map = recover(score_map, im_info)
bg = cv2.imread(args.background_image_path)
save_name = osp.basename(args.image_path)
save_path = osp.join(args.save_dir, save_name)
result = bg_replace(score_map, img, bg)
cv2.imwrite(save_path, result)
# 视频背景替换，如果提供背景视频则以背景视频作为背景，否则采用提供的背景图片
else:
is_video_bg = False
if args.background_video_path is not None:
if not osp.exists(args.background_video_path):
raise ('The --background_video_path is not existed: {}'.format(
args.background_video_path))
is_video_bg = True
elif args.background_image_path is not None:
if not osp.exists(args.background_image_path):
raise ('The --background_image_path is not existed: {}'.format(
args.background_image_path))
else:
raise (
'Please offer backgound image or video. You should set --backbground_iamge_paht or --background_video_path'
)
disflow = cv2.DISOpticalFlow_create(
cv2.DISOPTICAL_FLOW_PRESET_ULTRAFAST)
prev_gray = np.zeros((resize_h, resize_w), np.uint8)
prev_cfd = np.zeros((resize_h, resize_w), np.float32)
is_init = True
if args.video_path is not None:
print('Please waite. It is computing......')
if not osp.exists(args.video_path):
raise ('The --video_path is not existed: {}'.format(
args.video_path))
cap_video = cv2.VideoCapture(args.video_path)
fps = cap_video.get(cv2.CAP_PROP_FPS)
width = int(cap_video.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(cap_video.get(cv2.CAP_PROP_FRAME_HEIGHT))
save_name = osp.basename(args.video_path)
save_name = save_name.split('.')[0]
save_path = osp.join(args.save_dir, save_name + '.avi')
cap_out = cv2.VideoWriter(
save_path, cv2.VideoWriter_fourcc('M', 'J', 'P', 'G'), fps,
(width, height))
if is_video_bg:
cap_bg = cv2.VideoCapture(args.background_video_path)
frames_bg = cap_bg.get(cv2.CAP_PROP_FRAME_COUNT)
current_frame_bg = 1
else:
img_bg = cv2.imread(args.background_image_path)
while cap_video.isOpened():
ret, frame = cap_video.read()
if ret:
score_map, im_info = predict(frame, model, test_transforms)
cur_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
cur_gray = cv2.resize(cur_gray, (resize_w, resize_h))
score_map = 255 * score_map[:, :, 1]
optflow_map = postprocess(cur_gray, score_map, prev_gray, prev_cfd, \
disflow, is_init)
prev_gray = cur_gray.copy()
prev_cfd = optflow_map.copy()
is_init = False
optflow_map = cv2.GaussianBlur(optflow_map, (3, 3), 0)
optflow_map = threshold_mask(
optflow_map, thresh_bg=0.2, thresh_fg=0.8)
score_map = recover(optflow_map, im_info)
#循环读取背景帧
if is_video_bg:
ret_bg, frame_bg = cap_bg.read()
if ret_bg:
if current_frame_bg == frames_bg:
current_frame_bg = 1
cap_bg.set(cv2.CAP_PROP_POS_FRAMES, 0)
else:
break
current_frame_bg += 1
comb = bg_replace(score_map, frame, frame_bg)
else:
comb = bg_replace(score_map, frame, img_bg)
cap_out.write(comb)
else:
break
if is_video_bg:
cap_bg.release()
cap_video.release()
cap_out.release()
# 当没有输入预测图像和视频的时候，则打开摄像头
else:
cap_video = cv2.VideoCapture(0)
if not cap_video.isOpened():
raise IOError("Error opening video stream or file, "
"--video_path whether existing: {}"
" or camera whether working".format(
args.video_path))
return
if is_video_bg:
cap_bg = cv2.VideoCapture(args.background_video_path)
frames_bg = cap_bg.get(cv2.CAP_PROP_FRAME_COUNT)
current_frame_bg = 1
else:
img_bg = cv2.imread(args.background_image_path)
while cap_video.isOpened():
ret, frame = cap_video.read()
if ret:
score_map, im_info = predict(frame, model, test_transforms)
cur_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
cur_gray = cv2.resize(cur_gray, (resize_w, resize_h))
score_map = 255 * score_map[:, :, 1]
optflow_map = postprocess(cur_gray, score_map, prev_gray, prev_cfd, \
disflow, is_init)
prev_gray = cur_gray.copy()
prev_cfd = optflow_map.copy()
is_init = False
optflow_map = cv2.GaussianBlur(optflow_map, (3, 3), 0)
optflow_map = threshold_mask(
optflow_map, thresh_bg=0.2, thresh_fg=0.8)
score_map = recover(optflow_map, im_info)
#循环读取背景帧
if is_video_bg:
ret_bg, frame_bg = cap_bg.read()
if ret_bg:
if current_frame_bg == frames_bg:
current_frame_bg = 1
cap_bg.set(cv2.CAP_PROP_POS_FRAMES, 0)
else:
break
current_frame_bg += 1
comb = bg_replace(score_map, frame, frame_bg)
else:
comb = bg_replace(score_map, frame, img_bg)
cv2.imshow('HumanSegmentation', comb)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
else:
break
if is_video_bg:
cap_bg.release()
cap_video.release()
if __name__ == "__main__":
args = parse_args()
infer(args)
contrib/HumanSeg/utils/humanseg_postprocess.py
浏览文件 @ f5eccbd0
...@@ -14,13 +14,6 @@ ...@@ -14,13 +14,6 @@
# limitations under the License. # limitations under the License.
import numpy as np import numpy as np
import cv2
import os
def get_round(data):
round = 0.5 if data >= 0 else -0.5
return (int)(data + round)
def human_seg_tracking(pre_gray, cur_gray, prev_cfd, dl_weights, disflow): def human_seg_tracking(pre_gray, cur_gray, prev_cfd, dl_weights, disflow):
...@@ -41,26 +34,28 @@ def human_seg_tracking(pre_gray, cur_gray, prev_cfd, dl_weights, disflow): ...@@ -41,26 +34,28 @@ def human_seg_tracking(pre_gray, cur_gray, prev_cfd, dl_weights, disflow):
is_track = np.zeros_like(pre_gray) is_track = np.zeros_like(pre_gray)
flow_fw = disflow.calc(pre_gray, cur_gray, None) flow_fw = disflow.calc(pre_gray, cur_gray, None)
flow_bw = disflow.calc(cur_gray, pre_gray, None) flow_bw = disflow.calc(cur_gray, pre_gray, None)
for r in range(h): flow_fw = np.round(flow_fw).astype(np.int)
for c in range(w): flow_bw = np.round(flow_bw).astype(np.int)
fxy_fw = flow_fw[r, c] y_list = np.array(range(h))
dx_fw = get_round(fxy_fw[0]) x_list = np.array(range(w))
cur_x = dx_fw + c yv, xv = np.meshgrid(y_list, x_list)
dy_fw = get_round(fxy_fw[1]) yv, xv = yv.T, xv.T
cur_y = dy_fw + r cur_x = xv + flow_fw[:, :, 0]
if cur_x < 0 or cur_x >= w or cur_y < 0 or cur_y >= h: cur_y = yv + flow_fw[:, :, 1]
continue
fxy_bw = flow_bw[cur_y, cur_x] # 超出边界不跟踪
dx_bw = get_round(fxy_bw[0]) not_track = (cur_x < 0) + (cur_x >= w) + (cur_y < 0) + (cur_y >= h)
dy_bw = get_round(fxy_bw[1]) flow_bw[~not_track] = flow_bw[cur_y[~not_track], cur_x[~not_track]]
if ((dy_fw + dy_bw) * (dy_fw + dy_bw) + not_track += (np.square(flow_fw[:, :, 0] + flow_bw[:, :, 0]) +
(dx_fw + dx_bw) * (dx_fw + dx_bw)) >= check_thres: np.square(flow_fw[:, :, 1] + flow_bw[:, :, 1])) >= check_thres
continue track_cfd[cur_y[~not_track], cur_x[~not_track]] = prev_cfd[~not_track]
if abs(dy_fw) <= 0 and abs(dx_fw) <= 0 and abs(dy_bw) <= 0 and abs(
dx_bw) <= 0: is_track[cur_y[~not_track], cur_x[~not_track]] = 1
dl_weights[cur_y, cur_x] = 0.05
is_track[cur_y, cur_x] = 1 not_flow = np.all(
track_cfd[cur_y, cur_x] = prev_cfd[r, c] np.abs(flow_fw) == 0, axis=-1) * np.all(
np.abs(flow_bw) == 0, axis=-1)
dl_weights[cur_y[not_flow], cur_x[not_flow]] = 0.05
return track_cfd, is_track, dl_weights return track_cfd, is_track, dl_weights
...@@ -75,24 +70,27 @@ def human_seg_track_fuse(track_cfd, dl_cfd, dl_weights, is_track): ...@@ -75,24 +70,27 @@ def human_seg_track_fuse(track_cfd, dl_cfd, dl_weights, is_track):
cur_cfd: 光流跟踪图和人像分割结果融合图 cur_cfd: 光流跟踪图和人像分割结果融合图
""" """
fusion_cfd = dl_cfd.copy() fusion_cfd = dl_cfd.copy()
idxs = np.where(is_track > 0) is_track = is_track.astype(np.bool)
for i in range(len(idxs[0])): fusion_cfd[is_track] = dl_weights[is_track] * dl_cfd[is_track] + (
x, y = idxs[0][i], idxs[1][i] 1 - dl_weights[is_track]) * track_cfd[is_track]
dl_score = dl_cfd[x, y] # 确定区域
track_score = track_cfd[x, y] index_certain = ((dl_cfd > 0.9) + (dl_cfd < 0.1)) * is_track
fusion_cfd[x, y] = dl_weights[x, y] * dl_score + ( index_less01 = (dl_weights < 0.1) * index_certain
1 - dl_weights[x, y]) * track_score fusion_cfd[index_less01] = 0.3 * dl_cfd[index_less01] + 0.7 * track_cfd[
if dl_score > 0.9 or dl_score < 0.1: index_less01]
if dl_weights[x, y] < 0.1: index_larger09 = (dl_weights >= 0.1) * index_certain
fusion_cfd[x, y] = 0.3 * dl_score + 0.7 * track_score fusion_cfd[index_larger09] = 0.4 * dl_cfd[index_larger09] + 0.6 * track_cfd[
else: index_larger09]
fusion_cfd[x, y] = 0.4 * dl_score + 0.6 * track_score
else:
fusion_cfd[x, y] = dl_weights[x, y] * dl_score + (
1 - dl_weights[x, y]) * track_score
return fusion_cfd return fusion_cfd
def threshold_mask(img, thresh_bg, thresh_fg):
dst = (img / 255.0 - thresh_bg) / (thresh_fg - thresh_bg)
dst[np.where(dst > 1)] = 1
dst[np.where(dst < 0)] = 0
return dst.astype(np.float32)
def postprocess(cur_gray, scoremap, prev_gray, pre_cfd, disflow, is_init): def postprocess(cur_gray, scoremap, prev_gray, pre_cfd, disflow, is_init):
"""光流优化 """光流优化
Args: Args:
...@@ -105,13 +103,10 @@ def postprocess(cur_gray, scoremap, prev_gray, pre_cfd, disflow, is_init): ...@@ -105,13 +103,10 @@ def postprocess(cur_gray, scoremap, prev_gray, pre_cfd, disflow, is_init):
Returns: Returns:
fusion_cfd : 光流追踪图和预测结果融合图 fusion_cfd : 光流追踪图和预测结果融合图
""" """
height, width = scoremap.shape[0], scoremap.shape[1]
disflow = cv2.DISOpticalFlow_create(cv2.DISOPTICAL_FLOW_PRESET_ULTRAFAST)
h, w = scoremap.shape h, w = scoremap.shape
cur_cfd = scoremap.copy() cur_cfd = scoremap.copy()
if is_init: if is_init:
is_init = False
if h <= 64 or w <= 64: if h <= 64 or w <= 64:
disflow.setFinestScale(1) disflow.setFinestScale(1)
elif h <= 160 or w <= 160: elif h <= 160 or w <= 160:
...@@ -120,18 +115,9 @@ def postprocess(cur_gray, scoremap, prev_gray, pre_cfd, disflow, is_init): ...@@ -120,18 +115,9 @@ def postprocess(cur_gray, scoremap, prev_gray, pre_cfd, disflow, is_init):
disflow.setFinestScale(3) disflow.setFinestScale(3)
fusion_cfd = cur_cfd fusion_cfd = cur_cfd
else: else:
weights = np.ones((w, h), np.float32) * 0.3 weights = np.ones((h, w), np.float32) * 0.3
track_cfd, is_track, weights = human_seg_tracking( track_cfd, is_track, weights = human_seg_tracking(
prev_gray, cur_gray, pre_cfd, weights, disflow) prev_gray, cur_gray, pre_cfd, weights, disflow)
fusion_cfd = human_seg_track_fuse(track_cfd, cur_cfd, weights, is_track) fusion_cfd = human_seg_track_fuse(track_cfd, cur_cfd, weights, is_track)
fusion_cfd = cv2.GaussianBlur(fusion_cfd, (3, 3), 0)
return fusion_cfd return fusion_cfd
def threshold_mask(img, thresh_bg, thresh_fg):
dst = (img / 255.0 - thresh_bg) / (thresh_fg - thresh_bg)
dst[np.where(dst > 1)] = 1
dst[np.where(dst < 0)] = 0
return dst.astype(np.float32)
contrib/HumanSeg/video_infer.py
浏览文件 @ f5eccbd0
...@@ -109,7 +109,7 @@ def video_infer(args): ...@@ -109,7 +109,7 @@ def video_infer(args):
fps = cap.get(cv2.CAP_PROP_FPS) fps = cap.get(cv2.CAP_PROP_FPS)
if args.video_path: if args.video_path:
print('Please waite. It is computing......')
# 用于保存预测结果视频 # 用于保存预测结果视频
if not osp.exists(args.save_dir): if not osp.exists(args.save_dir):
os.makedirs(args.save_dir) os.makedirs(args.save_dir)
...@@ -123,8 +123,8 @@ def video_infer(args): ...@@ -123,8 +123,8 @@ def video_infer(args):
score_map, im_info = predict(frame, model, test_transforms) score_map, im_info = predict(frame, model, test_transforms)
cur_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) cur_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
cur_gray = cv2.resize(cur_gray, (resize_w, resize_h)) cur_gray = cv2.resize(cur_gray, (resize_w, resize_h))
scoremap = 255 * score_map[:, :, 1] score_map = 255 * score_map[:, :, 1]
optflow_map = postprocess(cur_gray, scoremap, prev_gray, prev_cfd, \ optflow_map = postprocess(cur_gray, score_map, prev_gray, prev_cfd, \
disflow, is_init) disflow, is_init)
prev_gray = cur_gray.copy() prev_gray = cur_gray.copy()
prev_cfd = optflow_map.copy() prev_cfd = optflow_map.copy()
...@@ -132,10 +132,11 @@ def video_infer(args): ...@@ -132,10 +132,11 @@ def video_infer(args):
optflow_map = cv2.GaussianBlur(optflow_map, (3, 3), 0) optflow_map = cv2.GaussianBlur(optflow_map, (3, 3), 0)
optflow_map = threshold_mask( optflow_map = threshold_mask(
optflow_map, thresh_bg=0.2, thresh_fg=0.8) optflow_map, thresh_bg=0.2, thresh_fg=0.8)
img_mat = np.repeat(optflow_map[:, :, np.newaxis], 3, axis=2) img_matting = np.repeat(
img_mat = recover(img_mat, im_info) optflow_map[:, :, np.newaxis], 3, axis=2)
bg_im = np.ones_like(img_mat) * 255 img_matting = recover(img_matting, im_info)
comb = (img_mat * frame + (1 - img_mat) * bg_im).astype( bg_im = np.ones_like(img_matting) * 255
comb = (img_matting * frame + (1 - img_matting) * bg_im).astype(
np.uint8) np.uint8)
out.write(comb) out.write(comb)
else: else:
...@@ -150,20 +151,20 @@ def video_infer(args): ...@@ -150,20 +151,20 @@ def video_infer(args):
score_map, im_info = predict(frame, model, test_transforms) score_map, im_info = predict(frame, model, test_transforms)
cur_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) cur_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
cur_gray = cv2.resize(cur_gray, (resize_w, resize_h)) cur_gray = cv2.resize(cur_gray, (resize_w, resize_h))
scoremap = 255 * score_map[:, :, 1] score_map = 255 * score_map[:, :, 1]
optflow_map = postprocess(cur_gray, scoremap, prev_gray, prev_cfd, \ optflow_map = postprocess(cur_gray, score_map, prev_gray, prev_cfd, \
disflow, is_init) disflow, is_init)
prev_gray = cur_gray.copy() prev_gray = cur_gray.copy()
prev_cfd = optflow_map.copy() prev_cfd = optflow_map.copy()
is_init = False is_init = False
# optflow_map = optflow_map/255.0
optflow_map = cv2.GaussianBlur(optflow_map, (3, 3), 0) optflow_map = cv2.GaussianBlur(optflow_map, (3, 3), 0)
optflow_map = threshold_mask( optflow_map = threshold_mask(
optflow_map, thresh_bg=0.2, thresh_fg=0.8) optflow_map, thresh_bg=0.2, thresh_fg=0.8)
img_mat = np.repeat(optflow_map[:, :, np.newaxis], 3, axis=2) img_matting = np.repeat(
img_mat = recover(img_mat, im_info) optflow_map[:, :, np.newaxis], 3, axis=2)
bg_im = np.ones_like(img_mat) * 255 img_matting = recover(img_matting, im_info)
comb = (img_mat * frame + (1 - img_mat) * bg_im).astype( bg_im = np.ones_like(img_matting) * 255
comb = (img_matting * frame + (1 - img_matting) * bg_im).astype(
np.uint8) np.uint8)
cv2.imshow('HumanSegmentation', comb) cv2.imshow('HumanSegmentation', comb)
if cv2.waitKey(1) & 0xFF == ord('q'): if cv2.waitKey(1) & 0xFF == ord('q'):
......
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