# coding: utf8 # Copyright (c) 2019 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)