import os import cv2 import argparse import glob import numpy as np from utils.general import imwrite from utils.restoration_helper import RestoreHelper if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('-i', '--input_path', type=str, default='./pic', help='Input image, video or folder. Default: inputs/whole_imgs') parser.add_argument('-o', '--output_path', type=str, default=None, help='Output folder. Default: results/_') parser.add_argument('-s', '--upscale', type=int, default=1, help='The final upsampling scale of the image. Default: 1') parser.add_argument('--detect_model', type=str, default='yolov5l-face.axmodel', help='face detection model path') parser.add_argument('--restore_model', type=str, default='codeformer.axmodel', help='face restore model path') parser.add_argument('--bg_model', type=str, default='realesrgan-x2.axmodel', help='background upsampler model path') parser.add_argument('--has_aligned', action='store_true', help='Input are cropped and aligned faces. Default: False') parser.add_argument('--only_center_face', action='store_true', help='Only restore the center face. Default: False') parser.add_argument('--draw_box', action='store_true', help='Draw the bounding box for the detected faces. Default: False') parser.add_argument('--suffix', type=str, default=None, help='Suffix of the restored faces. Default: None') args = parser.parse_args() # ------------------------ input & output ------------------------ if args.input_path.endswith(('jpg', 'jpeg', 'png', 'JPG', 'JPEG', 'PNG')): # input single img path input_img_list = [args.input_path] result_root = f'results/test_img_{args.upscale}' else: # input img folder if args.input_path.endswith('/'): # solve when path ends with / args.input_path = args.input_path[:-1] # scan all the jpg and png images input_img_list = sorted(glob.glob(os.path.join(args.input_path, '*.[jpJP][pnPN]*[gG]'))) result_root = 'results' if not args.output_path is None: # set output path result_root = args.output_path test_img_num = len(input_img_list) if test_img_num == 0: raise FileNotFoundError('No input image/video is found...\n' '\tNote that --input_path for video should end with .mp4|.mov|.avi') # ------------------ set up FaceRestoreHelper ------------------- restore_helper = RestoreHelper( args.upscale, face_size=512, crop_ratio=(1, 1), det_model=args.detect_model, res_model=args.restore_model, bg_model=args.bg_model, save_ext='png', use_parse=True ) # -------------------- start to processing --------------------- for i, img_path in enumerate(input_img_list): # clean all the intermediate results to process the next image restore_helper.clean_all() if isinstance(img_path, str): img_name = os.path.basename(img_path) basename, ext = os.path.splitext(img_name) print(f'[{i+1}/{test_img_num}] Processing: {img_name}') img = cv2.imread(img_path, cv2.IMREAD_COLOR) restore_helper.read_image(img) # get face landmarks for each face num_det_faces = restore_helper.get_face_landmarks_5( only_center_face=args.only_center_face, resize=640, eye_dist_threshold=5) print(f'\tdetect {num_det_faces} faces') # align and warp each face restore_helper.align_warp_face() # face restoration for each cropped face for idx, cropped_face in enumerate(restore_helper.cropped_faces): # prepare data cropped_face_t = (cropped_face.astype(np.float32) / 255.0) * 2.0 - 1.0 cropped_face_t = np.transpose( np.expand_dims(np.ascontiguousarray(cropped_face_t[...,::-1]), axis=0), (0,3,1,2) ) #print('cropped_face_t', cropped_face_t.shape) try: ort_outs = restore_helper.rs_sessison.run( restore_helper.rs_output, {restore_helper.rs_input: cropped_face_t} ) restored_face = ort_outs[0] restored_face = (restored_face.squeeze().transpose(1, 2, 0) * 0.5 + 0.5) * 255 restored_face = np.clip(restored_face[...,::-1], 0, 255).astype(np.uint8) except Exception as error: print(f'\tFailed inference for CodeFormer: {error}') restored_face = (cropped_face_t.squeeze().transpose(1, 2, 0) * 0.5 + 0.5) * 255 restored_face = np.clip(restored_face, 0, 255).astype(np.uint8) restored_face = restored_face.astype('uint8') restore_helper.add_restored_face(restored_face, cropped_face) # paste_back if not args.has_aligned: # upsample the background # Now only support RealESRGAN for upsampling background bg_img = restore_helper.background_upsampling(img) restore_helper.get_inverse_affine(None) # paste each restored face to the input image restored_img = restore_helper.paste_faces_to_input_image(upsample_img=bg_img, draw_box=args.draw_box) # save faces # for idx, (cropped_face, restored_face) in enumerate(zip(face_helper.cropped_faces, face_helper.restored_faces)): # # save cropped face # if not args.has_aligned: # save_crop_path = os.path.join(result_root, 'cropped_faces', f'{basename}_{idx:02d}.png') # imwrite(cropped_face, save_crop_path) # # save restored face # if args.has_aligned: # save_face_name = f'{basename}.png' # else: # save_face_name = f'{basename}_{idx:02d}.png' # if args.suffix is not None: # save_face_name = f'{save_face_name[:-4]}_{args.suffix}.png' # save_restore_path = os.path.join(result_root, 'restored_faces', save_face_name) # imwrite(restored_face, save_restore_path) # save restored img if not args.has_aligned and restored_img is not None: if args.suffix is not None: basename = f'{basename}_{args.suffix}' save_restore_path = os.path.join(result_root, 'final_results', f'{basename}.png') imwrite(restored_img, save_restore_path)