Delete inference_gfpgan.py

inference_gfpgan.py

@@ -1,116 +0,0 @@
-import argparse
-import cv2
-import glob
-import numpy as np
-import os
-import torch
-from basicsr.utils import imwrite
-
-from gfpgan import GFPGANer
-
-
-def main():
-    """Inference demo for GFPGAN.
-    """
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--upscale', type=int, default=2, help='The final upsampling scale of the image')
-    parser.add_argument('--arch', type=str, default='clean', help='The GFPGAN architecture. Option: clean | original')
-    parser.add_argument('--channel', type=int, default=2, help='Channel multiplier for large networks of StyleGAN2')
-    parser.add_argument('--model_path', type=str, default='experiments/pretrained_models/GFPGANCleanv1-NoCE-C2.pth')
-    parser.add_argument('--bg_upsampler', type=str, default='realesrgan', help='background upsampler')
-    parser.add_argument(
-        '--bg_tile', type=int, default=400, help='Tile size for background sampler, 0 for no tile during testing')
-    parser.add_argument('--test_path', type=str, default='inputs/whole_imgs', help='Input folder')
-    parser.add_argument('--suffix', type=str, default=None, help='Suffix of the restored faces')
-    parser.add_argument('--only_center_face', action='store_true', help='Only restore the center face')
-    parser.add_argument('--aligned', action='store_true', help='Input are aligned faces')
-    parser.add_argument('--paste_back', action='store_false', help='Paste the restored faces back to images')
-    parser.add_argument('--save_root', type=str, default='results', help='Path to save root')
-    parser.add_argument(
-        '--ext',
-        type=str,
-        default='auto',
-        help='Image extension. Options: auto | jpg | png, auto means using the same extension as inputs')
-    args = parser.parse_args()
-
-    args = parser.parse_args()
-    if args.test_path.endswith('/'):
-        args.test_path = args.test_path[:-1]
-    os.makedirs(args.save_root, exist_ok=True)
-
-    # background upsampler
-    if args.bg_upsampler == 'realesrgan':
-        if not torch.cuda.is_available():  # CPU
-            import warnings
-            warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. '
-                          'If you really want to use it, please modify the corresponding codes.')
-            bg_upsampler = None
-        else:
-            from basicsr.archs.rrdbnet_arch import RRDBNet
-            from realesrgan import RealESRGANer
-            model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=2)
-            bg_upsampler = RealESRGANer(
-                scale=2,
-                model_path='https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth',
-                model=model,
-                tile=args.bg_tile,
-                tile_pad=10,
-                pre_pad=0,
-                half=True)  # need to set False in CPU mode
-    else:
-        bg_upsampler = None
-    # set up GFPGAN restorer
-    restorer = GFPGANer(
-        model_path=args.model_path,
-        upscale=args.upscale,
-        arch=args.arch,
-        channel_multiplier=args.channel,
-        bg_upsampler=bg_upsampler)
-
-    img_list = sorted(glob.glob(os.path.join(args.test_path, '*')))
-    for img_path in img_list:
-        # read image
-        img_name = os.path.basename(img_path)
-        print(f'Processing {img_name} ...')
-        basename, ext = os.path.splitext(img_name)
-        input_img = cv2.imread(img_path, cv2.IMREAD_COLOR)
-
-        # restore faces and background if necessary
-        cropped_faces, restored_faces, restored_img = restorer.enhance(
-            input_img, has_aligned=args.aligned, only_center_face=args.only_center_face, paste_back=args.paste_back)
-
-        # save faces
-        for idx, (cropped_face, restored_face) in enumerate(zip(cropped_faces, restored_faces)):
-            # save cropped face
-            save_crop_path = os.path.join(args.save_root, 'cropped_faces', f'{basename}_{idx:02d}.png')
-            imwrite(cropped_face, save_crop_path)
-            # save restored face
-            if args.suffix is not None:
-                save_face_name = f'{basename}_{idx:02d}_{args.suffix}.png'
-            else:
-                save_face_name = f'{basename}_{idx:02d}.png'
-            save_restore_path = os.path.join(args.save_root, 'restored_faces', save_face_name)
-            imwrite(restored_face, save_restore_path)
-            # save comparison image
-            cmp_img = np.concatenate((cropped_face, restored_face), axis=1)
-            imwrite(cmp_img, os.path.join(args.save_root, 'cmp', f'{basename}_{idx:02d}.png'))
-
-        # save restored img
-        if restored_img is not None:
-            if args.ext == 'auto':
-                extension = ext[1:]
-            else:
-                extension = args.ext
-
-            if args.suffix is not None:
-                save_restore_path = os.path.join(args.save_root, 'restored_imgs',
-                                                 f'{basename}_{args.suffix}.{extension}')
-            else:
-                save_restore_path = os.path.join(args.save_root, 'restored_imgs', f'{basename}.{extension}')
-            imwrite(restored_img, save_restore_path)
-
-    print(f'Results are in the [{args.save_root}] folder.')
-
-
-if __name__ == '__main__':
-    main()