Upload inference_gfpgan.py

inference_gfpgan.py

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+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()