Update inference_realesrgan.py

inference_realesrgan.py

@@ -1,166 +1,54 @@
+import os
 import argparse
+import torch
 import cv2
-import glob
-import os
-from basicsr.archs.rrdbnet_arch import RRDBNet
-from basicsr.utils.download_util import load_file_from_url
-
+import numpy as np
+from PIL import Image
 from realesrgan import RealESRGANer
-from realesrgan.archs.srvgg_arch import SRVGGNetCompact
-
-
-def main():
-    """Inference demo for Real-ESRGAN.
-    """
-    parser = argparse.ArgumentParser()
-    parser.add_argument('-i', '--input', type=str, default='inputs', help='Input image or folder')
-    parser.add_argument(
-        '-n',
-        '--model_name',
-        type=str,
-        default='RealESRGAN_x4plus',
-        help=('Model names: RealESRGAN_x4plus | RealESRNet_x4plus | RealESRGAN_x4plus_anime_6B | RealESRGAN_x2plus | '
-              'realesr-animevideov3 | realesr-general-x4v3'))
-    parser.add_argument('-o', '--output', type=str, default='results', help='Output folder')
-    parser.add_argument(
-        '-dn',
-        '--denoise_strength',
-        type=float,
-        default=0.5,
-        help=('Denoise strength. 0 for weak denoise (keep noise), 1 for strong denoise ability. '
-              'Only used for the realesr-general-x4v3 model'))
-    parser.add_argument('-s', '--outscale', type=float, default=4, help='The final upsampling scale of the image')
-    parser.add_argument(
-        '--model_path', type=str, default=None, help='[Option] Model path. Usually, you do not need to specify it')
-    parser.add_argument('--suffix', type=str, default='out', help='Suffix of the restored image')
-    parser.add_argument('-t', '--tile', type=int, default=0, help='Tile size, 0 for no tile during testing')
-    parser.add_argument('--tile_pad', type=int, default=10, help='Tile padding')
-    parser.add_argument('--pre_pad', type=int, default=0, help='Pre padding size at each border')
-    parser.add_argument('--face_enhance', action='store_true', help='Use GFPGAN to enhance face')
-    parser.add_argument(
-        '--fp32', action='store_true', help='Use fp32 precision during inference. Default: fp16 (half precision).')
-    parser.add_argument(
-        '--alpha_upsampler',
-        type=str,
-        default='realesrgan',
-        help='The upsampler for the alpha channels. Options: realesrgan | bicubic')
-    parser.add_argument(
-        '--ext',
-        type=str,
-        default='auto',
-        help='Image extension. Options: auto | jpg | png, auto means using the same extension as inputs')
-    parser.add_argument(
-        '-g', '--gpu-id', type=int, default=None, help='gpu device to use (default=None) can be 0,1,2 for multi-gpu')
 
+# Define model path
+MODEL_PATH = "weight/RealESRGAN_x4plus.pth"
+
+# Ensure the model exists
+if not os.path.exists(MODEL_PATH):
+    print(f"[ERROR] Model file {MODEL_PATH} not found. Please download it using download_model.py.")
+    exit(1)
+
+# Load Real-ESRGAN model
+device = "cuda" if torch.cuda.is_available() else "cpu"
+upsampler = RealESRGANer(
+    scale=4, model_path=MODEL_PATH, model=None, tile=0, tile_pad=10, pre_pad=0,
+    half=True if torch.cuda.is_available() else False
+)
+
+def upscale_image(input_path, output_path):
+    """Upscales an image using Real-ESRGAN."""
+    try:
+        # Load image
+        img = cv2.imread(input_path, cv2.IMREAD_COLOR)
+        if img is None:
+            print(f"[ERROR] Failed to read image: {input_path}")
+            return
+
+        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+        img_pil = Image.fromarray(img)
+
+        # Apply super-resolution
+        print(f"[INFO] Upscaling image: {input_path}")
+        output, _ = upsampler.enhance(np.array(img_pil), outscale=2)
+        output = cv2.cvtColor(output, cv2.COLOR_RGB2BGR)
+
+        # Save upscaled image
+        cv2.imwrite(output_path, output)
+        print(f"[SUCCESS] Upscaled image saved at: {output_path}")
+
+    except Exception as e:
+        print(f"[ERROR] Failed to upscale image: {e}")
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Real-ESRGAN Image Upscaling")
+    parser.add_argument("-i", "--input", type=str, required=True, help="Path to input image")
+    parser.add_argument("-o", "--output", type=str, required=True, help="Path to save the upscaled image")
     args = parser.parse_args()
 
-    # determine models according to model names
-    args.model_name = args.model_name.split('.')[0]
-    if args.model_name == 'RealESRGAN_x4plus':  # x4 RRDBNet model
-        model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
-        netscale = 4
-        file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth']
-    elif args.model_name == 'RealESRNet_x4plus':  # x4 RRDBNet model
-        model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
-        netscale = 4
-        file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/RealESRNet_x4plus.pth']
-    elif args.model_name == 'RealESRGAN_x4plus_anime_6B':  # x4 RRDBNet model with 6 blocks
-        model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=6, num_grow_ch=32, scale=4)
-        netscale = 4
-        file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth']
-    elif args.model_name == 'RealESRGAN_x2plus':  # x2 RRDBNet model
-        model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=2)
-        netscale = 2
-        file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth']
-    elif args.model_name == 'realesr-animevideov3':  # x4 VGG-style model (XS size)
-        model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=4, act_type='prelu')
-        netscale = 4
-        file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-animevideov3.pth']
-    elif args.model_name == 'realesr-general-x4v3':  # x4 VGG-style model (S size)
-        model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu')
-        netscale = 4
-        file_url = [
-            'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-wdn-x4v3.pth',
-            'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth'
-        ]
-
-    # determine model paths
-    if args.model_path is not None:
-        model_path = args.model_path
-    else:
-        model_path = os.path.join('weights', args.model_name + '.pth')
-        if not os.path.isfile(model_path):
-            ROOT_DIR = os.path.dirname(os.path.abspath(__file__))
-            for url in file_url:
-                # model_path will be updated
-                model_path = load_file_from_url(
-                    url=url, model_dir=os.path.join(ROOT_DIR, 'weights'), progress=True, file_name=None)
-
-    # use dni to control the denoise strength
-    dni_weight = None
-    if args.model_name == 'realesr-general-x4v3' and args.denoise_strength != 1:
-        wdn_model_path = model_path.replace('realesr-general-x4v3', 'realesr-general-wdn-x4v3')
-        model_path = [model_path, wdn_model_path]
-        dni_weight = [args.denoise_strength, 1 - args.denoise_strength]
-
-    # restorer
-    upsampler = RealESRGANer(
-        scale=netscale,
-        model_path=model_path,
-        dni_weight=dni_weight,
-        model=model,
-        tile=args.tile,
-        tile_pad=args.tile_pad,
-        pre_pad=args.pre_pad,
-        half=not args.fp32,
-        gpu_id=args.gpu_id)
-
-    if args.face_enhance:  # Use GFPGAN for face enhancement
-        from gfpgan import GFPGANer
-        face_enhancer = GFPGANer(
-            model_path='https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth',
-            upscale=args.outscale,
-            arch='clean',
-            channel_multiplier=2,
-            bg_upsampler=upsampler)
-    os.makedirs(args.output, exist_ok=True)
-
-    if os.path.isfile(args.input):
-        paths = [args.input]
-    else:
-        paths = sorted(glob.glob(os.path.join(args.input, '*')))
-
-    for idx, path in enumerate(paths):
-        imgname, extension = os.path.splitext(os.path.basename(path))
-        print('Testing', idx, imgname)
-
-        img = cv2.imread(path, cv2.IMREAD_UNCHANGED)
-        if len(img.shape) == 3 and img.shape[2] == 4:
-            img_mode = 'RGBA'
-        else:
-            img_mode = None
-
-        try:
-            if args.face_enhance:
-                _, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True)
-            else:
-                output, _ = upsampler.enhance(img, outscale=args.outscale)
-        except RuntimeError as error:
-            print('Error', error)
-            print('If you encounter CUDA out of memory, try to set --tile with a smaller number.')
-        else:
-            if args.ext == 'auto':
-                extension = extension[1:]
-            else:
-                extension = args.ext
-            if img_mode == 'RGBA':  # RGBA images should be saved in png format
-                extension = 'png'
-            if args.suffix == '':
-                save_path = os.path.join(args.output, f'{imgname}.{extension}')
-            else:
-                save_path = os.path.join(args.output, f'{imgname}_{args.suffix}.{extension}')
-            cv2.imwrite(save_path, output)
-
-
-if __name__ == '__main__':
-    main()
+    upscale_image(args.input, args.output)