Support outscale; Add RealESRGANx2 model; Version 0.2.1

README.md

@@ -15,6 +15,7 @@ We extend the powerful ESRGAN to a practical restoration application (namely, Re
 
 :triangular_flag_on_post: **Updates**
 
+- :white_check_mark: Support arbitrary scale with `--outscale` (It actually further resizes outputs with `LANCZOS4`). Add *RealESRGAN_x2plus.pth* model.
 - :white_check_mark: [The inference code](inference_realesrgan.py) supports: 1) **tile** options; 2) images with **alpha channel**; 3) **gray** images; 4) **16-bit** images.
 - :white_check_mark: The training codes have been released. A detailed guide can be found in [Training.md](Training.md).
 
@@ -124,6 +125,7 @@ Results are in the `results` folder
 
 - [RealESRGAN-x4plus](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth)
 - [RealESRNet-x4plus](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/RealESRNet_x4plus.pth)
+- [RealESRGAN-x2plus](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.0/RealESRGAN_x2plus.pth)
 - [official ESRGAN-x4](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/ESRGAN_SRx4_DF2KOST_official-ff704c30.pth)
 
 ## :computer: Training

VERSION

@@ -1 +1 @@
-0.2.0
+0.2.1

inference_realesrgan.py

@@ -15,7 +15,8 @@ def main():
         default='experiments/pretrained_models/RealESRGAN_x4plus.pth',
         help='Path to the pre-trained model')
     parser.add_argument('--output', type=str, default='results', help='Output folder')
-    parser.add_argument('--scale', type=int, default=4, help='Upsample scale factor')
+    parser.add_argument('--netscale', type=int, default=4, help='Upsample scale factor of the network')
+    parser.add_argument('--outscale', type=float, default=4, help='The final upsampling scale of the image')
     parser.add_argument('--suffix', type=str, default='out', help='Suffix of the restored image')
     parser.add_argument('--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')
@@ -34,7 +35,7 @@ def main():
     args = parser.parse_args()
 
     upsampler = RealESRGANer(
-        scale=args.scale,
+        scale=args.netscale,
         model_path=args.model_path,
         tile=args.tile,
         tile_pad=args.tile_pad,
@@ -51,15 +52,25 @@ def main():
         print('Testing', idx, imgname)
 
         img = cv2.imread(path, cv2.IMREAD_UNCHANGED)
-        output, img_mode = upsampler.enhance(img)
-        if args.ext == 'auto':
-            extension = extension[1:]
+        h, w = img.shape[0:2]
+        if max(h, w) > 1000 and args.netscale == 4:
+            print('WARNING: The input image is large, try X2 model for better performace.')
+        if max(h, w) < 500 and args.netscale == 2:
+            print('WARNING: The input image is small, try X4 model for better performace.')
+
+        try:
+            output, img_mode = upsampler.enhance(img, outscale=args.outscale)
+        except Exception as error:
+            print('Error', error)
         else:
-            extension = args.ext
-        if img_mode == 'RGBA':  # RGBA images should be saved in png format
-            extension = 'png'
-        save_path = os.path.join(args.output, f'{imgname}_{args.suffix}.{extension}')
-        cv2.imwrite(save_path, output)
+            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'
+            save_path = os.path.join(args.output, f'{imgname}_{args.suffix}.{extension}')
+            cv2.imwrite(save_path, output)
 
 
 if __name__ == '__main__':

realesrgan/utils.py

@@ -63,12 +63,7 @@ def pre_process(self, img):
             self.img = F.pad(self.img, (0, self.mod_pad_w, 0, self.mod_pad_h), 'reflect')
 
     def process(self):
-        try:
-            # inference
-            with torch.no_grad():
-                self.output = self.model(self.img)
-        except Exception as error:
-            print('Error', error)
+        self.output = self.model(self.img)
 
     def tile_process(self):
         """Modified from: https://github.com/ata4/esrgan-launcher
@@ -143,7 +138,9 @@ def post_process(self):
             self.output = self.output[:, :, 0:h - self.pre_pad * self.scale, 0:w - self.pre_pad * self.scale]
         return self.output
 
-    def enhance(self, img, tile=False, alpha_upsampler='realesrgan'):
+    @torch.no_grad()
+    def enhance(self, img, outscale=None, alpha_upsampler='realesrgan'):
+        h_input, w_input = img.shape[0:2]
         # img: numpy
         img = img.astype(np.float32)
         if np.max(img) > 255:  # 16-bit image
@@ -203,6 +200,14 @@ def enhance(self, img, tile=False, alpha_upsampler='realesrgan'):
             output = (output_img * 65535.0).round().astype(np.uint16)
         else:
             output = (output_img * 255.0).round().astype(np.uint8)
+
+        if outscale is not None and outscale != float(self.scale):
+            output = cv2.resize(
+                output, (
+                    int(w_input * outscale),
+                    int(h_input * outscale),
+                ), interpolation=cv2.INTER_LANCZOS4)
+
         return output, img_mode