inference for all the images in one folder is added.

basicsr/demo_folder.py

@@ -0,0 +1,54 @@
+# ------------------------------------------------------------------------
+# Copyright (c) 2022 megvii-model. All Rights Reserved.
+# ------------------------------------------------------------------------
+# Modified from BasicSR (https://github.com/xinntao/BasicSR)
+# Copyright 2018-2020 BasicSR Authors
+# ------------------------------------------------------------------------
+import torch
+
+# from basicsr.data import create_dataloader, create_dataset
+from basicsr.models import create_model
+from basicsr.train import parse_options
+from basicsr.utils import FileClient, imfrombytes, img2tensor, padding, tensor2img, imwrite
+import os
+
+# from basicsr.utils import (get_env_info, get_root_logger, get_time_str,
+#                            make_exp_dirs)
+# from basicsr.utils.options import dict2str
+
+def main():
+     # parse options, set distributed setting, set ramdom seed
+    opt = parse_options(is_train=False)
+    opt['num_gpu'] = torch.cuda.device_count()
+    input_folder = opt['img_path'].get('input_folder')
+    output_folder = opt['img_path'].get('output_folder')
+    # Get a list of all image files in the input folder
+    image_files = [f for f in os.listdir(input_folder) if os.path.isfile(os.path.join(input_folder, f))]
+    opt['dist'] = False
+    model = create_model(opt)
+    for image_file in image_files:
+        # Construct the input and output paths for each image
+        img_path = os.path.join(input_folder, image_file)
+        output_path = os.path.join(output_folder, image_file)
+        ## 1. read image
+        file_client = FileClient('disk')
+        img_bytes = file_client.get(img_path, None)
+        try:
+            img = imfrombytes(img_bytes, float32=True)
+        except:
+            raise Exception("path {} not working".format(img_path))
+        img = img2tensor(img, bgr2rgb=True, float32=True)
+        ## 2. run inference
+        
+        model.feed_data(data={'lq': img.unsqueeze(dim=0)})
+        if model.opt['val'].get('grids', False):
+            model.grids()
+        model.test()
+        if model.opt['val'].get('grids', False):
+            model.grids_inverse()
+        visuals = model.get_current_visuals()
+        sr_img = tensor2img([visuals['result']])
+        imwrite(sr_img, output_path)
+        print(f'inference {img_path} .. finished. saved to {output_path}')
+if __name__ == '__main__':
+    main()

basicsr/train.py

@@ -35,9 +35,10 @@ def parse_options(is_train=True):
         default='none',
         help='job launcher')
     parser.add_argument('--local_rank', type=int, default=0)
-
     parser.add_argument('--input_path', type=str, required=False, help='The path to the input image. For single image inference only.')
+    parser.add_argument('--input_folder', type=str, required=False, help='The path to the input folder. For multiple image inference.')
     parser.add_argument('--output_path', type=str, required=False, help='The path to the output image. For single image inference only.')
+    parser.add_argument('--output_folder', type=str, required=False, help='The path to the output folder. For multiple image inference.')
 
     args = parser.parse_args()
     opt = parse(args.opt, is_train=is_train)
@@ -68,6 +69,11 @@ def parse_options(is_train=True):
             'input_img': args.input_path,
             'output_img': args.output_path
         }
+    elif args.input_folder is not None and args.output_folder is not None:
+        opt['img_path'] = {
+            'input_folder': args.input_folder,
+            'output_folder': args.output_folder
+        }
 
     return opt
 

basicsr/version.py

@@ -1,5 +1,5 @@
 # GENERATED VERSION FILE
-# TIME: Mon Apr 18 21:35:20 2022
+# TIME: Mon Jun 17 23:20:12 2024
-__version__ = '1.2.0+386ca20'
+__version__ = '1.2.0+2b4af71'
 short_version = '1.2.0'
 version_info = (1, 2, 0)