add generate_multiscale_DF2K

requirements.txt

@@ -3,4 +3,5 @@ facexlib>=0.2.0.3
 gfpgan>=0.2.1
 numpy
 opencv-python
+Pillow
 torch>=1.7

scripts/generate_multiscale_DF2K.py

@@ -0,0 +1,46 @@
+import argparse
+import glob
+import os
+from PIL import Image
+
+
+def main(args):
+
+    # For DF2K, we consider the following three scales,
+    # and the smallest image whose shortest edge is 400
+    scale_list = [0.75, 0.5, 1 / 3]
+    shortest_edge = 400
+
+    path_list = sorted(glob.glob(os.path.join(args.input, '*')))
+    for path in path_list:
+        print(path)
+        basename = os.path.splitext(os.path.basename(path))[0]
+
+        img = Image.open(path)
+        width, height = img.size
+        for idx, scale in enumerate(scale_list):
+            print(f'\t{scale:.2f}')
+            rlt = img.resize((int(width * scale), int(height * scale)), resample=Image.LANCZOS)
+            rlt.save(os.path.join(args.output, f'{basename}T{idx}.png'))
+
+        # save the smallest image which the shortest edge is 400
+        if width < height:
+            ratio = height / width
+            width = shortest_edge
+            height = int(width * ratio)
+        else:
+            ratio = width / height
+            height = shortest_edge
+            width = int(height * ratio)
+        rlt = img.resize((int(width), int(height)), resample=Image.LANCZOS)
+        rlt.save(os.path.join(args.output, f'{basename}T{idx+1}.png'))
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--input', type=str, default='datasets/DF2K/DF2K_HR', help='Input folder')
+    parser.add_argument('--output', type=str, default='datasets/DF2K/DF2K_multiscale', help='Output folder')
+    args = parser.parse_args()
+
+    os.makedirs(args.output, exist_ok=True)
+    main(args)

setup.cfg

@@ -17,6 +17,6 @@ line_length = 120
 multi_line_output = 0
 known_standard_library = pkg_resources,setuptools
 known_first_party = realesrgan
-known_third_party = basicsr,cv2,numpy,torch
+known_third_party = PIL,basicsr,cv2,numpy,torch
 no_lines_before = STDLIB,LOCALFOLDER
 default_section = THIRDPARTY