Upload demo.py

demo.py

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+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torchvision.transforms.functional as TF
+from PIL import Image
+import os
+import utils
+from skimage import img_as_ubyte
+from collections import OrderedDict
+from natsort import natsorted
+from glob import glob
+import cv2
+import argparse
+from model.CMFNet import CMFNet
+
+model = CMFNet()
+
+parser = argparse.ArgumentParser(description='Demo Image Restoration')
+parser.add_argument('--input_dir', default='./demo_samples/deraindrop', type=str, help='Input images folder')
+parser.add_argument('--result_dir', default='./demo_results', type=str, help='Directory for results')
+parser.add_argument('--weights', default='./pretrained_model/deraindrop_DeRainDrop_CMFNet.pth', type=str, help='Path to weights')
+
+args = parser.parse_args()
+
+def save_img(filepath, img):
+    cv2.imwrite(filepath, cv2.cvtColor(img, cv2.COLOR_RGB2BGR))
+
+def load_checkpoint(model, weights):
+    checkpoint = torch.load(weights)
+    try:
+        model.load_state_dict(checkpoint["state_dict"])
+    except:
+        state_dict = checkpoint["state_dict"]
+        new_state_dict = OrderedDict()
+        for k, v in state_dict.items():
+            name = k[7:]  # remove `module.`
+            new_state_dict[name] = v
+        model.load_state_dict(new_state_dict)
+
+inp_dir = args.input_dir
+out_dir = args.result_dir
+
+os.makedirs(out_dir, exist_ok=True)
+
+files = natsorted(glob(os.path.join(inp_dir, '*.jpg'))
+                  + glob(os.path.join(inp_dir, '*.JPG'))
+                  + glob(os.path.join(inp_dir, '*.png'))
+                  + glob(os.path.join(inp_dir, '*.PNG')))
+
+if len(files) == 0:
+    raise Exception(f"No files found at {inp_dir}")
+
+# Load corresponding model architecture and weights
+model = CMFNet()
+model.cuda()
+
+load_checkpoint(model, args.weights)
+model.eval()
+
+img_multiple_of = 8
+print('restoring images......')
+for file_ in files:
+    img = Image.open(file_).convert('RGB')
+    input_ = TF.to_tensor(img).unsqueeze(0).cuda()
+
+    # Pad the input if not_multiple_of 8
+    h, w = input_.shape[2], input_.shape[3]
+    H, W = ((h + img_multiple_of) // img_multiple_of) * img_multiple_of, (
+                (w + img_multiple_of) // img_multiple_of) * img_multiple_of
+    padh = H - h if h % img_multiple_of != 0 else 0
+    padw = W - w if w % img_multiple_of != 0 else 0
+    input_ = F.pad(input_, (0, padw, 0, padh), 'reflect')
+
+    with torch.no_grad():
+        restored = model(input_)
+    restored = restored[4]
+    restored = torch.clamp(restored, 0, 1)
+
+    # Un-pad the output
+    restored = restored[:, :, :h, :w]
+
+    restored = restored.permute(0, 2, 3, 1).cpu().detach().numpy()
+    restored = img_as_ubyte(restored[0])
+
+    f = os.path.splitext(os.path.split(file_)[-1])[0]
+    save_img((os.path.join(out_dir, f + '.png')), restored)
+
+print(f"Files saved at {out_dir}")
+print('finish !')