--- a
+++ b/U-Net/black_blood.py
@@ -0,0 +1,125 @@
+import argparse
+import logging
+import os
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+from PIL import Image
+from torchvision import transforms
+
+from utils.data_loading import BasicDataset
+from unet.unet_model import UNet
+from utils.utils import plot_img_and_mask
+
+device = torch.device('cuda:4' if torch.cuda.is_available() else 'cpu')
+
+directory = r'/home/data/spleen_blood/data/test/imgs'
+
+def predict_img(net,
+                full_img,
+                device,
+                scale_factor=1,
+                out_threshold=0.5):
+    net.eval()
+    img = torch.from_numpy(BasicDataset.preprocess(None, full_img, scale_factor, is_mask=False))
+    img = img.unsqueeze(0)
+    img = img.to(device=device, dtype=torch.float32)
+
+    with torch.no_grad():
+        output = net(img).cpu()
+        output = F.interpolate(output, (full_img.size[1], full_img.size[0]), mode='bilinear')
+        if net.n_classes > 1:
+            mask = output.argmax(dim=1)
+        else:
+            mask = torch.sigmoid(output) > out_threshold
+
+    return mask[0].long().squeeze().numpy()
+
+def get_output_filenames(in_files):
+    return f'{os.path.splitext(in_files)[0]}_OUT.png'
+
+
+def mask_to_image(mask: np.ndarray, mask_values):
+    if isinstance(mask_values[0], list):
+        out = np.zeros((mask.shape[-2], mask.shape[-1], len(mask_values[0])), dtype=np.uint8)
+    elif mask_values == [0, 1]:
+        out = np.zeros((mask.shape[-2], mask.shape[-1]), dtype=bool)
+    else:
+        out = np.zeros((mask.shape[-2], mask.shape[-1]), dtype=np.uint8)
+
+    if mask.ndim == 3:
+        mask = np.argmax(mask, axis=0)
+
+    for i, v in enumerate(mask_values):
+        out[mask == i] = v
+
+    return Image.fromarray(out)
+
+
+if __name__ == '__main__':
+    logging.basicConfig(level=logging.INFO, format='%(levelname)s: %(message)s')
+    
+    model = './epoch_26_acc_0.90_best_val_acc.pth'
+
+    for fn in os.listdir(directory):
+        in_files = os.path.join(directory, fn)
+        out_files = get_output_filenames(in_files)
+        #print(fn, in_files, out_files)
+
+        net = UNet(n_channels=1, n_classes=5, bilinear=True)
+        
+        logging.info(f'Loading model {model}')
+        logging.info(f'Using device {device}')
+
+        net.to(device=device)
+        state_dict = torch.load(model, map_location=device)
+        mask_values = state_dict.pop('mask_values', [0, 1])
+        net.load_state_dict(state_dict)
+
+        logging.info('Model loaded!')
+        
+        filename = in_files
+        logging.info(f'Predicting image {filename} ...')
+        img = Image.open(filename)
+
+        mask = predict_img(net=net,
+                           full_img=img,
+                           scale_factor=0.5,
+                           out_threshold=0.5,
+                           device=device)
+                           
+        out_filename = out_files
+        result = mask_to_image(mask, mask_values)
+        result.save(out_filename)
+        logging.info(f'Mask saved to {out_filename}')
+
+        #logging.info(f'Visualizing results for image {filename}, close to continue...')
+        #plot_img_and_mask(img, mask)
+
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