Update dataset/datasets.py

dataset/datasets.py

@@ -28,10 +28,10 @@ class TrainDataset(torch.utils.data.Dataset):
             mult_number = np.random.choice(range(self.mults_amount))
             
             bw_name = image_name[:image_name.rfind('.')] + '_' + str(mult_number) + '.png'
-            dfm_name = image_name[:image_name.rfind('.')] + '_' + str(mult_number) + '.png'
+            dfm_name = image_name[:image_name.rfind('.')] + '_' + str(mult_number) + '_dfm.png'
         else:
             bw_name = self.data[idx]
-            dfm_name =  os.path.splitext(self.data[idx])[0] + '0.png'
+            dfm_name =  os.path.splitext(self.data[idx])[0] + '0_dfm.png'
             
             
         bw_img =  np.expand_dims(plt.imread(os.path.join(self.data_path, 'bw', bw_name)), 2)
@@ -81,10 +81,10 @@ class FineTuningDataset(torch.utils.data.Dataset):
             mult_number = np.random.choice(range(self.mults_amount))
             
             bw_name = image_name[:image_name.rfind('.')] + '_' + str(self.mults_amount) + '.png'
-            dfm_name = image_name[:image_name.rfind('.')] + '_' + str(self.mults_amount) + 'dfm.png'
+            dfm_name = image_name[:image_name.rfind('.')] + '_' + str(self.mults_amount) + '_dfm.png'
         else:
             bw_name = self.data[idx]
-            dfm_name =  os.path.splitext(self.data[idx])[0] + 'dfm.png'
+            dfm_name =  os.path.splitext(self.data[idx])[0] + '_dfm.png'
         
         
         bw_img =  np.expand_dims(plt.imread(os.path.join(self.data_path, 'real_manga', image_name)), 2)
@@ -104,4 +104,74 @@ class FineTuningDataset(torch.utils.data.Dataset):
         
         color_img = (color_img - 0.5) / 0.5
         
+        return bw_img, dfm_img, color_img
+        
+        bw_img = np.concatenate([bw_img, dfm_img], axis = 2)
+        
+        if self.transform:
+            result = self.transform(image = color_img, mask = bw_img)
+            color_img = result['image']
+            bw_img = result['mask']
+          
+        dfm_img = bw_img[:, :, 1]
+        bw_img = bw_img[:, :, 0]
+        
+        color_img = self.ToTensor(color_img)
+        bw_img = self.ToTensor(bw_img)
+        
+        dfm_img = self.ToTensor(dfm_img)
+        
+        color_img = (color_img - 0.5) / 0.5
+        
+        mask = generate_mask(bw_img.shape[1], bw_img.shape[2])
+        hint = torch.cat((color_img * mask, mask), 0)
+        
+        return bw_img, color_img, hint, dfm_img
+    
+class FineTuningDataset(torch.utils.data.Dataset):
+    def __init__(self, data_path, transform=None, mult_amount=1):
+        self.data = [x for x in os.listdir(os.path.join(data_path, 'real_manga')) if x.find('_dfm') == -1]
+        self.color_data = [x for x in os.listdir(os.path.join(data_path, 'color'))]
+        self.data_path = data_path
+        self.transform = transform
+        self.mults_amount = mult_amount
+        self.ToTensor = transforms.ToTensor()
+
+        # Directorio para guardar las imágenes en blanco y negro
+        self.bw_directory = os.path.join(data_path, 'bw')
+        if not os.path.exists(self.bw_directory):
+            os.makedirs(self.bw_directory)
+
+    def __len__(self):
+        return len(self.data)
+
+    def __getitem__(self, idx):
+        color_img = plt.imread(os.path.join(self.data_path, 'color', self.color_data[idx]))
+
+        image_name = self.data[idx]
+        if self.mults_amount > 1:
+            mult_number = np.random.choice(range(self.mults_amount))
+            bw_name = image_name[:image_name.rfind('.')] + '_' + str(self.mults_amount) + '.png'
+            dfm_name = image_name[:image_name.rfind('.')] + '_' + str(self.mults_amount) + '_dfm.png'
+        else:
+            bw_name = image_name
+            dfm_name = os.path.splitext(image_name)[0] + '_dfm.png'
+
+        bw_img = np.expand_dims(plt.imread(os.path.join(self.bw_directory, bw_name)), 2)
+        dfm_img = np.expand_dims(plt.imread(os.path.join(self.bw_directory, dfm_name)), 2)
+        
+        if self.transform:
+            result = self.transform(image = color_img)
+            color_img = result['image']
+            
+            result = self.transform(image = bw_img,  mask = dfm_img)
+            bw_img = result['image']
+            dfm_img = result['mask']
+        
+        color_img = self.ToTensor(color_img)
+        bw_img = self.ToTensor(bw_img)
+        dfm_img = self.ToTensor(dfm_img)
+        
+        color_img = (color_img - 0.5) / 0.5
+        
         return bw_img, dfm_img, color_img