Update dataset/datasets.py

dataset/datasets.py

@@ -6,70 +6,102 @@ import numpy as np
 
 from utils.utils import generate_mask
 
+
 class TrainDataset(torch.utils.data.Dataset):
-    def __init__(self, data_path, transform=None):
+    def __init__(self, data_path, transform = None, mults_amount = 1):
         self.data = os.listdir(os.path.join(data_path, 'color'))
         self.data_path = data_path
         self.transform = transform
-
+        self.mults_amount = mults_amount
+        
         self.ToTensor = transforms.ToTensor()
-
     def __len__(self):
         return len(self.data)
-
+    
     def __getitem__(self, idx):
         image_name = self.data[idx]
+        
         color_img = plt.imread(os.path.join(self.data_path, 'color', image_name))
-
-        bw_name = image_name.replace('_dfm.png', '.png')  # Utiliza el mismo nombre en el directorio bw
-        bw_img = np.expand_dims(plt.imread(os.path.join(self.data_path, 'bw', bw_name)), 2)
-
+        
+
+        if self.mults_amount > 1:
+            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) + '_dfm.png'
+        else:
+            bw_name = self.data[idx]
+            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)
+        dfm_img =  np.expand_dims(plt.imread(os.path.join(self.data_path, 'bw', dfm_name)), 2)
+        
+        bw_img = np.concatenate([bw_img, dfm_img], axis = 2)
+        
         if self.transform:
-            result = self.transform(image=color_img, mask=bw_img)
+            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
-
+        
+        return bw_img, color_img, hint, dfm_img
+    
 class FineTuningDataset(torch.utils.data.Dataset):
-    def __init__(self, data_path, transform=None):
-        self.data = [x for x in os.listdir(os.path.join(data_path, 'real_manga')) if x.find('_dfm') == -1]
+    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
+        
         np.random.shuffle(self.color_data)
-
+        
         self.ToTensor = transforms.ToTensor()
-
     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]
-        bw_name = image_name.replace('_dfm.png', '.png')  # Utiliza el mismo nombre en el directorio real_manga
-        bw_img = np.expand_dims(plt.imread(os.path.join(self.data_path, 'real_manga', bw_name)), 2)
-
+        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 = self.data[idx]
+            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)
+        dfm_img =  np.expand_dims(plt.imread(os.path.join(self.data_path, 'real_manga', dfm_name)), 2)
+        
         if self.transform:
-            result = self.transform(image=color_img)
+            result = self.transform(image = color_img)
             color_img = result['image']
-
-            result = self.transform(image=bw_img)
+            
+            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, color_img
+        
+        return bw_img, dfm_img, color_img