Update dataset/datasets.py

dataset/datasets.py

@@ -1,133 +1,60 @@
-import torch
 import os
+import torch
 import torchvision.transforms as transforms
 import matplotlib.pyplot as plt
 import numpy as np
-
 from utils.utils import generate_mask
 
-
 class TrainDataset(torch.utils.data.Dataset):
-    def __init__(self, data_path, transform = None, mults_amount = 1):
+    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()
+
+        # 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):
         image_name = self.data[idx]
-        
         color_img = plt.imread(os.path.join(self.data_path, 'color', image_name))
-        
 
         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)
-            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
-        
-        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]
-        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)
-            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_name = image_name
+            dfm_name = os.path.splitext(image_name)[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)
+
+        # Guardar imágenes en el directorio bw_directory
+        bw_img_path = os.path.join(self.bw_directory, bw_name)
+        dfm_img_path = os.path.join(self.bw_directory, dfm_name)
+        plt.imsave(bw_img_path, bw_img.squeeze(), cmap='gray')
+        plt.imsave(dfm_img_path, dfm_img.squeeze(), cmap='gray')
+
+        # Normalización y generación de máscara
         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
-        
-        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]
@@ -159,19 +86,12 @@ class FineTuningDataset(torch.utils.data.Dataset):
 
         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)
+
+        # Normalización
         bw_img = self.ToTensor(bw_img)
+        color_img = self.ToTensor(color_img)
         dfm_img = self.ToTensor(dfm_img)
-        
+
         color_img = (color_img - 0.5) / 0.5
-        
-        return bw_img, dfm_img, color_img
+
+        return bw_img, dfm_img, color_img