Update dataset/datasets.py

dataset/datasets.py

@@ -4,11 +4,8 @@ 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
@@ -21,20 +18,21 @@ class TrainDataset(torch.utils.data.Dataset):
     def __getitem__(self, idx):
         image_name = self.data[idx]
         
-        color_img = plt.imread(get_file_path(image_name, self.data_path, self.mults_amount))
+        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] + '_dfm.png'
+            dfm_name = 'dfm_' + self.data[idx]
             
             
-        bw_img =  np.expand_dims(plt.imread(get_file_path(bw_name, self.data_path, self.mults_amount)), 2)
-        dfm_img =  np.expand_dims(plt.imread(get_file_path(dfm_name, self.data_path, self.mults_amount)), 2)
+        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)
         
@@ -53,24 +51,41 @@ class TrainDataset(torch.utils.data.Dataset):
         
         color_img = (color_img - 0.5) / 0.5
         
-        mask = generate_mask(bw_img.shape[1], bw_img.shape[2])
+        mask = generate_mask(bw_img.shape[1])
         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
-
-        bw_img = plt.imread(get_file_path(bw_name, self.data_path, self.mults_amount))
-        dfm_img = plt.imread(get_file_path(dfm_name, self.data_path, self.mults_amount))
-
-        bw_img = bw_img.astype('float32')
-        dfm_img = dfm_img.astype('float32')
-
-        color_img = bw_img[:, :, 0]
-        hint = dfm_img
-
-        return bw_img, color_img, hint, dfm_img
-
+    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] * 8
+        self.color_data = [x for x in os.listdir(os.path.join(data_path, 'color')) if x.find('left') == -1 and x.find('right') == -1] * 6
+        self.data_path = data_path
+        self.transform = transform
+        
+        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]))
+        bw_img =  np.expand_dims(plt.imread(os.path.join(self.data_path, 'real_manga', self.data[idx])), 2)
+        dfm_img =  np.expand_dims(plt.imread(os.path.join(self.data_path, 'real_manga', 'dfm_' + self.data[idx])), 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