Merge branch 'unet_crop' into 'main'

Adding the crop and padding as resizing features.

See merge request !17

qim3d/utils/augmentations.py

@@ -8,7 +8,7 @@ class Augmentation:
     Class for defining image augmentation transformations using the Albumentations library.
         
     Args:
-        resize ((int,tuple), optional): The target size to resize the image.
+        resize (str, optional): Specifies how the images should be reshaped to the appropriate size.
         trainsform_train (str, optional): level of transformation for the training set.
         transform_validation (str, optional): level of transformation for the validation set.
         transform_test (str, optional): level of transformation for the test set.
@@ -16,14 +16,14 @@ class Augmentation:
         std (float, optional): The standard deviation value for normalizing pixel intensities.
 
     Raises:
-        ValueError: If `resize` is neither a None, int nor tuple.  
+        ValueError: If the ´resize´ is neither 'crop', 'resize' or 'padding'.
     
     Example:
-        my_augmentation = Augmentation(resize = (256,256), transform_train = 'heavy')
+        my_augmentation = Augmentation(resize = 'crop', transform_train = 'heavy')
     """
     
     def __init__(self, 
-                 resize = None, 
+                 resize = 'crop', 
                  transform_train = 'moderate', 
                  transform_validation = None,
                  transform_test = None,
@@ -31,8 +31,8 @@ class Augmentation:
                  std: float = 0.5
                 ):
 
-        if not isinstance(resize,(type(None),int,tuple)):
-            raise ValueError(f"Invalid input for resize: {resize}. Use an integer or tuple to modify the data.")
+        if resize not in ['crop', 'reshape', 'padding']:
+            raise ValueError(f"Invalid resize type: {resize}. Use either 'crop', 'resize' or 'padding'.")
 
         self.resize = resize
         self.mean = mean
@@ -62,10 +62,21 @@ class Augmentation:
 
         # Baseline
         baseline_aug = [
-            A.Resize(im_h, im_w),
             A.Normalize(mean = (self.mean),std = (self.std)),
             ToTensorV2()
         ]
+        if self.resize == 'crop':
+            resize_aug = [
+                A.CenterCrop(im_h,im_w)
+            ]
+        elif self.resize == 'reshape':
+            resize_aug =[
+                A.Resize(im_h,im_w)
+            ]
+        elif self.resize == 'padding':
+            resize_aug = [
+                A.PadIfNeeded(im_h,im_w,border_mode = 0) # OpenCV border mode
+            ]
         
         # Level of augmentation
         if level == None:
@@ -91,6 +102,6 @@ class Augmentation:
                 A.Affine(scale = [0.8,1.4], translate_percent = (0.2,0.2), shear = (-15,15))
             ]
 
-        augment = A.Compose(level_aug + baseline_aug)
+        augment = A.Compose(level_aug + resize_aug + baseline_aug)
         
         return augment
\ No newline at end of file

qim3d/utils/data.py

@@ -101,27 +101,34 @@ class Dataset(torch.utils.data.Dataset):
         return Image.open(str(image_path)).size
 
 
-def check_resize(im_height: int, im_width: int, n_channels: int):
+def check_resize(im_height: int, im_width: int, resize: str, n_channels: int):
     """
     Checks the compatibility of the image shape with the depth of the model.
     If the image height and width cannot be divided by 2 `n_channels` times, then the image size is inappropriate.
-        If so, the image is reshaped to the closest appropriate dimension, and the user is notified with a warning.
+    If so, the image is changed to the closest appropriate dimension, and the user is notified with a warning.
 
     Args:
-            im_height (int): Height of the image chosen by the user.
-            im_width (int): Width of the image chosen by the user.
+        im_height (int) : Height of the original image from the dataset.
+        im_width (int)  : Width of the original image from the dataset.
+        resize (str)    : Type of resize to be used on the image if the size doesn't fit the model.
         n_channels (int): Number of channels in the model.
 
     Raises:
         ValueError: If the image size is smaller than minimum required for the model's depth.               
     """
+    # finding suitable size to upsize with padding 
+    if resize == 'padding':
+        h_adjust, w_adjust = (im_height // 2**n_channels+1) * 2**n_channels , (im_width // 2**n_channels+1) * 2**n_channels
         
+    # finding suitable size to downsize with crop / resize
+    else:
         h_adjust, w_adjust = (im_height // 2**n_channels) * 2**n_channels , (im_width // 2**n_channels) * 2**n_channels    
     
     if h_adjust == 0 or w_adjust == 0:
         raise ValueError("The size of the image is too small compared to the depth of the UNet. Choose a different 'resize' and/or a smaller model.")
-        elif (h_adjust!=im_height) or (w_adjust != im_width):
-            log.warning(f"The image size doesn't match the Unet model's depth. The image is resized to: {h_adjust,w_adjust}")
+    
+    elif h_adjust != im_height or w_adjust != im_width:
+        log.warning(f"The image size doesn't match the Unet model's depth. The image is changed with '{resize}', from {im_height, im_width} to {h_adjust, w_adjust}.")
     
     return h_adjust, w_adjust 
 
@@ -146,20 +153,13 @@ def prepare_datasets(path: str, val_fraction: float, model, augmentation):
     resize = augmentation.resize
     n_channels = len(model.channels)
 
-    if isinstance(resize,type(None)):
-        # OPEN THE FIRST IMAGE
+    # taking the size of the 1st image in the dataset
     im_path = Path(path) / 'train'
     first_img = sorted((im_path / "images").iterdir())[0]
     image = Image.open(str(first_img))
-        im_h, im_w = image.size[:2]
-        log.info("User did not choose a specific value for 'resize'. Checking the first image in the dataset...")
-        
-    elif isinstance(resize,int):
-        im_h, im_w = resize, resize
-    else:
-        im_h,im_w = resize
+    orig_h, orig_w = image.size[:2]
         
-    final_h, final_w = check_resize(im_h, im_w, n_channels)
+    final_h, final_w = check_resize(orig_h, orig_w, resize, n_channels)
     
     train_set = Dataset(root_path = path, transform = augmentation.augment(final_h, final_w, augmentation.transform_train))
     val_set   = Dataset(root_path = path, transform = augmentation.augment(final_h, final_w, augmentation.transform_validation))

qim3d/utils/models.py

@@ -140,9 +140,6 @@ def model_summary(dataloader,model):
         print(summary)
     """
     
-    device = "cuda" if torch.cuda.is_available() else "cpu"
-    model.to(device)
-
     images,_ = next(iter(dataloader)) 
     batch_size = tuple(images.shape)
     model_s = summary(model,batch_size,depth = torch.inf)