Merge pull request #57 from yashbhalgat/yash-preproc-fix

Add 'pad' mode in preprocessing to handle cases where height > width

vggt/utils/load_fn.py

@@ -9,33 +9,45 @@ from PIL import Image
 from torchvision import transforms as TF
 
 
-def load_and_preprocess_images(image_path_list):
+def load_and_preprocess_images(image_path_list, mode="crop"):
     """
     A quick start function to load and preprocess images for model input.
     This assumes the images should have the same shape for easier batching, but our model can also work well with different shapes.
 
     Args:
         image_path_list (list): List of paths to image files
+        mode (str, optional): Preprocessing mode, either "crop" or "pad".
+                             - "crop" (default): Sets width to 518px and center crops height if needed.
+                             - "pad": Preserves all pixels by making the largest dimension 518px
+                               and padding the smaller dimension to reach a square shape.
 
     Returns:
         torch.Tensor: Batched tensor of preprocessed images with shape (N, 3, H, W)
 
     Raises:
-        ValueError: If the input list is empty
+        ValueError: If the input list is empty or if mode is invalid
 
     Notes:
         - Images with different dimensions will be padded with white (value=1.0)
         - A warning is printed when images have different shapes
-        - The function ensures width=518px while maintaining aspect ratio
-        - Height is adjusted to be divisible by 14 for compatibility with model requirements
+        - When mode="crop": The function ensures width=518px while maintaining aspect ratio
+          and height is center-cropped if larger than 518px
+        - When mode="pad": The function ensures the largest dimension is 518px while maintaining aspect ratio
+          and the smaller dimension is padded to reach a square shape (518x518)
+        - Dimensions are adjusted to be divisible by 14 for compatibility with model requirements
     """
     # Check for empty list
     if len(image_path_list) == 0:
         raise ValueError("At least 1 image is required")
+    
+    # Validate mode
+    if mode not in ["crop", "pad"]:
+        raise ValueError("Mode must be either 'crop' or 'pad'")
 
     images = []
     shapes = set()
     to_tensor = TF.ToTensor()
+    target_size = 518
 
     # First process all images and collect their shapes
     for image_path in image_path_list:
@@ -54,28 +66,51 @@ def load_and_preprocess_images(image_path_list):
         img = img.convert("RGB")
 
         width, height = img.size
-        new_width = 518
-
-        # Calculate height maintaining aspect ratio, divisible by 14
-        new_height = round(height * (new_width / width) / 14) * 14
+        
+        if mode == "pad":
+            # Make the largest dimension 518px while maintaining aspect ratio
+            if width >= height:
+                new_width = target_size
+                new_height = round(height * (new_width / width) / 14) * 14  # Make divisible by 14
+            else:
+                new_height = target_size
+                new_width = round(width * (new_height / height) / 14) * 14  # Make divisible by 14
+        else:  # mode == "crop"
+            # Original behavior: set width to 518px
+            new_width = target_size
+            # Calculate height maintaining aspect ratio, divisible by 14
+            new_height = round(height * (new_width / width) / 14) * 14
 
         # Resize with new dimensions (width, height)
-
         img = img.resize((new_width, new_height), Image.Resampling.BICUBIC)
         img = to_tensor(img)  # Convert to tensor (0, 1)
 
-        # Center crop height if it's larger than 518
-
-        if new_height > 518:
-            start_y = (new_height - 518) // 2
-            img = img[:, start_y : start_y + 518, :]
+        # Center crop height if it's larger than 518 (only in crop mode)
+        if mode == "crop" and new_height > target_size:
+            start_y = (new_height - target_size) // 2
+            img = img[:, start_y : start_y + target_size, :]
+        
+        # For pad mode, pad to make a square of target_size x target_size
+        if mode == "pad":
+            h_padding = target_size - img.shape[1]
+            w_padding = target_size - img.shape[2]
+            
+            if h_padding > 0 or w_padding > 0:
+                pad_top = h_padding // 2
+                pad_bottom = h_padding - pad_top
+                pad_left = w_padding // 2
+                pad_right = w_padding - pad_left
+                
+                # Pad with white (value=1.0)
+                img = torch.nn.functional.pad(
+                    img, (pad_left, pad_right, pad_top, pad_bottom), mode="constant", value=1.0
+                )
 
         shapes.add((img.shape[1], img.shape[2]))
         images.append(img)
 
     # Check if we have different shapes
     # In theory our model can also work well with different shapes
-
     if len(shapes) > 1:
         print(f"Warning: Found images with different shapes: {shapes}")
         # Find maximum dimensions