Restore preprocessors.py to the original file

hy3dshape/hy3dshape/preprocessors.py

@@ -35,43 +35,20 @@ class ImageProcessorV2:
     @staticmethod
     def recenter(image, border_ratio: float = 0.2):
         """ recenter an image to leave some empty space at the image border.
-
         Args:
             image (ndarray): input image, float/uint8 [H, W, 3/4]
             mask (ndarray): alpha mask, bool [H, W]
             border_ratio (float, optional): border ratio, image will be resized to (1 - border_ratio). Defaults to 0.2.
-
         Returns:
             ndarray: output image, float/uint8 [H, W, 3/4]
         """
-        
-        # Validate image shape before accessing dimensions
-        if not isinstance(image, np.ndarray):
-            raise ValueError(f"Expected numpy array for image, got {type(image)}")
-            
-        # Handle different image shapes
-        if len(image.shape) == 2:
-            # Convert grayscale to RGB
-            image = np.stack([image, image, image], axis=-1)
-            mask = np.ones_like(image[..., 0:1]) * 255
-            image = np.concatenate([image, mask], axis=-1)
-            mask = mask[..., 0]
-        elif len(image.shape) >= 3 and image.shape[-1] == 4:
-            # RGBA image
+
+        if image.shape[-1] == 4:
             mask = image[..., 3]
-        elif len(image.shape) >= 3 and image.shape[-1] == 3:
-            # RGB image
+        else:
             mask = np.ones_like(image[..., 0:1]) * 255
             image = np.concatenate([image, mask], axis=-1)
             mask = mask[..., 0]
-        elif len(image.shape) >= 3 and image.shape[-1] == 1:
-            # Single channel image
-            mask = np.ones_like(image) * 255
-            image = np.concatenate([image, image, image, mask], axis=-1)
-            mask = mask[..., 0]
-        else:
-            # Handle unexpected shape
-            raise ValueError(f"Unexpected image shape: {image.shape}. Expected 2D array or array with 1, 3, or 4 channels.")
 
         H, W, C = image.shape
 
@@ -109,62 +86,17 @@ class ImageProcessorV2:
         return result, mask
 
     def load_image(self, image, border_ratio=0.15, to_tensor=True):
-        # Handle different input types
         if isinstance(image, str):
-            # Load from file path
             image = cv2.imread(image, cv2.IMREAD_UNCHANGED)
-            if image is None:
-                raise ValueError(f"Could not load image from path: {image}")
             image, mask = self.recenter(image, border_ratio=border_ratio)
             image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
         elif isinstance(image, Image.Image):
-            # Convert PIL Image to numpy array
             image = image.convert("RGBA")
             image = np.asarray(image)
             image, mask = self.recenter(image, border_ratio=border_ratio)
-        elif isinstance(image, np.ndarray):
-            # Handle numpy array input
-            if image.size == 0:
-                raise ValueError("Input image array is empty")
-                
-            try:
-                image, mask = self.recenter(image, border_ratio=border_ratio)
-            except Exception as e:
-                raise ValueError(f"Failed to process numpy array with shape {image.shape}: {str(e)}")
-        else:
-            # Handle any other type by trying to convert to numpy array
-            try:
-                if hasattr(image, 'convert'):
-                    # Assume it's a PIL-like image
-                    image = image.convert("RGBA")
-                    image = np.asarray(image)
-                else:
-                    # Try direct conversion to numpy array
-                    image = np.asarray(image)
-                
-                if image.size == 0:
-                    raise ValueError("Converted image array is empty")
-                    
-                # Print debug info before recenter
-                print(f"Debug: image shape before recenter: {image.shape}, type: {type(image)}")
-                
-                image, mask = self.recenter(image, border_ratio=border_ratio)
-            except Exception as e:
-                raise ValueError(f"Could not process image input of type {type(image)}: {e}")
-
-        # Validate that we have valid arrays before resizing
-        if not isinstance(image, np.ndarray) or image.size == 0:
-            raise ValueError("Image processing failed - invalid image array")
-        if not isinstance(mask, np.ndarray) or mask.size == 0:
-            raise ValueError("Image processing failed - invalid mask array")
-
-        # Resize with error handling
-        try:
-            image = cv2.resize(image, (self.size, self.size), interpolation=cv2.INTER_CUBIC)
-            mask = cv2.resize(mask, (self.size, self.size), interpolation=cv2.INTER_NEAREST)
-        except cv2.error as e:
-            raise ValueError(f"OpenCV resize failed: {e}. Image shape: {image.shape if hasattr(image, 'shape') else 'unknown'}, Mask shape: {mask.shape if hasattr(mask, 'shape') else 'unknown'}")
-        
+
+        image = cv2.resize(image, (self.size, self.size), interpolation=cv2.INTER_CUBIC)
+        mask = cv2.resize(mask, (self.size, self.size), interpolation=cv2.INTER_NEAREST)
         mask = mask[..., np.newaxis]
 
         if to_tensor:
@@ -230,4 +162,4 @@ IMAGE_PROCESSORS = {
     'mv_v2': MVImageProcessorV2,
 }
 
-DEFAULT_IMAGEPROCESSOR = 'v2'
+DEFAULT_IMAGEPROCESSOR = 'v2'