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paddleocr-vl-1.5.py

@@ -158,19 +158,17 @@ def smart_resize(
 
 def prepare_image(
     image: Union[Image.Image, Dict[str, Any], str],
-    task_mode: str = "ocr",
-    apply_smart_resize: bool = True,
 ) -> Image.Image:
     """
     Prepare image for PaddleOCR-VL-1.5 processing.
 
+    Note: Image resizing is handled by the processor via images_kwargs.
+
     Args:
         image: PIL Image, dict with bytes, or file path
-        task_mode: Task mode (affects max_pixels for spotting)
-        apply_smart_resize: Whether to apply smart resize
 
     Returns:
-        Processed PIL Image
+        Processed PIL Image in RGB format
     """
     # Convert to PIL Image if needed
     if isinstance(image, Image.Image):
@@ -185,17 +183,6 @@ def prepare_image(
     # Convert to RGB
     pil_img = pil_img.convert("RGB")
 
-    # Apply smart resize if requested
-    if apply_smart_resize:
-        max_pixels = MAX_PIXELS.get(task_mode, MAX_PIXELS["default"])
-        original_size = pil_img.size
-        new_height, new_width = smart_resize(
-            pil_img.height, pil_img.width, max_pixels=max_pixels
-        )
-        if (new_width, new_height) != (pil_img.width, pil_img.height):
-            pil_img = pil_img.resize((new_width, new_height), Image.Resampling.LANCZOS)
-            logger.debug(f"Resized image from {original_size} to {pil_img.size}")
-
     return pil_img
 
 
@@ -425,10 +412,9 @@ def main(
     )
 
     logger.info(f"Model loaded on {next(model.parameters()).device}")
+    max_pixels = MAX_PIXELS.get(task_mode, MAX_PIXELS["default"])
     logger.info(f"Processing {len(dataset)} images in batches of {batch_size}")
-    if apply_smart_resize:
-        max_pixels = MAX_PIXELS.get(task_mode, MAX_PIXELS["default"])
-        logger.info(f"Smart resize enabled (max_pixels={max_pixels:,})")
+    logger.info(f"Image resizing: max_pixels={max_pixels:,} (handled by processor)")
 
     # Process images in batches
     all_outputs = []
@@ -443,18 +429,16 @@ def main(
 
         try:
             # Prepare images and create messages
-            processed_images = [
-                prepare_image(
-                    img, task_mode=task_mode, apply_smart_resize=apply_smart_resize
-                )
-                for img in batch_images
-            ]
+            processed_images = [prepare_image(img) for img in batch_images]
 
             # Create messages for batch
             batch_messages = [
                 create_message(img, task_mode) for img in processed_images
             ]
 
+            # Get max_pixels for image processing
+            max_pixels = MAX_PIXELS.get(task_mode, MAX_PIXELS["default"])
+
             # Process with transformers batch inference
             inputs = processor.apply_chat_template(
                 batch_messages,
@@ -463,6 +447,12 @@ def main(
                 tokenize=True,
                 return_dict=True,
                 return_tensors="pt",
+                images_kwargs={
+                    "size": {
+                        "shortest_edge": processor.image_processor.min_pixels,
+                        "longest_edge": max_pixels,
+                    }
+                },
             ).to(model.device)
 
             # Generate outputs