Update README.md

README.md

@@ -1,11 +1,89 @@
----
-base_model: paddlepaddle/PP-DocLayoutV2
-tags:
-- ocr
-- onnx
-- layout-detection
-- paddle
-license: apache-2.0
----
-
-This model is an ONNX version of [`paddlepaddle/PP-DocLayoutV2`](https://huggingface.co/PaddlePaddle/PP-DocLayoutV2), created with [Paddle2ONNX](https://github.com/PaddlePaddle/Paddle2ONNX).
+---
+base_model: paddlepaddle/PP-DocLayoutV2
+tags:
+- ocr
+- onnx
+- layout-detection
+- paddle
+license: apache-2.0
+---
+
+This model is an ONNX version of [`paddlepaddle/PP-DocLayoutV2`](https://huggingface.co/PaddlePaddle/PP-DocLayoutV2), created with [Paddle2ONNX](https://github.com/PaddlePaddle/Paddle2ONNX).
+---
+
+Example Python code to run this model:
+
+
+```
+# Install dependencies:
+# pip install numpy opencv-python onnxruntime
+
+import numpy as np
+import cv2
+import onnxruntime as ort
+
+def preprocess_image_doclayout(image, target_input_size=(800, 800)):
+    """
+    Preprocessing for DocLayoutV2 with 800x800 input
+    """
+    orig_h, orig_w = image.shape[:2]
+    # Resize, do not preserve aspect ratio
+    target_h, target_w = target_input_size
+    scale_h = target_h / orig_h
+    scale_w = target_w / orig_w
+
+    new_h, new_w = int(orig_h * scale_h), int(orig_w * scale_w)
+    resized = cv2.resize(image, (new_w, new_h), interpolation=cv2.INTER_LINEAR)
+
+    # Convert to RGB and normalize
+    padded = cv2.cvtColor(resized, cv2.COLOR_BGR2RGB)
+    input_blob = padded.astype(np.float32) / 255.0
+
+    # ImageNet normalization
+    mean = np.array([0.485, 0.456, 0.406], dtype=np.float32)
+    std = np.array([0.229, 0.224, 0.225], dtype=np.float32)
+    input_blob = (input_blob - mean) / std
+
+    # Transpose to CHW format and add batch dimension
+    input_blob = input_blob.transpose(2, 0, 1)[np.newaxis, ...]
+
+    return input_blob, scale_h, scale_w
+
+
+def run_doclayout_onnx():
+    # Specify onnx model path here
+    model = ort.InferenceSession('path/to/PP-DocLayoutV2.onnx')  # Update your path to ONNX model here
+    input_names = [i.name for i in model.get_inputs()]
+    output_names = [o.name for o in model.get_outputs()]
+
+    image_path = 'path/to/input_image.png'  # Update path to your input image here
+    image = cv2.imread(image_path)
+    input_blob, scale_h, scale_w = preprocess_image_doclayout(image)
+    preprocess_shape = [np.array([800, 800], dtype=np.float32)]
+    input_feed = {input_names[0]: preprocess_shape,
+                  input_names[1]: input_blob,
+                  input_names[2]: [[scale_h, scale_w]]}
+
+    # shape=(300, 8), First 6 values are [label_index, score, xmin, ymin, xmax, ymax]
+    output = model.run(output_names, input_feed)[0]
+
+    # Filter out low-confidence boxes
+    boxes = output[output[:, 1] > 0.5]
+    print('--- DocLayoutV2 ONNX Output: ---')
+    print_doclayout_res(boxes[np.argsort(boxes[:, 2])])
+
+
+def print_doclayout_res(boxes):
+    print('cls_id\tscore\txmin\tymin\txmax\tymax')
+    if isinstance(boxes, np.ndarray):
+        for box in boxes:
+            print(f"{box[0]:.0f}\t{box[1]:.3f}\t{box[2]:.2f}\t{box[3]:.2f}\t{box[4]:.2f}\t{box[5]:.2f}")
+    else:
+        for box in sorted(boxes, key=lambda x: x['coordinate'][0]):
+            xmin, ymin, xmax, ymax = box['coordinate']
+            print(f"{box['cls_id']:.0f}\t{box['score']:.3f}"
+                  f"\t{xmin:.2f}\t{ymin:.2f}\t{xmax:.2f}\t{ymax:.2f}")
+
+if __name__ == '__main__':
+    run_doclayout_onnx()
+```