Create README.md

README.md

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+How do inference the pidinet onnx:
+
+```python
+"""
+Run the PiDiNet ONNX model on one image and save the fused edge map.
+
+Example:
+python test_pidinet_onnx.py \
+  --onnx model_PIDINET/pidinet_table5.onnx \
+  --image Images/example.jpg \
+  --output Results/example_edges.png
+"""
+
+import argparse
+from pathlib import Path
+
+import numpy as np
+import onnxruntime as ort
+from PIL import Image
+
+
+MEAN = np.array([0.485, 0.456, 0.406], dtype=np.float32)[:, None, None]
+STD = np.array([0.229, 0.224, 0.225], dtype=np.float32)[:, None, None]
+
+
+def preprocess(img_path: Path) -> np.ndarray:
+    img = Image.open(img_path).convert("RGB")
+    arr = np.asarray(img, dtype=np.float32) / 255.0          # HWC in [0,1]
+    arr = arr.transpose(2, 0, 1)                             # CHW
+    arr = (arr - MEAN) / STD
+    return arr[None, ...]                                    # BCHW
+
+
+def postprocess(edge_map: np.ndarray, out_path: Path):
+    out_path.parent.mkdir(parents=True, exist_ok=True)
+    edge_map = np.clip(edge_map, 0.0, 1.0)
+    edge_img = (edge_map * 255.0).astype(np.uint8)
+    Image.fromarray(edge_img).save(out_path)
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="Test PiDiNet ONNX on a single image.")
+    parser.add_argument(
+        "--onnx",
+        type=Path,
+        default=Path("model_PIDINET/pidinet_table5.onnx"),
+        help="Path to the PiDiNet ONNX file.",
+    )
+    parser.add_argument(
+        "--image",
+        type=Path,
+        required=True,
+        help="Input image path.",
+    )
+    parser.add_argument(
+        "--output",
+        type=Path,
+        default=Path("Results/pidinet_edges.png"),
+        help="Where to save the fused edge map.",
+    )
+    parser.add_argument(
+        "--provider",
+        type=str,
+        default="CPUExecutionProvider",
+        help="ONNX Runtime provider (e.g., CPUExecutionProvider or CUDAExecutionProvider).",
+    )
+    return parser.parse_args()
+
+
+def main():
+    args = parse_args()
+
+    session = ort.InferenceSession(
+        str(args.onnx),
+        providers=[args.provider],
+    )
+
+    inp = preprocess(args.image)
+    outputs = session.run(None, {"image": inp})
+
+    fused = np.array(outputs[-1])[0, 0]  # fused edge map
+    postprocess(fused, args.output)
+
+    print(f"Saved edge map to {args.output}")
+
+
+if __name__ == "__main__":
+    main()
+
+```