add script to repo

docs/source/docs/objectDetection/about-object-detection.md

@@ -45,7 +45,7 @@ First, download airockchip's YOLOv5 fork along with the onnx to rknn conversion
 
 ```bash
 git clone https://github.com/airockchip/yolov5.git airockchip-yolov5
-curl -O link-to-onnx2rknn.py
+wget https://raw.githubusercontent.com/PhotonVision/photonvision/refs/heads/master/scripts/onnx2rknn.py
 ```
 
 #### Export Command

scripts/onnx2rknn.py

@@ -0,0 +1,72 @@
+import argparse
+import cv2
+import numpy as np
+from rknn.api import RKNN
+import os
+
+def collect_images_from_directory(directory_path):
+    """Collect all image paths from the specified directory."""
+    image_extensions = ['.jpg', '.jpeg', '.png']
+    image_paths = []
+
+    for root, dirs, files in os.walk(directory_path):
+        for file in files:
+            if any(file.lower().endswith(ext) for ext in image_extensions):
+                image_paths.append(os.path.join(root, file))
+
+    return image_paths
+
+def convert(srcFileName, dstFilename, dataset):
+    platform = "rk3588"
+
+    print('--> Source file name: ' + srcFileName)
+    print('--> RKNN file name: ' + dstFilename)
+
+    rknn = RKNN()
+
+    rknn.config(mean_values=[[0, 0, 0]], std_values=[[255, 255, 255]], target_platform=platform)
+
+    # Load model
+    print('--> Loading model')
+    ret = rknn.load_onnx(srcFileName)
+    if ret != 0:
+        print('load model failed!')
+        exit(ret)
+    print('done')
+
+    # Build model with quantization
+    print('--> Building model')
+    ret = rknn.build(do_quantization=True, dataset=dataset)
+    if ret != 0:
+        print('build model failed.')
+        exit(ret)
+    print('done')
+
+    # Export model to rknn format for Rockchip NPU
+    print('--> Export rknn model')
+    ret = rknn.export_rknn(dstFilename)
+    if ret != 0:
+        print('Export rknn model failed!')
+        return ret
+
+    print('export done')
+
+    rknn.release()
+
+def main():
+
+    parser = argparse.ArgumentParser(description='Transform to RKNN model')
+    parser.add_argument('source_file', help='Path to the ONNX model file')
+    parser.add_argument('description_file', help='Output path for the RKNN model file')
+    parser.add_argument('quant_dir', help='Directory containing images for quantization')
+    args = parser.parse_args()
+
+    dataset = collect_images_from_directory(args.quant_dir)
+    if not dataset:
+        print(f"No images found in directory: {args.quant_dir}")
+        exit(1)
+
+    convert(args.source_file, args.description_file, dataset)
+
+if __name__ == '__main__':
+    main()