update docs & script

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

@@ -48,10 +48,17 @@ git clone https://github.com/airockchip/yolov5.git airockchip-yolov5
 wget https://raw.githubusercontent.com/PhotonVision/photonvision/refs/heads/master/scripts/onnx2rknn.py
 ```
 
+Now install the required packages, make sure you have Python 3.9 and pip downloaded.
+
+```bash
+cd airockchip-yolov5
+pip install -r requirements.txt
+```
+
 #### Export Command
 
 ```bash
-cd airockchip-yolov5 && python export.py --weights '/path/to/best.pt' --rknpu --include 'onnx'
+python3 export.py --weights '/path/to/model.pt' --rknpu --include 'onnx'
 ```
 
 ### Step 2: Converting ONNX to RKNN
@@ -60,10 +67,16 @@ Using the `onnx2rknn.py` script, convert the ONNX model to an RKNN file. This sc
 
 #### Conversion Command
 
-Run the script, passing in the ONNX model and a folder containing images from your dataset:
+First install RKNN Toolkit:
+
+```bash
+pip install rknn-toolkit2
+```
+
+Now, run the script, passing in the ONNX model and a folder containing images from your dataset:
 
 ```bash
-python onnx2rknn.py /path/to/best.onnx /path/to/export/best.rknn /path/to/dataset/valid/images
+python3 onnx2rknn.py /path/to/model.onnx /path/to/export/model.rknn /path/to/dataset/valid/images
 ```
 
 If you have any questions about the conversion process, ask in the PhotonVision Discord server.

scripts/onnx2rknn.py

@@ -16,7 +16,14 @@ def collect_images_from_directory(directory_path):
 
     return image_paths
 
-def convert(srcFileName, dstFilename, dataset):
+def save_dataset_to_file(dataset, output_path):
+    """Save the list of image paths to a text file."""
+    with open(output_path, 'w') as f:
+        for image_path in dataset:
+            f.write(image_path + '\n')
+    return output_path
+
+def convert(srcFileName, dstFilename, dataset_file):
     platform = "rk3588"
 
     print('--> Source file name: ' + srcFileName)
@@ -36,7 +43,7 @@ def convert(srcFileName, dstFilename, dataset):
 
     # Build model with quantization
     print('--> Building model')
-    ret = rknn.build(do_quantization=True, dataset=dataset)
+    ret = rknn.build(do_quantization=True, dataset=dataset_file)
     if ret != 0:
         print('build model failed.')
         exit(ret)
@@ -54,7 +61,6 @@ def convert(srcFileName, dstFilename, dataset):
     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')
@@ -66,7 +72,9 @@ def main():
         print(f"No images found in directory: {args.quant_dir}")
         exit(1)
 
-    convert(args.source_file, args.description_file, dataset)
+    dataset_file = save_dataset_to_file(dataset, 'dataset.txt')
+
+    convert(args.source_file, args.description_file, dataset_file)
 
 if __name__ == '__main__':
     main()