README.md

Perform object detection on NVIDIA Jetson with ONNX Runtime

1. Create a Dockerfile using the Jetson ONNX Runtime docker image and add application dependencies

   FROM mcr.microsoft.com/azureml/onnxruntime:v.1.4.0-jetpack4.4-l4t-base-r32.4.3
   WORKDIR .
   RUN apt-get update && apt-get install -y python3-pip libprotobuf-dev protobuf-compiler
   RUN python3 -m pip install onnx==1.6.0 easydict matplotlib
   CMD ["/bin/bash"]

2. Build a new image from the Dockerfile

   docker build -t jetson-onnxruntime-yolov4 .

3. Download the Yolov4 model, the object detection anchor locations, and class names from the ONNX model zoo

   wget https://github.com/onnx/models/blob/master/vision/object_detection_segmentation/yolov4/model/yolov4.onnx?raw=true -O yolov4.onnx
   wget https://raw.githubusercontent.com/onnx/models/master/vision/object_detection_segmentation/yolov4/dependencies/yolov4_anchors.txt
   wget https://raw.githubusercontent.com/natke/onnxruntime-jetson/master/coco.names

4. Download the Yolov4 object detection pre and post processing code

   wget https://raw.githubusercontent.com/natke/onnxruntime-jetson/master/preprocess_yolov4.py
   wget https://raw.githubusercontent.com/natke/onnxruntime-jetson/master/postprocess_yolov4.py

5. Download one or more test images

   wget https://raw.githubusercontent.com/SoloSynth1/tensorflow-yolov4/master/data/kite.jpg

6. Create an application called main.py to preprocess an image, run object detection and display the original image with the detected objects

   import cv2
   import numpy as np
   import preprocess_yolov4 as pre
   import postprocess_yolov4 as post
   from PIL import Image
   
   input_size = 416
   
   original_image = cv2.imread("kite.jpg")
   original_image = cv2.cvtColor(original_image, cv2.COLOR_BGR2RGB)
   original_image_size = original_image.shape[:2]
   
   image_data = pre.image_preprocess(np.copy(original_image), [input_size, input_size])
   image_data = image_data[np.newaxis, ...].astype(np.float32)
   
   print("Preprocessed image shape:",image_data.shape) # shape of the preprocessed input        
   
   import onnxruntime as rt
   
   sess = rt.InferenceSession("yolov4.onnx")
   
   output_name = sess.get_outputs()[0].name
   input_name = sess.get_inputs()[0].name
   
   detections = sess.run([output_name], {input_name: image_data})[0]
   
   print("Output shape:", detections.shape)
   
   image = post.image_postprocess(original_image, input_size, detections)
   
   image = Image.fromarray(image)
   image.save("kite-with-objects.jpg")

7. Run the application

   docker run -it --rm -v $PWD:/workspace/ --workdir=/workspace/ --gpus all jetson-onnxruntime-yolov4 python3 main.py

The application reads in the kite image and locates all the objects in the image. You can try it with different images, and extend the application to use a video stream as shown in the Azure IoT edge application above.