tiny_yolov2_onnx_cam

Tiny YOLO v2 Inference Application with NVIDIA TensorRT

What does this application do?

This application downloads the tiny YOLO v2 model from Open Neural Network eXchange (ONNX) Model Zoo and converts it to NVIDIA TensorRT plan, then starts the object detection for camera captured image.

Prerequisites

NVIDIA Jetson Nano Developer Kit
USB Web Camera or Raspberry Pi Camera V2
NVIDIA JetPack 4.2.1 or later

Installation

$ sudo apt-get update

$ sudo apt-get install python3-pip protobuf-compiler libprotoc-dev libjpeg-dev cmake

$ git clone https://github.com/tsutof/tiny_yolov2_onnx_cam

$ cd tiny_yolov2_onnx_cam

$ export PATH=$PATH:/usr/local/cuda/bin

$ python3 -m pip install -r requirements.txt

Usage

First, clock up your Jetson. Only the nvpmodel is not enough, the jetson_clocks command is also needed. Without the jetson_clocks, "select timeout" error happens at the frame capture.

$ sudo nvpmodel -m 0
$ sudo jetson_clocks

The following command starts this application.

$ python3 tiny_yolov2_onnx_cam.py [-h] [--camera CAMERA_NUM] [--width WIDTH]
                                  [--height HEIGHT] [--objth OBJ_THRESH]
                                  [--nmsth NMS_THRESH]

optional arguments:
  -h, --help            show this help message and exit
  --camera CAMERA_NUM, -c CAMERA_NUM
                        Camera number, use any negative integer for MIPI-CSI camera
  --width WIDTH         Capture width
  --height HEIGHT       Capture height
  --objth OBJ_THRESH    Threshold of object confidence score (between 0 and 1)
  --nmsth NMS_THRESH    Threshold of NMS algorithm (between 0 and 1)

For Raspberry Pi camera v2, use any negative number as the camera number.

$ python3 tiny_yolov2_onnx_cam.py --camera -1

For USB Web camera, if you camera is detected as /dev/video1, use 1 as the camera number.

$ python3 tiny_yolov2_onnx_cam.py --camera 1

Third Party License

This program is using open source software which is licensed with the following conditions:

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JordanMicahBennett / tiny_yolov2_onnx_cam