# Object Detection With The ONNX TensorRT Backend In Python

**Table Of Contents**
- [Description](#description)
- [How does this sample work?](#how-does-this-sample-work)
- [Prerequisites](#prerequisites)
- [Running the sample](#running-the-sample)
- [Additional resources](#additional-resources)
- [License](#license)
- [Changelog](#changelog)
- [Known issues](#known-issues)

## Description

This sample, yolov3_onnx, implements a full ONNX-based pipeline for performing inference with the YOLOv3 network, with an input size of 608 x 608 pixels, including pre and post-processing. This sample is based on the [YOLOv3-608](https://pjreddie.com/media/files/papers/YOLOv3.pdf) paper.

## How does this sample work?

First, the original YOLOv3 specification from the paper is converted to the Open Neural Network Exchange (ONNX) format in `yolov3_to_onnx.py` (only has to be done once).

Second, this ONNX representation of YOLOv3 is used to build a TensorRT engine, followed by inference on a sample image in `onnx_to_tensorrt.py`. The predicted bounding boxes are finally drawn to the original input image and saved to disk.

After inference, post-processing including bounding-box clustering is applied. The resulting bounding boxes are eventually drawn to a new image file and stored on disk for inspection.

**Note:** This sample is not supported on Ubuntu 14.04 and older.

## Prerequisites

For specific software versions, see the [TensorRT Installation Guide](https://docs.nvidia.com/deeplearning/sdk/tensorrt-archived/index.html).

1. Install the dependencies for Python.
    ```bash
    pip3 install -r requirements.txt
    ```

2.  Download sample data. See the "Download Sample Data" section of [the general setup guide](../README.md).


## Running the sample

The data directory needs to be specified (either via `-d /path/to/data` or environment varaiable `TRT_DATA_DIR`)
when running these scripts. An error will be thrown if not. Taking `TRT_DATA_DIR` approach in following example.

1.  Create an ONNX version of YOLOv3 with the following command.
    ```bash
    python3 yolov3_to_onnx.py
    ```
    When running the above command for the first time, the output should look similar to the following:
    ```
    [...]
    %106_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]]
    (%105_convolutional_lrelu, %106_convolutional_conv_weights, %106_convolutional_conv_bias)
    return %082_convolutional, %094_convolutional,%106_convolutional
    }
    ```

2.  Build a TensorRT engine from the generated ONNX file and run inference on a sample image
    ```bash
    python3 onnx_to_tensorrt.py
    ```
    When running the above command for the first time, the output should look similar to the following:
    ```
    Building an engine from file yolov3.onnx, this may take a while...
    Running inference on image dog.jpg...
    Saved image with bounding boxes of detected objects to dog_bboxes.jpg.
    ```

3.  Verify that the sample ran successfully. If the sample runs successfully you should see output similar to the following:
    ```
    Loading ONNX file from path yolov3.onnx...
    Beginning ONNX file parsing
    Completed parsing of ONNX file
    Building an engine from file yolov3.onnx; this may take a while...
    Completed creating Engine
    Running inference on image dog.jpg...
    [[135.14841333 219.59879284 184.30209195 324.0265199 ]
      [ 98.30805074 135.72613533 499.71263299 299.25579652]
      [478.00605802 81.25702449 210.57787895 86.91502688]] [0.99854713 0.99880403 0.93829258] [16 1 7]
    Saved image with bounding boxes of detected objects to dog_bboxes.png.
    ```
    You should be able to visually confirm whether the detection was correct.

# Additional resources

The following resources provide a deeper understanding about the model used in this sample, as well as the dataset it was trained on:

**Model**
- [YOLOv3: An Incremental Improvement](https://pjreddie.com/media/files/papers/YOLOv3.pdf)

**Dataset**
- [COCO dataset](http://cocodataset.org/#home)

**Documentation**
- [YOLOv3-608 paper](https://pjreddie.com/media/files/papers/YOLOv3.pdf)
- [Introduction To NVIDIA’s TensorRT Samples](https://docs.nvidia.com/deeplearning/sdk/tensorrt-sample-support-guide/index.html#samples)
- [Working With TensorRT Using The Python API](https://docs.nvidia.com/deeplearning/sdk/tensorrt-developer-guide/index.html#python_topics)
- [NVIDIA’s TensorRT Documentation Library](https://docs.nvidia.com/deeplearning/sdk/tensorrt-archived/index.html)

# License

For terms and conditions for use, reproduction, and distribution, see the [TensorRT Software License Agreement](https://docs.nvidia.com/deeplearning/sdk/tensorrt-sla/index.html) documentation.

# Changelog
August 2023
- Removed support for Python versions < 3.8.
- This sample was updated to support protobuf 3.20.3 for Python>=3.8
- Update ONNX version support to 1.14.0

March 2019
- This `README.md` file was recreated, updated and reviewed.


# Known issues

When installing the requirements with Python 3.10, there is a known issue for building onnx. The recommendation is to use a python version < 3.10 when running the sample.