# zed-yolo

**Repository Path**: yajun-wang11/zed-yolo

## Basic Information

- **Project Name**: zed-yolo
- **Description**: 3D Object detection using Yolo and the ZED in Python and C++
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 1
- **Created**: 2022-04-19
- **Last Updated**: 2022-04-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Stereolabs ZED - YOLO 3D

This package lets you use [YOLO (v2 or v3)](http://pjreddie.com/darknet/yolo/), the deep learning object detector using the ZED stereo camera in Python 3 or C++.

# [Update : the ZED is now natively supported in YOLO !](https://github.com/AlexeyAB/darknet)

## 1. Setup

The setup detailed setup instructions are available in the [Darknet repository](https://github.com/AlexeyAB/darknet).

This is a brief explanation on how to enable the ZED camera support.

### Prerequisites

- Windows 7 64bits or later, Ubuntu 16.04 or 18.04
- [ZED SDK](https://www.stereolabs.com/developers/) and its dependencies ([CUDA](https://developer.nvidia.com/cuda-downloads))
- **Darknet** : https://github.com/AlexeyAB/darknet and its dependencies ([cuDNN](https://developer.nvidia.com/cudnn) and OpenCV)

## Preparing Darknet installation

### cuDNN

In order to get the best performance, [cuDNN](https://developer.nvidia.com/cudnn) should be install before compiling darknet. Heads over to this [TensorFlow documentation article](https://www.tensorflow.org/install/gpu#install_cuda_with_apt) which explains how to setup both CUDA and cuDNN on Ubuntu and Windows.

### OpenCV

OpenCV binaries can be downloaded and install from [opencv.org](https://opencv.org/releases/).

Alternatively, on Ubuntu :

    sudo apt install pkg-config libopencv-dev

### CMake

On Windows, download and install CMAKE using the binary [available here](https://cmake.org/download/).

On Ubuntu, cmake can be installed using the package manager, i.e : `sudo apt install cmake`

However the default version of cmake might be too old, it can easily be updated using the script (located in this repository):

```bash
sudo bash cmake_apt_update.sh
```

## Compiling Darknet

[Download](https://github.com/AlexeyAB/darknet) and compile darknet, following the instructions:

- [How to compile on Linux](https://github.com/AlexeyAB/darknet#how-to-compile-on-linux)
- [How to compile on Windows](https://github.com/AlexeyAB/darknet#how-to-compile-on-windows-using-vcpkg)

### ZED Support Using CMake (recommended)

If the ZED SDK is installed, CMake will automatically detect it and compile with the ZED support. During the CMake configuration, a message will confirm that the ZED SDK was found.

    ...
    -- A library with BLAS API found.
    -- ZED SDK enabled
    -- Found OpenMP_C: -fopenmp (found version "4.5")
    ...


### ZED support Using Makefile

To enable the ZED support in YOLO using the Makefile, simply enable [`GPU` and `ZED_CAMERA`](https://github.com/AlexeyAB/darknet/blob/cce34712f6928495f1fbc5d69332162fc23491b9/Makefile#L8), it's also recommended to enable `CUDNN` for improved performances.

## 2. Launching the sample

Download the yolo weights, either [yolov3](https://pjreddie.com/media/files/yolov3.weights) or [yolov3-tiny](https://pjreddie.com/media/files/yolov3-tiny.weights) for instance, and put them in the local folder.


    ./uselib data/coco.names cfg/yolov3.cfg yolov3.weights zed_camera

SVO files are also supported :

    ./uselib data/coco.names cfg/yolov3.cfg yolov3.weights /path/to/svo/file.svo

## How to use YOLO 3D in Python

The native support is currently only in C++.

For the Python version please refer to instructions in [zed_python_sample](./zed_python_sample)

## Using Docker

A DockerFile is provided in the [docker folder](./docker)

## Legacy repository

The original YOLO 3D C++ sources are available in the [legacy branch](https://github.com/stereolabs/zed-yolo/tree/legacy)