mmdetection-ros

This is a ROS package for 2D object detection, which simply utilizes the toolbox MMDetection of OpenMMLab.

Requirements

ROS Melodic
Python 3.6+, PyTorch 1.3+, CUDA 9.2+ and MMCV

Installation

Clone all needed packages: this package, mmdetection and vision_msgs

cd {YOUR WORKSPACE}/src
git clone https://github.com/afengisme11/mmdetection_ros.git
git clone https://github.com/ros-perception/vision_msgs.git
cd mmdetection_ros
git clone https://github.com/open-mmlab/mmdetection.git

MMDetection is a toolbox for 2D object detection. For using it I used a virtual environment in python3

sudo apt-get install python-pip python-dev python-virtualenv
virtualenv --system-site-packages --python=python3 ~/mmdet
source ~/mmdet/bin/activate
pip --version # check the pip version

In this created environment we install all the requirements. Note that the following instructions for mmdetection are almost the same to the official one here, so you may also firstly have a look there if you have problems.
```
pip install mmdet
```
Then install PyTorch and torchvision following the official instructions here or for previous version here, e.g., I use CUDA 10.2 and install PyTorch 1.7.1, so I run this command:
```
pip install torch==1.7.1 torchvision==0.8.2 torchaudio==0.7.2
```

Install mmcv-full.

pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/{cu_version}/{torch_version}/index.html

Check the table here. Please replace {cu_version} and {torch_version} in the url to your desired one. For example, I use the following command:

pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu102/torch1.7.0/index.html

Install build requirements and then install MMDetection.

cd ./mmdetection
pip install -r requirements/build.txt
pip install -v -e .

Install rospkg.
```
pip install rospkg
```
Put your model in the scripts folder, and modify the model path and config path in the mmdetector.py. The default one now is a Faster R-CNN I trained on my custom dataset to detect 5 products in the robot TIAGo context. You can also get it here if you want

ROS Interfaces

params

~publish_rate: the debug image publish rate. default: 1hz
~is_service: whether or not to use service instead of subscribe-publish
~visualization: whether or not to show the debug image

topics

~debug_image: publish the debug image
~objects: publish the inference result, containing the information of detected objects
~image: subscribe the input image. The default one is ''/xtion/rgb/image_raw' for TIAGo

Example

If you are using the robot TIAGo, you could

source ~/mmdet/bin/activate # in the python3 virtual env
roslaunch mmdetection_ros mmdetector.launch

TIAGo will continously do the inference on the camera image and publish the detection results