mmdetection_ros

ROS 2 wrap for MMDetection. Object detection, instance segmentation and panoptic sementation are supported.

Table of Contents

1. Installation
2. Nodes
3. Usage
4. Demo

Installation

# Ubuntu 20.04 CUDA 10.1
$ pip3 install torch==1.8.1+cu101 torchvision==0.9.1+cu101 torchaudio==0.8.1 -f https://download.pytorch.org/whl/torch_stable.html
$ pip3 install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu101/torch1.8.0/index.html
$ pip3 install mmcv mmdet
$ cd ~/ros2_ws/src
$ git clone https://github.com/mgonzs13/mmdetection_ros.git
$ cd ~/ros2_ws
$ rosdep install --from-paths src -r -y --ignore-src
$ colcon build

Nodes

MmDetectionNode

This node run MMDetection.

Subscribers

• image_raw (type sensor_msgs/msg/Image) - Input images to feed to the detector

Publishers

• detections (type mmdetection_msgs/msg/Detections) - Detections found in the image (boxes and masks)

Services

• enable (type std_srvs::srv::SetBool) - Enable/disable the detector

Parameters

• config - a path to a file describing a network
• weights - a path to a file with weights/checkpoints for the given network
• device - device to use (GPU/CPU)
• threshold - minimum probability of a detection to be published
• detection.nms_threshold - Non-maximal Suppression threshold - controls filtering of overlapping boxes

VisualizationNode

This node publish the resulting image after applying the detections.

Subscribers

• image_raw (type sensor_msgs/msg/Image) - Input images to feed to the detector
• detections (type mmdetection_msgs/msg/Detections) - Detections found in the image (boxes and masks)

Publishers

• result_image (type sensor_msgs/msg/Image) - Output image after applying detections

Parameters

• alpha - alpha factor to apply in masks
• thickness - thickness for line drawing

Usage

$ ros2 launch mmdetection_bringup mmdetection.launch.py

Launch configuration

• network_config - Network configuration file (.py)
• weights - Weights (checkpoints) file (.pth)
• device - Device to use (GPU/CPU)
• enable - Wheter to start darknet enabled
• threshold - Minimum probability of a detection to be published
• publish_result_image - Whether to publish result images with detections
• show_debug_image - Whether show image with the detections
• input_image_topic - Name of the input image topic
• namespace - Namespace for the nodes

Demo

$ ros2 run usb_cam usb_cam_node_exe

$ wget https://download.openmmlab.com/mmdetection/v2.0/yolox/yolox_tiny_8x8_300e_coco/yolox_tiny_8x8_300e_coco_20211124_171234-b4047906.pth
$ ros2 launch mmdetection_bringup mmdetection.launch.py network_config:=~/ros2_ws/src/mmdetection_ros/mmdetection_bringup/config/yolox/yolox_tiny_8x8_300e_coco.py weights:=yolox_tiny_8x8_300e_coco_20211124_171234-b4047906.pth

Examples

• yolox_tiny_8x8_300e_coco_20211124_171234-b4047906.pth

• yolact_r50_1x8_coco_20200908-f38d58df.pth

• panoptic_fpn_r50_fpn_1x_coco_20210821_101153-9668fd13.pth