The ROS Package of Mask R-CNN for Object Detection and Segmentation

This is a ROS package of Mask R-CNN algorithm for object detection and segmentation.

The package contains ROS node of Mask R-CNN with topic-based ROS interface.

Most of core algorithm code was based on Mask R-CNN implementation by Matterport, Inc.

Training

This repository doesn't contain code for training Mask R-CNN network model. If you want to train the model on your own class definition or dataset, try it on the upstream reposity and give the result weight to model_path parameter.

Requirements

• ROS Indigo/kinetic
• TensorFlow 1.3+
• Keras 2.0.8+
• Numpy, skimage, scipy, Pillow, cython, h5py
• I only test code on Python 2.7, it may work on Python3.X.
• see more dependency and version details in requirements.txt

ROS Interfaces

Parameters

• ~model_path: string

  Path to the HDF5 model file. If the model_path is default value and the file doesn't exist, the node automatically downloads the file.

  Default: $ROS_HOME/mask_rcnn_coco.h5

• ~visualization: bool

  If true, the node publish visualized images to ~visualization topic. Default: true

• ~class_names: string[]

  Class names to be treated as detection targets. Default: All MS COCO classes.

Topics Published

• ~result: mask_rcnn_ros/Result

  Result of detection. See also Result.msg for detailed description.

• ~visualization: sensor_mgs/Image

  Visualized result over an input image.

Topics Subscribed

• ~input: sensor_msgs/Image

  Input image to be proccessed

Getting Started

1. Clone this repository to your catkin workspace, build workspace and source devel environment

   
   $ cd ~/.catkin_ws/src
   $ git clone https://github.com/qixuxiang/mask_rcnn_ros.git
   $ cd mask_rcnn_ros
   $ python2 -m pip install --upgrade pip
   $ python2 -m pip install -r requirements.txt
   $ cd ../..
   $ catkin_make
   $ source devel/setup.bash

2. Run mask_rcnn node
      ~~~bash
      $ rosrun mask_rcnn_ros mask_rcnn_node
      ~~~

## Example

There is a simple example launch file using [RGB-D SLAM Dataset](https://vision.in.tum.de/data/datasets/rgbd-dataset/download).

~~~bash
$ sudo chmod 777 scripts/download_freiburg3_rgbd_example_bag.sh
$ ./scripts/download_freiburg3_rgbd_example_bag.sh
$ roslaunch mask_rcnn_ros freiburg3_rgbd_example.launch
~~~

Then RViz window will appear and show result like following:

![example1](doc/mask_r-cnn_1.png)

![example2](doc/mask_r-cnn_2.png)

## Other issue

* If you have installed Anaconda|Python, Please delete or comment `export PATH=/home/soft/conda3/bin:$PATH` in you `~/.bashrc` file.

* When you run the code, please wait for a moment for the result because there will be delay when play bag file and process the images.

* Welcome to submit any issue if you have problems, and add your software system information details, such as Ubuntu 16/14,ROS Indigo/Kinetic, Python2/Python3, Tensorflow 1.4,etc..