# mask_rcnn_ros

**Repository Path**: gxdcode/mask_rcnn_ros

## Basic Information

- **Project Name**: mask_rcnn_ros
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-05-06
- **Last Updated**: 2024-06-22

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

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

This is a ROS package of [Mask R-CNN](https://arxiv.org/abs/1703.06870) 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. ](https://github.com/matterport/Mask_RCNN)

## 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](https://github.com/matterport/Mask_RCNN) and give the result weight to `model_path` parameter.


## Requirements
* ROS kinetic
* TensorFlow 1.3+
* Keras 2.0.8+
* Numpy, skimage, scipy, Pillow, cython, h5py

## ROS Interfaces

### Parameters

* `~input: string`

    Topic name of the input RGB image.

    Default: `'/camera/rgb/image_raw'`

* `~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.


## Getting Started

1. Clone this repository to your catkin workspace
2. Build workspace and source devel environment
3. 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
$ ./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:

<p align="center">
<img src="https://github.com/ethz-asl/mask_rcnn_ros/raw/master/doc/maskrcnnros.gif" width="700">
</p>