/mask_rcnn_ros

Custom Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow from USB camera in ROS

Primary LanguagePythonOtherNOASSERTION

This package is modification for custom model with USB camera stream.

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 youer own class definition or dataset, try it on the upstream reposity 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

  • ~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
  2. Build workspace and source devel environment
  3. Run mask_rcnn node 
    $ rosrun mask_rcnn_ros mask_rcnn_node

Example

There is a simple example launch file using RGB-D SLAM Dataset.

$ cd mask_rcnn_ros/examples
$ ./download_example_bag.sh
$ roslaunch example.launch

Then RViz window will appear and show result like following:

example1

example2