Tensorflow Object Detector with ROS

Requirements:

Tensorflow and ROS

This guide targets Ubuntu 16.04 and ROS Kinetic

 

Steps:

To run Default SSD (Single Shot Detection) algorithm:

1. Install ROS: http://wiki.ros.org/kinetic/Installation/Ubuntu

2. Install camera dependencies

   sudo apt-get install ros-kinetic-usb_cam ros-kinetic-openni2-launch

3. Install tensorflow into python virtualenv: https://www.tensorflow.org/install/install_linux

   sudo apt-get install python-pip python-dev python-virtualenv

   virtualenv --system-site-packages ~/tensorflow

   source ~/tensorflow/bin/activate

   easy_install -U pip

   pip install --upgrade tensorflow

4. mkdir ~/catkin_ws/ && mkdir ~/catkin_ws/src/

5. Clone standard Vision messages repository and this repository into catkin_ws/src:

   cd ~/catkin_ws/src

   git clone https://github.com/Kukanani/vision_msgs.git

   git clone https://github.com/osrf/tensorflow_object_detector.git

6. Build tensorflow_object_detector and Vision message

   cd ~/catkin_ws && catkin_make

7. Source catkin workspace's setup.bash:

   source ~/catkin_ws/devel/setup.bash

8. Plug in camera and launch Single Shot Detector (varies per camera, NOTE: object_detect.launch also launches the openni2.launch file for the camera. If you are using any other camera, please change the camera topic in the launch file before launching the file)

   roslaunch tensorflow_object_detector object_detect.launch

   OR

   roslaunch tensorflow_object_detector usb_cam_detector.launch

If you want to try any other ML model:

1. Download any Object Detection Models from the Tensorflow Object detection API and place it in data/models/. You can find the models in tensorflow Object Detection Model Zoo: https://github.com/tensorflow/models/blob/master/object_detection/g3doc/detection_model_zoo.md. Extract the tar.gz file.

2. Edit the MODEL_NAME and LABEL_NAME in detect_ros.py. By default it is ssd_mobilenet_v1_coco_11_06_2017 with mscoco_label_map.pbtxt respectively.

https://github.com/osrf/tensorflow_object_detector