This is a ROS Server served as an Object Detection using Faster_RCNN Method. Given an image and then output bbox information.

This idea comes form FasterRCNN

This code is modified from py-faster-rcnn, but I modified it as a ROS server to do specific task for object Recognition in PROGRESSLAB

To run this demo, you have to install caffe_model as mentioned in py-faster-rcnn in scripts folder and include a pretrained .caffemodel file. For model to download, please contact me [email protected]

1. Please recursive clone my repo, make sure you have included caffe-fast-rcnn folder in scripts folder
2. In scripts/caffe-fast-rcnn folder, make sure you have changed the branch to faster-rcnn branch, not master branch
3. In scripts/caffe-fast-rcnn folder, make a copy of Makefile.config.example as Make.config and uncomment the line: WITH_PYTHON_LAYER := 1
4. In scripts/caffe-fast-rcnn folder, add opencv_imgcodecs in file Makefile. It is around the line 174 in LIBRARIES variable. Then run make and make python. And pip install scikit-image, easydict, protobuf, cython, apt-get install python-opencv
5. In scripts/data/demo folder, include test images and make sure in client.py in scripts folder has specified the path of these test images.
6. In scripts/data/faster_rcnn_models, include pretrained model and make sure in demo.py in scripts folder has specified the path of pretrained model.
7. Run demo.py first, then run client.py

The demo video is shown on following youtube link demo

Use --net NET_NAME to indicate which trained model to use, as being specified in

NETS = {key: (name of folder, model name, pre-trained or not, tuples of class)}