This folder contains Python modules that are utilities for object detection networks.
To use the rpn component you need precompiled cython modules for nms (at least cpu_nms.cpXX-win_amd64.pyd for Windows or cpu_nms.cpython-XXm.so for Linux) and bbox (cython_bbox.cpXX-win_amd64.pyd for Windows or cython_bbox.cpython-XXm.so for Linux). To compile the cython modules for windows see (https://github.com/MrGF/py-faster-rcnn-windows):
git clone https://github.com/MrGF/py-faster-rcnn-windows
cd $FRCN_ROOT/lib
python setup.py build_ext --inplace
For Linux see (https://github.com/rbgirshick/py-faster-rcnn):
git clone https://github.com/rbgirshick/py-faster-rcnn
cd $FRCN_ROOT/lib
python setup.py build_ext --inplace
Copy the compiled .pyd (Windows) or .so (Linux) files into the cython_modules subfolder of this utils folder.
Contains all required parameters for using a region proposal network in training or evaluation. You can overwrite these parameters by specifying a config.py file of the same format inside your working directory.
The rpn module contains helper methods and required layers to generate region proposal networks for object detection.
Contains helper methods to create a region proposal network (rpn) and a proposal target layer for training the rpn.
Generates a regular grid of multi-scale, multi-aspect anchor boxes.
Converts RPN outputs (per-anchor scores and bbox regression estimates) into object proposals.
Generates training targets/labels for each anchor. Classification labels are 1 (object), 0 (not object) or -1 (ignore). Bbox regression targets are specified when the classification label is > 0.
Generates training targets/labels for each object proposal: classification labels 0 - K (bg or object class 1, ... , K) and bbox regression targets in that case that the label is > 0.
Generate object detection proposals from an imdb using an RPN.