Structured Edge Detection Toolbox V3.0, by Piotr Dollar (pdollar-at-gmail.com) FOR MORE DETAILS SEE https://github.com/pdollar/edges

This code is published under the MSR-LA Full Rights License. Please read license.txt for more info.

This fork adds a C++ and Python wrapper for structured edges and edge-boxes object proposals (see papers above), removing the Matlab dependency. To keep the code clean and reduce effort I used the C++ implementation of structured random forest edges from Philipp Krähenbühl (http://www.philkr.net/home/gop), included in ./cpp/external.

The Python wrapper requires

1. Boost::python
2. My fork of Sudeep Pillai's numpy-opencv-converter (see https://github.com/samarth-robo/numpy-opencv-converter)
3. My fork of Hilton Bristow's cvmatio (see https://github.com/samarth-robo/cvmatio)

1. Clone and compile numpy-opencv-converter:

git clone https://github.com/samarth-robo/numpy-opencv-converter.git
cd numpy-opencv-converter/build
cmake ..
make

2. Clone, make and install cvmatio:

git clone https://github.com/samarth-robo/cvmatio.git
cd cvmatio/build
cmake ..
make
make install

3. Clone edges and compile edges

git clone https://github.com/samarth-robo/edges.git
cd edges

Now edit CMakeLists.txt and set the values of CVMATIO_PATH and NUMPY_OPENCV_CONVERTER_PATH Then,

cmake ..
make -j6

1. For structured random forest edges use the function edge_detect(const Mat &im, Mat &E, Mat &O, string st_path) in ./cpp/src/edge_detect.cpp. st_path is the full path to the trained structured random forest, which can be obtained at http://googledrive.com/host/0B6qziMs8hVGieFg0UzE0WmZaOW8/code/gop_data.zip (link taken from http://www.philkr.net/home/gop)

2. For the C++ wrapper of edge-boxes see the file .cpp/src/edge_boxes_demo.cpp

3. For the Python wrapper, build the cpp directory using .cpp/CMakeLists.txt to get edge_boxes_python.so in the build folder. Add the build folder to your PYTHONPATH and then:

import cv2, os
from edge_boxes_python import edge_boxes_python
eb = edge_boxes_python(os.path.expanduser('~') + '/Documents/MATLAB/edges/cpp/external/gop_1.3/data/sf.dat') # string is path to the trained structured random forest, see 1.
im = cv2.imread('test.jpg')
bbs = eb.get_edge_boxes(im)