Edge Eval Python

A python implementation of edge eval.

The logic of the code is almost the same as that of the origin MATLAB implementation (see References).

Requirements

• Python3
• Numpy
• Scipy >= 1.6.0
• g++
• Matplotlib

Install

1. clone repository

git clone https://github.com/Walstruzz/edge_eval_python.git
cd edge_eval_python

2. compile cxx library

Most of the code in this folder is copied from davidstutz/extended-berkeley-segmentation-benchmark.

Actually, there is a more efficient function in Scipy that can solve the CSA problem without compiling the following cxx codes...

cd cxx/src
source build.sh

Usage

1. save your results

from scipy.io import savemat

key = "result"
result = your_method(image)
savemat(save_name, {key: image})

2.eval

python main.py --alg "HED" --model_name_list "hed" --result_dir examples/hed_result \
--save_dir examples/hed_eval_result --gt_dir examples/bsds500_gt --key result \
--file_format .mat --workers -1

Result(BSDS 500)

ODS	OIS	AP	R50
0.789	0.806	0.810	0.897

Note

• Because of the difference in calculation precision and the sensitivity of NMS threshold, the edge images may be slightly different.
• match_edge_maps samples points randomly (so as Matlab).
• Python and Matlab index files in different order, resulting in different order of eval_bdry_img.txt.
• Python version is slower than Matlab version. Should I implement more functions in cxx/lib/solve_cas.so?

References

• edge eval
• extended-berkeley-segmentation-benchmark.
• bwmorph_thin
• pdollar's image & video Matlab toolbox
• pdollar's edge detection toolbox
• PyTorch Reimplementation of HED