pyEdgeEval 0.1.3

Python Edge Evaluation Benchmark

Edge Evaluation Tools

Edge detection tasks heavily relies on the original codes used in BSDS300/500 written in MATLAB and C++. In the field of computer vision, various edge detection algorithms are now resorting to Python and the various machine learning libraries. However, due to the fact that not everyone has access to MATLAB and that the original benchmark codes are outdated, evaluating these algorithms, especially on remote servers (docker containers), has been difficult. This library aims to remove these limitations and make it easy for models to be evaluated and benchmarked without the need of MATLAB. The original C++ codes used in the MATLAB benchmarks are ported with Cython and the evaluation scripts are rewritten in Python. The codebase is created to be extensible and supports various tasks and datasets as well as different evaluation protocols. To test the validity of the evaluation code, pyEdgeEval's results are compared with the results of the original MATLAB codes.

pyEdgeEval is:

• an alternative to the original MATLAB benchmark
• light with minimal dependencies
• modular and easily customizable
• fast (uses multiprocessing and Cython)
• implements common preprocessing algorithms
• supports various tasks and datasets
• supports various evaluation protocols
• edge generation tools
• etc...

Supported tasks:

• Edge Detection
• Semantic Boundary Detection

Supported datasets:

• BSDS500
• SBD
• Cityscapes

Disclaimers:

• The evaluation code does not output results that exactly match the original MATLAB benchmark. This could be for various reasons. The results are, for the most part, close enough. I recommend NOT to compare the results from this evaluation directly with results obtained through the MATLAB code for this reason.
• The codes and algorithms are not perfect. I will not take responsibility for how the code is used. If there are some bugs or improvements, please submit issues or pull requests.

Installation

Dependencies

• python >= 3.8
• cv2

Installation guide

# Install dependencies
pip install -r requirements.txt

# install cv2 (e.g. use pip)
pip install opencv-python

# Option 1. install without cloning the project (only tested on ubuntu with python 3.8)
pip install pyEdgeEval

# Option 2. install as a pip package (install as a package)
git clone https://github.com/haruishi43/py-edge-eval.git
pip install -e .

Evaluation for each datasets

BSDS500

Script:

python scripts/evaluate_bsds500.py <path/to/bsds500> <path/to/pred> <path/to/output> --thresholds=5 --nproc=8

Tested with @xwjabc's HED implementation. Setting --nproc will drastically improve the evaluation. However, due to the randomness in the original MATLAB (C++) codebase, the results will be different (at most +-0.001 difference).

SBD

Script:

python scripts/evaluate_sbd.py <path/to/sbd> <path/to/pred> <path/to/output> --categories=15 --thresholds=5 --nproc=8

CityScapes

First, create GT data using this script:

# if you plan on evaluating with instance-sensitive edges (IS edges)
python scripts/convert_dataset/cityscapes.py --nproc 8
# if you plan on evaluating with instance-insensitive edges
python scripts/convert_dataset/cityscapes.py --insensitive --nproc 8

Evaluation script:

python scripts/evaluate_cityscapes.py <path/to/cityscapes> <path/to/predictions> <path/to/output> --categories='[1, 14]' --thresholds 99 --nproc 8

--thin will enable thinning on predictions and use thinned GTs. For instance-insensitive edges, you would need to supply --pre-seal argument.

Acknowledgements

• edges
  • tools for processing edges (written in MATLAB and C++)
• seal
  • semantic boundary detection protocols
• edge_eval_python:
  • referenced implementation for bwmorph thinning algorithm and nms preprocessing
  • referenced PR-curve visualization codes
• py-bsds500
  • referenced Cython ports for correspond_pixels used in the original MATLAB code
  • referenced python version of the evaluation script for BSDS500