pyEdgeEval 0.0.4

Python Edge Evaluation Benchmark

Python Binding for Edge Evaluation Code

Installation

# Install dependencies
pip install -r requirements.txt
pip install opencv-python  # cv2

# Option 1. build the project (doesn't install as a package)
python setup.py build_ext --inplace

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

# Option 3. install without cloning the project
pip install pyEdgeEval

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> --category=15 --thresholds=5 --nproc=8

Per-category evaluation is currently supported.

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
# if you plan on evaluating with instance-insensitive edges
python scripts/convert_dataset/cityscapes.py --insensitive

Evaluation script:

python scripts/evaluate_cityscapes.py <path/to/cityscapes> <path/to/predictions> <path/to/output> --category 14 --thresholds 99 --nproc 8

For instance-insensitive edges, you would need to supply --pre-seal argument.