orthomasker 0.9.5

A CLI tool and Python library for extracting vector features from geospatial raster (TIF) files using the Segment Anything Model (SAM), and exporting them as GeoJSON.

Raster Feature Extractor

A CLI tool and Python library for extracting vector features from geospatial raster (TIF) files using the Segment Anything Model (SAM), and exporting them as GeoJSON.

Installation

pip install orthomasker

Usage

# Using CLI
orthomasker your_input_filename.tif your_output_filename.geojson

# Using Python
from orthomasker.feature_extractor import RasterFeatureExtractor

# Provide your own test TIF file (upload or use a sample)
input_tif = "your_input_filename.tif"
output_geojson = "your_output_filename.geojson"

# Set up the extractor (use the path to your .pth file)
extractor = RasterFeatureExtractor()

extractor.convert(input_tif, output_geojson)

Options

• --sam-checkpoint: Path to SAM model weights (default: sam_vit_h_4b8939.pth)

• --model-type: SAM model type (vit_h, vit_l, vit_b; default: vit_h)

• --confidence-threshold: Minimum stability score to keep a mask (0–100; default: 0, no filter)

• --tile-size: Tile size for processing (default: 1024)

• --overlap: Tile overlap in pixels (default: 128)

• --class-name: Class label for output features (default: sam_object)

• --min-area: Minimum area (in square units of TIF CRS) for output features (optional)

• --max-area: Maximum area (in square units of TIF CRS) for output features (optional)

• --compactness: Minimum compactness threshold (0.0–1.0) using Polsby-Popper metric for filtering irregular shapes (optional)

• --fixed-bounds: Bounding box (minx, miny, maxx, maxy) in image CRS

• --merge: Merge overlapping polygons in output (optional)

• --verbose: Enable verbose output

Compactness Filtering

The --compactness option allows you to filter out irregular or elongated shapes by setting a minimum compactness threshold. This uses the Polsby-Popper compactness metric:

Compactness = (4π × Area) / (Perimeter²)

• Perfect circle: compactness = 1.0
• Square: compactness ≈ 0.785
• Elongated shapes: compactness approaches 0.0

Common threshold values:

• 0.1: Very permissive (removes only extremely irregular shapes)
• 0.3: Moderate filtering (removes highly irregular shapes)
• 0.6: Strict filtering (keeps only relatively compact shapes)
• 0.8: Very strict (keeps only very round/square shapes)

Development

Setup

git clone https://github.com/nickmccarty/orthomasker.git
cd orthomasker
pip install -r requirements.txt
pip install -e ".[ml,dev]"

Acknowledgments

This project leverages Meta AI’s Segment Anything Model (SAM) for automatic mask generation, which is faciliated by utilizing segment-anything-py as a dependency; many thanks to Qiusheng Wu, et al. for their work!

Citations

@article{kirillov2023segany,
title={Segment Anything},
author={Kirillov, Alexander and Mintun, Eric and Ravi, Nikhila and Mao, Hanzi and Rolland, Chloe and Gustafson, Laura and Xiao, Tete and Whitehead, Spencer and Berg, Alexander C. and Lo, Wan-Yen and Doll{'a}r, Piotr and Girshick, Ross},
journal={arXiv:2304.02643},
year={2023}
}

License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.