orthomasker 0.6.0

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 \
    --sam-checkpoint sam_vit_h_4b8939.pth \
    --confidence-threshold 80 \
    --min-area 100 \
    --max-area 10000 \
    --verbose

# Using Python
from orthomasker.converter import RasterFeatureExtractor

# Set up the extractor (use the path to your .pth file)
extractor = RasterFeatureExtractor(
    sam_checkpoint="sam_vit_h_4b8939.pth",
    confidence_threshold=80.0,
    min_area=100.0,      # Optional: filter by minimum area
    max_area=10000.0,    # Optional: filter by maximum area
    verbose=True,
)

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

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)

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

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

• --verbose: Enable verbose output

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 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

MIT License - see LICENSE file for details.