orthomosaic
A native orthomosaic generator for nadir drone imagery. It doesn't use Docker, VMs, OpenCV, GDAL or PROJ. The heavy lifting is in Cython-compiled C that releases the GIL, with an optional CUDA backend that is selected automatically when an NVIDIA GPU is present.
Runtime dependencies: numpy and pillow (plus cupy only if you want the GPU path).
Install
From PyPI (prebuilt wheels for Linux, Windows and macOS 14+):
pip install pyOrthomosaic # CPU
pip install "pyOrthomosaic[cuda]" # + NVIDIA GPU support via CuPy
The package is installed as pyOrthomosaic but imported as orthomosaic.
From source:
python3 -m venv .venv && source .venv/bin/activate
pip install numpy cython pillow setuptools
python setup.py build_ext --inplace # compiles orthomosaic/_core.pyx
# optional GPU (Linux/Windows + NVIDIA driver), pick the wheel matching your CUDA major:
pip install cupy-cuda12x
ORTHO_NATIVE=1 python setup.py build_ext --inplace adds -march=native for a few % extra.
Use
python -m orthomosaic /path/to/images -o ortho.tif
python odm.py /path/to/images ortho.tif
from orthomosaic import build_orthomosaic, Options
report = build_orthomosaic("images/", "ortho.tif", Options(backend="auto", render_scale=0.5))
Outputs:
ortho.tif: a tiled, Deflate-compressed RGBA GeoTIFF in UTM (EPSG:326xx/327xx). It opens in QGIS and GDAL.ortho_preview.jpg: a preview no larger than 2048 px.ortho_report.json: per-image affine transforms, exposure gains, statistics and the options used.
Useful flags:
| Flag | Effect |
|---|---|
--backend cpu|cuda|auto |
Choose the compute backend (auto uses CUDA if available). |
--render-scale 0.5 |
Decode images at half size: about 4x less RAM and faster, at half the output resolution. |
--resolution 0.05 |
Output GSD in metres per pixel. |
--cache-mb 256 |
Cap the decoded-image cache used during rendering. This is the main RAM knob. |
--blend seam |
Use a crisp "most-nadir pixel wins" blend instead of feathering. Less ghosting over tall objects. |
--gps-sigma 3 |
Expected GPS error in metres. Use 0.05 for RTK drones. |
--workers N |
Number of CPU threads. |
Pipeline
- Metadata: EXIF GPS is projected to UTM with a built-in transverse Mercator implementation.
- Features: ORB-style features, computed in
_core.pyx: a 4-level pyramid, FAST-9 corners scored by Harris, grid bucketing, intensity-centroid orientation and 256-bit steered BRIEF. - Pairs: the k nearest GPS neighbours. Without GPS, all pairs for small sets, or a capture-order window.
- Matching: a single-pass Hamming matcher (popcount) with a ratio test and a mutual check, followed by RANSAC affine and a least-squares refit. On a GPU, matching uses a shared-memory CUDA kernel.
- Global alignment: a linear least-squares bundle of one affine per image. X and Y decouple,
so the solve is a small 3N x 3N system:
- a relative solve,
- a robust similarity fit to GPS,
- a joint refinement with GPS priors. This step includes a scale-lock constraint that removes the shrinkage bias of world-space least squares.
- Exposure: per-channel gain compensation in the style of Brown & Lowe.
- Render: blocks of 1024 px are rendered in parallel and streamed into the GeoTIFF, with a serpentine traversal and an LRU image cache. Memory stays bounded no matter how large the mosaic is.
Results on the synthetic benchmark
The benchmark in tests/synthetic.py simulates:
- 228 images of 1200x900 px,
- a lawnmower flight with heading flips and ±4° yaw jitter,
- ±3% altitude changes,
- exposure changes and vignetting,
- 1.5 m GPS noise.
Results on an 8-core Apple Silicon machine, CPU only:
| Metric | Result |
|---|---|
| Absolute geolocation | 0.14 m RMS. The block averages out the 1.5 m per-image GPS noise. |
| Relative (shape) accuracy | 0.05 m RMS, about 1 ground pixel |
| Run time | 27 s end to end |
python tests/synthetic.py make /tmp/syn/images
python -m orthomosaic /tmp/syn/images -o /tmp/syn/ortho.tif
python tests/synthetic.py eval /tmp/syn/images /tmp/syn/ortho.tif
python tests/test_core.py
Scope and limitations
This is a planar (2D) orthomosaic: each image is mapped to the ground with an affine transform. It is accurate for nadir imagery over terrain that is flat or gently rolling relative to the flight altitude, which covers typical agricultural, thermal and mapping surveys.
It does not do:
- full structure-from-motion or DEM-based orthorectification,
- correction for lens distortion,
- correction for gimbal pitch.
Tall buildings and steep terrain will therefore show relief displacement or ghosting. --blend seam
reduces the ghosting. The natural next steps are:
- a Brown–Conrady undistortion pass,
- a homography or bundle-adjusted camera model,
- a coarse DEM from triangulated tie points.
1 maintainer
Metadata
1 maintainer
Release files for pyOrthomosaic 0.1.0
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Transparency logSecurity for pyOrthomosaic 0.1.0
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Attestation for pyorthomosaic-0.1.0.tar.gz
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Signed by GitHub Actions, verified by PyPI on Sep 27, 2026.
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