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 (CUDA, via CuPy)
pip install "pyOrthomosaic[mps]" # + Apple-silicon GPU (Metal, via MLX)
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[ctk]" # [ctk] brings the CUDA headers CuPy compiles kernels with
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 auto|cuda|mps|cpu |
Compute backend. auto picks CUDA, then the Apple GPU (mps), then the CPU. |
--palette rainbow |
Thermal palette (see below). |
--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
Digital Terrain Model
build_3d derives the bare ground from the DSM. You can also run it on any existing DSM GeoTIFF:
python -m orthomosaic.terrain recon/dsm.tif -o dtm.tif --ndsm height_above_ground.tif --max-object 80
from orthomosaic import dtm_from_dsm, TerrainOptions
dtm, ground_mask = dtm_from_dsm(dsm_array, cell_size_m, TerrainOptions(max_object_size=80))
How it works:
- The DSM is resampled to a coarse grid (0.25–1 m) using each block's 25th percentile, which is robust to stereo noise.
- Low blunders (cells more than 1 m below their neighbourhood) are dropped, so they can't dig pits into the terrain.
- A progressive morphological filter runs openings with windows growing up to
max_object_size. A slope-dependent height threshold separates objects from ground. - Detected objects are buffered by 1 m to remove stereo "fattening" at edges.
- The ground is interpolated underneath the objects, refined at full resolution, lightly smoothed, and never allowed above the real surface.
max_object_size (--dtm-max-object, default 60 m) must be larger than the short side of your
largest building. Anything wider is treated as terrain.
Synthetic check (known bare ground under 22 buildings):
- 99.4% of raised building cells removed;
- terrain error 0.16 m median in the open and 0.20 m under buildings.
Both are limited mainly by DSM noise. The largest errors sit at the edge of the surveyed area, where the DSM itself is extrapolated.
GPU backends
| Backend | Hardware | How |
|---|---|---|
cpu |
any | Cython kernels, all cores, no extra dependencies |
cuda |
NVIDIA | CuPy raw kernels compiled at runtime (pip install "pyOrthomosaic[cuda]", which includes the CUDA headers) |
mps |
Apple M1/M2/M3/M4 | Metal kernels through MLX (pip install "pyOrthomosaic[mps]") |
GPU backends run a self-test at start-up and fall back to the CPU, with a message saying what to
install, if anything fails. The GPU accelerates matching, rendering and the dense 3D sweep. Every
kernel is tested against the CPU implementation (tests/test_backends.py).
Timings on an Apple M1 (8 cores) with the same output on both backends:
| Job | CPU | Apple GPU |
|---|---|---|
| 2D, 228 synthetic images | 33 s | 19 s |
| 3D dense step, synthetic survey | 12 s | 7 s |
| 3D dense step, real 157-image RGB site | — | 80 s (a CPU run with the older, pre-batching code took 11 min) |
Thermal palettes
DJI radiometric thermal images (R-JPEG: M30T, M3T, M4T, H20T, ...) carry the raw 16-bit sensor image. When every image has it, the pipeline:
- mosaics the raw values on one survey-wide scale (palette colours are never blended, and exposure compensation is disabled);
- applies the palette to the finished product, with rainbow as the default;
- writes
*_thermal.tif, a float32 raster of raw values, and*_legend.png, a colour bar.
Available palettes are rainbow (default), iron, white_hot, black_hot, arctic, lava,
hot_metal, medical, green_hot and rainbow_hc. A custom list of RGB stops also works.
python -m orthomosaic thermal_images/ -o thermal.tif --palette iron
python -m orthomosaic.thermal list
python -m orthomosaic.thermal recolor thermal_thermal.tif -o thermal_arctic.tif --palette arctic
The last command switches the palette afterwards without re-processing.
from orthomosaic import Options, build_orthomosaic, recolor
build_orthomosaic("thermal/", "t.tif", Options(palette="white_hot"))
recolor("t_thermal.tif", "t_custom.tif", palette=[(0, 0, 0), (255, 0, 0), (255, 255, 0)])
Raw values are the camera's sensor units, which increase monotonically with temperature. Converting
them to degrees needs the camera's radiometric calibration (DJI Thermal SDK), which isn't done here.
The mosaic stores 256 levels over the survey's value range. Images without radiometric data keep
their JPEG colours; --no-thermal forces that behaviour.
3D reconstruction (DSM, true orthophoto, point cloud, mesh)
The 3D mode is for nadir grid flights. It produces a 2.5D reconstruction: surfaces seen from above (roofs, ground, trees), but not walls.
orthomosaic-3d /path/to/images -o recon/ # or: python -m orthomosaic.reconstruct ...
from orthomosaic import build_3d, Options3D
report = build_3d("images/", "recon/", Options3D(dsm_resolution=0.05))
Outputs, all in the same local height datum (height above take-off when DJI relative altitude is
present; report.json gives the offset to absolute altitude):
| File | Content |
|---|---|
dsm.tif |
Digital Surface Model: float32 elevation GeoTIFF including buildings and trees (nodata -9999) |
dtm.tif |
Digital Terrain Model: bare ground, with buildings, vegetation and cars removed |
ndsm.tif |
Height above ground (DSM − DTM): building and tree heights |
orthophoto.tif |
True orthophoto rendered on the DSM, so buildings don't lean |
dense.las, dense.ply |
Coloured dense point cloud (LAS 1.2 with EPSG, and binary PLY). LAS points are classified ground (2) or unclassified (1). |
mesh.glb, mesh.obj |
Textured surface mesh (glTF 2.0 and OBJ+MTL+JPEG) |
sparse.ply, report.json |
SfM points, camera poses and intrinsics, statistics |
dsm_preview.png, orthophoto_preview.jpg |
Quick looks |
How it works:
- Structure from motion. The 2D alignment gives the initial poses. Matches are verified with a plane+parallax test, which unlike the 8-point method doesn't degenerate on flat scenes. Matches are linked into multi-view tracks and triangulated. A Cython bundle adjustment (Levenberg–Marquardt, Schur complement, Huber loss) refines everything using GPS and DJI altitude priors, and self-calibrates the radial distortion.
- Dense matching. A coarse-to-fine height sweep over a ground grid. Photo-consistency is texture-weighted NCC against a per-cell reference view (the most nadir camera), taken over the best half of the other views to handle occlusion. The coarse level is regularised by 8-direction semi-global matching, so weakly textured roofs take their height from their edges. The same code runs on numpy (Cython sampler) or CuPy (CUDA kernel).
- Products. Outlier removal and smooth push-pull hole filling, a true orthophoto that blends only the views agreeing at each cell's height, point clouds, and a grid mesh that keeps ridges and walls sharp.
Focal length: straight-down imagery can't separate focal length from depth. Scaling both together
gives identical images, and relative altitude does not resolve this. The focal length is therefore
held at the EXIF value (35 mm-equivalent), and heights inherit its accuracy (about 1–2%). Pass
Options3D(refine_focal=True) only for flights with large altitude changes.
Synthetic check (tests/synthetic3d.py: 48 images, 22 buildings 4–15 m tall with walls, lens
distortion, 1° tilts, 1.5 m GPS noise):
- cameras 0.25 m / 0.2° from truth, distortion recovered;
- confidently matched DSM cells 0.12 m median error at a 10.5 cm cell size;
- 45 s on 8 CPU cores.
Flat, untextured areas and building edges are the weak spots: they are interpolated or slightly "fattened".
Scope and limitations
- 2D mode (
orthomosaic) is a planar mosaic. It is fast, but tall objects lean. For roofs and other relief, use the true orthophoto from the 3D mode. - 3D mode is 2.5D (one height per ground cell). Walls, overhangs and oblique/orbit flights aren't modelled.
- Thermal imagery has little texture, so its DSM is noticeably less reliable than RGB. Prefer the RGB DSM for geometry.
- Gimbal pitch isn't assumed to be nadir: tilts are solved in the bundle adjustment. Strongly oblique shots should still be excluded.
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Metadata
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