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pyOrthomosaic

Orthomosaics, DSM/DTM, dense point clouds and 3D meshes from drone photos — natively in Python. No Docker, no VM, no OpenCV, GDAL or PROJ. The heavy lifting runs in Cython-compiled C that uses every CPU core; NVIDIA (CUDA) and Apple-silicon (Metal) GPUs are used when available.

Two pipelines:

Command Output
2D orthomosaic Fast planar orthomosaic (tall objects lean)
2.5D / 3D orthomosaic-3d True orthophoto on a DSM, DSM / DTM / nDSM, dense point cloud (LAZ/PLY), 3D mesh (OBJ/GLB), contours, quality report

Package name pyOrthomosaic, import name orthomosaic. Python ≥ 3.9.


Install

pip install pyOrthomosaic                       # CPU (numpy + pillow only)
pip install "pyOrthomosaic[mesh,laz]"           # + 3D mesh (open3d) + LAZ point cloud (laspy/lazrs)
pip install "pyOrthomosaic[mps]"                # + Apple-silicon GPU (MLX)
pip install "pyOrthomosaic[cuda]"               # + NVIDIA GPU (CuPy)
pip install onnxruntime                         # optional: AI sky / background masks

macOS: open3d also needs brew install libusb.

From source (compiles the C kernels _core, _ba, _mvs, _dense):

git clone <repo> && cd ODM
python3 -m venv .venv && source .venv/bin/activate
pip install numpy cython pillow setuptools
pip install -e ".[mesh,laz]"

Optional extras only add outputs: without open3d the mesh is built from the DSM, without laspy[lazrs] the cloud is written as LAS.


Quick start

3D (DSM + true orthophoto + cloud + mesh)

orthomosaic-3d /path/to/images -o recon/
from orthomosaic import build_3d, Options3D

report = build_3d("images/", "recon/", Options3D())
print(report["dsm"]["gsd"], report["outputs"])

2D (planar orthomosaic)

orthomosaic /path/to/images -o ortho.tif
from orthomosaic import build_orthomosaic, Options

build_orthomosaic("images/", "ortho.tif", Options(render_scale=0.5))

Examples

Higher-resolution DSM, contours, no DTM

orthomosaic-3d images/ -o recon/ --dsm-resolution 0.03 --contours 1.0 --no-dtm

RTK drone, ground control points

from orthomosaic import build_3d, Options3D

opt = Options3D(gps_sigma=0.05,          # RTK-fixed positions (m)
                gcp="gcps.txt",          # WebODM / Pix4D GCP list
                gcp_sigma=0.02)          # surveyed accuracy (m)
build_3d("images/", "recon/", opt)

DJI electronic shutter, AI sky masks for oblique photos

orthomosaic-3d images/ -o recon/ --rolling-shutter --rolling-shutter-readout 25 --sky-removal

Fast run on an 8 GB laptop

opt = Options3D(depth_max_image=1200,     # smaller depth maps
                mesh_method="dsm",        # skip the Poisson mesh
                dtm=False, formats=("laz",),
                cache_mb=512)
build_3d("images/", "recon/", opt)

Pick the photos / thermal binding

from orthomosaic import build_3d, build_thermal_bound, Options3D
import glob

rgb = sorted(glob.glob("flight/*_V.JPG"))
build_3d(rgb, "recon_rgb/", Options3D())

# thermal placed from the RGB block via a rigid rig -> co-registered thermal DSM/ortho
build_thermal_bound("rgb/", "thermal/", "recon_bound/", Options3D(backend="mps"))

Quality report, DTM from an existing DSM, thermal palette

from orthomosaic import write_quality_report, dtm_from_dsm, TerrainOptions, recolor

write_quality_report("recon/report.json", "recon/quality_report.html")
dtm, ground = dtm_from_dsm(dsm_array, 0.05, TerrainOptions(max_object_size=80))
recolor("t_thermal.tif", "t_iron.tif", palette="iron")

3D outputs

All rasters share one north-up grid (same origin, cell size and CRS = UTM of the survey).

File Content
orthophoto.tif True orthophoto, RGBA GeoTIFF (alpha 0 = no data)
dsm.tif Digital Surface Model, float32 (nodata −9999)
dtm.tif, ndsm.tif Bare ground, height above ground
dsm_support.tif 1 = measured from the dense cloud, 2 = interpolated, 0 = none
valid_mask.tif DSM validity
source_image_id.tif Photo used per cell (index into report["source_images"])
coverage_count.tif Photos that see each cell (0 = hidden in all, coloured by fallback)
dense.laz / dense.ply Dense 3D point cloud (ASPRS classes ground / building / vegetation)
mesh.obj, mesh.glb Poisson mesh from the dense cloud (vertex colours)
contours.geojson Terrain contours (--contours)
sparse.ply, report.json Tie points, cameras, statistics, timing
*_preview.png/jpg Quick looks

Heights are in the take-off datum when DJI relative altitude is present; report.json gives the offset to absolute altitude.


How the 3D pipeline works

  1. Metadata — EXIF/XMP: GPS, focal length, DJI gimbal/flight speed/shutter type. Optional masks: <image>_mask.png next to an image, or AI sky/background masks; masked pixels are ignored everywhere.
  2. Aerial triangulation — ORB-style features, GPS-neighbour matching, plane + parallax verification, multi-view tracks, Levenberg–Marquardt bundle adjustment (Schur complement, Huber loss) with GPS / altitude / GCP priors and lens self-calibration. Optional rolling-shutter correction (ODM readout database, DJI flight speeds).
  3. Dense matching — one depth map per photo: coarse-to-fine multi-view plane sweep (NCC, ¼ → ½ → full of depth_max_image), geometric consistency across neighbouring depth maps, fusion into a 3D cloud where each point is confirmed by ≥ depth_min_views photos. Depth maps are temporary and always deleted, also when a run fails.
  4. DSM — per cell the median of the cloud's top layer (roofs never averaged with walls or ground below); empty cells filled from nearby points in radius steps (spacing × √2ᵏ); holes inside the area seen by ≥ 2 photos filled from the lower surrounding surface (no fake ramps beside buildings); spike removal and edge-preserving smoothing.
  5. True orthophoto — each DSM cell is projected into photos chosen from different sides of the tile, tested for occlusion against the DSM, and scored (viewing angle, resolution, border distance, exposure). An MRF picks one photo per cell with seams where photos agree; colours blend only near seams. Cells hidden in every photo take the best photo's colour (fill_hidden).
  6. DTM — progressive morphological ground filter; nDSM; contours on a 1 m DTM.
  7. Cloud + mesh — LAZ/PLY (LAS scale from the point spacing), Poisson mesh (open3d).

Resolution is never finer than GSD − 10 % (ignore_gsd=True to override).


Main options (Options3D)

Option CLI Default Meaning
dsm_resolution --dsm-resolution native GSD Cell size (m) of DSM and orthophoto
gps_sigma --gps-sigma 3.0 GPS accuracy (m); ~0.05 for RTK-fixed
dense_method --dense-method depthmap depthmap (3D cloud) or sweep (legacy 2.5D, used for thermal)
depth_max_image --depth-max-image 1600 Depth-map size (px, long side)
depth_min_views --depth-min-views 3 Photos that must agree on a point
dsm_max_fill --dsm-max-fill −1 Hole fill distance (m); −1 = everywhere photographed by ≥ 2 cameras
dem_gapfill_steps --dem-gapfill-steps 3 Radius steps for filling cells from nearby points
ortho_blend --ortho-blend seam seam (no ghosting) or feather
ortho_views — 8 Photos per tile, chosen from different sides
fill_hidden — True Colour fully occluded cells from the best photo
occlusion --no-occlusion True Occlusion test in the orthophoto
color_balance --no-color-balance True Per-image gain + offset
dtm --no-dtm True Bare-ground DTM
contour_interval --contours 0 Contour spacing (m), 0 = off
mesh_method — auto Poisson from the cloud (open3d) or DSM mesh
mesh_max_vertices --mesh-max-vertices 600 000 Mesh budget
formats --formats ply,laz,obj,glb Cloud / mesh files
rolling_shutter --rolling-shutter False Electronic-shutter correction
sky_removal, bg_removal --sky-removal, --bg-removal False AI masks (onnxruntime)
gcp, gcp_sigma --gcp, --gcp-sigma — , 0.05 Ground control
backend --backend auto auto, cuda, mps, cpu
cache_mb --cache-mb 1024 Image cache (main RAM knob)

orthomosaic-3d --help lists every flag.


Ground control points

WebODM / Pix4D format, one row per image mark:

EPSG:32646
230012.5 2635008.1 12.3 2456.0 1810.5 DJI_0001_V.JPG GCP1
230012.5 2635008.1 12.3 1203.0  905.0 DJI_0002_V.JPG GCP1
230040.0 2635050.0 10.0  812.0  640.0 DJI_0007_V.JPG GCP2

Three or more well-spread GCPs. report.json gives per-GCP residuals and RMSE. GCP residuals are not an independent accuracy check — keep separate checkpoints for that.


Thermal

DJI radiometric R-JPEGs (M30T, M3T, M4T, H20T …) are mosaicked on raw sensor values with one survey-wide scale, then coloured with a palette (rainbow, iron, white_hot, black_hot, arctic, lava, hot_metal, medical, green_hot, rainbow_hc). Raw values are written to *_thermal.tif; python -m orthomosaic.thermal recolor changes the palette afterwards.


Performance

Synthetic benchmark, Apple M1 8 GB, 48 photos at 12 MP, 40 M-cell DSM: ≈ 4.5 min end to end (depth maps 67 s, DSM 30 s, orthophoto 65 s, DTM 45 s, mesh 17 s). Expect ~7–8 min for 120 photos. On 8 GB machines close other heavy apps — swapping is the main slowdown.


Limitations

  • 2.5D: one height per cell; walls and overhangs are not represented in the DSM/orthophoto.
  • Areas hidden in every photo cannot be recovered; with fill_hidden they are coloured from the best photo (see coverage_count.tif = 0).
  • Low-texture, shadowed or moving surfaces are interpolated (dsm_support.tif = 2).
  • Absolute accuracy depends on GNSS/GCPs; low reprojection error is not proof of survey accuracy.

License: MIT.

Metadata

Release files for pyOrthomosaic 0.4.4

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Table of built distributions (wheels) for pyOrthomosaic 0.4.4
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pyorthomosaic-0.4.4-cp313-cp313-musllinux_1_2_x86_64.whl CPython 3.13 CPython 3.13 Linux musl 1.2+ x86-64 Details
pyorthomosaic-0.4.4-cp313-cp313-musllinux_1_2_aarch64.whl CPython 3.13 CPython 3.13 Linux musl 1.2+ ARM64 Details
pyorthomosaic-0.4.4-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_28_x86_64.whl CPython 3.13 CPython 3.13 Linux glibc 2.17+ x86-64, Linux glibc 2.28+ x86-64 Details
pyorthomosaic-0.4.4-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.manylinux_2_28_aarch64.whl CPython 3.13 CPython 3.13 Linux glibc 2.28+ ARM64, Linux glibc 2.17+ ARM64 Details
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pyorthomosaic-0.4.4-cp312-cp312-musllinux_1_2_aarch64.whl CPython 3.12 CPython 3.12 Linux musl 1.2+ ARM64 Details
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pyorthomosaic-0.4.4-cp310-cp310-macosx_14_0_arm64.whl CPython 3.10 CPython 3.10 macOS 14.0+ ARM64 Details
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pyorthomosaic-0.4.4-cp39-cp39-musllinux_1_2_x86_64.whl CPython 3.9 CPython 3.9 Linux musl 1.2+ x86-64 Details
pyorthomosaic-0.4.4-cp39-cp39-musllinux_1_2_aarch64.whl CPython 3.9 CPython 3.9 Linux musl 1.2+ ARM64 Details
pyorthomosaic-0.4.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_28_x86_64.whl CPython 3.9 CPython 3.9 Linux glibc 2.17+ x86-64, Linux glibc 2.28+ x86-64 Details
pyorthomosaic-0.4.4-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.manylinux_2_28_aarch64.whl CPython 3.9 CPython 3.9 Linux glibc 2.28+ ARM64, Linux glibc 2.17+ ARM64 Details
pyorthomosaic-0.4.4-cp39-cp39-macosx_14_0_x86_64.whl CPython 3.9 CPython 3.9 macOS 14.0+ x86-64 Details
pyorthomosaic-0.4.4-cp39-cp39-macosx_14_0_arm64.whl CPython 3.9 CPython 3.9 macOS 14.0+ ARM64 Details

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