Python bindings for COLMAP

This repository exposes to Python most COLMAP reconstruction objects, such as Cameras and Points3D, as well as estimators for absolute and relative poses.

Getting started

Wheels for Python 8/9/10 on Linux, macOS 11/12, and Windows can be install using pip:

pip install pycolmap

The wheels are automatically built and pushed to pypi at each release. They are currently not built with CUDA support, which requires building from source (see below).

Building from source

Alternatively, PyCOLMAP can be built from source. Here is how to build the most recent stable version (v0.4.0):

Install COLMAP 3.8 as a library following the official guide. Make sure to use the tag 3.8.
Clone and build the PyCOLMAP repository and its submodules:

git clone -b v0.4.0 --recursive https://github.com/colmap/pycolmap.git
cd pycolmap
pip install .

Important

The master branch is in active development and its API is unstable. It is currently only compatible with the COLMAP commit colmap/colmap@0d9ab40. Using an earlier commit might not work.

To build PyCOLMAP on Windows, install COLMAP via VCPKG following the official guide and run in powershell:

git clone https://github.com/colmap/pycolmap.git
cd pycolmap
py -m pip install . `
    --cmake.define.CMAKE_TOOLCHAIN_FILE="$VCPKG_INSTALLATION_ROOT/scripts/buildsystems/vcpkg.cmake" `
    --cmake.define.VCPKG_TARGET_TRIPLET="x64-windows"

Reconstruction pipeline

PyCOLMAP provides bindings for multiple steps of the standard reconstruction pipeline. They are defined in pipeline/ and include:

extracting and matching SIFT features
importing an image folder into a COLMAP database
inferring the camera parameters from the EXIF metadata of an image file
running two-view geometric verification of matches on a COLMAP database
triangulating points into an existing COLMAP model
running incremental reconstruction from a COLMAP database
dense reconstruction with multi-view stereo

Sparse & Dense reconstruction from a folder of images can be performed with:

output_path: pathlib.Path
image_dir: pathlib.Path

output_path.mkdir()
mvs_path = output_path / "mvs"
database_path = output_path / "database.db"

pycolmap.extract_features(database_path, image_dir)
pycolmap.match_exhaustive(database_path)
maps = pycolmap.incremental_mapping(database_path, image_dir, output_path)
maps[0].write(output_path)
# dense reconstruction
pycolmap.undistort_images(mvs_path, output_path, image_dir)
pycolmap.patch_match_stereo(mvs_path)  # requires compilation with CUDA
pycolmap.stereo_fusion(mvs_path / "dense.ply", mvs_path)

PyCOLMAP can leverage the GPU for feature extraction, matching, and multi-view stereo if COLMAP was compiled with CUDA support. Similarly, PyCOLMAP can run Delauney Triangulation if COLMAP was compiled with CGAL support. This requires to build the package from source and is not available with the PyPI wheels.

All of the above steps are easily configurable with python dicts which are recursively merged into their respective defaults, for example:

pycolmap.extract_features(database_path, image_dir, sift_options={"max_num_features": 512})
# equivalent to
ops = pycolmap.SiftExtractionOptions()
ops.max_num_features = 512
pycolmap.extract_features(database_path, image_dir, sift_options=ops)

To list available options, use

help(pycolmap.SiftExtractionOptions)

The default parameters can be looked up with

print(pycolmap.SiftExtractionOptions().summary())
# or
print(pycolmap.SiftExtractionOptions().todict())

For another example of usage, see hloc/reconstruction.py.

Reconstruction object

We can load and manipulate an existing COLMAP 3D reconstruction:

import pycolmap
reconstruction = pycolmap.Reconstruction("path/to/my/reconstruction/")
print(reconstruction.summary())

for image_id, image in reconstruction.images.items():
    print(image_id, image)

for point3D_id, point3D in reconstruction.points3D.items():
    print(point3D_id, point3D)

for camera_id, camera in reconstruction.cameras.items():
    print(camera_id, camera)

reconstruction.write("path/to/new/reconstruction/")

The object API mirrors the COLMAP C++ library. The bindings support many other operations, for example:

projecting a 3D point into an image with arbitrary camera model:

uv = camera.img_from_cam(image.cam_from_world * point3D.xyz)

aligning two 3D reconstructions by their camera poses:

rec2_from_rec1 = pycolmap.align_reconstructions_via_reprojections(reconstruction1, reconstrution2)
reconstruction1.transform(rec2_from_rec1)
print(rec2_from_rec1.scale, rec2_from_rec1.rotation, rec2_from_rec1.translation)

pycolmap also allows exporting models to TXT/NVM//CAM/Bundler/VRML/PLY (also supports pathlib.Path inputs). skip_distortion == True enables exporting more camera models, with the caveat of averaging the focal length parameters.

# Exports reconstruction to COLMAP text format.
reconstruction.write_text("path/to/new/reconstruction/")

# Exports in NVM format http://ccwu.me/vsfm/doc.html#nvm.
reconstruction.export_NVM("rec.nvm", skip_distortion=False)

# Creates a <img_name>.cam file for each image with pose/intrinsics information.
reconstruction.export_CAM("image_dir/", skip_distortion=False)

# Exports in Bundler format https://www.cs.cornell.edu/~snavely/bundler/.
reconstruction.export_bundler("rec.bundler.out", "rec.list.txt", skip_distortion=False)

# exports 3D points to PLY format.
reconstruction.export_PLY("rec.ply")

# Exports in VRML format https://en.wikipedia.org/wiki/VRML.
reconstruction.export_VRML("rec.images.wrl", "rec.points3D.wrl",
                           image_scale=1.0, image_rgb=[1.0, 0.0, 0.0])

Estimators

We provide robust RANSAC-based estimators for absolute camera pose (single-camera and multi-camera-rig), essential matrix, fundamental matrix, homography, and two-view relative pose for calibrated cameras.

All RANSAC and estimation parameters are exposed as objects that behave similarly as Python dataclasses. The RANSAC options are described in colmap/optim/ransac.h and their default values are:

ransac_options = pycolmap.RANSACOptions(
    max_error=4.0,  # reprojection error in pixels
    min_inlier_ratio=0.01,
    confidence=0.9999,
    min_num_trials=1000,
    max_num_trials=100000,
)

Absolute pose estimation

For instance, to estimate the absolute pose of a query camera given 2D-3D correspondences:

# Parameters:
# - points2D: Nx2 array; pixel coordinates
# - points3D: Nx3 array; world coordinates
# - camera: pycolmap.Camera
# Optional parameters:
# - estimation_options: dict or pycolmap.AbsolutePoseEstimationOptions
# - refinement_options: dict or pycolmap.AbsolutePoseRefinementOptions
answer = pycolmap.absolute_pose_estimation(points2D, points3D, camera)
# Returns: dictionary of estimation outputs or None if failure

2D and 3D points are passed as Numpy arrays or lists. The options are defined in estimators/absolute_pose.cc and can be passed as regular (nested) Python dictionaries:

pycolmap.absolute_pose_estimation(
    points2D, points3D, camera,
    estimation_options=dict(ransac=dict(max_error=12.0)),
    refinement_options=dict(refine_focal_length=True),
)

Absolute Pose Refinement

# Parameters:
# - cam_from_world: pycolmap.Rigid3d, initial pose
# - points2D: Nx2 array; pixel coordinates
# - points3D: Nx3 array; world coordinates
# - inlier_mask: array of N bool; inlier_mask[i] is true if correpondence i is an inlier
# - camera: pycolmap.Camera
# Optional parameters:
# - refinement_options: dict or pycolmap.AbsolutePoseRefinementOptions
answer = pycolmap.pose_refinement(cam_from_world, points2D, points3D, inlier_mask, camera)
# Returns: dictionary of refinement outputs or None if failure

Essential matrix estimation

# Parameters:
# - points1: Nx2 array; 2D pixel coordinates in image 1
# - points2: Nx2 array; 2D pixel coordinates in image 2
# - camera1: pycolmap.Camera of image 1
# - camera2: pycolmap.Camera of image 2
# Optional parameters:
# - options: dict or pycolmap.RANSACOptions (default inlier threshold is 4px)
answer = pycolmap.essential_matrix_estimation(points1, points2, camera1, camera2)
# Returns: dictionary of estimation outputs or None if failure

Fundamental matrix estimation

answer = pycolmap.fundamental_matrix_estimation(
    points1,
    points2,
    [options],       # optional dict or pycolmap.RANSACOptions
)

Homography estimation

answer = pycolmap.homography_matrix_estimation(
    points1,
    points2,
    [max_error_px],  # optional RANSAC inlier threshold in pixels
    [options],       # optional dict or pycolmap.RANSACOptions
)

Two-view geometry estimation

COLMAP can also estimate a relative pose between two calibrated cameras by estimating both E and H and accounting for the degeneracies of each model.

# Parameters:
# - camera1: pycolmap.Camera of image 1
# - points1: Nx2 array; 2D pixel coordinates in image 1
# - camera2: pycolmap.Camera of image 2
# - points2: Nx2 array; 2D pixel coordinates in image 2
# Optional parameters:
# - matches: Nx2 integer array; correspondences across images
# - options: dict or pycolmap.TwoViewGeometryOptions
answer = pycolmap.estimate_calibrated_two_view_geometry(camera1, points1, camera2, points2)
# Returns: pycolmap.TwoViewGeometry

The TwoViewGeometryOptions control how each model is selected. The output structure contains the geometric model, inlier matches, the relative pose (if options.compute_relative_pose=True), and the type of camera configuration, which is an instance of the enum pycolmap.TwoViewGeometryConfiguration.

Camera argument

Some estimators expect a COLMAP camera object, which can be created as follow:

camera = pycolmap.Camera(
    model=camera_model_name_or_id,
    width=width,
    height=height,
    params=params,
)

The different camera models and their extra parameters are defined in colmap/src/colmap/sensor/models.h. For example for a pinhole camera:

camera = pycolmap.Camera(
    model='SIMPLE_PINHOLE',
    width=width,
    height=height,
    params=[focal_length, cx, cy],
)

Alternatively, we can also pass a camera dictionary:

camera_dict = {
    'model': COLMAP_CAMERA_MODEL_NAME,
    'width': IMAGE_WIDTH,
    'height': IMAGE_HEIGHT,
    'params': EXTRA_CAMERA_PARAMETERS_LIST
}

SIFT feature extraction

import numpy as np
import pycolmap
from PIL import Image, ImageOps

# Input should be grayscale image with range [0, 1].
img = Image.open('image.jpg').convert('RGB')
img = ImageOps.grayscale(img)
img = np.array(img).astype(np.float) / 255.

# Optional parameters:
# - options: dict or pycolmap.SiftExtractionOptions
# - device: default pycolmap.Device.auto uses the GPU if available
sift = pycolmap.Sift()

# Parameters:
# - image: HxW float array
keypoints, descriptors = sift.extract(img)
# Returns:
# - keypoints: Nx4 array; format: x (j), y (i), scale, orientation
# - descriptors: Nx128 array; L2-normalized descriptors

TODO

Add documentation
Add more detailed examples
Add unit tests for reconstruction bindings

Created and maintained by Mihai Dusmanu, Philipp Lindenberger, John Lambert, Paul-Edouard Sarlin, and other contributors.

safijari / pycolmap