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توضیحات

Aruco Scale Factor Estimation

ویژگی	مقدار
سیستم عامل	OS Independent
نام فایل	aruco-estimator-1.1.5
نام	aruco-estimator
نسخه کتابخانه	1.1.5
نگهدارنده	[]
ایمیل نگهدارنده	[]
نویسنده	Lukas Meyer
ایمیل نویسنده	lukas.meyer@fau.de
آدرس صفحه اصلی	https://github.com/meyerls/aruco-estimator
آدرس اینترنتی	https://pypi.org/project/aruco-estimator/
مجوز	MIT

<p align="center" width="100%"> <img width="100%" src="https://github.com/meyerls/aruco-estimator/blob/dev/img/wood.png?raw=true"> </p> # Automatic Aruco marker-based scale factor estimation (Work in Progress!) <a href="https://pypi.org/project/aruco-estimator/"><img alt="PyPI - Python Version" src="https://img.shields.io/pypi/pyversions/aruco-estimator"></a> <a href="https://pypi.org/project/aruco-estimator/"><img alt="PyPI" src="https://img.shields.io/pypi/v/aruco-estimator"></a> <a href="https://github.com/meyerls/aruco-estimator/actions"><img alt="GitHub Workflow Status" src="https://img.shields.io/github/workflow/status/meyerls/aruco-estimator/Python%20package"></a> <a href="https://github.com/meyerls/aruco-estimator/blob/main/LICENSE"><img alt="license" src="https://img.shields.io/github/license/meyerls/aruco-estimator"></a>   ## About This project aims to automatically compute the correct scale of a point cloud generated with [COLMAP](https://colmap.github.io/) by placing an aruco marker into the scene. ## Installation ### PyPi This repository is tested on Python 3.6+ and can be installed from [PyPi](https://pypi.org/project/aruco-estimator)  ````angular2html pip install aruco-estimator ```` ## Usage ### Dataset An exemplary data set is provided. The dataset shows a simple scene of a door with an aruco marker. Other dataset might follow in future work. It can be downloaded by using ````python from aruco_estimator import download dataset = download.Dataset() dataset.download_door_dataset(output_path='.') ```` ### API A use of the code on the provided dataset can be seen in the following block. The most important function is ``aruco_scale_factor.run()``. Here, an aruco marker is searched for in each image. If a marker is found in at least 2 images, the position of the aruco corner in 3D is calculated based on the camera poses and the corners of the aruco maker.Based on the positions of the corners of the square aruco marker, the size of the marker in the unscaled reconstruction can be determined. With the correct metric size of the marker, the scene can be scaled true to scale using ``aruco_scale_factor.apply(true_scale)``. ````python from aruco_estimator.aruco_scale_factor import ArucoScaleFactor from aruco_estimator import download import os import open3d as o3d # Download example dataset. Door dataset is roughly 200 MB dataset = download.Dataset() dataset.download_door_dataset() # Init & run pose estimation of corners in 3D & estimate mean L2 distance between the four aruco corners aruco_scale_factor = ArucoScaleFactor(project_path=dataset.dataset_path) aruco_distance = aruco_scale_factor.run() print('Size of the unscaled aruco markers: ', aruco_distance) # Calculate scaling factor, apply it to the scene and save scaled point cloud dense, scale_factor = aruco_scale_factor.apply(true_scale=dataset.scale) # scale in cm print('Point cloud and poses are scaled by: ', scale_factor) print('Size of the scaled (true to scale) aruco markers in meters: ', aruco_distance * scale_factor) # Visualization of the scene and rays BEFORE scaling. This might be necessary for debugging aruco_scale_factor.visualize_estimation(frustum_scale=0.4) o3d.io.write_point_cloud(os.path.join(dataset.colmap_project, 'scaled.ply'), dense) aruco_scale_factor.write_data() ```` ## Source If you want to install the repo from source make sure that conda is installed. Afterwards clone this repository, give the bash file executable rights and install the conda env: ````angular2html git clone https://github.com/meyerls/aruco-estimator.git cd aruco-estimator chmod u+x init_env.sh ./init_env.sh ```` Finally install all python dependencies in the activated conda environment via ````angular2html pip install -r requirements.txt ```` ### Usage of Command Line ````angular2html usage: scale_estimator.py [-h] [--colmap_project COLMAP_PROJECT] [--dense_model DENSE_MODEL] [--aruco_size ARUCO_SIZE] [--visualize] [--point_size POINT_SIZE] [--frustum_size FRUSTUM_SIZE] [--test_data] Estimate scale factor for COLMAP projects with aruco markers. optional arguments: -h, --help show this help message and exit --colmap_project COLMAP_PROJECT Path to COLMAP project --dense_model DENSE_MODEL name to the dense model --aruco_size ARUCO_SIZE Size of the aruco marker in cm. --visualize Flag to enable visualization --point_size POINT_SIZE Point size of the visualized dense point cloud. Depending on the number of points in the model. Between 0.001 and 2 --frustum_size FRUSTUM_SIZE Size of the visualized camera frustums. Between 0 (small) and 1 (large) --test_data Download and try out test data ```` To test the code on your local machine try the example project by using: ````angular2html python scale_estimator.py --test_data ```` <p align="center" width="100%"> <img width="100%" src="https://github.com/meyerls/aruco-estimator/blob/dev/img/door.png?raw=true"> </p> <p align="center" width="100%"> <img width="100%" src="https://github.com/meyerls/aruco-estimator/blob/dev/img/output.gif?raw=true"> </p> ## Limitation / Improvements - [ ] Up to now only SIMPLE_RADIAL and PINHOLE camera models are supported. Extend all models - [ ] Install CLI Tool vi PyPi - [ ] Up to now only one aruco marker per scene can be detected. Multiple aruco marker could improve the scale estimation - [ ] Different aruco marker settings and marker types should be investigated for different scenarios to make it either more robust to false detections - [ ] Geo referencing of aruco markers with earth coordinate system using GPS or RTK - [ ] Only COLMAP is supported. Add additional reconstruction software. ## Acknowledgement * The Code to read out the binary COLMAP data is partly borrowed from the repo [COLMAP Utility Scripts](https://github.com/uzh-rpg/colmap_utils) by [uzh-rpg](https://github.com/uzh-rpg). * The visualization of the wooden block is created from the dataset found in [[1](https://robocip-aist.github.io/sii_nerf_scans/)] ## Trouble Shooting - In some cases cv2 does not detect the aruco marker module. Reinstalling opencv-python and opencv-python-python might help [Source](https://stackoverflow.com/questions/45972357/python-opencv-aruco-no-module-named-cv2-aruco) ## References <div class="csl-entry">[1] Erich, F., Bourreau, B., <i>Neural Scanning: Rendering and determining geometry of household objects using Neural Radiance Fields</i> <a href="https://robocip-aist.github.io/sii_nerf_scans/">Link</a>. 2022</div> ## Citation Please cite this paper, if this work helps you with your research: ``` @InProceedings{ , author="H", title="", booktitle="", year="", pages="", isbn="" } ```

نیازمندی

مقدار	نام
-	numpy
-	colmap-wrapper
-	matplotlib
-	open3d
-	opencv-contrib-python
-	pyquaternion
-	tqdm
-	wget

نحوه نصب

نصب پکیج whl aruco-estimator-1.1.5:

pip install aruco-estimator-1.1.5.whl

نصب پکیج tar.gz aruco-estimator-1.1.5:

pip install aruco-estimator-1.1.5.tar.gz