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توضیحات

Aruco Scale Factor Estimation

ویژگی	مقدار
سیستم عامل	-
نام فایل	aruco-estimator-1.1.8
نام	aruco-estimator
نسخه کتابخانه	1.1.8
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://media.githubusercontent.com/media/meyerls/aruco-estimator/main/img/wood.png"> </p> # Automatic Estimation of the Scale Factor Based on Aruco Markers (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='.') ```` ### Scale Factor Estimation An example of how to use the aruco estimator is shown below. ````python from aruco_estimator.aruco_scale_factor import ArucoScaleFactor from aruco_estimator.visualization import ArucoVisualization from aruco_estimator import download from colmap_wrapper.colmap import COLMAP import os import open3d as o3d # Download example dataset. Door dataset is roughly 200 MB dataset = download.Dataset() dataset.download_door_dataset() # Load Colmap project folder project = COLMAP(project_path=dataset.dataset_path, image_resize=0.4) # Init & run pose estimation of corners in 3D & estimate mean L2 distance between the four aruco corners aruco_scale_factor = ArucoScaleFactor(photogrammetry_software=project, aruco_size=dataset.scale) aruco_distance, aruco_corners_3d = 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() 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 vis = ArucoVisualization(aruco_colmap=aruco_scale_factor) vis.visualization(frustum_scale=0.7, point_size=0.1) # Write Data aruco_scale_factor.write_data() ```` ### Registration and Scaling In some cases COLMAP is not able to registrate all images into one dense reconstruction. If appears to be reconstructed into two seperated reconstruction. To registrate both (up to know only two are possible) reconstructions the aruco markers are used to registrate both sides using ```ArucoMarkerScaledRegistration```. ```python from aruco_estimator.registration import ArucoMarkerScaledRegistration scaled_registration = ArucoMarkerScaledRegistration(project_path_a=[path2part1], project_path_b=[path2part2]) scaled_registration.scale(debug=True) scaled_registration.registrate(manual=False, debug=True) scaled_registration.write() ``` ## 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 # You might need a password to install exiftools ./init_env.sh ```` To test the code on your local machine try the example project by using: ````angular2html python3 aruco_estimator/test.py --test_data --visualize --frustum_size 0.4 ```` <p align="center" width="100%"> <img width="100%" src="https://github.com/meyerls/aruco-estimator/blob/main/img/door.png?raw=true"> </p> <p align="center" width="100%"> <img width="100%" src="https://github.com/meyerls/aruco-estimator/blob/main/img/output.gif?raw=true"> </p> ## Limitation / Improvements - [ ] Up to now only SIMPLE_RADIAL and PINHOLE camera models are supported. Extend all models - [ ] 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). * Thanks to [Baptiste](https://github.com/Baptiste-AIST) for providing the data for the wooden block reconstruction. Source from [[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) * [PyExifTool](https://github.com/sylikc/pyexiftool): A library to communicate with the [ExifTool](https://exiftool.org) command- application. If you have trouble installing it please refer to the PyExifTool-Homepage. ```bash # For Ubuntu users: wget https://exiftool.org/Image-ExifTool-12.51.tar.gz gzip -dc Image-ExifTool-12.51.tar.gz | tar -xf - cd Image-ExifTool-12.51 perl Makefile.PL make test sudo make install ``` ## 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="", title="", booktitle="", year="", pages="", isbn="" } ```

نیازمندی

مقدار	نام
-	numpy
==1.1.5	colmap-wrapper
-	matplotlib
-	open3d
==4.6.0.66	opencv-contrib-python
-	pyquaternion
-	pycolmap
-	tqdm
-	wget
-	pyexiftool

نحوه نصب

نصب پکیج whl aruco-estimator-1.1.8:

pip install aruco-estimator-1.1.8.whl

نصب پکیج tar.gz aruco-estimator-1.1.8:

pip install aruco-estimator-1.1.8.tar.gz