Initial snapshot: hand motion pipeline (Dyn-HaMR + dex-retargeting + SPIDER)
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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Copyright (c) 2020, Carnegie Mellon University
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All rights reserved.
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Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
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1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
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2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
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3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.
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THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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# TartanAir dataset: AirSim Simulation Dataset for Simultaneous Localization and Mapping
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This repository provides sample code and scripts for accessing the training and testing data, as well as evaluation tools. Please refer to [TartanAir](http://theairlab.org/tartanair-dataset) for more information about the dataset.
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You can also reach out to contributors on the associated [AirSim GitHub](https://github.com/microsoft/AirSim).
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This dataset was used to train the first generalizable learning-based visual odometry [TartanVO](http://theairlab.org/tartanvo/), which achieved better performance than geometry-based VO methods in challenging cases. Please check out the [paper](https://arxiv.org/pdf/2011.00359.pdf) and published [TartanVO code](https://github.com/castacks/tartanvo).
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## Download the training data
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The data is divided into two levels (Easy and Hard) in terms of the motion patterns. It is organized in trajectory folders. You can download data from different cameras (left or right), with different data types (RGB, depth, segmentation, camera pose, and flow). Please see [data type](data_type.md) page for the camera intrinsics, extrinsics and other information.
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<p style="color:red"> <b> !! NOTE: The size of all the data is up to 3TB! It could take days to download. We also added the option to use the dataset directly on Azure without requiring a download. Please select the data type you really need before download. You can also go to
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<a href=http://theairlab.org/tartanair-dataset>TartanAir</a>
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to download the sample files for a better understanding of the data types. </b> </p>
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### Data directory structure
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```
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ROOT
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|
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--- ENV_NAME_0 # environment folder
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| |
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| ---- Easy # difficulty level
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| | |
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| | ---- P000 # trajectory folder
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| | | |
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| | | +--- depth_left # 000000_left_depth.npy - 000xxx_left_depth.npy
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| | | +--- depth_right # 000000_right_depth.npy - 000xxx_right_depth.npy
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| | | +--- flow # 000000_000001_flow/mask.npy - 000xxx_000xxx_flow/mask.npy
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| | | +--- image_left # 000000_left.png - 000xxx_left.png
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| | | +--- image_right # 000000_right.png - 000xxx_right.png
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| | | +--- seg_left # 000000_left_seg.npy - 000xxx_left_seg.npy
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| | | +--- seg_right # 000000_right_seg.npy - 000xxx_right_seg.npy
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| | | ---- pose_left.txt
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| | | ---- pose_right.txt
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| | |
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| | +--- P001
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| | .
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| | .
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| | |
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| | +--- P00K
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| |
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| +--- Hard
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|
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+-- ENV_NAME_1
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.
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.
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|
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+-- ENV_NAME_N
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```
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### Download data to your local machine
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We provide a python script `download_training.py` for the data downloading. You can also take look at the [URL list](download_training_zipfiles.txt) to download the spesific files you want.
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* Specify an output directory
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--output-dir OUTPUTDIR
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* Select file type:
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--rgb
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--depth
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--seg
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--flow
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* Select difficulty level:
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--only-hard
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--only-easy
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[NO TAG]: both 'hard' and 'easy' levels
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* Select camera:
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--only-left
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--only-right
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[NO TAG]: both 'left' and 'right' cameras
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* Select flow type when --flow is set:
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--only-flow
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--only-mask
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[NO TAG]: both 'flow' and 'mask' files
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For example, download all the RGB images from the left camera:
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```
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python download_training.py --output-dir OUTPUTDIR --rgb --only-left
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```
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Download all the depth data from both cameras in hard level:
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```
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python download_training.py --output-dir OUTPUTDIR --depth --only-hard
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```
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Download all optical flow data without flow-mask:
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```
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python download_training.py --output-dir OUTPUTDIR --flow --only-flow
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```
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Download all the files in the dataset (could be very slow due to the large size):
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```
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python download_training.py --output-dir OUTPUTDIR --rgb --depth --seg --flow
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```
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---
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**NOTE**
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We found that using AzCopy, which is a tool provided by MicroSoft, is much faster than directly downloading by the URLs. Please try the following steps if you want to accelerate the downloading process.
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1. Download the [AzCopy](https://docs.microsoft.com/en-us/azure/storage/common/storage-use-azcopy-v10) and put the executable to your system path.
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2. Run the above commands with a --azcopy tag.
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---
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### Access the data using Azure virtual machine
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Yet another way to access the data is to use an Azure virtual machine. In this way, you don't have to download the data to your local machine. You can use [Azure SDKs](https://github.com/Azure/azure-sdk-for-python) to access the data on the cloud. [Here](TartanAir_Sample.ipynb) is a sample notebook for accessing and visualizing the data. **NOTE: This sample notebook can only be used on Azure. To download the data to your local machine, please refer to the download instructions [here](https://github.com/castacks/tartanair_tools#download-data-to-your-local-machine) or the [dataset website](http://theairlab.org/tartanair-dataset) for the sample data.**
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## Download the testing data for the CVPR Visual SLAM challenge
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You can click the download links below. In Linux system, you can also run the following command to download all the files:
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```
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wget -r -i download_cvpr_slam_test.txt
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```
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* [Monocular track](https://tartanair.blob.core.windows.net/tartanair-testing1/tartanair-test-mono-release.tar.gz) (Size: 7.65 GB)
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MD5 hash: 009b52e7d7b224ffb8a203db294ac9fb
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```
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mono
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|
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--- ME000 # monocular easy trajectory 0
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| |
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| ---- 000000.png # RGB image 000000
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| ---- 000001.png # RGB image 000001
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| .
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| .
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| ---- 000xxx.png # RGB image 000xxx
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|
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+-- ME001 # monocular easy trajectory 1
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.
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.
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+-- ME007 # monocular easy trajectory 7
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|
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+-- MH000 # monocular hard trajectory 0
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.
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.
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|
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+-- MH007 # monocular hard trajectory 7
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```
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* [Stereo track](https://tartanair.blob.core.windows.net/tartanair-testing1/tartanair-test-stereo-release.tar.gz) (Size: 17.51 GB)
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MD5 hash: 8a3363ff2013f147c9495d5bb161c48e
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```
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stereo
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|
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--- SE000 # stereo easy trajectory 0
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| |
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| ---- image_left # left image folder
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| | |
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| | ---- 000000_left.png # RGB left image 000000
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| | ---- 000001_left.png # RGB left image 000001
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| | .
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| | .
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| | ---- 000xxx_left.png # RGB left image 000xxx
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| |
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| ---- image_right # right image folder
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| |
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| ---- 000000_right.png # RGB right image 000000
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| ---- 000001_right.png # RGB right image 000001
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| .
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| .
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| ---- 000xxx_right.png # RGB right image 000xxx
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|
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+-- SE001 # stereo easy trajectory 1
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.
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.
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+-- SE007 # stereo easy trajectory 7
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|
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+-- SH000 # stereo hard trajectory 0
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.
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.
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|
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+-- SH007 # stereo hard trajectory 7
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```
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* [Both monocular and stereo tracks](https://tartanair.blob.core.windows.net/tartanair-testing1/tartanair-test-release.tar.gz) (Size: 25.16 GB)
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MD5 hash: ea176ca274135622cbf897c8fa462012
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More information about the [CVPR Visual SLAM challenge](https://sites.google.com/view/vislocslamcvpr2020/slam-challenge)
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* The [monocular track](https://www.aicrowd.com/challenges/tartanair-visual-slam-mono-track)
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* The [stereo track](https://www.aicrowd.com/challenges/tartanair-visual-slam-stereo-track)
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Now the CVPR challenge has completed, if you need the <b> ground truth poses </b> for the above testing trajectories, please send an email to [tartanair@hotmail.com](tartanair@hotmail.com).
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## Evaluation tools
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Following the [TUM dataset](https://vision.in.tum.de/data/datasets/rgbd-dataset) and the [KITTI dataset](http://www.cvlibs.net/datasets/kitti/eval_odometry.php), we adopt three metrics: absolute trajectory error (ATE), the relative pose error (RPE), a modified version of KITTI VO metric.
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[More details](https://vision.in.tum.de/data/datasets/rgbd-dataset/tools#evaluation)
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Check out the sample code:
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```
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cd evaluation
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python tartanair_evaluator.py
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```
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Note that our camera poses are defined in the NED frame. That is to say, the x-axis is pointing to the camera's forward, the y-axis is pointing to the camera's right, the z-axis is pointing to the camera's downward. You can use the `cam2ned` function in the `evaluation/trajectory_transform.py` to transform the trajectory from the camera frame to the NED frame.
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## Paper
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More technical details are available in the [TartanAir paper](https://arxiv.org/abs/2003.14338). Please cite this as:
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```
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@article{tartanair2020iros,
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title = {TartanAir: A Dataset to Push the Limits of Visual SLAM},
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author = {Wang, Wenshan and Zhu, Delong and Wang, Xiangwei and Hu, Yaoyu and Qiu, Yuheng and Wang, Chen and Hu, Yafei and Kapoor, Ashish and Scherer, Sebastian},
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booktitle = {2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
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year = {2020}
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}
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```
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## Contact
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Email tartanair@hotmail.com if you have any questions about the data source. To post problems in the Github issue is also welcome. You can also reach out to contributors on the associated [GitHub](https://github.com/microsoft/AirSim).
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## License
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[This software is BSD licensed.](https://opensource.org/licenses/BSD-3-Clause)
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Copyright (c) 2020, Carnegie Mellon University All rights reserved.
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Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
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|
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Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
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||||
|
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Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
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Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.
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THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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Vendored
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### GRB Image
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The color images are stored as 640x480 8-bit RGB images in PNG format.
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* Load the image using OpenCV:
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```
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import cv2
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img = cv2.imread(FILENAME)
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cv2.imshow('img', img)
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cv2.waitKey(0)
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```
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* Load the image using Pillow:
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```
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from PIL import Image
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img = Image.open(FILENAME)
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img.show()
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```
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### Camera intrinsics
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```
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fx = 320.0 # focal length x
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fy = 320.0 # focal length y
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cx = 320.0 # optical center x
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cy = 240.0 # optical center y
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fov = 90 deg # field of view
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width = 640
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height = 480
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```
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### Depth image
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The depth maps are stored as 640x480 16-bit numpy array in NPY format. In the Unreal Engine, the environment usually has a sky sphere at a large distance. So the infinite distant object such as the sky has a large depth value (e.g. 10000) instead of an infinite number.
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The unit of the depth value is meter. The baseline between the left and right cameras is 0.25m.
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* Load the depth image:
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```
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import numpy as np
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depth = np.load(FILENAME)
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# change to disparity image
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disparity = 80.0 / depth
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```
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### Segmentation image
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The segmentation images are saved as a uint8 numpy array. AirSim assigns value 0 to 255 to each mesh available in the environment.
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[More details](https://github.com/microsoft/AirSim/blob/master/docs/image_apis.md#segmentation)
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* Load the segmentation image
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```
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import numpy as np
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depth = np.load(FILENAME)
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```
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### Optical flow
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The optical flow maps are saved as a float32 numpy array, which is calculated based on the ground truth depth and ground truth camera motion, using [this](https://github.com/huyaoyu/ImageFlow) code. Dynamic objects and occlusions are masked by the mask file, which is a uint8 numpy array. We currently provide the optical flow for the left camera.
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* Load the optical flow
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```
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import numpy as np
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flow = np.load(FILENAME)
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# load the mask
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mask = np.load(MASKFILENAME)
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```
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### Pose file
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The camera pose file is a text file containing the translation and orientation of the camera in a fixed coordinate frame. Note that our automatic evaluation tool expects both the ground truth trajectory and the estimated trajectory to be in this format.
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* Each line in the text file contains a single pose.
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* The number of lines/poses is the same as the number of image frames in that trajectory.
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* The format of each line is '**tx ty tz qx qy qz qw**'.
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* **tx ty tz** (3 floats) give the position of the optical center of the color camera with respect to the world origin in the world frame.
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* **qx qy qz qw** (4 floats) give the orientation of the optical center of the color camera in the form of a unit quaternion with respect to the world frame.
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* The camera motion is defined in the NED frame. That is to say, the x-axis is pointing to the camera's forward, the y-axis is pointing to the camera's right, the z-axis is pointing to the camera's downward.
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* Load the pose file:
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```
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import numpy as np
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flow = np.loadtxt(FILENAME)
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```
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Vendored
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https://tartanair.blob.core.windows.net/tartanair-testing1/tartanair-test-mono-release.tar.gz
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https://tartanair.blob.core.windows.net/tartanair-testing1/tartanair-test-stereo-release.tar.gz
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||||
https://tartanair.blob.core.windows.net/tartanair-testing1/tartanair-test-release.tar.gz
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Vendored
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from os import system, mkdir
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import argparse
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from os.path import isdir, isfile
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def get_args():
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parser = argparse.ArgumentParser(description='TartanAir')
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parser.add_argument('--output-dir', default='./',
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help='root directory for downloaded files')
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parser.add_argument('--rgb', action='store_true', default=False,
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help='download rgb image')
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||||
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||||
parser.add_argument('--depth', action='store_true', default=False,
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help='download depth image')
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parser.add_argument('--flow', action='store_true', default=False,
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help='download optical flow')
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parser.add_argument('--seg', action='store_true', default=False,
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help='download segmentation image')
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parser.add_argument('--only-easy', action='store_true', default=False,
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help='download only easy trajectories')
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parser.add_argument('--only-hard', action='store_true', default=False,
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help='download only hard trajectories')
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parser.add_argument('--only-left', action='store_true', default=False,
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help='download only left camera')
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parser.add_argument('--only-right', action='store_true', default=False,
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help='download only right camera')
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parser.add_argument('--only-flow', action='store_true', default=False,
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help='download only optical flow wo/ mask')
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||||
parser.add_argument('--only-mask', action='store_true', default=False,
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help='download only mask wo/ flow')
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parser.add_argument('--azcopy', action='store_true', default=False,
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help='download the data with AzCopy, which is 10x faster in our test')
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||||
args = parser.parse_args()
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||||
return args
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||||
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||||
def _help():
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||||
print ''
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||||
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||||
if __name__ == '__main__':
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||||
args = get_args()
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||||
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||||
# output directory
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||||
outdir = args.output_dir
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||||
if not isdir(outdir):
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||||
print('Output dir {} does not exists!'.format(outdir))
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||||
exit()
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||||
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||||
# difficulty level
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||||
levellist = ['Easy', 'Hard']
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||||
if args.only_easy:
|
||||
levellist = ['Easy']
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||||
if args.only_hard:
|
||||
levellist = ['Hard']
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||||
if args.only_easy and args.only_hard:
|
||||
print('--only-eazy and --only-hard tags can not be set at the same time!')
|
||||
exit()
|
||||
|
||||
|
||||
# filetype
|
||||
typelist = []
|
||||
if args.rgb:
|
||||
typelist.append('image')
|
||||
if args.depth:
|
||||
typelist.append('depth')
|
||||
if args.seg:
|
||||
typelist.append('seg')
|
||||
if args.flow:
|
||||
typelist.append('flow')
|
||||
if len(typelist)==0:
|
||||
print('Specify the type of data you want to download by --rgb/depth/seg/flow')
|
||||
exit()
|
||||
|
||||
# camera
|
||||
cameralist = ['left', 'right', 'flow', 'mask']
|
||||
if args.only_left:
|
||||
cameralist.remove('right')
|
||||
if args.only_right:
|
||||
cameralist.remove('left')
|
||||
if args.only_flow:
|
||||
cameralist.remove('mask')
|
||||
if args.only_mask:
|
||||
cameralist.remove('flow')
|
||||
if args.only_left and args.only_right:
|
||||
print('--only-left and --only-right tags can not be set at the same time!')
|
||||
exit()
|
||||
if args.only_flow and args.only_mask:
|
||||
print('--only-flow and --only-mask tags can not be set at the same time!')
|
||||
exit()
|
||||
|
||||
# read all the zip file urls
|
||||
with open('download_training_zipfiles.txt') as f:
|
||||
lines = f.readlines()
|
||||
ziplist = [ll.strip() for ll in lines if ll.strip().endswith('.zip')]
|
||||
|
||||
downloadlist = []
|
||||
for zipfile in ziplist:
|
||||
zf = zipfile.split('/')
|
||||
filename = zf[-1]
|
||||
difflevel = zf[-2]
|
||||
|
||||
# image/depth/seg/flow
|
||||
filetype = filename.split('_')[0]
|
||||
# left/right/flow/mask
|
||||
cameratype = filename.split('.')[0].split('_')[-1]
|
||||
|
||||
if (difflevel in levellist) and (filetype in typelist) and (cameratype in cameralist):
|
||||
downloadlist.append(zipfile)
|
||||
|
||||
if len(downloadlist)==0:
|
||||
print('No file meets the condition!')
|
||||
exit()
|
||||
|
||||
print('{} files are going to be downloaded...'.format(len(downloadlist)))
|
||||
for fileurl in downloadlist:
|
||||
print fileurl
|
||||
|
||||
for fileurl in downloadlist:
|
||||
zf = fileurl.split('/')
|
||||
filename = zf[-1]
|
||||
difflevel = zf[-2]
|
||||
envname = zf[-3]
|
||||
|
||||
envfolder = outdir + '/' + envname
|
||||
if not isdir(envfolder):
|
||||
mkdir(envfolder)
|
||||
print('Created a new env folder {}..'.format(envfolder))
|
||||
# else:
|
||||
# print('Env folder {} already exists..'.format(envfolder))
|
||||
|
||||
levelfolder = envfolder + '/' + difflevel
|
||||
if not isdir(levelfolder):
|
||||
mkdir(levelfolder)
|
||||
print(' Created a new level folder {}..'.format(levelfolder))
|
||||
# else:
|
||||
# print('Level folder {} already exists..'.format(levelfolder))
|
||||
|
||||
targetfile = levelfolder + '/' + filename
|
||||
if isfile(targetfile):
|
||||
print('Target file {} already exists..'.format(targetfile))
|
||||
exit()
|
||||
|
||||
if args.azcopy:
|
||||
cmd = 'azcopy copy ' + fileurl + ' ' + targetfile
|
||||
else:
|
||||
cmd = 'wget -r -O ' + targetfile + ' ' + fileurl
|
||||
print cmd
|
||||
system(cmd)
|
||||
+288
@@ -0,0 +1,288 @@
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/abandonedfactory/Easy/depth_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/abandonedfactory/Easy/depth_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/abandonedfactory/Easy/flow_flow.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/abandonedfactory/Easy/flow_mask.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/abandonedfactory/Easy/image_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/abandonedfactory/Easy/image_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/abandonedfactory/Easy/seg_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/abandonedfactory/Easy/seg_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/abandonedfactory/Hard/depth_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/abandonedfactory/Hard/depth_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/abandonedfactory/Hard/flow_flow.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/abandonedfactory/Hard/flow_mask.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/abandonedfactory/Hard/image_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/abandonedfactory/Hard/image_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/abandonedfactory/Hard/seg_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/abandonedfactory/Hard/seg_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/abandonedfactory_night/Easy/depth_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/abandonedfactory_night/Easy/depth_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/abandonedfactory_night/Easy/flow_flow.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/abandonedfactory_night/Easy/flow_mask.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/abandonedfactory_night/Easy/image_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/abandonedfactory_night/Easy/image_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/abandonedfactory_night/Easy/seg_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/abandonedfactory_night/Easy/seg_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/abandonedfactory_night/Hard/depth_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/abandonedfactory_night/Hard/depth_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/abandonedfactory_night/Hard/flow_flow.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/abandonedfactory_night/Hard/flow_mask.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/abandonedfactory_night/Hard/image_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/abandonedfactory_night/Hard/image_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/abandonedfactory_night/Hard/seg_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/abandonedfactory_night/Hard/seg_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/amusement/Easy/depth_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/amusement/Easy/depth_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/amusement/Easy/flow_flow.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/amusement/Easy/flow_mask.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/amusement/Easy/image_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/amusement/Easy/image_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/amusement/Easy/seg_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/amusement/Easy/seg_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/amusement/Hard/depth_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/amusement/Hard/depth_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/amusement/Hard/flow_flow.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/amusement/Hard/flow_mask.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/amusement/Hard/image_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/amusement/Hard/image_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/amusement/Hard/seg_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/amusement/Hard/seg_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/carwelding/Easy/depth_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/carwelding/Easy/depth_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/carwelding/Easy/flow_flow.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/carwelding/Easy/flow_mask.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/carwelding/Easy/image_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/carwelding/Easy/image_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/carwelding/Easy/seg_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/carwelding/Easy/seg_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/carwelding/Hard/depth_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/carwelding/Hard/depth_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/carwelding/Hard/flow_flow.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/carwelding/Hard/flow_mask.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/carwelding/Hard/image_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/carwelding/Hard/image_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/carwelding/Hard/seg_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/carwelding/Hard/seg_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/endofworld/Easy/depth_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/endofworld/Easy/depth_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/endofworld/Easy/flow_flow.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/endofworld/Easy/flow_mask.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/endofworld/Easy/image_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/endofworld/Easy/image_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/endofworld/Easy/seg_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/endofworld/Easy/seg_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/endofworld/Hard/depth_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/endofworld/Hard/depth_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/endofworld/Hard/flow_flow.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/endofworld/Hard/flow_mask.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/endofworld/Hard/image_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/endofworld/Hard/image_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/endofworld/Hard/seg_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/endofworld/Hard/seg_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/gascola/Easy/depth_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/gascola/Easy/depth_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/gascola/Easy/flow_flow.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/gascola/Easy/flow_mask.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/gascola/Easy/image_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/gascola/Easy/image_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/gascola/Easy/seg_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/gascola/Easy/seg_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/gascola/Hard/depth_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/gascola/Hard/depth_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/gascola/Hard/flow_flow.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/gascola/Hard/flow_mask.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/gascola/Hard/image_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/gascola/Hard/image_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/gascola/Hard/seg_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/gascola/Hard/seg_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/hospital/Easy/depth_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/hospital/Easy/depth_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/hospital/Easy/flow_flow.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/hospital/Easy/flow_mask.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/hospital/Easy/image_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/hospital/Easy/image_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/hospital/Easy/seg_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/hospital/Easy/seg_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/hospital/Hard/depth_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/hospital/Hard/depth_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/hospital/Hard/flow_flow.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/hospital/Hard/flow_mask.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/hospital/Hard/image_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/hospital/Hard/image_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/hospital/Hard/seg_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/hospital/Hard/seg_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/japanesealley/Easy/depth_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/japanesealley/Easy/depth_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/japanesealley/Easy/flow_flow.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/japanesealley/Easy/flow_mask.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/japanesealley/Easy/image_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/japanesealley/Easy/image_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/japanesealley/Easy/seg_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/japanesealley/Easy/seg_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/japanesealley/Hard/depth_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/japanesealley/Hard/depth_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/japanesealley/Hard/flow_flow.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/japanesealley/Hard/flow_mask.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/japanesealley/Hard/image_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/japanesealley/Hard/image_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/japanesealley/Hard/seg_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/japanesealley/Hard/seg_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/neighborhood/Easy/depth_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/neighborhood/Easy/depth_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/neighborhood/Easy/flow_flow.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/neighborhood/Easy/flow_mask.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/neighborhood/Easy/image_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/neighborhood/Easy/image_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/neighborhood/Easy/seg_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/neighborhood/Easy/seg_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/neighborhood/Hard/depth_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/neighborhood/Hard/depth_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/neighborhood/Hard/flow_flow.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/neighborhood/Hard/flow_mask.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/neighborhood/Hard/image_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/neighborhood/Hard/image_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/neighborhood/Hard/seg_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/neighborhood/Hard/seg_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/ocean/Easy/depth_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/ocean/Easy/depth_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/ocean/Easy/flow_flow.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/ocean/Easy/flow_mask.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/ocean/Easy/image_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/ocean/Easy/image_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/ocean/Easy/seg_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/ocean/Easy/seg_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/ocean/Hard/depth_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/ocean/Hard/depth_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/ocean/Hard/flow_flow.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/ocean/Hard/flow_mask.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/ocean/Hard/image_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/ocean/Hard/image_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/ocean/Hard/seg_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/ocean/Hard/seg_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/office/Easy/depth_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/office/Easy/depth_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/office/Easy/flow_flow.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/office/Easy/flow_mask.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/office/Easy/image_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/office/Easy/image_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/office/Easy/seg_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/office/Easy/seg_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/office/Hard/depth_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/office/Hard/depth_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/office/Hard/flow_flow.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/office/Hard/flow_mask.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/office/Hard/image_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/office/Hard/image_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/office/Hard/seg_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/office/Hard/seg_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/office2/Easy/depth_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/office2/Easy/depth_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/office2/Easy/flow_flow.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/office2/Easy/flow_mask.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/office2/Easy/image_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/office2/Easy/image_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/office2/Easy/seg_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/office2/Easy/seg_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/office2/Hard/depth_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/office2/Hard/depth_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/office2/Hard/flow_flow.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/office2/Hard/flow_mask.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/office2/Hard/image_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/office2/Hard/image_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/office2/Hard/seg_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/office2/Hard/seg_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/oldtown/Easy/depth_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/oldtown/Easy/depth_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/oldtown/Easy/flow_flow.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/oldtown/Easy/flow_mask.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/oldtown/Easy/image_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/oldtown/Easy/image_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/oldtown/Easy/seg_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/oldtown/Easy/seg_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/oldtown/Hard/depth_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/oldtown/Hard/depth_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/oldtown/Hard/flow_flow.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/oldtown/Hard/flow_mask.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/oldtown/Hard/image_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/oldtown/Hard/image_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/oldtown/Hard/seg_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/oldtown/Hard/seg_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/seasidetown/Easy/depth_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/seasidetown/Easy/depth_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/seasidetown/Easy/flow_flow.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/seasidetown/Easy/flow_mask.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/seasidetown/Easy/image_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/seasidetown/Easy/image_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/seasidetown/Easy/seg_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/seasidetown/Easy/seg_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/seasidetown/Hard/depth_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/seasidetown/Hard/depth_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/seasidetown/Hard/flow_flow.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/seasidetown/Hard/flow_mask.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/seasidetown/Hard/image_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/seasidetown/Hard/image_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/seasidetown/Hard/seg_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/seasidetown/Hard/seg_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/seasonsforest/Easy/depth_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/seasonsforest/Easy/depth_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/seasonsforest/Easy/flow_flow.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/seasonsforest/Easy/flow_mask.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/seasonsforest/Easy/image_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/seasonsforest/Easy/image_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/seasonsforest/Easy/seg_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/seasonsforest/Easy/seg_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/seasonsforest/Hard/depth_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/seasonsforest/Hard/depth_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/seasonsforest/Hard/flow_flow.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/seasonsforest/Hard/flow_mask.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/seasonsforest/Hard/image_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/seasonsforest/Hard/image_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/seasonsforest/Hard/seg_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/seasonsforest/Hard/seg_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/seasonsforest_winter/Easy/depth_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/seasonsforest_winter/Easy/depth_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/seasonsforest_winter/Easy/flow_flow.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/seasonsforest_winter/Easy/flow_mask.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/seasonsforest_winter/Easy/image_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/seasonsforest_winter/Easy/image_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/seasonsforest_winter/Easy/seg_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/seasonsforest_winter/Easy/seg_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/seasonsforest_winter/Hard/depth_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/seasonsforest_winter/Hard/depth_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/seasonsforest_winter/Hard/flow_flow.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/seasonsforest_winter/Hard/flow_mask.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/seasonsforest_winter/Hard/image_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/seasonsforest_winter/Hard/image_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/seasonsforest_winter/Hard/seg_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/seasonsforest_winter/Hard/seg_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/soulcity/Easy/depth_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/soulcity/Easy/depth_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/soulcity/Easy/flow_flow.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/soulcity/Easy/flow_mask.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/soulcity/Easy/image_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/soulcity/Easy/image_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/soulcity/Easy/seg_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/soulcity/Easy/seg_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/soulcity/Hard/depth_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/soulcity/Hard/depth_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/soulcity/Hard/flow_flow.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/soulcity/Hard/flow_mask.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/soulcity/Hard/image_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/soulcity/Hard/image_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/soulcity/Hard/seg_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/soulcity/Hard/seg_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/westerndesert/Easy/depth_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/westerndesert/Easy/depth_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/westerndesert/Easy/flow_flow.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/westerndesert/Easy/flow_mask.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/westerndesert/Easy/image_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/westerndesert/Easy/image_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/westerndesert/Easy/seg_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/westerndesert/Easy/seg_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/westerndesert/Hard/depth_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/westerndesert/Hard/depth_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/westerndesert/Hard/flow_flow.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/westerndesert/Hard/flow_mask.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/westerndesert/Hard/image_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/westerndesert/Hard/image_right.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/westerndesert/Hard/seg_left.zip
|
||||
https://tartanair.blob.core.windows.net/tartanair-release1/westerndesert/Hard/seg_right.zip
|
||||
Vendored
+133
@@ -0,0 +1,133 @@
|
||||
#!/usr/bin/python
|
||||
|
||||
# Modified by Wenshan Wang
|
||||
# Modified by Raul Mur-Artal
|
||||
# Automatically compute the optimal scale factor for monocular VO/SLAM.
|
||||
|
||||
# Software License Agreement (BSD License)
|
||||
#
|
||||
# Copyright (c) 2013, Juergen Sturm, TUM
|
||||
# All rights reserved.
|
||||
#
|
||||
# Redistribution and use in source and binary forms, with or without
|
||||
# modification, are permitted provided that the following conditions
|
||||
# are met:
|
||||
#
|
||||
# * Redistributions of source code must retain the above copyright
|
||||
# notice, this list of conditions and the following disclaimer.
|
||||
# * Redistributions in binary form must reproduce the above
|
||||
# copyright notice, this list of conditions and the following
|
||||
# disclaimer in the documentation and/or other materials provided
|
||||
# with the distribution.
|
||||
# * Neither the name of TUM nor the names of its
|
||||
# contributors may be used to endorse or promote products derived
|
||||
# from this software without specific prior written permission.
|
||||
#
|
||||
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
|
||||
# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
|
||||
# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
|
||||
# FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
|
||||
# COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
|
||||
# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
|
||||
# BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
|
||||
# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
|
||||
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
|
||||
# LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
|
||||
# ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
|
||||
# POSSIBILITY OF SUCH DAMAGE.
|
||||
#
|
||||
# Requirements:
|
||||
# sudo apt-get install python-argparse
|
||||
|
||||
"""
|
||||
This script computes the absolute trajectory error from the ground truth
|
||||
trajectory and the estimated trajectory.
|
||||
"""
|
||||
|
||||
import numpy
|
||||
|
||||
def align(model,data,calc_scale=False):
|
||||
"""Align two trajectories using the method of Horn (closed-form).
|
||||
|
||||
Input:
|
||||
model -- first trajectory (3xn)
|
||||
data -- second trajectory (3xn)
|
||||
|
||||
Output:
|
||||
rot -- rotation matrix (3x3)
|
||||
trans -- translation vector (3x1)
|
||||
trans_error -- translational error per point (1xn)
|
||||
|
||||
"""
|
||||
numpy.set_printoptions(precision=3,suppress=True)
|
||||
model_zerocentered = model - model.mean(1)
|
||||
data_zerocentered = data - data.mean(1)
|
||||
|
||||
W = numpy.zeros( (3,3) )
|
||||
for column in range(model.shape[1]):
|
||||
W += numpy.outer(model_zerocentered[:,column],data_zerocentered[:,column])
|
||||
U,d,Vh = numpy.linalg.linalg.svd(W.transpose())
|
||||
S = numpy.matrix(numpy.identity( 3 ))
|
||||
if(numpy.linalg.det(U) * numpy.linalg.det(Vh)<0):
|
||||
S[2,2] = -1
|
||||
rot = U*S*Vh
|
||||
|
||||
if calc_scale:
|
||||
rotmodel = rot*model_zerocentered
|
||||
dots = 0.0
|
||||
norms = 0.0
|
||||
for column in range(data_zerocentered.shape[1]):
|
||||
dots += numpy.dot(data_zerocentered[:,column].transpose(),rotmodel[:,column])
|
||||
normi = numpy.linalg.norm(model_zerocentered[:,column])
|
||||
norms += normi*normi
|
||||
# s = float(dots/norms)
|
||||
s = float(norms/dots)
|
||||
else:
|
||||
s = 1.0
|
||||
|
||||
# trans = data.mean(1) - s*rot * model.mean(1)
|
||||
# model_aligned = s*rot * model + trans
|
||||
# alignment_error = model_aligned - data
|
||||
|
||||
# scale the est to the gt, otherwise the ATE could be very small if the est scale is small
|
||||
trans = s*data.mean(1) - rot * model.mean(1)
|
||||
model_aligned = rot * model + trans
|
||||
data_alingned = s * data
|
||||
alignment_error = model_aligned - data_alingned
|
||||
|
||||
trans_error = numpy.sqrt(numpy.sum(numpy.multiply(alignment_error,alignment_error),0)).A[0]
|
||||
|
||||
return rot,trans,trans_error, s
|
||||
|
||||
def plot_traj(ax,stamps,traj,style,color,label):
|
||||
"""
|
||||
Plot a trajectory using matplotlib.
|
||||
|
||||
Input:
|
||||
ax -- the plot
|
||||
stamps -- time stamps (1xn)
|
||||
traj -- trajectory (3xn)
|
||||
style -- line style
|
||||
color -- line color
|
||||
label -- plot legend
|
||||
|
||||
"""
|
||||
stamps.sort()
|
||||
interval = numpy.median([s-t for s,t in zip(stamps[1:],stamps[:-1])])
|
||||
x = []
|
||||
y = []
|
||||
last = stamps[0]
|
||||
for i in range(len(stamps)):
|
||||
if stamps[i]-last < 2*interval:
|
||||
x.append(traj[i][0])
|
||||
y.append(traj[i][1])
|
||||
elif len(x)>0:
|
||||
ax.plot(x,y,style,color=color,label=label)
|
||||
label=""
|
||||
x=[]
|
||||
y=[]
|
||||
last= stamps[i]
|
||||
if len(x)>0:
|
||||
ax.plot(x,y,style,color=color,label=label)
|
||||
|
||||
|
||||
Vendored
+126
@@ -0,0 +1,126 @@
|
||||
# Copyright (c) 2020 Carnegie Mellon University, Wenshan Wang <wenshanw@andrew.cmu.edu>
|
||||
# For License information please see the LICENSE file in the root directory.
|
||||
# This is a python reinplementation of the KITTI metric: http://www.cvlibs.net/datasets/kitti/eval_odometry.php
|
||||
# Cridit: Xiangwei Wang https://github.com/TimingSpace
|
||||
|
||||
import numpy as np
|
||||
import sys
|
||||
|
||||
def trajectory_distances(poses):
|
||||
distances = []
|
||||
distances.append(0)
|
||||
for i in range(1,len(poses)):
|
||||
p1 = poses[i-1]
|
||||
p2 = poses[i]
|
||||
delta = p1[0:3,3] - p2[0:3,3]
|
||||
distances.append(distances[i-1]+np.linalg.norm(delta))
|
||||
return distances
|
||||
|
||||
def last_frame_from_segment_length(dist,first_frame,length):
|
||||
for i in range(first_frame,len(dist)):
|
||||
if dist[i]>dist[first_frame]+length:
|
||||
return i
|
||||
return -1
|
||||
|
||||
def rotation_error(pose_error):
|
||||
a = pose_error[0,0]
|
||||
b = pose_error[1,1]
|
||||
c = pose_error[2,2]
|
||||
d = 0.5*(a+b+c-1)
|
||||
rot_error = np.arccos(max(min(d,1.0),-1.0))
|
||||
return rot_error
|
||||
|
||||
def translation_error(pose_error):
|
||||
dx = pose_error[0,3]
|
||||
dy = pose_error[1,3]
|
||||
dz = pose_error[2,3]
|
||||
return np.sqrt(dx*dx+dy*dy+dz*dz)
|
||||
|
||||
# def line2matrix(pose_line):
|
||||
# pose_line = np.array(pose_line)
|
||||
# pose_m = np.matrix(np.eye(4))
|
||||
# pose_m[0:3,:] = pose_line.reshape(3,4)
|
||||
# return pose_m
|
||||
|
||||
def calculate_sequence_error(poses_gt,poses_result,lengths=[10,20,30,40,50,60,70,80]):
|
||||
# error_vetor
|
||||
errors = []
|
||||
|
||||
# paramet
|
||||
step_size = 1 #10; # every second
|
||||
num_lengths = len(lengths)
|
||||
|
||||
# import ipdb;ipdb.set_trace()
|
||||
# pre-compute distances (from ground truth as reference)
|
||||
dist = trajectory_distances(poses_gt)
|
||||
# for all start positions do
|
||||
for first_frame in range(0, len(poses_gt), step_size):
|
||||
# for all segment lengths do
|
||||
for i in range(0,num_lengths):
|
||||
# current length
|
||||
length = lengths[i];
|
||||
|
||||
# compute last frame
|
||||
last_frame = last_frame_from_segment_length(dist,first_frame,length);
|
||||
# continue, if sequence not long enough
|
||||
if (last_frame==-1):
|
||||
continue;
|
||||
|
||||
# compute rotational and translational errors
|
||||
pose_delta_gt = np.linalg.inv(poses_gt[first_frame]).dot(poses_gt[last_frame])
|
||||
pose_delta_result = np.linalg.inv(poses_result[first_frame]).dot(poses_result[last_frame])
|
||||
pose_error = np.linalg.inv(pose_delta_result).dot(pose_delta_gt)
|
||||
r_err = rotation_error(pose_error);
|
||||
t_err = translation_error(pose_error);
|
||||
|
||||
# compute speed
|
||||
num_frames = (float)(last_frame-first_frame+1);
|
||||
speed = length/(0.1*num_frames);
|
||||
|
||||
# write to file
|
||||
error = [first_frame,r_err/length,t_err/length,length,speed]
|
||||
errors.append(error)
|
||||
# return error vector
|
||||
return errors
|
||||
|
||||
def calculate_ave_errors(errors,lengths=[10,20,30,40,50,60,70,80]):
|
||||
rot_errors=[]
|
||||
tra_errors=[]
|
||||
for length in lengths:
|
||||
rot_error_each_length =[]
|
||||
tra_error_each_length =[]
|
||||
for error in errors:
|
||||
if abs(error[3]-length)<0.1:
|
||||
rot_error_each_length.append(error[1])
|
||||
tra_error_each_length.append(error[2])
|
||||
|
||||
if len(rot_error_each_length)==0:
|
||||
# import ipdb;ipdb.set_trace()
|
||||
continue
|
||||
else:
|
||||
rot_errors.append(sum(rot_error_each_length)/len(rot_error_each_length))
|
||||
tra_errors.append(sum(tra_error_each_length)/len(tra_error_each_length))
|
||||
return np.array(rot_errors)*180/np.pi, tra_errors
|
||||
|
||||
def evaluate(gt, data,rescale_=False):
|
||||
lens = [5,10,15,20,25,30,35,40] #[1,2,3,4,5,6] #
|
||||
errors = calculate_sequence_error(gt, data, lengths=lens)
|
||||
rot,tra = calculate_ave_errors(errors, lengths=lens)
|
||||
return np.mean(rot), np.mean(tra)
|
||||
|
||||
def main():
|
||||
# usage: python main.py path_to_ground_truth path_to_predict_pose
|
||||
# load and preprocess data
|
||||
ground_truth_data = np.loadtxt(sys.argv[1])
|
||||
predict_pose__data = np.loadtxt(sys.argv[2])
|
||||
errors = calculate_sequence_error(ground_truth_data,predict_pose__data)
|
||||
rot,tra = calculate_ave_errors(errors)
|
||||
print(rot,'\n',tra)
|
||||
#print(error)
|
||||
# evaluate the vo result
|
||||
# save and visualization the evaluatation result
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
|
||||
|
||||
Vendored
+219
@@ -0,0 +1,219 @@
|
||||
#!/usr/bin/python
|
||||
# Software License Agreement (BSD License)
|
||||
#
|
||||
# Modified by Wenshan Wang
|
||||
# Copyright (c) 2013, Juergen Sturm, TUM
|
||||
# All rights reserved.
|
||||
#
|
||||
# Redistribution and use in source and binary forms, with or without
|
||||
# modification, are permitted provided that the following conditions
|
||||
# are met:
|
||||
#
|
||||
# * Redistributions of source code must retain the above copyright
|
||||
# notice, this list of conditions and the following disclaimer.
|
||||
# * Redistributions in binary form must reproduce the above
|
||||
# copyright notice, this list of conditions and the following
|
||||
# disclaimer in the documentation and/or other materials provided
|
||||
# with the distribution.
|
||||
# * Neither the name of TUM nor the names of its
|
||||
# contributors may be used to endorse or promote products derived
|
||||
# from this software without specific prior written permission.
|
||||
#
|
||||
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
|
||||
# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
|
||||
# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
|
||||
# FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
|
||||
# COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
|
||||
# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
|
||||
# BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
|
||||
# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
|
||||
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
|
||||
# LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
|
||||
# ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
|
||||
# POSSIBILITY OF SUCH DAMAGE.
|
||||
|
||||
"""
|
||||
This script computes the relative pose error from the ground truth trajectory
|
||||
and the estimated trajectory.
|
||||
"""
|
||||
|
||||
import random
|
||||
import numpy as np
|
||||
import sys
|
||||
|
||||
def ominus(a,b):
|
||||
"""
|
||||
Compute the relative 3D transformation between a and b.
|
||||
|
||||
Input:
|
||||
a -- first pose (homogeneous 4x4 matrix)
|
||||
b -- second pose (homogeneous 4x4 matrix)
|
||||
|
||||
Output:
|
||||
Relative 3D transformation from a to b.
|
||||
"""
|
||||
return np.dot(np.linalg.inv(a),b)
|
||||
|
||||
def compute_distance(transform):
|
||||
"""
|
||||
Compute the distance of the translational component of a 4x4 homogeneous matrix.
|
||||
"""
|
||||
return np.linalg.norm(transform[0:3,3])
|
||||
|
||||
def compute_angle(transform):
|
||||
"""
|
||||
Compute the rotation angle from a 4x4 homogeneous matrix.
|
||||
"""
|
||||
# an invitation to 3-d vision, p 27
|
||||
return np.arccos( min(1,max(-1, (np.trace(transform[0:3,0:3]) - 1)/2) ))
|
||||
|
||||
def distances_along_trajectory(traj):
|
||||
"""
|
||||
Compute the translational distances along a trajectory.
|
||||
"""
|
||||
motion = [ominus(traj[i+1],traj[i]) for i in range(len(traj)-1)]
|
||||
distances = [0]
|
||||
sum = 0
|
||||
for t in motion:
|
||||
sum += compute_distance(t)
|
||||
distances.append(sum)
|
||||
return distances
|
||||
|
||||
|
||||
def evaluate_trajectory(traj_gt, traj_est, param_max_pairs=10000, param_fixed_delta=False,
|
||||
param_delta=1.00):
|
||||
"""
|
||||
Compute the relative pose error between two trajectories.
|
||||
|
||||
Input:
|
||||
traj_gt -- the first trajectory (ground truth)
|
||||
traj_est -- the second trajectory (estimated trajectory)
|
||||
param_max_pairs -- number of relative poses to be evaluated
|
||||
param_fixed_delta -- false: evaluate over all possible pairs
|
||||
true: only evaluate over pairs with a given distance (delta)
|
||||
param_delta -- distance between the evaluated pairs
|
||||
param_delta_unit -- unit for comparison:
|
||||
"s": seconds
|
||||
"m": meters
|
||||
"rad": radians
|
||||
"deg": degrees
|
||||
"f": frames
|
||||
param_offset -- time offset between two trajectories (to model the delay)
|
||||
param_scale -- scale to be applied to the second trajectory
|
||||
|
||||
Output:
|
||||
list of compared poses and the resulting translation and rotation error
|
||||
"""
|
||||
|
||||
if not param_fixed_delta:
|
||||
if(param_max_pairs==0 or len(traj_est)<np.sqrt(param_max_pairs)):
|
||||
pairs = [(i,j) for i in range(len(traj_est)) for j in range(len(traj_est))]
|
||||
else:
|
||||
pairs = [(random.randint(0,len(traj_est)-1),random.randint(0,len(traj_est)-1)) for i in range(param_max_pairs)]
|
||||
else:
|
||||
pairs = []
|
||||
for i in range(len(traj_est)):
|
||||
j = i + param_delta
|
||||
if j < len(traj_est):
|
||||
pairs.append((i,j))
|
||||
if(param_max_pairs!=0 and len(pairs)>param_max_pairs):
|
||||
pairs = random.sample(pairs,param_max_pairs)
|
||||
|
||||
result = []
|
||||
for i,j in pairs:
|
||||
|
||||
error44 = ominus( ominus( traj_est[j], traj_est[i] ),
|
||||
ominus( traj_gt[j], traj_gt[i] ) )
|
||||
|
||||
trans = compute_distance(error44)
|
||||
rot = compute_angle(error44)
|
||||
|
||||
result.append([i,j,trans,rot])
|
||||
|
||||
if len(result)<2:
|
||||
raise Exception("Couldn't find pairs between groundtruth and estimated trajectory!")
|
||||
|
||||
return result
|
||||
|
||||
# import argparse
|
||||
# if __name__ == '__main__':
|
||||
# random.seed(0)
|
||||
|
||||
# parser = argparse.ArgumentParser(description='''
|
||||
# This script computes the relative pose error from the ground truth trajectory and the estimated trajectory.
|
||||
# ''')
|
||||
# parser.add_argument('groundtruth_file', help='ground-truth trajectory file (format: "timestamp tx ty tz qx qy qz qw")')
|
||||
# parser.add_argument('estimated_file', help='estimated trajectory file (format: "timestamp tx ty tz qx qy qz qw")')
|
||||
# parser.add_argument('--max_pairs', help='maximum number of pose comparisons (default: 10000, set to zero to disable downsampling)', default=10000)
|
||||
# parser.add_argument('--fixed_delta', help='only consider pose pairs that have a distance of delta delta_unit (e.g., for evaluating the drift per second/meter/radian)', action='store_true')
|
||||
# parser.add_argument('--delta', help='delta for evaluation (default: 1.0)',default=1.0)
|
||||
# parser.add_argument('--delta_unit', help='unit of delta (options: \'s\' for seconds, \'m\' for meters, \'rad\' for radians, \'f\' for frames; default: \'s\')',default='s')
|
||||
# parser.add_argument('--offset', help='time offset between ground-truth and estimated trajectory (default: 0.0)',default=0.0)
|
||||
# parser.add_argument('--scale', help='scaling factor for the estimated trajectory (default: 1.0)',default=1.0)
|
||||
# parser.add_argument('--save', help='text file to which the evaluation will be saved (format: stamp_est0 stamp_est1 stamp_gt0 stamp_gt1 trans_error rot_error)')
|
||||
# parser.add_argument('--plot', help='plot the result to a file (requires --fixed_delta, output format: png)')
|
||||
# parser.add_argument('--verbose', help='print all evaluation data (otherwise, only the mean translational error measured in meters will be printed)', action='store_true')
|
||||
# args = parser.parse_args()
|
||||
|
||||
# if args.plot and not args.fixed_delta:
|
||||
# sys.exit("The '--plot' option can only be used in combination with '--fixed_delta'")
|
||||
|
||||
# traj_gt = np.loadtxt(args.groundtruth_file)
|
||||
# traj_est = np.loadtxt(args.estimated_file)
|
||||
|
||||
# from trajectory_transform import trajectory_transform
|
||||
# traj_gt, traj_est = trajectory_transform(traj_gt, traj_est)
|
||||
|
||||
# traj_gt = tf.pos_quats2SE_matrices(traj_gt)
|
||||
# traj_est = tf.pos_quats2SE_matrices(traj_est)
|
||||
|
||||
# result = evaluate_trajectory(traj_gt,
|
||||
# traj_est,
|
||||
# int(args.max_pairs),
|
||||
# args.fixed_delta,
|
||||
# float(args.delta),
|
||||
# args.delta_unit)
|
||||
|
||||
# trans_error = np.array(result)[:,2]
|
||||
# rot_error = np.array(result)[:,3]
|
||||
|
||||
# if args.save:
|
||||
# f = open(args.save,"w")
|
||||
# f.write("\n".join([" ".join(["%f"%v for v in line]) for line in result]))
|
||||
# f.close()
|
||||
|
||||
# if args.verbose:
|
||||
# print "compared_pose_pairs %d pairs"%(len(trans_error))
|
||||
|
||||
# print "translational_error.rmse %f m"%np.sqrt(np.dot(trans_error,trans_error) / len(trans_error))
|
||||
# print "translational_error.mean %f m"%np.mean(trans_error)
|
||||
# print "translational_error.median %f m"%np.median(trans_error)
|
||||
# print "translational_error.std %f m"%np.std(trans_error)
|
||||
# print "translational_error.min %f m"%np.min(trans_error)
|
||||
# print "translational_error.max %f m"%np.max(trans_error)
|
||||
|
||||
# print "rotational_error.rmse %f deg"%(np.sqrt(np.dot(rot_error,rot_error) / len(rot_error)) * 180.0 / np.pi)
|
||||
# print "rotational_error.mean %f deg"%(np.mean(rot_error) * 180.0 / np.pi)
|
||||
# print "rotational_error.median %f deg"%(np.median(rot_error) * 180.0 / np.pi)
|
||||
# print "rotational_error.std %f deg"%(np.std(rot_error) * 180.0 / np.pi)
|
||||
# print "rotational_error.min %f deg"%(np.min(rot_error) * 180.0 / np.pi)
|
||||
# print "rotational_error.max %f deg"%(np.max(rot_error) * 180.0 / np.pi)
|
||||
# else:
|
||||
# print np.mean(trans_error)
|
||||
|
||||
# import ipdb;ipdb.set_trace()
|
||||
|
||||
# if args.plot:
|
||||
# import matplotlib
|
||||
# matplotlib.use('Agg')
|
||||
# import matplotlib.pyplot as plt
|
||||
# import matplotlib.pylab as pylab
|
||||
# fig = plt.figure()
|
||||
# ax = fig.add_subplot(111)
|
||||
# ax.plot(stamps - stamps[0],trans_error,'-',color="blue")
|
||||
# #ax.plot([t for t,e in err_rot],[e for t,e in err_rot],'-',color="red")
|
||||
# ax.set_xlabel('time [s]')
|
||||
# ax.set_ylabel('translational error [m]')
|
||||
# plt.savefig(args.plot,dpi=300)
|
||||
|
||||
|
||||
Vendored
+91
@@ -0,0 +1,91 @@
|
||||
# Copyright (c) 2020 Carnegie Mellon University, Wenshan Wang <wenshanw@andrew.cmu.edu>
|
||||
# For License information please see the LICENSE file in the root directory.
|
||||
|
||||
import numpy as np
|
||||
from .trajectory_transform import trajectory_transform, rescale
|
||||
from .transformation import pos_quats2SE_matrices, SE2pos_quat
|
||||
|
||||
|
||||
np.set_printoptions(suppress=True, precision=2, threshold=100000)
|
||||
|
||||
def transform_trajs(gt_traj, est_traj, cal_scale):
|
||||
gt_traj, est_traj = trajectory_transform(gt_traj, est_traj)
|
||||
if cal_scale :
|
||||
est_traj, s = rescale(gt_traj, est_traj)
|
||||
print(' Scale, {}'.format(s))
|
||||
else:
|
||||
s = 1.0
|
||||
return gt_traj, est_traj, s
|
||||
|
||||
def quats2SEs(gt_traj, est_traj):
|
||||
gt_SEs = pos_quats2SE_matrices(gt_traj)
|
||||
est_SEs = pos_quats2SE_matrices(est_traj)
|
||||
return gt_SEs, est_SEs
|
||||
|
||||
from .evaluate_ate_scale import align, plot_traj
|
||||
|
||||
|
||||
class ATEEvaluator(object):
|
||||
def __init__(self):
|
||||
super(ATEEvaluator, self).__init__()
|
||||
|
||||
|
||||
def evaluate(self, gt_traj, est_traj, scale):
|
||||
gt_xyz = np.matrix(gt_traj[:,0:3].transpose())
|
||||
est_xyz = np.matrix(est_traj[:, 0:3].transpose())
|
||||
|
||||
rot, trans, trans_error, s = align(gt_xyz, est_xyz, scale)
|
||||
print(' ATE scale: {}'.format(s))
|
||||
error = np.sqrt(np.dot(trans_error,trans_error) / len(trans_error))
|
||||
|
||||
# align two trajs
|
||||
est_SEs = pos_quats2SE_matrices(est_traj)
|
||||
T = np.eye(4)
|
||||
T[:3,:3] = rot
|
||||
T[:3,3:] = trans
|
||||
T = np.linalg.inv(T)
|
||||
est_traj_aligned = []
|
||||
for se in est_SEs:
|
||||
se[:3,3] = se[:3,3] * s
|
||||
se_new = T.dot(se)
|
||||
se_new = SE2pos_quat(se_new)
|
||||
est_traj_aligned.append(se_new)
|
||||
|
||||
|
||||
return error, gt_traj, est_traj_aligned
|
||||
|
||||
# =======================
|
||||
|
||||
from .evaluate_rpe import evaluate_trajectory
|
||||
|
||||
class RPEEvaluator(object):
|
||||
def __init__(self):
|
||||
super(RPEEvaluator, self).__init__()
|
||||
|
||||
|
||||
def evaluate(self, gt_SEs, est_SEs):
|
||||
result = evaluate_trajectory(gt_SEs, est_SEs)
|
||||
|
||||
trans_error = np.array(result)[:,2]
|
||||
rot_error = np.array(result)[:,3]
|
||||
|
||||
trans_error_mean = np.mean(trans_error)
|
||||
rot_error_mean = np.mean(rot_error)
|
||||
|
||||
# import ipdb;ipdb.set_trace()
|
||||
|
||||
return (rot_error_mean, trans_error_mean)
|
||||
|
||||
# =======================
|
||||
|
||||
from .evaluate_kitti import evaluate as kittievaluate
|
||||
|
||||
class KittiEvaluator(object):
|
||||
def __init__(self):
|
||||
super(KittiEvaluator, self).__init__()
|
||||
|
||||
# return rot_error, tra_error
|
||||
def evaluate(self, gt_SEs, est_SEs):
|
||||
# trajectory_scale(est_SEs, 0.831984631412)
|
||||
error = kittievaluate(gt_SEs, est_SEs)
|
||||
return error
|
||||
Vendored
+734
@@ -0,0 +1,734 @@
|
||||
0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00
|
||||
-7.763581722974777222e-02 8.129438757896423340e-02 8.294846117496490479e-02 1.040334086665092687e-02 1.041169190809397287e-05 2.011712313020339732e-02 9.997435032193935367e-01
|
||||
-1.614615394973635265e-01 1.753936329237417158e-01 1.824119399153587007e-01 2.189289735848180471e-02 3.260965583645257884e-04 4.095062368656528229e-02 9.989212336112042179e-01
|
||||
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|
||||
1.115369224548339844e+01 -2.610171318054199219e+01 -2.590780019760131836e+00 1.523126568645238876e-02 2.583893537521362305e-01 -6.804589182138442993e-02 9.635209441184997559e-01
|
||||
1.103638744354248047e+01 -2.610457420349121094e+01 -2.596641778945922852e+00 2.056868188083171844e-02 2.580237686634063721e-01 -6.429013609886169434e-02 9.637777209281921387e-01
|
||||
1.092376804351806641e+01 -2.610856437683105469e+01 -2.603400707244873047e+00 2.580112963914871216e-02 2.577296495437622070e-01 -5.986779555678367615e-02 9.640153646469116211e-01
|
||||
1.081586933135986328e+01 -2.611361312866210938e+01 -2.610776424407958984e+00 3.083451092243194580e-02 2.575255334377288818e-01 -5.467998981475830078e-02 9.642302393913269043e-01
|
||||
1.070365047454833984e+01 -2.612030220031738281e+01 -2.619052648544311523e+00 3.557673841714859009e-02 2.574303746223449707e-01 -4.862414672970771790e-02 9.644167423248291016e-01
|
||||
1.058720302581787109e+01 -2.612927246093750000e+01 -2.628027200698852539e+00 3.993793204426765442e-02 2.574616968631744385e-01 -4.159193113446235657e-02 9.645665287971496582e-01
|
||||
1.046674633026123047e+01 -2.614217758178710938e+01 -2.637519359588623047e+00 4.383187368512153625e-02 2.576360106468200684e-01 -3.347089886665344238e-02 9.646668434143066406e-01
|
||||
1.034272193908691406e+01 -2.616141510009765625e+01 -2.647081136703491211e+00 4.717654734849929810e-02 2.579675316810607910e-01 -2.414281293749809265e-02 9.646990299224853516e-01
|
||||
1.021537590026855469e+01 -2.618688964843750000e+01 -2.656219482421875000e+00 4.989557713270187378e-02 2.584675848484039307e-01 -1.348417624831199646e-02 9.646362066268920898e-01
|
||||
1.008484077453613281e+01 -2.621813964843750000e+01 -2.664593458175659180e+00 5.191991105675697327e-02 2.591438591480255127e-01 -1.366918906569480896e-03 9.644412398338317871e-01
|
||||
9.950877189636230469e+00 -2.625335884094238281e+01 -2.672272920608520508e+00 5.318967998027801514e-02 2.599999010562896729e-01 1.234182342886924744e-02 9.640635251998901367e-01
|
||||
9.813652038574218750e+00 -2.629250335693359375e+01 -2.680364847183227539e+00 5.374157801270484924e-02 2.600106000900268555e-01 2.781768701970577240e-02 9.637076854705810547e-01
|
||||
9.673460960388183594e+00 -2.633624458312988281e+01 -2.690360069274902344e+00 5.356618016958236694e-02 2.605955302715301514e-01 4.487821832299232483e-02 9.629156589508056641e-01
|
||||
9.531424522399902344e+00 -2.638529205322265625e+01 -2.703675508499145508e+00 5.271744728088378906e-02 2.616814076900482178e-01 6.338652223348617554e-02 9.616267085075378418e-01
|
||||
9.388124465942382812e+00 -2.644035530090332031e+01 -2.721546649932861328e+00 5.124986916780471802e-02 2.631950974464416504e-01 8.320283889770507812e-02 9.597807526588439941e-01
|
||||
9.244585037231445312e+00 -2.650260734558105469e+01 -2.745594501495361328e+00 4.921869561076164246e-02 2.650635242462158203e-01 1.041846200823783875e-01 9.573215246200561523e-01
|
||||
9.102452278137207031e+00 -2.657352447509765625e+01 -2.777873516082763672e+00 4.668008908629417419e-02 2.672145068645477295e-01 1.261866539716720581e-01 9.541982412338256836e-01
|
||||
8.964381217956542969e+00 -2.665489768981933594e+01 -2.820977687835693359e+00 4.369113594293594360e-02 2.695775926113128662e-01 1.490615159273147583e-01 9.503681659698486328e-01
|
||||
8.832632064819335938e+00 -2.674715232849121094e+01 -2.876063108444213867e+00 4.040694609284400940e-02 2.716771364212036133e-01 1.727234572172164917e-01 9.458992481231689453e-01
|
||||
8.707996368408203125e+00 -2.684913444519042969e+01 -2.942261934280395508e+00 3.691590949892997742e-02 2.734744250774383545e-01 1.970320641994476318e-01 9.407589435577392578e-01
|
||||
8.590589523315429688e+00 -2.695952033996582031e+01 -3.018403291702270508e+00 3.319668024778366089e-02 2.753017246723175049e-01 2.217792868614196777e-01 9.348374009132385254e-01
|
||||
8.477613449096679688e+00 -2.707590675354003906e+01 -3.101557493209838867e+00 2.929470688104629517e-02 2.771500647068023682e-01 2.468045502901077271e-01 9.281256198883056641e-01
|
||||
8.368250846862792969e+00 -2.719570159912109375e+01 -3.189133167266845703e+00 2.524992823600769043e-02 2.790117561817169189e-01 2.719464898109436035e-01 9.206302165985107422e-01
|
||||
8.290827751159667969e+00 -2.728365135192871094e+01 -3.255115985870361328e+00 2.109719812870025635e-02 2.808790802955627441e-01 2.970450520515441895e-01 9.123739004135131836e-01
|
||||
+80
@@ -0,0 +1,80 @@
|
||||
# Copyright (c) 2020 Carnegie Mellon University, Wenshan Wang <wenshanw@andrew.cmu.edu>
|
||||
# For License information please see the LICENSE file in the root directory.
|
||||
|
||||
import numpy as np
|
||||
from os.path import isdir, isfile
|
||||
|
||||
from .evaluator_base import ATEEvaluator, RPEEvaluator, KittiEvaluator, transform_trajs, quats2SEs
|
||||
|
||||
# from trajectory_transform import timestamp_associate
|
||||
|
||||
|
||||
def plot_traj(gtposes, estposes, vis=False, savefigname=None, title=''):
|
||||
import matplotlib.pyplot as plt
|
||||
fig = plt.figure(figsize=(4,4))
|
||||
|
||||
|
||||
cm = plt.cm.get_cmap('Spectral')
|
||||
|
||||
plt.subplot(111)
|
||||
plt.plot(gtposes[:,2],gtposes[:,0], linestyle='dashed',c='k')
|
||||
plt.plot(estposes[:, 2], estposes[:, 0],c='#ff7f0e')
|
||||
plt.xlabel('x (m)')
|
||||
plt.ylabel('y (m)')
|
||||
plt.legend(['Ground Truth','Ours'])
|
||||
plt.title(title)
|
||||
|
||||
plt.axis('equal')
|
||||
|
||||
if savefigname is not None:
|
||||
plt.savefig(savefigname)
|
||||
|
||||
if vis:
|
||||
plt.show()
|
||||
|
||||
plt.close(fig)
|
||||
|
||||
|
||||
#
|
||||
|
||||
class TartanAirEvaluator:
|
||||
def __init__(self, scale = False, round=1):
|
||||
self.ate_eval = ATEEvaluator()
|
||||
self.rpe_eval = RPEEvaluator()
|
||||
self.kitti_eval = KittiEvaluator()
|
||||
|
||||
def evaluate_one_trajectory(self, gt_traj, est_traj, scale=False, title=''):
|
||||
"""
|
||||
scale = True: calculate a global scale
|
||||
"""
|
||||
|
||||
if gt_traj.shape[0] != est_traj.shape[0]:
|
||||
raise Exception("POSEFILE_LENGTH_ILLEGAL")
|
||||
|
||||
if gt_traj.shape[1] != 7 or est_traj.shape[1] != 7:
|
||||
raise Exception("POSEFILE_FORMAT_ILLEGAL")
|
||||
|
||||
gt_traj = gt_traj.astype(np.float64)
|
||||
est_traj = est_traj.astype(np.float64)
|
||||
|
||||
ate_score, gt_ate_aligned, est_ate_aligned = self.ate_eval.evaluate(gt_traj, est_traj, scale)
|
||||
|
||||
plot_traj(np.matrix(gt_ate_aligned), np.matrix(est_ate_aligned), vis=False, savefigname="figures/%s.pdf"%title, title=title)
|
||||
|
||||
est_ate_aligned = np.array(est_ate_aligned)
|
||||
gt_SEs, est_SEs = quats2SEs(gt_ate_aligned, est_ate_aligned)
|
||||
|
||||
|
||||
|
||||
rpe_score = self.rpe_eval.evaluate(gt_SEs, est_SEs)
|
||||
kitti_score = self.kitti_eval.evaluate(gt_SEs, est_SEs)
|
||||
|
||||
return {'ate_score': ate_score, 'rpe_score': rpe_score, 'kitti_score': kitti_score}
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
||||
# scale = True for monocular track, scale = False for stereo track
|
||||
aicrowd_evaluator = TartanAirEvaluator()
|
||||
result = aicrowd_evaluator.evaluate_one_trajectory('pose_gt.txt', 'pose_est.txt', scale=True)
|
||||
print(result)
|
||||
+162
@@ -0,0 +1,162 @@
|
||||
# Copyright (c) 2020 Carnegie Mellon University, Wenshan Wang <wenshanw@andrew.cmu.edu>
|
||||
# For License information please see the LICENSE file in the root directory.
|
||||
|
||||
import numpy as np
|
||||
from . import transformation as tf
|
||||
|
||||
def shift0(traj):
|
||||
'''
|
||||
Traj: a list of [t + quat]
|
||||
Return: translate and rotate the traj
|
||||
'''
|
||||
traj_ses = tf.pos_quats2SE_matrices(np.array(traj))
|
||||
traj_init = traj_ses[0]
|
||||
traj_init_inv = np.linalg.inv(traj_init)
|
||||
new_traj = []
|
||||
for tt in traj_ses:
|
||||
ttt=traj_init_inv.dot(tt)
|
||||
new_traj.append(tf.SE2pos_quat(ttt))
|
||||
return np.array(new_traj)
|
||||
|
||||
def ned2cam(traj):
|
||||
'''
|
||||
transfer a ned traj to camera frame traj
|
||||
'''
|
||||
T = np.array([[0,1,0,0],
|
||||
[0,0,1,0],
|
||||
[1,0,0,0],
|
||||
[0,0,0,1]], dtype=np.float32)
|
||||
T_inv = np.linalg.inv(T)
|
||||
new_traj = []
|
||||
traj_ses = tf.pos_quats2SE_matrices(np.array(traj))
|
||||
|
||||
for tt in traj_ses:
|
||||
ttt=T.dot(tt).dot(T_inv)
|
||||
new_traj.append(tf.SE2pos_quat(ttt))
|
||||
|
||||
return np.array(new_traj)
|
||||
|
||||
def cam2ned(traj):
|
||||
'''
|
||||
transfer a camera traj to ned frame traj
|
||||
'''
|
||||
T = np.array([[0,0,1,0],
|
||||
[1,0,0,0],
|
||||
[0,1,0,0],
|
||||
[0,0,0,1]], dtype=np.float32)
|
||||
T_inv = np.linalg.inv(T)
|
||||
new_traj = []
|
||||
traj_ses = tf.pos_quats2SE_matrices(np.array(traj))
|
||||
|
||||
for tt in traj_ses:
|
||||
ttt=T.dot(tt).dot(T_inv)
|
||||
new_traj.append(tf.SE2pos_quat(ttt))
|
||||
|
||||
return np.array(new_traj)
|
||||
|
||||
|
||||
def trajectory_transform(gt_traj, est_traj):
|
||||
'''
|
||||
1. center the start frame to the axis origin
|
||||
2. align the GT frame (NED) with estimation frame (camera)
|
||||
'''
|
||||
gt_traj_trans = shift0(gt_traj)
|
||||
est_traj_trans = shift0(est_traj)
|
||||
|
||||
# gt_traj_trans = ned2cam(gt_traj_trans)
|
||||
# est_traj_trans = cam2ned(est_traj_trans)
|
||||
|
||||
return gt_traj_trans, est_traj_trans
|
||||
|
||||
def rescale_bk(poses_gt, poses):
|
||||
motion_gt = tf.pose2motion(poses_gt)
|
||||
motion = tf.pose2motion(poses)
|
||||
|
||||
speed_square_gt = np.sum(motion_gt[:,0:3,3]*motion_gt[:,0:3,3],1)
|
||||
speed_gt = np.sqrt(speed_square_gt)
|
||||
speed_square = np.sum(motion[:,0:3,3]*motion[:,0:3,3],1)
|
||||
speed = np.sqrt(speed_square)
|
||||
# when the speed is small, the scale could become very large
|
||||
# import ipdb;ipdb.set_trace()
|
||||
mask = (speed_gt>0.0001) # * (speed>0.00001)
|
||||
scale = np.mean((speed[mask])/speed_gt[mask])
|
||||
scale = 1.0/scale
|
||||
motion[:,0:3,3] = motion[:,0:3,3]*scale
|
||||
pose_update = tf.motion2pose(motion)
|
||||
return pose_update, scale
|
||||
|
||||
def pose2trans(pose_data):
|
||||
data_size = len(pose_data)
|
||||
trans = []
|
||||
for i in range(0,data_size-1):
|
||||
tran = np.array(pose_data[i+1][:3]) - np.array(pose_data[i][:3]) # np.linalg.inv(data[i]).dot(data[i+1])
|
||||
trans.append(tran)
|
||||
|
||||
return np.array(trans) # N x 3
|
||||
|
||||
|
||||
def rescale(poses_gt, poses):
|
||||
'''
|
||||
similar to rescale
|
||||
poses_gt/poses: N x 7 poselist in quaternion format
|
||||
'''
|
||||
trans_gt = pose2trans(poses_gt)
|
||||
trans = pose2trans(poses)
|
||||
|
||||
speed_square_gt = np.sum(trans_gt*trans_gt,1)
|
||||
speed_gt = np.sqrt(speed_square_gt)
|
||||
speed_square = np.sum(trans*trans,1)
|
||||
speed = np.sqrt(speed_square)
|
||||
# when the speed is small, the scale could become very large
|
||||
# import ipdb;ipdb.set_trace()
|
||||
mask = (speed_gt>0.0001) # * (speed>0.00001)
|
||||
scale = np.mean((speed[mask])/speed_gt[mask])
|
||||
scale = 1.0/scale
|
||||
poses[:,0:3] = poses[:,0:3]*scale
|
||||
return poses, scale
|
||||
|
||||
def trajectory_scale(traj, scale):
|
||||
for ttt in traj:
|
||||
ttt[0:3,3] = ttt[0:3,3]*scale
|
||||
return traj
|
||||
|
||||
def timestamp_associate(first_list, second_list, max_difference):
|
||||
"""
|
||||
Associate two trajectory of [stamp,data]. As the time stamps never match exactly, we aim
|
||||
to find the closest match for every input tuple.
|
||||
|
||||
Input:
|
||||
first_list -- first list of (stamp,data)
|
||||
second_list -- second list of (stamp,data)
|
||||
max_difference -- search radius for candidate generation
|
||||
|
||||
Output:
|
||||
first_res: matched data from the first list
|
||||
second_res: matched data from the second list
|
||||
|
||||
"""
|
||||
first_dict = dict([(l[0],l[1:]) for l in first_list if len(l)>1])
|
||||
second_dict = dict([(l[0],l[1:]) for l in second_list if len(l)>1])
|
||||
|
||||
first_keys = first_dict.keys()
|
||||
second_keys = second_dict.keys()
|
||||
potential_matches = [(abs(a - b ), a, b)
|
||||
for a in first_keys
|
||||
for b in second_keys
|
||||
if abs(a - b) < max_difference]
|
||||
potential_matches.sort()
|
||||
matches = []
|
||||
for diff, a, b in potential_matches:
|
||||
if a in first_keys and b in second_keys:
|
||||
first_keys.remove(a)
|
||||
second_keys.remove(b)
|
||||
matches.append((a, b))
|
||||
|
||||
matches.sort()
|
||||
|
||||
first_res = []
|
||||
second_res = []
|
||||
for t1, t2 in matches:
|
||||
first_res.append(first_dict[t1])
|
||||
second_res.append(second_dict[t2])
|
||||
return np.array(first_res), np.array(second_res)
|
||||
Vendored
Executable
+164
@@ -0,0 +1,164 @@
|
||||
# Copyright (c) 2020 Carnegie Mellon University, Wenshan Wang <wenshanw@andrew.cmu.edu>
|
||||
# For License information please see the LICENSE file in the root directory.
|
||||
# Cridit: Xiangwei Wang https://github.com/TimingSpace
|
||||
|
||||
import numpy as np
|
||||
from scipy.spatial.transform import Rotation as R
|
||||
|
||||
def line2mat(line_data):
|
||||
mat = np.eye(4)
|
||||
mat[0:3,:] = line_data.reshape(3,4)
|
||||
return np.matrix(mat)
|
||||
|
||||
def motion2pose(data):
|
||||
data_size = len(data)
|
||||
all_pose = [] # np.zeros((data_size+1, 4, 4))
|
||||
all_pose.append(np.eye(4,4)) #[0,:] = np.eye(4,4)
|
||||
pose = np.eye(4,4)
|
||||
for i in range(0,data_size):
|
||||
pose = pose.dot(data[i])
|
||||
all_pose.append(pose)
|
||||
return all_pose
|
||||
|
||||
def pose2motion(data):
|
||||
data_size = len(data)
|
||||
all_motion = []
|
||||
for i in range(0,data_size-1):
|
||||
motion = np.linalg.inv(data[i]).dot(data[i+1])
|
||||
all_motion.append(motion)
|
||||
|
||||
return np.array(all_motion) # N x 4 x 4
|
||||
|
||||
def SE2se(SE_data):
|
||||
result = np.zeros((6))
|
||||
result[0:3] = np.array(SE_data[0:3,3].T)
|
||||
result[3:6] = SO2so(SE_data[0:3,0:3]).T
|
||||
return result
|
||||
|
||||
def SO2so(SO_data):
|
||||
return R.from_matrix(SO_data).as_rotvec()
|
||||
|
||||
def so2SO(so_data):
|
||||
return R.from_rotvec(so_data).as_matrix()
|
||||
|
||||
def se2SE(se_data):
|
||||
result_mat = np.matrix(np.eye(4))
|
||||
result_mat[0:3,0:3] = so2SO(se_data[3:6])
|
||||
result_mat[0:3,3] = np.matrix(se_data[0:3]).T
|
||||
return result_mat
|
||||
### can get wrong result
|
||||
def se_mean(se_datas):
|
||||
all_SE = np.matrix(np.eye(4))
|
||||
for i in range(se_datas.shape[0]):
|
||||
se = se_datas[i,:]
|
||||
SE = se2SE(se)
|
||||
all_SE = all_SE*SE
|
||||
all_se = SE2se(all_SE)
|
||||
mean_se = all_se/se_datas.shape[0]
|
||||
return mean_se
|
||||
|
||||
def ses_mean(se_datas):
|
||||
se_datas = np.array(se_datas)
|
||||
se_datas = np.transpose(se_datas.reshape(se_datas.shape[0],se_datas.shape[1],se_datas.shape[2]*se_datas.shape[3]),(0,2,1))
|
||||
se_result = np.zeros((se_datas.shape[0],se_datas.shape[2]))
|
||||
for i in range(0,se_datas.shape[0]):
|
||||
mean_se = se_mean(se_datas[i,:,:])
|
||||
se_result[i,:] = mean_se
|
||||
return se_result
|
||||
|
||||
def ses2poses(data):
|
||||
data_size = data.shape[0]
|
||||
all_pose = np.zeros((data_size+1,12))
|
||||
temp = np.eye(4,4).reshape(1,16)
|
||||
all_pose[0,:] = temp[0,0:12]
|
||||
pose = np.matrix(np.eye(4,4))
|
||||
for i in range(0,data_size):
|
||||
data_mat = se2SE(data[i,:])
|
||||
pose = pose*data_mat
|
||||
pose_line = np.array(pose[0:3,:]).reshape(1,12)
|
||||
all_pose[i+1,:] = pose_line
|
||||
return all_pose
|
||||
|
||||
def SEs2ses(motion_data):
|
||||
data_size = motion_data.shape[0]
|
||||
ses = np.zeros((data_size,6))
|
||||
for i in range(0,data_size):
|
||||
SE = np.matrix(np.eye(4))
|
||||
SE[0:3,:] = motion_data[i,:].reshape(3,4)
|
||||
ses[i,:] = SE2se(SE)
|
||||
return ses
|
||||
|
||||
def so2quat(so_data):
|
||||
so_data = np.array(so_data)
|
||||
theta = np.sqrt(np.sum(so_data*so_data))
|
||||
axis = so_data/theta
|
||||
quat=np.zeros(4)
|
||||
quat[0:3] = np.sin(theta/2)*axis
|
||||
quat[3] = np.cos(theta/2)
|
||||
return quat
|
||||
|
||||
def quat2so(quat_data):
|
||||
quat_data = np.array(quat_data)
|
||||
sin_half_theta = np.sqrt(np.sum(quat_data[0:3]*quat_data[0:3]))
|
||||
axis = quat_data[0:3]/sin_half_theta
|
||||
cos_half_theta = quat_data[3]
|
||||
theta = 2*np.arctan2(sin_half_theta,cos_half_theta)
|
||||
so = theta*axis
|
||||
return so
|
||||
|
||||
# input so_datas batch*channel*height*width
|
||||
# return quat_datas batch*numner*channel
|
||||
def sos2quats(so_datas,mean_std=[[1],[1]]):
|
||||
so_datas = np.array(so_datas)
|
||||
so_datas = so_datas.reshape(so_datas.shape[0],so_datas.shape[1],so_datas.shape[2]*so_datas.shape[3])
|
||||
so_datas = np.transpose(so_datas,(0,2,1))
|
||||
quat_datas = np.zeros((so_datas.shape[0],so_datas.shape[1],4))
|
||||
for i_b in range(0,so_datas.shape[0]):
|
||||
for i_p in range(0,so_datas.shape[1]):
|
||||
so_data = so_datas[i_b,i_p,:]
|
||||
quat_data = so2quat(so_data)
|
||||
quat_datas[i_b,i_p,:] = quat_data
|
||||
return quat_datas
|
||||
|
||||
def SO2quat(SO_data):
|
||||
rr = R.from_matrix(SO_data)
|
||||
return rr.as_quat()
|
||||
|
||||
def quat2SO(quat_data):
|
||||
return R.from_quat(quat_data).as_matrix()
|
||||
|
||||
|
||||
def pos_quat2SE(quat_data):
|
||||
SO = R.from_quat(quat_data[3:7]).as_matrix()
|
||||
SE = np.matrix(np.eye(4))
|
||||
SE[0:3,0:3] = np.matrix(SO)
|
||||
SE[0:3,3] = np.matrix(quat_data[0:3]).T
|
||||
SE = np.array(SE[0:3,:]).reshape(1,12)
|
||||
return SE
|
||||
|
||||
|
||||
def pos_quats2SEs(quat_datas):
|
||||
data_len = quat_datas.shape[0]
|
||||
SEs = np.zeros((data_len,12))
|
||||
for i_data in range(0,data_len):
|
||||
SE = pos_quat2SE(quat_datas[i_data,:])
|
||||
SEs[i_data,:] = SE
|
||||
return SEs
|
||||
|
||||
|
||||
def pos_quats2SE_matrices(quat_datas):
|
||||
data_len = quat_datas.shape[0]
|
||||
SEs = []
|
||||
for quat in quat_datas:
|
||||
SO = R.from_quat(quat[3:7]).as_matrix()
|
||||
SE = np.eye(4)
|
||||
SE[0:3,0:3] = SO
|
||||
SE[0:3,3] = quat[0:3]
|
||||
SEs.append(SE)
|
||||
return SEs
|
||||
|
||||
def SE2pos_quat(SE_data):
|
||||
pos_quat = np.zeros(7)
|
||||
pos_quat[3:] = SO2quat(SE_data[0:3,0:3])
|
||||
pos_quat[:3] = SE_data[0:3,3].T
|
||||
return pos_quat
|
||||
+256
@@ -0,0 +1,256 @@
|
||||
0 0 0
|
||||
153 108 6
|
||||
112 105 191
|
||||
89 121 72
|
||||
190 225 64
|
||||
206 190 59
|
||||
81 13 36
|
||||
115 176 195
|
||||
161 171 27
|
||||
135 169 180
|
||||
29 26 199
|
||||
102 16 239
|
||||
242 107 146
|
||||
156 198 23
|
||||
49 89 160
|
||||
68 218 116
|
||||
11 236 9
|
||||
196 30 8
|
||||
121 67 28
|
||||
0 53 65
|
||||
146 52 70
|
||||
226 149 143
|
||||
151 126 171
|
||||
194 39 7
|
||||
205 120 161
|
||||
212 51 60
|
||||
211 80 208
|
||||
189 135 188
|
||||
54 72 205
|
||||
103 252 157
|
||||
124 21 123
|
||||
19 132 69
|
||||
195 237 132
|
||||
94 253 175
|
||||
182 251 87
|
||||
90 162 242
|
||||
199 29 1
|
||||
254 12 229
|
||||
35 196 244
|
||||
220 163 49
|
||||
86 254 214
|
||||
152 3 129
|
||||
92 31 106
|
||||
207 229 90
|
||||
125 75 48
|
||||
98 55 74
|
||||
126 129 238
|
||||
222 153 109
|
||||
85 152 34
|
||||
173 69 31
|
||||
37 128 125
|
||||
58 19 33
|
||||
134 57 119
|
||||
218 124 115
|
||||
120 0 200
|
||||
225 131 92
|
||||
246 90 16
|
||||
51 155 241
|
||||
202 97 155
|
||||
184 145 182
|
||||
96 232 44
|
||||
133 244 133
|
||||
180 191 29
|
||||
1 222 192
|
||||
99 242 104
|
||||
91 168 219
|
||||
65 54 217
|
||||
148 66 130
|
||||
203 102 204
|
||||
216 78 75
|
||||
234 20 250
|
||||
109 206 24
|
||||
164 194 17
|
||||
157 23 236
|
||||
158 114 88
|
||||
245 22 110
|
||||
67 17 35
|
||||
181 213 93
|
||||
170 179 42
|
||||
52 187 148
|
||||
247 200 111
|
||||
25 62 174
|
||||
100 25 240
|
||||
191 195 144
|
||||
252 36 67
|
||||
241 77 149
|
||||
237 33 141
|
||||
119 230 85
|
||||
28 34 108
|
||||
78 98 254
|
||||
114 161 30
|
||||
75 50 243
|
||||
66 226 253
|
||||
46 104 76
|
||||
8 234 216
|
||||
15 241 102
|
||||
93 14 71
|
||||
192 255 193
|
||||
253 41 164
|
||||
24 175 120
|
||||
185 243 231
|
||||
169 233 97
|
||||
243 215 145
|
||||
72 137 21
|
||||
160 113 101
|
||||
214 92 13
|
||||
167 140 147
|
||||
101 109 181
|
||||
53 118 126
|
||||
3 177 32
|
||||
40 63 99
|
||||
186 139 153
|
||||
88 207 100
|
||||
71 146 227
|
||||
236 38 187
|
||||
215 4 215
|
||||
18 211 66
|
||||
113 49 134
|
||||
47 42 63
|
||||
219 103 127
|
||||
57 240 137
|
||||
227 133 211
|
||||
145 71 201
|
||||
217 173 183
|
||||
250 40 113
|
||||
208 125 68
|
||||
224 186 249
|
||||
69 148 46
|
||||
239 85 20
|
||||
108 116 224
|
||||
56 214 26
|
||||
179 147 43
|
||||
48 188 172
|
||||
221 83 47
|
||||
155 166 218
|
||||
62 217 189
|
||||
198 180 122
|
||||
201 144 169
|
||||
132 2 14
|
||||
128 189 114
|
||||
163 227 112
|
||||
45 157 177
|
||||
64 86 142
|
||||
118 193 163
|
||||
14 32 79
|
||||
200 45 170
|
||||
74 81 2
|
||||
59 37 212
|
||||
73 35 225
|
||||
95 224 39
|
||||
84 170 220
|
||||
159 58 173
|
||||
17 91 237
|
||||
31 95 84
|
||||
34 201 248
|
||||
63 73 209
|
||||
129 235 107
|
||||
231 115 40
|
||||
36 74 95
|
||||
238 228 154
|
||||
61 212 54
|
||||
13 94 165
|
||||
141 174 0
|
||||
140 167 255
|
||||
117 93 91
|
||||
183 10 186
|
||||
165 28 61
|
||||
144 238 194
|
||||
12 158 41
|
||||
76 110 234
|
||||
150 9 121
|
||||
142 1 246
|
||||
230 136 198
|
||||
5 60 233
|
||||
232 250 80
|
||||
143 112 56
|
||||
187 70 156
|
||||
2 185 62
|
||||
138 223 226
|
||||
122 183 222
|
||||
166 245 3
|
||||
175 6 140
|
||||
240 59 210
|
||||
248 44 10
|
||||
83 82 52
|
||||
223 248 167
|
||||
87 15 150
|
||||
111 178 117
|
||||
197 84 22
|
||||
235 208 124
|
||||
9 76 45
|
||||
176 24 50
|
||||
154 159 251
|
||||
149 111 207
|
||||
168 231 15
|
||||
209 247 202
|
||||
80 205 152
|
||||
178 221 213
|
||||
27 8 38
|
||||
244 117 51
|
||||
107 68 190
|
||||
23 199 139
|
||||
171 88 168
|
||||
136 202 58
|
||||
6 46 86
|
||||
105 127 176
|
||||
174 249 197
|
||||
172 172 138
|
||||
228 142 81
|
||||
7 204 185
|
||||
22 61 247
|
||||
233 100 78
|
||||
127 65 105
|
||||
33 87 158
|
||||
139 156 252
|
||||
42 7 136
|
||||
20 99 179
|
||||
79 150 223
|
||||
131 182 184
|
||||
110 123 37
|
||||
60 138 96
|
||||
210 96 94
|
||||
123 48 18
|
||||
137 197 162
|
||||
188 18 5
|
||||
39 219 151
|
||||
204 143 135
|
||||
249 79 73
|
||||
77 64 178
|
||||
41 246 77
|
||||
16 154 4
|
||||
116 134 19
|
||||
4 122 235
|
||||
177 106 230
|
||||
21 119 12
|
||||
104 5 98
|
||||
50 130 53
|
||||
30 192 25
|
||||
26 165 166
|
||||
10 160 82
|
||||
106 43 131
|
||||
44 216 103
|
||||
255 101 221
|
||||
32 151 196
|
||||
213 220 89
|
||||
70 209 228
|
||||
97 184 83
|
||||
82 239 232
|
||||
251 164 128
|
||||
193 11 245
|
||||
38 27 159
|
||||
229 141 203
|
||||
130 56 55
|
||||
147 210 11
|
||||
162 203 118
|
||||
255 255 255
|
||||
Reference in New Issue
Block a user