82 lines
2.7 KiB
Markdown
82 lines
2.7 KiB
Markdown
# Monoloco library
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<img src="docs/monoloco.gif" alt="gif" />
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This repository contains the code for two research projects:
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1. **Perceiving Humans: from Monocular 3D Localization to Social Distancing (MonoLoco++)**
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[README](https://github.com/vita-epfl/monstereo/blob/master/docs/MonoLoco%2B%2B.md) & [Article](https://arxiv.org/abs/2009.00984)
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2. **MonStereo: When Monocular and Stereo Meet at the Tail of 3D Human Localization**
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[README](https://github.com/vita-epfl/monstereo/blob/master/docs/MonStereo.md) & [Article](https://arxiv.org/abs/2008.10913)
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Both projects has been built upon the CVPR'19 project [Openpifpaf](https://github.com/vita-epfl/openpifpaf)
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for 2D pose estimation and the ICCV'19 project [MonoLoco](https://github.com/vita-epfl/monoloco) for monocular 3D localization.
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All projects share the AGPL Licence.
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# Setup
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Installation steps are the same for both projects.
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### Install
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The installation has been tested on OSX and Linux operating systems, with Python 3.6 or Python 3.7.
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Packages have been installed with pip and virtual environments.
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For quick installation, do not clone this repository,
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and make sure there is no folder named monstereo in your current directory.
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A GPU is not required, yet highly recommended for real-time performances.
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MonoLoco++ and MonStereo can be installed as a single package, by:
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```
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pip3 install monstereo
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```
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For development of the monstereo source code itself, you need to clone this repository and then:
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```
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pip3 install sdist
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cd monstereo
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python3 setup.py sdist bdist_wheel
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pip3 install -e .
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```
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### Interfaces
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All the commands are run through a main file called `main.py` using subparsers.
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To check all the commands for the parser and the subparsers (including openpifpaf ones) run:
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* `python3 -m monstereo.run --help`
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* `python3 -m monstereo.run predict --help`
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* `python3 -m monstereo.run train --help`
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* `python3 -m monstereo.run eval --help`
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* `python3 -m monstereo.run prep --help`
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or check the file `monstereo/run.py`
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### Data structure
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Data
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├── arrays
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├── models
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├── kitti
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├── figures
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├── logs
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Run the following to create the folders:
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```
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mkdir data
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cd data
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mkdir arrays models kitti figures logs
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```
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Further instructions for prediction, preprocessing, training and evaluation can be found here:
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* [MonoLoco++ README](https://github.com/vita-epfl/monstereo/blob/master/docs/MonoLoco%2B%2B.md)
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* [MonStereo README](https://github.com/vita-epfl/monstereo/blob/master/docs/MonStereo.md)
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