Metadata-Version: 2.1
Name: openpifpafwebdemo
Version: 0.12a3
Summary: Web-browser demo for openpifpaf.
Home-page: https://github.com/vita-epfl/openpifpafwebdemo
Author: Sven Kreiss
Author-email: research@svenkreiss.com
License: MIT
Description: # openpifpafwebdemo
        
        ![Tests](https://github.com/vita-epfl/openpifpafwebdemo/workflows/Tests/badge.svg)
        
        Links:
        [main repository](https://github.com/vita-epfl/openpifpaf) and
        [CVPR2019 paper](http://openaccess.thecvf.com/content_CVPR_2019/html/Kreiss_PifPaf_Composite_Fields_for_Human_Pose_Estimation_CVPR_2019_paper.html).<br />
        Live demo: https://vitademo.epfl.ch<br />
        Serverless live demo: https://vita-epfl.github.io/openpifpafwebdemo/
        
        
        # Use Locally
        
        Run the full pipeline (neural network and decoder) in Python
        and visualize the output in the browser:
        
        ```sh
        pip3 install openpifpafwebdemo
        python3 -m openpifpafwebdemo.server
        ```
        
        Open a web browser at `http://localhost:5000` to view the web interface.
        
        _Troubleshooting_: Make sure you are using Python3 and have the latest pip and setuptools with `pip install --upgrade pip setuptools`. Do not clone this repository. Make sure there is no folder named `openpifpafwebdemo` in your current directory.
        
        
        __Example:__
        
        <img src="docs/wave3.gif" height=250 alt="example image" />
        
        
        # API
        
        Example using cURL:
        
        ```sh
        curl --data-binary @docs/me_nyc_square_500.jpeg http://localhost:5000/v1/human-poses
        ```
        
        which produces:
        
        ```json
        [{"coordinates": [[0.588631883263588, 0.41628291457891464, 3.5567557387194797], [0.621234196703881, 0.36160339042544365, 3.524825929280572], [0.546875, 0.375, 3.744302039019678], [0.6724068783223629, 0.44710323959589005, 3.459401266884038], [0.494683139026165, 0.4611567258834839, 3.5954212359489217], [0.78733691573143, 0.8311769068241119, 2.1321910543190827], [0.3859005756676197, 0.8252473473548889, 2.158424186304439], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]], "score": 0.26909651332876167}]
        ```
        
        Keep-alive connection test:
        
        ```sh
        curl -v --data-binary @docs/me_nyc_square_500.jpeg http://localhost:5000/v1/human-poses --next --data-binary @docs/me_nyc_square_500.jpeg http://localhost:5000/v1/human-poses 2>&1 | grep '#0'
        * Connected to localhost (127.0.0.1) port 5000 (#0)
        * Connection #0 to host localhost left intact
        * Re-using existing connection! (#0) with host localhost
        * Connected to localhost (127.0.0.1) port 5000 (#0)
        * Connection #0 to host localhost left intact
        ```
        
        
        # Dev
        
        To install from source and set up for development use
        `pip install --editable ".[test]"`, install the frontend dependencies with
        `npm install` and then create the frontend JavaScript code with `npm run build`. For continuous rebuilds of the js package, use `npm run watch`.
        
        Run the server process with `--debug` to get salted version numbers to break
        the browser cache for static assets and autoreload when source files change.
        
        
        # Citation
        
        ```
        @InProceedings{kreiss2019pifpaf,
          author = {Kreiss, Sven and Bertoni, Lorenzo and Alahi, Alexandre},
          title = {PifPaf: Composite Fields for Human Pose Estimation},
          booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
          month = {June},
          year = {2019}
        }
        ```
        
Platform: UNKNOWN
Description-Content-Type: text/markdown
Provides-Extra: test
