Metadata-Version: 2.1
Name: olympic_sports
Version: 0.0.1
Summary: Olympic sports tools package
Home-page: https://github.com/aiporre/olympic_sports_tools
Author: Georgios Zoumpourlis
License: MIT
Description: # olympic_sports_tools
        
        This repo contains two scripts that help parsing the Olympic Sports dataset, from the following publication:
        
        Juan Carlos Niebles, Chih-Wei Chen and Li Fei-Fei, "Modeling Temporal Structure of Decomposable Motion Segments for Activity Classification", ECCV 2010
        
        Dataset's paper : http://vision.stanford.edu/documents/NieblesChenFei-Fei_ECCV2010.pdf
        
        Dataset website : http://vision.stanford.edu/Datasets/OlympicSports/
        
        The scripts are the following:
        
         1. parse_attributes.py (used to save a numpy array with some class-related attributes)
        
         2. convert_Olympic.py (used to convert the videos from .seq to .avi format)
        
        ----------------------------------
        
        ### Parsing class-related attributes
        
        The class-related attributes are taken from the following publication:
        
        J. Liu, B. Kuipers, S. Savarese, "Recognizing Human Actions by Attributes", CVPR 2011
        
        Publication link : http://cvgl.stanford.edu/papers/cvpr11_liu_a.pdf
        
        Technical report : http://www.cs.ucf.edu/%7Eliujg/papers/cvpr11_liu_a_tech_report.pdf
        
        The unprocessed attributes are copied from Table 3 in the paper's technical report, and pasted in this file :
        ```
        Olympic_Attributes.txt
        ```
        
        We have 16 classes of the Olympic Sports dataset, and 40 attributes.
        
        Then, we execute
        ```python
        from olympic_sports.parse_attributes import read_attributes
        attributes, class_list, attribute_list = read_attributes()
        ```
        and save the following 3 variables in the 'attributes.npz' file:
        
        - *attributes* :
        The attributes are stored in a numpy array of size 16x40, containing ones and zeros.
        
        Values of 1 (integer) in the cell (i,j), mean that the i-th class has the j-th attribute.
        
        Values of 0 (integer) mean that the i-th class does not have the j-th attribute.
        
        - *attribute_names* :
        List of the attribute names
        
        - *class_names* :
        List of the class names
        
        You can see the correspondences in the saved Attributes_Olympic.png image.
        
        <p>
          <img src="https://github.com/gzoumpourlis/olympic_sports_tools/raw/master/Attributes_Olympic.png" width="800" title="Class names and class-related attributes">
        </p>
        
        
        ----------------------------------
        
        ### Converting the videos from .seq to .avi format
        
        To convert the videos of the dataset from .seq file format to .avi, we execute:
        ```python
        convert-olympic [path-seq-files]
        ```
        
        This script will need a file named "video_Olympic.list", containing all the videos. This file already exists in this repo.
        If you want to create this on your own for a modified version of the dataset, you can execute the following command:
        ```
        ls /path/to/your/dataset/*/*.seq > video_Olympic.list
        ```
        Then using a text editor you can simply find and replace the substring of the exported directories, so that you only keep the class names and the video names in your final file (as in the already saved file). The folder location should be in the repo under `olympic_sports/resources` or in the enviroment directory.
         
        
        
        ----------------------------------
        
        Credits go to @psycharo, for his useful gist to read .seq files, that is used in this repo.
        https://gist.github.com/psycharo/7e6422a491d93e1e3219/
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
