Metadata-Version: 2.1
Name: SoccerNet
Version: 0.1.7
Summary: SoccerNet SDK
Home-page: https://github.com/SilvioGiancola/SoccerNetv2
Author: Silvio Giancola
Author-email: silvio.giancola@kaust.edu.sa
License: MIT
Description: # SOCCERNETV2
        
        ```bash
        conda create -n SoccerNet python pip
        pip install SoccerNet
        ```
        
        ## Structure of the data data for each game
        
        - SoccerNet main folder
          - Leagues (england_epl/europe_uefa-champions-league/france_ligue-1/...)
            - Seasons (2014-2015/2015-2016/2016-2017)
              - Games (format: "{Date} - {Time} - {HomeTeam} {Score} {AwayTeam}")
                - SoccerNet-v2 - Labels / Manual Annotations
                  - **video.ini**: information on start/duration for each half of the game in the HQ video, in second
                  - **Labels-v2.json**: Labels from SoccerNet-v2 - action spotting
                  - **Labels-cameras.json**: Labels from SoccerNet-v1 - camera shot segmentation
        
                - SoccerNet-v2 - Videos / Automatically Extracted Features
                  - **1_HQ.mkv**: HQ video 1st half
                  - **2_HQ.mkv**: HQ video 2nd half
                  - **1.mkv**: LQ video 1st half - timmed with start/duration from HQ video - resolution 224*398 - 25 fps
                  - **2.mkv**: LQ video 2nd half - timmed with start/duration from HQ video - resolution 224*398 - 25 fps
                  - **1_ResNET_TF2.npy**: ResNET features @2fps for 1st half from SoccerNet-v2, [extracted using TF2](https://github.com/SilvioGiancola/SoccerNetv2-DevKit)
                  - **2_ResNET_TF2.npy**: ResNET features @2fps for 2nd half from SoccerNet-v2, [extracted using TF2](https://github.com/SilvioGiancola/SoccerNetv2-DevKit)
                  - **1_ResNET_TF2_PCA512.npy**: ResNET features @2fps for 1st half from SoccerNet-v2, [extracted using TF2](https://github.com/SilvioGiancola/SoccerNetv2-DevKit), with dimensionality reduced to 512 using PCA
                  - **2_ResNET_TF2_PCA512.npy**: ResNET features @2fps for 2nd half from SoccerNet-v2, [extracted using TF2](https://github.com/SilvioGiancola/SoccerNetv2-DevKit), with dimensionality reduced to 512 using PCA
                  - **1_ResNET_5fps_TF2.npy**: ResNET features @5fps for 1st half from SoccerNet-v2, [extracted using TF2](https://github.com/SilvioGiancola/SoccerNetv2-DevKit)
                  - **2_ResNET_5fps_TF2.npy**: ResNET features @5fps for 2nd half from SoccerNet-v2, [extracted using TF2](https://github.com/SilvioGiancola/SoccerNetv2-DevKit)
                  - **1_ResNET_5fps_TF2_PCA512.npy**: ResNET features @5fps for 1st half from SoccerNet-v2, [extracted using TF2](https://github.com/SilvioGiancola/SoccerNetv2-DevKit), with dimensionality reduced to 512 using PCA
                  - **2_ResNET_5fps_TF2_PCA512.npy**: ResNET features @5fps for 2nd half from SoccerNet-v2, [extracted using TF2](https://github.com/SilvioGiancola/SoccerNetv2-DevKit), with dimensionality reduced to 512 using PCA
                  - **1_ResNET_25fps_TF2.npy**: ResNET features @25fps for 1st half from SoccerNet-v2, [extracted using TF2](https://github.com/SilvioGiancola/SoccerNetv2-DevKit)
                  - **2_ResNET_25fps_TF2.npy**: ResNET features @25fps for 2nd half from SoccerNet-v2, [extracted using TF2](https://github.com/SilvioGiancola/SoccerNetv2-DevKit)
                  - **1_player_boundingbox_maskrcnn.json**: Player Bounding Boxes @2fps for 1st half, extracted with MaskRCNN
                  - **2_player_boundingbox_maskrcnn.json**: Player Bounding Boxes @2fps for 2nd half, extracted with MaskRCNN
                  - **1_field_calib_ccbv.json**: Field Camera Calibration @2fps for 1st half, extracted with CCBV
                  - **2_field_calib_ccbv.json**: Field Camera Calibration @2fps for 2nd half, extracted with CCBV
        
                - Legacy from SoccerNet-v1
                  - **Labels.json**: Labels from SoccerNet-v1 - action spotting for goals/cards/subs only
                  - **1_C3D.npy**: C3D features @2fps for 1st half from SoccerNet-v1
                  - **2_C3D.npy**: C3D features @2fps for 2nd half from SoccerNet-v1
                  - **1_C3D_PCA512.npy**: C3D features @2fps for 1st half from SoccerNet-v1, with dimensionality reduced to 512 using PCA
                  - **2_C3D_PCA512.npy**: C3D features @2fps for 2nd half from SoccerNet-v1, with dimensionality reduced to 512 using PCA
                  - **1_I3D.npy**: I3D features @2fps for 1st half from SoccerNet-v1
                  - **2_I3D.npy**: I3D features @2fps for 2nd half from SoccerNet-v1
                  - **1_I3D_PCA512.npy**: I3D features @2fps for 1st half from SoccerNet-v1, with dimensionality reduced to 512 using PCA
                  - **2_I3D_PCA512.npy**: I3D features @2fps for 2nd half from SoccerNet-v1, with dimensionality reduced to 512 using PCA
                  - **1_ResNET.npy**: ResNET features @2fps for 1st half from SoccerNet-v1
                  - **2_ResNET.npy**: ResNET features @2fps for 2nd half from SoccerNet-v1
                  - **1_ResNET_PCA512.npy**: ResNET features @2fps for 1st half from SoccerNet-v1, with dimensionality reduced to 512 using PCA
                  - **2_ResNET_PCA512.npy**: ResNET features @2fps for 2nd half from SoccerNet-v1, with dimensionality reduced to 512 using PCA
        
        
        ## How to Download Games (Python)
        
        ```python
        from SoccerNet.Downloader import SoccerNetDownloader
        
        mySoccerNetDownloader = SoccerNetDownloader(LocalDirectory="/path/to/soccernet")
        
        # Download SoccerNet labels
        mySoccerNetDownloader.downloadGames(files=["Labels.json"], split=["train","valid","test"]) # download labels
        mySoccerNetDownloader.downloadGames(files=["Labels-v2.json"], split=["train","valid","test"]) # download labels SN v2
        mySoccerNetDownloader.downloadGames(files=["Labels-cameras.json"], split=["train","valid","test"]) # download labels for camera shot
        
        # Download SoccerNet features
        mySoccerNetDownloader.downloadGames(files=["1_ResNET_TF2.npy", "2_ResNET_TF2.npy"], split=["train","valid","test"]) # download Features
        mySoccerNetDownloader.downloadGames(files=["1_ResNET_TF2_PCA512.npy", "2_ResNET_TF2_PCA512.npy"], split=["train","valid","test"]) # download Features reduced with PCA
        mySoccerNetDownloader.downloadGames(files=["1_player_boundingbox_maskrcnn.json", "2_player_boundingbox_maskrcnn.json"], split=["train","valid","test"]) # download Player Bounding Boxes inferred with MaskRCNN
        mySoccerNetDownloader.downloadGames(files=["1_field_calib_ccbv.json", "2_field_calib_ccbv.json"], split=["train","valid","test"]) # download Field Calibration inferred with CCBV
        
        # Download SoccerNet videos (require password from NDA to download videos)
        mySoccerNetDownloader.password = input("Password for videos? (contact the author):\n")
        mySoccerNetDownloader.downloadGames(files=["1.mkv", "2.mkv"], split=["train","valid","test"]) # download LQ Videos
        mySoccerNetDownloader.downloadGames(files=["1_HQ.mkv", "2_HQ.mkv", "video.ini"], split=["train","valid","test"]) # download HQ Videos
        
        # Download SoccerNet Challenge set (require password from NDA to download videos)
        mySoccerNetDownloader.downloadGames(files=["1_ResNET_TF2.npy", "2_ResNET_TF2.npy"], split=["challenge"]) # download ResNET Features
        mySoccerNetDownloader.downloadGames(files=["1_ResNET_TF2_PCA512.npy", "2_ResNET_TF2_PCA512.npy"], split=["challenge"]) # download ResNET Features reduced with PCA
        mySoccerNetDownloader.downloadGames(files=["1.mkv", "2.mkv", "video.ini"], split=["challenge"]) # download LQ Videos (require password from NDA)
        mySoccerNetDownloader.downloadGames(files=["1_HQ.mkv", "2_HQ.mkv", "video.ini"], split=["challenge"]) # download HQ Videos (require password from NDA)
        mySoccerNetDownloader.downloadGames(files=["1_player_boundingbox_maskrcnn.json", "2_player_boundingbox_maskrcnn.json"], split=["challenge"]) # download Player Bounding Boxes inferred with MaskRCNN 
        mySoccerNetDownloader.downloadGames(files=["1_field_calib_ccbv.json", "2_field_calib_ccbv.json"], split=["challenge"]) # download Field Calibration inferred with CCBV 
        
        ```
        
        ## How to read the list Games (Python)
        
        ```python
        from SoccerNet.utils import getListGames
        print(getListGames(split="train")) # return list of games recommended for training
        print(getListGames(split="valid")) # return list of games recommended for validation
        print(getListGames(split="test")) # return list of games recommended for testing
        print(getListGames(split="challenge")) # return list of games recommended for challenge
        print(getListGames(split=["train", "valid", "test", "challenge"])) # return list of games for training, validation and testing
        print(getListGames(split="v1")) # return list of games from SoccerNetv1 (train/valid/test)
        ```
        
        
        
Keywords: SoccerNet,SDK,Spotting,Football,Soccer,Video
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Topic :: Software Development :: Build Tools
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Description-Content-Type: text/markdown
