Skip to main content

SoccerNet SDK

Project description

SOCCERNETV2

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_224p.mkv: 224p video 1st half - timmed with start/duration from HQ video - resolution 224*398 - 25 fps
            • 2_224p.mkv: 224p video 2nd half - timmed with start/duration from HQ video - resolution 224*398 - 25 fps
            • 1_720p.mkv: 720p video 1st half - timmed with start/duration from HQ video - resolution 720*1280 - 25 fps
            • 2_720p.mkv: 720p video 2nd half - timmed with start/duration from HQ video - resolution 720*1280 - 25 fps
            • 1_ResNET_TF2.npy: ResNET features @2fps for 1st half from SoccerNet-v2, extracted using TF2
            • 2_ResNET_TF2.npy: ResNET features @2fps for 2nd half from SoccerNet-v2, extracted using TF2
            • 1_ResNET_TF2_PCA512.npy: ResNET features @2fps for 1st half from SoccerNet-v2, extracted using TF2, with dimensionality reduced to 512 using PCA
            • 2_ResNET_TF2_PCA512.npy: ResNET features @2fps for 2nd half from SoccerNet-v2, extracted using TF2, with dimensionality reduced to 512 using PCA
            • 1_ResNET_5fps_TF2.npy: ResNET features @5fps for 1st half from SoccerNet-v2, extracted using TF2
            • 2_ResNET_5fps_TF2.npy: ResNET features @5fps for 2nd half from SoccerNet-v2, extracted using TF2
            • 1_ResNET_5fps_TF2_PCA512.npy: ResNET features @5fps for 1st half from SoccerNet-v2, extracted using TF2, 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, with dimensionality reduced to 512 using PCA
            • 1_ResNET_25fps_TF2.npy: ResNET features @25fps for 1st half from SoccerNet-v2, extracted using TF2
            • 2_ResNET_25fps_TF2.npy: ResNET features @25fps for 2nd half from SoccerNet-v2, extracted using TF2
            • 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
            • 1_baidu_soccer_embeddings.npy: Frame Embeddings for 1st half from https://github.com/baidu-research/vidpress-sports
            • 2_baidu_soccer_embeddings.npy: Frame Embeddings for 2nd half from https://github.com/baidu-research/vidpress-sports
          • 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)

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
mySoccerNetDownloader.downloadGames(files=["1_baidu_soccer_embeddings.npy","2_baidu_soccer_embeddings.npy"], split=["train","valid","test"]) # download Frame Embeddings from https://github.com/baidu-research/vidpress-sports

# Download SoccerNet videos (require password from NDA to download videos)
mySoccerNetDownloader.password = input("Password for videos? (contact the author):\n")
mySoccerNetDownloader.downloadGames(files=["1_224p.mkv", "2_224p.mkv"], split=["train","valid","test"]) # download 224p Videos
mySoccerNetDownloader.downloadGames(files=["1_720p.mkv", "2_720p.mkv"], split=["train","valid","test"]) # download 720p 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_224p.mkv", "2_224p.mkv"], split=["challenge"]) # download 224p Videos (require password from NDA)
mySoccerNetDownloader.downloadGames(files=["1_720p.mkv", "2_720p.mkv"], split=["challenge"]) # download 720p 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 
mySoccerNetDownloader.downloadGames(files=["1_baidu_soccer_embeddings.npy","2_baidu_soccer_embeddings.npy"], split=["challenge"]) # download Frame Embeddings from https://github.com/baidu-research/vidpress-sports

How to read the list Games (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)

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

SoccerNet-0.1.44.tar.gz (67.2 kB view hashes)

Uploaded Source

Built Distribution

SoccerNet-0.1.44-py2.py3-none-any.whl (86.6 kB view hashes)

Uploaded Python 2 Python 3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page