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|
%global _empty_manifest_terminate_build 0
Name: python-SoccerNet
Version: 0.1.51
Release: 1
Summary: SoccerNet SDK
License: MIT
URL: https://github.com/SilvioGiancola/SoccerNetv2
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/d8/8c/d2fce8eba4092d61b57e1a61e670131ae948b609383cfa144f143811a819/SoccerNet-0.1.51.tar.gz
BuildArch: noarch
Requires: python3-tqdm
Requires: python3-scikit-video
Requires: python3-matplotlib
Requires: python3-google-measurement-protocol
Requires: python3-pycocoevalcap
%description
[](https://img.shields.io/pypi/pyversions/SoccerNet)
[](https://pypi.org/project/SoccerNet/)
[](https://pepy.tech/project/SoccerNet)
[](https://pepy.tech/project/SoccerNet)
[](https://github.com/SoccerNet/SoccerNet/blob/master/LICENSE)
<!-- [](https://github.com/SoccerNet/SoccerNet/) -->
<!-- [](https://github.com/SoccerNet/SoccerNet/network) -->
<!-- [](https://github.com/SoccerNet/SoccerNet/issues) -->
<!-- [](http://www.repostatus.org/#active) -->
# SoccerNet package
```bash
conda create -n SoccerNet python pip
pip install SoccerNet
# pip install -e https://github.com/SoccerNet/SoccerNet
# pip install -e .
```
## 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](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
- **1_baidu_soccer_embeddings.npy**: Frame Embeddings for 1st half from [https://github.com/baidu-research/vidpress-sports](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](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)
```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 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
# Download development kit per task
mySoccerNetDownloader.downloadDataTask(task="calib-2023", split=["train", "valid", "test", "challenge"])
mySoccerNetDownloader.downloadDataTask(task="caption-2023", split=["train", "valid", "test", "challenge"])
mySoccerNetDownloader.downloadDataTask(task="jersey-2023", split=["train", "test", "challenge"])
mySoccerNetDownloader.downloadDataTask(task="reid-2023", split=["train", "valid", "test", "challenge"])
mySoccerNetDownloader.downloadDataTask(task="spotting-2023", split=["train", "valid", "test", "challenge"])
mySoccerNetDownloader.downloadDataTask(task="spotting-ball-2023", split=["train", "valid", "test", "challenge"], password=<PW_FROM_NDA>)
mySoccerNetDownloader.downloadDataTask(task="tracking-2023", split=["train", "test", "challenge"])
# 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
mySoccerNetDownloader.downloadRAWVideo(dataset="SoccerNet") # download 720p Videos
mySoccerNetDownloader.downloadRAWVideo(dataset="SoccerNet-Tracking") # download single camera RAW Videos
```
## 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)
```
%package -n python3-SoccerNet
Summary: SoccerNet SDK
Provides: python-SoccerNet
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-SoccerNet
[](https://img.shields.io/pypi/pyversions/SoccerNet)
[](https://pypi.org/project/SoccerNet/)
[](https://pepy.tech/project/SoccerNet)
[](https://pepy.tech/project/SoccerNet)
[](https://github.com/SoccerNet/SoccerNet/blob/master/LICENSE)
<!-- [](https://github.com/SoccerNet/SoccerNet/) -->
<!-- [](https://github.com/SoccerNet/SoccerNet/network) -->
<!-- [](https://github.com/SoccerNet/SoccerNet/issues) -->
<!-- [](http://www.repostatus.org/#active) -->
# SoccerNet package
```bash
conda create -n SoccerNet python pip
pip install SoccerNet
# pip install -e https://github.com/SoccerNet/SoccerNet
# pip install -e .
```
## 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](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
- **1_baidu_soccer_embeddings.npy**: Frame Embeddings for 1st half from [https://github.com/baidu-research/vidpress-sports](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](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)
```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 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
# Download development kit per task
mySoccerNetDownloader.downloadDataTask(task="calib-2023", split=["train", "valid", "test", "challenge"])
mySoccerNetDownloader.downloadDataTask(task="caption-2023", split=["train", "valid", "test", "challenge"])
mySoccerNetDownloader.downloadDataTask(task="jersey-2023", split=["train", "test", "challenge"])
mySoccerNetDownloader.downloadDataTask(task="reid-2023", split=["train", "valid", "test", "challenge"])
mySoccerNetDownloader.downloadDataTask(task="spotting-2023", split=["train", "valid", "test", "challenge"])
mySoccerNetDownloader.downloadDataTask(task="spotting-ball-2023", split=["train", "valid", "test", "challenge"], password=<PW_FROM_NDA>)
mySoccerNetDownloader.downloadDataTask(task="tracking-2023", split=["train", "test", "challenge"])
# 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
mySoccerNetDownloader.downloadRAWVideo(dataset="SoccerNet") # download 720p Videos
mySoccerNetDownloader.downloadRAWVideo(dataset="SoccerNet-Tracking") # download single camera RAW Videos
```
## 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)
```
%package help
Summary: Development documents and examples for SoccerNet
Provides: python3-SoccerNet-doc
%description help
[](https://img.shields.io/pypi/pyversions/SoccerNet)
[](https://pypi.org/project/SoccerNet/)
[](https://pepy.tech/project/SoccerNet)
[](https://pepy.tech/project/SoccerNet)
[](https://github.com/SoccerNet/SoccerNet/blob/master/LICENSE)
<!-- [](https://github.com/SoccerNet/SoccerNet/) -->
<!-- [](https://github.com/SoccerNet/SoccerNet/network) -->
<!-- [](https://github.com/SoccerNet/SoccerNet/issues) -->
<!-- [](http://www.repostatus.org/#active) -->
# SoccerNet package
```bash
conda create -n SoccerNet python pip
pip install SoccerNet
# pip install -e https://github.com/SoccerNet/SoccerNet
# pip install -e .
```
## 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](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
- **1_baidu_soccer_embeddings.npy**: Frame Embeddings for 1st half from [https://github.com/baidu-research/vidpress-sports](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](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)
```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 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
# Download development kit per task
mySoccerNetDownloader.downloadDataTask(task="calib-2023", split=["train", "valid", "test", "challenge"])
mySoccerNetDownloader.downloadDataTask(task="caption-2023", split=["train", "valid", "test", "challenge"])
mySoccerNetDownloader.downloadDataTask(task="jersey-2023", split=["train", "test", "challenge"])
mySoccerNetDownloader.downloadDataTask(task="reid-2023", split=["train", "valid", "test", "challenge"])
mySoccerNetDownloader.downloadDataTask(task="spotting-2023", split=["train", "valid", "test", "challenge"])
mySoccerNetDownloader.downloadDataTask(task="spotting-ball-2023", split=["train", "valid", "test", "challenge"], password=<PW_FROM_NDA>)
mySoccerNetDownloader.downloadDataTask(task="tracking-2023", split=["train", "test", "challenge"])
# 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
mySoccerNetDownloader.downloadRAWVideo(dataset="SoccerNet") # download 720p Videos
mySoccerNetDownloader.downloadRAWVideo(dataset="SoccerNet-Tracking") # download single camera RAW Videos
```
## 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)
```
%prep
%autosetup -n SoccerNet-0.1.51
%build
%py3_build
%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .
%files -n python3-SoccerNet -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Tue Apr 25 2023 Python_Bot <Python_Bot@openeuler.org> - 0.1.51-1
- Package Spec generated
|
