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@@ -0,0 +1 @@ +/SoccerNet-0.1.51.tar.gz diff --git a/python-soccernet.spec b/python-soccernet.spec new file mode 100644 index 0000000..c97a24f --- /dev/null +++ b/python-soccernet.spec @@ -0,0 +1,455 @@ +%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 +* Wed Apr 12 2023 Python_Bot <Python_Bot@openeuler.org> - 0.1.51-1 +- Package Spec generated @@ -0,0 +1 @@ +dea486a89a8d53543f2f02f809888055 SoccerNet-0.1.51.tar.gz |