automatic import of python-soccernet

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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
+[![Python](https://img.shields.io/pypi/pyversions/SoccerNet)](https://img.shields.io/pypi/pyversions/SoccerNet)
+[![Pypi](https://img.shields.io/pypi/v/SoccerNet)](https://pypi.org/project/SoccerNet/)
+[![Downloads](https://static.pepy.tech/personalized-badge/SoccerNet?period=month&units=international_system&left_color=grey&right_color=brightgreen&left_text=PyPI%20downloads/month)](https://pepy.tech/project/SoccerNet)
+[![Downloads](https://static.pepy.tech/personalized-badge/SoccerNet?period=total&units=international_system&left_color=grey&right_color=brightgreen&left_text=Downloads)](https://pepy.tech/project/SoccerNet)
+[![License](https://img.shields.io/badge/license-MIT-green.svg)](https://github.com/SoccerNet/SoccerNet/blob/master/LICENSE)
+<!-- [![LOC](https://sloc.xyz/github/SoccerNet/SoccerNet/?category=code)](https://github.com/SoccerNet/SoccerNet/) -->
+<!-- [![Forks](https://img.shields.io/github/forks/SoccerNet/SoccerNet.svg)](https://github.com/SoccerNet/SoccerNet/network) -->
+<!-- [![Issues](https://img.shields.io/github/issues/SoccerNet/SoccerNet.svg)](https://github.com/SoccerNet/SoccerNet/issues) -->
+<!-- [![Project Status](http://www.repostatus.org/badges/latest/active.svg)](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
+[![Python](https://img.shields.io/pypi/pyversions/SoccerNet)](https://img.shields.io/pypi/pyversions/SoccerNet)
+[![Pypi](https://img.shields.io/pypi/v/SoccerNet)](https://pypi.org/project/SoccerNet/)
+[![Downloads](https://static.pepy.tech/personalized-badge/SoccerNet?period=month&units=international_system&left_color=grey&right_color=brightgreen&left_text=PyPI%20downloads/month)](https://pepy.tech/project/SoccerNet)
+[![Downloads](https://static.pepy.tech/personalized-badge/SoccerNet?period=total&units=international_system&left_color=grey&right_color=brightgreen&left_text=Downloads)](https://pepy.tech/project/SoccerNet)
+[![License](https://img.shields.io/badge/license-MIT-green.svg)](https://github.com/SoccerNet/SoccerNet/blob/master/LICENSE)
+<!-- [![LOC](https://sloc.xyz/github/SoccerNet/SoccerNet/?category=code)](https://github.com/SoccerNet/SoccerNet/) -->
+<!-- [![Forks](https://img.shields.io/github/forks/SoccerNet/SoccerNet.svg)](https://github.com/SoccerNet/SoccerNet/network) -->
+<!-- [![Issues](https://img.shields.io/github/issues/SoccerNet/SoccerNet.svg)](https://github.com/SoccerNet/SoccerNet/issues) -->
+<!-- [![Project Status](http://www.repostatus.org/badges/latest/active.svg)](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
+[![Python](https://img.shields.io/pypi/pyversions/SoccerNet)](https://img.shields.io/pypi/pyversions/SoccerNet)
+[![Pypi](https://img.shields.io/pypi/v/SoccerNet)](https://pypi.org/project/SoccerNet/)
+[![Downloads](https://static.pepy.tech/personalized-badge/SoccerNet?period=month&units=international_system&left_color=grey&right_color=brightgreen&left_text=PyPI%20downloads/month)](https://pepy.tech/project/SoccerNet)
+[![Downloads](https://static.pepy.tech/personalized-badge/SoccerNet?period=total&units=international_system&left_color=grey&right_color=brightgreen&left_text=Downloads)](https://pepy.tech/project/SoccerNet)
+[![License](https://img.shields.io/badge/license-MIT-green.svg)](https://github.com/SoccerNet/SoccerNet/blob/master/LICENSE)
+<!-- [![LOC](https://sloc.xyz/github/SoccerNet/SoccerNet/?category=code)](https://github.com/SoccerNet/SoccerNet/) -->
+<!-- [![Forks](https://img.shields.io/github/forks/SoccerNet/SoccerNet.svg)](https://github.com/SoccerNet/SoccerNet/network) -->
+<!-- [![Issues](https://img.shields.io/github/issues/SoccerNet/SoccerNet.svg)](https://github.com/SoccerNet/SoccerNet/issues) -->
+<!-- [![Project Status](http://www.repostatus.org/badges/latest/active.svg)](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