wunderscout 0.1.8

Scouting and vision tools for YOLO and sports analytics.

wunderscout

A Python library for extracting player and ball tracking data from soccer match footage using YOLO, Siglip embeddings, and homography.

Features

• Detection & Tracking: Uses YOLO for player/ball/pitch-keypoint detection and ByteTrack for temporal consistency.
• Automated Team Clustering: Groups players into teams using Siglip vision transformer embeddings and K-Means clustering via UMAP dimensionality reduction.
• Pitch Mapping: Transforms 2D image coordinates to a normalized 0-1 coordinate system using pitch keypoint homography.
• Goalkeeper Attribution: Assigns goalkeepers to teams based on proximity to team centroids.
• Data Export: Generates Home and Away CSV files containing frame-by-frame XY coordinates.

Installation

uv add wunderscout

Usage

The ScoutingPipeline class manages calibration, tracking, and data export.

from wunderscout import ScoutingPipeline

# Initialize with paths to trained YOLO weights
pipeline = ScoutingPipeline(
    player_weights="players.pt",
    field_weights="pitch.pt"
)

# Run processing
pipeline.run(
    video_path="input_match.mp4",
    output_video_path="ouput_match.mp4"
)

Internal Components

• VisionEngine: Manages YOLO models and generates Siglip embeddings for player crops.
• PitchMapper: Computes homography matrices based on a 32-point pitch configuration. Handles RANSAC-based perspective transforms.
• TeamClassifier: Performs unsupervised clustering on player embeddings. Uses a rolling consensus buffer to stabilize team assignments across frames.
• DataExporter: Formats tracking results into CSV files with frame indices and normalized pitch coordinates.

Dependencies

• ultralytics
• supervision
• transformers
• umap-learn
• scikit-learn
• opencv-python

License

MIT