ureca07/lol_viewport

LoL Viewport Prediction

This repository contains the minimal public release package for the Scientific Reports revision of a screen-only League of Legends viewport prediction study. It includes only the files needed to reproduce the viewport prediction experiments: source-video references, replay splits, derived role-map/viewport-mask data, evaluation boxes, and clean training/evaluation code.

Raw broadcast videos are not included because the authors do not own their copyright. The file data/source_videos.csv lists the YouTube sources used to reconstruct the raw broadcasts. Availability of third-party videos can change over time.

Contents

• data/source_videos.csv: YouTube source references for the 20 replay sets.
• data/splits.json: 15 train replay sets and 5 held-out test replay sets.
• data/metadata/matches.yaml: champion-to-role mapping for each replay.
• data/processed/manifest.json: manifest for compressed role-map and viewport-mask shards.
• data/evaluation_boxes/: paper-style *_gt_boxes.npy and *_pred_boxes.npy files for IoU evaluation.
• lol_viewport/: dataset loader, Mask R-CNN model builder, and IoU metrics.
• scripts/: conversion, training, inference, and evaluation entry points.

Dataset Format

The processed dataset stores compressed .npz shards. Each shard contains:

• frames: uint8 array with shape (N, 2, 256, 256). The two channels correspond to the two teams. Pixel values encode roles: 0=background, 1=TOP, 2=JUNGLE, 3=MID, 4=BOT, 5=SUPPORT.
• masks: uint8 array with shape (N, 1, 256, 256). The mask is the professional observer viewport region.
• frame_indices: original processed frame indices within the replay.

The training loader stacks five consecutive role-map frames into a 10-channel tensor, matching the manuscript implementation.

Install

pip install -r requirements.txt
pip install -e .

Evaluate Released Box Files

python scripts/evaluate_iou.py --box-dir data/evaluation_boxes/2ch_role_ours

Expected output for the released archived 2-ch + Role (Ours) boxes is:

Method / Setting	Mean IoU	IoU >= 0.3 (%)	IoU >= 0.5 (%)	IoU >= 0.7 (%)
2-ch + Role (Ours)	0.4730	64.23	56.14	37.94

The manuscript reports the following reference table:

Method / Setting	Mean IoU	IoU >= 0.3 (%)	IoU >= 0.5 (%)	IoU >= 0.7 (%)
Random Champion Selection	0.2903	40.45	28.97	15.93
Riot Observer System Supervision	0.2999	52.61	25.32	6.24
10-ch + One-hot (per champion)	0.4496	61.62	53.02	34.61
1-ch (10 champions)	0.4455	61.20	52.67	33.88
2-ch + One-hot	0.4574	62.27	53.80	35.83
2-ch + Role (Ours)	0.4730	64.23	56.14	37.94

The released random_champion_selection box files were generated from a stochastic baseline and may not reproduce the exact random-seed value in the manuscript table.

Train

python scripts/train_maskrcnn.py \
  --manifest data/processed/manifest.json \
  --output-dir checkpoints/2ch_role \
  --epochs 20 \
  --batch-size 4 \
  --lr 0.005 \
  --pretrained

The code samples a small validation subset from the training pool during optimization, matching the revision implementation.

Inference

python scripts/infer_maskrcnn.py \
  --manifest data/processed/manifest.json \
  --checkpoint checkpoints/2ch_role/best.pth \
  --output-dir outputs/2ch_role_boxes

Rebuilding the Processed Shards

If you have the legacy per-frame files in data_viewport_youtube_1118, rebuild the compressed release dataset with:

python scripts/export_legacy_dataset.py \
  --legacy-root /path/to/data_viewport_youtube_1118 \
  --output-root data/processed

Sharing Constraints

This release intentionally excludes raw .mp4 files, generated overlay videos, model checkpoints, API keys, IDE files, notebooks, and temporary experiment folders. The released derived data are intended to support reproducible training, validation sampling, testing, and IoU evaluation without redistributing copyrighted broadcast videos.