BlackPymer2/BrawlStars-gameplay

Brawl Stars Gameplay Dataset & Models

This repository contains labeled gameplay footage of Brawl Stars, along with trained computer vision models for detection and HP (health points) OCR.

Contents

YOLO Detection Model

• best.pt — Trained YOLO model weights (yolo26s architecture). Detects: player, enemy, powercube, powercube-box, wall, water, bush, zone.

from ultralytics import YOLO
model = YOLO("best.pt")
results = model.predict("screenshot.png")

HP OCR Dataset

health_dataset/ — A labeled dataset of 3259 cropped HP regions extracted from gameplay frames:

• images/ — 100×115 px cropped HP bars (3-channel)
• labels.csv — Ground truth HP values (regression targets)
• labels_with_predictions.csv — Labels augmented with model predictions for unlabelled entries

Each filename encodes metadata: {source}_frame{number}_{player|enemy}_{instance_id}.jpg

Distribution

HP range	Labeled samples	Common values
0 (dead)	176	0
4800–6800	230	4800, 5189, 5316, 5716, 5846, 6260, 6480, 6800
7200–8600	367	7200, 7600, 7649, 7800, 8076, 8173, 8200, 8476, 8600
9000–11400	248	9000, 9400, 9800, 10200, 10600, 10630, 11000, 11200
12600–13000	141	12600, 13000
Other	295	Mixed partial-HP values
Unlabelled	1800	—

HP OCR Model

• hp_crnn_best.pt — CRNN (CNN + BiLSTM + CTC) for reading HP values from cropped health bars.
• Accuracy: 93.75% on the validation split.

Loading & Inference

import cv2
import torch
from train_hp import CRNN, preprocess, decode
model = CRNN()
model.load_state_dict(torch.load("hp_crnn_best.pt", map_location="cpu"))
model.eval()
# Load a cropped HP region
img = cv2.imread("health_dataset/images/frame_000306_player_0.jpg")
# Preprocess (grayscale, resize to H=48, normalize)
inp = torch.from_numpy(preprocess(img)).unsqueeze(0)
# Inference
with torch.no_grad():
    logits = model(inp).permute(1, 0, 2)
pred_ids = logits.argmax(dim=2).permute(1, 0)[0]
hp_text = decode(pred_ids.tolist())
print(f"HP: {hp_text}")

Training

The model is a lightweight CRNN trained via CTC loss on the HP dataset. See train_hp.py for the full training pipeline.

Video Demo

A video demonstrating the trained network in action is included in this repository.

License

Apache 2.0