pixeler 0.0.18

An automation framework for python using OpenCV

Pixeler

A Python framework for building game automation bots that can see, understand, and interact with Windows applications in a human-like way.

---

How it works

Capture screenshots  →  Train a model  →  Run a GameBot  →  Write game modules
      (CLI)               (CLI/API)        (event loop)        (plugin API)

1. Capture — point the pixeler-capture tool at your game window and draw bounding boxes around the objects you care about (enemies, health bars, loot, UI elements). Frames are saved as a labeled dataset.
2. Train — run pixeler-train to fine-tune a YOLOv8 nano model on your dataset and export it as an ONNX file.
3. Detect — load the ONNX into a GameBot alongside color filters, templates, and OCR regions. Every frame, the ScreenAnalyzer runs all detectors and fires events.
4. React — write a GameModule that subscribes to events ("detection.enemy", "color.health_low", "ocr.health_bar") and calls mouse/keyboard actions in response.

---

Installation

pip install pixeler

Training support (YOLOv8 via Ultralytics) is an optional extra — skip it if you only need inference at runtime:

# Runtime only
pip install pixeler

# With training support
pip install "pixeler[train]"

Additional requirement: Tesseract OCR must be installed separately and its directory added to your system PATH before OCR functions will work.

---

Quickstart

Step 1 — Capture a dataset

pixeler-capture --game "MyGame" --dataset datasets/mygame --classes enemy,loot,health_bar

An OpenCV annotation window opens on top of a frozen game screenshot.

Key	Action
Click + drag	Draw a bounding box
0–9	Assign class to the last box
s	Save this frame
Space	Grab a new frame (auto-saves)
c	Clear boxes
q	Quit

Step 2 — Train

pixeler-train --dataset datasets/mygame --out models/mygame.onnx --epochs 100

Exports the dataset into a YOLO-compatible structure, trains yolov8n, and writes the final model to models/mygame.onnx. Training artifacts (charts, confusion matrices, checkpoints) land in training_runs/mygame/.

Step 3 — Write a bot

# my_game_bot.py
from pathlib import Path
from src.pixeler.bot.game_bot import GameBot          # coming in phase 4
from src.pixeler.window.win32_window import Win32Window
from src.pixeler.vision.classifier import YOLOClassifier
from src.pixeler.vision.color import ColorFilter
from src.pixeler.math.rectangle import Rectangle
from my_game.combat_module import CombatModule

CLASSES = ["enemy", "loot", "health_bar"]

bot = GameBot(window=Win32Window("MyGame"))
bot.analyzer.add_yolo("detector", YOLOClassifier("models/mygame.onnx", CLASSES))
bot.analyzer.add_color("health_low", ColorFilter.from_rgb(220, 30, 30, hue_tol=10))
bot.analyzer.add_ocr("gold", Rectangle(10, 50, 120, 18), throttle_s=1.0)
bot.add_module(CombatModule())
bot.start()

Step 4 — Write a game module

# my_game/combat_module.py
from src.pixeler.modules.base_module import GameModule   # coming in phase 5
from src.pixeler.events.payloads import DetectionPayload, ColorRegionPayload

class CombatModule(GameModule):
    name = "combat"

    def on_register(self, bus, bot):
        self._bot = bot
        self.on("detection.enemy", self._attack)
        self.on("color.health_low", self._eat_food)

    def _attack(self, event):
        payload: DetectionPayload = event.data
        self.log(f"Enemy at {payload.center}")
        from src.pixeler.input.mouse import move_and_right_click
        move_and_right_click(*payload.center)

    def _eat_food(self, event):
        from src.pixeler.input.keyboard import press
        press("1")

---

Feature Overview

Vision

Module	What it does
vision/color.py	ColorFilter — HSV-range pixel detection (illumination-independent). Color — BGR solid colors for drawing.
vision/detection.py	find_color_regions(), find_template(), find_all_templates() — results include .center, .rect, .confidence.
vision/classifier.py	YOLOClassifier — ONNX multi-class detection via cv2.dnn. ORBMatcher — feature-based sprite matching, no training needed.
vision/ocr.py	read_text(), read_number(), read_words(). Always call with preprocess=True on game screenshots.
vision/utils.py	preprocess_for_ocr() — upscale → CLAHE → threshold → denoise.

Training

Module	What it does
training/dataset.py	Dataset — manages labeled screenshot storage in YOLO format. BoundingBox — normalized coordinates with pixel ↔ YOLO conversion.
training/capturer.py	CaptureSession — interactive OpenCV annotation UI against a live game window.
training/trainer.py	YOLOTrainer — trains YOLOv8, exports to ONNX (requires [train] extra). ORBTrainer — sprite library for ORBMatcher.

Event System

Module	What it does
events/event_bus.py	EventBus — synchronous pub/sub. Wildcard subscriptions ("detection.*"), fault-isolated handlers, catch-all ("*").
events/event_names.py	String constants and builder functions: event_names.detection("enemy") → "detection.enemy".
events/payloads.py	Typed payloads for every event: DetectionPayload, ColorRegionPayload, OCRPayload, etc. All have .center, .rect shortcuts.

Input

Module	What it does
input/mouse.py	move_to() with WindMouse algorithm. click(), right_click(), scroll(), move_and_click().
input/keyboard.py	write(text, wpm=65) with human timing. press(), hotkey(). Optional mistakes=True for realistic typos.

Window

Module	What it does
window/win32_window.py	Win32Window — Windows HWND targeting. create_overlay() factory.
window/overlay.py	Overlay — transparent always-on-top GDI drawing layer. draw_rect(), draw_circle(), draw_text(), draw_label().
window/window.py	Window — cross-platform targeting via pywinctl.

Math & Geometry

Module	What it does
math/point.py	Point(x, y) — immutable. distance_to(), lerp(), normalize(), dot().
math/rectangle.py	Rectangle(x, y, w, h). random_point() biased toward center. screenshot().
math/bezier.py	natural_path(start, end) — human-like curved mouse paths.
math/circle.py	Circle — random_point_normal() for human-like aiming.
math/polygon.py	Polygon — ray-casting containment, random_point() via rejection sampling.
math/random.py	reaction_delay(), idle_delay(), gaussian_jitter() — human timing distributions.

---

Development Setup

git clone https://github.com/klobbix/Pixeler
cd Pixeler

# Install runtime dependencies
uv sync

# Install with training support
uv sync --extra train

# Run examples
uv run python examples/example_bot.py

---

License

MIT — see LICENSE.

Acknowledgments

• Ultralytics YOLOv8 — object detection training
• pytesseract — OCR
• OpenCV — computer vision
• PyAutoGUI — input simulation
• WindMouse algorithm — Jack Tasia

Contact

[e-mail] · GitHub Issues