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https://github.com/h8d13/ceauron
https://github.com/h8d13/ceauron
Last synced: about 7 hours ago
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- Host: GitHub
- URL: https://github.com/h8d13/ceauron
- Owner: h8d13
- Created: 2024-08-22T10:24:06.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2024-12-30T01:30:42.000Z (14 days ago)
- Last Synced: 2024-12-30T02:26:32.766Z (14 days ago)
- Language: Python
- Size: 644 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.MD
Awesome Lists containing this project
README
# Ceauron - Automation Tool
Computer vision system to detects templates, checks pixel colors based on ANY visual input.
## Features
- Multiple capture modes (Window, Fullscreen, Camera)
- Camera processing time: ~0.03s to 0.1s average
- HD Screenshot processing time: ~0.1s average on Linux and 0.15s on Windows (could be reduced with downsizing and scale of template matches)
- ROI (Region of Interest) breakdown for targeted monitoring
- Template matching with configurable confidence thresholds + Scaling (useful for changing or small items)
- Color detection and pixel analysis
- Motion detection
- Logging system for detailed information
## Performance oriented
- Multi-threaded processing
- Configurable
###
## Installation
1. Create and activate virtual environment:
```bash
# Create virtual environment
python -m venv .venv
# Activate on Linux/Mac
source .venv/bin/activate
# Activate on Windows
.venv\Scripts\activate
```
## Prerequisites
### Linux
```bash
pip3 install numpy opencv-python pillow mss pyautogui wmctrl
```
### Windows
```bash
pip install numpy opencv-python pillow mss pyautogui pywin32
```
### MacOS
```bash
pip3 install numpy opencv-python pillow mss pyautogui pyobjc-framework-Quartz
```
2. Run setup script:
```bash
python setup.py
```
This will create:
- Template folder
- Screenshots/processed directories
- Default configuration files (templates metadata, regions config, main config)
## Configuration
### Main Configuration
```json
{
"target_window": "Chromium",
"capture_interval": 4,
"template_dir": "templates",
"confidence_thresholds": {
"high": 0.8,
"medium": 0.5
},
"fullscreen": true,
"use_camera": false,
"camera_index": 0,
"camera_width": 640,
"camera_height": 480,
"enable_pixel_checks": true,
"enable_motion_detection": true
}
```
Fullscreen or camera mode overides the target_window setting.
Careful that naming differs on platforms and is usually case sensitive (you can go in your ressource monitor to find the exact process handles):
```bash
# Windows
GoogleChrome.exe
# Linux
Chromium
# Mac
Google Chrome
```
### Template Configuration
```json
{
"Capture.PNG": {
"category": "Chrome Icons",
"value": 10
}
}
```
### Regions, Pixels
```json
{
"regions": {
"full": {
"name": "full",
"enabled": true,
"x": 0,
"y": 0,
"width": -1,
"height": -1,
"description": "Full capture area"
},
"top_bar": {
"name": "top_bar",
"enabled": true,
"x": 0,
"y": 0,
"width": 1920,
"height": 100,
"description": "Top bar area"
}
},
"color_checks": {
"black_pixel": {
"name": "black_pixel",
"enabled": true,
"x": 200,
"y": 200,
"color_space": "BGR",
"values": [0, 0, 0],
"tolerance": 30,
"description": "Check for black pixel"
}
}
}
```
## Usage
To start the automation:
- Make sure to have at least 1 template (again metadata is case sensitive)
Then run the main script:
```bash
python csauron.py
```
## Note
This project is currently being tested primarily on Linux, with ongoing work to ensure cross-platform compatibility.
```bash
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```