Metadata-Version: 2.3
Name: mindar
Version: 0.1.0
Summary: High-performance MindAR implementation for real-time image recognition on edge devices. Developed by FANSEE LAB.
License: MIT
Author: FANSEE LAB
Author-email: info@fansee.com
Maintainer: FANSEE LAB
Maintainer-email: info@fansee.com
Requires-Python: >=3.9,<4.0
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Requires-Dist: numpy (>=1.26.0,<2.0.0)
Requires-Dist: opencv-python (>=4.12.0,<5.0.0)
Requires-Dist: scipy (>=1.13.0,<2.0.0)
Project-URL: Documentation, https://github.com/FANSEE-LAB/mind-ar#readme
Project-URL: Homepage, https://fansee.studio
Project-URL: Repository, https://github.com/FANSEE-LAB/mind-ar
Description-Content-Type: text/markdown

# MindAR Python

High-performance MindAR implementation for real-time image recognition on edge devices.

## Features

- **MindAR Compatible**: Full compatibility with MindAR .mind file format
- **High Performance**: Optimized for Raspberry Pi and edge devices
- **Real-time Detection**: Efficient feature detection and matching
- **Pure Python**: No complex dependencies, easy deployment

## Installation

```bash
cd mindar
pip install -e .
```

## Usage

### Basic Detection

```python
import cv2
from mindar import Detector, Matcher, MindARCompiler

# Load target images and create detector
detector = Detector(width=640, height=480)
matcher = Matcher()

# Detect features in image
image = cv2.imread("target.jpg", cv2.IMREAD_GRAYSCALE)
result = detector.detect(image)
feature_points = result["feature_points"]

print(f"Detected {len(feature_points)} features")
```

### Compile .mind Files

```python
from mindar import MindARCompiler

compiler = MindARCompiler()

# Compile images to .mind file
success = compiler.compile_directory("./images", "./targets.mind")
if success:
    print("✅ Compilation successful")

# Load compiled targets
mind_data = compiler.load_mind_file("./targets.mind")
```

## Performance

Optimized for edge devices like Raspberry Pi 4:

- **Detection**: ~50ms per frame (640x480)
- **Matching**: ~20ms per target
- **Memory**: <100MB usage

## License

MIT License - Compatible with original MindAR project

