Metadata-Version: 2.4
Name: ungyoface
Version: 0.1.0
Summary: Ultimate Fast Face Recognition System with ungyoseries Integration
Author: 운교와 친구들
Author-email: chldnsry1214@gmail.com
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.8
Description-Content-Type: text/markdown
Requires-Dist: opencv-python
Requires-Dist: deepface
Requires-Dist: numpy
Requires-Dist: scipy
Requires-Dist: fasthardware
Dynamic: author
Dynamic: author-email
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
Dynamic: requires-dist
Dynamic: requires-python
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Markdown
# ungyoface

Ultimate Fast Face Recognition System powered by DeepFace and optimized with `ungyoseries` Core Framework.

## 🚀 Features
- **Zero Box Delay**: Real-time face tracking using OpenCV Haar Cascades aligned perfectly with the camera FPS.
- **Asynchronous Processing**: Background DeepFace embedding and emotion recognition to eliminate main thread freezing.
- **RAM Caching**: Pre-computed VGG-Face embeddings for lightning-fast matching.

---

## 🛠️ Installation

1. Create a directory to store reference images (Default path: `./my_faces/`) and add your face photos.
   * The filename will be used as the recognition identity (e.g., `ungyo.jpg` -> displayed as `ungyo` on screen).

2. Install the package in editable (development) mode from the root directory:
   ```bash
   pip install -e .
💻 Usage
1. Run Directly via Terminal Command
Once installed, you can launch the real-time face acceleration radar system from anywhere in your terminal using a single command:

Bash
ungyoface
2. Import as a Module in Python Scripts
You can import and control the core pipeline of the ungyoface package directly within other Python projects:

```Python
from ungyoface import main_pipeline

if __name__ == "__main__":
    # Start the main pipeline integrated with hardware booster and real-time detection stream
    main_pipeline()
```
⚙️ Core API Reference
Below is the specification of the core functions operating inside the ungyoface package. Refer to this when custom extensions are required.
```
pre_compute_db_embeddings()
Description: Extracts VGG-Face feature vectors from all image files inside the specified directory (./my_faces) at startup and caches them into RAM.

Key Detail: Minimizes initial file injection overhead by warming up the DeepFace model structure in memory exactly once.

detect_faces_cv2(frame)
Description: Swiftly extracts face bounding box coordinates from the input frame using the OpenCV Haar Cascade algorithm.

Returns: A list of dictionaries in the format of [{'box': (x, y, w, h)}, ...].

async_analysis_worker(face_crops_with_id)
Description: A background worker thread completely isolated from the main rendering thread. It receives cropped face images, processes heavy deep learning computations (DeepFace.analyze for emotion and scipy.spatial.distance.cosine for verification) asynchronously, and updates the global tracking table in real time.

main_pipeline()
```
Description: The top-level main loop of the system running under the @ungyoseries.boost() hardware acceleration. It opens an ultra-fast video stream and outputs the final frame by seamlessly combining the synchronous real-time tracking box rendering with the asynchronous feature identification layer.
