Metadata-Version: 2.4
Name: cognitive-face-scanner
Version: 2.0.0
Summary: A real-time face recognition and object detection radar HUD scanner with database integration
Requires-Python: >=3.8
Description-Content-Type: text/markdown
Requires-Dist: ultralytics>=8.0.0
Requires-Dist: opencv-python>=4.0.0
Requires-Dist: dill>=0.3.0
Requires-Dist: psycopg2-binary>=2.9.0
Requires-Dist: pillow>=9.0.0

# YOLOv8 Object and Face Detection

A lightweight Python project that performs combined object detection and face detection on local images using YOLOv8. It runs the standard YOLOv8n detector to locate general classes (people, cars, cats, etc.) and overlays specialized face detection predictions on top of the same frame.

## Sample Results
*The script saves output images with a `result_` prefix showing predicted bounding boxes and labels for both standard objects and detected faces.*

## How the Public Can Use It

### 1. Prerequisites
You need **Python 3.8+** installed. We recommend using a Virtual Environment or Anaconda/Miniconda.

### 2. Installation

1. **Clone this repository:**
   ```bash
   git clone https://github.com/YOUR_USERNAME/YOUR_REPO_NAME.git
   cd YOUR_REPO_NAME
   ```

2. **Install the dependencies:**
   ```bash
   pip install -r requirements.txt
   ```

### 3. Model Weights
This project uses two YOLOv8 models:
- **`yolov8n.pt`** (General Object Detection): Auto-downloaded by the Ultralytics library on first run.
- **`yolov8m-face.pt`** (Face Detection): Place your YOLOv8 face detection weights in the root directory.

### 4. Running the Detections

Place any `.jpg`, `.jpeg`, or `.png` images in the project root, then run:

```bash
python face.py
```

- Bounding boxes for objects and faces will be displayed in an on-screen preview.
- Press **any key** (or wait 5 seconds) to cycle to the next image. Press **ESC** to stop.
- Processed images will be saved in the directory with the prefix `result_`.
