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A Python library for face detection using YOLOv8. This library enables real-time face detection via webcam, leveraging the YOLOv8 model for high-performance object detection.
You can install the library via pip. Run the following command in your terminal:
pip install face_detection_lib
To use the face_detection_lib
library for face detection, follow these steps:
from face_detection_lib.detector import FaceDetector
# Initialize the FaceDetector
detector = FaceDetector()
# Start the webcam and run face detection
detector.run_webcam()
You can customize the face detection by passing parameters to the FaceDetector
class. For example, you can set a custom model path, labels, or confidence threshold:
from face_detection_lib.detector import FaceDetector
# Define custom parameters
custom_model_path = 'path/to/your/custom/model.pt'
custom_labels = {0: 'Person A', 1: 'Person B'}
custom_confidence_threshold = 0.7
# Initialize the FaceDetector with custom parameters
detector = FaceDetector(
model_path=custom_model_path,
labels=custom_labels,
confidence_threshold=custom_confidence_threshold
)
# Start the webcam with custom settings
detector.run_webcam()
Ensure that your library functions as expected by writing tests in the tests/test_detector.py
file. Here's a basic example of how you might start testing:
def test_face_detection():
# Initialize the FaceDetector
detector = FaceDetector()
# Add tests to verify the functionality
assert detector is not None
# Further tests can be added here
To run your tests, execute:
pytest
Contributions are welcome! If you have suggestions or improvements, please submit a pull request or open an issue.
This project is licensed under the MIT License. See the LICENSE file for details.
Thank you for using face_detection_lib
!
FAQs
A library for face detection using YOLOv8
We found that face-detection-lib demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 2 open source maintainers collaborating on the project.
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