InspireFace provides an easy-to-use Python API that wraps the underlying dynamic link library through ctypes. You can install the latest release version via pip or configure it using the project's self-compiled dynamic library.

Quick Installation

Install via pip (Recommended)

pip install inspireface

Manual Installation

First install the necessary dependencies:

pip install loguru tqdm opencv-python

Copy the compiled dynamic library to the specified directory:

# Copy the compiled dynamic library to the corresponding system architecture directory
cp YOUR_BUILD_DIR/libInspireFace.so inspireface/modules/core/SYSTEM/CORE_ARCH/

Install the Python package:

python setup.py install

Quick Start

Here's a simple example showing how to use InspireFace for face detection and landmark drawing:

import cv2
import inspireface as isf

# Create session with required features enabled
session = isf.InspireFaceSession(
    opt=isf.HF_ENABLE_NONE,  # Optional features
    detect_mode=isf.HF_DETECT_MODE_ALWAYS_DETECT  # Detection mode
)

# Set detection confidence threshold
session.set_detection_confidence_threshold(0.5)

# Read image
image = cv2.imread("path/to/your/image.jpg")
assert image is not None, "Please check if the image path is correct"

# Perform face detection
faces = session.face_detection(image)
print(f"Detected {len(faces)} faces")

# Draw detection results on image
draw = image.copy()
for idx, face in enumerate(faces):
    # Get face bounding box coordinates
    x1, y1, x2, y2 = face.location
    
    # Calculate rotated box parameters
    center = ((x1 + x2) / 2, (y1 + y2) / 2)
    size = (x2 - x1, y2 - y1)
    angle = face.roll
    
    # Draw rotated box
    rect = ((center[0], center[1]), (size[0], size[1]), angle)
    box = cv2.boxPoints(rect)
    box = box.astype(int)
    cv2.drawContours(draw, [box], 0, (100, 180, 29), 2)
    
    # Draw landmarks
    landmarks = session.get_face_dense_landmark(face)
    for x, y in landmarks.astype(int):
        cv2.circle(draw, (x, y), 0, (220, 100, 0), 2)

More Examples

The project provides multiple example files demonstrating different features:

sample_face_detection.py: Basic face detection
sample_face_track_from_video.py: Video face tracking
sample_face_recognition.py: Face recognition
sample_face_comparison.py: Face comparison
sample_feature_hub.py: Feature extraction
sample_system_resource_statistics.py: System resource statistics

Running Tests

The project includes unit tests. You can adjust test content by modifying parameters in test/test_settings.py:

python -m unittest discover -s test

Notes

Ensure that OpenCV and other necessary dependencies are installed on your system
Make sure the dynamic library is correctly installed before use
Python 3.7 or higher is recommended