inspireface 1.2.3.post5

InspireFace Python SDK

InspireFace Python API

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

1. First install the necessary dependencies:

pip install loguru tqdm opencv-python

2. 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/

3. 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

1. Ensure that OpenCV and other necessary dependencies are installed on your system
2. Make sure the dynamic library is correctly installed before use
3. Python 3.7 or higher is recommended
4. The default version is CPU, if you want to use the GPU, CoreML, or NPU backend version, you can refer to the documentation to replace the so and make a Python installation package