【昉·星光2 RISC-V 单板计算机】人脸识别

本文介绍了昉·星光2单板计算机实现 人脸识别 的板端推理的项目设计。

人脸识别

基于 Haar 特征的级联分类器进行目标检测是 Paul Viola 和 Michael Jones 在2001年发表的论文《Rapid Object Detection using a Boosted Cascade of Simple Features》中提出的一种有效的目标检测方法。

Haar Cascade 基于机器学习，由大量的正负样本图像训练级联函数，用于检测其他图像中的对象。

详见：Rapid object detection using a boosted cascade of simple features | IEEE Conference Publication | IEEE Xplore .

流程图

代码

调用本地 haarcascades 的人脸识别 XML 文件，默认路径为

/usr/share/opencv4/haarcascades/haarcascade_frontalface_default.xml 实现人脸识别的板端推理。

import cv2
import argparse
import os
import numpy as np

def detect_faces(image_path, cascade_path="/usr/share/opencv4/haarcascades/haarcascade_frontalface_default.xml"):
    """
    Detect faces in an image using Haar Cascade.

    Parameters:
        image_path: Path to the input image.
        cascade_path: Path to the Haar Cascade model file.
    """
    
    # Check if the files exist
    if not os.path.isfile(image_path):
        print(f"Error: The image file {image_path} does not exist!")
        return
        
    if not os.path.isfile(cascade_path):
        print(f"Error: The cascade file {cascade_path} does not exist!")
        print("Please download it from OpenCV's GitHub: https://github.com/opencv/opencv/tree/master/data/haarcascades")
        return
    
    # Load the image
    image = cv2.imread(image_path)
    if image is None:
        print(f"Error: Unable to load the image {image_path}")
        return
        
    # Convert to grayscale (face detection is usually performed on grayscale images)
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    
    # Load the Haar Cascade classifier
    face_cascade = cv2.CascadeClassifier(cascade_path)
    
    # Detect faces
    faces, _, weights = face_cascade.detectMultiScale3(
        gray,
        scaleFactor=1.1,
        minNeighbors=5,
        minSize=(30, 30),
        outputRejectLevels=True
    )
    
    print(f"Detected {len(faces)} faces")
    
    # Draw green rectangles around detected faces and label them with "confidence"
    for i, (x, y, w, h) in enumerate(faces):
        # Calculate "pseudo-confidence" (normalized neighbor count to the range 0-1)
        pseudo_confidence = min(weights[i] / 20.0, 1.0)  # Assuming maximum neighbor count is 20
        
        # Draw a green rectangle (in BGR format)
        color = (0, 255, 0)  # green
        thickness = 2
        cv2.rectangle(image, (x, y), (x+w, y+h), color, thickness)
        
        # Display "confidence" above the rectangle
        label = f"{pseudo_confidence:.2f}"
        font = cv2.FONT_HERSHEY_SIMPLEX
        font_scale = 0.6
        text_size = cv2.getTextSize(label, font, font_scale, thickness)[0]
        
        # Ensure the text does not go out of the image boundaries
        text_x = min(x, image.shape[1] - text_size[0] - 5)
        text_y = max(y - 5, text_size[1] + 5)
        
        # Draw a background for the text
        '''
        cv2.rectangle(image, 
                     (text_x, text_y - text_size[1] - 5),
                     (text_x + text_size[0], text_y + 5),
                     color, -1)  # fill the rectangle
        '''
        # Draw the text
        cv2.putText(image, label, 
                   (text_x, text_y), 
                   font, font_scale, 
                   (0, 255, 0),  # color text
                   thickness, cv2.LINE_AA)
    
    # Save the result
    output_path = "output_" + os.path.basename(image_path)
    cv2.imwrite(output_path, image)
    print(f"The result has been saved to {output_path}")
    
    # If you have a graphical interface, you can uncomment the following lines to display the image
    # cv2.imshow("Face Detection Result", image)
    # cv2.waitKey(0)
    # cv2.destroyAllWindows()

if __name__ == "__main__":
    # Set up command-line argument parsing
    parser = argparse.ArgumentParser(description='Detect faces in an image using Haar Cascade')
    parser.add_argument('image_path', type=str, help='Path to the input image')
    parser.add_argument('--cascade', type=str, default="/usr/share/opencv4/haarcascades/haarcascade_frontalface_default.xml",
                       help='Path to the Haar Cascade model file (default is haarcascade_frontalface_default.xml in the current directory)')
    
    args = parser.parse_args()
    
    # Perform face detection
    detect_faces(args.image_path, args.cascade)

保存代码，终端执行指令 python3 face_detection.py test.jpg 运行识别程序；

终端输出检测结果和保存路径