opencv人脸打马赛克

import base64

import cv2


def FaceFind(imgPath: str) -> list:
    image = cv2.imread(imgPath)

    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    # https://github.com/opencv/opencv/tree/4.x/data/haarcascades
    face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_alt.xml')
    # 返回人脸坐标列表
    faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))

    # 保存图片
    for (x, y, w, h) in faces:
        cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 3)
        cv2.imwrite('face.jpg', image)

    return faces


def ApplyMosaic(ImagePath: str, BoxList: list):
    # 加载原始图像
    image = cv2.imread(ImagePath)
    # 马赛克坐标
    for box in BoxList:
        (x, y, w, h) = box
        # 从原始图片中获取马赛克图片位置
        roi = image[y:y + h, x:x + w]
        # 马赛克块大小 10x10
        roi_small = cv2.resize(roi, (10, 10), interpolation=cv2.INTER_LINEAR)
        roi_back = cv2.resize(roi_small, (w, h), interpolation=cv2.INTER_NEAREST)
        image[y:y + h, x:x + w] = roi_back
    # 输出图片
    cv2.imwrite('output_image.jpg', image)
    # ret, buffer = cv2.imencode('.jpg', image)
    # if ret:
    #     # 转base64
    #     base64_data = base64.b64encode(buffer).decode('utf-8')
    #     print(base64_data)


def main():
    # 图片路径
    ImagePath = "img_4.png"
    # 马赛克应用的区域
    BoxList = FaceFind(ImagePath)
    if len(BoxList) > 0:
        ApplyMosaic(ImagePath, BoxList)
    else:
        print("没有识别到人脸，不做处理")


if __name__ == '__main__':
    main()

效果: