opencv-python人脸识别

pip install opencv-contrib-python

opencv官网下载完整版 opencv-python
安装到当前目录下

创建文件夹：trainer
准备放入 人脸特征文件

创建文件夹：img
将人像图片放入里面

import os
import cv2
import numpy as np


def getImageAndLabels(path):
    facesSamples = []
    ids = []
    imagePaths = [os.path.join(path, f) for f in os.listdir(path)]
    # 检测人脸
    face_detector = cv2.CascadeClassifier('./sources/data/haarcascades/haarcascade_frontalface_alt2.xml')
    # 打印数组imagePaths
    # print('数据排列：', imagePaths)
    # 遍历列表中的图片
    for imagePath in imagePaths:

        img_numpy = cv2.imread(imagePath)
        img_numpy = cv2.cvtColor(img_numpy, cv2.COLOR_BGR2GRAY)

        # 获取图片人脸特征
        faces = face_detector.detectMultiScale(img_numpy)
        # 获取每张图片的id和姓名
        id = int(os.path.split(imagePath)[1].split('.')[0])
        # 预防无面容照片
        for x, y, w, h in faces:
            ids.append(id)
            facesSamples.append(img_numpy[y:y + h, x:x + w])
        # 打印脸部特征和id
        # print('fs:', facesSamples)
        # print('id:', id)
        # print('fs:', facesSamples[id])
    # print('fs:', facesSamples)
    # print('脸部例子：',facesSamples[0])
    # print('身份信息：',ids[0])
    return facesSamples, ids


if __name__ == '__main__':
    # 图片路径
    path = './img'
    # 获取图像数组和id标签数组和姓名
    faces, ids = getImageAndLabels(path)
    # 获取训练对象
    recognizer = cv2.face.LBPHFaceRecognizer_create()
    # recognizer.train(faces,names)#np.array(ids)
    recognizer.train(np.array(faces), np.array(ids))
    # 保存文件
    recognizer.write('trainer/trainer.yml')
    # save_to_file('names.txt',names)

import cv2

# 加载训练数据集文件
recogizer = cv2.face.LBPHFaceRecognizer_create()
recogizer.read('trainer/trainer.yml')

warningtime = 0


# 准备识别的图片
def face_detect_demo(img):
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)  # 转换为灰度
    face_detector = cv2.CascadeClassifier('./sources/data/haarcascades/haarcascade_frontalface_alt2.xml')
    face = face_detector.detectMultiScale(gray, 1.1, 5, cv2.CASCADE_SCALE_IMAGE, (100, 100), (300, 300))
    # face=face_detector.detectMultiScale(gray)
    for x, y, w, h in face:
        cv2.rectangle(img, (x, y), (x + w, y + h), color=(0, 0, 255), thickness=2)
        cv2.circle(img, center=(x + w // 2, y + h // 2), radius=w // 2, color=(0, 255, 0), thickness=1)
        # 人脸识别
        ids, confidence = recogizer.predict(gray[y:y + h, x:x + w])
        # print('标签id:',ids,'置信评分：', confidence)

        if confidence > 80:
            global warningtime
            warningtime += 1
            if warningtime > 100:
                # 添加识别失败后的逻辑代码
                warningtime = 0
            cv2.putText(img, 'unkonw', (x + 10, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (0, 255, 0), 1)
        else:
            cv2.putText(img, '0', (x + 10, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (0, 255, 0), 1)
    cv2.imshow('result', img)
    # print('bug:',ids)


cap = cv2.VideoCapture(0)

while True:
    flag, frame = cap.read()
    if not flag:
        break
    face_detect_demo(frame)
    if ord(' ') == cv2.waitKey(10):
        break
cv2.destroyAllWindows()
cap.release()
# print(names)