用OpenCv-Python自带的LBPH识别器实现简单人脸识别（下）

介绍

本文附录了通过LBPH实现简单人脸识别的源代码，分类效果并不是很好，供个人学习使用。

人脸录入.py

import cv2

cap = cv2.VideoCapture(0)

flag = 1
num = 0

while (cap.isOpened()):
    ret_flag, Vshow = cap.read()
    cv2.imshow("Capture_Test", Vshow)
    k = cv2.waitKey(1) & 0xFF
    if k == ord('s'):
        cv2.imwrite("F:/pythonProject/test/Lao_Wang/" + "0.WangZhenHui" + str(num) + ".jpg", Vshow)
#  路径需要自己修改 名称里的id和名字也要自己修改，每个人一个id和一个名字  num表示的每个id所对应的图片的数量
        print("success to save" + str(num) + ".jgp")
        print("----------------------------------")
        num += 1
    elif k == ord(' '):
        break

cap.release()

cv2.destroyAllWindows()

训练数据.py

import os
import cv2
import sys
from PIL import Image
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('D:/Python_venv/tf/Lib/site-packages/cv2/data'
                                          '/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:
        # 打开图片,黑白化
        PIL_img = Image.open(imagePath).convert('L')
        # 将图像转换为数组，以黑白深浅
        img_numpy = np.array(PIL_img, 'uint8')
        # 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])

        print('id:', id)
    print('fs:', facesSamples)

    return facesSamples, ids


if __name__ == '__main__':
    # 图片路径
    path = 'F:/pythonProject/test/Lao_Wang/'
    # 获取图像数组和id标签数组和姓名
    faces, ids = getImageAndLabels(path)
    # 创建LBPH实例对象
    recognizer = cv2.face.LBPHFaceRecognizer_create()
    # 训练模型
    recognizer.train(faces, np.array(ids))
    # 保存数据
    recognizer.write('trainer/trainer.yml')

人脸识别.py

import cv2
import numpy as np
import os
# coding=utf-8
import urllib
import urllib.request
import hashlib

# 加载训练数据集文件
recogizer = cv2.face.LBPHFaceRecognizer_create()
recogizer.read('trainer/trainer.yml')
names = []
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('D:/Python_venv/tf/Lib/site-packages/cv2/data'
                                          '/haarcascade_frontalface_alt2.xml ')
    face = face_detector.detectMultiScale(gray, 1.1, 5, cv2.CASCADE_SCALE_IMAGE, (100, 100), (300, 300))  # 人脸检测

    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)  # 这里的置信评分其实可以理解为差异值，超过80就代表着差异值过大
        if confidence > 80:
            global warningtime
            warningtime += 1
            if warningtime > 100:
                warningtime = 0
            cv2.putText(img, 'unknown', (x + 10, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (0, 255, 0), 1)
        else:
            cv2.putText(img, str(names[ids - 1]), (x + 10, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (0, 255, 0), 1)
    cv2.imshow('result', img)
# name函数读取特定路径下的名字
def name():
    path = 'F:/pythonProject/test/Lao_Wang/'
    imagePaths = [os.path.join(path, f) for f in os.listdir(path)]
    for imagePath in imagePaths:
        name = str(os.path.split(imagePath)[1].split('.', 2)[1])
        names.append(name)


# cap=cv2.VideoCapture('1.mp4')
cap = cv2.VideoCapture(0)
name()
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)

__EOF__

本文作者：WangZhenHui
本文链接：https://www.cnblogs.com/index-12/p/17280693.html
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