人脸识别的步骤：

人脸检测

    haar人脸检测，

    lbp人脸检测

特征处理

    图片大小尺寸统一

    彩色灰度转换

    图片编成一维矩阵

特征提取处理

    归一化

    特征选择-删除低方差的特征

    分析进行特征降维

    训练集与测试集以一定比例数据分割

预测与训练

    朴素贝叶斯算法的预测

    决策树进行预测

    K-近邻预测

    得出准确率

# camera.py 运行会调用笔记本摄像头，鼠标右击会保存摄像头检测的人脸，保存到0和1文件夹，但是保存文件的路径需要自己手改
import cv2 as cv

import time

def zh_cv(string):

    return  string.encode("gbk").decode(errors="ignore")

def get_video():

    capture =cv.VideoCapture()# VideoCapture() 开发默认摄像头，如果你有多个摄像头可以试试除0之外的其他参数

    print("-----打开摄像头--------")

    while(capture.isOpened()) :

        ret, frame=capture.read()

        if ret ==False :

            break;

        # print(frame)

        cv.flip(frame,)# 左右变换

        # print("---------haar检测算法----------")

        face_detect_dome(frame)

        # face_lbp_dome(frame)

        # cv.imshow("voide",frame)

        # cv.imshow(zh_cv("摄像头"),frame)

        c=cv.waitKey()

        if c == :

            break;

def face_detect_dome(image):

    num =

    gray=cv.cvtColor(image,cv.COLOR_BGR2GRAY)

    face_detect=cv.CascadeClassifier("./face_xml/haarcascades/haarcascade_frontalface_alt.xml")

    #识别出人脸数量

    # facerect = face_detect.detectMultiScale(gray, scaleFactor=1.2, minNeighbors=, minSize=(, ))

    faces=face_detect.detectMultiScale(gray,1.1,)

    for x,y,w,h in faces:

        num +=

        global f

        while(f ==True):

            file_name=""#  保存文件路径

            img_head = "zheng"#  wang20200203211958

            image_save = image[y:y+h, x:x+w] # 将当前帧含人脸部分保存为图片，注意这里存的还是彩色图片，前面检测时灰度化是为了降低计算量；这里访问的是从y位开始到y+h-1位

            # print(resize_image(image_save,,))

            image_save_resize=cv.resize(image_save,(,))

            gray=cv.cvtColor(image_save_resize,cv.COLOR_BGR2GRAY)# 灰度

            print(gray.shape)

            # cv.imshow("-----",image_save)

            cv.imwrite('./train_img/%s/%s%s.jpg' %(file_name,img_head,face_time()), gray)

            show_save =cv.imread('./train_img/%s/%s%s.jpg' %(file_name,img_head,face_time()))

            cv.imshow(zh_cv("save_%s%s"%(img_head,face_time())),show_save)

            f=False

            print("保存图片","%s%s"%(img_head,face_time()))

        cv.rectangle(image,(x,y),(x+w,y+h),(,,),)

        #          原图         位置  ，h         颜色

        # cv.namedWindow("face lbp",cv.WINDOW_NORMAL)

        # 显示当前捕捉到了多少人脸图片了

        font = cv.FONT_HERSHEY_SIMPLEX

        cv.putText(image, 'num:%d' % (num), (x + , y + ), font, , (, , ), )

        cv.putText(image, 'name:%d' % (num), (x + , y -), font, , (, , ), )

        # cv.namedWindow("face_haar",cv.WINDOW_NORMAL)

        cv.setMouseCallback('face_haar',img_save)

        cv.imshow("face_haar",image)

def face_lbp_dome(image):

    gray=cv.cvtColor(image,cv.COLOR_BGR2GRAY)

    face_detect=cv.CascadeClassifier("./face_xml/lbpcascades/lbpcascade_frontalface.xml")

    faces=face_detect.detectMultiScale(gray,1.1,)

    print("---------lbp----------")

    for x,y,w,h in faces:

        cv.rectangle(image,(x,y),(x+w,y+h),(,,),)

        #          原图         位置  w，h         颜色

        cv.namedWindow("face_lbp",cv.WINDOW_NORMAL)

        cv.setMouseCallback('face_lbp',img_save)

        cv.imshow("face_lbp",image)

def img_save(event, x, y, flags, param):

    global f

    if event == cv.EVENT_RBUTTONDOWN:

        f = True

        print(f)

        print("---------截取人脸----------")

        # cv.waitKey()

def face_time():

    # print(time.strftime("%Y%m%d%H%M%S", time.localtime()))

    n_time =str(time.strftime("%Y%m%d%H%M%S", time.localtime()))

    return n_time

if __name__ == '__main__':

    f = False

    get_video()

#read_image 会调用保存两个文件夹采集的人脸数据图片，test_pre方法读取的图片路径使用自己人脸照片（属于上面采集两个人脸之一）
import os

from sklearn.preprocessing import MinMaxScaler, StandardScaler

from sklearn.decomposition import PCA

import numpy as np

from sklearn.model_selection import train_test_split, GridSearchCV

from sklearn.preprocessing import StandardScaler

from sklearn.naive_bayes import MultinomialNB

import cv2

images = []

labels = []

img_list =[]

# path_name是当前工作目录，后面会由os.getcwd()获得

def read_path(path_name):

    for dir_item in os.listdir(path_name): # os.listdir() 方法用于返回指定的文件夹包含的文件或文件夹的名字的列表

        # 从当前工作目录寻找训练集图片的文件夹

        full_path = os.path.abspath(os.path.join(path_name, dir_item))

        if os.path.isdir(full_path): # 如果是文件夹，继续递归调用，去读取文件夹里的内容

            read_path(full_path)

        else: # 如果是文件了

            if dir_item.endswith('.jpg'):

                image = cv2.imread(full_path)

                if image is None: # 遇到部分数据有点问题，报错'NoneType' object has no attribute 'shape'

                    pass

                else:

                    image_resize=cv2.resize(image,(,))

                    gray=cv2.cvtColor(image_resize,cv2.COLOR_BGR2GRAY)# 灰度

                    weight,height = gray.shape

                  # 取reshape后的矩阵的第一维度数据，即所需要的数据列表

                    img_reshape = gray.reshape(,weight*height)[]

                    # print(list(img_reshape))

                    #   转换列表添加images

                    image_list=list(img_reshape)

                    images.append(image_list)

                    # global labels

                    labels.append(path_name)

                    # 标注数据，me文件夹下是我，指定为0，其他指定为1，这里的0和1不是logistic regression二分类输出下的0和1，而是softmax下的多分类的类别

        label = np.array(["%s"%"zhaoban" if label.endswith("%d"%)   else "unknow" for label in labels])

    # label = np.array([endwith(labels)])

    return images,label

def mm(img_mm):

    """

    归一化处理

    :return: NOne

    """

    mm = MinMaxScaler(feature_range=(,))

    data = mm.fit_transform(img_mm)

    print(data)

    print("----------归一化处理-------------")

    return data

def pca(img_pca):

    """

    主成分分析进行特征降维

    :return: None

    """

    pca = PCA(n_components=0.9)

    data = pca.fit_transform(img_pca)

    print("------------主成分分析进行特征降维---------------")

    # print(data)

    return data

def stand(stand_data):

    """

    标准化缩放

    :return:

    """

    std = StandardScaler()

    data = std.fit_transform(stand_data)

    # print(data)

    return data

def naviebayes(data,target):

    """

    朴素贝叶斯进行文本分类

    :return: None,t

    """

    # news = fetch_20newsgroups(subset='all')

    # 进行数据分割

    x_train, x_test, y_train, y_test = train_test_split(data,target, test_size=0.25)

    mlt = MultinomialNB(alpha=1.0)

    mlt.fit(x_train, y_train)

    # print(x_test)

    # print(test_pre())

    y_predict = mlt.predict(test_pre())

    print("分类类别为：", y_predict)

    # # 得出准确率

    print("准确率为：", mlt.score(x_test, y_test))

    return None

def test_pre():

    image_test =[]

    image =cv2.imread("qq.jpg")

    image_resize=cv2.resize(image,(,))

    gray=cv2.cvtColor(image_resize,cv2.COLOR_BGR2GRAY)# 灰度

    weight,height = gray.shape

  # 取reshape后的矩阵的第一维度数据，即所需要的数据列表

    img_reshape = gray.reshape(,weight*height)[]

    # print(list(img_reshape))

    #   转换列表添加images

    image_list=list(img_reshape)

    image_test.append(image_list)

    return image_test

if __name__ == "__main__":

    # print(read_path("./train_img/"))

    images ,labels= read_path("./train_img/")

    # image_mm =mm(images)

    # image_stand =stand(image_mm)

    # image_pca=pca(image_mm)

    print("---------------------------------------")

    naviebayes(images,labels)

    print("-----------------")

#load_face_test.py 是把俩个程序结合出来，再有人脸数据集和haarcascades和lbp（opencv自带人脸检测网上可下就是慢，lbp是对比用的，我这里最后没有使用）

import cv2 as cv

import time

import os

from sklearn.preprocessing import MinMaxScaler, StandardScaler

from sklearn.decomposition import PCA

import numpy as np

from sklearn.model_selection import train_test_split, GridSearchCV

from sklearn.preprocessing import StandardScaler

from sklearn.naive_bayes import MultinomialNB

import cv2

images = []

labels = []

img_list =[]

# path_name是当前工作目录，后面会由os.getcwd()获得

def read_path(path_name):

    for dir_item in os.listdir(path_name): # os.listdir() 方法用于返回指定的文件夹包含的文件或文件夹的名字的列表

        # 从当前工作目录寻找训练集图片的文件夹

        full_path = os.path.abspath(os.path.join(path_name, dir_item))

        if os.path.isdir(full_path): # 如果是文件夹，继续递归调用，去读取文件夹里的内容

            read_path(full_path)

        else: # 如果是文件了

            if dir_item.endswith('.jpg'):

                image = cv2.imread(full_path)

                if image is None: # 遇到部分数据有点问题，报错'NoneType' object has no attribute 'shape'

                    pass

                else:

                    image_resize=cv2.resize(image,(,))

                    gray=cv2.cvtColor(image_resize,cv2.COLOR_BGR2GRAY)# 灰度

                    weight,height = gray.shape

                  # 取reshape后的矩阵的第一维度数据，即所需要的数据列表

                    img_reshape = gray.reshape(,weight*height)[]

                    # print(list(img_reshape))

                    #   转换列表添加images

                    image_list=list(img_reshape)

                    images.append(image_list)

                    # global labels

                    labels.append(path_name)

                    # 标注数据，me文件夹下是我，指定为0，其他指定为1，这里的0和1不是logistic regression二分类输出下的0和1，而是softmax下的多分类的类别

    # dict ={:"办",:"long"}

    # for key in range():

    #     print(dict[key])

    label = np.array(["%s"%"zheng" if label.endswith("%d"%)   else "zhaoban" for label in labels])

    # label = np.array(["%s"%dict[i] if label.endswith("%d"%(i for i in range())) for label in labels])

    return images,label

def mm(img_mm):

    """

    归一化处理

    :return: NOne

    """

    mm = MinMaxScaler(feature_range=(,))

    data = mm.fit_transform(img_mm)

    print(data)

    print("----------归一化处理-------------")

    return data

def pca(img_pca):

    """

    主成分分析进行特征降维

    :return: None

    """

    pca = PCA(n_components=0.9)

    data = pca.fit_transform(img_pca)

    print("------------主成分分析进行特征降维---------------")

    # print(data)

    return data

def stand(stand_data):

    """

    标准化缩放

    :return:

    """

    std = StandardScaler()

    data = std.fit_transform(stand_data)

    # print(data)

    return data

def naviebayes(data,target,test_img):

    """

    朴素贝叶斯进行文本分类

    :return: None,t

    """

    # news = fetch_20newsgroups(subset='all')

    # 进行数据分割

    x_train, x_test, y_train, y_test = train_test_split(data,target, test_size=0.25)

    mlt = MultinomialNB(alpha=1.0)

    mlt.fit(x_train, y_train)

    # print(x_test)

    # print(test_pre())

    y_predict = mlt.predict(test_img)

    print("分类类别为：", y_predict)

    # # 得出准确率

    print("准确率为：", mlt.score(x_test, y_test))

    return y_predict,mlt.score(x_test, y_test)

def test_pre(gray):

    image_test =[]

    weight,height = gray.shape

  # 取reshape后的矩阵的第一维度数据，即所需要的数据列表

    img_reshape = gray.reshape(,weight*height)[]

    # print(list(img_reshape))

    #   转换列表添加images

    image_list=list(img_reshape)

    image_test.append(image_list)

    return image_test

def zh_cv(string):

    return  string.encode("gbk").decode(errors="ignore")

def get_video():

    capture =cv.VideoCapture()# VideoCapture() 开发默认摄像头，如果你有多个摄像头可以试试除0之外的其他参数

    print("-----打开摄像头--------")

    while(capture.isOpened()) :

        ret, frame=capture.read()

        if ret ==False :

            break;

        # print(frame)

        cv.flip(frame,)# 左右变换

        # print("---------haar检测算法----------")

        face_detect_dome(frame)

        # face_lbp_dome(frame)

        # cv.imshow("voide",frame)

        # cv.imshow(zh_cv("摄像头"),frame)

        c=cv.waitKey()

        if c == :

            break;

def face_detect_dome(image):

    num =

    gray=cv.cvtColor(image,cv.COLOR_BGR2GRAY)

    face_detect=cv.CascadeClassifier("./face_xml/haarcascades/haarcascade_frontalface_alt.xml")

    #识别出人脸数量

    # facerect = face_detect.detectMultiScale(gray, scaleFactor=1.2, minNeighbors=, minSize=(, ))

    faces=face_detect.detectMultiScale(gray,1.1,)

    for x,y,w,h in faces:

        num +=

        image_save = image[y:y+h, x:x+w] # 将当前帧含人脸部分保存为图片，注意这里存的还是彩色图片，前面检测时灰度化是为了降低计算量；这里访问的是从y位开始到y+h-1位++

        # print(resize_image(image_save,,))

        image_save_resize=cv.resize(image_save,(,))

        gray=cv.cvtColor(image_save_resize,cv.COLOR_BGR2GRAY)# 灰度

        predict,score =naviebayes(images,labels,test_pre(gray))

        print(score)

        global f

        # while(f ==True):

        #     file_name=""

        #     img_head = "wang"#  wang20200203211958

        #     print(gray.shape)

        #     cv.imwrite('./train_img/%s/%s%s.jpg' %(file_name,img_head,face_time()), gray)

        #     show_save =cv.imread('./train_img/%s/%s%s.jpg' %(file_name,img_head,face_time()))

        #     cv.imshow(zh_cv("save_%s%s"%(img_head,face_time())),show_save)

        #     f=False

        #     print("保存图片","%s%s"%(img_head,face_time()))

            # image_test =cv.imread('./train_img/%s/%s%s.jpg' %(file_name,img_head,face_time()))

            # cv.imshow("-----",image_save)

        cv.rectangle(image,(x,y),(x+w,y+h),(,,),)

        #          原图         位置  ，h         颜色

        # cv.namedWindow("face lbp",cv.WINDOW_NORMAL)

        # 显示当前捕捉到了多少人脸图片了

        font = cv.FONT_HERSHEY_SIMPLEX

        cv.putText(image, 'num:%d' % (num), (x + , y + ), font, , (, , ), )

        cv.putText(image, 'name:%s precision:%3.2f %%' % (predict[],score*),(x -, y -), font, 0.8, (, , ), )

        #cv.namedWindow("face_haar",cv.WINDOW_NORMAL)

        cv.setMouseCallback('face_haar',img_save)

        cv.imshow("face_haar",image)

def face_lbp_dome(image):

    gray=cv.cvtColor(image,cv.COLOR_BGR2GRAY)

    face_detect=cv.CascadeClassifier("./face_xml/lbpcascades/lbpcascade_frontalface.xml")

    faces=face_detect.detectMultiScale(gray,1.1,)

    print("---------lbp----------")

    for x,y,w,h in faces:

        cv.rectangle(image,(x,y),(x+w,y+h),(,,),)

        #          原图         位置  w，h         颜色

        cv.namedWindow("face_lbp",cv.WINDOW_NORMAL)

        cv.setMouseCallback('face_lbp',img_save)

        cv.imshow("face_lbp",image)

def img_save(event, x, y, flags, param):

    global f

    if event == cv.EVENT_RBUTTONDOWN:

        f = True

        print(f)

        print("---------截取人脸----------")

        # cv.waitKey()

def face_time():

    # print(time.strftime("%Y%m%d%H%M%S", time.localtime()))

    n_time =str(time.strftime("%Y%m%d%H%M%S", time.localtime()))

    return n_time

if __name__ == '__main__':

    f = False

    images ,labels= read_path("./train_img/")

    get_video()