python人脸识别

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,labeldef 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_testif __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 MultinomialNBimport 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,labeldef 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_testdef 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_timeif __name__ == '__main__':
f = False
images ,labels= read_path("./train_img/")
get_video()