windows hello的低阶板本，没有Windows hello的3D景深镜头，因此这是一个基于图片的识别机主的程序。 具体运行时，解锁时，判断是否是本人；若不是本人或无人（10s），锁屏；若是本人，正常使用；(采取无密码原始界面)
人脸的检测采取opencv cv2.CascadeClassifier
关于模型则采取
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d (Conv2D) (None, 62, 62, 128) 3584
_________________________________________________________________
conv2d_1 (Conv2D) (None, 60, 60, 64) 73792
_________________________________________________________________
flatten (Flatten) (None, 230400) 0
_________________________________________________________________
dense (Dense) (None, 40) 9216040
=================================================================
Total params: 9,293,416
Trainable params: 9,293,416
Non-trainable params: 0
_________________________________________________________________
None
基础需要由四部分组成。
| face_1.py | face_2.py | face_3.py | face_4.py |
|---|---|---|---|
| 制作自己人脸训练数据 | 由face_1.py 和 face_2.py制作的数据来进行CNN深度学习，并保存模型 | 由已知其他人脸来制作数据 | 最后的检测程序 |
主要是在tensorflow2.0-gpu下运行； 这里略微吐槽下tensorflow2.0.keras模块部分无提示，对于新手不太友好。 conda list：
| Name | Version | Build Channel |
|---|---|---|
| _tflow_select | 2.1.0 | gpu |
| absl-py | 0.8.1 | py37_0 |
| altgraph | 0.17 | pypi_0 pypi |
| astor | 0.8.0 | py37_0 |
| astroid | 2.3.3 | py37_0 |
| backcall | 0.1.0 | py37_0 |
| blas | 1.0 | mkl |
| ca-certificates | 2019.11.27 | 0 |
| certifi | 2019.11.28 | py37_0 |
| colorama | 0.4.3 | py_0 |
| cudatoolkit | 10.0.130 | 0 |
| cudnn | 7.6.5 | cuda10.0_0 |
| cycler | 0.10.0 | pypi_0 pypi |
| decorator | 4.4.1 | py_0 |
| future | 0.18.2 | pypi_0 pypi |
| gast | 0.2.2 | py37_0 |
| google-pasta | 0.1.8 | py_0 |
| grpcio | 1.16.1 | py37h351948d_1 |
| h5py | 2.9.0 | py37h5e291fa_0 |
| hdf5 | 1.10.4 | h7ebc959_0 |
| icc_rt | 2019.0.0 | h0cc432a_1 |
| intel-openmp | 2019.4 | 245 |
| ipykernel | 5.1.3 | py37h39e3cac_1 |
| ipython | 7.11.1 | py37h39e3cac_0 |
| ipython_genutils | 0.2.0 | py37_0 |
| isort | 4.3.21 | py37_0 |
| jedi | 0.15.2 | py37_0 |
| joblib | 0.14.1 | py_0 |
| jupyter_client | 5.3.4 | py37_0 |
| jupyter_core | 4.6.1 | py37_0 |
| keras | 2.3.1 | pypi_0 pypi |
| keras-applications | 1.0.8 | py_0 |
| keras-preprocessing | 1.1.0 | py_1 |
| kiwisolver | 1.1.0 | pypi_0 pypi |
| lazy-object-proxy | 1.4.3 | py37he774522_0 |
| libprotobuf | 3.11.2 | h7bd577a_0 |
| libsodium | 1.0.16 | h9d3ae62_0 |
| markdown | 3.1.1 | py37_0 |
| matplotlib | 3.1.2 | pypi_0 pypi |
| mccabe | 0.6.1 | py37_1 |
| mkl | 2019.4 | 245 |
| mkl-service | 2.3.0 | py37hb782905_0 |
| mkl_fft | 1.0.15 | py37h14836fe_0 |
| mkl_random | 1.1.0 | py37h675688f_0 |
| mouseinfo | 0.1.2 | pypi_0 pypi |
| numpy | 1.17.4 | py37h4320e6b_0 |
| numpy-base | 1.17.4 | py37hc3f5095_0 |
| opencv-python | 4.1.2.30 | pypi_0 pypi |
| openssl | 1.1.1d | he774522_3 |
| opt_einsum | 3.1.0 | py_0 |
| pandas | 0.25.3 | pypi_0 |
| parso | 0.5.2 | py_0 |
| pefile | 2019.4.18 | pypi_0 |
| pickleshare | 0.7.5 | py37_0 |
| pillow | 7.0.0 | pypi_0 |
| pip | 19.3.1 | py37_0 |
| prompt_toolkit | 3.0.2 | py_0 |
| protobuf | 3.11.2 | py37h33f27b4_0 |
| pyautogui | 0.9.48 | pypi_0 pypi |
| pygetwindow | 0.0.8 | pypi_0 pypi |
| pygments | 2.5.2 | py_0 |
| pyinstaller | 3.6 | pypi_0 pypi |
| pylint | 2.4.4 | py37_0 |
| pymsgbox | 1.0.7 | pypi_0 pypi |
| pyparsing | 2.4.6 | pypi_0 pypi |
| pyperclip | 1.7.0 | pypi_0 pypi |
| pyreadline | 2.1 | py37_1 |
| pyrect | 0.1.4 | pypi_0 pypi |
| pyscreeze | 0.1.26 | pypi_0 pypi |
| python | 3.7.6 | h60c2a47_2 |
| python-dateutil | 2.8.1 | py_0 |
| pytweening | 1.0.3 | pypi_0 pypi |
| pytz | 2019.3 | pypi_0 pypi |
| pywin32 | 227 | py37he774522_1 |
| pywin32-ctypes | 0.2.0 | pypi_0 pypi |
| pyyaml | 5.3 | pypi_0 pypi |
| pyzmq | 18.1.0 | py37ha925a31_0 |
| scikit-learn | 0.22.1 | py37h6288b17_0 |
| scipy | 1.3.2 | py37h29ff71c_0 |
| setuptools | 44.0.0 | py37_0 |
| six | 1.13.0 | py37_0 |
| sqlite | 3.30.1 | he774522_0 |
| tensorboard | 2.0.0 | pyhb38c66f_1 |
| tensorflow | 2.0.0 | gpu_py37h57d29ca_0 |
| tensorflow-base | 2.0.0 | gpu_py37h390e234_0 |
| tensorflow-estimator | 2.0.0 | pyh2649769_0 |
| tensorflow-gpu | 2.0.0 | h0d30ee6_0 |
| termcolor | 1.1.0 | py37_1 |
| tornado | 6.0.3 | py37he774522_0 |
| traitlets | 4.3.3 | py37_0 |
| vc | 14.1 | h0510ff6_4 |
| vs2015_runtime | 14.16.27012 | hf0eaf9b_1 |
| wcwidth | 0.1.7 | py37_0 |
| werkzeug | 0.16.0 | py_0 |
| wheel | 0.33.6 | py37_0 |
| wincertstore | 0.2 | py37_0 |
| wrapt | 1.11.2 py37he774522_0 | |
| zeromq | 4.3.1 | h33f27b4_3 |
| zlib | 1.2.11 | h62dcd97_3 |
人脸数据存储至my_faces 可自己命名
face_1.py
# 制作自己人脸数据
from cv2 import cv2
import os
import sys
import random
out_dir = './my_faces'
if not os.path.exists(out_dir):
os.makedirs(out_dir)
# 改变亮度与对比度
def relight(img, alpha=1, bias=0):
w = img.shape[1]
h = img.shape[0]
#image = []
for i in range(0,w):
for j in range(0,h):
for c in range(3):
tmp = int(img[j,i,c]*alpha + bias)
if tmp > 255:
tmp = 255
elif tmp < 0:
tmp = 0
img[j,i,c] = tmp
return img
# 获取分类器
haar = cv2.CascadeClassifier(r'E:\ProgramData\Anaconda3\envs\tenserflow02\Lib\site-packages\cv2\data\haarcascade_frontalface_default.xml')
# 打开摄像头 参数为输入流，可以为摄像头或视频文件
camera = cv2.VideoCapture(0)
n = 1
while 1:
if (n <= 5000):
print('It`s processing %s image.' % n)
# 读帧
success, img = camera.read()
gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = haar.detectMultiScale(gray_img, 1.3, 5)
for f_x, f_y, f_w, f_h in faces:
face = img[f_y:f_y+f_h, f_x:f_x+f_w]
face = cv2.resize(face, (64,64))
face = relight(face, random.uniform(0.5, 1.5), random.randint(-50, 50))
cv2.imshow('img', face)
cv2.imwrite(out_dir+'/'+str(n)+'.jpg', face)
n+=1
key = cv2.waitKey(30) & 0xff
if key == 27:
break
else:
break
需要收集一个其他人脸的图片集，只要不是自己的人脸都可以，可以在网上找到，这里我给出一个我用到的图片集： 网站地址:http://vis-www.cs.umass.edu/lfw/ 图片集下载:http://vis-www.cs.umass.edu/lfw/lfw.tgz 先将下载的图片集，解压到项目目录下的lfw目录下，也可以自己指定目录(修改代码中的input_dir变量)
face_3.py
# -*- codeing: utf-8 -*-
import sys
import os
from cv2 import cv2
input_dir = './lfw'
output_dir = './other_faces'
size = 64
if not os.path.exists(output_dir):
os.makedirs(output_dir)
def close_cv2():
"""删除cv窗口"""
while(1):
if(cv2.waitKey(100)==27):
break
cv2.destroyAllWindows()
# 获取分类器
haar = cv2.CascadeClassifier(r'E:\ProgramData\Anaconda3\envs\tenserflow02\Lib\site-packages\cv2\data\haarcascade_frontalface_default.xml')
index = 1
for (path, dirnames, filenames) in os.walk(input_dir):
for filename in filenames:
if filename.endswith('.jpg'):
print('Being processed picture %s' % index)
img_path = path+'/'+filename
# # 从文件读取图片
print(img_path)
img = cv2.imread(img_path)
# cv2.imshow(" ",img)
# close_cv2()
# 转为灰度图片
gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = haar.detectMultiScale(gray_img, 1.3, 5)
for f_x, f_y, f_w, f_h in faces:
face = img[f_y:f_y+f_h, f_x:f_x+f_w]
face = cv2.resize(face, (64,64))
# face = relight(face, random.uniform(0.5, 1.5), random.randint(-50, 50))
cv2.imshow('img', face)
cv2.imwrite(output_dir+'/'+str(index)+'.jpg', face)
index+=1
key = cv2.waitKey(30) & 0xff
if key == 27:
sys.exit(0)读取上文的 my_faces和other_faces文件夹下的训练数据进行训练
face_2.py
# -*- codeing: utf-8 -*-
from __future__ import absolute_import, division, print_function
import tensorflow as tf
from cv2 import cv2
import numpy as np
import os
import random
import sys
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
# from keras import backend as K
def getPaddingSize(img):
h, w, _ = img.shape
top, bottom, left, right = (0,0,0,0)
longest = max(h, w)
if w < longest:
tmp = longest - w
# //表示整除符号
left = tmp // 2
right = tmp - left
elif h < longest:
tmp = longest - h
top = tmp // 2
bottom = tmp - top
else:
pass
return top, bottom, left, right
def readData(path, h,w,imgs,labs):
for filename in os.listdir(path):
if filename.endswith('.jpg'):
filename = path + '/' + filename
img = cv2.imread(filename)
# cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
top,bottom,left,right = getPaddingSize(img)
# 将图片放大， 扩充图片边缘部分
img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=[0,0,0])
img = cv2.resize(img, (h, w))
imgs.append(img)
labs.append(path)
return imgs,labs
def get_model():
model = tf.keras.Sequential()
# 第一层卷积，卷积的数量为128，卷积的高和宽是3x3，激活函数使用relu
model.add(tf.keras.layers.Conv2D(128, kernel_size=3, activation='relu', input_shape=(64, 64, 3)))
# 第二层卷积
model.add(tf.keras.layers.Conv2D(64, kernel_size=3, activation='relu'))
#把多维数组压缩成一维，里面的操作可以简单理解为reshape，方便后面Dense使用
model.add(tf.keras.layers.Flatten())
#对应cnn的全链接层，可以简单理解为把上面的小图汇集起来，进行分类
model.add(tf.keras.layers.Dense(40, activation='softmax'))
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
return model
def facemain():
my_faces_path = './my_faces'
other_faces_path = './other_faces'
size = 64
imgs = []
labs = []
imgs,labs=readData(my_faces_path,size,size,imgs,labs)
imgs,labs=readData(other_faces_path,size,size,imgs,labs)
# 将图片数据与标签转换成数组
imgs = np.array(imgs)
# labs = np.array([[0,1] if lab == my_faces_path else [1,0] for lab in labs])
labs = np.array([[1] if lab == my_faces_path else [0] for lab in labs])
print(imgs.shape)
print(labs.shape)
# 随机划分测试集与训练集
train_x,test_x,train_y,test_y = train_test_split(imgs, labs, test_size=0.8, random_state=random.randint(0,100))
# 参数：图片数据的总数，图片的高、宽、通道
train_x = train_x.reshape(train_x.shape[0], size, size, 3)
test_x = test_x.reshape(test_x.shape[0], size, size, 3)
# 将数据转换成小于1的数
train_x = train_x.astype('float32')/255.0
test_x = test_x.astype('float32')/255.0
print('train size:%s, test size:%s' % (len(train_x), len(test_x)))
# 图片块，每次取100张图片
batch_size = 100
num_batch = len(train_x) // batch_size
model=get_model()
model.fit(train_x, train_y, epochs=5)
model.save(r'C:\Users\Administrator\Desktop\my_model.h5')
facemain()face_4.py
#识别自己
from __future__ import absolute_import, division, print_function
import tensorflow as tf
from cv2 import cv2
import os
import sys
import random
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
from sklearn.metrics import cohen_kappa_score
from ctypes import *
import time
import sys
def getPaddingSize(img):
h, w, _ = img.shape
top, bottom, left, right = (0,0,0,0)
longest = max(h, w)
if w < longest:
tmp = longest - w
# //表示整除符号
left = tmp // 2
right = tmp - left
elif h < longest:
tmp = longest - h
top = tmp // 2
bottom = tmp - top
else:
pass
return top, bottom, left, right
def readData(path, h,w,imgs,labs):
for filename in os.listdir(path):
if filename.endswith('.jpg'):
filename = path + '/' + filename
img = cv2.imread(filename)
# cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
top,bottom,left,right = getPaddingSize(img)
# 将图片放大， 扩充图片边缘部分
img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=[0,0,0])
img = cv2.resize(img, (h, w))
imgs.append(img)
labs.append(path)
return imgs,labs
# 改变亮度与对比度
def relight(img, alpha=1, bias=0):
w = img.shape[1]
h = img.shape[0]
#image = []
for i in range(0,w):
for j in range(0,h):
for c in range(3):
tmp = int(img[j,i,c]*alpha + bias)
if tmp > 255:
tmp = 255
elif tmp < 0:
tmp = 0
img[j,i,c] = tmp
return img
out_dir = './temp_faces'
if not os.path.exists(out_dir):
os.makedirs(out_dir)
# 获取分类器
haar = cv2.CascadeClassifier(r'E:\ProgramData\Anaconda3\envs\tenserflow02\Lib\site-packages\cv2\data\haarcascade_frontalface_default.xml')
# 打开摄像头 参数为输入流，可以为摄像头或视频文件
camera = cv2.VideoCapture(0)
n = 1
start = time.clock()
while 1:
if (n <= 20):
print('It`s processing %s image.' % n)
# 读帧
success, img = camera.read()
gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = haar.detectMultiScale(gray_img, 1.3, 5)
for f_x, f_y, f_w, f_h in faces:
face = img[f_y:f_y+f_h, f_x:f_x+f_w]
face = cv2.resize(face, (64,64))
# face = relight(face, random.uniform(0.5, 1.5), random.randint(-50, 50))
cv2.imshow('img', face)
cv2.imwrite(out_dir+'/'+str(n)+'.jpg', face)
n+=1
key = cv2.waitKey(30) & 0xff
if key == 27:
break
end = time.clock()
print(str(end-start))
if (end-start)>10:
user32 = windll.LoadLibrary('user32.dll')
user32.LockWorkStation()
sys.exit()
else:
break
my_faces_path = out_dir
size = 64
imgs = []
labs = []
imgs,labs=readData(my_faces_path,size,size,imgs,labs)
# 将图片数据与标签转换成数组
imgs = np.array(imgs)
# labs = np.array([[0,1] if lab == my_faces_path else [1,0] for lab in labs])
labs = np.array([[1] if lab == my_faces_path else [0] for lab in labs])
# 随机划分测试集与训练集
train_x,test_x,train_y,test_y = train_test_split(imgs, labs, test_size=0.9, random_state=random.randint(0,100))
# 参数：图片数据的总数，图片的高、宽、通道
train_x = train_x.reshape(train_x.shape[0], size, size, 3)
test_x = test_x.reshape(test_x.shape[0], size, size, 3)
# 将数据转换成小于1的数
train_x = train_x.astype('float32')/255.0
test_x = test_x.astype('float32')/255.0
restored_model = tf.keras.models.load_model(r'C:\Users\Administrator\Desktop\my_model.h5')
pre_result=restored_model.predict_classes(test_x)
print(pre_result.shape)
print(pre_result)
acc=sum(pre_result==1)/pre_result.shape[0]
print("相似度： "+str(acc))
if acc > 0.8:
print("你是***")
else:
user32 = windll.LoadLibrary('user32.dll')
user32.LockWorkStation()激活Anaconda环境 切CD至face_4.py的位置
call activate tensorflow02
cd /d E:\ziliao\LearningPy\face
python face_4.py
Set ws = CreateObject("Wscript.Shell")
ws.run "cmd /c E:\ziliao\LearningPy\face\myface.bat",vbhide
创建任务
| 常规中 | 触发器 | 操作 |
|---|---|---|
| 最高权限 选择对应系统win10 | 添加 工作站解锁时 | 添加hide.vbs |