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facelearned.py
483 lines (455 loc) · 22.4 KB
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facelearned.py
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import dlib # 人脸识别的库dlib
import numpy # 数据处理的库numpy
import cv2 # 图像处理的库OpenCv
from PyQt5.QtCore import QThread,QMutex,QMutexLocker,pyqtSignal
from PyQt5.QtGui import QImage
from skimage import io as iio
import os
from time import localtime,strftime
from LearnDatabase import LearnDatabase # 数据库类 存储访问日志、录入人脸数据等
import random # 陌生人随机ID
from traceback import print_exc # 异常处理打印错误信息，仅调试用
# 人脸识别处理类
class FaceLearning(QThread):
ID_WORKER_UNAVIABLE = -2
FACE_EXISTS = -1
# 载入数据模式
WORKER_INFO = 1
LOGCAT_INFO = 2
OTHERS_INFO = 3
ACCOUNT_INFO = 4
# 信号处理模式
AUTO_CHECK_SINGAL = 12
INSPECT_FACE_SINGAL = 5
EXIT_SINGAL = 6
REGISTER_SINGAL = 7
Register_FINISHSINGAL = 8
Register_FAILED = 13
# 线程事件处理
IS_EXIT = False
CHECKFACE_SINGAL = False
AUTO_CHECKFACE_SINGAL = True
UPDATE_LOGIC = 11
update_LoadSignal = pyqtSignal(int) # 重载日志信号
Register_FinishSignal = pyqtSignal(int) # 注册完成信号
Label_Sender = pyqtSignal(QImage)
def __init__(self,file):
super(FaceLearning,self).__init__()
self._mutex = QMutex()
self.PATH_FACE = "data/face_img_database/"
# face recognition model, the object maps human faces into 128D vectors
self.facerec = dlib.face_recognition_model_v1("model/dlib_face_recognition_resnet_model_v1.dat")
# Dlib 预测器
self.detector = dlib.get_frontal_face_detector()
self.predictor = dlib.shape_predictor('model/shape_predictor_68_face_landmarks.dat')
# 数据库
self.file = file
self.database = LearnDatabase(self.file)
# 摄像头
self.cap = cv2.VideoCapture(0)
self.initData()
# 静态方法
@staticmethod
def return_euclidean_distance(feature_1, feature_2):
feature_1 = numpy.array(feature_1)
feature_2 = numpy.array(feature_2)
dist = numpy.sqrt(numpy.sum(numpy.square(feature_1 - feature_2)))
print("欧式距离: ", dist)
if dist > 0.4:
return "diff"
else:
return "same"
def initData(self):
self.name = ""
self.id = self.ID_WORKER_UNAVIABLE
self.face_feature = ""
self.image = None
self.flag = 0
self.pic_num = 0
self.flag_registed = False
self.puncard_time = "09:00:00"
with QMutexLocker(self._mutex):
self.database.loadDataBase(self.WORKER_INFO)
self.database.loadDataBase(self.LOGCAT_INFO)
self.database.loadDataBase(self.OTHERS_INFO)
self.database.loadDataBase(self.ACCOUNT_INFO)
pass
def getDateAndTime(self):
dateandtime = strftime("%Y-%m-%d %H:%M:%S",localtime())
return "["+dateandtime+"]"
pass
# 开启摄像头注册
def registerCap(self):
with QMutexLocker(self._mutex):
# 加载录入用户数据库
self.database.loadDataBase(self.WORKER_INFO)
while self.cap.isOpened():
# cap.read()
# 返回两个值：
# 一个布尔值true/false，用来判断读取视频是否成功/是否到视频末尾
# 图像对象，图像的三维矩阵
flag, im_rd = self.cap.read()
cv2.waitKey(1)
# 人脸数 dets
dets = self.detector(im_rd, 1)
# 检测到人脸
if len(dets) != 0:
biggest_face = dets[0]
# 取占比最大的脸
maxArea = 0
for det in dets:
w = det.right() - det.left()
h = det.top() - det.bottom()
if w * h > maxArea:
biggest_face = det
maxArea = w * h
# 绘制矩形框
cv2.rectangle(im_rd, tuple([biggest_face.left(), biggest_face.top()]),
tuple([biggest_face.right(), biggest_face.bottom()]),
(255, 0, 0), 2)
# 获取图片的长和宽
img_height, img_width = im_rd.shape[:2]
image = cv2.resize(im_rd, (int(img_height * 0.8), int(img_width * 0.8)))
image1 = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
imgx = QImage(image1.data, image1.shape[1], image1.shape[0], QImage.Format_RGB888)
# TODO:SEND SIGNAL TO MAINWINDOW TO DIS(Get)
# 获取当前捕获到的图像的所有人脸的特征，存储到 features_cap_arr
shape = self.predictor(im_rd, biggest_face)
features_cap = self.facerec.compute_face_descriptor(im_rd, shape)
# 对于某张人脸，遍历所有存储的人脸特征
for i, knew_face_feature in enumerate(self.database.knew_face_feature):
# 将某张人脸与存储的所有人脸数据进行比对
compare = self.return_euclidean_distance(features_cap, knew_face_feature)
if compare == "same": # 找到了相似脸
print("此人已经存在了呀")
# TODO:SEND SIGNAL TO MAINWINDOW TO REMAIN THE PASSAGE
self.flag_registed = True
try:
self.onFinishRegister()
except:
print('traceback.print_exc():', print_exc())
return self.FACE_EXISTS # 人员存在
# print(features_known_arr[i][-1])
face_height = biggest_face.bottom() - biggest_face.top()
face_width = biggest_face.right() - biggest_face.left()
im_blank = numpy.zeros((face_height, face_width, 3), numpy.uint8)
try:
for ii in range(face_height):
for jj in range(face_width):
im_blank[ii][jj] = im_rd[biggest_face.top() + ii][biggest_face.left() + jj]
self.pic_num += 1
# 解决python3下使用cv2.imwrite存储带有中文路径图片
if len(self.name) > 0:
print("开启写入数据")
cv2.imencode('.jpg', im_blank)[1].tofile(
self.PATH_FACE + self.name + "/img_face_" + str(self.pic_num) + ".jpg") # 正确方法
print("写入本地：", str(self.PATH_FACE + self.name) + "/img_face_" + str(self.pic_num) + ".jpg")
except:
print('traceback.print_exc():',print_exc())
print("保存照片异常,请对准摄像头")
return False
if self.pic_num == 10:
try:
self.onFinishRegister()
except:
print('traceback.print_exc():', print_exc())
return True
# 是否写入数据库
def onFinishRegister(self):
if self.flag_registed == True:
dir = self.PATH_FACE + self.name
for file in os.listdir(dir):
os.remove(dir + "/" + file)
print("已删除已录入人脸的图片", dir + "/" + file)
os.rmdir(self.PATH_FACE + self.name)
print("已删除已录入人脸的姓名文件夹", dir)
self.initData()
return
if self.pic_num > 0:
pics = os.listdir(self.PATH_FACE + self.name)
feature_list = []
feature_average = []
for i in range(len(pics)):
pic_path = self.PATH_FACE + self.name + "/" + pics[i]
print("正在读的人脸图像：", pic_path)
img = iio.imread(pic_path)
img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
dets = self.detector(img_gray, 1)
if len(dets) != 0:
shape = self.predictor(img_gray, dets[0])
face_descriptor = self.facerec.compute_face_descriptor(img_gray, shape)
feature_list.append(face_descriptor)
else:
face_dssaescriptor = 0
print("未在照片中识别到人脸")
if len(feature_list) > 0:
for j in range(128):
# 防止越界
feature_average.append(0)
for i in range(len(feature_list)):
feature_average[j] += feature_list[i][j]
feature_average[j] = (feature_average[j]) / len(feature_list)
# 数据库写入
with QMutexLocker(self._mutex):
self.database.insertRow([self.id, self.name, feature_average], self.WORKER_INFO)
print("写入数据库成功")
# 删除处理图片的缓存
if True :
dir = self.PATH_FACE + self.name
for file in os.listdir(dir):
os.remove(dir + "/" + file)
print("已删除已录入人脸的图片", dir + "/" + file)
os.rmdir(self.PATH_FACE + self.name)
print("已删除已录入人脸的姓名文件夹", dir)
self.initData()
return
pass
else:
os.rmdir(self.PATH_FACE + self.name)
print("已删除空文件夹", self.PATH_FACE + self.name)
self.initData()
# 陌生人入库
def othersRegister(self):
if self.flag_registed == True:
dir = self.PATH_FACE + self.id
for file in os.listdir(dir):
os.remove(dir + "/" + file)
print("已删除已录入人脸的图片", dir + "/" + file)
os.rmdir(self.PATH_FACE + self.id)
print("已删除已录入人脸的姓名文件夹", dir)
self.initData()
return
if self.pic_num > 0:
pics = os.listdir(self.PATH_FACE + self.id)
feature_list = []
feature_average = []
for i in range(len(pics)):
pic_path = self.PATH_FACE + self.id + "/" + pics[i]
print("正在读的人脸图像：", pic_path)
img = iio.imread(pic_path)
img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
dets = self.detector(img_gray, 1)
if len(dets) != 0:
shape = self.predictor(img_gray, dets[0])
face_descriptor = self.facerec.compute_face_descriptor(img_gray, shape)
feature_list.append(face_descriptor)
else:
face_dssaescriptor = 0
print("未在照片中识别到人脸")
if len(feature_list) > 0:
for j in range(128):
# 防止越界
feature_average.append(0)
for i in range(len(feature_list)):
feature_average[j] += feature_list[i][j]
feature_average[j] = (feature_average[j]) / len(feature_list)
# 数据库写入
with QMutexLocker(self._mutex):
datetime1 = self.getDateAndTime()
self.database.insertRow([self.id,datetime1,feature_average], self.OTHERS_INFO)
print("写入数据库成功")
# self.infoText.AppendText(self.getDateAndTime() + "工号:" + str(self.id)
# + " 姓名:" + self.name + " 的人脸数据已成功存入\r\n")
pass
else:
os.rmdir(self.PATH_FACE + self.id)
print("已删除空文件夹", self.PATH_FACE + self.id)
self.initData()
# 核验人脸
def punchCardCap(self,Unlock):
with QMutexLocker(self._mutex):
# 加载录入用户数据库
self.database.loadDataBase(self.WORKER_INFO)
# 加载陌生人用户数据库
# self.database.loadDataBase(self.OTHERS_INFO)
# 加载访问日志用户数据库
# self.database.loadDataBase(self.LOGCAT_INFO)
# cap是否初始化成功
if self.cap.isOpened():
# cap.read()
# 返回两个值：
# 一个布尔值true/false，用来判断读取视频是否成功/是否到视频末尾
# 图像对象，图像的三维矩阵
flag, im_rd = self.cap.read()
cv2.waitKey(1)
# 人脸数 dets
dets = self.detector(im_rd, 1)
# 检测到人脸
if len(dets) != 0:
biggest_face = dets[0]
# 取占比最大的脸
maxArea = 0
for det in dets:
w = det.right() - det.left()
h = det.top() - det.bottom()
if w * h > maxArea:
biggest_face = det
maxArea = w * h
# 绘制矩形框
cv2.rectangle(im_rd, tuple([biggest_face.left(), biggest_face.top()]),
tuple([biggest_face.right(), biggest_face.bottom()]),
(255, 0, 255), 2)
img_height, img_width = im_rd.shape[:2]
image = cv2.resize(im_rd, (int(img_height*0.8), int(img_width*0.8)))
image1 = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
# 获取当前捕获到的图像的所有人脸的特征，存储到 features_cap_arr
shape = self.predictor(im_rd, biggest_face)
features_cap = self.facerec.compute_face_descriptor(im_rd, shape)
# 对于某张人脸，遍历所有存储的注册用户人脸特征
for i, knew_face_feature in enumerate(self.database.knew_face_feature):
# 将某张人脸与存储的所有人脸数据进行比对
compare = self.return_euclidean_distance(features_cap, knew_face_feature)
if compare == "same": # 找到了相似脸
if Unlock == True:
return True
self.randomid = str(self.database.knew_id[i]) + "--" +str(random.randint(1000,65535))
print("已注册数据库人员")
flag = 0
nowdt = self.getDateAndTime()
try:
for j, logcat_name in enumerate(self.database.logcat_name):
# 如果存在同时同人就退出写入
if logcat_name == self.database.knew_name[i] and nowdt[0:nowdt.index(" ")] == \
self.database.logcat_datetime[j]\
and nowdt[nowdt.index(" "):] == self.database.logcat_late[j]:
flag = 1
break
except:
print('traceback.print_exc():', print_exc())
if flag == 1:
break
logicdata = [self.randomid,self.database.knew_name[i],nowdt[0:nowdt.index(" ")],nowdt[nowdt.index(" ")+1:]]
self.database.insertRow(logicdata,self.LOGCAT_INFO)
self.update_LoadSignal.emit(self.UPDATE_LOGIC)
if Unlock == True:
return False
'''
# 对于某张人脸，遍历所有存储的陌生人脸特征
for i, others_face_feature in enumerate(self.database.others_face_feature):
# 将某张人脸与存储的所有人脸数据进行比对
compare = self.return_euclidean_distance(features_cap, others_face_feature)
if compare == "same": # 找到了相似脸
print("已录入陌生人数据库人员")
flag = 0
nowdt = self.getDateAndTime()
try:
for j, others_id in enumerate(self.database.others_id):
# 如果存在同时同人就退出写入
if others_id == self.database.others_id[i] and nowdt[0:nowdt.index(" ")] == \
self.database.logcat_datetime[j] \
and nowdt[nowdt.index(" "):] == self.database.logcat_late[j]:
flag = 1
break
except:
print('traceback.print_exc():', traceback.print_exc())
if flag == 1:
break
logicdata = [self.database.others_id[i], "陌生人",
nowdt[0:nowdt.index(" ")], nowdt[nowdt.index(" ") + 1:]]
self.database.insertRow(logicdata, self.LOGCAT_INFO)
self.update_LoadSignal.emit(self.UPDATE_LOGIC)
return
elif compare == "diff":
print("未录入陌生人数据库人员")
self.id = str(random.randint(10000,99999))
face_height = biggest_face.bottom() - biggest_face.top()
face_width = biggest_face.right() - biggest_face.left()
im_blank = numpy.zeros((face_height, face_width, 3), numpy.uint8)
try:
for ii in range(face_height):
for jj in range(face_width):
im_blank[ii][jj] = im_rd[biggest_face.top() + ii][biggest_face.left() + jj]
self.pic_num += 1
# 解决python3下使用cv2.imwrite存储带有中文路径图片
if len(self.id) > 0:
if self.id not in (os.listdir(self.PATH_FACE)):
os.makedirs(self.PATH_FACE + self.id)
print("开启写入数据")
cv2.imencode('.jpg', im_blank)[1].tofile(
self.PATH_FACE + self.id + "/img_face_" + str(self.pic_num) + ".jpg") # 正确方法
print("写入本地：",
str(self.PATH_FACE + self.id) + "/img_face_" + str(self.pic_num) + ".jpg")
else:
self.flag_registed = True
print("此人已经存在")
except:
print('traceback.print_exc():', traceback.print_exc())
print("保存照片异常,请对准摄像头")
return False
if self.pic_num == 10:
self.othersRegister()
self.update_LoadSignal.emit(self.UPDATE_LOGIC)
return True
pass
'''
# 信号槽处理
def signalHandle(self, event):
if event == self.REGISTER_SINGAL:
self.start() # 开启线程，处理自动核验人脸时线程挂起无法运行的情况
self.AUTO_CHECKFACE_SINGAL = False
self.CHECKFACE_SINGAL = False
elif event == self.INSPECT_FACE_SINGAL:
self.start() # 开启线程，处理自动核验人脸时线程挂起无法运行的情况
self.AUTO_CHECKFACE_SINGAL = False
self.CHECKFACE_SINGAL = True
elif event == self.AUTO_CHECK_SINGAL:
self.start() # 开启线程，处理自动核验人脸时线程挂起无法运行的情况
self.AUTO_CHECKFACE_SINGAL = True
self.CHECKFACE_SINGAL = False
elif event == self.EXIT_SINGAL:
self.IS_EXIT == True
# 获取注册用户名称id
def setName(self,name,id):
self.name = name
self.id = id
# 获取图片
def setImage(self,img):
self.image = img
# 解锁处理TODO
def unlockHandle(self):
print("UNLOCK")
pass
# 线程执行
def run(self):
while True:
if self.EXIT_SINGAL == True:
break # 退出程序处理
if self.AUTO_CHECKFACE_SINGAL == True:
self.punchCardCap(False)
self.sleep(3) # 自动扫描处理
print("自动扫描处理")
elif self.CHECKFACE_SINGAL == True:
temp = self.punchCardCap(True)
if temp == True:
self.unlockHandle() # 人脸解锁处理
else:
print("未识别的人脸")
print("人脸解锁处理")
else:
try:
temp = self.registerCap() # 用户注册处理
except:
print('traceback.print_exc():', print_exc())
# temp = True
print("用户注册处理")
if temp == True:
try:
self.Register_FinishSignal.emit(self.Register_FINISHSINGAL)
self.AUTO_CHECKFACE_SINGAL = True
self.CHECKFACE_SINGAL = False
print("注册成功")
except:
print('traceback.print_exc():', print_exc())
# QMessageBox.critical(self, '识别提示', '没有检查到人脸，请靠近一点')
elif temp == self.FACE_EXISTS:
self.Register_FinishSignal.emit(self.Register_FAILED)
self.AUTO_CHECKFACE_SINGAL = True
self.CHECKFACE_SINGAL = False
print("此人已经存在")
pass
if __name__ == "__main__":
x = "test.db"
a = FaceLearning(x)
a.registerCap()
print("ok")
pass