import os

import cv2

import sys

import time

import numpy as np

import face_recognition

from PyQt5.QtGui import QPixmap

from PyQt5.QtWidgets import QApplication,QLabel

# 全局变量

path = './data'  # 人像存储路径

showflag = True  # 是否实时显示图像

# 利用 PyQt5 打开全屏窗口，实现窗口替换效果

def lock_screen(image_path='lock.jpg'):

     app = QApplication(sys.argv)

     pixmap = QPixmap(image_path)

     screen = QLabel()

     screen.setPixmap(pixmap)

     screen.showFullScreen()

     sys.exit(app.exec_())

# 读取本地肖像库，建立识别白名单

def load_portrait(path=path):

     '''

     path ：肖像库路径

     '''

     # 消息提示

     print('>>> 本地图像库读取中，请稍后 ',end='')

     for i in range(5):

         print('.', end='')

         time.sleep(0.3)

     # 建立白名单

     white_map = {}

     for name in os.listdir(path):

         filepath = '%s/%s'%(path, name)

         avg_coding = np.zeros(128)

         n = 0

         for file in os.listdir(filepath):

             if '.jpg' in file:

                 image = face_recognition.load_image_file('%s/%s'%(filepath, file))

                 encoding = face_recognition.face_encodings(image)[0]

                 avg_coding += encoding

                 n += 1

         avg_coding /= n

         white_map[name] = avg_coding

         print('>>"%s" 人脸数据加载完成！ '%name)

     return white_map

# 人脸识别，判断当前画面的人像是否与白名单中的匹配

def recognize(frame, white_map):

     '''

     frame: 捕获的摄像头帧

     white_map: 人像库白名单

     '''

     # 根据白名单，提取肖像编码

     known_face_encodings = list(white_map.values())

     known_face_names = list(white_map.keys())

     # 图像预处理（包括大小调整、格式转换）

     frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25) # 调整图像大小，以减小计算需求

     frame = frame[:, :, ::-1] # BGR->RGB

     # 计算人脸的编码值

     face_locations = face_recognition.face_locations(frame)

     face_encodings =外汇跟单gendan5.com face_recognition.face_encodings(frame, face_locations)

     # 计算的编码与白名单比较，获得其匹配的名字

     face_names = []

     for face_encoding in face_encodings:

         # 默认为 " 未知 "

         name = ' 未知 '

         # 匹配

         matches = face_recognition.compare_faces(known_face_encodings, face_encoding)

         if True in matches:

             index = matches.index(True)

             name = known_face_names[index]

         face_names.append(name)

     return face_names, face_locations

if __name__ == '__main__':

     # 加载白名单

     white_map = load_portrait(path)

     # 开启摄像头

     video_capture = cv2.VideoCapture(0, cv2.CAP_DSHOW)

     # 采集图像

     flag = 0

     while True:

         flag %= 3

         _, frame = video_capture.read()

         if flag == 0: # 每 3 帧处理因此（提高处理速度，防止视频卡顿）

             face_names, face_locations = recognize(frame, white_map)

             if ' 未知 ' in face_names: # 如果有白名单之外的人

                 lock_screen()

                 break

         flag += 1

         if showflag:

             # 将人脸框出

             for (top, right, bottom, left), name in zip(face_locations, face_names):

                 # 改变坐标位置（因为处理时原图被缩小了 4*4 ）

                 top *= 4

                 right *= 4

                 bottom *= 4

                 left *= 4

                 # 矩形框

                 cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)

                 # 加上姓名

                 cv2.rectangle(frame, (left, bottom - 35), (right, bottom), (0, 0, 255), cv2.FILLED)

                 font = cv2.FONT_HERSHEY_DUPLEX

                 cv2.putText(frame, name, (left + 6, bottom - 6), font, 1.0, (255, 255, 255), 1)

             # 显示图像

             cv2.imshow('monitor', frame)

         # 按 Q 退出

         if cv2.waitKey(1) & 0xFF == ord('q'):

             break

     video_capture.release()

     cv2.destroyAllWindows()