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# 实验环境：python 3.6 + opencv-python 3.4.14.51
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import cv2
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import numpy as np
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import os
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import shutil
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import threading
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import tkinter as tk
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from PIL import Image, ImageTk
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# 首先读取config文件，第一行代表当前已经储存的人名个数，接下来每一行是（id，name）标签和对应的人名
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id_dict = {} # 字典里存的是id——name键值对
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Total_face_num = 999 # 已经被识别有用户名的人脸个数,
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def init(): # 将config文件内的信息读入到字典中
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f = open('config.txt')
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global Total_face_num
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Total_face_num = int(f.readline())
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for i in range(int(Total_face_num)):
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line = f.readline()
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id_name = line.split(' ')
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id_dict[int(id_name[0])] = id_name[1]
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f.close()
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init()
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# 加载OpenCV人脸检测分类器Haar
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face_cascade = cv2.CascadeClassifier("haarcascade_frontalface_default.xml")
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# 准备好识别方法LBPH方法
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recognizer = cv2.face.LBPHFaceRecognizer_create()
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# 打开标号为0的摄像头
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camera = cv2.VideoCapture(0) # 摄像头
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success, img = camera.read() # 从摄像头读取照片
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W_size = 0.1 * camera.get(3)
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H_size = 0.1 * camera.get(4)
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system_state_lock = 0 # 标志系统状态的量 0表示无子线程在运行 1表示正在刷脸 2表示正在录入新面孔。
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# 相当于mutex锁，用于线程同步
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'''
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============================================================================================
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以上是初始化
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============================================================================================
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'''
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def Get_new_face():
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print("正在从摄像头录入新人脸信息 \n")
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# 存在目录data就清空，不存在就创建，确保最后存在空的data目录
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filepath = "data"
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if not os.path.exists(filepath):
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os.mkdir(filepath)
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else:
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shutil.rmtree(filepath)
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os.mkdir(filepath)
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sample_num = 0 # 已经获得的样本数
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while True: # 从摄像头读取图片
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global success
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global img # 因为要显示在可视化的控件内，所以要用全局的
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success, img = camera.read()
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# 转为灰度图片
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if success is True:
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gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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else:
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break
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# 检测人脸，将每一帧摄像头记录的数据带入OpenCv中，让Classifier判断人脸
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# 其中gray为要检测的灰度图像，1.3为每次图像尺寸减小的比例，5为minNeighbors
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faces = face_cascade.detectMultiScale(gray, 1.3, 5)
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# 框选人脸，for循环保证一个能检测的实时动态视频流
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for (x, y, w, h) in faces:
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# xy为左上角的坐标,w为宽，h为高，用rectangle为人脸标记画框
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cv2.rectangle(img, (x, y), (x + w, y + w), (255, 0, 0))
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# 样本数加1
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sample_num += 1
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# 保存图像，把灰度图片看成二维数组来检测人脸区域，这里是保存在data缓冲文件夹内
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T = Total_face_num
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cv2.imwrite("./data/User." + str(T) + '.' + str(sample_num) + '.jpg', gray[y:y + h, x:x + w])
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pictur_num = 1000 # 表示摄像头拍摄取样的数量,越多效果越好，但获取以及训练的越慢
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cv2.waitKey(1)
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if sample_num > pictur_num:
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break
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else: # 控制台内输出进度条
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l = int(sample_num / pictur_num * 50)
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r = int((pictur_num - sample_num) / pictur_num * 50)
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print("\r" + "%{:.1f}".format(sample_num / pictur_num * 100) + "=" * l + "->" + "_" * r, end="")
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var.set("%{:.1f}".format(sample_num / pictur_num * 100)) # 控件可视化进度信息
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# tk.Tk().update()
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window.update() # 刷新控件以实时显示进度
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