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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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import jiqiren as jqr
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import yuyin as yy
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import MySQL as m
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import time
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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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global Total_face_num
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Total_face_num = m.rl_select('count(name)')[0][0]
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def begin():
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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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a = (face_cascade, recognizer, success, W_size, H_size, system_state_lock, camera)
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return a
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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(a):
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# a = (face_cascade, recognizer, success, W_size, H_size, system_state_lock ,camera)
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face_cascade = a[0]
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recognizer = a[1]
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W_size = a[3]
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H_size = a[4]
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camera = a[6]
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jqr.Key("请对该面部信息进行命名：")
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yy.record()
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name = yy.asr_updata()
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l = len(name)
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name = name[0: l-1]
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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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cv2.imshow("capture", img) # 生成摄像头窗口
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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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return name
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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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face_detector = face_cascade
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faces = face_detector.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 = 30 # 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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return name
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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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def Train_new_face():
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print("\n正在训练")
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# cv2.destroyAllWindows()
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path = 'data'
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# 初始化识别的方法
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recog = cv2.face.LBPHFaceRecognizer_create()
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# 调用函数并将数据喂给识别器训练
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faces, ids = get_images_and_labels(path)
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print('本次用于训练的识别码为:') # 调试信息
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print(ids) # 输出识别码
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# 训练模型 #将输入的所有图片转成四维数组
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recog.train(faces, np.array(ids))
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# 保存模型
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yml ='/home/cxg/BYSJ/RL-data/' + str(Total_face_num) + ".yml"
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print(yml)
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rec_f = open(yml, "w+")
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rec_f.close()
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recog.save(yml)
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# recog.save('aaa.yml')
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# 创建一个函数，用于从数据集文件夹中获取训练图片,并获取id
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# 注意图片的命名格式为User.id.sampleNum
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def get_images_and_labels(path):
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image_paths = [os.path.join(path, f) for f in os.listdir(path)]
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# 新建连个list用于存放
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face_samples = []
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ids = []
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# 遍历图片路径，导入图片和id添加到list中
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for image_path in image_paths:
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# 通过图片路径将其转换为灰度图片
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img = Image.open(image_path).convert('L')
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# 将图片转化为数组
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img_np = np.array(img, 'uint8')
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if os.path.split(image_path)[-1].split(".")[-1] != 'jpg':
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continue
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# 为了获取id，将图片和路径分裂并获取
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id = int(os.path.split(image_path)[-1].split(".")[1])
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# 调用熟悉的人脸分类器
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detector = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
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faces = detector.detectMultiScale(img_np)
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# 将获取的图片和id添加到list中
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for (x, y, w, h) in faces:
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face_samples.append(img_np[y:y + h, x:x + w])
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ids.append(id)
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return face_samples, ids
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def write_config(name):
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print("新人脸训练结束")
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T = Total_face_num
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m.rl_insert(str(T),name,)
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jqr.Key("是否要设置其他个人信息？")
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text = yy.record_text()
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if ("是" in text):
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information_update(str(T), "年龄")
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information_update(str(T), "性别")
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information_update(str(T), "电话号码")
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information_update(str(T), "生日")
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jqr.Key("完成录入信息")
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'''
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============================================================================================
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以上是录入新人脸信息功能的实现
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============================================================================================
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'''
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def scan_face(a):
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# 使用之前训练好的模型
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# a = (face_cascade, recognizer, success, W_size, H_size, system_state_lock ,camera)
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face_cascade = a[0]
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recognizer = a[1]
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W_size = a[3]
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H_size = a[4]
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camera = a[6]
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for i in range(Total_face_num): # 每个识别器都要用
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i += 1
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yml ='/home/cxg/BYSJ/RL-data/' + str(i) + ".yml"
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print("\n本次:" + yml) # 调试信息
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recognizer.read(yml)
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ave_poss = 0
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for times in range(10): # 每个识别器扫描十遍
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times += 1
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cur_poss = 0
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global success
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global img
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'''
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global system_state_lock
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while system_state_lock == 2: # 如果正在录入新面孔就阻塞
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print("\r刷脸被录入面容阻塞", end="")
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pass
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'''
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success, img = camera.read()
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cv2.imshow("capture", img) # 生成摄像头窗口
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gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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# 识别人脸
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faces = face_cascade.detectMultiScale(
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gray,
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scaleFactor=1.2,
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minNeighbors=5,
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minSize=(int(W_size), int(H_size))
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)
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# 进行校验
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for (x, y, w, h) in faces:
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'''
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global system_state_lock
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while system_state_lock == 2: # 如果正在录入新面孔就阻塞
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print("\r刷脸被录入面容阻塞", end="")
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pass
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'''
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# 这里调用Cv2中的rectangle函数 在人脸周围画一个矩形
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cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 2)
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# 调用分类器的预测函数，接收返回值标签和置信度
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idnum, confidence = recognizer.predict(gray[y:y + h, x:x + w])
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conf = confidence
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# 计算出一个检验结果
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if confidence < 100: # 可以识别出已经训练的对象——直接输出姓名在屏幕上
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if idnum in id_dict:
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user_name = id_dict[idnum]
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else:
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# print("无法识别的ID:{}\t".format(idnum), end="")
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user_name = "Untagged user:" + str(idnum)
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confidence = "{0}%", format(round(100 - confidence))
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else: # 无法识别此对象，那么就开始训练
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user_name = "unknown"
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# print("检测到陌生人脸\n")
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# cv2.destroyAllWindows()
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# global Total_face_num
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# Total_face_num += 1
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# Get_new_face() # 采集新人脸
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# Train_new_face() # 训练采集到的新人脸
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# write_config() # 修改配置文件
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# recognizer.read('aaa.yml') # 读取新识别器
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# 加载一个字体用于输出识别对象的信息
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font = cv2.FONT_HERSHEY_SIMPLEX
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# 输出检验结果以及用户名
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cv2.putText(img, str(user_name), (x + 5, y - 5), font, 1, (0, 0, 255), 1)
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cv2.putText(img, str(confidence), (x + 5, y + h - 5), font, 1, (0, 0, 0), 1)
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# 展示结果
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# cv2.imshow('camera', img)
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print("conf=" + str(conf), end="\t")
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if 35 > conf > 0:
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cur_poss = 1 # 表示可以识别
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elif 75 > conf > 35:
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cur_poss = 1 # 表示可以识别
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else:
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cur_poss = 0 # 表示不可以识别
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k = cv2.waitKey(1)
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if k == 27:
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# cam.release() # 释放资源
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cv2.destroyAllWindows()
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break
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ave_poss += cur_poss
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if ave_poss >= 5: # 有一半以上识别说明可行则返回
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return i
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return 0 # 全部过一遍还没识别出说明无法识别
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'''
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============================================================================================
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以上是关于刷脸功能的设计
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============================================================================================
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'''
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def information_update(T, str):
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if ("生日" in str):
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birthday_update(T)
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elif (str in "年龄，性别，姓名，电话"):
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jqr.Key("该用户"+str+"为：")
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str = change(str)
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text = yy.record_text()
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text = text[0: len(text) - 1]
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# print(text,T)
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m.rl_update(T, str, text)
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def birthday_update(T):
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message = ['','','']
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jqr.Key("该用户出生年份为：")
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text = yy.record_text()
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message[0] = text[0: len(text) - 1]
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jqr.Key("该用户出生月份为：")
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text = yy.record_text()
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message[1] = text[0: len(text) - 1]
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jqr.Key("该用户出生日期为：")
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text = yy.record_text()
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message[2] = text[0: len(text) - 1]
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m.rl_update(str(T), "birthday", str(message[0]) + "-" + str(message[1]) + "-" + str(message[2]))
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def change(str):
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if ("姓名" in str):
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return "name"
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elif ("年龄" in str):
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return "age"
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elif ("性别" in str):
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return "sex"
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elif ("电话" in str):
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return "phone"
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def rl_message_update(id):
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while True:
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jqr.Key("需要修改什么信息？")
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text = yy.record_text()
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if ("取消" in text):
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break
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elif (text not in ""):
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text = text[0: len(text) - 1]
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information_update(id, text)
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'''
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============================================================================================
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以上是修改人脸信息功能的实现
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============================================================================================
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'''
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def f_scan_face():
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# 使用之前训练好的模型
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# recognizer.read('aaa.yml')
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# var.set('刷脸')
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a = begin() #申请摄像头资源
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ans = scan_face(a)
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if ans == 0:
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# print("最终结果：无法识别")
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# var.set("最终结果：无法识别")
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jqr.Key("人脸识别完毕。最终结果：检测失败")
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cv2.destroyAllWindows()
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a[6].release()
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return 0,0
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else:
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id_dict = m.rl_select('id')
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name_dict = m.rl_select("name")
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ans_name = "最终结果：User" + str(id_dict[ans-1][0]) + name_dict[ans-1][0]
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# print(ans_name)
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# var.set(ans_name)
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jqr.Key("人脸识别完毕。"+ ans_name)
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jqr.Key("人脸检测成功。。。")
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cv2.destroyAllWindows()
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a[6].release()
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return 1,str(id_dict[ans-1][0])
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def f_rec_face():
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a = begin() #申请摄像头资源
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# var.set('录入')
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global Total_face_num
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Total_face_num += 1
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name = Get_new_face(a) # 采集新人脸
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print("采集完毕，开始训练")
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jqr.Key("人脸录入完毕。")
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global system_state_lock # 采集完就可以解开锁
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# print("锁被释放0")
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system_state_lock = 0
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|
|
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Train_new_face() # 训练采集到的新人脸
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write_config(name) # 修改数据库
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cv2.destroyAllWindows()
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a[6].release()
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|
|
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|
|
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# recognizer.read('aaa.yml') # 读取新识别器
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|
|
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# global system_state_lock
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# print("锁被释放0")
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# system_state_lock = 0 # 修改system_state_lock,释放资源
|
|
|
|
|
|
|
|
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def check_face():
|
|
|
while True:
|
|
|
jqr.Key("即将进行人脸检测。。。。")
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|
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sign = f_scan_face()
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|
|
# print(sign)
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|
|
if(0 in sign):
|
|
|
jqr.Key("人脸检测失败，是否要重新检测。。。")
|
|
|
yy.record()
|
|
|
text = yy.asr_updata()
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|
|
print("本人说：", text)
|
|
|
if("是" in text or "重新检测" in text):
|
|
|
continue
|
|
|
elif("否" in text or "取消" in text):
|
|
|
return 0
|
|
|
else:
|
|
|
jqr.Key("未检测到关键字！将继续进行人脸检测。。。")
|
|
|
continue
|
|
|
|
|
|
else:
|
|
|
return sign[1]
|
|
|
|
|
|
'''
|
|
|
============================================================================================
|
|
|
以上是关于人脸的执行设计
|
|
|
============================================================================================
|
|
|
'''
|
|
|
|
|
|
def manual_video(): #视频监控
|
|
|
camera = cv2.VideoCapture(0) # 摄像头
|
|
|
print('开始视频监控')
|
|
|
id = m.hcsr_select("m_photo", "max(num_id)")[0][0] + 1 # 确认操作编号
|
|
|
t = time.strftime('%Y-%m-%d %H:%M:%S')
|
|
|
photoname = m.hcsr_select("m_photo", "max(last_photo)")[0][0] + 1 # 文件名序号初始值
|
|
|
temp = photoname # 记录第一张照片的临时变量
|
|
|
m.hcsr_insert("m_photo", str(id), t, str(photoname)) # 保存数据
|
|
|
while True:
|
|
|
# get a frame
|
|
|
ret, frame = camera.read()
|
|
|
frame = cv2.flip(frame, 1) # 摄像头是和人对立的，将图像左右调换回来正常显示
|
|
|
# show a frame
|
|
|
cv2.imshow("capture", frame) # 生成摄像头窗口
|
|
|
if cv2.waitKey(1) & 0xFF == ord('q'): # 如果按下q 就截图保存并退出
|
|
|
cv2.imwrite('/home/cxg/BYSJ/manual-photo/' +"m_video" + str(photoname) + ".png", frame) # 保存路径
|
|
|
cv2.destroyAllWindows()
|
|
|
m.hcsr_update("m_photo", "last_photo", str(photoname), str(id)) # 保存最后一张照片的数据
|
|
|
m.hcsr_update("m_photo", "num", str(photoname - temp + 1), str(id))
|
|
|
break
|
|
|
elif cv2.waitKey(1) & 0xFF == ord('z'):
|
|
|
cv2.imwrite('/home/cxg/BYSJ/manual-photo/' +"m_video" + str(photoname) + ".png", frame) # 保存路径
|
|
|
m.hcsr_update("m_photo", "last_photo", str(photoname), str(id)) # 保存最后一张照片的数据
|
|
|
m.hcsr_update("m_photo", "num", str(photoname - temp + 1), str(id))
|
|
|
photoname += 1
|
|
|
continue
|
|
|
camera.release()
|
|
|
|
|
|
|
|
|
|
|
|
def auto_video(): #自动监控
|
|
|
camera = cv2.VideoCapture(0) # 摄像头
|
|
|
print('开始自动监控')
|
|
|
i = 0 # 定时装置初始值
|
|
|
id = m.hcsr_select("hcsr", "max(num_id)")[0][0] + 1 #确认操作编号
|
|
|
t = time.strftime('%Y-%m-%d %H:%M:%S')
|
|
|
photoname = m.hcsr_select("hcsr", "max(last_photo)")[0][0] + 1 # 文件名序号初始值
|
|
|
temp = photoname #记录第一张照片的临时变量
|
|
|
m.hcsr_insert("hcsr", str(id), t, str(photoname)) #保存数据
|
|
|
while True:
|
|
|
i = i + 1 #计时
|
|
|
reg, frame = camera.read()
|
|
|
frame = cv2.flip(frame, 1) # 图片左右调换
|
|
|
cv2.imshow('window', frame)
|
|
|
if i == 50: # 定时装置，定时截屏，可以修改。
|
|
|
|
|
|
filename = "a_video" + str(photoname) + '.png' # filename为图像名字，将photoname作为编号命名保存的截图
|
|
|
cv2.imwrite('/home/cxg/BYSJ/aut-photo/' + filename, frame) # 截图 前面为放在桌面的路径 frame为此时的图像
|
|
|
print(filename + '保存成功') # 打印保存成功
|
|
|
i = 0 # 清零
|
|
|
|
|
|
m.hcsr_update("hcsr", "last_photo", str(photoname), str(id)) #保存最后一张照片的数据
|
|
|
m.hcsr_update("hcsr", "num", str(photoname - temp+1), str(id))
|
|
|
photoname += 1
|
|
|
if photoname - temp >= 20: # 最多截图20张 然后退出（如果调用photoname = 1 不用break为不断覆盖图片）
|
|
|
# photoname = 1
|
|
|
cv2.destroyAllWindows()
|
|
|
# 释放资源
|
|
|
camera.release()
|
|
|
return str(id),1
|
|
|
|
|
|
if cv2.waitKey(1) & 0xff == ord('q'):
|
|
|
cv2.destroyAllWindows()
|
|
|
# 释放资源
|
|
|
camera.release()
|
|
|
return str(id),0
|
|
|
|
|
|
cv2.destroyAllWindows()
|
|
|
# 释放资源
|
|
|
camera.release()
|
|
|
|
|
|
|
|
|
'''
|
|
|
============================================================================================
|
|
|
以上是关于监控的设计
|
|
|
============================================================================================
|
|
|
'''
|
