face_recognize.py

import dlib  # 人脸识别的库dlib
import numpy as np  # 数据处理的库numpy
import cv2  # 图像处理的库OpenCv
import pandas as pd  # 数据处理的库Pandas
from PIL import Image, ImageDraw, ImageFont
import wx
import os
import csv
import datetime
import _thread

# face recognition model, the object maps human faces into 128D vectors
facerec = dlib.face_recognition_model_v1("model/dlib_face_recognition_resnet_model_v1.dat")

# Dlib 检测器
detector = dlib.get_frontal_face_detector()

# Dlib 预测器
#predictor = dlib.shape_predictor('model/shape_predictor_68_face_landmarks.dat')
# 自己训练的预测器
predictor = dlib.shape_predictor('train_shape_detector/predictor.dat')

loading = 'icon/loading.png'
rec_fail = 'icon/rec_fail.png'
rec_repeat = 'icon/rec_repeat.png'
rec_success = 'icon/rec_success.png'

path_logcat_csv = "data/logcat.csv"


def read_csv_to_recoders():
    recodes = []
    if os.path.exists(path_logcat_csv):
        with open(path_logcat_csv, "r", newline="") as csvfiler:
            reader = csv.reader(csvfiler)
            for row in reader:
                recodes.append(row)  # 包括header
    else:
        with open(path_logcat_csv, "w", newline="") as csvfilew:
            writer = csv.writer(csvfilew)
            header = ["姓名", "日期", "时间"]
            writer.writerow(header)
    return recodes
    pass


# 计算两个向量间的欧式距离
def return_euclidean_distance(feature_1, feature_2):
    feature_1 = np.array(feature_1)
    feature_2 = np.array(feature_2)
    dist = np.sqrt(np.sum(np.square(feature_1 - feature_2)))
    print("欧式距离: ", dist)

    if dist > 0.4:
        return "diff"
    else:
        return "same"


# 处理存放所有人脸特征的csv
path_feature_known_csv = "data/feature_all.csv"

# path_features_known_csv= "/media/con/data/code/python/P_dlib_face_reco/data/csvs/features_all.csv"
csv_rd = pd.read_csv(path_feature_known_csv, header=None, encoding='gbk')

# 存储的特征人脸个数
# print(csv_rd.shape)
# （2，129）

# 用来存放所有录入人脸特征的数组
features_known_arr = []

# print("s0",csv_rd.shape[0],"s1",csv_rd.shape[1])
for i in range(csv_rd.shape[0]):
    features_someone_arr = []
    for j in range(0, len(csv_rd.ix[i, :])):
        features_someone_arr.append(csv_rd.ix[i, :][j])
    #    print(features_someone_arr)
    features_known_arr.append(features_someone_arr)
print("数据库人脸数:", len(features_known_arr))


# 返回一张图像多张人脸的128D特征  只返回一张人脸
def get_128d_features(img_gray):
    dets = detector(img_gray, 1)
    shape = predictor(img_gray, dets[0])
    face_des = facerec.compute_face_descriptor(img_gray, shape)
    return face_des
    # if len(dets) != 0:
    #     face_des = []
    #     for i in range(len(dets)):
    #         shape = predictor(img_gray, dets[i])
    #         face_des.append(facerec.compute_face_descriptor(img_gray, shape))
    # else:
    #     face_des = []
    # return face_des[0]


class RecognizeUi(wx.Frame):
    def __init__(self, superion):
        wx.Frame.__init__(self, parent=superion, title="人脸识别", size=(800, 590),
                          style=wx.DEFAULT_FRAME_STYLE | wx.STAY_ON_TOP)
        self.SetBackgroundColour('white')
        self.Center()

        self.OpenCapButton = wx.Button(parent=self, pos=(50, 120), size=(90, 60), label='开始/重新识别')

        self.resultText = wx.StaticText(parent=self, style=wx.ALIGN_CENTER_VERTICAL, pos=(10, 320), size=(90, 60),
                                        label="来访天数:0")

        self.resultText.SetBackgroundColour('white')
        self.resultText.SetForegroundColour('blue')
        font = wx.Font(14, wx.DECORATIVE, wx.ITALIC, wx.NORMAL)
        self.resultText.SetFont(font)

        self.rec_day_num = 1

        # 封面图片
        self.image_loading = wx.Image(loading, wx.BITMAP_TYPE_ANY).Scale(600, 480)

        self.image_fail = wx.Image(rec_fail, wx.BITMAP_TYPE_ANY).Scale(600, 480)
        self.image_repeat = wx.Image(rec_repeat, wx.BITMAP_TYPE_ANY).Scale(600, 480)
        self.image_success = wx.Image(rec_success, wx.BITMAP_TYPE_ANY).Scale(600, 480)

        # 显示图片
        self.bmp = wx.StaticBitmap(parent=self, pos=(200, 20), bitmap=wx.Bitmap(self.image_loading))

        self.Bind(wx.EVT_BUTTON, self.OnOpenCapButtonClicked, self.OpenCapButton)

    def OnOpenCapButtonClicked(self, event):

        """使用多线程，子线程运行后台的程序，主线程更新前台的UI，这样不会互相影响"""
        # 创建子线程，按钮调用这个方法，
        _thread.start_new_thread(self._open_cap, (event,))

    def _open_cap(self, event):
        # 创建 cv2 摄像头对象
        self.cap = cv2.VideoCapture(0)
        # cap.set(propId, value)
        # 设置视频参数，propId设置的视频参数，value设置的参数值
        self.cap.set(3, 480)

        # cap是否初始化成功
        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()

            # 每帧数据延时1ms，延时为0读取的是静态帧
            kk = cv2.waitKey(1)

            # 人脸数 dets
            dets = detector(im_rd, 1)

            # 待会要写的字体
            font = cv2.FONT_HERSHEY_SIMPLEX


            # 存储人脸名字和位置的两个 list
            # list 1 (dets): store the name of faces                Jack    unknown unknown Mary
            # list 2 (pos_namelist): store the positions of faces   12,1    1,21    1,13    31,1

            # 人脸的名字
            name = ''
            pos = ''
            # pos_namelist = []
            # name_namelist = []

            if len(dets) != 0:
                # 检测到人脸
                start = datetime.datetime.now()
                # 获取当前捕获到的图像的所有人脸的特征，存储到 features_cap_arr
                features_cap = ''
                shape = predictor(im_rd, dets[0])
                features_cap = facerec.compute_face_descriptor(im_rd, shape)

                # 遍历捕获到的图像中所有的人脸
                # 让人名跟随在矩形框的下方
                # 确定人名的位置坐标
                # 先默认所有人不认识，是 unknown
                name = "unknown"

                # 每个捕获人脸的名字坐标
                pos = tuple([(int)((dets[0].left() + dets[0].right()) / 2) - 50
                                , dets[0].bottom() + 20])

                # 对于某张人脸，遍历所有存储的人脸特征

                for i in range(len(features_known_arr)):
                    # 将某张人脸与存储的所有人脸数据进行比对
                    self.rec_day_num = 0;
                    compare = return_euclidean_distance(features_cap, features_known_arr[i][0:-1])
                    if compare == "same":  # 找到了相似脸
                        end = datetime.datetime.now()
                        print(end - start)
                        name = features_known_arr[i][-1]
                        recoder = []
                        recoder.append(name)
                        localtime = datetime.datetime.now()
                        date = str(localtime.year) + "/" + str(localtime.month) + "/" + str(localtime.day)
                        time = str(localtime.hour) + ":" + str(localtime.minute) + ":" + str(localtime.second)
                        recoder.append(date)
                        recoder.append(time)
                        recoders = read_csv_to_recoders()

                        for item in recoders:
                            if item[0] == recoder[0]:
                                self.rec_day_num += 1

                        #self.resultText.SetLabel("来访天数:" + str(self.rec_day_num))
                        self.resultText.SetLabel(
                            "来访天数:" + str(self.rec_day_num) + "\n来访姓名：" + name + "\n来访日期:" + recoder[1] + "\n来访时间 " +
                            recoder[2])
                        for item in recoders:
                            if item[0] == recoder[0] and item[1] == recoder[1]:
                                #wx.MessageBox(message=name + "您好，今天已经识别到了\n请勿重复识别", caption="温馨提示")
                                self.bmp.SetBitmap(wx.Bitmap(self.image_repeat))
                                _thread.exit()

                        # wx.MessageBox(message="已成功识别" + name + "\n来访时间:" + recoder[1] + " " + recoder[2],
                        #               caption="温馨提示")
                        self.bmp.SetBitmap(wx.Bitmap(self.image_success))
                        self.rec_day_num += 1
                        self.resultText.SetLabel(
                            "来访天数:" + str(self.rec_day_num) + "\n来访姓名：" + name + "\n来访日期:" + recoder[1] + "\n来访时间 " + recoder[2])

                        with open(path_logcat_csv, "a+", newline="") as csvfilew:
                            writer = csv.writer(csvfilew)
                            writer.writerow(recoder)
                        _thread.exit()

                        # print(features_known_arr[i][-1])


                # 绘制矩形框
                cv2.rectangle(im_rd, tuple([dets[0].left(), dets[0].top()]), tuple([dets[0].right(), dets[0].bottom()]),
                              (255, 0, 0), 2)

                # 写人脸名字
                cv2.putText(im_rd, name, pos, font, 0.8, (0, 0, 255), 1, cv2.LINE_AA)


            cv2.putText(im_rd, "Faces: " + str(len(dets)), (50, 80), font, 1, (255, 0, 0), 1, cv2.LINE_AA)

            height, width = im_rd.shape[:2]
            image1 = cv2.cvtColor(im_rd, cv2.COLOR_BGR2RGB)
            pic = wx.Bitmap.FromBuffer(width, height, image1)
            # 显示图片在panel上
            self.bmp.SetBitmap(pic)