目錄

• 一、采集人臉
• 二、采集對應(yīng)20張圖片的68個(gè)特征點(diǎn)數(shù)組和平均特征數(shù)組
• 三、人臉識別
• 四、總結(jié)
• 五、參考資料

一、采集人臉

• 代碼：

import cv2 import dlib import os import sys import random # 存儲(chǔ)位置 output_dir = 'F:/my/631907060127' size = 64if not os.path.exists(output_dir):os.makedirs(output_dir) # 改變圖片的亮度與對比度def relight(img, light=1, bias=0):w = img.shape[1]h = img.shape[0]#image = []for i in range(0,w):for j in range(0,h):for c in range(3):tmp = int(img[j,i,c]*light + bias)if tmp > 255:tmp = 255elif tmp < 0:tmp = 0img[j,i,c] = tmpreturn img#使用dlib自帶的frontal_face_detector作為我們的特征提取器 detector = dlib.get_frontal_face_detector() # 打開攝像頭 參數(shù)為輸入流，可以為攝像頭或視頻文件 camera = cv2.VideoCapture(0) #camera = cv2.VideoCapture('C:/Users/CUNGU/Videos/Captures/wang.mp4')index = 1 while True:if (index <= 20):#存儲(chǔ)20張人臉特征圖像print('Being processed picture %s' % index)# 從攝像頭讀取照片success, img = camera.read()# 轉(zhuǎn)為灰度圖片gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)# 使用detector進(jìn)行人臉檢測dets = detector(gray_img, 1)for i, d in enumerate(dets):x1 = d.top() if d.top() > 0 else 0y1 = d.bottom() if d.bottom() > 0 else 0x2 = d.left() if d.left() > 0 else 0y2 = d.right() if d.right() > 0 else 0face = img[x1:y1,x2:y2]# 調(diào)整圖片的對比度與亮度， 對比度與亮度值都取隨機(jī)數(shù)，這樣能增加樣本的多樣性face = relight(face, random.uniform(0.5, 1.5), random.randint(-50, 50))face = cv2.resize(face, (size,size))cv2.imshow('image', face)cv2.imwrite(output_dir+'/'+str(index)+'.jpg', face)index += 1key = cv2.waitKey(30) & 0xffif key == 27:breakelse:print('Finished!')# 釋放攝像頭 release cameracamera.release()# 刪除建立的窗口 delete all the windowscv2.destroyAllWindows()break

• 運(yùn)行結(jié)果：
  
  采集的時(shí)候最好露出眉毛和完整的五官，否則后面的特征點(diǎn)容易識別不了
  

二、采集對應(yīng)20張圖片的68個(gè)特征點(diǎn)數(shù)組和平均特征數(shù)組

• 代碼：

from cv2 import cv2 as cv2 import os import dlib from skimage import io import csv import numpy as np# 要讀取人臉圖像文件的路徑 path_images_from_camera = "F:/my/"# Dlib 正向人臉檢測器 detector = dlib.get_frontal_face_detector()# Dlib 人臉預(yù)測器 predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")# Dlib 人臉識別模型 # Face recognition model, the object maps human faces into 128D vectors face_rec = dlib.face_recognition_model_v1("dlib_face_recognition_resnet_model_v1.dat")# 返回單張圖像的 128D 特征 def return_128d_features(path_img):img_rd = io.imread(path_img)s=path_imga=s[16:17]i1=str(a)a1=s[17:]str1="/"b=a1[a1.index(str1):-4]b1=b[1:]i2=str(b1)img_gray = cv2.cvtColor(img_rd, cv2.COLOR_BGR2RGB)faces = detector(img_gray, 1)for i in range(len(faces)):landmarks = np.matrix([[p.x, p.y] for p in predictor(img_rd,faces[i]).parts()]) for idx, point in enumerate(landmarks):# 68點(diǎn)的坐標(biāo)pos = (point[0, 0], point[0, 1])add="F:/jupyter/feature/face"+i1+"_feature"+i2+".csv"with open(add, "a", newline="") as csvfile:writer1 = csv.writer(csvfile)writer1.writerow((idx,pos))print(add)print("%-40s %-20s" % ("檢測到人臉的圖像 / image with faces detected:", path_img), '\n')# 因?yàn)橛锌赡芙叵聛淼娜四樤偃z測，檢測不出來人臉了# 所以要確保是 檢測到人臉的人臉圖像 拿去算特征if len(faces) != 0:shape = predictor(img_gray, faces[0])face_descriptor = face_rec.compute_face_descriptor(img_gray, shape)else:face_descriptor = 0print("no face")return face_descriptor# 將文件夾中照片特征提取出來, 寫入 CSV def return_features_mean_personX(path_faces_personX):features_list_personX = []photos_list = os.listdir(path_faces_personX)if photos_list:for i in range(len(photos_list)):# 調(diào)用return_128d_features()得到128d特征print("%-40s %-20s" % ("正在讀的人臉圖像 / image to read:", path_faces_personX + "/" + photos_list[i]))features_128d = return_128d_features(path_faces_personX + "/" + photos_list[i])# print(features_128d)# 遇到?jīng)]有檢測出人臉的圖片跳過if features_128d == 0:i += 1else:features_list_personX.append(features_128d)else:print("文件夾內(nèi)圖像文件為空 / Warning: No images in " + path_faces_personX + '/', '\n')# 計(jì)算 128D 特征的均值# N x 128D -> 1 x 128Dif features_list_personX:features_mean_personX = np.array(features_list_personX).mean(axis=0)else:features_mean_personX = '0'return features_mean_personX# 讀取某人所有的人臉圖像的數(shù)據(jù) people = os.listdir(path_images_from_camera) people.sort() with open("F:/jupyter/feature/face_feature_mean.csv", "w", newline="") as csvfile: #程序會(huì)新建一個(gè)表格文件來保存特征值，方便以后比對writer = csv.writer(csvfile)for person in people:print("##### " + person + " #####")# Get the mean/average features of face/personX, it will be a list with a length of 128Dfeatures_mean_personX = return_features_mean_personX(path_images_from_camera + person)writer.writerow(features_mean_personX)print("特征均值 / The mean of features:", list(features_mean_personX))print('\n')print("所有錄入人臉數(shù)據(jù)存入 / Save all the features of faces registered into: F:/jupyter/feature/face_feature_mean.csv")

• 運(yùn)行結(jié)果：
  
  
  

三、人臉識別

• 代碼：

import os import winsound # 系統(tǒng)音效 from playsound import playsound # 音頻播放 import dlib # 人臉處理的庫 Dlib import csv # 存入表格 import time import sys import numpy as np # 數(shù)據(jù)處理的庫 numpy from cv2 import cv2 as cv2 # 圖像處理的庫 OpenCv import pandas as pd # 數(shù)據(jù)處理的庫 Pandas# 人臉識別模型，提取128D的特征矢量 # face recognition model, the object maps human faces into 128D vectors # Refer this tutorial: http://dlib.net/python/index.html#dlib.face_recognition_model_v1 facerec = dlib.face_recognition_model_v1("dlib_face_recognition_resnet_model_v1.dat")#我這是在同一路徑下的，dlib_face_recognition_resnet_model_v1.dat不在 #要寫為絕對路徑："D:/****/****/dlib_face_recognition_resnet_model_v1.dat"# 計(jì)算兩個(gè)128D向量間的歐式距離 # compute the e-distance between two 128D features 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)))return dist# 處理存放所有人臉特征的 csv path_features_known_csv = "F:/jupyter/feature/face_feature_mean.csv" csv_rd = pd.read_csv(path_features_known_csv, header=None)# 用來存放所有錄入人臉特征的數(shù)組 # the array to save the features of faces in the database features_known_arr = []# 讀取已知人臉數(shù)據(jù) # print known faces for i in range(csv_rd.shape[0]):features_someone_arr = []for j in range(0, len(csv_rd.loc[i, :])):features_someone_arr.append(csv_rd.loc[i, :][j])features_known_arr.append(features_someone_arr) print("Faces in Database：", len(features_known_arr))# Dlib 檢測器和預(yù)測器 # The detector and predictor will be used detector = dlib.get_frontal_face_detector() predictor = dlib.shape_predictor('shape_predictor_68_face_landmarks.dat')#我這是在同一路徑下的，shape_predictor_68_face_landmarks.dat不在 #要寫為絕對路徑："D:/****/****/shape_predictor_68_face_landmarks.dat# 創(chuàng)建 cv2 攝像頭對象 # cv2.VideoCapture(0) to use the default camera of PC, # and you can use local video name by use cv2.VideoCapture(filename) cap = cv2.VideoCapture(0)# cap.set(propId, value) # 設(shè)置視頻參數(shù)，propId 設(shè)置的視頻參數(shù)，value 設(shè)置的參數(shù)值 cap.set(3, 480)# cap.isOpened() 返回 true/false 檢查初始化是否成功 # when the camera is open while cap.isOpened():flag, img_rd = cap.read()kk = cv2.waitKey(1)# 取灰度img_gray = cv2.cvtColor(img_rd, cv2.COLOR_RGB2GRAY)# 人臉數(shù) facesfaces = detector(img_gray, 0)# 待會(huì)要寫的字體 font to write laterfont = cv2.FONT_HERSHEY_COMPLEX# 存儲(chǔ)當(dāng)前攝像頭中捕獲到的所有人臉的坐標(biāo)/名字# the list to save the positions and names of current faces capturedpos_namelist = []name_namelist = []# 按下 q 鍵退出# press 'q' to exitif kk == ord('q'):breakelse:# 檢測到人臉 when face detectedif len(faces) != 0: # 獲取當(dāng)前捕獲到的圖像的所有人臉的特征，存儲(chǔ)到 features_cap_arr# get the features captured and save into features_cap_arrfeatures_cap_arr = []for i in range(len(faces)):shape = predictor(img_rd, faces[i])features_cap_arr.append(facerec.compute_face_descriptor(img_rd, shape))# 遍歷捕獲到的圖像中所有的人臉# traversal all the faces in the databasefor k in range(len(faces)):print("##### camera person", k+1, "#####")# 讓人名跟隨在矩形框的下方# 確定人名的位置坐標(biāo)# 先默認(rèn)所有人不認(rèn)識，是 unknown# set the default names of faces with "unknown"name_namelist.append("unknown")# 每個(gè)捕獲人臉的名字坐標(biāo) the positions of faces capturedpos_namelist.append(tuple([faces[k].left(), int(faces[k].bottom() + (faces[k].bottom() - faces[k].top())/4)]))# 對于某張人臉，遍歷所有存儲(chǔ)的人臉特征# for every faces detected, compare the faces in the databasee_distance_list = []for i in range(len(features_known_arr)):# 如果 person_X 數(shù)據(jù)不為空if str(features_known_arr[i][0]) != '0.0':print("with person", str(i + 1), "the e distance: ", end='')e_distance_tmp = return_euclidean_distance(features_cap_arr[k], features_known_arr[i])print(e_distance_tmp)e_distance_list.append(e_distance_tmp)else:# 空數(shù)據(jù) person_Xe_distance_list.append(999999999)# 找出最接近的一個(gè)人臉數(shù)據(jù)是第幾個(gè)# Find the one with minimum e distancesimilar_person_num = e_distance_list.index(min(e_distance_list))print("Minimum e distance with person", int(similar_person_num)+1)# 計(jì)算人臉識別特征與數(shù)據(jù)集特征的歐氏距離# 距離小于0.4則標(biāo)出為可識別人物if min(e_distance_list) < 0.4:# 這里可以修改攝像頭中標(biāo)出的人名# Here you can modify the names shown on the camera# 1、遍歷文件夾目錄folder_name = 'F:/my/'# 最接近的人臉sum=similar_person_num+1key_id=1 # 從第一個(gè)人臉數(shù)據(jù)文件夾進(jìn)行對比# 獲取文件夾中的文件名:1wang、2zhou、3...file_names = os.listdir(folder_name)for name in file_names:# print(name+'->'+str(key_id))if sum ==key_id:#winsound.Beep(300,500)# 響鈴：300頻率，500持續(xù)時(shí)間name_namelist[k] = name[1:]#人名刪去第一個(gè)數(shù)字（用于視頻輸出標(biāo)識）key_id += 1# 播放歡迎光臨音效#playsound('D:/myworkspace/JupyterNotebook/People/music/welcome.wav')# print("May be person "+str(int(similar_person_num)+1))# -----------篩選出人臉并保存到visitor文件夾------------for i, d in enumerate(faces):x1 = d.top() if d.top() > 0 else 0y1 = d.bottom() if d.bottom() > 0 else 0x2 = d.left() if d.left() > 0 else 0y2 = d.right() if d.right() > 0 else 0face = img_rd[x1:y1,x2:y2]size = 64face = cv2.resize(face, (size,size))# 要存儲(chǔ)visitor人臉圖像文件的路徑path_visitors_save_dir = "F:/my/known" #自己在faces路徑下先建一個(gè)known文件，否則可能會(huì)報(bào)錯(cuò)# 存儲(chǔ)格式：2019-06-24-14-33-40wang.jpgnow_time = time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime())save_name = str(now_time)+str(name_namelist[k])+'.jpg'# print(save_name)# 本次圖片保存的完整urlsave_path = path_visitors_save_dir+'/'+ save_name # 遍歷visitor文件夾所有文件名visitor_names = os.listdir(path_visitors_save_dir)visitor_name=''for name in visitor_names:# 名字切片到分鐘數(shù)：2019-06-26-11-33-00wangyu.jpgvisitor_name=(name[0:16]+'-00'+name[19:])# print(visitor_name)visitor_save=(save_name[0:16]+'-00'+save_name[19:])# print(visitor_save)# 一分鐘之內(nèi)重復(fù)的人名不保存if visitor_save!=visitor_name:cv2.imwrite(save_path, face)print('新存儲(chǔ)：'+path_visitors_save_dir+'/'+str(now_time)+str(name_namelist[k])+'.jpg')else:print('重復(fù)，未保存！')else:# 播放無法識別音效#playsound('D:/myworkspace/JupyterNotebook/People/music/sorry.wav')print("Unknown person")# -----保存圖片-------# -----------篩選出人臉并保存到visitor文件夾------------for i, d in enumerate(faces):x1 = d.top() if d.top() > 0 else 0y1 = d.bottom() if d.bottom() > 0 else 0x2 = d.left() if d.left() > 0 else 0y2 = d.right() if d.right() > 0 else 0face = img_rd[x1:y1,x2:y2]size = 64face = cv2.resize(face, (size,size))# 要存儲(chǔ)visitor-》unknown人臉圖像文件的路徑path_visitors_save_dir = "F:/my/unknown"#自己在faces路徑下先建一個(gè)unknown文件，否則可能會(huì)報(bào)錯(cuò)# 存儲(chǔ)格式：2019-06-24-14-33-40unknown.jpgnow_time = time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime())# print(save_name)# 本次圖片保存的完整urlsave_path = path_visitors_save_dir+'/'+ str(now_time)+'unknown.jpg'cv2.imwrite(save_path, face)print('新存儲(chǔ)：'+path_visitors_save_dir+'/'+str(now_time)+'unknown.jpg')# 矩形框# draw rectanglefor kk, d in enumerate(faces):# 繪制矩形框cv2.rectangle(img_rd, tuple([d.left(), d.top()]), tuple([d.right(), d.bottom()]), (0, 255, 255), 2)print('\n')# 在人臉框下面寫人臉名字# write names under rectanglefor i in range(len(faces)):cv2.putText(img_rd, name_namelist[i], pos_namelist[i], font, 0.8, (0, 255, 255), 1, cv2.LINE_AA)print("Faces in camera now:", name_namelist, "\n")#cv2.putText(img_rd, "Press 'q': Quit", (20, 450), font, 0.8, (84, 255, 159), 1, cv2.LINE_AA)cv2.putText(img_rd, "Face Recognition", (20, 40), font, 1, (0, 0, 255), 1, cv2.LINE_AA)cv2.putText(img_rd, "Visitors: " + str(len(faces)), (20, 100), font, 1, (0, 0, 255), 1, cv2.LINE_AA)# 窗口顯示 show with opencvcv2.imshow("camera", img_rd)k = cv2.waitKey(1)if k == 27: # press 'ESC' to quitbreak # 釋放攝像頭 release camera cap.release()# 刪除建立的窗口 delete all the windows cv2.destroyAllWindows()

• 運(yùn)行結(jié)果：
  

四、總結(jié)

本次實(shí)驗(yàn)簡單實(shí)現(xiàn)了一下人臉識別，總的來說還是不難，而且也比較有趣。

五、參考資料

簡單的人臉識別
人臉識別數(shù)據(jù)集建立及應(yīng)用

總結(jié)

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