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            首頁 編程技術Python實現進階版人臉識別

            Python實現進階版人臉識別

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            使用到的庫:dlib+Opencvpython版本:3.8編譯環境:Jupyter Notebook (Anaconda3)

            0.Dlib人臉特征檢測原理

            • 提取特征點:請參考
            • 首選抓取多張圖片,從中獲取特征數據集和平均特征值然后寫入csv文件 - 計算特征數據集的歐式距離作對比:首先使用Opencv庫將攝像頭中的人臉框出來,再將攝像頭中采取到的人臉特征值與數據集中的每個人的特征均值作對比,選取最接近(歐氏距離最?。┑闹?,將其標注為歐氏距離最小的數據集的人名

            Python實現進階版人臉識別插圖

            Python實現進階版人臉識別插圖1

            一、構建人臉特征數據集

            1. 安裝Dlib

            請參考

            2. 構建自己的數據集

            2.1 抓取人臉圖片

            Python實現進階版人臉識別插圖2

            在視頻流中抓取人臉特征,并保存為256*256大小的圖片文件共20張,這就是我們建立數據集的第一步,用來訓練人臉識別。

            不一定是256*256的尺寸,可以根據自己的需求來調整大小,圖片越大訓練結果會愈加精確,但也會影響訓練模型的時間。

            其中:

            • 光線:曝光和黑暗的圖片需手動剔除- 請使用同一個設備進行數據采集,不同設備的攝像頭采集到的數據集會有出入- 這里采用的是從視頻流中進行捕捉截圖,也可以自己準備20張左右的人臉圖片

            代碼:

            import cv2  
            import dlib  
            import os  
            import sys  
            import random  
            # 存儲位置  
            output_dir = 'D:/No1WorkSpace/JupyterNotebook/Facetrainset/Num&Name' #這里填編號+人名  
            size = 256 #圖片邊長  
            
            if 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 = 255  
                            elif tmp < 0:  
                                tmp = 0  
                            img[j,i,c] = tmp  
                return 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15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()  
                    # 轉為灰度圖片  
                    gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)  
                    # 使用detector進行人臉檢測  
                    dets = detector(gray_img, 1)  
            
                    for i, d in enumerate(dets):  
                        x1 = d.top() if d.top() > 0 else 0  
                        y1 = d.bottom() if d.bottom() > 0 else 0  
                        x2 = d.left() if d.left() > 0 else 0  
                        y2 = d.right() if d.right() > 0 else 0  
            
                        face = img[x1:y1,x2:y2]  
                        # 調整圖片的對比度與亮度, 對比度與亮度值都取隨機數,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 += 1  
                    key = cv2.waitKey(30) & 0xff  
                    if key == 27:  
                        break  
                else:  
                    print('Finished!')  
                    # 釋放攝像頭 release camera  
                    camera.release()  
                    # 刪除建立的窗口 delete all the windows  
                    cv2.destroyAllWindows()  
                    break
            

            運行效果:

            Python實現進階版人臉識別插圖3Python實現進階版人臉識別插圖4

            2.2 分析每張人臉的特征值并存入csv文件

            根據抓取的圖片和人臉識別模型->訓練得到的20個的68個特征數據集以及1個平均特征值存入csv文件

            每張圖片的68個特征數據集可以不用存取,他們只是中間量,計算平均值以后就可以拋棄了,這里把他們輸出出來只是為了方便學習。

            代碼:

            # 從人臉圖像文件中提取人臉特征存入 CSV  
            # Features extraction from images and save into features_all.csv  
            
            # return_128d_features()          獲取某張圖像的128D特征  
            # compute_the_mean()              計算128D特征均值  
            
            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 = "D:/No1WorkSpace/JupyterNotebook/Facetrainset/"  
            
            # Dlib 正向人臉檢測器  
            detector = dlib.get_frontal_face_detector()  
            
            # Dlib 人臉預測器  
            predictor = dlib.shape_predictor("D:/No1WorkSpace/JupyterNotebook/model/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("D:/No1WorkSpace/JupyterNotebook/model/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)  
                img_gray = cv2.cvtColor(img_rd, cv2.COLOR_BGR2RGB)  
                faces = detector(img_gray, 1)  
            
                print("%-40s %-20s" % ("檢測到人臉的圖像 / image with faces detected:", path_img), '\n')  
            
                # 因為有可能截下來的人臉再去檢測,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 = 0  
                    print("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)):  
                        with open("D:/No1WorkSpace/JupyterNotebook/feature/featuresGiao"+str(i)+".csv", "w", newline="") as csvfile:  
                            writer = csv.writer(csvfile)  
                        # 調用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)  
                            writer.writerow(features_128d)  
                        # 遇到沒有檢測出人臉的圖片跳過  
                            if features_128d == 0:  
                                i += 1  
                            else:  
                                features_list_personX.append(features_128d)  
                else:  
                    print("文件夾內圖像文件為空 / Warning: No images in " + path_faces_personX + '/', '\n')  
            
                # 計算 128D 特征的均值  
                # N x 128D -> 1 x 128D  
                if 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據  
            people = os.listdir(path_images_from_camera)  
            people.sort()  
            
            with open("D:/No1WorkSpace/JupyterNotebook/feature/features_all.csv", "w", newline="") as csvfile:  
                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 128D  
                    features_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("所有錄入人臉數據存入 / Save all the features of faces registered into: D:/myworkspace/JupyterNotebook/People/feature/features_all2.csv")  
            

            如果要輸出每一張圖片的特征數據集,這里要用到Python的文件批量生成。

            代碼運行效果

            Python實現進階版人臉識別插圖5Python實現進階版人臉識別插圖6Python實現進階版人臉識別插圖7

            二、識別人臉并匹配數據集

            1. 原理:

            通過計算特征數據集的歐氏距離作對比來識別人臉,取歐氏距離最小的數據集進行匹配。

            歐氏距離也稱歐幾里得距離或歐幾里得度量,是一個通常采用的距離定義,它是在m維空間中兩個點之間的真實距離。在二維和三維空間中的歐氏距離的就是兩點之間的距離。使用這個距離,歐氏空間成為度量空間。相關聯的范數稱為歐幾里得范數。較早的文獻稱之為畢達哥拉斯度量。二維空間公式:Python實現進階版人臉識別插圖8Python實現進階版人臉識別插圖9

            2. 視頻流實時識別人臉數據

            代碼:

            # 攝像頭實時人臉識別  
            import os  
            import dlib          # 人臉處理的庫 Dlib  
            import csv # 存入表格  
            import time  
            import sys  
            import numpy as np   # 數據處理的庫 numpy  
            from cv2 import cv2 as cv2           # 圖像處理的庫 OpenCv  
            import pandas as pd  # 數據處理的庫 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("D:/No1WorkSpace/JupyterNotebook/model/dlib_face_recognition_resnet_model_v1.dat")  
            
            
            # 計算兩個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 = "D:/No1WorkSpace/JupyterNotebook/feature/features_all.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組  
            # the array to save the features of faces in the database  
            features_known_arr = []  
            
            # 讀取已知人臉數據  
            # 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 檢測器和預測器  
            # The detector and predictor will be used  
            detector = dlib.get_frontal_face_detector()  
            predictor = dlib.shape_predictor('D:/No1WorkSpace/JupyterNotebook/model/shape_predictor_68_face_landmarks.dat')  
            
            # 創建 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)  
            # 設置視頻參數,propId 設置的視頻參數,value 設置的參數值  
            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 faces  
                faces = detector(img_gray, 0)  
            
                # 待會要寫的字體 font to write later  
                font = cv2.FONT_HERSHEY_COMPLEX  
            
                # 存儲當前攝像頭中捕獲到的所有人臉的坐標/名字  
                # the list to save the positions and names of current faces captured  
                pos_namelist = []  
                name_namelist = []  
            
                # 按下 q 鍵退出  
                # press 'q' to exit  
                if kk == ord('q'):  
                    break  
                else:  
                    # 檢測到人臉 when face detected  
                    if len(faces) != 0:    
                        # 獲取當前捕獲到的圖像的所有人臉的特征,存儲到 features_cap_arr  
                        # get the features captured and save into features_cap_arr  
                        features_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 database  
                        for 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  
                            # 先默認所有人不認識,是 unknown  
                            # set the default names of faces with "unknown"  
                            name_namelist.append("unknown")  
            
                            # 每個捕獲人臉的名字坐標 the positions of faces captured  
                            pos_namelist.append(tuple([faces[k].left(), int(faces[k].bottom() + (faces[k].bottom() - faces[k].top())/4)]))  
            
                            # 對于某張人臉,遍歷所有存儲的人臉特征  
                            # for every faces detected, compare the faces in the database  
                            e_distance_list = []  
                            for i in range(len(features_known_arr)):  
                                # 如果 person_X 數據不為空  
                                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:  
                                    # 空數據 person_X  
                                    e_distance_list.append(999999999)  
                            # 找出最接近的一個人臉數據是第幾個  
                            # Find the one with minimum e distance  
                            similar_person_num = e_distance_list.index(min(e_distance_list))  
                            print("Minimum e distance with person", int(similar_person_num)+1)  
            
                            # 計算人臉識別特征與數據集特征的歐氏距離  
                            # 距離小于0.4則標出為可識別人物  
                            if min(e_distance_list) < 0.4:  
                                # 這里可以修改攝像頭中標出的人名  
                                # Here you can modify the names shown on the camera  
                                # 1、遍歷文件夾目錄  
                                folder_name = 'D:/No1WorkSpace/JupyterNotebook/Facetrainset/'  
                                # 最接近的人臉  
                                sum=similar_person_num+1  
                                key_id=1 # 從第一個人臉數據文件夾進行對比  
                                # 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持續時間  
                                        name_namelist[k] = name[1:]#人名刪去第一個數字(用于視頻輸出標識)  
                                    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 0  
                                    y1 = d.bottom() if d.bottom() > 0 else 0  
                                    x2 = d.left() if d.left() > 0 else 0  
                                    y2 = d.right() if d.right() > 0 else 0  
                                    face = img_rd[x1:y1,x2:y2]  
                                    size = 64  
                                    face = cv2.resize(face, (size,size))  
                                    # 要存儲visitor人臉圖像文件的路徑  
                                    path_visitors_save_dir = "D:/No1WorkSpace/JupyterNotebook/KnownFacetrainset/"  
                                    # 存儲格式:2019-06-24-14-33-40wang.jpg  
                                    now_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url  
                                    save_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:2019-06-26-11-33-00wangyu.jpg  
                                        visitor_name=(name[0:16]+'-00'+name[19:])  
                                    # print(visitor_name)  
                                    visitor_save=(save_name[0:16]+'-00'+save_name[19:])  
                                    # print(visitor_save)  
                                    # 一分鐘之內重復的人名不保存  
                                    if visitor_save!=visitor_name:  
                                        cv2.imwrite(save_path, face)  
                                        print('新存儲:'+path_visitors_save_dir+'/'+str(now_time)+str(name_namelist[k])+'.jpg')  
                                    else:  
                                        print('重復,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 0  
                                    y1 = d.bottom() if d.bottom() > 0 else 0  
                                    x2 = d.left() if d.left() > 0 else 0  
                                    y2 = d.right() if d.right() > 0 else 0  
                                    face = img_rd[x1:y1,x2:y2]  
                                    size = 64  
                                    face = cv2.resize(face, (size,size))  
                                    # 要存儲visitor-》unknown人臉圖像文件的路徑  
                                    path_visitors_save_dir = "D:/No1WorkSpace/JupyterNotebook/UnKnownFacetrainset/"  
                                    # 存儲格式:2019-06-24-14-33-40unknown.jpg  
                                    now_time = time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime())  
                                    # print(save_name)  
                                    # 本次圖片保存的完整url  
                                    save_path = path_visitors_save_dir+'/'+ str(now_time)+'unknown.jpg'  
                                    cv2.imwrite(save_path, face)  
                                    print('新存儲:'+path_visitors_save_dir+'/'+str(now_time)+'unknown.jpg')  
            
                            # 矩形框  
                            # draw rectangle  
                            for 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 rectangle  
                        for 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 opencv  
                cv2.imshow("camera", img_rd)  
            
            # 釋放攝像頭 release camera  
            cap.release()  
            
            # 刪除建立的窗口 delete all the windows  
            cv2.destroyAllWindows()  
            

            若直接使用本代碼,文件目錄弄成中文會亂碼

            運行效果:Python實現進階版人臉識別插圖10圖中兩人的特征數據集均已被收集并錄入,所以可以識別出來,如果沒有被錄入的人臉就會出現unknown。

            Python實現進階版人臉識別插圖11

            沒有吳京叔叔的數據集,所以他是陌生人

            原文鏈接:https://blog.csdn.net/ChenJ_1012/article/details/121323101

            本文鏈接:http://m.abandonstatusquo.com/40724.html

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