目錄

• 采集人臉照片20張
• 總結
• 參考

采集人臉照片20張

用dlib庫采取自己的照片20張并保存：

import cv2 import dlib import os import sys import random # 存儲位置 output_dir = 'D:/631907060224' 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 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 += 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

可以看到路徑下：

之后采集對應20張圖片的68個特征點數組計算出平均特征數組：

# 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:/631907060224/person/"# 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:/BaiduNetdiskDownload/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:/BaiduNetdiskDownload/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 = 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)# 遇到沒有檢測出人臉的圖片跳過if features_128d == 0:i += 1else:features_list_personX.append(features_128d)i1=str(i+1)add="D:/631907060224/feature/face_feature"+i1+".csv"print(add)with open(add, "w", newline="") as csvfile:writer1 = csv.writer(csvfile)writer1.writerow(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 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據 people = os.listdir(path_images_from_camera) people.sort()with open("D:/631907060224/feature/features2_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 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("所有錄入人臉數據存入 / Save all the features of faces registered into: D:/631907060224/feature/features_all2.csv")

運行：

路徑下有：

總結

通過本次實驗，進一步熟悉對于dlib庫的使用，對人臉采集的一些方法也更加熟悉。

參考

Dlib模型實現人臉識別
基于dlib庫人臉特征提取【構建自己的人臉識別數據集】

總結

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