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import cv2 import dlib import os import sys import random # 存储位置 output_dir = 'D:/z/photo' size = 64 if not os.path.exists(output_dir): os.makedirs(output_dir) # 改变图片的亮度与对比度 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自带的frontal_face_detector作为我们的特征提取器 detector = dlib.get_frontal_face_detector() # 打开摄像头 参数为输入流,可以为摄像头或视频文件 camera = cv2.VideoCapture(0) #camera = cv2.VideoCapture('D:/z7z8/yy.mp4') index = 1 while True: if (index <= 20):#存储20张人脸特征图像 print('Being processed picture %s' % index) # 从摄像头读取照片 success, img = camera.read() # 转为灰度图片 gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # 使用detector进行人脸检测 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] # 调整图片的对比度与亮度, 对比度与亮度值都取随机数,这样能增加样本的多样性 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!') # 释放摄像头 release camera camera.release() # 删除建立的窗口 delete all the windows cv2.destroyAllWindows() break
# 从人脸图像文件中提取人脸特征存入 CSV # Features extraction from images and save into features_all.csv # return_128d_features() 获取某张图像的128D特征 # compute_the_mean() 计算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 # 要读取人脸图像文件的路径 path_images_from_camera = "D:/z" # Dlib 正向人脸检测器 detector = dlib.get_frontal_face_detector() # Dlib 人脸预测器 predictor = dlib.shape_predictor("D:/shape_predictor_68_face_landmarks.dat") # Dlib 人脸识别模型 # Face recognition model, the object maps human faces into 128D vectors face_rec = dlib.face_recognition_model_v1("D:/dlib_face_recognition_resnet_model_v1.dat") # 返回单张图像的 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" % ("检测到人脸的图像 / image with faces detected:", path_img), '\n') # 因为有可能截下来的人脸再去检测,检测不出来人脸了 # 所以要确保是 检测到人脸的人脸图像 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 # 将文件夹中照片特征提取出来, 写入 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)): # 调用return_128d_features()得到128d特征 print("%-40s %-20s" % ("正在读的人脸图像 / image to read:", path_faces_personX + "/" + photos_list[i])) features_128d = return_128d_features(path_faces_personX + "/" + photos_list[i]) # print(features_128d) # 遇到没有检测出人脸的图片跳过 if features_128d == 0: i += 1 else: features_list_personX.append(features_128d) i1=str(i+1) add="D:/face/csv/face_feature"+i1+".csv" print(add) with open(add, "w", newline="") as csvfile: writer1 = csv.writer(csvfile) writer1.writerow(features_128d) else: print("文件夹内图像文件为空 / Warning: No images in " + path_faces_personX + '/', '\n') # 计算 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 # 读取某人所有的人脸图像的数据 people = os.listdir(path_images_from_camera) people.sort() with open("D:/face/csv/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 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("所有录入人脸数据存入 / Save all the features of faces registered into: D:/face/csv/features_all2.csv")
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