前言

在前面的博客中（OpenFace学习（1）：安装配置及人脸比对），介绍了OpenFace的安装配置，以及一个人脸匹配的demo。其中只是匹配了几张图片中人脸，对每个人脸的特征向量很粗略地采用欧氏距离测量，效果也还不错。本文中将使用SVM来对每个人脸的特征向量进行分类，进行人脸比对。

demo

代码文件有四个：

• featrure_extract.py：提取人脸信息，每一张人脸提取为一个128维的特征向量；
• feature_train.py：训练SVM分类器；
• feature_classify.py：使用SVM分类人脸特征向量。
• logs.py：控制台打印日志信息配置。

不说废话了，下面直接上代码。

logs.py

# *_*coding:utf-8 *_* # author: 许鸿斌 # 邮箱:2775751197@qq.comimport logging import sys# 获取logger实例，如果参数为空则返回root logger logger = logging.getLogger('Test') # 指定logger输出格式 formatter = logging.Formatter('%(asctime)s %(levelname)-8s: %(message)s') # 文件日志 # file_handler = logging.FileHandler("test.log") # file_handler.setFormatter(formatter) # 可以通过setFormatter指定输出格式 # 控制台日志 console_handler = logging.StreamHandler(sys.stdout) console_handler.formatter = formatter # 也可以直接给formatter赋值 # 为logger添加的日志处理器 # logger.addHandler(file_handler) logger.addHandler(console_handler) # 指定日志的最低输出级别，默认为WARN级别 logger.setLevel(logging.INFO)

featrure_extract.py

# *_*coding:utf-8 *_* # author: 许鸿斌 # 邮箱:2775751197@qq.comfrom log import logger import os import cv2 import time import openface import cPickle# dlib和openface模型配置 imgDim = 96 dlib_model_dir = '/home/xhb/文档/Packages/openface/models/dlib' openface_model_dir = '/home/xhb/文档/Packages/openface/models/openface' # 导入dlib人脸特征点检测器 align = openface.AlignDlib(os.path.join(dlib_model_dir, "shape_predictor_68_face_landmarks.dat")) # 导入FaceNet net = openface.TorchNeuralNet(os.path.join(openface_model_dir, 'nn4.small2.v1.t7'), imgDim)def getRep(imgPath, verbose=False):logger.info("Processing {}.".format(imgPath))bgrImg = cv2.imread(imgPath)if bgrImg is None:raise Exception("Unable to load image: {}".format(imgPath))rgbImg = cv2.cvtColor(bgrImg, cv2.COLOR_BGR2RGB)if verbose:logger.info("Original size: {}".format(rgbImg.shape)) start = time.time()faceBoundingBox = align.getLargestFaceBoundingBox(rgbImg)if faceBoundingBox is None:raise Exception("Unable to find a face: {}".format(imgPath))if verbose:logger.info("Face detection took {} seconds.".format(time.time() - start))start = time.time()alignedFace = align.align(imgDim, rgbImg, faceBoundingBox, landmarkIndices=openface.AlignDlib.OUTER_EYES_AND_NOSE) if alignedFace is None:raise Exception("Unable to align image: {}".format(imgPath))if verbose:logger.info("Face alignment took {} seconds.".format(time.time() - start))start = time.time()rep = net.forward(alignedFace) if verbose:logger.info("OpenFace forward pass took {} seconds.".format(time.time()-start))logger.info("Representation:")logger.info(rep)return reppwd = os.getcwd() img_dir = os.path.join(pwd, 'training_images') people_name_list = os.listdir(img_dir) # logger.info(res) people_rep_dict = {} for people_name in people_name_list:face_dir = os.path.join(img_dir, people_name)img_name_list = os.listdir(face_dir)# logger.info(img_path_list)people_rep_list = []for img_name in img_name_list:img_path = os.path.join(face_dir, img_name)# logger.info('Processing: {}'.format(img_path))rep = getRep(img_path, verbose=False)people_rep_list.append(rep)people_rep_dict[people_name] = people_rep_listlogger.info('Face vectors has been generated!')# logger.info(people_rep_dict) rep_dir = os.path.join(pwd, 'representation_vectors') # 保存生成的特征向量 cPickle.dump(people_rep_dict, open(os.path.join(rep_dir, 'rep.pkl'), 'wb')) logger.info('Vectors saved in {}'.format(os.path.join(rep_dir, 'rep.pkl')))

feature_train.py

# *_*coding:utf-8 *_* # author: 许鸿斌 # 邮箱:2775751197@qq.comfrom log import logger import os import cPickle from sklearn.svm import SVCpwd = os.getcwd() rep_dir = os.path.join(pwd, 'representation_vectors') people_rep_dict = cPickle.load(open(os.path.join(rep_dir, 'rep.pkl'), 'rb'))people_names = people_rep_dict.keys() logger.info(people_names) nClasses = len(people_names) label = 0 vector_list = [] label_list = [] for people_name in people_names:vectors = people_rep_dict[people_name]label += 1for vector in vectors:vector_list.append(vector)label_list.append(label)logger.info('vector_list: {}'.format(len(vector_list))) logger.info('label_list: {}'.format(len(label_list)))# linearSVM logger.info('Start training linear svm classifier.') clf = SVC(C=1, kernel='linear', probability=True) clf.fit(vector_list, label_list)# 保存模型和labels clf_name = 'classifier_{}.pkl'.format('linear_svm') logger.info('classifier saved as: {}'.format(os.path.join(pwd, clf_name))) cPickle.dump(clf, open(os.path.join(pwd, clf_name), 'wb')) labels_name = 'labels.pkl' logger.info('labels saved as: {}'.format(os.path.join(pwd, labels_name))) cPickle.dump(people_names, open(os.path.join(pwd, labels_name), 'wb'))

feature_classify.py

# *_*coding:utf-8 *_* # author: 许鸿斌 # 邮箱:2775751197@qq.comfrom log import logger import os import cPickle from sklearn.svm import SVC import cv2 import time import openface import numpy as np# dlib和openface模型配置 imgDim = 96 dlib_model_dir = '/home/xhb/文档/Packages/openface/models/dlib' openface_model_dir = '/home/xhb/文档/Packages/openface/models/openface' # 导入dlib人脸特征点检测器 align = openface.AlignDlib(os.path.join(dlib_model_dir, "shape_predictor_68_face_landmarks.dat")) # 导入FaceNet net = openface.TorchNeuralNet(os.path.join(openface_model_dir, 'nn4.small2.v1.t7'), imgDim)def getRep(imgPath, verbose=False, multiple=False):logger.info("Processing {}.".format(imgPath))bgrImg = cv2.imread(imgPath)if bgrImg is None:raise Exception("Unable to load image: {}".format(imgPath))rgbImg = cv2.cvtColor(bgrImg, cv2.COLOR_BGR2RGB)if verbose:logger.info("Original size: {}".format(rgbImg.shape)) start = time.time()if multiple:faceBoundingBoxs = align.getAllFaceBoundingBoxes(rgbImg)else:faceBoundingBox = align.getLargestFaceBoundingBox(rgbImg)faceBoundingBoxs = [faceBoundingBox]if len(faceBoundingBoxs) == 0 or (not multiple and faceBoundingBox is None):raise Exception("Unable to find a face: {}".format(imgPath))if verbose:logger.info("Face detection took {} seconds.".format(time.time() - start))reps = []for bb in faceBoundingBoxs:start = time.time()alignedFace = align.align(imgDim, rgbImg, bb, landmarkIndices=openface.AlignDlib.OUTER_EYES_AND_NOSE)if alignedFace is None:raise Exception("Unable to align image: {}".format(imgPath))if verbose:logger.info("Face alignment took {} seconds.".format(time.time() - start))logger.info("This bbox is centered at {}, {}".format(bb.center().x, bb.center().y))start = time.time()rep = net.forward(alignedFace) if verbose:logger.info("OpenFace forward pass took {} seconds.".format(time.time()-start))# logger.info("Representation:")# logger.info(rep)reps.append((bb.center().x, rep))sreps = sorted(reps, key=lambda x: x[0])return srepspwd = os.getcwd() clf_name = 'classifier_{}.pkl'.format('linear_svm') clf = cPickle.load(open(os.path.join(pwd, clf_name), 'r')) label_name = 'labels.pkl' labels = cPickle.load(open(os.path.join(pwd, label_name), 'r'))# logger.info(clf) # logger.info(labels)test_image_path = os.path.join(pwd, 'test_images', 'inesta1.jpg') reps = getRep(test_image_path, multiple=True) if len(reps) > 1:logger.info("List of faces in image from left to right") for r in reps:rep = r[1].reshape(1, -1)bbx = r[0]start = time.time()pred = clf.predict_proba(rep).ravel()for name, prob in zip(labels, pred):logger.info('Probablity of {} is: {}'.format(name, prob))max_prob_index = np.argmax(pred)person_name = labels[max_prob_index]confidence = pred[max_prob_index]logger.info("Prediction took {} seconds.".format(time.time() - start))logger.info("Predict {} @ x={} with {:.2f} confidence.".format(person_name.decode('utf-8'), bbx, confidence))

补充说明

代码部分很简单，大部分看看就能懂了，不多做介绍了。稍微补充几个要注意的地方：
1、收集人脸图片
在代码文件同一个目录下建立一个training_images文件夹，其中对应每个人再建立相应的文件夹：

比如说，我这里就直接建了三个文件夹：inesta、obama、trump。
里面对应的就是收集的若干张图片：



注意图片只能包含单张人脸，因为后面要训练SVM分类这些人脸，多张人脸会造成混淆。
2、模型路径
跟上篇博客中一样，需要自己修改一下对应模型的路径。模型下载链接在上篇博客中已给出，请自行下载：OpenFace学习（1）：安装配置及人脸比对。

# dlib和openface模型配置 imgDim = 96 dlib_model_dir = '/home/xhb/文档/Packages/openface/models/dlib' openface_model_dir = '/home/xhb/文档/Packages/openface/models/openface' # 导入dlib人脸特征点检测器 align = openface.AlignDlib(os.path.join(dlib_model_dir, "shape_predictor_68_face_landmarks.dat")) # 导入FaceNet net = openface.TorchNeuralNet(os.path.join(openface_model_dir, 'nn4.small2.v1.t7'), imgDim)

imgDim用这里的默认值就可以了，dlib_model_dir是存放dlib的人脸特征点检测器模型（shape_predictor_68_face_landmarks.dat）的路径，openface_model_dir是存放OpenFace开源的FaceNet网络训练好的模型（nn4.small2.v1.t7）的路径。

运行结果

运行featrure_extract.py，会自动解析training_images文件夹中的图片，并将生成的特征向量和标签保存在rep.pkl文件中。

运行feature_train.py，读取rep.pkl中的特征向量，训练SVM分类器。SVM分类器保存在’classifier_linear_svm.pkl’中，标签保存在labels.pkl中。

运行feature_classify.py，对测试图片使用SVM进行分类，给出预测结果。

总结

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