人脸检测、人脸定位、人脸对齐、MTCNN、人脸识别(衡量人脸的相似或不同：softmax、三元组损失Triplet Loss、中心损失Center Loss、ArcFace)

我就是我 2023-07-13 04:21 1阅读 0赞

# ![20191009191333910.png][][日萌社][Link 1] #

[人工智能AI：Keras PyTorch MXNet TensorFlow PaddlePaddle 深度学习实战（不定时更新）][AI_Keras PyTorch MXNet TensorFlow PaddlePaddle]

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# ArcFace
    class ArcMarginProduct(nn.Module):
        r"""Implement of large margin arc distance: :
            Args:
                in_features: size of each input sample
                out_features: size of each output sample
                s: norm of input feature
                m: margin
    
                cos(theta + m)
            """
    
        def __init__(self, in_features, out_features, s=30.0, m=0.50, easy_margin=False):
            super(ArcMarginProduct, self).__init__()
            self.in_features = in_features
            self.out_features = out_features
            self.s = s
            self.m = m
            # 初始化权重
            self.weight = Parameter(torch.FloatTensor(out_features, in_features))
            nn.init.xavier_uniform_(self.weight)
    
            self.easy_margin = easy_margin
            self.cos_m = math.cos(m)
            self.sin_m = math.sin(m)
            self.th = math.cos(math.pi - m)
            self.mm = math.sin(math.pi - m) * m
    
        def forward(self, input, label):
            # cos(theta) & phi(theta)
            # torch.nn.functional.linear(input, weight, bias=None)
            # y=x*W^T+b
            cosine = F.linear(F.normalize(input), F.normalize(self.weight))
            sine = torch.sqrt(1.0 - torch.pow(cosine, 2))
            # cos(a+b)=cos(a)*cos(b)-size(a)*sin(b)
            phi = cosine * self.cos_m - sine * self.sin_m
            if self.easy_margin:
                # torch.where(condition, x, y) → Tensor
                # condition (ByteTensor) – When True (nonzero), yield x, otherwise yield y
                # x (Tensor) – values selected at indices where condition is True
                # y (Tensor) – values selected at indices where condition is False
                # return:
                # A tensor of shape equal to the broadcasted shape of condition, x, y
                # cosine>0 means two class is similar, thus use the phi which make it
                phi = torch.where(cosine > 0, phi, cosine)
            else:
                phi = torch.where(cosine > self.th, phi, cosine - self.mm)
            # convert label to one-hot
            # one_hot = torch.zeros(cosine.size(), requires_grad=True, device='cuda')
            # 将cos(\theta + m)更新到tensor相应的位置中
            one_hot = torch.zeros(cosine.size(), device='cuda')
            # scatter_(dim, index, src)
            one_hot.scatter_(1, label.view(-1, 1).long(), 1)
            # torch.where(out_i = {x_i if condition_i else y_i) 
            output = (one_hot * phi) + ((1.0 - one_hot) * cosine)
            output *= self.s
    
            return output

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from PIL import Image
    from detector import detect_faces
    from visualization_utils import show_results
    
    img = Image.open('some_img.jpg') # modify the image path to yours
    bounding_boxes, landmarks = detect_faces(img) # detect bboxes and landmarks for all faces in the image
    show_results(img, bounding_boxes, landmarks) # visualize the results

from PIL import Image
    from face_dect_recong.align.detector import detect_faces
    from face_dect_recong.align.visualization_utils import show_results
    %matplotlib inline
    
    img = Image.open('./data/other_my_face/my/my/myf112.jpg') 
    bounding_boxes, landmarks = detect_faces(img) # detect bboxes and landmarks for all faces in the image
    show_results(img, bounding_boxes, landmarks) # visualize the results

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from PIL import Image
    from face_dect_recong.align.detector import detect_faces
    from face_dect_recong.align.visualization_utils import show_results
    %matplotlib inline
    
    img = Image.open('./data/other_my_face/others/Woody_Allen/Woody_Allen_0001.jpg') 
    bounding_boxes, landmarks = detect_faces(img) 
    show_results(img, bounding_boxes, landmarks)

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#对其他人的图像进行检测
    %run face_dect_recong/align/face_align.py -source_root './data/other_my_face/others/' -dest_root './data/other_my_face_align/others' -crop_size 128
    
    
    #对我的图像进行检测
    %run face_dect_recong/align/face_align.py -source_root './data/other_my_face/my/' -dest_root './data/other_my_face_align/others/' -crop_size 128

import matplotlib.pyplot as plt
    from matplotlib.image import imread
    %matplotlib inline
    
    img=imread('./data/other_my_face_align/others/my/myf112.jpg')
    plt.imshow(img)
    plt.show

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img=imread('./data/other_my_face_align/others/Woody_Allen/Woody_Allen_0002.jpg')
    plt.imshow(img)
    plt.show

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#删除小于4张的一些人
    %run face_dect_recong/balance/remove_lowshot.py -root './data/other_my_face_align/others' -min_num 4
    
    img=imread('./data/dataset/lfw/lfw-align-128/Zico/Zico_0001.jpg')
    plt.imshow(img)
    plt.show

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class Config(object):
        env = 'default'
        backbone = 'resnet18'
        classify = 'softmax'
        
        metric = 'arc_margin'
        easy_margin = False
        #是否使用压缩奖惩网络模块（Squeeze-and-Excitation Blocks）
        use_se = False
        loss = 'focal_loss'
    
        display = False
        finetune = False
     
        lfw_root = '/home/wumg/data/data/other_my_face_align/others'
     
        lfw_test_list = '/home/wumg/data/data/other_my_face_align/others_test_pair.txt'
        test_model_path = '/home/wumg/data/data/dataset/lfw/resnet18_110.pth'
        save_interval = 10
    
        train_batch_size = 16  # batch size
        test_batch_size = 60
    
        input_shape = (1, 128, 128)
    
        optimizer = 'sgd'
    
        use_gpu = True  # use GPU or not
        gpu_id = '0, 1'
        num_workers = 4  # how many workers for loading data
       
        max_epoch = 2
        lr = 1e-1  # initial learning rate
        lr_step = 10
        lr_decay = 0.95  # when val_loss increase, lr = lr*lr_decay
        weight_decay = 5e-4

from __future__ import print_function
    import os
    import cv2
    from models import *
    import torch
    import torch.nn as nn
    import numpy as np
    import time
    from torch.nn import DataParallel
    import torch.utils.model_zoo as model_zoo
    import torch.nn.functional as F
    
    model_urls = {
        'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
        'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth'
        }
    
    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
    
    
    def conv3x3(in_planes, out_planes, stride=1):
        """3x3 convolution with padding"""
        return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
                         padding=1, bias=False)
    
    
    class BasicBlock(nn.Module):
        expansion = 1
    
        def __init__(self, inplanes, planes, stride=1, downsample=None):
            super(BasicBlock, self).__init__()
            self.conv1 = conv3x3(inplanes, planes, stride)
            self.bn1 = nn.BatchNorm2d(planes)
            self.relu = nn.ReLU(inplace=True)
            self.conv2 = conv3x3(planes, planes)
            self.bn2 = nn.BatchNorm2d(planes)
            self.downsample = downsample
            self.stride = stride
    
        def forward(self, x):
            residual = x
    
            out = self.conv1(x)
            out = self.bn1(out)
            out = self.relu(out)
    
            out = self.conv2(out)
            out = self.bn2(out)
    
            if self.downsample is not None:
                residual = self.downsample(x)
    
            out += residual
            out = self.relu(out)
    
            return out
        
    class IRBlock(nn.Module):
        expansion = 1
    
        def __init__(self, inplanes, planes, stride=1, downsample=None, use_se=True):
            super(IRBlock, self).__init__()
            self.bn0 = nn.BatchNorm2d(inplanes)
            self.conv1 = conv3x3(inplanes, inplanes)
            self.bn1 = nn.BatchNorm2d(inplanes)
            self.prelu = nn.PReLU()
            self.conv2 = conv3x3(inplanes, planes, stride)
            self.bn2 = nn.BatchNorm2d(planes)
            self.downsample = downsample
            self.stride = stride
            self.use_se = use_se
            if self.use_se:
                self.se = SEBlock(planes)
    
        def forward(self, x):
            residual = x
            out = self.bn0(x)
            out = self.conv1(out)
            out = self.bn1(out)
            out = self.prelu(out)
    
            out = self.conv2(out)
            out = self.bn2(out)
            if self.use_se:
                out = self.se(out)
    
            if self.downsample is not None:
                residual = self.downsample(x)
    
            out += residual
            out = self.prelu(out)
    
            return out
    
    
    class ResNetFace(nn.Module):
        def __init__(self, block, layers, use_se=True):
            self.inplanes = 64
            self.use_se = use_se
            super(ResNetFace, self).__init__()
            self.conv1 = nn.Conv2d(1, 64, kernel_size=3, padding=1, bias=False)
            self.bn1 = nn.BatchNorm2d(64)
            self.prelu = nn.PReLU()
            self.maxpool = nn.MaxPool2d(kernel_size=2, stride=2)
            self.layer1 = self._make_layer(block, 64, layers[0])
            self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
            self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
            self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
            self.bn4 = nn.BatchNorm2d(512)
            self.dropout = nn.Dropout()
            self.fc5 = nn.Linear(512 * 8 * 8, 512)
            self.bn5 = nn.BatchNorm1d(512)
    
            for m in self.modules():
                if isinstance(m, nn.Conv2d):
                    nn.init.xavier_normal_(m.weight)
                elif isinstance(m, nn.BatchNorm2d) or isinstance(m, nn.BatchNorm1d):
                    nn.init.constant_(m.weight, 1)
                    nn.init.constant_(m.bias, 0)
                elif isinstance(m, nn.Linear):
                    nn.init.xavier_normal_(m.weight)
                    nn.init.constant_(m.bias, 0)
    
        def _make_layer(self, block, planes, blocks, stride=1):
            downsample = None
            if stride != 1 or self.inplanes != planes * block.expansion:
                downsample = nn.Sequential(
                    nn.Conv2d(self.inplanes, planes * block.expansion,
                              kernel_size=1, stride=stride, bias=False),
                    nn.BatchNorm2d(planes * block.expansion),
                )
            layers = []
            layers.append(block(self.inplanes, planes, stride, downsample, use_se=self.use_se))
            self.inplanes = planes
            for i in range(1, blocks):
                layers.append(block(self.inplanes, planes, use_se=self.use_se))
    
            return nn.Sequential(*layers)
    
        def forward(self, x):
            x = self.conv1(x)
            x = self.bn1(x)
            x = self.prelu(x)
            x = self.maxpool(x)
    
            x = self.layer1(x)
            x = self.layer2(x)
            x = self.layer3(x)
            x = self.layer4(x)
            x = self.bn4(x)
            x = self.dropout(x)
            x = x.view(x.size(0), -1)
            x = self.fc5(x)
            x = self.bn5(x)
    
            return x
    
    
    
    class ResNet(nn.Module):
    
        def __init__(self, block, layers):
            self.inplanes = 64
            super(ResNet, self).__init__()
            # self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
            #                        bias=False)
            self.conv1 = nn.Conv2d(1, 64, kernel_size=3, stride=1, padding=1,
                                   bias=False)
            self.bn1 = nn.BatchNorm2d(64)
            self.relu = nn.ReLU(inplace=True)
            # self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
            self.layer1 = self._make_layer(block, 64, layers[0], stride=2)
            self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
            self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
            self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
            self.fc5 = nn.Linear(512 * 8 * 8, 512)
    
            for m in self.modules():
                if isinstance(m, nn.Conv2d):
                    nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
                elif isinstance(m, nn.BatchNorm2d):
                    nn.init.constant_(m.weight, 1)
                    nn.init.constant_(m.bias, 0)
    
        def _make_layer(self, block, planes, blocks, stride=1):
            downsample = None
            if stride != 1 or self.inplanes != planes * block.expansion:
                downsample = nn.Sequential(
                    nn.Conv2d(self.inplanes, planes * block.expansion,
                              kernel_size=1, stride=stride, bias=False),
                    nn.BatchNorm2d(planes * block.expansion),
                )
    
            layers = []
            layers.append(block(self.inplanes, planes, stride, downsample))
            self.inplanes = planes * block.expansion
            for i in range(1, blocks):
                layers.append(block(self.inplanes, planes))
    
            return nn.Sequential(*layers)
    
        def forward(self, x):
            x = self.conv1(x)
            x = self.bn1(x)
            x = self.relu(x)
            # x = self.maxpool(x)
    
            x = self.layer1(x)
            x = self.layer2(x)
            x = self.layer3(x)
            x = self.layer4(x)
            # x = nn.AvgPool2d(kernel_size=x.size()[2:])(x)
            # x = self.avgpool(x)
            x = x.view(x.size(0), -1)
            x = self.fc5(x)
    
            return x
    
    
    def resnet18(pretrained=False, **kwargs):
        """Constructs a ResNet-18 model.
        Args:
            pretrained (bool): If True, returns a model pre-trained on ImageNet
        """
        model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)
        if pretrained:
            model.load_state_dict(model_zoo.load_url(model_urls['resnet18']))
        return model
    
    def resnet_face18(use_se=True, **kwargs):
        model = ResNetFace(IRBlock, [2, 2, 2, 2], use_se=use_se, **kwargs)
        return model

def get_lfw_list(pair_list):
        with open(pair_list, 'r') as fd:
            pairs = fd.readlines()
        data_list = []
        for pair in pairs:
            splits = pair.split()
    
            if splits[0] not in data_list:
                data_list.append(splits[0])
    
            if splits[1] not in data_list:
                data_list.append(splits[1])
        return data_list
    
    
    def load_image(img_path):
        image = cv2.imread(img_path, 0)
        if image is None:
            return None
        image = np.dstack((image, np.fliplr(image)))
        image = image.transpose((2, 0, 1))
        image = image[:, np.newaxis, :, :]
        image = image.astype(np.float32, copy=False)
        image -= 127.5
        image /= 127.5
        return image
    
    
    def get_featurs(model, test_list, batch_size=10):
        images = None
        features = None
        cnt = 0
        for i, img_path in enumerate(test_list):
            image = load_image(img_path)
            if image is None:
                print('read {} error'.format(img_path))
    
            if images is None:
                images = image
            else:
                images = np.concatenate((images, image), axis=0)
    
            if images.shape[0] % batch_size == 0 or i == len(test_list) - 1:
                cnt += 1
    
                data = torch.from_numpy(images)
                data = data.to(device)
                output = model(data)
                output = output.data.cpu().numpy()
    
                fe_1 = output[::2]
                fe_2 = output[1::2]
                feature = np.hstack((fe_1, fe_2))
                # print(feature.shape)
    
                if features is None:
                    features = feature
                else:
                    features = np.vstack((features, feature))
    
                images = None
    
        return features, cnt
    
    
    def load_model(model, model_path):
        model_dict = model.state_dict()
        pretrained_dict = torch.load(model_path)
        pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict}
        model_dict.update(pretrained_dict)
        model.load_state_dict(model_dict)
    
    
    def get_feature_dict(test_list, features):
        fe_dict = {}
        for i, each in enumerate(test_list):
            # key = each.split('/')[1]
            fe_dict[each] = features[i]
        return fe_dict
    
    
    def cosin_metric(x1, x2):
        return np.dot(x1, x2) / (np.linalg.norm(x1) * np.linalg.norm(x2))
    
    
    def cal_accuracy(y_score, y_true):
        y_score = np.asarray(y_score)
        y_true = np.asarray(y_true)
        best_acc = 0
        best_th = 0
        for i in range(len(y_score)):
            th = y_score[i]
            y_test = (y_score >= th)
            acc = np.mean((y_test == y_true).astype(int))
            if acc > best_acc:
                best_acc = acc
                best_th = th
    
        return (best_acc, best_th)
    
    
    def test_performance(fe_dict, pair_list):
        with open(pair_list, 'r') as fd:
            pairs = fd.readlines()
    
        sims = []
        labels = []
        for pair in pairs:
            splits = pair.split()
            fe_1 = fe_dict[splits[0]]
            fe_2 = fe_dict[splits[1]]
            label = int(splits[2])
            sim = cosin_metric(fe_1, fe_2)
    
            sims.append(sim)
            labels.append(label)
    
        acc, th = cal_accuracy(sims, labels)
        return acc, th
    
    
    def lfw_test(model, img_paths, identity_list, compair_list, batch_size):
        s = time.time()
        features, cnt = get_featurs(model, img_paths, batch_size=batch_size)
        #print(features.shape)
        t = time.time() - s
        #print('共用时间 {}, average time is {}'.format(t, t / cnt))
        fe_dict = get_feature_dict(identity_list, features)
        acc, th = test_performance(fe_dict, compair_list)
        print('准确率: ', acc, '阀值: ', th)
        return acc

opt = Config()
    model = resnet_face18(opt.use_se)
    #采用多GPU的数据并行处理机制
    model = DataParallel(model)
    #装载预训练模型
    model.load_state_dict(torch.load(opt.test_model_path))
    model.to(device)
    
    identity_list = get_lfw_list(opt.lfw_test_list)
    img_paths = [os.path.join(opt.lfw_root, each) for each in identity_list]
    
    model.eval()
    lfw_test(model, img_paths, identity_list, opt.lfw_test_list, opt.test_batch_size)

![20200310201304918.png][]

#img_path='/home/wumg/data/data/other_my_face_align/others/Wen_Jiabao/Wen_Jiabao_0002.jpg'
    img_path='/home/wumg/data/data/other_my_face_align/others/my/myf241.jpg'
    #img_path='/home/wumg/data/data/dataset/lfw/lfw-align-128/Wen_Jiabao/Wen_Jiabao_0002.jpg'
    image = cv2.imread(img_path, 0)
    if image is None:
        print("ok")
    
    image = np.dstack((image, np.fliplr(image)))
    image = image.transpose((2, 0, 1))
    image = image[:, np.newaxis, :, :]
    image = image.astype(np.float32, copy=False)
    image -= 127.5
    image /= 127.5
    
    image.shape

![20200310201314308.png][]

from __future__ import print_function
    import os
    import cv2
    from models import *
    import torch
    import numpy as np
    import time
    #from config import Config
    from torch.nn import DataParallel
    
    
    def get_lfw_list(pair_list):
        with open(pair_list, 'r') as fd:
            pairs = fd.readlines()
        data_list = []
        for pair in pairs:
            splits = pair.split()
    
            if splits[0] not in data_list:
                data_list.append(splits[0])
    
            if splits[1] not in data_list:
                data_list.append(splits[1])
        return data_list
    
    
    def load_image(img_path):
        image = cv2.imread(img_path, 0)
        if image is None:
            return None
        image = np.dstack((image, np.fliplr(image)))
        image = image.transpose((2, 0, 1))
        image = image[:, np.newaxis, :, :]
        image = image.astype(np.float32, copy=False)
        image -= 127.5
        image /= 127.5
        return image
    
    
    def get_featurs(model, test_list, batch_size=10):
        images = None
        features = None
        cnt = 0
        for i, img_path in enumerate(test_list):
            image = load_image(img_path)
            if image is None:
                print('read {} error'.format(img_path))
    
            if images is None:
                images = image
            else:
                images = np.concatenate((images, image), axis=0)
    
            if images.shape[0] % batch_size == 0 or i == len(test_list) - 1:
                cnt += 1
    
                data = torch.from_numpy(images)
                data = data.to(torch.device("cuda"))
                output = model(data)
                output = output.data.cpu().numpy()
    
                fe_1 = output[::2]
                fe_2 = output[1::2]
                feature = np.hstack((fe_1, fe_2))
                # print(feature.shape)
    
                if features is None:
                    features = feature
                else:
                    features = np.vstack((features, feature))
    
                images = None
    
        return features, cnt
    
    
    def load_model(model, model_path):
        model_dict = model.state_dict()
        pretrained_dict = torch.load(model_path)
        pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict}
        model_dict.update(pretrained_dict)
        model.load_state_dict(model_dict)
    
    
    def get_feature_dict(test_list, features):
        fe_dict = {}
        for i, each in enumerate(test_list):
            # key = each.split('/')[1]
            fe_dict[each] = features[i]
        return fe_dict
    
    
    def cosin_metric(x1, x2):
        return np.dot(x1, x2) / (np.linalg.norm(x1) * np.linalg.norm(x2))
    
    
    def cal_accuracy(y_score, y_true):
        y_score = np.asarray(y_score)
        y_true = np.asarray(y_true)
        best_acc = 0
        best_th = 0
        for i in range(len(y_score)):
            th = y_score[i]
            y_test = (y_score >= th)
            acc = np.mean((y_test == y_true).astype(int))
            if acc > best_acc:
                best_acc = acc
                best_th = th
    
        return (best_acc, best_th)
    
    
    def test_performance(fe_dict, pair_list):
        with open(pair_list, 'r') as fd:
            pairs = fd.readlines()
    
        sims = []
        labels = []
        for pair in pairs:
            splits = pair.split()
            fe_1 = fe_dict[splits[0]]
            fe_2 = fe_dict[splits[1]]
            label = int(splits[2])
            sim = cosin_metric(fe_1, fe_2)
    
            sims.append(sim)
            labels.append(label)
    
        acc, th = cal_accuracy(sims, labels)
        return acc, th
    
    
    def lfw_test(model, img_paths, identity_list, compair_list, batch_size):
        s = time.time()
        features, cnt = get_featurs(model, img_paths, batch_size=batch_size)
        print(features.shape)
        t = time.time() - s
        print('total time is {}, average time is {}'.format(t, t / cnt))
        fe_dict = get_feature_dict(identity_list, features)
        acc, th = test_performance(fe_dict, compair_list)
        print('lfw face verification accuracy: ', acc, 'threshold: ', th)
        return acc
    
    
    if __name__ == '__main__':
    
        opt = Config()
        if opt.backbone == 'resnet18':
            model = resnet_face18(opt.use_se)
        elif opt.backbone == 'resnet34':
            model = resnet34()
        elif opt.backbone == 'resnet50':
            model = resnet50()
    
        model = DataParallel(model)
        # load_model(model, opt.test_model_path)
        model.load_state_dict(torch.load(opt.test_model_path))
        model.to(torch.device("cuda"))
    
        identity_list = get_lfw_list(opt.lfw_test_list)
        img_paths = [os.path.join(opt.lfw_root, each) for each in identity_list]
    
        model.eval()
        lfw_test(model, img_paths, identity_list, opt.lfw_test_list, opt.test_batch_size)

![20200310201326966.png][]

[20191009191333910.png]: https://img-blog.csdnimg.cn/20191009191333910.png
[Link 1]: http://www.rimengshe.com/
[AI_Keras PyTorch MXNet TensorFlow PaddlePaddle]: https://blog.csdn.net/zimiao552147572/article/details/88867161
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[20200310201304918.png]: https://img-blog.csdnimg.cn/20200310201304918.png
[20200310201314308.png]: https://img-blog.csdnimg.cn/20200310201314308.png
[20200310201326966.png]: https://img-blog.csdnimg.cn/20200310201326966.png