脚本之家 服务器常用软件
快捷导航
您的位置：首页 → 脚本专栏 → python → facenet人脸检测keras

python神经网络facenet人脸检测及keras实现

 更新时间：2022年05月10日 18:36:07   作者：Bubbliiiing  
这篇文章主要为大家介绍了python神经网络facenet人脸检测及keras实现，有需要的朋友可以借鉴参考下，希望能够有所帮助，祝大家多多进步，早日升职加薪

什么是facenet

最近学了我最喜欢的mtcnn，可是光有人脸有啥用啊，咱得知道who啊，开始facenet提取特征之旅。

谷歌人脸检测算法，发表于 CVPR 2015，利用相同人脸在不同角度等姿态的照片下有高内聚性，不同人脸有低耦合性，提出使用 cnn + triplet mining 方法，在 LFW 数据集上准确度达到 99.63%。

通过 CNN 将人脸映射到欧式空间的特征向量上，实质上：不同图片人脸特征的距离较大；通过相同个体的人脸的距离，总是小于不同个体的人脸这一先验知识训练网络。

测试时只需要计算人脸特征EMBEDDING，然后计算距离使用阈值即可判定两张人脸照片是否属于相同的个体。

简单来讲，在使用阶段，facenet即是：

1、输入一张人脸图片

2、通过深度学习网络提取特征

3、L2标准化

4、得到128维特征向量。

代码下载链接：https://pan.baidu.com/s/1T2b5u2mZ9yMtKt3TvLxTaw 

提取码：xmg0 

Inception-ResNetV1

Inception-ResNetV1是facenet使用的主干网络。

它的结构很有意思！

如图所示为整个网络的主干结构：

可以看到里面的结构分为几个重要的部分

1、stem

2、Inception-resnet-A

3、Inception-resnet-B

4、Inception-resnet-C

1、Stem的结构：

在facenet里，它的Input为160x160x3大小，输入后进行：

两次卷积 -> 一次最大池化 -> 两次卷积

python实现代码如下：

inputs = Input(shape=input_shape)
# 160,160,3 -> 77,77,64
x = conv2d_bn(inputs, 32, 3, strides=2, padding='valid', name='Conv2d_1a_3x3')
x = conv2d_bn(x, 32, 3, padding='valid', name='Conv2d_2a_3x3')
x = conv2d_bn(x, 64, 3, name='Conv2d_2b_3x3')
# 77,77,64 -> 38,38,64
x = MaxPooling2D(3, strides=2, name='MaxPool_3a_3x3')(x)
# 38,38,64 -> 17,17,256
x = conv2d_bn(x, 80, 1, padding='valid', name='Conv2d_3b_1x1')
x = conv2d_bn(x, 192, 3, padding='valid', name='Conv2d_4a_3x3')
x = conv2d_bn(x, 256, 3, strides=2, padding='valid', name='Conv2d_4b_3x3')

2、Inception-resnet-A的结构：

Inception-resnet-A的结构分为四个分支

1、未经处理直接输出

2、经过一次1x1的32通道的卷积处理

3、经过一次1x1的32通道的卷积处理和一次3x3的32通道的卷积处理

4、经过一次1x1的32通道的卷积处理和两次3x3的32通道的卷积处理

234步的结果堆叠后j进行一次卷积，并与第一步的结果相加，实质上这是一个残差网络结构。

实现代码如下：

branch_0 = conv2d_bn(x, 32, 1, name=name_fmt('Conv2d_1x1', 0))
branch_1 = conv2d_bn(x, 32, 1, name=name_fmt('Conv2d_0a_1x1', 1))
branch_1 = conv2d_bn(branch_1, 32, 3, name=name_fmt('Conv2d_0b_3x3', 1))
branch_2 = conv2d_bn(x, 32, 1, name=name_fmt('Conv2d_0a_1x1', 2))
branch_2 = conv2d_bn(branch_2, 32, 3, name=name_fmt('Conv2d_0b_3x3', 2))
branch_2 = conv2d_bn(branch_2, 32, 3, name=name_fmt('Conv2d_0c_3x3', 2))
branches = [branch_0, branch_1, branch_2]
mixed = Concatenate(axis=channel_axis, name=name_fmt('Concatenate'))(branches)
up = conv2d_bn(mixed,K.int_shape(x)[channel_axis],1,activation=None,use_bias=True,
                name=name_fmt('Conv2d_1x1'))
up = Lambda(scaling,
            output_shape=K.int_shape(up)[1:],
            arguments={'scale': scale})(up)
x = add([x, up])
if activation is not None:
    x = Activation(activation, name=name_fmt('Activation'))(x)

3、Inception-resnet-B的结构：

Inception-resnet-B的结构分为四个分支

1、未经处理直接输出

2、经过一次1x1的128通道的卷积处理

3、经过一次1x1的128通道的卷积处理、一次1x7的128通道的卷积处理和一次7x1的128通道的卷积处理

23步的结果堆叠后j进行一次卷积，并与第一步的结果相加，实质上这是一个残差网络结构。

实现代码如下：

branch_0 = conv2d_bn(x, 128, 1, name=name_fmt('Conv2d_1x1', 0))
branch_1 = conv2d_bn(x, 128, 1, name=name_fmt('Conv2d_0a_1x1', 1))
branch_1 = conv2d_bn(branch_1, 128, [1, 7], name=name_fmt('Conv2d_0b_1x7', 1))
branch_1 = conv2d_bn(branch_1, 128, [7, 1], name=name_fmt('Conv2d_0c_7x1', 1))
branches = [branch_0, branch_1]
mixed = Concatenate(axis=channel_axis, name=name_fmt('Concatenate'))(branches)
up = conv2d_bn(mixed,K.int_shape(x)[channel_axis],1,activation=None,use_bias=True,
                name=name_fmt('Conv2d_1x1'))
up = Lambda(scaling,
            output_shape=K.int_shape(up)[1:],
            arguments={'scale': scale})(up)
x = add([x, up])
if activation is not None:
    x = Activation(activation, name=name_fmt('Activation'))(x)

4、Inception-resnet-C的结构：

Inception-resnet-B的结构分为四个分支

1、未经处理直接输出

2、经过一次1x1的128通道的卷积处理

3、经过一次1x1的192通道的卷积处理、一次1x3的192通道的卷积处理和一次3x1的128通道的卷积处理

23步的结果堆叠后j进行一次卷积，并与第一步的结果相加，实质上这是一个残差网络结构。

实现代码如下：

branch_0 = conv2d_bn(x, 192, 1, name=name_fmt('Conv2d_1x1', 0))
branch_1 = conv2d_bn(x, 192, 1, name=name_fmt('Conv2d_0a_1x1', 1))
branch_1 = conv2d_bn(branch_1, 192, [1, 3], name=name_fmt('Conv2d_0b_1x3', 1))
branch_1 = conv2d_bn(branch_1, 192, [3, 1], name=name_fmt('Conv2d_0c_3x1', 1))
branches = [branch_0, branch_1]
mixed = Concatenate(axis=channel_axis, name=name_fmt('Concatenate'))(branches)
up = conv2d_bn(mixed,K.int_shape(x)[channel_axis],1,activation=None,use_bias=True,
                name=name_fmt('Conv2d_1x1'))
up = Lambda(scaling,
            output_shape=K.int_shape(up)[1:],
            arguments={'scale': scale})(up)
x = add([x, up])
if activation is not None:
    x = Activation(activation, name=name_fmt('Activation'))(x)

5、全部代码

from functools import partial
from keras.models import Model
from keras.layers import Activation
from keras.layers import BatchNormalization
from keras.layers import Concatenate
from keras.layers import Conv2D
from keras.layers import Dense
from keras.layers import Dropout
from keras.layers import GlobalAveragePooling2D
from keras.layers import Input
from keras.layers import Lambda
from keras.layers import MaxPooling2D
from keras.layers import add
from keras import backend as K
def scaling(x, scale):
    return x * scale
def _generate_layer_name(name, branch_idx=None, prefix=None):
    if prefix is None:
        return None
    if branch_idx is None:
        return '_'.join((prefix, name))
    return '_'.join((prefix, 'Branch', str(branch_idx), name))
def conv2d_bn(x,filters,kernel_size,strides=1,padding='same',activation='relu',use_bias=False,name=None):
    x = Conv2D(filters,
               kernel_size,
               strides=strides,
               padding=padding,
               use_bias=use_bias,
               name=name)(x)
    if not use_bias:
        x = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001,
                               scale=False, name=_generate_layer_name('BatchNorm', prefix=name))(x)
    if activation is not None:
        x = Activation(activation, name=_generate_layer_name('Activation', prefix=name))(x)
    return x
def _inception_resnet_block(x, scale, block_type, block_idx, activation='relu'):
    channel_axis = 3
    if block_idx is None:
        prefix = None
    else:
        prefix = '_'.join((block_type, str(block_idx)))
    name_fmt = partial(_generate_layer_name, prefix=prefix)
    if block_type == 'Block35':
        branch_0 = conv2d_bn(x, 32, 1, name=name_fmt('Conv2d_1x1', 0))
        branch_1 = conv2d_bn(x, 32, 1, name=name_fmt('Conv2d_0a_1x1', 1))
        branch_1 = conv2d_bn(branch_1, 32, 3, name=name_fmt('Conv2d_0b_3x3', 1))
        branch_2 = conv2d_bn(x, 32, 1, name=name_fmt('Conv2d_0a_1x1', 2))
        branch_2 = conv2d_bn(branch_2, 32, 3, name=name_fmt('Conv2d_0b_3x3', 2))
        branch_2 = conv2d_bn(branch_2, 32, 3, name=name_fmt('Conv2d_0c_3x3', 2))
        branches = [branch_0, branch_1, branch_2]
    elif block_type == 'Block17':
        branch_0 = conv2d_bn(x, 128, 1, name=name_fmt('Conv2d_1x1', 0))
        branch_1 = conv2d_bn(x, 128, 1, name=name_fmt('Conv2d_0a_1x1', 1))
        branch_1 = conv2d_bn(branch_1, 128, [1, 7], name=name_fmt('Conv2d_0b_1x7', 1))
        branch_1 = conv2d_bn(branch_1, 128, [7, 1], name=name_fmt('Conv2d_0c_7x1', 1))
        branches = [branch_0, branch_1]
    elif block_type == 'Block8':
        branch_0 = conv2d_bn(x, 192, 1, name=name_fmt('Conv2d_1x1', 0))
        branch_1 = conv2d_bn(x, 192, 1, name=name_fmt('Conv2d_0a_1x1', 1))
        branch_1 = conv2d_bn(branch_1, 192, [1, 3], name=name_fmt('Conv2d_0b_1x3', 1))
        branch_1 = conv2d_bn(branch_1, 192, [3, 1], name=name_fmt('Conv2d_0c_3x1', 1))
        branches = [branch_0, branch_1]
    mixed = Concatenate(axis=channel_axis, name=name_fmt('Concatenate'))(branches)
    up = conv2d_bn(mixed,K.int_shape(x)[channel_axis],1,activation=None,use_bias=True,
                   name=name_fmt('Conv2d_1x1'))
    up = Lambda(scaling,
                output_shape=K.int_shape(up)[1:],
                arguments={'scale': scale})(up)
    x = add([x, up])
    if activation is not None:
        x = Activation(activation, name=name_fmt('Activation'))(x)
    return x
def InceptionResNetV1(input_shape=(160, 160, 3),
                      classes=128,
                      dropout_keep_prob=0.8):
    channel_axis = 3
    inputs = Input(shape=input_shape)
    # 160,160,3 -> 77,77,64
    x = conv2d_bn(inputs, 32, 3, strides=2, padding='valid', name='Conv2d_1a_3x3')
    x = conv2d_bn(x, 32, 3, padding='valid', name='Conv2d_2a_3x3')
    x = conv2d_bn(x, 64, 3, name='Conv2d_2b_3x3')
    # 77,77,64 -> 38,38,64
    x = MaxPooling2D(3, strides=2, name='MaxPool_3a_3x3')(x)
    # 38,38,64 -> 17,17,256
    x = conv2d_bn(x, 80, 1, padding='valid', name='Conv2d_3b_1x1')
    x = conv2d_bn(x, 192, 3, padding='valid', name='Conv2d_4a_3x3')
    x = conv2d_bn(x, 256, 3, strides=2, padding='valid', name='Conv2d_4b_3x3')
    # 5x Block35 (Inception-ResNet-A block):
    for block_idx in range(1, 6):
        x = _inception_resnet_block(x,scale=0.17,block_type='Block35',block_idx=block_idx)
    # Reduction-A block:
    # 17,17,256 -> 8,8,896
    name_fmt = partial(_generate_layer_name, prefix='Mixed_6a')
    branch_0 = conv2d_bn(x, 384, 3,strides=2,padding='valid',name=name_fmt('Conv2d_1a_3x3', 0))
    branch_1 = conv2d_bn(x, 192, 1, name=name_fmt('Conv2d_0a_1x1', 1))
    branch_1 = conv2d_bn(branch_1, 192, 3, name=name_fmt('Conv2d_0b_3x3', 1))
    branch_1 = conv2d_bn(branch_1,256,3,strides=2,padding='valid',name=name_fmt('Conv2d_1a_3x3', 1))
    branch_pool = MaxPooling2D(3,strides=2,padding='valid',name=name_fmt('MaxPool_1a_3x3', 2))(x)
    branches = [branch_0, branch_1, branch_pool]
    x = Concatenate(axis=channel_axis, name='Mixed_6a')(branches)
    # 10x Block17 (Inception-ResNet-B block):
    for block_idx in range(1, 11):
        x = _inception_resnet_block(x,
                                    scale=0.1,
                                    block_type='Block17',
                                    block_idx=block_idx)
    # Reduction-B block
    # 8,8,896 -> 3,3,1792
    name_fmt = partial(_generate_layer_name, prefix='Mixed_7a')
    branch_0 = conv2d_bn(x, 256, 1, name=name_fmt('Conv2d_0a_1x1', 0))
    branch_0 = conv2d_bn(branch_0,384,3,strides=2,padding='valid',name=name_fmt('Conv2d_1a_3x3', 0))
    branch_1 = conv2d_bn(x, 256, 1, name=name_fmt('Conv2d_0a_1x1', 1))
    branch_1 = conv2d_bn(branch_1,256,3,strides=2,padding='valid',name=name_fmt('Conv2d_1a_3x3', 1))
    branch_2 = conv2d_bn(x, 256, 1, name=name_fmt('Conv2d_0a_1x1', 2))
    branch_2 = conv2d_bn(branch_2, 256, 3, name=name_fmt('Conv2d_0b_3x3', 2))
    branch_2 = conv2d_bn(branch_2,256,3,strides=2,padding='valid',name=name_fmt('Conv2d_1a_3x3', 2))
    branch_pool = MaxPooling2D(3,strides=2,padding='valid',name=name_fmt('MaxPool_1a_3x3', 3))(x)
    branches = [branch_0, branch_1, branch_2, branch_pool]
    x = Concatenate(axis=channel_axis, name='Mixed_7a')(branches)
    # 5x Block8 (Inception-ResNet-C block):
    for block_idx in range(1, 6):
        x = _inception_resnet_block(x,
                                    scale=0.2,
                                    block_type='Block8',
                                    block_idx=block_idx)
    x = _inception_resnet_block(x,scale=1.,activation=None,block_type='Block8',block_idx=6)
    # 平均池化
    x = GlobalAveragePooling2D(name='AvgPool')(x)
    x = Dropout(1.0 - dropout_keep_prob, name='Dropout')(x)
    # 全连接层到128
    x = Dense(classes, use_bias=False, name='Bottleneck')(x)
    bn_name = _generate_layer_name('BatchNorm', prefix='Bottleneck')
    x = BatchNormalization(momentum=0.995, epsilon=0.001, scale=False,
                           name=bn_name)(x)
    # 创建模型
    model = Model(inputs, x, name='inception_resnet_v1')
    return model

检测人脸并实现比较：

利用opencv自带的cv2.CascadeClassifier检测人脸并实现人脸的比较：根目录摆放方式如下：

demo文件如下：

import numpy as np
import cv2
from net.inception import InceptionResNetV1
from keras.models import load_model
import face_recognition
#---------------------------------#
#   图片预处理
#   高斯归一化
#---------------------------------#
def pre_process(x):
    if x.ndim == 4:
        axis = (1, 2, 3)
        size = x[0].size
    elif x.ndim == 3:
        axis = (0, 1, 2)
        size = x.size
    else:
        raise ValueError('Dimension should be 3 or 4')
    mean = np.mean(x, axis=axis, keepdims=True)
    std = np.std(x, axis=axis, keepdims=True)
    std_adj = np.maximum(std, 1.0/np.sqrt(size))
    y = (x - mean) / std_adj
    return y
#---------------------------------#
#   l2标准化
#---------------------------------#
def l2_normalize(x, axis=-1, epsilon=1e-10):
    output = x / np.sqrt(np.maximum(np.sum(np.square(x), axis=axis, keepdims=True), epsilon))
    return output
#---------------------------------#
#   计算128特征值
#---------------------------------#
def calc_128_vec(model,img):
    face_img = pre_process(img)
    pre = model.predict(face_img)
    pre = l2_normalize(np.concatenate(pre))
    pre = np.reshape(pre,[1,128])
    return pre
#---------------------------------#
#   获取人脸框
#---------------------------------#
def get_face_img(cascade,filepaths,margin):
    aligned_images = []
    img = cv2.imread(filepaths)
    img = cv2.cvtColor(img,cv2.COLOR_BGRA2RGB)
    faces = cascade.detectMultiScale(img,
                                        scaleFactor=1.1,
                                        minNeighbors=3)
    (x, y, w, h) = faces[0]
    print(x, y, w, h)
    cropped = img[y-margin//2:y+h+margin//2,
                    x-margin//2:x+w+margin//2, :]
    aligned = cv2.resize(cropped, (160, 160))
    aligned_images.append(aligned)
    return np.array(aligned_images)
#---------------------------------#
#   计算人脸距离
#---------------------------------#
def face_distance(face_encodings, face_to_compare):
    if len(face_encodings) == 0:
        return np.empty((0))
    return np.linalg.norm(face_encodings - face_to_compare, axis=1)
if __name__ == "__main__":
    cascade_path = './model/haarcascade_frontalface_alt2.xml'
    cascade = cv2.CascadeClassifier(cascade_path)
    image_size = 160
    model = InceptionResNetV1()
    # model.summary()
    model_path = './model/facenet_keras.h5'
    model.load_weights(model_path)
    img1 = get_face_img(cascade,r"img/Larry_Page_0000.jpg",10)
    img2 = get_face_img(cascade,r"img/Larry_Page_0001.jpg",10)
    img3 = get_face_img(cascade,r"img/Mark_Zuckerberg_0000.jpg",10)
    print(face_distance(calc_128_vec(model,img1),calc_128_vec(model,img2)))
    print(face_distance(calc_128_vec(model,img2),calc_128_vec(model,img3)))

实现效果为：

[0.6534328]
[1.3536944]

以上就是python神经网络facenet人脸检测及keras实现的详细内容，更多关于facenet人脸检测keras实现的资料请关注脚本之家其它相关文章！

您可能感兴趣的文章:

相关文章

  • Python汉字转拼音pypinyin库、输出excel的xlwt库

    Python汉字转拼音pypinyin库、输出excel的xlwt库

    本文介绍了如何使用Python的pypinyin库和xlwt库,将汉字文本转换为带有拼音标注的Excel文件,通过读取文本、获取拼音并写入Excel,实现了汉字和拼音的一一对应,同时,文章也指出了潜在的问题
    2025-04-04
  • Python3 虚拟开发环境搭建过程(图文详解)

    Python3 虚拟开发环境搭建过程(图文详解)

    这篇文章主要介绍了Python3 虚拟开发环境搭建过程，本文通过图文实例代码相结合给大家介绍的非常详细，具有一定的参考借鉴价值,需要的朋友可以参考下
    2020-01-01
  • Python运行的17个时新手常见错误小结

    Python运行的17个时新手常见错误小结

    当初学 Python 时，想要弄懂 Python 的错误信息的含义可能有点复杂。这里列出了常见的的一些让你程序 crash 的运行时错误
    2012-08-08
  • Python实现指定范围内筛选并剔除Excel表格中的数据

    Python实现指定范围内筛选并剔除Excel表格中的数据

    这篇文章主要为大家详细介绍了Python如何实现在指定范围内筛选并剔除Excel表格中的数据，文中的示例代码讲解详细，感兴趣的可以了解一下
    2023-06-06
  • PHP基于phpqrcode类库生成二维码过程解析

    PHP基于phpqrcode类库生成二维码过程解析

    这篇文章主要介绍了PHP基于phpqrcode类库生成二维码过程解析,文中通过示例代码介绍的非常详细，对大家的学习或者工作具有一定的参考学习价值,需要的朋友可以参考下
    2020-05-05
  • Python数据库编程之pymysql详解

    Python数据库编程之pymysql详解

    本文主要介绍了Python数据库编程中pymysql，文中通过示例代码介绍的非常详细，对大家的学习或者工作具有一定的参考学习价值，需要的朋友们下面随着小编来一起学习学习吧
    2023-05-05
  • Python实现简易的图书管理系统

    Python实现简易的图书管理系统

    这篇文章主要为大家详细介绍了Python实现简易的图书管理系统，文中示例代码介绍的非常详细，具有一定的参考价值，感兴趣的小伙伴们可以参考一下
    2022-03-03
  • python numpy--数组的组合和分割实例

    python numpy--数组的组合和分割实例

    这篇文章主要介绍了python numpy--数组的组合和分割实例，具有很好的参考价值，希望对大家有所帮助。一起跟随小编过来看看吧
    2020-02-02
  • python错误提示:Errno 2] No such file or directory的解决方法

    python错误提示:Errno 2] No such file or directory的解决方法

    我相信很多人在学习Python的时候,特别是在open文件的时候总还碰到,还报错IOError:[Errno 2]没有这样的文件或目录:'E：//aaa.txt',这篇文章主要给大家介绍了关于python错误提示:Errno 2] No such file or directory的解决方法,需要的朋友可以参考下
    2022-02-02
  • python词云库wordcloud的使用方法与实例详解

    python词云库wordcloud的使用方法与实例详解

    这篇文章主要介绍了python词云库wordcloud的使用方法与实例详解,需要的朋友可以参考下
    2020-02-02

最新评论