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python神經(jīng)網(wǎng)絡(luò)facenet人臉檢測(cè)及keras實(shí)現(xiàn)

 更新時(shí)間：2022年05月10日 18:36:07   作者：Bubbliiiing  
這篇文章主要為大家介紹了python神經(jīng)網(wǎng)絡(luò)facenet人臉檢測(cè)及keras實(shí)現(xiàn)，有需要的朋友可以借鑒參考下，希望能夠有所幫助，祝大家多多進(jìn)步，早日升職加薪

什么是facenet

最近學(xué)了我最喜歡的mtcnn，可是光有人臉有啥用啊，咱得知道who啊，開(kāi)始facenet提取特征之旅。

谷歌人臉檢測(cè)算法，發(fā)表于 CVPR 2015，利用相同人臉在不同角度等姿態(tài)的照片下有高內(nèi)聚性，不同人臉有低耦合性，提出使用 cnn + triplet mining 方法，在 LFW 數(shù)據(jù)集上準(zhǔn)確度達(dá)到 99.63%。

通過(guò) CNN 將人臉映射到歐式空間的特征向量上，實(shí)質(zhì)上：不同圖片人臉特征的距離較大；通過(guò)相同個(gè)體的人臉的距離，總是小于不同個(gè)體的人臉這一先驗(yàn)知識(shí)訓(xùn)練網(wǎng)絡(luò)。

測(cè)試時(shí)只需要計(jì)算人臉特征EMBEDDING，然后計(jì)算距離使用閾值即可判定兩張人臉照片是否屬于相同的個(gè)體。

簡(jiǎn)單來(lái)講，在使用階段，facenet即是：

1、輸入一張人臉圖片

2、通過(guò)深度學(xué)習(xí)網(wǎng)絡(luò)提取特征

3、L2標(biāo)準(zhǔn)化

4、得到128維特征向量。

代碼下載鏈接：https://pan.baidu.com/s/1T2b5u2mZ9yMtKt3TvLxTaw 

提取碼：xmg0 

Inception-ResNetV1

Inception-ResNetV1是facenet使用的主干網(wǎng)絡(luò)。

它的結(jié)構(gòu)很有意思！

如圖所示為整個(gè)網(wǎng)絡(luò)的主干結(jié)構(gòu)：

可以看到里面的結(jié)構(gòu)分為幾個(gè)重要的部分

1、stem

2、Inception-resnet-A

3、Inception-resnet-B

4、Inception-resnet-C

1、Stem的結(jié)構(gòu)：

在facenet里，它的Input為160x160x3大小，輸入后進(jìn)行：

兩次卷積 -> 一次最大池化 -> 兩次卷積

python實(shí)現(xiàn)代碼如下：

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的結(jié)構(gòu)：

Inception-resnet-A的結(jié)構(gòu)分為四個(gè)分支

1、未經(jīng)處理直接輸出

2、經(jīng)過(guò)一次1x1的32通道的卷積處理

3、經(jīng)過(guò)一次1x1的32通道的卷積處理和一次3x3的32通道的卷積處理

4、經(jīng)過(guò)一次1x1的32通道的卷積處理和兩次3x3的32通道的卷積處理

234步的結(jié)果堆疊后j進(jìn)行一次卷積，并與第一步的結(jié)果相加，實(shí)質(zhì)上這是一個(gè)殘差網(wǎng)絡(luò)結(jié)構(gòu)。

實(shí)現(xiàn)代碼如下：

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的結(jié)構(gòu)：

Inception-resnet-B的結(jié)構(gòu)分為四個(gè)分支

1、未經(jīng)處理直接輸出

2、經(jīng)過(guò)一次1x1的128通道的卷積處理

3、經(jīng)過(guò)一次1x1的128通道的卷積處理、一次1x7的128通道的卷積處理和一次7x1的128通道的卷積處理

23步的結(jié)果堆疊后j進(jìn)行一次卷積，并與第一步的結(jié)果相加，實(shí)質(zhì)上這是一個(gè)殘差網(wǎng)絡(luò)結(jié)構(gòu)。

實(shí)現(xiàn)代碼如下：

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的結(jié)構(gòu)：

Inception-resnet-B的結(jié)構(gòu)分為四個(gè)分支

1、未經(jīng)處理直接輸出

2、經(jīng)過(guò)一次1x1的128通道的卷積處理

3、經(jīng)過(guò)一次1x1的192通道的卷積處理、一次1x3的192通道的卷積處理和一次3x1的128通道的卷積處理

23步的結(jié)果堆疊后j進(jìn)行一次卷積，并與第一步的結(jié)果相加，實(shí)質(zhì)上這是一個(gè)殘差網(wǎng)絡(luò)結(jié)構(gòu)。

實(shí)現(xiàn)代碼如下：

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、全部代碼

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)
    # 全連接層到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)
    # 創(chuàng)建模型
    model = Model(inputs, x, name='inception_resnet_v1')
    return model

檢測(cè)人臉并實(shí)現(xiàn)比較：

利用opencv自帶的cv2.CascadeClassifier檢測(cè)人臉并實(shí)現(xiàn)人臉的比較：根目錄擺放方式如下：

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
#---------------------------------#
#   圖片預(yù)處理
#   高斯歸一化
#---------------------------------#
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標(biāo)準(zhǔn)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
#---------------------------------#
#   計(jì)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
#---------------------------------#
#   獲取人臉框
#---------------------------------#
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)
#---------------------------------#
#   計(jì)算人臉距離
#---------------------------------#
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)))

實(shí)現(xiàn)效果為：

[0.6534328]
[1.3536944]

以上就是python神經(jīng)網(wǎng)絡(luò)facenet人臉檢測(cè)及keras實(shí)現(xiàn)的詳細(xì)內(nèi)容，更多關(guān)于facenet人臉檢測(cè)keras實(shí)現(xiàn)的資料請(qǐng)關(guān)注腳本之家其它相關(guān)文章！

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