python神經網絡facenet人臉檢測及keras實現(xiàn)
更新時間：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神經網絡facenet人臉檢測及keras實現(xiàn)，有需要的朋友可以借鑒參考下，希望能夠有所幫助，祝大家多多進步，早日升職加薪
什么是facenet
最近學了我最喜歡的mtcnn，可是光有人臉有啥用啊，咱得知道who啊，開始facenet提取特征之旅。
谷歌人臉檢測算法，發(fā)表于 CVPR 2015，利用相同人臉在不同角度等姿態(tài)的照片下有高內聚性，不同人臉有低耦合性，提出使用 cnn + triplet mining 方法，在 LFW 數據集上準確度達到 99.63%。
通過 CNN 將人臉映射到歐式空間的特征向量上，實質上：不同圖片人臉特征的距離較大；通過相同個體的人臉的距離，總是小于不同個體的人臉這一先驗知識訓練網絡。
測試時只需要計算人臉特征EMBEDDING，然后計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體。
簡單來講，在使用階段，facenet即是：
1、輸入一張人臉圖片
2、通過深度學習網絡提取特征
3、L2標準化
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使用的主干網絡。
它的結構很有意思！
如圖所示為整個網絡的主干結構：
可以看到里面的結構分為幾個重要的部分
1、stem
2、Inception-resnet-A
3、Inception-resnet-B
4、Inception-resnet-C
1、Stem的結構：
在facenet里，它的Input為160x160x3大小，輸入后進行：
兩次卷積 -> 一次最大池化 -> 兩次卷積
python實現(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的結構：
Inception-resnet-A的結構分為四個分支
1、未經處理直接輸出
2、經過一次1x1的32通道的卷積處理
3、經過一次1x1的32通道的卷積處理和一次3x3的32通道的卷積處理
4、經過一次1x1的32通道的卷積處理和兩次3x3的32通道的卷積處理
234步的結果堆疊后j進行一次卷積，并與第一步的結果相加，實質上這是一個殘差網絡結構。
實現(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的結構：
Inception-resnet-B的結構分為四個分支
1、未經處理直接輸出
2、經過一次1x1的128通道的卷積處理
3、經過一次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步的結果堆疊后j進行一次卷積，并與第一步的結果相加，實質上這是一個殘差網絡結構。
實現(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的結構：
Inception-resnet-B的結構分為四個分支
1、未經處理直接輸出
2、經過一次1x1的128通道的卷積處理
3、經過一次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步的結果堆疊后j進行一次卷積，并與第一步的結果相加，實質上這是一個殘差網絡結構。
實現(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
檢測人臉并實現(xiàn)比較：
利用opencv自帶的cv2.CascadeClassifier檢測人臉并實現(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標準化
#---------------------------------#
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)))
實現(xiàn)效果為：
[0.6534328]
[1.3536944]
以上就是python神經網絡facenet人臉檢測及keras實現(xiàn)的詳細內容，更多關于facenet人臉檢測keras實現(xiàn)的資料請關注腳本之家其它相關文章！
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