前言
基于深度学习的人脸识别系统,一共用到了5个开源库:OpenCV(计算机视觉库)、Caffe(深度学习库)、Dlib(机器学习库)、libfacedetection(人脸检测库)、cudnn(gpu加速库)。
用到了一个开源的深度学习模型:VGG model。
最终的效果是很赞的,识别一张人脸的速度是0.039秒,而且最重要的是:精度高啊!!!
CPU:intel i5-4590
GPU:GTX 980
系统:Win 10
OpenCV版本:3.1(这个无所谓)
Caffe版本:Microsoft caffe (微软编译的Caffe,安装方便,在这里安利一波)
Dlib版本:19.0(也无所谓
CUDA版本:7.5
cudnn版本:4
libfacedetection:6月份之后的(这个有所谓,6月后出了64位版本的)
这个系列纯C++构成,有问题的各位朋同学可以直接在博客下留言,我们互相交流学习。
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本篇是该系列的第三篇博客,介绍如何使用VGG网络模型与Caffe的 MemoryData层去提取一个OpenCV矩阵类型Mat的特征。
思路
VGG网络模型是牛津大学视觉几何组提出的一种深度模型,在LFW数据库上取得了97%的准确率。VGG网络由5个卷积层,两层fc图像特征,一层fc分类特征组成,具体我们可以去读它的prototxt文件。这里是模型与配置文件的下载地址。
http://www.robots.ox.ac.uk/~vgg/software/vgg_face/
话题回到Caffe。在Caffe中提取图片的特征是很容易的,其提供了extract_feature.exe让我们来实现,提取格式为lmdb与leveldb。关于这个的做法,可以看我的这篇博客:
/mr_curry/article/details/52097529
显然,我们在程序中肯定是希望能够灵活利用的,使用这种方法不太可行。Caffe的Data层提供了type:MemoryData,我们可以使用它来进行Mat类型特征的提取。
注:你需要先按照本系列第一篇博客的方法去配置好Caffe的属性表。
/mr_curry/article/details/52443126
实现
首先我们打开VGG_FACE_deploy.prototxt,观察VGG的网络结构。
有意思的是,MemoryData层需要图像均值,但是官方网站上并没有给出mean文件。我们可以通过这种方式进行输入:
mean_value:129.1863mean_value:104.7624mean_value:93.5940
我们还需要修改它的data层:(你可以用下面这部分的代码去替换下载下来的prototxt文件的data层)
layer {name: "data"type: "MemoryData"top: "data"top: "label"transform_param {mirror: falsecrop_size: 224mean_value:129.1863mean_value:104.7624mean_value:93.5940}memory_data_param {batch_size: 1channels:3height:224width:224}}
为了不破坏原来的文件,把它另存为vgg_extract_feature_memorydata.prototxt。
好的,然后我们开始编写。添加好这个属性表:
然后,新建caffe_net_memorylayer.h、ExtractFeature_.h、ExtractFeature_.cpp开始编写。
caffe_net_memorylayer.h:
#include "caffe/layers/input_layer.hpp" #include "caffe/layers/inner_product_layer.hpp" #include "caffe/layers/dropout_layer.hpp" #include "caffe/layers/conv_layer.hpp" #include "caffe/layers/relu_layer.hpp" #include <iostream> #include "caffe/caffe.hpp"#include <opencv.hpp>#include <caffe/layers/memory_data_layer.hpp>#include "caffe/layers/pooling_layer.hpp" #include "caffe/layers/lrn_layer.hpp" #include "caffe/layers/softmax_layer.hpp" // must predefinedcaffe::MemoryDataLayer<float> *memory_layer;caffe::Net<float>* net;
ExtractFeature_.h
#include <opencv.hpp>using namespace cv;using namespace std;std::vector<float> ExtractFeature(Mat FaceROI);//给一个图片 返回一个vector<float>容器void Caffe_Predefine();
ExtractFeature_.cpp:
#include <ExtractFeature_.h>#include <caffe_net_memorylayer.h>namespace caffe{extern INSTANTIATE_CLASS(InputLayer);extern INSTANTIATE_CLASS(InnerProductLayer);extern INSTANTIATE_CLASS(DropoutLayer);extern INSTANTIATE_CLASS(ConvolutionLayer);REGISTER_LAYER_CLASS(Convolution);extern INSTANTIATE_CLASS(ReLULayer);REGISTER_LAYER_CLASS(ReLU);extern INSTANTIATE_CLASS(PoolingLayer);REGISTER_LAYER_CLASS(Pooling);extern INSTANTIATE_CLASS(LRNLayer);REGISTER_LAYER_CLASS(LRN);extern INSTANTIATE_CLASS(SoftmaxLayer);REGISTER_LAYER_CLASS(Softmax);extern INSTANTIATE_CLASS(MemoryDataLayer);}template <typename Dtype>caffe::Net<Dtype>* Net_Init_Load(std::string param_file, std::string pretrained_param_file, caffe::Phase phase){caffe::Net<Dtype>* net(new caffe::Net<Dtype>("vgg_extract_feature_memorydata.prototxt", caffe::TEST));net->CopyTrainedLayersFrom("VGG_FACE.caffemodel");return net;}void Caffe_Predefine()//when our code begining run must add it{caffe::Caffe::set_mode(caffe::Caffe::GPU);net = Net_Init_Load<float>("vgg_extract_feature_memorydata.prototxt", "VGG_FACE.caffemodel", caffe::TEST);memory_layer = (caffe::MemoryDataLayer<float> *)net->layers()[0].get();}std::vector<float> ExtractFeature(Mat FaceROI){caffe::Caffe::set_mode(caffe::Caffe::GPU);std::vector<Mat> test;std::vector<int> testLabel;std::vector<float> test_vector;test.push_back(FaceROI);testLabel.push_back(0);memory_layer->AddMatVector(test, testLabel);// memory_layer and net , must be define be a global variable.test.clear(); testLabel.clear();std::vector<caffe::Blob<float>*> input_vec;net->Forward(input_vec);boost::shared_ptr<caffe::Blob<float>> fc8 = net->blob_by_name("fc8");int test_num = 0;while (test_num < 2622){test_vector.push_back(fc8->data_at(0, test_num++, 1, 1));}return test_vector;}
=============注意上面这个地方可以这么改:==============
(直接可以知道这个向量的首地址、尾地址,我们直接用其来定义vector)
float* begin = nullptr;float* end = nullptr;begin = fc8->mutable_cpu_data();end = begin + fc8->channels();CHECK(begin != nullptr);CHECK(end != nullptr);std::vector<float> FaceVector{ begin,end };return std::move(FaceVector);
请特别注意这个地方:
namespace caffe{extern INSTANTIATE_CLASS(InputLayer);extern INSTANTIATE_CLASS(InnerProductLayer);extern INSTANTIATE_CLASS(DropoutLayer);extern INSTANTIATE_CLASS(ConvolutionLayer);REGISTER_LAYER_CLASS(Convolution);extern INSTANTIATE_CLASS(ReLULayer);REGISTER_LAYER_CLASS(ReLU);extern INSTANTIATE_CLASS(PoolingLayer);REGISTER_LAYER_CLASS(Pooling);extern INSTANTIATE_CLASS(LRNLayer);REGISTER_LAYER_CLASS(LRN);extern INSTANTIATE_CLASS(SoftmaxLayer);REGISTER_LAYER_CLASS(Softmax);extern INSTANTIATE_CLASS(MemoryDataLayer);}
为什么要加这些?因为在提取过程中发现,如果不加,会导致有一些层没有注册的情况。我在Github的Microsoft/Caffe上帮一外国哥们解决了这个问题。我把问题展现一下:
如果我们加了上述代码,就相当于注册了这些层,自然就不会有这样的问题。
在提取过程中,我提取的是fc8层的特征,2622维。当然,最后一层都已经是分类特征了,最好还是提取fc7层的4096维特征。
在这个地方:
void Caffe_Predefine()//when our code begining run must add it{caffe::Caffe::set_mode(caffe::Caffe::GPU);net = Net_Init_Load<float>("vgg_extract_feature_memorydata.prototxt", "VGG_FACE.caffemodel", caffe::TEST);memory_layer = (caffe::MemoryDataLayer<float> *)net->layers()[0].get();}
是一个初始化的函数,用于将VGG网络模型与提取特征的配置文件进行传入,所以很明显地,在提取特征之前,需要先:
Caffe_Predefine();
进行了这个之后,这些全局量我们就能一直用了。
我们可以试试提取特征的这个接口。新建一个main.cpp,调用之:
#include <ExtractFeature_.h>int main(){Caffe_Predefine();Mat lena = imread("lena.jpg");if (!lena.empty()){ExtractFeature(lena);}}
因为我们得到的是一个vector< float>类型,所以我们可以把它逐一输出出来看看。当然,在ExtractFeature()的函数中你就可以这么做了。我们还是在main()函数里这么做。
来看看:
#include <ExtractFeature_.h>int main(){Caffe_Predefine();Mat lena = imread("lena.jpg");if (!lena.empty()){int i = 0;vector<float> print=ExtractFeature(lena);while (i<print.size()){cout << print[i++] << endl;}}imshow("Extract feature",lena);waitKey(0);}
那么对于这张图片,提取出的特征,就是很多的这些数字:
提取一张224*224图片特征的时间为:0.019s。我们可以看到,GPU加速的效果是非常明显的。而且我这块显卡也就是GTX980。不知道泰坦X的提取速度如何(泪)。
附:net结构 (prototxt),注意layer和layers的区别:
name: "VGG_FACE_16_layer"layer {name: "data"type: "MemoryData"top: "data"top: "label"transform_param {mirror: falsecrop_size: 224mean_value:129.1863mean_value:104.7624mean_value:93.5940}memory_data_param {batch_size: 1channels:3height:224width:224}}layer {bottom: "data"top: "conv1_1"name: "conv1_1"type: "Convolution"convolution_param {num_output: 64pad: 1kernel_size: 3}}layer {bottom: "conv1_1"top: "conv1_1"name: "relu1_1"type: "ReLU"}layer {bottom: "conv1_1"top: "conv1_2"name: "conv1_2"type: "Convolution"convolution_param {num_output: 64pad: 1kernel_size: 3}}layer {bottom: "conv1_2"top: "conv1_2"name: "relu1_2"type: "ReLU"}layer {bottom: "conv1_2"top: "pool1"name: "pool1"type: "Pooling"pooling_param {pool: MAXkernel_size: 2stride: 2}}layer {bottom: "pool1"top: "conv2_1"name: "conv2_1"type: "Convolution"convolution_param {num_output: 128pad: 1kernel_size: 3}}layer {bottom: "conv2_1"top: "conv2_1"name: "relu2_1"type: "ReLU"}layer {bottom: "conv2_1"top: "conv2_2"name: "conv2_2"type: "Convolution"convolution_param {num_output: 128pad: 1kernel_size: 3}}layer {bottom: "conv2_2"top: "conv2_2"name: "relu2_2"type: "ReLU"}layer {bottom: "conv2_2"top: "pool2"name: "pool2"type: "Pooling"pooling_param {pool: MAXkernel_size: 2stride: 2}}layer {bottom: "pool2"top: "conv3_1"name: "conv3_1"type: "Convolution"convolution_param {num_output: 256pad: 1kernel_size: 3}}layer {bottom: "conv3_1"top: "conv3_1"name: "relu3_1"type: "ReLU"}layer {bottom: "conv3_1"top: "conv3_2"name: "conv3_2"type: "Convolution"convolution_param {num_output: 256pad: 1kernel_size: 3}}layer {bottom: "conv3_2"top: "conv3_2"name: "relu3_2"type: "ReLU"}layer {bottom: "conv3_2"top: "conv3_3"name: "conv3_3"type: "Convolution"convolution_param {num_output: 256pad: 1kernel_size: 3}}layer {bottom: "conv3_3"top: "conv3_3"name: "relu3_3"type: "ReLU"}layer {bottom: "conv3_3"top: "pool3"name: "pool3"type: "Pooling"pooling_param {pool: MAXkernel_size: 2stride: 2}}layer {bottom: "pool3"top: "conv4_1"name: "conv4_1"type: "Convolution"convolution_param {num_output: 512pad: 1kernel_size: 3}}layer {bottom: "conv4_1"top: "conv4_1"name: "relu4_1"type: "ReLU"}layer {bottom: "conv4_1"top: "conv4_2"name: "conv4_2"type: "Convolution"convolution_param {num_output: 512pad: 1kernel_size: 3}}layer {bottom: "conv4_2"top: "conv4_2"name: "relu4_2"type: "ReLU"}layer {bottom: "conv4_2"top: "conv4_3"name: "conv4_3"type: "Convolution"convolution_param {num_output: 512pad: 1kernel_size: 3}}layer {bottom: "conv4_3"top: "conv4_3"name: "relu4_3"type: "ReLU"}layer {bottom: "conv4_3"top: "pool4"name: "pool4"type: "Pooling"pooling_param {pool: MAXkernel_size: 2stride: 2}}layer {bottom: "pool4"top: "conv5_1"name: "conv5_1"type: "Convolution"convolution_param {num_output: 512pad: 1kernel_size: 3}}layer {bottom: "conv5_1"top: "conv5_1"name: "relu5_1"type: "ReLU"}layer {bottom: "conv5_1"top: "conv5_2"name: "conv5_2"type: "Convolution"convolution_param {num_output: 512pad: 1kernel_size: 3}}layer {bottom: "conv5_2"top: "conv5_2"name: "relu5_2"type: "ReLU"}layer {bottom: "conv5_2"top: "conv5_3"name: "conv5_3"type: "Convolution"convolution_param {num_output: 512pad: 1kernel_size: 3}}layer {bottom: "conv5_3"top: "conv5_3"name: "relu5_3"type: "ReLU"}layer {bottom: "conv5_3"top: "pool5"name: "pool5"type: "Pooling"pooling_param {pool: MAXkernel_size: 2stride: 2}}layer {bottom: "pool5"top: "fc6"name: "fc6"type: "InnerProduct"inner_product_param {num_output: 4096}}layer {bottom: "fc6"top: "fc6"name: "relu6"type: "ReLU"}layer {bottom: "fc6"top: "fc6"name: "drop6"type: "Dropout"dropout_param {dropout_ratio: 0.5}}layer {bottom: "fc6"top: "fc7"name: "fc7"type: "InnerProduct"inner_product_param {num_output: 4096}}layer {bottom: "fc7"top: "fc7"name: "relu7"type: "ReLU"}layer {bottom: "fc7"top: "fc7"name: "drop7"type: "Dropout"dropout_param {dropout_ratio: 0.5}}layer {bottom: "fc7"top: "fc8"name: "fc8"type: "InnerProduct"inner_product_param {num_output: 2622}}layer {bottom: "fc8"top: "prob"name: "prob"type: "Softmax"}
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基于深度学习的人脸识别系统系列(Caffe+OpenCV+Dlib)——【三】使用Caffe的MemoryData层与VGG网络模型提取Mat的特征 完结,如果在代码过程中出现了任何问题,直接在博客下留言即可,共同交流学习。
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