caffe 提取特徵caffe介面
//CaffeExFeat.h
#ifndef CAFFEEXFEAT_H
#define CAFFEEXFEAT_H
#include "caffe/caffe.hpp"
#include <string>
#include <vector>
#include "opencv2/opencv.hpp"
https://blog.csdn.net/AP1005834/article/details/79820947
using namespace caffe;
class CaffeExFeat
{
public:
explicit CaffeExFeat(std::string proto,std::string model,char* nameLayer,std::string meanFile,float scale=-1);
explicit CaffeExFeat(std::string proto,std::string model,char* nameLayer,float v1=0.0,float v2=0.0,float v3=0.0,float scale=-1);
~CaffeExFeat();
double*extractFeat(const cv::Mat& img);
double calSimilarity(const cv::Mat& img1 ,const cv::Mat& img2);
private:
unsigned int blob_id_;
boost::shared_ptr< Net<float> > net_;
cv::Size input_geometry_;
int num_channels_;
cv::Mat mean_;
Blob<float>* input_blobs_;
unsigned int featNum_;
float scale_;
void init(std::string proto,std::string model,float scale);
void getMeanData(std::string mean_file);
void getMeanData(float v1,float v2,float v3 );
unsigned int get_blob_index( char *query_blob_name);
void wrapInputLayer(std::vector<cv::Mat>* input_channels);
void preprocess(const cv::Mat& img,std::vector<cv::Mat>* input_channels);
};
#endif
//CaffeExFeat.cpp
#include "CaffeExFeat.h"
CaffeExFeat::CaffeExFeat(std::string proto,std::string model, char* nLayer,std::string meanFile,
float scale)
{
init(proto,model,scale);
getMeanData(meanFile);
blob_id_ = get_blob_index(nLayer);
}
CaffeExFeat::CaffeExFeat(std::string proto,std::string model, char* nLayer,float v1,float v2,float v3,
float scale)
{
init(proto,model,scale);
getMeanData(v1,v2,v3);
blob_id_ = get_blob_index(nLayer);
}
void CaffeExFeat::init(std::string proto,std::string model,float scale)
{
scale_=scale;
Phase phase = TEST;
Caffe::set_mode(Caffe::CPU);
net_ = boost::shared_ptr< Net<float> >(new caffe::Net<float>(proto, phase));
net_->CopyTrainedLayersFrom(model);
input_blobs_ = net_->input_blobs()[0];
num_channels_ = input_blobs_->channels();
input_geometry_ = cv::Size(input_blobs_->width(),input_blobs_->height());
input_blobs_->Reshape(1, num_channels_,input_geometry_.height, input_geometry_.width); //難道可輸入多張圖
net_->Reshape(); //維度改變
}
CaffeExFeat::~CaffeExFeat()
{
}
double* CaffeExFeat::extractFeat(const cv::Mat& img)
{
std::vector<cv::Mat> input_channels;
wrapInputLayer(&input_channels);
preprocess(img, &input_channels);
net_->ForwardPrefilled();
boost::shared_ptr<Blob<float> > featBlob = net_->blobs()[blob_id_];
featNum_ = featBlob->count();
const float *featData = (const float *) featBlob->cpu_data();
double* out = new double[featNum_];
for(int k=0;k<featNum_;++k) out[k]=featData[k];
return out;
}
double CaffeExFeat::calSimilarity(const cv::Mat& img1 ,const cv::Mat& img2)
{
double* feat_1 = extractFeat(img1);
double* feat_2 = extractFeat(img2);
double sim = cblas_ddot(featNum_,feat_1,1,feat_2,1)/(std::sqrt(cblas_ddot(featNum_,feat_1,1,feat_1,1))*std::sqrt(cblas_ddot(featNum_,feat_2,1,feat_2,1)));
delete []feat_1;
delete []feat_2;
return sim;
}
void CaffeExFeat::getMeanData(std::string mean_file)
{
BlobProto blob_proto;
ReadProtoFromBinaryFileOrDie(mean_file.c_str(), &blob_proto);
/* Convert from BlobProto to Blob<float> */
Blob<float> mean_blob;
mean_blob.FromProto(blob_proto);
/* The format of the mean file is planar 32-bit float BGR or grayscale. */
std::vector<cv::Mat> channels;
float* data = mean_blob.mutable_cpu_data();
for (int i = 0; i < num_channels_; ++i) {
/* Extract an individual channel. */
cv::Mat channel(mean_blob.height(), mean_blob.width(), CV_32FC1, data);
channels.push_back(channel);
data += mean_blob.height() * mean_blob.width();
}
/* Merge the separate channels into a single image. */
cv::Mat mean;
cv::merge(channels, mean);
/* Compute the global mean pixel value and create a mean image
* filled with this value. */
cv::Scalar channel_mean = cv::mean(mean);
mean_ = cv::Mat(input_geometry_, mean.type(), channel_mean);
}
void CaffeExFeat::getMeanData(float v1,float v2, float v3)
{
cv::Scalar channel_mean(v1,v2,v3);
mean_ = cv::Mat(input_geometry_,CV_32FC3,channel_mean );
}
unsigned int CaffeExFeat::get_blob_index( char *query_blob_name)
{
std::string str_query(query_blob_name);
vector< string > const & blob_names = net_->blob_names();
for( unsigned int i = 0; i != blob_names.size(); ++i )
{
if( str_query == blob_names[i] )
{
return i;
}
}
LOG(FATAL) << "Unknown blob name: " << str_query;
}
void CaffeExFeat::wrapInputLayer(std::vector<cv::Mat>* input_channels) {
Blob<float>* input_layer = net_->input_blobs()[0];
int width = input_layer->width();
int height = input_layer->height();
float* input_data = input_layer->mutable_cpu_data();
for (int i = 0; i < input_layer->channels(); ++i) {
cv::Mat channel(height, width, CV_32FC1, input_data);
input_channels->push_back(channel);
input_data += width * height;
}
}
void CaffeExFeat::preprocess(const cv::Mat& img,std::vector<cv::Mat>* input_channels)
{
cv::Mat sample;
if (img.channels() == 3 && num_channels_ == 1)
cv::cvtColor(img, sample, CV_BGR2GRAY);
else if (img.channels() == 4 && num_channels_ == 1)
cv::cvtColor(img, sample, CV_BGRA2GRAY);
else if (img.channels() == 4 && num_channels_ == 3)
cv::cvtColor(img, sample, CV_BGRA2BGR);
else if (img.channels() == 1 && num_channels_ == 3)
cv::cvtColor(img, sample, CV_GRAY2BGR);
else
sample = img;
cv::Mat sample_resized;
if (sample.size() != input_geometry_)
cv::resize(sample, sample_resized, input_geometry_);
else
sample_resized = sample;
cv::Mat sample_float;
if (num_channels_ == 3)
sample_resized.convertTo(sample_float, CV_32FC3);
else
sample_resized.convertTo(sample_float, CV_32FC1);
cv::Mat sample_normalized;
cv::subtract(sample_float, mean_, sample_normalized);
if(scale_!=-1){
cv::multiply(scale_ ,sample_normalized,sample_normalized);
}
cv::split(sample_normalized, *input_channels);
}
參考<深度學習21天實戰caffe>,P136,所用到的boost需要是boot_1_58_0版本。
編寫檔案 net_demo.cpp，並儲存在/home/sf/demo下：
#include <vector>
#include <iostream>
#include "caffe/net.hpp"
using namespace caffe;
using namespace std;
int main(void)
{
std::string proto("deploy.prototxt");
Net<float> nn(proto,caffe::TEST);
vector<string> bn=nn.blob_names();
for(int i=0;i<bn.size();i++)
{
cout<<"Blob #"<<i<<" : "<<bn[i]<<endl;
}
return 0;
}
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---------------------
作者：woneil
來源：CSDN
原文：https://blog.csdn.net/ahbbshenfeng/article/details/52077605
版權宣告：本文為博主原創文章，轉載請附上博文連結！
Net在Caffe中代表一個完整的CNN模型，它包含若干個Layer例項。前面看到的各類prototxt的經典網路結構如LeNet、AlexNet等都是Caffe程式碼實現的一個Net物件。
1、Net基本用法
#include <caffe\net.hpp>
在main（）中新增
std::string proto("deploy.prototxt");
Net<float> nn(proto, caffe::TEST);
vector<string> bn = nn.blob_names(); //獲取Net中所有blob物件名
vector<string> ln = nn.layer_names();
for (int i = 0; i < bn.size(); i++)
{
cout << "Blob #" << i << ":" << bn[i] << endl;
}
for (int j = 0; j < ln.size(); j++)
{
cout << "Layer #" << j << ":" << ln[j] << endl;
}
執行結果如下：
F0103 13:36:44.474290 5904 cudnn_conv_layer.cpp:53] Check failed: status == CUDNN_STATUS_SUCCESS (6 vs. 0) CUDNN_STATUS_ARCH_MISMATCH
*** Check failure stack trace: ***
由於我的GPU硬體不支援cuda 3.0以上版本，關閉掉前處理器中的USE_CUDNN（修改CommonSettings.props檔案相應選項）。可以看到結果如下：
I0103 13:54:55.282647 13792 net.cpp:283] Network initialization done.
Blob #0:data
Blob #1:conv1
Blob #2:pool1
Blob #3:norm1
Blob #4:conv2
Blob #5:pool2
Blob #6:norm2
Blob #7:conv3
Blob #8:conv4
Blob #9:conv5
Blob #10:pool5
Blob #11:fc6
Blob #12:fc7
Blob #13:fc8
Blob #14:prob
Layer #0:data
Layer #1:conv1
Layer #2:relu1
Layer #3:pool1
Layer #4:norm1
Layer #5:conv2
Layer #6:relu2
Layer #7:pool2
Layer #8:norm2
Layer #9:conv3
Layer #10:relu3
Layer #11:conv4
Layer #12:relu4
Layer #13:conv5
Layer #14:relu5
Layer #15:pool5
Layer #16:fc6
Layer #17:relu6
Layer #18:drop6
Layer #19:fc7
Layer #20:relu7
Layer #21:drop7
Layer #22:fc8
Layer #23:prob
---------------------
作者：阿爾法旺旺
來源：CSDN
原文：https://blog.csdn.net/yingwei13mei/article/details/53997389
版權宣告：本文為博主原創文章，轉載請附上博文連結！