caffe - - 在windows上实施 前向

caffe-window搭建自己的小项目例子

           手头有一个实际的视觉检测的项目，用的是caffe来分类，于是需要用caffe新建自己的项目的例子。在网上找了好久都没有找到合适的，于是自己开始弄。

1 首先是配置caffe的VC++目录中的include和库文件。配置include lib dll都是坑，而且还分debug和release两个版本。添加输入项目需要注意，而且需要把编译好的caffe.lib等等一系列东西拷贝到当前项目下。也就是caffe bulid文件夹下面的东西，包括caffe.lib 、libcaffe.lib、还有很多dll.

这个是debug_include配置图

这个是debug_lib配置图

这个是release_include配置图

这个是release_lib配置图

同时也需要在，项目属性页的链接器输入中，填写相应的lib，其中debug和release是不同的。以下是需要填写的相应lib

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//debug opencv_calib3d2413d.lib opencv_contrib2413d.lib opencv_core2413d.lib opencv_features2d2413d.lib opencv_flann2413d.lib opencv_gpu2413d.lib opencv_highgui2413d.lib opencv_imgproc2413d.lib opencv_legacy2413d.lib opencv_ml2413d.lib opencv_objdetect2413d.lib opencv_ts2413d.lib opencv_video2413d.lib caffe.lib libcaffe.lib cudart.lib cublas.lib curand.lib gflagsd.lib libglog.lib libopenblas.dll.a libprotobuf.lib leveldb.lib hdf5.lib hdf5_hl.lib Shlwapi.lib //release opencv_calib3d2413.lib opencv_contrib2413.lib opencv_core2413.lib opencv_features2d2413.lib opencv_flann2413.lib opencv_gpu2413.lib opencv_highgui2413.lib opencv_imgproc2413.lib opencv_legacy2413.lib opencv_ml2413.lib opencv_objdetect2413.lib opencv_ts2413.lib opencv_video2413.lib caffe.lib libcaffe.lib cudart.lib cublas.lib curand.lib gflags.lib libglog.lib libopenblas.dll.a libprotobuf.lib leveldb.lib lmdb.lib hdf5.lib hdf5_hl.lib Shlwapi.lib

 2 新建一个Classifier的c++类，其中头文件为

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#include "stdafx.h"   #include <caffe caffe.hpp=""> #include <opencv2 core="" core.hpp=""> #include <opencv2 highgui="" highgui.hpp=""> #include <opencv2 imgproc="" imgproc.hpp=""> #include <algorithm> #include <iosfwd> #include <memory> #include <string> #include <utility> #include <vector>     #pragma once   using  namespace  caffe;   // NOLINT(build/namespaces) using  std::string; //using namespace boost; 注意不需要添加这个   /* Pair (label, confidence) representing a prediction. */ typedef  std::pair<string,  float = "" > Prediction;     class  Classifier { public :      Classifier( const  string& model_file,          const  string& trained_file,          const  string& mean_file,          const  string& label_file);        std::vector<prediction> Classify( const  cv::Mat& img,  int  N = 5);        ~Classifier();   private :      void  SetMean( const  string& mean_file);        std::vector< float > Predict( const  cv::Mat& img);        void  WrapInputLayer(std::vector<cv::mat>* input_channels);        void  Preprocess( const  cv::Mat& img,          std::vector<cv::mat>* input_channels);   private :      boost::shared_ptr<net< float > > net_;      cv::Size input_geometry_;      int  num_channels_;      cv::Mat mean_;      std::vector<string> labels_; }; </string></net< float ></cv::mat></cv::mat></ float ></prediction></string,></vector></utility></string></memory></iosfwd></algorithm></opencv2></opencv2></opencv2></caffe>

　　c++文件为

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#include "stdafx.h" #include "Classifier.h"       Classifier::Classifier( const  string& model_file,      const  string& trained_file,      const  string& mean_file,      const  string& label_file) { #ifdef CPU_ONLY      Caffe::set_mode(Caffe::CPU); #else      Caffe::set_mode(Caffe::GPU); #endif        /* Load the network. */      net_.reset( new  Net< float >(model_file, TEST));      net_->CopyTrainedLayersFrom(trained_file);        CHECK_EQ(net_->num_inputs(), 1) <<  "Network should have exactly one input." ;      CHECK_EQ(net_->num_outputs(), 1) <<  "Network should have exactly one output." ;        Blob< float >* input_layer = net_->input_blobs()[0];      num_channels_ = input_layer->channels();      CHECK(num_channels_ == 3 || num_channels_ == 1)          <<  "Input layer should have 1 or 3 channels." ;      input_geometry_ = cv::Size(input_layer->width(), input_layer->height());        /* Load the binaryproto mean file. */      SetMean(mean_file);        /* Load labels. */      std::ifstream labels(label_file.c_str());      CHECK(labels) <<  "Unable to open labels file "  << label_file;      string line;      while  (std::getline(labels, line))          labels_.push_back(string(line));        Blob< float >* output_layer = net_->output_blobs()[0];      CHECK_EQ(labels_.size(), output_layer->channels())          <<  "Number of labels is different from the output layer dimension." ; }   static  bool  PairCompare( const  std::pair< float ,  int >& lhs,      const  std::pair< float ,  int >& rhs) {      return  lhs.first > rhs.first; }   /* Return the indices of the top N values of vector v. */ static  std::vector< int > Argmax( const  std::vector< float >& v,  int  N) {      std::vector<std::pair< float ,  int > > pairs;      for  ( size_t  i = 0; i < v.size(); ++i)          pairs.push_back(std::make_pair(v[i],  static_cast < int >(i)));      std::partial_sort(pairs.begin(), pairs.begin() + N, pairs.end(), PairCompare);        std::vector< int > result;      for  ( int  i = 0; i < N; ++i)          result.push_back(pairs[i].second);      return  result; }   /* Return the top N predictions. */ std::vector<Prediction> Classifier::Classify( const  cv::Mat& img,  int  N) {      std::vector< float > output = Predict(img);        N = std::min< int >(labels_.size(), N);      std::vector< int > maxN = Argmax(output, N);      std::vector<Prediction> predictions;      for  ( int  i = 0; i < N; ++i) {          int  idx = maxN[i];          predictions.push_back(std::make_pair(labels_[idx], output[idx]));      }        return  predictions; }   /* Load the mean file in binaryproto format. */ void  Classifier::SetMean( const  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);      CHECK_EQ(mean_blob.channels(), num_channels_)          <<  "Number of channels of mean file doesn't match input layer." ;        /* 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); }   std::vector< float > Classifier::Predict( const  cv::Mat& img) {      Blob< float >* input_layer = net_->input_blobs()[0];      input_layer->Reshape(1, num_channels_,          input_geometry_.height, input_geometry_.width);      /* Forward dimension change to all layers. */      net_->Reshape();        std::vector<cv::Mat> input_channels;      WrapInputLayer(&input_channels);        Preprocess(img, &input_channels);        net_->Forward();        /* Copy the output layer to a std::vector */      Blob< float >* output_layer = net_->output_blobs()[0];      const  float * begin = output_layer->cpu_data();      const  float * end = begin + output_layer->channels();      return  std::vector< float >(begin, end); }   /* Wrap the input layer of the network in separate cv::Mat objects * (one per channel). This way we save one memcpy operation and we * don't need to rely on cudaMemcpy2D. The last preprocessing * operation will write the separate channels directly to the input * layer. */ void  Classifier::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  Classifier::Preprocess( const  cv::Mat& img,      std::vector<cv::Mat>* input_channels) {      /* Convert the input image to the input image format of the network. */      cv::Mat sample;      if  (img.channels() == 3 && num_channels_ == 1)          cv::cvtColor(img, sample, cv::COLOR_BGR2GRAY);      else  if  (img.channels() == 4 && num_channels_ == 1)          cv::cvtColor(img, sample, cv::COLOR_BGRA2GRAY);      else  if  (img.channels() == 4 && num_channels_ == 3)          cv::cvtColor(img, sample, cv::COLOR_BGRA2BGR);      else  if  (img.channels() == 1 && num_channels_ == 3)          cv::cvtColor(img, sample, cv::COLOR_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);        /* This operation will write the separate BGR planes directly to the      * input layer of the network because it is wrapped by the cv::Mat      * objects in input_channels. */      cv::split(sample_normalized, *input_channels);        CHECK( reinterpret_cast < float *>(input_channels->at(0).data)          == net_->input_blobs()[0]->cpu_data())          <<  "Input channels are not wrapping the input layer of the network." ; }   Classifier::~Classifier() { }

　　c++,文件来自于\caffe-master\examples\cpp_classification中的classification.cpp文件

3 直接编译后会出现的问题是F0519 14:54:12.494139 14504 layer_factory.hpp:77] Check failed: registry.count(t ype) == 1 (0 vs. 1) Unknown layer type: Input (known types: Input ),百度后发现是要加头文件！http://blog.youkuaiyun.com/fangjin_kl/article/details/50936952#0-tsina-1-63793-397232819ff9a47a7b7e80a40613cfe1

因此安装上面说的新建一个head.h    

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#include "caffe/common.hpp" #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 "caffe/layers/pooling_layer.hpp" #include "caffe/layers/lrn_layer.hpp" #include "caffe/layers/softmax_layer.hpp"     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);   }

注意上述网络可能不全，需要根据实际的网络添加层。参考

 

  View Code

 

 

 

4 出现的第二个问题是有些符号GLOG_NO_ABBREVIATED_SEVERITIES未定义，因此在项目属性页 c++预处理器中添加下面两个：

GLOG_NO_ABBREVIATED_SEVERITIES
_SCL_SECURE_NO_WARNINGS

5 同时需要把

#include <caffe/proto/caffe.pb.h>
#include "head.h"

这两个头文件放到stdafx.h中，必须放到里面。

6 编译通过后，编写测试分类的程序，首先加载caffermodle.

string model_file = "D:\\caffe\\caffe-master\\mypower\\deploy.prototxt";//prototxt 这个必须是depoly，这个是计算输出的类别概率
string trained_file = "D:\\caffe\\caffe-master\\mypower_iter_2000.caffemodel"; //这个是训练好的model
string mean_file = "D:\\caffe\\caffe-master\\mypower\\imagenet_mean.binaryproto";//这个是均值文件
string label_file ="D:\\caffe\\caffe-master\\mypower\\label.txt"; //这个是样本标签 ,如果两类，可以新建一个txt文件，里面写作如下

1 2	0 good 1 bad

定义一个指针  Classifier *classifier;
classifier = new Classifier(model_file, trained_file, mean_file, label_file);

 分类程序：

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cv::Mat img(roiimage, 0); //加载图像   //CHECK(!img.empty()) << "Unable to decode image " ;   std::vector<Prediction> predictions = classifier->Classify(img);   /* Print the top N predictions. */   string precision_p0= "" ;   for  ( size_t  i = 0; i < predictions.size()-1; ++i) //只输出了概率最大的那一类,通常就是第一类   {    Prediction p = predictions[i];    precision_p0 = p.first;    std::cout << std::fixed << std::setprecision(4) << p.second <<  " - \""    << p.first <<  "\""  << std::endl;<br>   }   char  firstc = precision_p0[0];   if  (firstc ==  '0' ) //第一类正样本 好的<br>   {    //AfxMessageBox("good");              }   else  //第二类负样本 存在缺陷   {    //AfxMessageBox("bad");         }