caffe - - 在windows上实施 前向

           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

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55

//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

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54

#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

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189

#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.csdn.net/fangjin_kl/article/details/50936952#0-tsina-1-63793-397232819ff9a47a7b7e80a40613cfe1

因此安装上面说的新建一个head.h    

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

#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

 

 

 

 

 

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：

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19

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");         }