深度学习caffe中的分类程序（classification）制作成动态链接库（dll）

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最近因公司项目需要把caffe中的classification分类程序融入到自己的项目程序中，因此把Caffe中的classification封装成dll供源程序进行调用。经过两天的不懈努力，终于把caffe中的分类程序封装成dll，以供项目程序调用。在此中间遇到各种库依赖的问题，在这进行相应的总结，以备文档留存。

第一：在VS2013下新建Win32控制台程序，命名为ClassJpg，下一步，勾选Dll与空项目，点击完成。

第二：点击属性VC++目录在包含目录中添加以下头文件路径，同时将caffe原工程的include目录复制到 caffe-class\include中。

// Debug & RleaseE:\caffe-class\includeE:\caffe-class\NugetPackages\gflags.2.1.2.1\build\native\includeE:\caffe-class\NugetPackages\glog.0.3.3.0\build\native\includeC:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0\includeE:\caffe-class\NugetPackages\protobuf-v120.2.6.1\build\native\includeE:\caffe-class\NugetPackages\OpenCV.2.4.10\build\native\includeE:\caffe-class\NugetPackages\OpenBLAS.0.2.14.1\lib\native\includeE:\caffe-class\NugetPackages\boost.1.59.0.0\lib\native\include

第三：为工程添加相应的lib，点击属性VC++目录在库目录中添加以下库文件路径；

/ DebugE:\caffe-class\caffelibE:\caffe-class\NugetPackages\OpenCV.2.4.10\build\native\lib\x64\v120\DebugC:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0\lib\x64E:\caffe-class\NugetPackages\gflags.2.1.2.1\build\native\x64\v120\dynamic\LibE:\caffe-class\NugetPackages\glog.0.3.3.0\build\native\lib\x64\v120\Debug\dynamicE:\caffe-class\NugetPackages\OpenBLAS.0.2.14.1\lib\native\lib\x64E:\caffe-class\NugetPackages\protobuf-v120.2.6.1\build\native\lib\x64\v120\DebugE:\caffe-class\NugetPackages\LevelDB-vc120.1.2.0.0\build\native\lib\x64\v120\DebugE:\caffe-class\NugetPackages\hdf5-v120-complete.1.8.15.2\lib\native\lib\x64E:\caffe-class\NugetPackages\boost_date_time-vc120.1.59.0.0\lib\native\address-model-64\libE:\caffe-class\NugetPackages\boost_filesystem-vc120.1.59.0.0\lib\native\address-model-64\libE:\caffe-class\NugetPackages\boost_system-vc120.1.59.0.0\lib\native\address-model-64\libE:\caffe-class\NugetPackages\boost_thread-vc120.1.59.0.0\lib\native\address-model-64\libE:\caffe-class\NugetPackages\boost_chrono-vc120.1.59.0.0\lib\native\address-model-64\lib

第四：添加相关的库依赖，打开属性-连接器-输入-附加依赖项中输入以下内容；

// Debuglibglog.lib libcaffe.lib         gflagsd.lib   gflags_nothreadsd.lib hdf5.lib         hdf5_hl.lib     libprotobuf.lib     libopenblas.dll.a  cublas.lib cuda.lib   curand.lib cudart.lib cudnn.lib Shlwapi.libLevelDb.lib lmdbD.lib opencv_core2410d.lib       opencv_highgui2410d.lib    opencv_imgproc2410d.lib   opencv_video2410d.lib      opencv_objdetect2410d.lib// Releaselibglog.liblibcaffe.libgflags.libgflags_nothreads.libhdf5.libhdf5_hl.liblibprotobuf.liblibopenblas.dll.acublas.libcuda.libcurand.libcudart.libcudnn.libShlwapi.libLevelDb.liblmdb.libopencv_core2410.libopencv_highgui2410.libopencv_imgproc2410.libopencv_video2410.libopencv_objdetect2410.lib

第五：在属性—C/C++—预处理器中添加以下命令；
_SCL_SECURE_NO_WARNINGS
第六：新建头文件命名为class-jpg-caffe.h，输入以下的类定义：
#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 onceusing namespace caffe;  // NOLINT(build/namespaces)using std::string;/* Pair (label, confidence) representing a prediction. */typedef std::pair<string, float> Prediction;class  _declspec(dllexport)  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:shared_ptr<Net<float> > net_;cv::Size input_geometry_;int num_channels_;cv::Mat mean_;std::vector<string> labels_;};
第七：新建头文件head.h，输入以下语句；
#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);}第八：新建源文件命名为class-jpg-caffe.cpp，输入以下语句；#include "class-jpg-caffe.h"#include "head.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);//#endifCaffe::set_mode(Caffe::GPU);/* 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);elsesample = img;cv::Mat sample_resized;if (sample.size() != input_geometry_)cv::resize(sample, sample_resized, input_geometry_);elsesample_resized = sample;cv::Mat sample_float;if (num_channels_ == 3)sample_resized.convertTo(sample_float, CV_32FC3);elsesample_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(){｝
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