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封装caffe-windows-cpu(支持模型有多个输出)
小編覺得挺不錯的,現(xiàn)在分享給大家,幫大家做個參考.
注意：該版本為CPU版本。
用到的caffe-windows來自：https://github.com/happynear/caffe-windows
先下載caffe-windows，解壓；然后下載第三方庫：https://pan.baidu.com/s/1eStyfrc??解壓到caffe-windows-master，看起來是這樣：caffe-windows-master\3rdparty
把3rdparty的bin加入環(huán)境變量或者復制里面的dll到build_cpu_only\caffelib下（cudnn的不需要）。
打開caffe-windows-master\src\caffe\proto，雙擊extract_proto.bat，然后用VS2013打開./build_cpu_only/MainBuilder.sln。請確保為Release x64
1.右鍵caffelib項目，重命名為：multi_recognition_cpu（按個人愛好，其他名字也行，不改也可以）；再右鍵該項目——>屬性——>配置屬性——>常規(guī)：
配置類型修改為動態(tài)庫(.dll)，目標擴展名修改為.dll。
2.C/C++——>常規(guī)：
附加包含目錄：
../../3rdparty/include
../../src
../../include
C/C++——>預處理器：
添加?MULTI_RECOGNITION_API_EXPORTS
3.鏈接器——>常規(guī)：
附加庫目錄：
../../3rdparty/lib
鏈接器——>輸入：
去掉cuda和cudnn的lib（cu開頭和cudnn開頭的lib）
4.修改net.hpp和net.cpp
為了支持模型多輸出，要知道輸出的順序，所以把輸出blob的名字輸出到控制臺，打開net.hpp，給Net類添加：
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protected:?? ?????std::vector<std::string>?outputblobnames;??
以及：
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public:?? ??inline?std::vector<std::string>?output_blobs_names()?const?? ??{?? ??????return?outputblobnames;?? ??}??
net.cpp修改：（最后一行，把輸出blob名字保存到vector中）
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for?(set<string>::iterator?it?=?available_blobs.begin();?? ??????it?!=?available_blobs.end();?++it)?{?? ????LOG_IF(INFO,?Caffe::root_solver())?? ????????<<?"This?network?produces?output?"?<<?*it;?? ????net_output_blobs_.push_back(blobs_[blob_name_to_idx[*it]].get());?? ????net_output_blob_indices_.push_back(blob_name_to_idx[*it]);?? ????outputblobnames.push_back(*it);?? ??}??
這樣，屬性就配置好了代碼也修改完了，再右鍵該項目，添加新建項，有四個：
classification.h
classification.cpp
multi_recognition_cpu.h
multi_recognition_cpu.cpp
classification.h：
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#ifndef?CLASSIFICATION_H_?? #define?CLASSIFICATION_H_?? ?? #include?<caffe/caffe.hpp>?? #include?<opencv2/core/core.hpp>?? #include?<opencv2/highgui/highgui.hpp>?? #include?<opencv2/imgproc/imgproc.hpp>?? #include?<iosfwd>?? #include?<memory>?? #include?<utility>?? #include?<vector>?? #include?<iostream>?? #include?<string>?? #include?<time.h>?? ?? using?namespace?caffe;?? using?std::string;?? typedef?std::pair<int,?float>?Prediction;?? ?? class??ClassifierImpl?{?? public:?? ????ClassifierImpl(const?string&?model_file,?? ????????const?string&?trained_file,?? ????????const?string&?mean_file?? ????????);?? ?? ????std::vector<std::vector<Prediction>?>?Classify(const?cv::Mat&?img,?int?N?=?2);?? private:?? ????void?SetMean(const?string&?mean_file);?? ?? ????std::vector<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_;?? };?? #endif??
classification.cpp：
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#include?"classification.h"?? ?? ClassifierImpl::ClassifierImpl(const?string&?model_file,?? ????const?string&?trained_file,?? ????const?string&?mean_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.";?? ????std::cout?<<?"Network?have?"?<<?net_->num_outputs()?<<?"?outputs.\n";?? ????vector<string>?names?=?net_->output_blobs_names();?? ?? ????for?(int?n?=?0;?n?<?net_->num_outputs();?++n)?? ????{?? ????????std::cout?<<?"Output?"?<<?n?+?1?<<?":"?<<?names[n]?<<?";?have?"?<<?net_->output_blobs()[n]->channels()?<<?"?outputs.\n";?? ????}?? ?? ?? ????Blob<float>*?input_layer?=?net_->input_blobs()[0];?? ????std::cout?<<?"Input?width:"?<<?input_layer->width()?<<?";"?<<?"Input?height:"?<<?input_layer->height()?<<?"\n";?? ????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);?? ?? }?? ?? 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],?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<std::vector<Prediction>?>?ClassifierImpl::Classify(const?cv::Mat&?img,?int?N)?{?? ????std::vector<std::vector<Prediction>?>?outputPredict;?? ????std::vector<std::vector<float>?>?output?=?Predict(img);?? ?? ????for?(auto?bg?=?output.begin();?bg?!=?output.end();?++bg)?? ????{?? ????????std::vector<int>?maxN?=?Argmax(*bg,?N);?? ????????std::vector<Prediction>?predictions;?? ????????for?(int?i?=?0;?i?<?N;?++i)?{?? ????????????int?idx?=?maxN[i];?? ????????????predictions.push_back(std::make_pair(idx,?(*bg)[idx]));?? ????????}?? ????????outputPredict.push_back(predictions);?? ????????predictions.clear();?? ????????maxN.clear();?? ????}?? ?? ????return?outputPredict;?? }?? ?? /*?Load?the?mean?file?in?binaryproto?format.?*/?? void?ClassifierImpl::SetMean(const?string&?mean_file)?{?? ????BlobProto?blob_proto;?? ????ReadProtoFromBinaryFileOrDie(mean_file.c_str(),?&blob_proto);?? ????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.";?? ????std::vector<cv::Mat>?channels;?? ????float*?data?=?mean_blob.mutable_cpu_data();?? ????for?(int?i?=?0;?i?<?num_channels_;?++i)?{?? ????????cv::Mat?channel(mean_blob.height(),?mean_blob.width(),?CV_32FC1,?data);?? ????????channels.push_back(channel);?? ????????data?+=?mean_blob.height()?*?mean_blob.width();?? ????}?? ?? ????cv::Mat?mean;?? ????cv::merge(channels,?mean);?? ????cv::Scalar?channel_mean?=?cv::mean(mean);?? ????mean_?=?cv::Mat(input_geometry_,?mean.type(),?channel_mean);?? }?? std::vector<std::vector<float>?>?ClassifierImpl::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);?? ????net_->Reshape();?? ????std::vector<cv::Mat>?input_channels;?? ????WrapInputLayer(&input_channels);?? ????Preprocess(img,?&input_channels);?? ????net_->ForwardPrefilled();?? ?? ????std::vector<std::vector<float>?>?outPredict;?? ????for?(int?i?=?0;?i?<?net_->output_blobs().size();?++i)?? ????{?? ????????Blob<float>*?output_layer?=?net_->output_blobs()[i];?? ????????const?float*?begin?=?output_layer->cpu_data();?? ????????const?float*?end?=?begin?+?output_layer->channels();?? ????????std::vector<float>?temp(begin,?end);?? ????????outPredict.push_back(temp);?? ????????temp.clear();?? ????}?? ?? ????return?outPredict;?? }?? ?? void?ClassifierImpl::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?ClassifierImpl::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);?? ????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.";?? }??
導出類：
multi_recognition_cpu.h：
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#ifndef?MULTI_RECOGNITION_CPU_H_?? #define?MULTI_RECOGNITION_CPU_H_?? ?? #ifdef?MULTI_RECOGNITION_API_EXPORTS?? #define?MULTI_RECOGNITION_API?__declspec(dllexport)?? #else?? #define?MULTI_RECOGNITION_API?__declspec(dllimport)?? #endif?? #include?<opencv2/core/core.hpp>?? #include?<opencv2/highgui/highgui.hpp>?? #include?<opencv2/imgproc/imgproc.hpp>?? #include?<string>?? #include?<vector>?? #include?<iostream>?? #include?<io.h>?? class?ClassifierImpl;?? using?std::string;?? using?std::vector;?? typedef?std::pair<int,?float>?Prediction;?? ?? class?MULTI_RECOGNITION_API?MultiClassifier?? {?? public:?? ????MultiClassifier(const?string&?model_file,?? ????????const?string&?trained_file,?? ????????const?string&?mean_file);?? ?? ????~MultiClassifier();?? ????std::vector<std::vector<Prediction>?>Classify(const?cv::Mat&?img,?int?N?=?2);?? ????void?getFiles(std::string?path,?std::vector<std::string>&?files);?? private:?? ????ClassifierImpl?*Impl;?? };?? ?? #endif??
multi_recognition_cpu.cpp：
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#include?"multi_recognition_cpu.h"?? #include?"classification.h"?? ?? MultiClassifier::MultiClassifier(const?string&?model_file,?const?string&?trained_file,?const?string&?mean_file)?? {?? ????Impl?=?new?ClassifierImpl(model_file,?trained_file,?mean_file);?? }?? MultiClassifier::~MultiClassifier()?? {?? ????delete?Impl;?? }?? std::vector<std::vector<Prediction>?>?MultiClassifier::Classify(const?cv::Mat&?img,?int?N?/*?=?2?*/)?? {?? ????return?Impl->Classify(img,?N);?? }?? void?MultiClassifier::getFiles(string?path,?vector<string>&?files)?? {?? ????//文件句柄?? ????long???hFile?=?0;?? ????//文件信息?? ????struct?_finddata_t?fileinfo;?? ????string?p;?? ????if?((hFile?=?_findfirst(p.assign(path).append("\\*").c_str(),?&fileinfo))?!=?-1)?? ????{?? ????????do?? ????????{?? ????????????if?((fileinfo.attrib?&??_A_SUBDIR))?? ????????????{?? ????????????????if?(strcmp(fileinfo.name,?".")?!=?0?&&?strcmp(fileinfo.name,?"..")?!=?0)?? ????????????????????getFiles(p.assign(path).append("\\").append(fileinfo.name),?files);?? ????????????}?? ????????????else?? ????????????{?? ????????????????files.push_back(p.assign(path).append("\\").append(fileinfo.name));?? ????????????}?? ????????}?while?(_findnext(hFile,?&fileinfo)?==?0);?? ????????_findclose(hFile);?? ????}?? }??
右鍵項目，生成就可以了。
最后得到：
模型可以有多個輸出：
封裝的代碼下載地址：caffe-windows-cpu
總結
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