【深度学习之Caffe】将模型测试Classification过程生成动态链接库dll以方便其他项目调用

1. #ifndef CAFFE_CLASSIFY_H_  
   #define CAFFE_CLASSIFY_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>  
2. 
   #pragma once  
   
   using namespace caffe;  // NOLINT(build/namespaces)  
   using std::string;
   
   /* 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);
   
   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_;
   };
   
   #endif

1. #include "caffe_classify.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);
   #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.";
   }

1. #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);
   }

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