封装caffe-windows-cpu(支持模型有多个输出)

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注意：该版本为CPU版本。

用到的caffe-windows来自：https://github.com/happynear/caffe-windows

先下载caffe-windows，解压；然后下载第三方库：https://pan.baidu.com/s/1eStyfrc  解压到caffe-windows-master，看起来是这样：caffe-windows-master\3rdparty

把3rdparty的bin加入环境变量或者复制里面的dll到build_cpu_only\caffelib下（cudnn的不需要）。

打开caffe-windows-master\src\caffe\proto，双击extract_proto.bat，然后用VS2013打开./build_cpu_only/MainBuilder.sln。请确保为Release x64

1.右键caffelib项目，重命名为：multi_recognition_cpu（按个人爱好，其他名字也行，不改也可以）；再右键该项目——>属性——>配置属性——>常规：

配置类型修改为动态库(.dll)，目标扩展名修改为.dll。

2.C/C++——>常规：

附加包含目录：

../../3rdparty/include

../../src

../../include

C/C++——>预处理器：

添加 MULTI_RECOGNITION_API_EXPORTS

3.链接器——>常规：

附加库目录：

../../3rdparty/lib

链接器——>输入：

去掉cuda和cudnn的lib（cu开头和cudnn开头的lib）

4.修改net.hpp和net.cpp

为了支持模型多输出，要知道输出的顺序，所以把输出blob的名字输出到控制台，打开net.hpp，给Net类添加：

protected:
	 std::vector<std::string> outputblobnames;

以及：

public:
  inline std::vector<std::string> output_blobs_names() const
  {
	  return outputblobnames;
  }

net.cpp修改：（最后一行，把输出blob名字保存到vector中）

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);
  }

这样，属性就配置好了代码也修改完了，再右键该项目，添加新建项，有四个：

classification.h

classification.cpp

multi_recognition_cpu.h

multi_recognition_cpu.cpp

classification.h：

#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：

#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.";
}

导出类：

multi_recognition_cpu.h：

#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：

#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);
	}
}

右键项目，生成就可以了。

最后得到：

模型可以有多个输出：

封装的代码下载地址：caffe-windows-cpu