#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 <sstream>
#include "CvxText.h" //英文標籤去掉該標頭檔案

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_;
};

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);
	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], 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);

	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_->ForwardPrefilled();

	/* 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_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);

	/* 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.";
}
//獲取路徑path下的檔案，並儲存在files容器中
void getFiles(string path, vector<string>& files)
{
	//檔案控制代碼
	long   hFile = 0;
	//檔案資訊
	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);
	}
}

int main(int argc, char** argv) {
	//caffe的準備工作
#ifdef _MSC_VER
#pragma comment( linker, "/subsystem:\"windows\" /entry:\"mainCRTStartup\"" )
#endif
	//::google::InitGoogleLogging(argv[0]);
	string model_file("../../model/deploy.prototxt");
	string trained_file("../../model/type.caffemodel");
	string mean_file("../../model/type_mean.binaryproto");
	string label_file("../../model/labels.txt");
	string picture_path("../../type");

  	Classifier classifier(model_file, trained_file, mean_file, label_file);
	vector<string> files;
	getFiles(picture_path, files);

	
	for (int i = 0; i < files.size(); i++)
	{
		cv::Mat img = cv::imread(files[i], -1);
		cv::Mat img2;

		std::vector<Prediction> predictions = classifier.Classify(img);
		Prediction p = predictions[0];
		
		CvSize sz;
		sz.width = img.cols;
		sz.height = img.rows;
		float scal = 0;
		scal = sz.width > sz.height ? (300.0 / (float)sz.height) : (300.0 / (float)sz.width);
		sz.width *= scal;
		sz.height *= scal;
		resize(img, img2, sz, 0, 0, CV_INTER_LINEAR);
		IplImage* show = cvCreateImage(sz, IPL_DEPTH_8U, 3);
		
		string text = p.first;
		char buff[20];
		_gcvt(p.second, 4, buff);
		text = text + ":" + buff;

		/************************輸出中文(用到Freetype庫)****************************/
		/*CvxText mytext("../../STZHONGS.TTF");// 字型檔案   
		const char *msg = text.c_str();
		CvScalar size;
		size.val[0] = 26;
		size.val[1] = 0.5;
		size.val[2] = 0.1;
		size.val[3] = 0;
		mytext.setFont(NULL,&size, NULL, NULL);   // 設定字型大小
		mytext.putText(&IplImage(img2), msg, cvPoint(10, 30), cvScalar(0, 0, 255, NULL));
		//輸出影象名
		text = files[i].substr(files[i].find_last_of("\\")+1);
		msg = text.c_str();
		mytext.putText(&IplImage(img2), msg, cvPoint(10, 55), cvScalar(0, 0, 255, NULL));
		cvCopy(&(IplImage)img2, show);*/
		/*******************************************************************************/

		/***************************輸出英文標籤*****************************************/
		cvCopy(&(IplImage)img2, show);
		CvFont font;
		cvInitFont(&font, CV_FONT_HERSHEY_COMPLEX, 1.0, 1.0, 0, 2, 8);  //初始化字型
		cvPutText(show, text.c_str(), cvPoint(10, 30), &font, cvScalar(0, 0, 255, NULL));

		text = files[i].substr(files[i].find_last_of("\\")+1);
		cvPutText(show, text.c_str(), cvPoint(10, 55), &font, cvScalar(0, 0, 255, NULL));
		/**********************************************************************************/
		
		cvNamedWindow("結果展示");
		cvShowImage("結果展示", show);
		int c = cvWaitKey();
		cvDestroyWindow("結果展示");
		cvReleaseImage(&show);
		
		if (c == 27)
		{
			return 0;
		}
	}
	return 0;
}

 

3.生成

設定好之後，右鍵caffelib，生成。

4.結果

將Classification\CLassificationDLL\bin加入環境變數，然後加入你的模型檔案即可。