使用Opencv::dnn模块实现多任务识别

1、参考https://blog.csdn.net/u014696921/article/details/70245141 使用caffe训练多任务模型

2、使用opencv3.4.0读取caffe模型并输出识别结果；

3、关键点，常用的Mat prob_type    = net.forward("prob_type");形式只能获取第一个输出，并不适用于多任务的输出，注意看forward函数的说明。需要使用以下形式 获取输出

std::vector> prob_test ;

vector prob_name = { "fc8_type","prob_type","fc8_surface", "prob_surface" };

net.forward(prob_test, prob_name);

/**M///////////////////////////////////////////////////////////////////////////////////////
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#include "stdafx.h"
#include 
#include 
#include 
#include 
using namespace cv;
using namespace cv::dnn;

#include 
#include 
#include 
using namespace std;

/* Find best class for the blob (i. e. class with maximal probability) */
static void getMaxClass(const Mat &probBlob, int *classId, double *classProb)
{
	Mat probMat = probBlob.reshape(1, 1); //reshape the blob to 1x1000 matrix
	Point classNumber;

	minMaxLoc(probMat, NULL, classProb, NULL, &classNumber);
	*classId = classNumber.x;
}

static std::vector readClassNames(const char *filename = "synset_words.txt")
{
	std::vector classNames;

	std::ifstream fp(filename);
	if (!fp.is_open())
	{
		std::cerr << "File with classes labels not found: " << filename << std::endl;
		exit(-1);
	}

	std::string name;
	while (!fp.eof())
	{
		std::getline(fp, name);
		if (name.length())
			classNames.push_back(name.substr(name.find(' ') + 1));
	}

	fp.close();
	return classNames;
}

const char* params
= "{ help           | false | Sample app for loading googlenet model }"
"{ proto          | bvlc_googlenet.prototxt | model configuration }"
"{ model          | bvlc_googlenet.caffemodel | model weights }"
"{ image          | space_shuttle.jpg | path to image file }"
"{ opencl         | false | enable OpenCL }"
;

int main(int argc, char **argv)
{
	CV_TRACE_FUNCTION();

	//CommandLineParser parser(argc, argv, params);

	//if (parser.get("help"))
	//{
	//	parser.printMessage();
	//	return 0;
	//}

	//String modelTxt = parser.get("proto");
	//String modelBin = parser.get("model");
	//String imageFile = parser.get("image");
	String modelTxt = "E:\\data\\car_multilabel\\Caffe_MultiLabel_Classification-master\\deploy.prototxt";
	String modelBin = "E:\\data\\car_multilabel\\ZnCar\\train_snapshot_iter_1000.caffemodel";
	String imageFile = "E:\\data\\car_multilabel\\ZnCar\\Test\\4.jpg";

	Net net;
	try {
		//! [Read and initialize network]
		net = dnn::readNetFromCaffe(modelTxt, modelBin);
		//! [Read and initialize network]
	}
	catch (cv::Exception& e) {
		std::cerr << "Exception: " << e.what() << std::endl;
		//! [Check that network was read successfully]
		if (net.empty())
		{
			std::cerr << "Can't load network by using the following files: " << std::endl;
			std::cerr << "prototxt:   " << modelTxt << std::endl;
			std::cerr << "caffemodel: " << modelBin << std::endl;
			std::cerr << "bvlc_googlenet.caffemodel can be downloaded here:" << std::endl;
			std::cerr << "http://dl.caffe.berkeleyvision.org/bvlc_googlenet.caffemodel" << std::endl;
			exit(-1);
		}
		//! [Check that network was read successfully]
	}

	//if (parser.get("opencl"))
	//{
	//	net.setPreferableTarget(DNN_TARGET_OPENCL);
	//}

	//! [Prepare blob]
	Mat img = imread(imageFile);
	if (img.empty())
	{
		std::cerr << "Can't read image from the file: " << imageFile << std::endl;
		exit(-1);
	}

	//GoogLeNet accepts only 224x224 BGR-images
	Mat inputBlob = blobFromImage(img, 1.0f, Size(227, 227),
		Scalar(123, 124, 121), false);   //Convert Mat to batch of images
										 //! [Prepare blob]
	net.setInput(inputBlob, "data");        //set the network input
	
	std::vector> prob_test ;
	vector prob_name = { "fc8_type","prob_type","fc8_surface", "prob_surface" };

	

//关键在这里，常用的Mat prob_type    = net.forward("prob_type");形式只能获取第一个输出，并不适用于多任务的输出，注意看forward函数的说明

net.forward(prob_test, prob_name);for (int i = 0; i < prob_test.size(); i++){for (int j = 0; j < prob_test[i].size(); j++){std::cout << i << "-" << j << std::endl;cout << prob_test[i][j] << endl;}}return 0;} //main