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<std::vector<cv::Mat>> prob_test ;
vector<cv::String> prob_name = { "fc8_type","prob_type","fc8_surface", "prob_surface" };
net.forward(prob_test, prob_name);
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#include "stdafx.h"
#include <opencv2/dnn.hpp>
#include <opencv2/imgproc.hpp>
#include <opencv2/highgui.hpp>
#include <opencv2/core/utils/trace.hpp>
using namespace cv;
using namespace cv::dnn;
#include <fstream>
#include <iostream>
#include <cstdlib>
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<String> readClassNames(const char *filename = "synset_words.txt")
{
std::vector<String> 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<bool>("help"))
//{
// parser.printMessage();
// return 0;
//}
//String modelTxt = parser.get<string>("proto");
//String modelBin = parser.get<string>("model");
//String imageFile = parser.get<String>("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<bool>("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<std::vector<cv::Mat>> prob_test ;
vector<cv::String> 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