将
name: "CaffeNet" layer { name: "data" type: "Input" top: "data" input_param { shape: { dim: 10 dim: 3 dim: 227 dim: 227 } } } 修改为: name: "CaffeNet" input: "data" input_dim: 10 input_dim: 3 input_dim: 227 input_dim: 227 即可。 2、我将三个文件命名为：caffenet.prototxt、caffenet.caffemodel、labels.txt 3、VS新建工程，复制下面代码： #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; //寻找出概率最高的一类 static void getMaxClass(const Mat &probBlob, int *classId, double *classProb) { Mat probMat = probBlob.reshape(1, 1); Point classNumber; minMaxLoc(probMat, NULL, classProb, NULL, &classNumber); *classId = classNumber.x; } //从标签文件读取分类 空格为标志 static std::vector<String> readClassNames(const char *filename = "labels.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; } //主程序 int main(int argc, char **argv) { //初始化 CV_TRACE_FUNCTION(); //读取模型参数和模型结构文件 String modelTxt = "caffenet.prototxt"; String modelBin = "caffenet.caffemodel"; //读取图片 String imageFile = (argc > 1) ? argv[1] : "test0.jpg"; //合成网络 Net net = dnn::readNetFromCaffe(modelTxt, modelBin); //判断网络是否生成成功 if (net.empty()) { std::cerr << "Can't load network by using the following files: " << std::endl; exit(-1); } cerr << "net read successfully" << endl; //读取图片 Mat img = imread(imageFile); imshow("image", img); if (img.empty()) { std::cerr << "Can't read image from the file: " << imageFile << std::endl; exit(-1); } cerr << "image read sucessfully" << endl; /* Mat inputBlob = blobFromImage(img, 1, Size(224, 224), Scalar(104, 117, 123)); */ //构造blob，为传入网络做准备，图片不能直接进入网络 Mat inputBlob = blobFromImage(img, 1, Size(227, 227)); Mat prob; cv::TickMeter t; for (int i = 0; i < 10; i++) { CV_TRACE_REGION("forward"); //将构建的blob传入网络data层 net.setInput(inputBlob,"data"); //计时 t.start(); //前向预测 prob = net.forward("prob"); //停止计时 t.stop(); } int classId; double classProb; //找出最高的概率ID存储在classId，对应的标签在classProb中 getMaxClass(prob, &classId, &classProb); //打印出结果 std::vector<String> classNames = readClassNames(); std::cout << "Best class: #" << classId << " '" << classNames.at(classId) << "'" << std::endl; std::cout << "Probability: " << classProb * 100 << "%" << std::endl; //打印出花费时间 std::cout << "Time: " << (double)t.getTimeMilli() / t.getCounter() << " ms (average from " << t.getCounter() << " iterations)" << std::endl; //便于观察结果 waitKey(0); return 0; } 4、运行程序得到下面结果：OK，输出上述信息，就说明你的模型在opencv中调用没问题了，接下来就是深入学习网络结构，理解各种参数，学习各种框架，从中总结。希望大家戒骄戒躁，都能达到自己的目标，谢谢大家。
