OpenCV调用caffe分类模型

//#include "pch.h" #include <iostream> #include <fstream> #include <sstream> #include <opencv2/opencv.hpp> #include <opencv2/dnn.hpp> //dnn模块 #include <time.h> using namespace std; using namespace cv; using namespace ::dnn; //调用DNN命名空间 clock_t start, finish; String model_file = "caffe/model/model.caffemodel"; //模型结构文件 String model_text = "caffe/model/deploy.prototxt"; //模型数据 //图像深度学习检测 double detect_NN(Mat detectImg, Net net) { if (net.empty()) { cout << "no model!" << endl; return -1; } //initialize images(输入图像初始化) Mat src = detectImg.clone(); if (src.empty()) { return -1; } //图像识别转换 //第一个参数输入图像，第二个参数图像放缩大小，第三个参数输入图像尺寸,第四个参数模型训练图像三个通道RGB的均值（均值文件） start = clock(); Mat inputBlob; //resize(src, src, Size(227, 227)); // 参数分别为输入图像，归一化参数，模型大小，BGR均值 inputBlob = blobFromImage(src, 1.0, Size(227, 227), Scalar(92.71, 106.44, 118.11)); Mat prob; //输出结果 //循环 for (int i = 0; i < 1; i++) { net.setInput(inputBlob, "data"); prob = net.forward("prob"); //输出层2 } Mat probMat = prob.reshape(1, 1); //转化为1行2列 Point classNumber; //最大值的位置 double classProb; //最大值多少 //最大最小值查找，忽略最小值 minMaxLoc(probMat, NULL, &classProb, NULL, &classNumber); int classidx = classNumber.x; printf("classidx is:%d\n", classidx); printf("prob is %f\n", classProb); finish = clock(); double duration = (double)(finish - start); printf("run time is %f ms\n", duration); return duration; } int main() { Net net = readNetFromCaffe(model_text, model_file); Mat detectImg = imread("caffe/image/husky.jpg"); double runTime = detect_NN(detectImg, net); system("pause"); return 0; }