opencv - The result of opencv3.3 dnn module not match the caffe prediction -


i used opencv dnn classification, result not match caffe prediction. confused me images similar result caffe,a small number of images not.when changed bgr rgb, of results ware wrong.

similar result: similar result:

different result: different result:

blobfromimage(norm_img, 1.0, cv::size(64, 64));when used default parameters changed bgr rgb ,but result wrong .so used blobfromimage(norm_img, 1.0, cv::size(64, 64), cv::scalar(),false); .most of result matched caffe prediction,why small number of images not?

#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 blob (i. e. class maximal probability) */ static void getmaxclass(const mat &probblob, int *classid, double *classprob) {     mat probmat = probblob.reshape(1, 1); //reshape blob 1x1000 matrix     point classnumber;      minmaxloc(probmat, null, classprob, null, &classnumber);     *classid = classnumber.x; }  static std::vector<string> readclassnames(const char *filename = "./config/type.txt") {     std::vector<string> classnames;      std::ifstream fp(filename);     if (!fp.is_open())     {         std::cerr << "file 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; }  int main(int argc, char **argv) {     cv_trace_function();      string modeltxt = "./config/hccr3755_res20_deploy.prototxt";     string modelbin = "./config/hccr3755-res20_iter_790000.caffemodel";     string imagefile = "./config/b9.jpg";     net net = dnn::readnetfromcaffe(modeltxt, modelbin);     if (net.empty())     {         std::cerr << "can't load network using following files: " << std::endl;         std::cerr << "prototxt:   " << modeltxt << std::endl;         std::cerr << "caffemodel: " << modelbin << std::endl;         exit(-1);     }     mat img = imread(imagefile);     filestorage fs("./config/mean.xml", filestorage::read);     mat _mean;     fs["vocabulary"] >> _mean;     if (img.empty())     {         std::cerr << "can't read image file: " << imagefile << std::endl;         exit(-1);     }     cv::mat img_resize;     resize(img, img_resize, size(64, 64));      cv::mat img_float;     img_resize.convertto(img_float, cv_32fc3);     cv::mat norm_img;     cv::subtract(img_float, _mean, norm_img);      mat inputblob = blobfromimage(norm_img, 1.0, cv::size(64, 64), cv::scalar(),false);   //convert mat batch of images      mat prob;      cv::tickmeter t;     (int = 0; < 1; i++)     {         cv_trace_region("forward");         //! [set input blob]         net.setinput(inputblob, "data");        //set network input         //! [set input blob]         t.start();         //! [make forward pass]         prob = net.forward("prob");          //std::cout << prob << std::endl;//compute output         //! [make forward pass]         t.stop();     }      int classid;     double classprob;     getmaxclass(prob, &classid, &classprob);//find best class     //! [gather output]      //! [print results]     std::vector<string> classnames = readclassnames();     std::cout << "best class: #" << classid << " '" << classnames.at(classid) << "'" << std::endl;     std::cout << "probability: " << classprob * 100 << "%" << std::endl;     //! [print results]     std::cout << "time: " << (double)t.gettimemilli() / t.getcounter() << " ms (average " << t.getcounter() << " iterations)" << std::endl;     getchar();     return 0; } //main


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