int main(int argc, char** argv) {	/***  if (argc != 6) {    std::cerr << "Usage: " << argv[0]              << " deploy.prototxt network.caffemodel"              << " mean.binaryproto labels.txt img.jpg" << std::endl;    return 1;  }  ::google::InitGoogleLogging(argv[0]);  string model_file   = argv[1];  string trained_file = argv[2];  string mean_file    = argv[3];  string label_file   = argv[4];  Classifier classifier(model_file, trained_file, mean_file, label_file);  string file = argv[5];  **/	if (argc != 2) {		std::cerr << "Usage: " << argv[0]			<< " deploy.prototxt network.caffemodel"			<< " mean.binaryproto labels.txt img.jpg" << std::endl;		return 1;	}	::google::InitGoogleLogging(argv[0]);	string model_file = "lenet.prototxt";	string trained_file = "lenet_iter_10000.caffemodel";	string mean_file = "mean.binaryproto";	string label_file = "synset_words.txt";	Classifier classifier(model_file, trained_file, mean_file, label_file);	string file = argv[1];	IplImage *image = cvLoadImage(file.c_str());	IplImage *desc;	if (!image){		printf(" No image data \n ");		return -1;	}	CvSize sz;	sz.width = 28;	sz.height = 28;	desc = cvCreateImage(sz, image->depth, image->nChannels);	cvResize(image, desc, CV_INTER_AREA);	cv::Mat imge;	imge=cv::cvarrToMat(desc);	cv::Mat gray_image;	cvtColor(imge, gray_image, CV_BGR2GRAY);	imwrite("gray_image.bmp", gray_image);	file = "gray_image.bmp";  std::cout << "---------- Prediction for "            << file << " ----------" << std::endl;  cv::Mat img = cv::imread(file, -1);  CHECK(!img.empty()) << "Unable to decode image " << file;  std::vector<Prediction> predictions = classifier.Classify(img);  /* Print the top N predictions. */  for (size_t i = 0; i < predictions.size(); ++i) {    Prediction p = predictions[i];    std::cout << std::fixed << std::setprecision(4) << p.second << " - \""              << p.first << "\"" << std::endl;  }}