example of mat of cv to tensor of tfl siample code

工程化需求，解决一个问题，如何通过 cv::Mat of opencv convert to tensor tensorflow 步骤解析：
1- 通过指针，指向tensor数据位置；
2- 通过指针，指向mat数据位置
3- 通过遍历，把mat的数据赋值到tensor上；

//Loading Opencv fIles for processing

#include 
#include 

#include 
#include 

#include "tensorflow/core/public/session.h"
#include "tensorflow/core/platform/env.h"
#include "tensorflow/core/framework/tensor.h"
#include 

int main(int argc, char** argv)
{

  // Loading the file path provided in the arg into a mat objects
  std::string path = argv[1];
  cv::Mat readImage = cv::imread(path);
  std::cerr << "read image=" << path << std::endl;

  // converting the image to the necessary dim and for normalization
  int height = 299;
  int width = 299;
  int mean = 128;
  int std = 128;

  cv::Size s(height,width);
  cv::Mat Image;
  std::cerr << "resizing\n";
  cv::resize(readImage,Image,s,0,0,cv::INTER_CUBIC);
  std::cerr << "success resizing\n";

  int depth = Image.channels();

  //std::cerr << "height=" << height << " / width=" << width << " / depth=" << depth << std::endl;

  // creating a Tensor for storing the data
  tensorflow::Tensor input_tensor(tensorflow::DT_FLOAT, tensorflow::TensorShape({1,height,width,depth}));
  auto input_tensor_mapped = input_tensor.tensor<float, 4>();

  cv::Mat Image2;
  Image.convertTo(Image2, CV_32FC1);
  Image = Image2;
  Image = Image-mean;
  Image = Image/std;
  const float * source_data = (float*) Image.data;

  // copying the data into the corresponding tensor
  for (int y = 0; y < height; ++y) {
    const float* source_row = source_data + (y * width * depth);
    for (int x = 0; x < width; ++x) {
      const float* source_pixel = source_row + (x * depth);
      for (int c = 0; c < depth; ++c) {
  const float* source_value = source_pixel + c;
  input_tensor_mapped(0, y, x, c) = *source_value;
      }
    }
  }

  // initializing the graph 
  tensorflow::GraphDef graph_def;

  // Name of the folder in which inception graph is present
  std::string graphFile = "../../model/tensorflow_inception_graph.pb";

  // Loading the graph to the given variable
  tensorflow::Status graphLoadedStatus = ReadBinaryProto(tensorflow::Env::Default(),graphFile,&graph_def);
  if (!graphLoadedStatus.ok()){
    std::cout << graphLoadedStatus.ToString()<<std::endl;
    return 1;
  }

  // creating a session with the grap
  std::unique_ptr session_inception(tensorflow::NewSession(tensorflow::SessionOptions()));
  //session->reset(tensorflow::NewSession(tensorflow::SessionOptions()));
  tensorflow::Status session_create_status = session_inception->Create(graph_def);

  if (!session_create_status.ok()){
    return 1;
  }
  // running the loaded graph 
  std::vector finalOutput;

  std::string InputName = "Mul";
  std::string OutputName = "softmax";
  tensorflow::Status run_status  = session_inception->Run({{InputName,input_tensor}},{OutputName},{},&finalOutput);

  // finding the labels for prediction
  std::cerr << "final output size=" << finalOutput.size() << std::endl;
  tensorflow::Tensor output = std::move(finalOutput.at(0));
  auto scores = output.flat<float>();
  std::cerr << "scores size=" << scores.size() << std::endl;

  // Label File Name
  std::string labelfile = "../../model/imagenet_comp_graph_label_strings.txt";
  std::ifstream label(labelfile);
  std::string line;

  // sorting the file to find the top labels
  std::vector<std::pair<float,std::string>> sorted;

  for (unsigned int i =0; i<=1000 ;++i){
    std::getline(label,line);
    sorted.emplace_back(scores(i),line);
    //std::cout << scores(i) << " / line=" << line << std::endl;
  }

  std::sort(sorted.begin(),sorted.end());
  std::reverse(sorted.begin(),sorted.end());
  std::cout << "size of the sorted file is "<std::endl;
  for(unsigned int i =0 ; i< 5;++i){

    std::cout << "The output of the current graph has category  " << sorted[i].second << " with probability "<< sorted[i].first << std::endl; 
  }

  /*cv::namedWindow("imageOpencv",CV_WINDOW_KEEPRATIO);
  cv::imshow("imgOpencv",Image);

  */
}