Tracker trainer test separately

tracker_trainer_test.py

#include <iostream>
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>

using namespace cv;
using namespace std;
// Number of images in each batch.
const int kBatchSize = 50;

// Number of examples that we generate (by applying synthetic transformations)
// to each image.
const int kGeneratedExamplesPerImage = 10;

std::vector<cv::Mat> images_batch_; # I put it outside cause it is a class member
# for this member function, it is global
void Train(cv::Mat image){

  // Make training examples.
  std::vector<cv::Mat> images;

  for (int i=0; i < 11; i++){
    images.push_back(image);
  }

  while (images.size() > 0) {
    // Compute the number of images left to complete the batch.
    const int num_in_batch = images_batch_.size();
    const int num_left_in_batch = kBatchSize - num_in_batch;
    cout << "num_left_in_batch " << num_left_in_batch << endl;

    // The number of images to use is upper-bounded by the number left in the batch.
    // The rest go into the next batch.
    const int num_use = std::min(static_cast<int>(images.size()), num_left_in_batch);
    cout << "num_use " << num_use << endl;

    if (num_use < 0) {
      cout << "Error num use" << num_use << endl;
    }

    // Add the approrpriate number of images to the batch.
    images_batch_.insert(images_batch_.end(),
                         images.begin(), images.begin() + num_use);

    // If we have a full batch, then train!  Otherwise, save this batch for later.
    if (images_batch_.size() == kBatchSize) {
      // Increment the batch count.

      cout << "Process batch" << endl;
      // After training, clear the batch.
      images_batch_.clear();

      // Reserve the appropriate amount of space for the next batch.
      images_batch_.reserve(kBatchSize);
    }

    // Remove the images that were used.
    images.erase(images.begin(), images.begin() + num_use);
  }
}

int main(int argc, char** argv){
    //cv::Mat image = imread(argv[1], CV_LOAD_IMAGE_COLOR);
    cv::Mat image = Mat::zeros(100, 100, 0);
    cout << "start";
    Train(image);
    Train(image);
    Train(image);
    Train(image);
    Train(image);
    Train(image);
    return 0;
}

Compile it with

 g++ -o test tracker_trainer.cpp `pkg-config opencv --cflags --libs`