text: #ifdef USE_OPENCV
  #include <opencv2/core/core.hpp>
  
  #include <fstream>  // NOLINT(readability/streams)
  #include <iostream>  // NOLINT(readability/streams)
  #include <string>
  #include <utility>
  #include <vector>
  
  #include "caffe/data_transformer.hpp"
  #include "caffe/layers/base_data_layer.hpp"
  #include "caffe/layers/image_data_layer.hpp"
  #include "caffe/util/benchmark.hpp"
  #include "caffe/util/io.hpp"
  #include "caffe/util/math_functions.hpp"
  #include "caffe/util/rng.hpp"
  
  namespace caffe {
  
  template <typename Dtype>
  ImageDataLayer<Dtype>::~ImageDataLayer<Dtype>() {
    this->StopInternalThread();
  }
  
  template <typename Dtype>
  void ImageDataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
        const vector<Blob<Dtype>*>& top) {
    const int new_height = this->layer_param_.image_data_param().new_height();
    const int new_width  = this->layer_param_.image_data_param().new_width();
    const bool is_color  = this->layer_param_.image_data_param().is_color();
    string root_folder = this->layer_param_.image_data_param().root_folder();
  
    CHECK((new_height == 0 && new_width == 0) ||
        (new_height > 0 && new_width > 0)) << "Current implementation requires "
        "new_height and new_width to be set at the same time.";
    // Read the file with filenames and labels
    const string& source = this->layer_param_.image_data_param().source();
    LOG(INFO) << "Opening file " << source;
    std::ifstream infile(source.c_str());
    string line;
    size_t pos;
    int label;
    while (std::getline(infile, line)) {
      pos = line.find_last_of(' ');
      label = atoi(line.substr(pos + 1).c_str());
      lines_.push_back(std::make_pair(line.substr(0, pos), label));
    }
  
    CHECK(!lines_.empty()) << "File is empty";
  
    if (this->layer_param_.image_data_param().shuffle()) {
      // randomly shuffle data
      LOG(INFO) << "Shuffling data";
      const unsigned int prefetch_rng_seed = caffe_rng_rand();
      prefetch_rng_.reset(new Caffe::RNG(prefetch_rng_seed));
      ShuffleImages();
    }
    LOG(INFO) << "A total of " << lines_.size() << " images.";
  
    lines_id_ = 0;
    // Check if we would need to randomly skip a few data points
    if (this->layer_param_.image_data_param().rand_skip()) {
      unsigned int skip = caffe_rng_rand() %
          this->layer_param_.image_data_param().rand_skip();
      LOG(INFO) << "Skipping first " << skip << " data points.";
      CHECK_GT(lines_.size(), skip) << "Not enough points to skip";
      lines_id_ = skip;
    }
    // Read an image, and use it to initialize the top blob.
    cv::Mat cv_img = ReadImageToCVMat(root_folder + lines_[lines_id_].first,
                                      new_height, new_width, is_color);
    CHECK(cv_img.data) << "Could not load " << lines_[lines_id_].first;
    // Use data_transformer to infer the expected blob shape from a cv_image.
    vector<int> top_shape = this->data_transformer_->InferBlobShape(cv_img);
    this->transformed_data_.Reshape(top_shape);
    // Reshape prefetch_data and top[0] according to the batch_size.
    const int batch_size = this->layer_param_.image_data_param().batch_size();
    CHECK_GT(batch_size, 0) << "Positive batch size required";
    top_shape[0] = batch_size;
    for (int i = 0; i < this->PREFETCH_COUNT; ++i) {
      this->prefetch_[i].data_.Reshape(top_shape);
    }
    top[0]->Reshape(top_shape);
  
    LOG(INFO) << "output data size: " << top[0]->num() << ","
        << top[0]->channels() << "," << top[0]->height() << ","
        << top[0]->width();
    // label
    vector<int> label_shape(1, batch_size);
    top[1]->Reshape(label_shape);
    for (int i = 0; i < this->PREFETCH_COUNT; ++i) {
      shared_ptr<Blob<Dtype> > label_blob_pointer(new Blob<Dtype>());
      label_blob_pointer->Reshape(label_shape);
      this->prefetch_[i].labels_.push_back(label_blob_pointer);
    }
  }
  
  template <typename Dtype>
  void ImageDataLayer<Dtype>::ShuffleImages() {
    caffe::rng_t* prefetch_rng =
        static_cast<caffe::rng_t*>(prefetch_rng_->generator());
    shuffle(lines_.begin(), lines_.end(), prefetch_rng);
  }
  
  // This function is called on prefetch thread
  template <typename Dtype>
  void ImageDataLayer<Dtype>::load_batch(Batch<Dtype>* batch) {
    CPUTimer batch_timer;
    batch_timer.Start();
    double read_time = 0;
    double trans_time = 0;
    CPUTimer timer;
    CHECK(batch->data_.count());
    CHECK(this->transformed_data_.count());
    ImageDataParameter image_data_param = this->layer_param_.image_data_param();
    const int batch_size = image_data_param.batch_size();
    const int new_height = image_data_param.new_height();
    const int new_width = image_data_param.new_width();
    const bool is_color = image_data_param.is_color();
    string root_folder = image_data_param.root_folder();
  
    // Reshape according to the first image of each batch
    // on single input batches allows for inputs of varying dimension.
    cv::Mat cv_img = ReadImageToCVMat(root_folder + lines_[lines_id_].first,
        new_height, new_width, is_color);
    CHECK(cv_img.data) << "Could not load " << lines_[lines_id_].first;
    // Use data_transformer to infer the expected blob shape from a cv_img.
    vector<int> top_shape = this->data_transformer_->InferBlobShape(cv_img);
    this->transformed_data_.Reshape(top_shape);
    // Reshape batch according to the batch_size.
    top_shape[0] = batch_size;
    batch->data_.Reshape(top_shape);
  
    Dtype* prefetch_data = batch->data_.mutable_cpu_data();
    CHECK_EQ(batch->labels_.size(),1);
    Dtype* prefetch_label = batch->labels_[0]->mutable_cpu_data();
  
    // datum scales
    const int lines_size = lines_.size();
    for (int item_id = 0; item_id < batch_size; ++item_id) {
      // get a blob
      timer.Start();
      CHECK_GT(lines_size, lines_id_);
      cv::Mat cv_img = ReadImageToCVMat(root_folder + lines_[lines_id_].first,
          new_height, new_width, is_color);
      CHECK(cv_img.data) << "Could not load " << lines_[lines_id_].first;
      read_time += timer.MicroSeconds();
      timer.Start();
      // Apply transformations (mirror, crop...) to the image
      int offset = batch->data_.offset(item_id);
      this->transformed_data_.set_cpu_data(prefetch_data + offset);
      this->data_transformer_->Transform(cv_img, &(this->transformed_data_));
      trans_time += timer.MicroSeconds();
  
      prefetch_label[item_id] = lines_[lines_id_].second;
      // go to the next iter
      lines_id_++;
      if (lines_id_ >= lines_size) {
        // We have reached the end. Restart from the first.
        DLOG(INFO) << "Restarting data prefetching from start.";
        lines_id_ = 0;
        if (this->layer_param_.image_data_param().shuffle()) {
          ShuffleImages();
        }
      }
    }
    batch_timer.Stop();
    DLOG(INFO) << "Prefetch batch: " << batch_timer.MilliSeconds() << " ms.";
    DLOG(INFO) << "     Read time: " << read_time / 1000 << " ms.";
    DLOG(INFO) << "Transform time: " << trans_time / 1000 << " ms.";
  }
  
  INSTANTIATE_CLASS(ImageDataLayer);
  REGISTER_LAYER_CLASS(ImageData);
  
  }  // namespace caffe
  #endif  // USE_OPENCV
  