Tensor resize()

in src/io/image_transformer.cc [87:198]
108 lines of code
40 McCabe index (conditional complexity)

Tensor resize(Tensor& input, const size_t resize_height,
              const size_t resize_width, const string& image_dim_order) {
  CHECK_LE(input.nDim(), 4u);
  CHECK_GE(input.nDim(), 2u);
  if (!resize_height || !resize_width) return input;
  Tensor output;
  cv::Mat mat;
  const auto* in = input.data<float>();
  if (input.nDim() == 4u) {
    /// TODO
    /// batch based resize
    LOG(FATAL) << "Not implemented";
  } else if (input.nDim() == 3u) {
    if (image_dim_order == "CHW") {
      size_t height = input.shape(1), width = input.shape(2),
             channel = input.shape(0);
      if (channel == 3u) {
        mat = cv::Mat(height, width, CV_32FC3, cv::Scalar(0, 0, 0));
        for (size_t i = 0; i < height; i++)
          for (size_t j = 0; j < width; j++)
            for (size_t k = 0; k < channel; k++)
              mat.at<cv::Vec3f>(i, j)[k] = in[k * height * width + i * width + j];
      } else if (channel == 1u) {
        mat = cv::Mat(height, width, CV_32FC1);
        for (size_t i = 0; i < height; i++)
          for (size_t j = 0; j < width; j++)
            mat.at<cv::Vec<float, 1>>(i, j)[0] = in[i * width + j];
      } else LOG(FATAL) << "Invalid channel size: " << channel;
    } else if (image_dim_order == "HWC") {
      size_t height = input.shape(0), width = input.shape(1),
             channel = input.shape(2);
      if (channel == 3u) {
        mat = cv::Mat(height, width, CV_32FC3, cv::Scalar(0, 0, 0));
        for (size_t i = 0; i < height; i++)
          for (size_t j = 0; j < width; j++)
            for (size_t k = 0; k < channel; k++)
              mat.at<cv::Vec3f>(i, j)[k] =
                in[i * width * channel + j * channel + k];
      } else if (channel == 1u) { /// 2D gray image
        mat = cv::Mat(height, width, CV_32FC1);
        for (size_t i = 0; i < height; i++)
          for (size_t j = 0; j < width; j++)
            mat.at<cv::Vec<float, 1>>(i, j)[0] = in[i * width + j];
      } else LOG(FATAL) << "Invalid channel size: " << channel;
    } else {
      LOG(FATAL) << "Unknow dimension order for images " << image_dim_order
                 << " Only support 'HWC' and 'CHW'";
    }
  } else { /// 2D gray image
    size_t height = input.shape(0), width = input.shape(1);
    mat = cv::Mat(height, width, CV_32FC1);
    for (size_t i = 0; i < height; i++)
      for (size_t j = 0; j < width; j++)
        mat.at<cv::Vec<float, 1>>(i, j)[0] = in[i * width + j];
  }
  cv::Size size(resize_width, resize_height);
  cv::Mat resized;
  cv::resize(mat, resized, size);
  CHECK_EQ(resized.size().height, resize_height);
  CHECK_EQ(resized.size().width, resize_width);
  size_t new_size = resize_height * resize_width * resized.channels();
  float* out = new float[new_size];
  if (input.nDim() == 4u) {
    /// TODO
    /// batch based resize
    LOG(FATAL) << "Not implemented";
  } else if (input.nDim() == 3u) {
    if (image_dim_order == "CHW") {
      size_t height = resize_height, width = resize_width,
             channel = input.shape(0);
      if (channel == 3u) {
        for (size_t i = 0; i < height; i++)
          for (size_t j = 0; j < width; j++)
            for (size_t k = 0; k < channel; k++)
              out[k * height * width + i * width + j] = resized.at<cv::Vec3f>(i, j)[k];
      } else { /// 2D gray image
        for (size_t i = 0; i < height; i++)
          for (size_t j = 0; j < width; j++)
            out[i * width + j] = resized.at<cv::Vec<float, 1>>(i, j)[0];
      }
      Tensor temp(Shape{channel, height, width});
      temp.CopyDataFromHostPtr<float>(out, new_size);
      output = temp;
    } else {
      size_t height = resize_height, width = resize_width,
             channel = input.shape(2);
      if (channel == 3u) {
        for (size_t i = 0; i < height; i++)
          for (size_t j = 0; j < width; j++)
            for (size_t k = 0; k < channel; k++)
              out[i * width * channel + j * channel + k] = resized.at<cv::Vec3f>(i, j)[k];
      } else { /// 1 channel
        for (size_t i = 0; i < height; i++)
          for (size_t j = 0; j < width; j++)
            out[i * width + j] = resized.at<cv::Vec<float, 1>>(i, j)[0];
      }
      Tensor temp(Shape{height, width, channel});
      temp.CopyDataFromHostPtr<float>(out, new_size);
      output = temp;
    }
  } else { /// 2D gray image
    size_t height = resize_height, width = resize_width;
    for (size_t i = 0; i < height; i++)
      for (size_t j = 0; j < width; j++)
        out[i * width + j] = resized.at<cv::Vec<float, 1>>(i, j)[0];
    Tensor temp(Shape{height, width});
    temp.CopyDataFromHostPtr<float>(out, new_size);
    output = temp;
  }
  delete[] out;
  return output;
}