C++ ImageType::size方法代码示例

void
pcl::pcl_2d::convolution_2d::conv  (ImageType &output, ImageType &kernel, ImageType &input){
  int rows = input.size ();
  int cols = input[0].size ();
  int k_rows = kernel.size ();
  int k_cols = kernel[0].size ();

  /*default boundary option : zero padding*/
  output.resize (input.size ());
  for (int i = 0; i < rows; i++)
  {
    output[i].resize (cols);
    for (int j = 0; j < cols; j++)
    {
      output[i][j] = 0;
      for (int k = 0; k < k_rows; k++)
      {
        for (int l = 0; l < k_cols; l++)
        {
          if ((i + k - k_rows / 2) < 0 || (i + k - k_rows / 2) >= rows || (j + l - k_cols / 2) < 0 || (j + l - k_cols / 2) >= cols)
          {
            continue;
          }
          else
          {
            output[i][j] += kernel[k][l] * input[i + k - k_rows / 2][j + l - k_cols / 2];
          }
        }
      }
    }
  }
}

void
pcl::pcl_2d::edge::robertsMagnitudeDirection  (ImageType &G, ImageType &thet, ImageType &input){
  ImageType Gx;
  ImageType Gy;
  robertsXY (Gx, Gy, input);
  G.resize (input.size ());
  thet.resize (input.size ());
  for (int i = 0; i < input.size (); i++)
  {
    G[i].resize (input[i].size ());
    thet[i].resize (input[i].size ());
    for (int j = 0; j < input[i].size (); j++)
    {
      G[i][j] = sqrt (Gx[i][j] * Gx[i][j] + Gy[i][j] * Gy[i][j]);
      thet[i][j] = atan2 (Gy[i][j], Gx[i][j]);
    }
  }
}

void
pcl::keypoint::hessianBlob (ImageType &output, ImageType &input, const float sigma, bool SCALED){
  /*creating the gaussian kernels*/
  ImageType kernel, cornerness;
  conv_2d.gaussianKernel  (5, sigma, kernel);

  /*scaling the image with differentiation scale*/
  ImageType smoothed_image;
  conv_2d.convolve  (smoothed_image, kernel, input);

  /*image derivatives*/
  ImageType I_x, I_y;
  edge_detection.ComputeDerivativeXCentral  (I_x, smoothed_image);
  edge_detection.ComputeDerivativeYCentral  (I_y, smoothed_image);

  /*second moment matrix*/
  ImageType I_xx, I_yy, I_xy;
  edge_detection.ComputeDerivativeXCentral  (I_xx, I_x);
  edge_detection.ComputeDerivativeYCentral  (I_xy, I_x);
  edge_detection.ComputeDerivativeYCentral  (I_yy, I_y);
  /*Determinant of Hessian*/
  const size_t height = input.size ();
  const size_t width = input[0].size ();
  float min = std::numeric_limits<float>::max();
  float max = std::numeric_limits<float>::min();
  cornerness.resize (height);
  for (size_t i = 0; i < height; i++)
  {
    cornerness[i].resize (width);
    for (size_t j = 0; j < width; j++)
    {
      cornerness[i][j] = sigma*sigma*(I_xx[i][j]+I_yy[i][j]-I_xy[i][j]*I_xy[i][j]);
      if(SCALED){
        if(cornerness[i][j]  < min)
          min = cornerness[i][j];
        if(cornerness[i][j] > max)
          max = cornerness[i][j];
      }
    }

    /*local maxima*/
    output.resize (height);
    output[0].resize (width);
    output[height-1].resize (width);
    for (size_t i = 1; i < height - 1; i++)
    {
      output[i].resize (width);
      for (size_t j = 1; j < width - 1; j++)
      {
        if(SCALED)
          output[i][j] = ((cornerness[i][j]-min)/(max-min));
        else
          output[i][j] = cornerness[i][j];
      }
    }
  }
}

void
pcl::keypoint::hessianBlob (ImageType &output, ImageType &input, const float start_scale, const float scaling_factor, const int num_scales){
  const size_t height = input.size();
  const size_t width = input[0].size();
  const int local_search_radius = 1;
  float scale = start_scale;
  std::vector<ImageType> cornerness;
  cornerness.resize(num_scales);
  for(int i = 0;i < num_scales;i++){
    hessianBlob(cornerness[i], input, scale, false);
    scale *= scaling_factor;
  }
  bool non_max_flag = false;
  float scale_max, local_max;
  for(size_t i = 0;i < height;i++){
    for(size_t j = 0;j < width;j++){
      scale_max = std::numeric_limits<float>::min();
      /*default output in case of no blob at the current point is 0*/
      output[i][j] = 0;
      for(int k = 0;k < num_scales;k++){
        /*check if the current point (k,i,j) is a maximum in the defined search radius*/
        non_max_flag = false;
        local_max = cornerness[k][i][j];
        for(int n = -local_search_radius; n <= local_search_radius;n++){
          if(n+k < 0 || n+k >= num_scales)
            continue;
          for(int l = -local_search_radius;l <= local_search_radius;l++){
            if(l+i < 0 || l+i >= height)
              continue;
            for(int m = -local_search_radius; m <= local_search_radius;m++){
              if(m+j < 0 || m+j >= width)
                continue;
              if(cornerness[n+k][l+i][m+j] > local_max){
                non_max_flag = true;
                break;
              }
            }
            if(non_max_flag)
              break;
          }
          if(non_max_flag)
            break;
        }
        /*if the current point is a point of local maximum, check if it is a maximum point across scales*/
        if(!non_max_flag){
          if(cornerness[k][i][j] > scale_max){
            scale_max = cornerness[k][i][j];
            /*output indicates the scale at which the blob is found at the current location in the image*/
            output[i][i] = start_scale*pow(scaling_factor, k);
          }
        }
      }
    }
  }
}

void
pcl::pcl_2d::edge::cannyTraceEdge  (int rowOffset, int colOffset, int row, int col, float theta, float tLow, float tHigh, ImageType &G, ImageType &thet){
  int newRow = row + rowOffset;
  int newCol = col + colOffset;
  if (newRow > 0 && newRow < G.size () && newCol > 0 && newCol < G[0].size ())
  {
    if (G[newRow][newCol] < tLow || G[newRow][newCol] > tHigh || thet[newRow][newCol] != theta)
      return;
    G[newRow][newCol] = tHigh + 1;
    cannyTraceEdge  (rowOffset,colOffset, newRow, newCol, theta, tLow, tHigh, G, thet);
  }
}

void
pcl::pcl_2d::edge::canny  (ImageType &output, ImageType &input)
{
  float tHigh = 50;
  float tLow = 20;
  const int height = input.size();
  const int width = input[0].size();
  /*noise reduction using gaussian blurring*/
  ImageType gaussian_kernel;
  conv_2d->gaussianKernel  (5, 1.4, gaussian_kernel);
  conv_2d->convolve  (output, gaussian_kernel, input);

  /*edge detection usign Sobel*/
  ImageType G;
  ImageType thet;
  sobelMagnitudeDirection  (G, thet, input);

  /*edge discretization*/
  float angle;
  for (int i = 0; i < height; i++)
  {
    for (int j = 0; j < width; j++)
    {
      angle = (thet[i][j] / 3.14f) * 180;
      if (((angle < 22.5) && (angle > -22.5)) || (angle > 157.5) || (angle < -157.5))
        thet[i][j] = 0;
      else
        if (((angle > 22.5) && (angle < 67.5)) || ((angle < -112.5) && (angle > -157.5)))
          thet[i][j] = 45;
        else
          if (((angle > 67.5) && (angle < 112.5)) || ((angle < -67.5) && (angle > -112.5)))
            thet[i][j] = 90;
          else
            if (((angle > 112.5) && (angle < 157.5)) || ((angle < -22.5) && (angle > -67.5)))
              thet[i][j] = 135;
    }
  }

  float max;
  /*tHigh and non-maximal supression*/
  for (int i = 1; i < height - 1; i++)
  {
    for (int j = 1; j < width - 1; j++)
    {
      if (G[i][j] < tHigh)
        continue;
      max = G[i][j];
      switch ((int)thet[i][j])
      {
        case 0:
          if(G[i][j] < G[i][j-1] || G[i][j] < G[i][j+1])
            G[i][j] = 0;
          break;
        case 45:
          if(G[i][j] < G[i-1][j+1] || G[i][j] < G[i+1][j-1])
            G[i][j] = 0;
          break;
        case 90:
          if(G[i][j] < G[i-1][j] || G[i][j] < G[i+1][j])
            G[i][j] = 0;
          break;
        case 135:
          if(G[i][j] < G[i-1][j-1] || G[i][j] < G[i+1][j+1])
            G[i][j] = 0;
          break;
      }
    }
  }

  /*edge tracing*/
  for (int i = 0; i < height; i++)
  {
    for (int j = 0; j < width; j++)
    {
      if (G[i][j] < tHigh)
        continue;
      switch ((int)thet[i][j])
      {
        case 0:
          cannyTraceEdge  (1, 0, i, j, 0, tLow, tHigh, G, thet);
          cannyTraceEdge  (-1, 0, i, j, 0, tLow, tHigh, G, thet);
          break;
        case 45:
          cannyTraceEdge  (1, 1, i, j, 45, tLow, tHigh, G, thet);
          cannyTraceEdge  (-1, -1, i, j, 45, tLow, tHigh, G, thet);
          break;
        case 90:
          cannyTraceEdge  (0, -1, i, j, 90, tLow, tHigh, G, thet);
          cannyTraceEdge  (0, 1, i, j, 90, tLow, tHigh, G, thet);
          break;
        case 135:
          cannyTraceEdge  (-1, 1, i, j, 135, tLow, tHigh, G, thet);
          cannyTraceEdge  (1, -1, i, j, 135, tLow, tHigh, G, thet);
          break;
      }
    }
  }

  /*final thresholding*/
  output.resize (height);
//.........这里部分代码省略.........

void
pcl::keypoint::harrisCorner (ImageType &output, ImageType &input, const float sigma_d, const float sigma_i, const float alpha, const float thresh){

  /*creating the gaussian kernels*/
  ImageType kernel_d;
  ImageType kernel_i;
  conv_2d.gaussianKernel  (5, sigma_d, kernel_d);
  conv_2d.gaussianKernel  (5, sigma_i, kernel_i);

  /*scaling the image with differentiation scale*/
  ImageType smoothed_image;
  conv_2d.convolve  (smoothed_image, kernel_d, input);

  /*image derivatives*/
  ImageType I_x, I_y;
  edge_detection.ComputeDerivativeXCentral  (I_x, smoothed_image);
  edge_detection.ComputeDerivativeYCentral  (I_y, smoothed_image);

  /*second moment matrix*/
  ImageType I_x2, I_y2, I_xI_y;
  imageElementMultiply  (I_x2, I_x, I_x);
  imageElementMultiply  (I_y2, I_y, I_y);
  imageElementMultiply  (I_xI_y, I_x, I_y);

  /*scaling second moment matrix with integration scale*/
  ImageType M00, M10, M11;
  conv_2d.convolve  (M00, kernel_i, I_x2);
  conv_2d.convolve  (M10, kernel_i, I_xI_y);
  conv_2d.convolve  (M11, kernel_i, I_y2);

  /*harris function*/
  const size_t height = input.size ();
  const size_t width = input[0].size ();
  output.resize (height);
  for (size_t i = 0; i < height; i++)
  {
    output[i].resize (width);
    for (size_t j = 0; j < width; j++)
    {
      output[i][j] = M00[i][j] * M11[i][j] - (M10[i][j] * M10[i][j]) - alpha * ((M00[i][j] + M11[i][j]) * (M00[i][j] + M11[i][j]));
      if (thresh != 0)
      {
        if (output[i][j] < thresh)
          output[i][j] = 0;
        else
          output[i][j] = 255;
      }
    }
  }

  /*local maxima*/
  for (size_t i = 1; i < height - 1; i++)
  {
    for (size_t j = 1; j < width - 1; j++)
    {
      if (output[i][j] > output[i - 1][j - 1] && output[i][j] > output[i - 1][j] && output[i][j] > output[i - 1][j + 1] &&
          output[i][j] > output[i][j - 1] && output[i][j] > output[i][j + 1] &&
          output[i][j] > output[i + 1][j - 1] && output[i][j] > output[i + 1][j] && output[i][j] > output[i + 1][j + 1])
        ;
      else
        output[i][j] = 0;
    }
  }
}

void
pcl::pcl_2d::convolution_2d::conv  (ImageType &output, ImageType &kernel, ImageType &input, BOUNDARY_OPTIONS_ENUM boundary_option){
  int rows = input.size ();
  int cols = input[0].size ();
  int k_rows = kernel.size ();
  int k_cols = kernel[0].size ();
  int input_row = 0, input_col = 0;

  output.resize (input.size ());

  if (boundary_option == BOUNDARY_OPTION_CLAMP)
  {
    for (int i = 0; i < rows; i++)
    {
      output[i].resize (cols);
      for (int j = 0; j < cols; j++)
      {
        output[i][j] = 0;
        for (int k = 0; k < k_rows; k++)
        {
          for (int l = 0; l < k_cols; l++)
          {
            if ((i + k - k_rows / 2) < 0)
              input_row = 0;
            else
              if ((i + k - k_rows / 2) >= rows)
              {
                input_row = rows - 1;
              }
              else
                input_row = i + k - k_rows / 2;
            if ((j + l - k_cols / 2) < 0)
              input_col = 0;
            else
              if ((j + l - k_cols / 2) >= cols)
                input_col = cols - 1;
              else
                input_col = j + l - k_cols / 2;
            output[i][j] += kernel[k][l] * input[input_row][input_col];
          }
        }
      }
    }
  }
  else
    if (boundary_option == BOUNDARY_OPTION_MIRROR)
    {
      for (int i = 0; i < rows; i++)
      {
        output[i].resize (cols);
        for (int j = 0; j < cols; j++)
        {
          output[i][j] = 0;
          for (int k = 0; k < k_rows; k++)
          {
            for (int l = 0; l < k_cols; l++)
            {
              if ((i + k - (k_rows / 2)) < 0)
                input_row = -(i + k - (k_rows / 2));
              else
                if ((i + k - (k_rows / 2)) >= rows)
                {
                  input_row = 2 * rows - 1 - (i + k - (k_rows / 2));
                }
                else
                  input_row = i + k - (k_rows / 2);

              if ((j + l - (k_cols / 2)) < 0)
                input_col = -(j + l - (k_cols / 2));
              else
                if ((j + l - (k_cols / 2)) >= cols)
                  input_col = 2 * cols - 1 - (j + l - (k_cols / 2));
                else
                  input_col = j + l - (k_cols / 2);
              output[i][j] += kernel[k][l] * input[input_row][input_col];
            }
          }
        }
      }
    }
    else
      if (boundary_option == BOUNDARY_OPTION_ZERO_PADDING)
      {
        conv (output, kernel, input);
      }
}