private void addFeatureOrientations(GaussianScaleSpacePyramid i_pyramid, DogFeaturePoint dfp, DogFeaturePointStack i_ot_fps)
{
double[] tmp = this._addFeatureOrientations_tmp;
double x, y, s;
// Down sample the point to the detected octave
bilinear_downsample_point(tmp, dfp.x, dfp.y, dfp.sigma, dfp.octave);
x = tmp[0];
y = tmp[1];
s = tmp[2];
// Downsampling the point can cause (x,y) to leave the image bounds
// by
// a tiny amount. Here we just clip it to be within the image
// bounds.
x = ClipScalar(x, 0, i_pyramid.get(dfp.octave, 0).getWidth() - 1);
y = ClipScalar(y, 0, i_pyramid.get(dfp.octave, 0).getHeight() - 1);
// Compute dominant orientations
int num_angles = mOrientationAssignment.compute(dfp.octave, dfp.scale, x, y, s, this.mOrientations);
// Create a feature point for each angle
for (int j = 0; j < num_angles; j++)
{
// Copy the feature point
DogFeaturePoint fp = i_ot_fps.prePush();
if (fp == null)
{
//中断
// prepush_warning();
break;
}
fp.x = dfp.x;
fp.y = dfp.y;
fp.octave = dfp.octave;
fp.scale = dfp.scale;
fp.sp_scale = dfp.sp_scale;
fp.score = dfp.score;
fp.sigma = dfp.sigma;
fp.edge_score = dfp.edge_score;
fp.angle = mOrientations[j];
}
return;
}
/**
* Extract the minima/maxima.
*/
private void extractFeatures(GaussianScaleSpacePyramid pyramid, DoGPyramid laplacian, DogFeaturePointStack i_dog_fp)
{
double laplacianSqrThreshold = (this.mLaplacianThreshold * this.mLaplacianThreshold);
for (int i = 1; i < mLaplacianPyramid.size() - 1; i++)
{
LaplacianImage im0 = laplacian.get(i - 1);
LaplacianImage im1 = laplacian.get(i);
LaplacianImage im2 = laplacian.get(i + 1);
double[] im0b = (double[])im0.getBuffer();
double[] im1b = (double[])im1.getBuffer();
double[] im2b = (double[])im2.getBuffer();
int octave = laplacian.octaveFromIndex((int)i);
int scale = laplacian.scaleFromIndex((int)i);
if (im0.getWidth() == im1.getWidth() && im0.getWidth() == im2.getWidth())
{ // All images are the
// same size
// ASSERT(im0.height() == im1.height(), "Height is inconsistent");
// ASSERT(im0.height() == im2.height(), "Height is inconsistent");
int width_minus_1 = im1.getWidth() - 1;
int heigh_minus_1 = im1.getHeight() - 1;
for (int row = 1; row < heigh_minus_1; row++)
{
int im0_ym1 = im0.get(row - 1);
int im0_y = im0.get(row);
int im0_yp1 = im0.get(row + 1);
int im1_ym1 = im1.get(row - 1);
int im1_y = im1.get(row);
int im1_yp1 = im1.get(row + 1);
int im2_ym1 = im2.get(row - 1);
int im2_y = im2.get(row);
int im2_yp1 = im2.get(row + 1);
for (int col = 1; col < width_minus_1; col++)
{
double value = im1b[im1_y + col];
// Check laplacian score
if ((value * value) < laplacianSqrThreshold)
{
continue;
}
bool extrema = false;
if (value > im0b[im0_ym1 + col - 1] && value > im0b[im0_ym1 + col] &&
value > im0b[im0_ym1 + col + 1] && value > im0b[im0_y + col - 1] &&
value > im0b[im0_y + col] && value > im0b[im0_y + col + 1] &&
value > im0b[im0_yp1 + col - 1] && value > im0b[im0_yp1 + col] &&
value > im0b[im0_yp1 + col + 1] &&
/* im1 - 8 evaluations */
value > im1b[im1_ym1 + col - 1] && value > im1b[im1_ym1 + col] &&
value > im1b[im1_ym1 + col + 1] && value > im1b[im1_y + col - 1] &&
value > im1b[im1_y + col + 1] && value > im1b[im1_yp1 + col - 1] &&
value > im1b[im1_yp1 + col] && value > im1b[im1_yp1 + col + 1] &&
/* im2 - 9 evaluations */
value > im2b[im2_ym1 + col - 1] && value > im2b[im2_ym1 + col] &&
value > im2b[im2_ym1 + col + 1] && value > im2b[im2_y + col - 1] &&
value > im2b[im2_y + col] && value > im2b[im2_y + col + 1] &&
value > im2b[im2_yp1 + col - 1] && value > im2b[im2_yp1 + col] &&
value > im2b[im2_yp1 + col + 1])
{
extrema = true;
}
else if (value < im0b[im0_ym1 + col - 1] && value < im0b[im0_ym1 + col] &&
value < im0b[im0_ym1 + col + 1] && value < im0b[im0_y + col - 1] &&
value < im0b[im0_y + col] && value < im0b[im0_y + col + 1] &&
value < im0b[im0_yp1 + col - 1] && value < im0b[im0_yp1 + col] &&
value < im0b[im0_yp1 + col + 1] &&
/* im1 - 8 evaluations */
value < im1b[im1_ym1 + col - 1] && value < im1b[im1_ym1 + col] &&
value < im1b[im1_ym1 + col + 1] && value < im1b[im1_y + col - 1] &&
value < im1b[im1_y + col + 1] && value < im1b[im1_yp1 + col - 1] &&
value < im1b[im1_yp1 + col] && value < im1b[im1_yp1 + col + 1] &&
/* im2 - 9 evaluations */
value < im2b[im2_ym1 + col - 1] && value < im2b[im2_ym1 + col] &&
value < im2b[im2_ym1 + col + 1] && value < im2b[im2_y + col - 1] &&
value < im2b[im2_y + col] && value < im2b[im2_y + col + 1] &&
value < im2b[im2_yp1 + col - 1] && value < im2b[im2_yp1 + col] &&
value < im2b[im2_yp1 + col + 1])
{
extrema = true;
}
if (extrema)
{
DogFeaturePoint fp = i_dog_fp.prePush();
if (fp == null)
{
prepush_warning();
break;
}
fp.octave = octave;
fp.scale = scale;
fp.score = value;
fp.sigma = pyramid.effectiveSigma(octave, scale);
double[] tmp = new double[2];
bilinear_upsample_point(tmp, col, row, octave);
fp.x = tmp[0];
fp.y = tmp[1];
}
}
}
}
else if (im0.getWidth() == im1.getWidth() && (im1.getWidth() >> 1) == im2.getWidth())
{
int end_x = (int)Math.Floor(((im2.getWidth() - 1) - 0.5f) * 2.0f + 0.5f);
int end_y = (int)Math.Floor(((im2.getHeight() - 1) - 0.5f) * 2.0f + 0.5f);
for (int row = 2; row < end_y; row++)
{
int im0_ym1 = im0.get(row - 1);
int im0_y = im0.get(row);
int im0_yp1 = im0.get(row + 1);
int im1_ym1 = im1.get(row - 1);
int im1_y = im1.get(row);
int im1_yp1 = im1.get(row + 1);
for (int col = 2; col < end_x; col++)
{
double value = im1b[im1_y + col];
// Check laplacian score
if ((value * value) < laplacianSqrThreshold)
{
continue;
}
// Compute downsampled point location
double ds_x = col * 0.5f - 0.25f;
double ds_y = row * 0.5f - 0.25f;
bool extrema = false;
if (
/* im0 - 9 evaluations */
value > im0b[im0_ym1 + col - 1] && value > im0b[im0_ym1 + col] &&
value > im0b[im0_ym1 + col + 1] && value > im0b[im0_y + col - 1] &&
value > im0b[im0_y + col] && value > im0b[im0_y + col + 1] &&
value > im0b[im0_yp1 + col - 1] && value > im0b[im0_yp1 + col] &&
value > im0b[im0_yp1 + col + 1] &&
/* im1 - 8 evaluations */
value > im1b[im1_ym1 + col - 1] && value > im1b[im1_ym1 + col] &&
value > im1b[im1_ym1 + col + 1] && value > im1b[im1_y + col - 1] &&
value > im1b[im1_y + col + 1] && value > im1b[im1_yp1 + col - 1] &&
value > im1b[im1_yp1 + col] && value > im1b[im1_yp1 + col + 1] &&
/* im2 - 9 evaluations */
value > im2.bilinearInterpolation(ds_x - 0.5f, ds_y - 0.5f) &&
value > im2.bilinearInterpolation(ds_x, ds_y - 0.5f) &&
value > im2.bilinearInterpolation(ds_x + 0.5f, ds_y - 0.5f) &&
value > im2.bilinearInterpolation(ds_x - 0.5f, ds_y) &&
value > im2.bilinearInterpolation(ds_x, ds_y) &&
value > im2.bilinearInterpolation(ds_x + 0.5f, ds_y) &&
value > im2.bilinearInterpolation(ds_x - 0.5f, ds_y + 0.5f) &&
value > im2.bilinearInterpolation(ds_x, ds_y + 0.5f) &&
value > im2.bilinearInterpolation(ds_x + 0.5f, ds_y + 0.5f))
{
extrema = true;
}
else if (
/* im0 - 9 evaluations */
value < im0b[im0_ym1 + col - 1] && value < im0b[im0_ym1 + col] &&
value < im0b[im0_ym1 + col + 1] && value < im0b[im0_y + col - 1] &&
value < im0b[im0_y + col] && value < im0b[im0_y + col + 1] &&
value < im0b[im0_yp1 + col - 1] && value < im0b[im0_yp1 + col] &&
value < im0b[im0_yp1 + col + 1] &&
/* im1 - 8 evaluations */
value < im1b[im1_ym1 + col - 1] && value < im1b[im1_ym1 + col] &&
value < im1b[im1_ym1 + col + 1] && value < im1b[im1_y + col - 1] &&
value < im1b[im1_y + col + 1] && value < im1b[im1_yp1 + col - 1] &&
value < im1b[im1_yp1 + col] && value < im1b[im1_yp1 + col + 1] &&
/* im2 - 9 evaluations */
value < im2.bilinearInterpolation(ds_x - 0.5f, ds_y - 0.5f) &&
value < im2.bilinearInterpolation(ds_x, ds_y - 0.5f) &&
value < im2.bilinearInterpolation(ds_x + 0.5f, ds_y - 0.5f) &&
value < im2.bilinearInterpolation(ds_x - 0.5f, ds_y) &&
value < im2.bilinearInterpolation(ds_x, ds_y) &&
value < im2.bilinearInterpolation(ds_x + 0.5f, ds_y) &&
value < im2.bilinearInterpolation(ds_x - 0.5f, ds_y + 0.5f) &&
value < im2.bilinearInterpolation(ds_x, ds_y + 0.5f) &&
value < im2.bilinearInterpolation(ds_x + 0.5f, ds_y + 0.5f))
{
extrema = true;
}
if (extrema)
{
DogFeaturePoint fp = i_dog_fp.prePush();
if (fp == null)
{
prepush_warning();
break;
}
fp.octave = octave;
fp.scale = scale;
fp.score = value;
fp.sigma = pyramid.effectiveSigma(octave, scale);
double[] tmp = new double[2];
bilinear_upsample_point(tmp, col, row, octave);
fp.x = tmp[0];
fp.y = tmp[1];
}
}
}
}
else if ((im0.getWidth() >> 1) == im1.getWidth() && (im0.getWidth() >> 1) == im2.getWidth())
{
int width_minus_1 = im1.getWidth() - 1;
int height_minus_1 = im1.getHeight() - 1;
for (int row = 1; row < height_minus_1; row++)
{
int im1_ym1 = im1.get(row - 1);
int im1_y = im1.get(row);
int im1_yp1 = im1.get(row + 1);
int im2_ym1 = im2.get(row - 1);
int im2_y = im2.get(row);
int im2_yp1 = im2.get(row + 1);
for (int col = 1; col < width_minus_1; col++)
{
double value = im1b[im1_y + col];
// Check laplacian score
if ((value * value) < laplacianSqrThreshold)
{
continue;
}
double us_x = (col << 1) + 0.5f;
double us_y = (row << 1) + 0.5f;
bool extrema = false;
if (value > im1b[im1_ym1 + col - 1] && value > im1b[im1_ym1 + col] &&
value > im1b[im1_ym1 + col + 1] && value > im1b[im1_y + col - 1] &&
value > im1b[im1_y + col + 1] && value > im1b[im1_yp1 + col - 1] &&
value > im1b[im1_yp1 + col] && value > im1b[im1_yp1 + col + 1] &&
/* im2 - 9 evaluations */
value > im2b[im2_ym1 + col - 1] && value > im2b[im2_ym1 + col] &&
value > im2b[im2_ym1 + col + 1] && value > im2b[im2_y + col - 1] &&
value > im2b[im2_y + col] && value > im2b[im2_y + col + 1] &&
value > im2b[im2_yp1 + col - 1] && value > im2b[im2_yp1 + col] &&
value > im2b[im2_yp1 + col + 1] &&
/* im2 - 9 evaluations */
value > im0.bilinearInterpolation(us_x - 2.0f, us_y - 2.0f) &&
value > im0.bilinearInterpolation(us_x, us_y - 2.0f) &&
value > im0.bilinearInterpolation(us_x + 2.0f, us_y - 2.0f) &&
value > im0.bilinearInterpolation(us_x - 2.0f, us_y) &&
value > im0.bilinearInterpolation(us_x, us_y) &&
value > im0.bilinearInterpolation(us_x + 2.0f, us_y) &&
value > im0.bilinearInterpolation(us_x - 2.0f, us_y + 2.0f) &&
value > im0.bilinearInterpolation(us_x, us_y + 2.0f) &&
value > im0.bilinearInterpolation(us_x + 2.0f, us_y + 2.0f))
{
extrema = true;
}
else if (value < im1b[im1_ym1 + col - 1] && value < im1b[im1_ym1 + col] &&
value < im1b[im1_ym1 + col + 1] && value < im1b[im1_y + col - 1] &&
value < im1b[im1_y + col + 1] && value < im1b[im1_yp1 + col - 1] &&
value < im1b[im1_yp1 + col] && value < im1b[im1_yp1 + col + 1] &&
/* im2 - 9 evaluations */
value < im2b[im2_ym1 + col - 1] && value < im2b[im2_ym1 + col] &&
value < im2b[im2_ym1 + col + 1] && value < im2b[im2_y + col - 1] &&
value < im2b[im2_y + col] && value < im2b[im2_y + col + 1] &&
value < im2b[im2_yp1 + col - 1] && value < im2b[im2_yp1 + col] &&
value < im2b[im2_yp1 + col + 1] &&
/* im2 - 9 evaluations */
value < im0.bilinearInterpolation(us_x - 2.0f, us_y - 2.0f) &&
value < im0.bilinearInterpolation(us_x, us_y - 2.0f) &&
value < im0.bilinearInterpolation(us_x + 2.0f, us_y - 2.0f) &&
value < im0.bilinearInterpolation(us_x - 2.0f, us_y) &&
value < im0.bilinearInterpolation(us_x, us_y) &&
value < im0.bilinearInterpolation(us_x + 2.0f, us_y) &&
value < im0.bilinearInterpolation(us_x - 2.0f, us_y + 2.0f) &&
value < im0.bilinearInterpolation(us_x, us_y + 2.0f) &&
value < im0.bilinearInterpolation(us_x + 2.0f, us_y + 2.0f))
{
extrema = true;
}
if (extrema)
{
DogFeaturePoint fp = i_dog_fp.prePush();
if (fp == null)
{
prepush_warning();
break;
}
fp.octave = octave;
fp.scale = scale;
fp.score = value;
fp.sigma = pyramid.effectiveSigma(octave, scale);
double[] tmp = new double[2];
bilinear_upsample_point(tmp, col, row, octave);
fp.x = tmp[0];
fp.y = tmp[1];
}
}
}
}
}
return;
}