/**
* Extract a descriptor from the pyramid for a single point.
*/
bool ExtractFREAK84(
LongDescripter768 i_desc, // unsigned char desc[84],
GaussianScaleSpacePyramid pyramid, DogFeaturePoint point,
double[] points_ring0, double[] points_ring1, double[] points_ring2,
double[] points_ring3, double[] points_ring4, double[] points_ring5,
double sigma_center, double sigma_ring0, double sigma_ring1,
double sigma_ring2, double sigma_ring3, double sigma_ring4,
double sigma_ring5, double expansion_factor)
{
double[] samples = new double[37];
// Create samples
if (!SamplePyramidFREAK84(samples, pyramid, point, points_ring0,
points_ring1, points_ring2, points_ring3, points_ring4,
points_ring5, sigma_center, sigma_ring0, sigma_ring1,
sigma_ring2, sigma_ring3, sigma_ring4, sigma_ring5,
expansion_factor))
{
return(false);
}
// Once samples are created compute descriptor
CompareFREAK84(i_desc, samples);
return(true);
}
/**
* Detect scale-invariant feature points given a pyramid.
* @param _i_dog_feature_points
* 検出したDOG特徴点
*/
public void detect(GaussianScaleSpacePyramid i_pyramid, DogFeaturePointStack i_dog_feature_points)
{
//clean up 1st feature stack
DogFeaturePointStack tmp_fp = this._tmp_fps;
tmp_fp.clear();
// Compute Laplacian images (DoG)
this.mLaplacianPyramid.compute(i_pyramid);
// Detect minima and maximum in Laplacian images
this.extractFeatures(i_pyramid, this.mLaplacianPyramid, tmp_fp);
// Sub-pixel refinement
this.findSubpixelLocations(i_pyramid, tmp_fp);
// Compute the gradient pyramid
this.mOrientationAssignment.computeGradients(i_pyramid);
AreaBuckit abuckit = this.mBuckets;
if (tmp_fp.getLength() <= abuckit._buckit.Length)
{
//特徴点の数が要求数以下なら全てのポイントを使う。
for (int i = 0; i < tmp_fp.getLength(); i++)
{
this.addFeatureOrientations(i_pyramid, tmp_fp.getItem(i), i_dog_feature_points);
}
}
else
{
//特徴点を選別(Prune features)
// Clear the previous state
abuckit.clear();
// Insert each features into a bucket
for (int i = 0; i < tmp_fp.getLength(); i++)
{
DogFeaturePoint p = tmp_fp.getItem(i);
abuckit.put(p.x, p.y, i, Math.Abs(p.score));
}
// Compute an orientation for each feature point
for (int i = 0; i < abuckit._buckit.Length; i++)
{
if (abuckit._buckit[i].first == 0)
{
continue;
}
this.addFeatureOrientations(i_pyramid, tmp_fp.getItem(abuckit._buckit[i].second), i_dog_feature_points);
}
}
return;
}
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;
}
/**
* Sub-pixel refinement.
*/
private void findSubpixelLocations(GaussianScaleSpacePyramid pyramid, DogFeaturePointStack i_dog_fp)
{
int num_points;
double laplacianSqrThreshold;
double hessianThreshold;
num_points = 0;
laplacianSqrThreshold = (this.mLaplacianThreshold * this.mLaplacianThreshold);
double te = (mEdgeThreshold + 1);
hessianThreshold = ((te * te) / mEdgeThreshold);
for (int i = 0; i < i_dog_fp.getLength(); i++)
{
DogFeaturePoint kp = i_dog_fp.getItem(i);
//assert kp.scale < mLaplacianPyramid.numScalePerOctave();
// ASSERT(kp.scale < mLaplacianPyramid.numScalePerOctave(),
// "Feature point scale is out of bounds");
int lap_index = kp.octave * mLaplacianPyramid.numScalePerOctave() + kp.scale;
// Get Laplacian images
LaplacianImage lap0 = mLaplacianPyramid.get(lap_index - 1);
LaplacianImage lap1 = mLaplacianPyramid.get(lap_index);
LaplacianImage lap2 = mLaplacianPyramid.get(lap_index + 1);
// Compute the Hessian
if (!this.updateLocation(kp, lap0, lap1, lap2))
{
continue;
}
if (Math.Abs(kp.edge_score) < hessianThreshold && (kp.score * kp.score) >= laplacianSqrThreshold &&
kp.x >= 0 && kp.x < mLaplacianPyramid.get(0).getWidth() && kp.y >= 0 &&
kp.y < mLaplacianPyramid.get(0).getHeight())
{
// Update the sigma
kp.sigma = pyramid.effectiveSigma(kp.octave, kp.sp_scale);
i_dog_fp.swap(i, num_points++);
}
}
i_dog_fp.setLength(num_points);
}
/**
* Extract the descriptors for all the feature points.
*/
private void ExtractFREAK84(FreakFeaturePointStack store,
GaussianScaleSpacePyramid pyramid, DogFeaturePointStack points,
double[] points_ring0, double[] points_ring1, double[] points_ring2,
double[] points_ring3, double[] points_ring4, double[] points_ring5,
double sigma_center, double sigma_ring0, double sigma_ring1,
double sigma_ring2, double sigma_ring3, double sigma_ring4,
double sigma_ring5, double expansion_factor)
{
// ASSERT(pyramid, "Pyramid is NULL");
// ASSERT(store.size() == points.size(),
// "Feature store has not been allocated");
for (int i = 0; i < points.getLength(); i++)
{
FreakFeaturePoint sp = store.prePush();
if (sp == null)
{
prepush_wawning();
break;
}
DogFeaturePoint pt = points.getItem(i);
if (!ExtractFREAK84(sp.descripter,
pyramid, pt, points_ring0, points_ring1,
points_ring2, points_ring3, points_ring4, points_ring5,
sigma_center, sigma_ring0, sigma_ring1, sigma_ring2,
sigma_ring3, sigma_ring4, sigma_ring5, expansion_factor
))
{
store.pop();
continue;
}
sp.angle = pt.angle;
sp.x = pt.x;
sp.y = pt.y;
sp.scale = pt.sigma;
sp.maxima = pt.score > 0;
// store.point(num_points).set(points[i]);
}
}
/**
* Sample all the receptors from the pyramid given a single point.
*/
bool SamplePyramidFREAK84(double[] samples,
GaussianScaleSpacePyramid pyramid, DogFeaturePoint point,
double[] points_ring0, double[] points_ring1, double[] points_ring2,
double[] points_ring3, double[] points_ring4, double[] points_ring5,
double sigma_center, double sigma_ring0, double sigma_ring1,
double sigma_ring2, double sigma_ring3, double sigma_ring4,
double sigma_ring5, double expansion_factor)
{
double[] S = new double[9];
double[] c = new double[2];
double[] r0 = new double[2 * 6];
double[] r1 = new double[2 * 6];
double[] r2 = new double[2 * 6];
double[] r3 = new double[2 * 6];
double[] r4 = new double[2 * 6];
double[] r5 = new double[2 * 6];
double sc, s0, s1, s2, s3, s4, s5;
// Ensure the scale of the similarity transform is at least "1".
// double transform_scale = point.scale * expansion_factor;
double transform_scale = point.sigma * expansion_factor;
if (transform_scale < 1)
{
transform_scale = 1;
}
// Transformation from canonical test locations to image
Similarity(S, point.x, point.y, point.angle, transform_scale);
// Locate center points
c[0] = S[2];
c[1] = S[5];
// Locate ring 0 points
MultiplyPointSimilarityInhomogenous(r0, 0, S, points_ring0, 0);
MultiplyPointSimilarityInhomogenous(r0, 2, S, points_ring0, 2);
MultiplyPointSimilarityInhomogenous(r0, 4, S, points_ring0, 4);
MultiplyPointSimilarityInhomogenous(r0, 6, S, points_ring0, 6);
MultiplyPointSimilarityInhomogenous(r0, 8, S, points_ring0, 8);
MultiplyPointSimilarityInhomogenous(r0, 10, S, points_ring0, 10);
// Locate ring 1 points
MultiplyPointSimilarityInhomogenous(r1, 0, S, points_ring1, 0);
MultiplyPointSimilarityInhomogenous(r1, 2, S, points_ring1, 2);
MultiplyPointSimilarityInhomogenous(r1, 4, S, points_ring1, 4);
MultiplyPointSimilarityInhomogenous(r1, 6, S, points_ring1, 6);
MultiplyPointSimilarityInhomogenous(r1, 8, S, points_ring1, 8);
MultiplyPointSimilarityInhomogenous(r1, 10, S, points_ring1, 10);
// Locate ring 2 points
MultiplyPointSimilarityInhomogenous(r2, 0, S, points_ring2, 0);
MultiplyPointSimilarityInhomogenous(r2, 2, S, points_ring2, 2);
MultiplyPointSimilarityInhomogenous(r2, 4, S, points_ring2, 4);
MultiplyPointSimilarityInhomogenous(r2, 6, S, points_ring2, 6);
MultiplyPointSimilarityInhomogenous(r2, 8, S, points_ring2, 8);
MultiplyPointSimilarityInhomogenous(r2, 10, S, points_ring2, 10);
// Locate ring 3 points
MultiplyPointSimilarityInhomogenous(r3, 0, S, points_ring3, 0);
MultiplyPointSimilarityInhomogenous(r3, 2, S, points_ring3, 2);
MultiplyPointSimilarityInhomogenous(r3, 4, S, points_ring3, 4);
MultiplyPointSimilarityInhomogenous(r3, 6, S, points_ring3, 6);
MultiplyPointSimilarityInhomogenous(r3, 8, S, points_ring3, 8);
MultiplyPointSimilarityInhomogenous(r3, 10, S, points_ring3, 10);
// Locate ring 4 points
MultiplyPointSimilarityInhomogenous(r4, 0, S, points_ring4, 0);
MultiplyPointSimilarityInhomogenous(r4, 2, S, points_ring4, 2);
MultiplyPointSimilarityInhomogenous(r4, 4, S, points_ring4, 4);
MultiplyPointSimilarityInhomogenous(r4, 6, S, points_ring4, 6);
MultiplyPointSimilarityInhomogenous(r4, 8, S, points_ring4, 8);
MultiplyPointSimilarityInhomogenous(r4, 10, S, points_ring4, 10);
// Locate ring 5 points
MultiplyPointSimilarityInhomogenous(r5, 0, S, points_ring5, 0);
MultiplyPointSimilarityInhomogenous(r5, 2, S, points_ring5, 2);
MultiplyPointSimilarityInhomogenous(r5, 4, S, points_ring5, 4);
MultiplyPointSimilarityInhomogenous(r5, 6, S, points_ring5, 6);
MultiplyPointSimilarityInhomogenous(r5, 8, S, points_ring5, 8);
MultiplyPointSimilarityInhomogenous(r5, 10, S, points_ring5, 10);
// Transfer all the SIGMA values to the image
sc = sigma_center * transform_scale;
s0 = sigma_ring0 * transform_scale;
s1 = sigma_ring1 * transform_scale;
s2 = sigma_ring2 * transform_scale;
s3 = sigma_ring3 * transform_scale;
s4 = sigma_ring4 * transform_scale;
s5 = sigma_ring5 * transform_scale;
//
// Locate and sample ring 5
//
GaussianScaleSpacePyramid.LocateResult lr = new GaussianScaleSpacePyramid.LocateResult();
pyramid.locate(s5, lr);
samples[0] = SampleReceptor(pyramid, r5[0], r5[1], lr.octave, lr.scale);
samples[1] = SampleReceptor(pyramid, r5[2], r5[3], lr.octave, lr.scale);
samples[2] = SampleReceptor(pyramid, r5[4], r5[5], lr.octave, lr.scale);
samples[3] = SampleReceptor(pyramid, r5[6], r5[7], lr.octave, lr.scale);
samples[4] = SampleReceptor(pyramid, r5[8], r5[9], lr.octave, lr.scale);
samples[5] = SampleReceptor(pyramid, r5[10], r5[11], lr.octave,
lr.scale);
//
// Locate and sample ring 4
//
pyramid.locate(s4, lr);
samples[6] = SampleReceptor(pyramid, r4[0], r4[1], lr.octave, lr.scale);
samples[7] = SampleReceptor(pyramid, r4[2], r4[3], lr.octave, lr.scale);
samples[8] = SampleReceptor(pyramid, r4[4], r4[5], lr.octave, lr.scale);
samples[9] = SampleReceptor(pyramid, r4[6], r4[7], lr.octave, lr.scale);
samples[10] = SampleReceptor(pyramid, r4[8], r4[9], lr.octave, lr.scale);
samples[11] = SampleReceptor(pyramid, r4[10], r4[11], lr.octave,
lr.scale);
//
// Locate and sample ring 3
//
pyramid.locate(s3, lr);
samples[12] = SampleReceptor(pyramid, r3[0], r3[1], lr.octave, lr.scale);
samples[13] = SampleReceptor(pyramid, r3[2], r3[3], lr.octave, lr.scale);
samples[14] = SampleReceptor(pyramid, r3[4], r3[5], lr.octave, lr.scale);
samples[15] = SampleReceptor(pyramid, r3[6], r3[7], lr.octave, lr.scale);
samples[16] = SampleReceptor(pyramid, r3[8], r3[9], lr.octave, lr.scale);
samples[17] = SampleReceptor(pyramid, r3[10], r3[11], lr.octave,
lr.scale);
//
// Locate and sample ring 2
//
pyramid.locate(s2, lr);
samples[18] = SampleReceptor(pyramid, r2[0], r2[1], lr.octave, lr.scale);
samples[19] = SampleReceptor(pyramid, r2[2], r2[3], lr.octave, lr.scale);
samples[20] = SampleReceptor(pyramid, r2[4], r2[5], lr.octave, lr.scale);
samples[21] = SampleReceptor(pyramid, r2[6], r2[7], lr.octave, lr.scale);
samples[22] = SampleReceptor(pyramid, r2[8], r2[9], lr.octave, lr.scale);
samples[23] = SampleReceptor(pyramid, r2[10], r2[11], lr.octave,
lr.scale);
//
// Locate and sample ring 1
//
pyramid.locate(s1, lr);
samples[24] = SampleReceptor(pyramid, r1[0], r1[1], lr.octave, lr.scale);
samples[25] = SampleReceptor(pyramid, r1[2], r1[3], lr.octave, lr.scale);
samples[26] = SampleReceptor(pyramid, r1[4], r1[5], lr.octave, lr.scale);
samples[27] = SampleReceptor(pyramid, r1[6], r1[7], lr.octave, lr.scale);
samples[28] = SampleReceptor(pyramid, r1[8], r1[9], lr.octave, lr.scale);
samples[29] = SampleReceptor(pyramid, r1[10], r1[11], lr.octave, lr.scale);
//
// Locate and sample ring 0
//
pyramid.locate(s0, lr);
samples[30] = SampleReceptor(pyramid, r0[0], r0[1], lr.octave, lr.scale);
samples[31] = SampleReceptor(pyramid, r0[2], r0[3], lr.octave, lr.scale);
samples[32] = SampleReceptor(pyramid, r0[4], r0[5], lr.octave, lr.scale);
samples[33] = SampleReceptor(pyramid, r0[6], r0[7], lr.octave, lr.scale);
samples[34] = SampleReceptor(pyramid, r0[8], r0[9], lr.octave, lr.scale);
samples[35] = SampleReceptor(pyramid, r0[10], r0[11], lr.octave,
lr.scale);
//
// Locate and sample center
//
pyramid.locate(sc, lr);
samples[36] = SampleReceptor(pyramid, c[0], c[1], lr.octave, lr.scale);
return(true);
}
// private boolean ComputeSubpixelHessian(
// float H[9],float b[3],
// const Image& lap0,const Image& lap1,const Image& lap2,
// int x,int y)
private bool updateLocation(DogFeaturePoint kp, LaplacianImage lap0, LaplacianImage lap1, LaplacianImage lap2)
{
double[] tmp = new double[2];
double[] b = new double[3];
// Downsample the feature point to the detection octave
bilinear_downsample_point(tmp, kp.x, kp.y, kp.octave);
double xp = tmp[0];
double yp = tmp[1];
// Compute the discrete pixel location
int x = (int)(xp + 0.5f);
int y = (int)(yp + 0.5f);
double[] H = new double[9];
if (lap0.getWidth() == lap1.getWidth() && lap1.getWidth() == lap2.getWidth())
{
//すべての画像サイズが同じ
//assert lap0.getHeight() == lap1.getHeight() && lap1.getHeight() == lap2.getHeight();// "Width/height are not consistent");
ComputeSubpixelHessianSameOctave(H, b, lap0, lap1, lap2, x, y);
}
else if ((lap0.getWidth() == lap1.getWidth()) && ((lap1.getWidth() >> 1) == lap2.getWidth()))
{
//0,1が同じで2がその半分
//assert (lap0.getHeight() == lap1.getHeight()) && ((lap1.getHeight() >> 1) == lap2.getHeight());// Width/height are not consistent");
ComputeSubpixelHessianFineOctavePair(H, b, lap0, lap1, lap2, x, y);
}
else if (((lap0.getWidth() >> 1) == lap1.getWidth()) && (lap1.getWidth() == lap2.getWidth()))
{
//0の半分が1,2
//assert ((lap0.getWidth() >> 1) == lap1.getWidth()) && (lap1.getWidth() == lap2.getWidth());// Width/height are not consistent");
ComputeSubpixelHessianCoarseOctavePair(H, b, lap0, lap1, lap2, x, y);
}
else
{
// ASSERT(0, "Image sizes are inconsistent");
return(false);
}
// A*u=b // if (!SolveSymmetricLinearSystem3x3(u, H, b)) {
NyARDoubleMatrix33 m = new NyARDoubleMatrix33();
m.m00 = H[0];
m.m01 = H[1];
m.m02 = H[2];
m.m10 = H[3];
m.m11 = H[4];
m.m12 = H[5];
m.m20 = H[6];
m.m21 = H[7];
m.m22 = H[8];
if (!m.inverse(m))
{
return(false);
}
double u0 = (m.m00 * b[0] + m.m01 * b[1] + m.m02 * b[2]);
double u1 = (m.m10 * b[0] + m.m11 * b[1] + m.m12 * b[2]);
double u2 = (m.m20 * b[0] + m.m21 * b[1] + m.m22 * b[2]);
// If points move too much in the sub-pixel update, then the point probably unstable.
if ((u0 * u0) + (u1 * u1) > mMaxSubpixelDistanceSqr)
{
return(false);
}
// Compute the edge score
if (!ComputeEdgeScore(tmp, H))
{
return(false);
}
kp.edge_score = tmp[0];
// Compute a linear estimate of the intensity
// ASSERT(kp.score == lap1.get<float>(y)[x],
// "Score is not consistent with the DoG image");
double[] lap1_buf = (double[])lap1.getBuffer();
kp.score = lap1_buf[lap1.get(y) + x] - (b[0] * u0 + b[1] * u1 + b[2] * u2);
// Update the location:
// Apply the update on the downsampled location and then upsample
// the result.
// bilinear_upsample_point(kp.x, kp.y, xp+u[0], yp+u[1], kp.octave);
bilinear_upsample_point(tmp, xp + u0, yp + u1, kp.octave);
kp.x = tmp[0];
kp.y = tmp[1];
// Update the scale
kp.sp_scale = kp.scale + u2;
kp.sp_scale = ClipScalar(kp.sp_scale, 0, mLaplacianPyramid.numScalePerOctave());
return(true);
}
/**
* 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;
}
