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do multiplication right in the nlp diffusity kernel

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This commit is contained in:
Jiri Horner
2017-06-30 23:17:16 +02:00
parent 554c47e0cb
commit c133518918
1 changed files with 25 additions and 19 deletions
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modules/features2d/src/kaze/AKAZEFeatures.cpp
+25 -19
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@@ -123,7 +123,7 @@ static inline int getGaussianKernelSize(float sigma) {
* dL_by_ds = d(c dL_by_dx)_by_dx + d(c dL_by_dy)_by_dy
*/
static inline void
nld_step_scalar_one_lane(const Mat& Lt, const Mat& Lf, Mat& Lstep, int row_begin, int row_end)
nld_step_scalar_one_lane(const Mat& Lt, const Mat& Lf, Mat& Lstep, float step_size, int row_begin, int row_end)
{
CV_INSTRUMENT_REGION()
/* The labeling scheme for this five star stencil:
@@ -139,6 +139,7 @@ nld_step_scalar_one_lane(const Mat& Lt, const Mat& Lf, Mat& Lstep, int row_begin
const float *lt_a, *lt_c, *lt_b;
const float *lf_a, *lf_c, *lf_b;
float *dst;
float step_r = 0.f;
// Process the top row
if (row == 0) {
@@ -149,9 +150,10 @@ nld_step_scalar_one_lane(const Mat& Lt, const Mat& Lf, Mat& Lstep, int row_begin
dst = Lstep.ptr<float>(0) + 1;
for (int j = 0; j < cols; j++) {
dst[j] = (lf_c[j] + lf_c[j + 1])*(lt_c[j + 1] - lt_c[j]) +
(lf_c[j] + lf_c[j - 1])*(lt_c[j - 1] - lt_c[j]) +
(lf_c[j] + lf_b[j ])*(lt_b[j ] - lt_c[j]);
step_r = (lf_c[j] + lf_c[j + 1])*(lt_c[j + 1] - lt_c[j]) +
(lf_c[j] + lf_c[j - 1])*(lt_c[j - 1] - lt_c[j]) +
(lf_c[j] + lf_b[j ])*(lt_b[j ] - lt_c[j]);
dst[j] = step_r * step_size;
}
++row;
}
@@ -169,9 +171,10 @@ nld_step_scalar_one_lane(const Mat& Lt, const Mat& Lf, Mat& Lstep, int row_begin
dst = Lstep.ptr<float>(row);
// The left-most column
dst[0] = (lf_c[0] + lf_c[1])*(lt_c[1] - lt_c[0]) +
step_r = (lf_c[0] + lf_c[1])*(lt_c[1] - lt_c[0]) +
(lf_c[0] + lf_b[0])*(lt_b[0] - lt_c[0]) +
(lf_c[0] + lf_a[0])*(lt_a[0] - lt_c[0]);
dst[0] = step_r * step_size;
lt_a++; lt_c++; lt_b++;
lf_a++; lf_c++; lf_b++;
@@ -180,16 +183,18 @@ nld_step_scalar_one_lane(const Mat& Lt, const Mat& Lf, Mat& Lstep, int row_begin
// The middle columns
for (int j = 0; j < cols; j++)
{
dst[j] = (lf_c[j] + lf_c[j + 1])*(lt_c[j + 1] - lt_c[j]) +
step_r = (lf_c[j] + lf_c[j + 1])*(lt_c[j + 1] - lt_c[j]) +
(lf_c[j] + lf_c[j - 1])*(lt_c[j - 1] - lt_c[j]) +
(lf_c[j] + lf_b[j ])*(lt_b[j ] - lt_c[j]) +
(lf_c[j] + lf_a[j ])*(lt_a[j ] - lt_c[j]);
dst[j] = step_r * step_size;
}
// The right-most column
dst[cols] = (lf_c[cols] + lf_c[cols - 1])*(lt_c[cols - 1] - lt_c[cols]) +
(lf_c[cols] + lf_b[cols ])*(lt_b[cols ] - lt_c[cols]) +
(lf_c[cols] + lf_a[cols ])*(lt_a[cols ] - lt_c[cols]);
step_r = (lf_c[cols] + lf_c[cols - 1])*(lt_c[cols - 1] - lt_c[cols]) +
(lf_c[cols] + lf_b[cols ])*(lt_b[cols ] - lt_c[cols]) +
(lf_c[cols] + lf_a[cols ])*(lt_a[cols ] - lt_c[cols]);
dst[cols] = step_r * step_size;
}
// Process the bottom row (row == Lt.rows - 1)
@@ -201,9 +206,10 @@ nld_step_scalar_one_lane(const Mat& Lt, const Mat& Lf, Mat& Lstep, int row_begin
dst = Lstep.ptr<float>(row) +1;
for (int j = 0; j < cols; j++) {
dst[j] = (lf_c[j] + lf_c[j + 1])*(lt_c[j + 1] - lt_c[j]) +
(lf_c[j] + lf_c[j - 1])*(lt_c[j - 1] - lt_c[j]) +
(lf_c[j] + lf_a[j ])*(lt_a[j ] - lt_c[j]);
step_r = (lf_c[j] + lf_c[j + 1])*(lt_c[j + 1] - lt_c[j]) +
(lf_c[j] + lf_c[j - 1])*(lt_c[j - 1] - lt_c[j]) +
(lf_c[j] + lf_a[j ])*(lt_a[j ] - lt_c[j]);
dst[j] = step_r * step_size;
}
}
}
@@ -211,19 +217,20 @@ nld_step_scalar_one_lane(const Mat& Lt, const Mat& Lf, Mat& Lstep, int row_begin
class NonLinearScalarDiffusionStep : public ParallelLoopBody
{
public:
NonLinearScalarDiffusionStep(const Mat& Lt, const Mat& Lf, Mat& Lstep)
: Lt_(&Lt), Lf_(&Lf), Lstep_(&Lstep)
NonLinearScalarDiffusionStep(const Mat& Lt, const Mat& Lf, Mat& Lstep, float step_size)
: Lt_(&Lt), Lf_(&Lf), Lstep_(&Lstep), step_size_(step_size)
{}
void operator()(const Range& range) const
{
nld_step_scalar_one_lane(*Lt_, *Lf_, *Lstep_, range.start, range.end);
nld_step_scalar_one_lane(*Lt_, *Lf_, *Lstep_, step_size_, range.start, range.end);
}
private:
const Mat* Lt_;
const Mat* Lf_;
Mat* Lstep_;
float step_size_;
};
/**
@@ -310,10 +317,9 @@ int AKAZEFeatures::Create_Nonlinear_Scale_Space(const Mat& img)
std::vector<float> &tsteps = tsteps_[i - 1];
for (size_t j = 0; j < tsteps.size(); j++) {
// Lstep must be preallocated before this parallel loop
parallel_for_(Range(0, e.Lt.rows), NonLinearScalarDiffusionStep(e.Lt, Lflow, Lstep),
(double)e.Lt.total()/(1 << 16));
const float step_size = tsteps[j];
e.Lt += Lstep * (0.5f * step_size);
const float step_size = tsteps[j] * 0.5f;
parallel_for_(Range(0, e.Lt.rows), NonLinearScalarDiffusionStep(e.Lt, Lflow, Lstep, step_size));
e.Lt += Lstep;
}
}