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akaze: replace ceil()

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- integer division => divUp()
- cast to 'int' => cvCeil()
pull/9303/head
Alexander Alekhin 7 years ago
parent 9ca39821c8
commit 8aca8d90d6
4 changed files with 50 additions and 38 deletions
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  1. 4
      modules/features2d/src/akaze.cpp
  2. 80
      modules/features2d/src/kaze/AKAZEFeatures.cpp
  3. 2
      modules/features2d/src/kaze/fed.cpp
  4. 2
      modules/features2d/src/kaze/nldiffusion_functions.cpp

4
modules/features2d/src/akaze.cpp
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@ -113,12 +113,12 @@ namespace cv
if (descriptor_size == 0)
{
int t = (6 + 36 + 120) * descriptor_channels;
return (int)ceil(t / 8.);
return divUp(t, 8);
}
else
{
// We use the random bit selection length binary descriptor
return (int)ceil(descriptor_size / 8.);
return divUp(descriptor_size, 8);
}
default:

80
modules/features2d/src/kaze/AKAZEFeatures.cpp
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@ -106,7 +106,7 @@ void AKAZEFeatures::Allocate_Memory_Evolution(void) {
*/
static inline int getGaussianKernelSize(float sigma) {
// Compute an appropriate kernel size according to the specified sigma
int ksize = (int)ceil(2.0f*(1.0f + (sigma - 0.8f) / (0.3f)));
int ksize = (int)cvCeil(2.0f*(1.0f + (sigma - 0.8f) / (0.3f)));
ksize |= 1; // kernel should be odd
return ksize;
}
@ -1131,20 +1131,17 @@ void AKAZEFeatures::Compute_Descriptors(std::vector<KeyPoint>& kpts, OutputArray
}
// Allocate memory for the matrix with the descriptors
if (options_.descriptor < AKAZE::DESCRIPTOR_MLDB_UPRIGHT) {
descriptors.create((int)kpts.size(), 64, CV_32FC1);
}
else {
// We use the full length binary descriptor -> 486 bits
if (options_.descriptor_size == 0) {
int t = (6 + 36 + 120)*options_.descriptor_channels;
descriptors.create((int)kpts.size(), (int)ceil(t / 8.), CV_8UC1);
}
else {
// We use the random bit selection length binary descriptor
descriptors.create((int)kpts.size(), (int)ceil(options_.descriptor_size / 8.), CV_8UC1);
}
int descriptor_size = 64;
int descriptor_type = CV_32FC1;
if (options_.descriptor >= AKAZE::DESCRIPTOR_MLDB_UPRIGHT)
{
int descriptor_bits = (options_.descriptor_size == 0)
? (6 + 36 + 120)*options_.descriptor_channels // the full length binary descriptor -> 486 bits
: options_.descriptor_size; // the random bit selection length binary descriptor
descriptor_size = divUp(descriptor_bits, 8);
descriptor_type = CV_8UC1;
}
descriptors.create((int)kpts.size(), descriptor_size, descriptor_type);
Mat desc = descriptors.getMat();
@ -1701,10 +1698,11 @@ void Upright_MLDB_Full_Descriptor_Invoker::Get_Upright_MLDB_Full_Descriptor(cons
// For 2x2 grid, 3x3 grid and 4x4 grid
const int pattern_size = options_->descriptor_pattern_size;
int sample_step[3] = {
CV_Assert((pattern_size & 1) == 0);
const int sample_step[3] = {
pattern_size,
static_cast<int>(ceil(pattern_size*2./3.)),
pattern_size / 2
divUp(pattern_size * 2, 3),
divUp(pattern_size, 2)
};
// For the three grids
@ -1873,8 +1871,16 @@ void MLDB_Full_Descriptor_Invoker::Get_MLDB_Full_Descriptor(const KeyPoint& kpt,
const int max_channels = 3;
CV_Assert(options_->descriptor_channels <= max_channels);
const int pattern_size = options_->descriptor_pattern_size;
float values[16*max_channels];
const double size_mult[3] = {1, 2.0/3.0, 1.0/2.0};
CV_Assert((pattern_size & 1) == 0);
//const double size_mult[3] = {1, 2.0/3.0, 1.0/2.0};
const int sample_step[3] = { // static_cast<int>(ceil(pattern_size * size_mult[lvl]))
pattern_size,
divUp(pattern_size * 2, 3),
divUp(pattern_size, 2)
};
float ratio = (float)(1 << kpt.octave);
float scale = (float)fRound(0.5f*kpt.size / ratio);
@ -1883,14 +1889,12 @@ void MLDB_Full_Descriptor_Invoker::Get_MLDB_Full_Descriptor(const KeyPoint& kpt,
float angle = (kpt.angle * static_cast<float>(CV_PI)) / 180.f;
float co = cos(angle);
float si = sin(angle);
int pattern_size = options_->descriptor_pattern_size;
int dpos = 0;
for(int lvl = 0; lvl < 3; lvl++) {
int val_count = (lvl + 2) * (lvl + 2);
int sample_step = static_cast<int>(ceil(pattern_size * size_mult[lvl]));
MLDB_Fill_Values(values, sample_step, kpt.class_id, xf, yf, co, si, scale);
MLDB_Fill_Values(values, sample_step[lvl], kpt.class_id, xf, yf, co, si, scale);
MLDB_Binary_Comparisons(values, desc, val_count, dpos);
}
}
@ -1930,14 +1934,18 @@ void MLDB_Descriptor_Subset_Invoker::Get_MLDB_Descriptor_Subset(const KeyPoint&
Mat values((4 + 9 + 16)*options.descriptor_channels, 1, CV_32FC1);
// Sample everything, but only do the comparisons
vector<int> steps(3);
steps.at(0) = options.descriptor_pattern_size;
steps.at(1) = (int)ceil(2.f*options.descriptor_pattern_size / 3.f);
steps.at(2) = options.descriptor_pattern_size / 2;
const int pattern_size = options.descriptor_pattern_size;
CV_Assert((pattern_size & 1) == 0);
const int sample_steps[3] = {
pattern_size,
divUp(pattern_size * 2, 3),
divUp(pattern_size, 2)
};
for (int i = 0; i < descriptorSamples_.rows; i++) {
const int *coords = descriptorSamples_.ptr<int>(i);
int sample_step = steps.at(coords[0]);
CV_Assert(coords[0] >= 0 && coords[0] < 3);
const int sample_step = sample_steps[coords[0]];
di = 0.0f;
dx = 0.0f;
dy = 0.0f;
@ -2025,14 +2033,18 @@ void Upright_MLDB_Descriptor_Subset_Invoker::Get_Upright_MLDB_Descriptor_Subset(
// Allocate memory for the matrix of values
Mat values ((4 + 9 + 16)*options.descriptor_channels, 1, CV_32FC1);
vector<int> steps(3);
steps.at(0) = options.descriptor_pattern_size;
steps.at(1) = static_cast<int>(ceil(2.f*options.descriptor_pattern_size / 3.f));
steps.at(2) = options.descriptor_pattern_size / 2;
const int pattern_size = options.descriptor_pattern_size;
CV_Assert((pattern_size & 1) == 0);
const int sample_steps[3] = {
pattern_size,
divUp(pattern_size * 2, 3),
divUp(pattern_size, 2)
};
for (int i = 0; i < descriptorSamples_.rows; i++) {
const int *coords = descriptorSamples_.ptr<int>(i);
int sample_step = steps.at(coords[0]);
CV_Assert(coords[0] >= 0 && coords[0] < 3);
int sample_step = sample_steps[coords[0]];
di = 0.0f, dx = 0.0f, dy = 0.0f;
for (int k = coords[1]; k < coords[1] + sample_step; k++) {
@ -2120,7 +2132,7 @@ void generateDescriptorSubsample(Mat& sampleList, Mat& comparisons, int nbits,
for (int i = 0, c = 0; i < 3; i++) {
int gdiv = i + 2; //grid divisions, per row
int gsz = gdiv*gdiv;
int psz = (int)ceil(2.f*pattern_size / (float)gdiv);
int psz = divUp(2*pattern_size, gdiv);
for (int j = 0; j < gsz; j++) {
for (int k = j + 1; k < gsz; k++, c++) {
@ -2134,12 +2146,12 @@ void generateDescriptorSubsample(Mat& sampleList, Mat& comparisons, int nbits,
}
RNG rng(1024);
Mat_<int> comps = Mat_<int>(nchannels * (int)ceil(nbits / (float)nchannels), 2);
const int npicks = divUp(nbits, nchannels);
Mat_<int> comps = Mat_<int>(nchannels * npicks, 2);
comps = 1000;
// Select some samples. A sample includes all channels
int count = 0;
int npicks = (int)ceil(nbits / (float)nchannels);
Mat_<int> samples(29, 3);
Mat_<int> fullcopy = fullM.clone();
samples = -1;

2
modules/features2d/src/kaze/fed.cpp
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@ -72,7 +72,7 @@ int fed_tau_by_cycle_time(const float& t, const float& tau_max,
float scale = 0.0; // Ratio of t we search to maximal t
// Compute necessary number of time steps
n = (int)(ceilf(sqrtf(3.0f*t/tau_max+0.25f)-0.5f-1.0e-8f)+ 0.5f);
n = cvCeil(sqrtf(3.0f*t/tau_max+0.25f)-0.5f-1.0e-8f);
scale = 3.0f*t/(tau_max*(float)(n*(n+1)));
// Call internal FED time step creation routine

2
modules/features2d/src/kaze/nldiffusion_functions.cpp
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@ -49,7 +49,7 @@ void gaussian_2D_convolution(const cv::Mat& src, cv::Mat& dst, int ksize_x, int
// Compute an appropriate kernel size according to the specified sigma
if (sigma > ksize_x || sigma > ksize_y || ksize_x == 0 || ksize_y == 0) {
ksize_x_ = (int)ceil(2.0f*(1.0f + (sigma - 0.8f) / (0.3f)));
ksize_x_ = cvCeil(2.0f*(1.0f + (sigma - 0.8f) / (0.3f)));
ksize_y_ = ksize_x_;
}

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