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Merge pull request #9303 from alalek:akaze_update

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Alexander Alekhin 2017-08-03 17:17:03 +00:00 committed by GitHub
commit 922ac1a1ec
14 changed files with 326 additions and 299 deletions
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17
modules/core/include/opencv2/core/utility.hpp
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@ -444,6 +444,23 @@ static inline size_t alignSize(size_t sz, int n)
return (sz + n-1) & -n;
}
/** @brief Integer division with result round up.
Use this function instead of `ceil((float)a / b)` expressions.
@sa alignSize
*/
static inline int divUp(int a, unsigned int b)
{
CV_DbgAssert(a >= 0);
return (a + b - 1) / b;
}
/** @overload */
static inline size_t divUp(size_t a, unsigned int b)
{
return (a + b - 1) / b;
}
/** @brief Enables or disables the optimized code.
The function can be used to dynamically turn on and off optimized code (code that uses SSE2, AVX,

5
modules/core/src/matrix.cpp
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@ -3406,11 +3406,6 @@ static TransposeInplaceFunc transposeInplaceTab[] =
#ifdef HAVE_OPENCL
static inline int divUp(int a, int b)
{
return (a + b - 1) / b;
}
static bool ocl_transpose( InputArray _src, OutputArray _dst )
{
const ocl::Device & dev = ocl::Device::getDefault();

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:

354
modules/features2d/src/kaze/AKAZEFeatures.cpp
View File

@ -74,12 +74,12 @@ void AKAZEFeatures::Allocate_Memory_Evolution(void) {
Evolution step;
step.size = Size(level_width, level_height);
step.esigma = options_.soffset*pow(2.f, (float)(j) / (float)(options_.nsublevels) + i);
step.sigma_size = fRound(step.esigma * options_.derivative_factor / power); // In fact sigma_size only depends on j
step.sigma_size = cvRound(step.esigma * options_.derivative_factor / power); // In fact sigma_size only depends on j
step.etime = 0.5f * (step.esigma * step.esigma);
step.octave = i;
step.sublevel = j;
step.octave_ratio = (float)power;
step.border = fRound(smax * step.sigma_size) + 1;
step.border = cvRound(smax * step.sigma_size) + 1;
evolution_.push_back(step);
}
@ -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;
}
@ -890,11 +890,11 @@ public:
{
for (int i = range.start; i < range.end; i++)
{
Get_SURF_Descriptor_Upright_64((*keypoints_)[i], descriptors_->ptr<float>(i));
Get_SURF_Descriptor_Upright_64((*keypoints_)[i], descriptors_->ptr<float>(i), descriptors_->cols);
}
}
void Get_SURF_Descriptor_Upright_64(const KeyPoint& kpt, float* desc) const;
void Get_SURF_Descriptor_Upright_64(const KeyPoint& kpt, float* desc, int desc_size) const;
private:
std::vector<KeyPoint>* keypoints_;
@ -916,11 +916,11 @@ public:
{
for (int i = range.start; i < range.end; i++)
{
Get_SURF_Descriptor_64((*keypoints_)[i], descriptors_->ptr<float>(i));
Get_SURF_Descriptor_64((*keypoints_)[i], descriptors_->ptr<float>(i), descriptors_->cols);
}
}
void Get_SURF_Descriptor_64(const KeyPoint& kpt, float* desc) const;
void Get_SURF_Descriptor_64(const KeyPoint& kpt, float* desc, int desc_size) const;
private:
std::vector<KeyPoint>* keypoints_;
@ -942,11 +942,11 @@ public:
{
for (int i = range.start; i < range.end; i++)
{
Get_MSURF_Upright_Descriptor_64((*keypoints_)[i], descriptors_->ptr<float>(i));
Get_MSURF_Upright_Descriptor_64((*keypoints_)[i], descriptors_->ptr<float>(i), descriptors_->cols);
}
}
void Get_MSURF_Upright_Descriptor_64(const KeyPoint& kpt, float* desc) const;
void Get_MSURF_Upright_Descriptor_64(const KeyPoint& kpt, float* desc, int desc_size) const;
private:
std::vector<KeyPoint>* keypoints_;
@ -968,11 +968,11 @@ public:
{
for (int i = range.start; i < range.end; i++)
{
Get_MSURF_Descriptor_64((*keypoints_)[i], descriptors_->ptr<float>(i));
Get_MSURF_Descriptor_64((*keypoints_)[i], descriptors_->ptr<float>(i), descriptors_->cols);
}
}
void Get_MSURF_Descriptor_64(const KeyPoint& kpt, float* desc) const;
void Get_MSURF_Descriptor_64(const KeyPoint& kpt, float* desc, int desc_size) const;
private:
std::vector<KeyPoint>* keypoints_;
@ -995,11 +995,11 @@ public:
{
for (int i = range.start; i < range.end; i++)
{
Get_Upright_MLDB_Full_Descriptor((*keypoints_)[i], descriptors_->ptr<unsigned char>(i));
Get_Upright_MLDB_Full_Descriptor((*keypoints_)[i], descriptors_->ptr<unsigned char>(i), descriptors_->cols);
}
}
void Get_Upright_MLDB_Full_Descriptor(const KeyPoint& kpt, unsigned char* desc) const;
void Get_Upright_MLDB_Full_Descriptor(const KeyPoint& kpt, unsigned char* desc, int desc_size) const;
private:
std::vector<KeyPoint>* keypoints_;
@ -1030,11 +1030,11 @@ public:
{
for (int i = range.start; i < range.end; i++)
{
Get_Upright_MLDB_Descriptor_Subset((*keypoints_)[i], descriptors_->ptr<unsigned char>(i));
Get_Upright_MLDB_Descriptor_Subset((*keypoints_)[i], descriptors_->ptr<unsigned char>(i), descriptors_->cols);
}
}
void Get_Upright_MLDB_Descriptor_Subset(const KeyPoint& kpt, unsigned char* desc) const;
void Get_Upright_MLDB_Descriptor_Subset(const KeyPoint& kpt, unsigned char* desc, int desc_size) const;
private:
std::vector<KeyPoint>* keypoints_;
@ -1061,11 +1061,11 @@ public:
{
for (int i = range.start; i < range.end; i++)
{
Get_MLDB_Full_Descriptor((*keypoints_)[i], descriptors_->ptr<unsigned char>(i));
Get_MLDB_Full_Descriptor((*keypoints_)[i], descriptors_->ptr<unsigned char>(i), descriptors_->cols);
}
}
void Get_MLDB_Full_Descriptor(const KeyPoint& kpt, unsigned char* desc) const;
void Get_MLDB_Full_Descriptor(const KeyPoint& kpt, unsigned char* desc, int desc_size) const;
void MLDB_Fill_Values(float* values, int sample_step, int level,
float xf, float yf, float co, float si, float scale) const;
void MLDB_Binary_Comparisons(float* values, unsigned char* desc,
@ -1100,11 +1100,11 @@ public:
{
for (int i = range.start; i < range.end; i++)
{
Get_MLDB_Descriptor_Subset((*keypoints_)[i], descriptors_->ptr<unsigned char>(i));
Get_MLDB_Descriptor_Subset((*keypoints_)[i], descriptors_->ptr<unsigned char>(i), descriptors_->cols);
}
}
void Get_MLDB_Descriptor_Subset(const KeyPoint& kpt, unsigned char* desc) const;
void Get_MLDB_Descriptor_Subset(const KeyPoint& kpt, unsigned char* desc, int desc_size) const;
private:
std::vector<KeyPoint>* keypoints_;
@ -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();
@ -1208,12 +1205,11 @@ void Sample_Derivative_Response_Radius6(const Mat &Lx, const Mat &Ly,
{ 0.00344629f, 0.00318132f, 0.00250252f, 0.00167749f, 0.00095820f, 0.00046640f, 0.00019346f },
{ 0.00142946f, 0.00131956f, 0.00103800f, 0.00069579f, 0.00039744f, 0.00019346f, 0.00008024f }
};
static const int id[] = { 6, 5, 4, 3, 2, 1, 0, 1, 2, 3, 4, 5, 6 };
static const struct gtable
{
float weight[109];
int8_t xidx[109];
int8_t yidx[109];
int xidx[109];
int yidx[109];
explicit gtable(void)
{
@ -1222,29 +1218,28 @@ void Sample_Derivative_Response_Radius6(const Mat &Lx, const Mat &Ly,
for (int i = -6; i <= 6; ++i) {
for (int j = -6; j <= 6; ++j) {
if (i*i + j*j < 36) {
weight[k] = gauss25[id[i + 6]][id[j + 6]];
yidx[k] = static_cast<int8_t>(i);
xidx[k] = static_cast<int8_t>(j);
CV_Assert(k < 109);
weight[k] = gauss25[abs(i)][abs(j)];
yidx[k] = i;
xidx[k] = j;
++k;
}
}
}
CV_DbgAssert(k == 109);
}
} g;
const float * lx = Lx.ptr<float>(0);
const float * ly = Ly.ptr<float>(0);
int cols = Lx.cols;
CV_Assert(x0 - 6 * scale >= 0 && x0 + 6 * scale < Lx.cols);
CV_Assert(y0 - 6 * scale >= 0 && y0 + 6 * scale < Lx.rows);
for (int i = 0; i < 109; i++) {
int j = (y0 + g.yidx[i] * scale) * cols + (x0 + g.xidx[i] * scale);
for (int i = 0; i < 109; i++)
{
int y = y0 + g.yidx[i] * scale;
int x = x0 + g.xidx[i] * scale;
resX[i] = g.weight[i] * lx[j];
resY[i] = g.weight[i] * ly[j];
CV_DbgAssert(isfinite(resX[i]));
CV_DbgAssert(isfinite(resY[i]));
float w = g.weight[i];
resX[i] = w * Lx.at<float>(y, x);
resY[i] = w * Ly.at<float>(y, x);
}
}
@ -1253,7 +1248,7 @@ void Sample_Derivative_Response_Radius6(const Mat &Lx, const Mat &Ly,
* @param a[] Input floating point array to sort
* @param n The length of a[]
* @param quantum The interval to convert a[i]'s float values to integers
* @param max The upper bound of a[], meaning a[i] must be in [0, max]
* @param nkeys a[i] < nkeys * quantum
* @param idx[] Output array of the indices: a[idx[i]] forms a sorted array
* @param cum[] Output array of the starting indices of quantized floats
* @note The values of a[] in [k*quantum, (k + 1)*quantum) is labeled by
@ -1263,25 +1258,35 @@ void Sample_Derivative_Response_Radius6(const Mat &Lx, const Mat &Ly,
*/
static inline
void quantized_counting_sort(const float a[], const int n,
const float quantum, const float max,
uint8_t idx[], uint8_t cum[])
const float quantum, const int nkeys,
int idx[/*n*/], int cum[/*nkeys + 1*/])
{
const int nkeys = (int)(max / quantum);
// The size of cum[] must be nkeys + 1
memset(cum, 0, nkeys + 1);
memset(cum, 0, sizeof(cum[0]) * (nkeys + 1));
// Count up the quantized values
for (int i = 0; i < n; i++)
cum[(int)(a[i] / quantum)]++;
{
int b = (int)(a[i] / quantum);
if (b < 0 || b >= nkeys)
b = 0;
cum[b]++;
}
// Compute the inclusive prefix sum i.e. the end indices; cum[nkeys] is the total
for (int i = 1; i <= nkeys; i++)
{
cum[i] += cum[i - 1];
}
CV_Assert(cum[nkeys] == n);
// Generate the sorted indices; cum[] becomes the exclusive prefix sum i.e. the start indices of keys
for (int i = 0; i < n; i++)
idx[--cum[(int)(a[i] / quantum)]] = static_cast<uint8_t>(i);
{
int b = (int)(a[i] / quantum);
if (b < 0 || b >= nkeys)
b = 0;
idx[--cum[b]] = i;
}
}
/**
@ -1296,9 +1301,9 @@ void Compute_Main_Orientation(KeyPoint& kpt, const std::vector<Evolution>& evolu
// get the right evolution level for this keypoint
const Evolution& e = evolution[kpt.class_id];
// Get the information from the keypoint
int scale = fRound(0.5f * kpt.size / e.octave_ratio);
int x0 = fRound(kpt.pt.x / e.octave_ratio);
int y0 = fRound(kpt.pt.y / e.octave_ratio);
int scale = cvRound(0.5f * kpt.size / e.octave_ratio);
int x0 = cvRound(kpt.pt.x / e.octave_ratio);
int y0 = cvRound(kpt.pt.y / e.octave_ratio);
// Sample derivatives responses for the points within radius of 6*scale
const int ang_size = 109;
@ -1312,17 +1317,18 @@ void Compute_Main_Orientation(KeyPoint& kpt, const std::vector<Evolution>& evolu
// Sort by the angles; angles are labeled by slices of 0.15 radian
const int slices = 42;
const float ang_step = (float)(2.0 * CV_PI / slices);
uint8_t slice[slices + 1];
uint8_t sorted_idx[ang_size];
quantized_counting_sort(Ang, ang_size, ang_step, (float)(2.0 * CV_PI), sorted_idx, slice);
int slice[slices + 1];
int sorted_idx[ang_size];
quantized_counting_sort(Ang, ang_size, ang_step, slices, sorted_idx, slice);
// Find the main angle by sliding a window of 7-slice size(=PI/3) around the keypoint
const int win = 7;
float maxX = 0.0f, maxY = 0.0f;
for (int i = slice[0]; i < slice[win]; i++) {
maxX += resX[sorted_idx[i]];
maxY += resY[sorted_idx[i]];
const int idx = sorted_idx[i];
maxX += resX[idx];
maxY += resY[idx];
}
float maxNorm = maxX * maxX + maxY * maxY;
@ -1333,8 +1339,9 @@ void Compute_Main_Orientation(KeyPoint& kpt, const std::vector<Evolution>& evolu
float sumX = 0.0f, sumY = 0.0f;
for (int i = slice[sn]; i < slice[sn + win]; i++) {
sumX += resX[sorted_idx[i]];
sumY += resY[sorted_idx[i]];
const int idx = sorted_idx[i];
sumX += resX[idx];
sumY += resY[idx];
}
float norm = sumX * sumX + sumY * sumY;
@ -1350,12 +1357,14 @@ void Compute_Main_Orientation(KeyPoint& kpt, const std::vector<Evolution>& evolu
float sumX = 0.0f, sumY = 0.0f;
for (int i = slice[sn]; i < slice[slices]; i++) {
sumX += resX[sorted_idx[i]];
sumY += resY[sorted_idx[i]];
const int idx = sorted_idx[i];
sumX += resX[idx];
sumY += resY[idx];
}
for (int i = slice[0]; i < slice[remain]; i++) {
sumX += resX[sorted_idx[i]];
sumY += resY[sorted_idx[i]];
const int idx = sorted_idx[i];
sumX += resX[idx];
sumY += resY[idx];
}
float norm = sumX * sumX + sumY * sumY;
@ -1410,7 +1419,10 @@ void AKAZEFeatures::Compute_Keypoints_Orientation(std::vector<KeyPoint>& kpts) c
* from Agrawal et al., CenSurE: Center Surround Extremas for Realtime Feature Detection and Matching,
* ECCV 2008
*/
void MSURF_Upright_Descriptor_64_Invoker::Get_MSURF_Upright_Descriptor_64(const KeyPoint& kpt, float *desc) const {
void MSURF_Upright_Descriptor_64_Invoker::Get_MSURF_Upright_Descriptor_64(const KeyPoint& kpt, float *desc, int desc_size) const {
const int dsize = 64;
CV_Assert(desc_size == dsize);
float dx = 0.0, dy = 0.0, mdx = 0.0, mdy = 0.0, gauss_s1 = 0.0, gauss_s2 = 0.0;
float rx = 0.0, ry = 0.0, len = 0.0, xf = 0.0, yf = 0.0, ys = 0.0, xs = 0.0;
@ -1418,7 +1430,7 @@ void MSURF_Upright_Descriptor_64_Invoker::Get_MSURF_Upright_Descriptor_64(const
int x1 = 0, y1 = 0, sample_step = 0, pattern_size = 0;
int x2 = 0, y2 = 0, kx = 0, ky = 0, i = 0, j = 0, dcount = 0;
float fx = 0.0, fy = 0.0, ratio = 0.0, res1 = 0.0, res2 = 0.0, res3 = 0.0, res4 = 0.0;
int scale = 0, dsize = 0;
int scale = 0;
// Subregion centers for the 4x4 gaussian weighting
float cx = -0.5f, cy = 0.5f;
@ -1426,13 +1438,12 @@ void MSURF_Upright_Descriptor_64_Invoker::Get_MSURF_Upright_Descriptor_64(const
const std::vector<Evolution>& evolution = *evolution_;
// Set the descriptor size and the sample and pattern sizes
dsize = 64;
sample_step = 5;
pattern_size = 12;
// Get the information from the keypoint
ratio = (float)(1 << kpt.octave);
scale = fRound(0.5f*kpt.size / ratio);
scale = cvRound(0.5f*kpt.size / ratio);
const int level = kpt.class_id;
Mat Lx = evolution[level].Mx;
Mat Ly = evolution[level].My;
@ -1469,11 +1480,11 @@ void MSURF_Upright_Descriptor_64_Invoker::Get_MSURF_Upright_Descriptor_64(const
//Get the gaussian weighted x and y responses
gauss_s1 = gaussian(xs - sample_x, ys - sample_y, 2.50f*scale);
y1 = (int)(sample_y - .5);
x1 = (int)(sample_x - .5);
y1 = (int)(sample_y - .5f);
x1 = (int)(sample_x - .5f);
y2 = (int)(sample_y + .5);
x2 = (int)(sample_x + .5);
y2 = (int)(sample_y + .5f);
x2 = (int)(sample_x + .5f);
fx = sample_x - x1;
fy = sample_y - y1;
@ -1517,6 +1528,8 @@ void MSURF_Upright_Descriptor_64_Invoker::Get_MSURF_Upright_Descriptor_64(const
i += 9;
}
CV_Assert(dcount == desc_size);
// convert to unit vector
len = sqrt(len);
@ -1535,7 +1548,10 @@ void MSURF_Upright_Descriptor_64_Invoker::Get_MSURF_Upright_Descriptor_64(const
* from Agrawal et al., CenSurE: Center Surround Extremas for Realtime Feature Detection and Matching,
* ECCV 2008
*/
void MSURF_Descriptor_64_Invoker::Get_MSURF_Descriptor_64(const KeyPoint& kpt, float *desc) const {
void MSURF_Descriptor_64_Invoker::Get_MSURF_Descriptor_64(const KeyPoint& kpt, float *desc, int desc_size) const {
const int dsize = 64;
CV_Assert(desc_size == dsize);
float dx = 0.0, dy = 0.0, mdx = 0.0, mdy = 0.0, gauss_s1 = 0.0, gauss_s2 = 0.0;
float rx = 0.0, ry = 0.0, rrx = 0.0, rry = 0.0, len = 0.0, xf = 0.0, yf = 0.0, ys = 0.0, xs = 0.0;
@ -1543,7 +1559,7 @@ void MSURF_Descriptor_64_Invoker::Get_MSURF_Descriptor_64(const KeyPoint& kpt, f
float fx = 0.0, fy = 0.0, ratio = 0.0, res1 = 0.0, res2 = 0.0, res3 = 0.0, res4 = 0.0;
int x1 = 0, y1 = 0, x2 = 0, y2 = 0, sample_step = 0, pattern_size = 0;
int kx = 0, ky = 0, i = 0, j = 0, dcount = 0;
int scale = 0, dsize = 0;
int scale = 0;
// Subregion centers for the 4x4 gaussian weighting
float cx = -0.5f, cy = 0.5f;
@ -1551,14 +1567,13 @@ void MSURF_Descriptor_64_Invoker::Get_MSURF_Descriptor_64(const KeyPoint& kpt, f
const std::vector<Evolution>& evolution = *evolution_;
// Set the descriptor size and the sample and pattern sizes
dsize = 64;
sample_step = 5;
pattern_size = 12;
// Get the information from the keypoint
ratio = (float)(1 << kpt.octave);
scale = fRound(0.5f*kpt.size / ratio);
angle = (kpt.angle * static_cast<float>(CV_PI)) / 180.f;
scale = cvRound(0.5f*kpt.size / ratio);
angle = kpt.angle * static_cast<float>(CV_PI / 180.f);
const int level = kpt.class_id;
Mat Lx = evolution[level].Mx;
Mat Ly = evolution[level].My;
@ -1598,11 +1613,11 @@ void MSURF_Descriptor_64_Invoker::Get_MSURF_Descriptor_64(const KeyPoint& kpt, f
// Get the gaussian weighted x and y responses
gauss_s1 = gaussian(xs - sample_x, ys - sample_y, 2.5f*scale);
y1 = fRound(sample_y - 0.5f);
x1 = fRound(sample_x - 0.5f);
y1 = cvRound(sample_y - 0.5f);
x1 = cvRound(sample_x - 0.5f);
y2 = fRound(sample_y + 0.5f);
x2 = fRound(sample_x + 0.5f);
y2 = cvRound(sample_y + 0.5f);
x2 = cvRound(sample_x + 0.5f);
// fix crash: indexing with out-of-bounds index, this might happen near the edges of image
// clip values so they fit into the image
@ -1655,6 +1670,8 @@ void MSURF_Descriptor_64_Invoker::Get_MSURF_Descriptor_64(const KeyPoint& kpt, f
i += 9;
}
CV_Assert(dcount == desc_size);
// convert to unit vector
len = sqrt(len);
@ -1670,7 +1687,7 @@ void MSURF_Descriptor_64_Invoker::Get_MSURF_Descriptor_64(const KeyPoint& kpt, f
* @param kpt Input keypoint
* @param desc Descriptor vector
*/
void Upright_MLDB_Full_Descriptor_Invoker::Get_Upright_MLDB_Full_Descriptor(const KeyPoint& kpt, unsigned char *desc) const {
void Upright_MLDB_Full_Descriptor_Invoker::Get_Upright_MLDB_Full_Descriptor(const KeyPoint& kpt, unsigned char *desc, int desc_size) const {
float di = 0.0, dx = 0.0, dy = 0.0;
float ri = 0.0, rx = 0.0, ry = 0.0, xf = 0.0, yf = 0.0;
@ -1682,16 +1699,14 @@ void Upright_MLDB_Full_Descriptor_Invoker::Get_Upright_MLDB_Full_Descriptor(cons
const AKAZEOptions & options = *options_;
const std::vector<Evolution>& evolution = *evolution_;
// Matrices for the M-LDB descriptor
Mat values[3] = {
Mat(4, options.descriptor_channels, CV_32FC1),
Mat(9, options.descriptor_channels, CV_32FC1),
Mat(16, options.descriptor_channels, CV_32FC1)
};
// Buffer for the M-LDB descriptor
const int max_channels = 3;
CV_Assert(options.descriptor_channels <= max_channels);
float values[16*max_channels];
// Get the information from the keypoint
ratio = (float)(1 << kpt.octave);
scale = fRound(0.5f*kpt.size / ratio);
scale = cvRound(0.5f*kpt.size / ratio);
const int level = kpt.class_id;
Mat Lx = evolution[level].Mx;
Mat Ly = evolution[level].My;
@ -1701,12 +1716,15 @@ 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)
};
memset(desc, 0, desc_size);
// For the three grids
for (int z = 0; z < 3; z++) {
dcount2 = 0;
@ -1723,8 +1741,8 @@ void Upright_MLDB_Full_Descriptor_Invoker::Get_Upright_MLDB_Full_Descriptor(cons
sample_y = yf + l*scale;
sample_x = xf + k*scale;
y1 = fRound(sample_y);
x1 = fRound(sample_x);
y1 = cvRound(sample_y);
x1 = cvRound(sample_x);
ri = *(Lt.ptr<float>(y1)+x1);
rx = *(Lx.ptr<float>(y1)+x1);
@ -1741,7 +1759,7 @@ void Upright_MLDB_Full_Descriptor_Invoker::Get_Upright_MLDB_Full_Descriptor(cons
dx /= nsamples;
dy /= nsamples;
float *val = values[z].ptr<float>(dcount2);
float *val = &values[dcount2*max_channels];
*(val) = di;
*(val+1) = dx;
*(val+2) = dy;
@ -1753,13 +1771,11 @@ void Upright_MLDB_Full_Descriptor_Invoker::Get_Upright_MLDB_Full_Descriptor(cons
const int num = (z + 2) * (z + 2);
for (int i = 0; i < num; i++) {
for (int j = i + 1; j < num; j++) {
const float * valI = values[z].ptr<float>(i);
const float * valJ = values[z].ptr<float>(j);
const float * valI = &values[i*max_channels];
const float * valJ = &values[j*max_channels];
for (int k = 0; k < 3; ++k) {
if (*(valI + k) > *(valJ + k)) {
desc[dcount1 / 8] |= (1 << (dcount1 % 8));
} else {
desc[dcount1 / 8] &= ~(1 << (dcount1 % 8));
}
dcount1++;
}
@ -1767,6 +1783,9 @@ void Upright_MLDB_Full_Descriptor_Invoker::Get_Upright_MLDB_Full_Descriptor(cons
}
} // for (int z = 0; z < 3; z++)
CV_Assert(dcount1 <= desc_size*8);
CV_Assert(divUp(dcount1, 8) == desc_size);
}
void MLDB_Full_Descriptor_Invoker::MLDB_Fill_Values(float* values, int sample_step, const int level,
@ -1791,8 +1810,8 @@ void MLDB_Full_Descriptor_Invoker::MLDB_Fill_Values(float* values, int sample_st
float sample_y = yf + (l*co * scale + k*si*scale);
float sample_x = xf + (-l*si * scale + k*co*scale);
int y1 = fRound(sample_y);
int x1 = fRound(sample_x);
int y1 = cvRound(sample_y);
int x1 = cvRound(sample_x);
// fix crash: indexing with out-of-bounds index, this might happen near the edges of image
// clip values so they fit into the image
@ -1852,10 +1871,6 @@ void MLDB_Full_Descriptor_Invoker::MLDB_Binary_Comparisons(float* values, unsign
if (ival > ivalues[chan * j + pos]) {
desc[dpos >> 3] |= (1 << (dpos & 7));
}
else {
desc[dpos >> 3] &= ~(1 << (dpos & 7));
}
dpos++;
}
}
@ -1869,30 +1884,41 @@ void MLDB_Full_Descriptor_Invoker::MLDB_Binary_Comparisons(float* values, unsign
* @param kpt Input keypoint
* @param desc Descriptor vector
*/
void MLDB_Full_Descriptor_Invoker::Get_MLDB_Full_Descriptor(const KeyPoint& kpt, unsigned char *desc) const {
void MLDB_Full_Descriptor_Invoker::Get_MLDB_Full_Descriptor(const KeyPoint& kpt, unsigned char *desc, int desc_size) const {
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);
float scale = (float)cvRound(0.5f*kpt.size / ratio);
float xf = kpt.pt.x / ratio;
float yf = kpt.pt.y / ratio;
float angle = (kpt.angle * static_cast<float>(CV_PI)) / 180.f;
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;
memset(desc, 0, desc_size);
int dpos = 0;
for(int lvl = 0; lvl < 3; lvl++) {
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);
}
CV_Assert(dpos == 486);
CV_Assert(divUp(dpos, 8) == desc_size);
}
/* ************************************************************************* */
@ -1903,7 +1929,7 @@ void MLDB_Full_Descriptor_Invoker::Get_MLDB_Full_Descriptor(const KeyPoint& kpt,
* @param kpt Input keypoint
* @param desc Descriptor vector
*/
void MLDB_Descriptor_Subset_Invoker::Get_MLDB_Descriptor_Subset(const KeyPoint& kpt, unsigned char *desc) const {
void MLDB_Descriptor_Subset_Invoker::Get_MLDB_Descriptor_Subset(const KeyPoint& kpt, unsigned char *desc, int desc_size) const {
float di = 0.f, dx = 0.f, dy = 0.f;
float rx = 0.f, ry = 0.f;
@ -1915,8 +1941,8 @@ void MLDB_Descriptor_Subset_Invoker::Get_MLDB_Descriptor_Subset(const KeyPoint&
// Get the information from the keypoint
float ratio = (float)(1 << kpt.octave);
int scale = fRound(0.5f*kpt.size / ratio);
float angle = (kpt.angle * static_cast<float>(CV_PI)) / 180.f;
int scale = cvRound(0.5f*kpt.size / ratio);
float angle = kpt.angle * static_cast<float>(CV_PI / 180.f);
const int level = kpt.class_id;
Mat Lx = evolution[level].Mx;
Mat Ly = evolution[level].My;
@ -1927,17 +1953,25 @@ void MLDB_Descriptor_Subset_Invoker::Get_MLDB_Descriptor_Subset(const KeyPoint&
float si = sin(angle);
// Allocate memory for the matrix of values
Mat values((4 + 9 + 16)*options.descriptor_channels, 1, CV_32FC1);
// Buffer for the M-LDB descriptor
const int max_channels = 3;
const int channels = options.descriptor_channels;
CV_Assert(channels <= max_channels);
float values[(4 + 9 + 16)*max_channels];
// 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;
@ -1949,8 +1983,8 @@ void MLDB_Descriptor_Subset_Invoker::Get_MLDB_Descriptor_Subset(const KeyPoint&
sample_y = yf + (l*scale*co + k*scale*si);
sample_x = xf + (-l*scale*si + k*scale*co);
y1 = fRound(sample_y);
x1 = fRound(sample_x);
y1 = cvRound(sample_y);
x1 = cvRound(sample_x);
di += *(Lt.ptr<float>(y1)+x1);
@ -1970,26 +2004,27 @@ void MLDB_Descriptor_Subset_Invoker::Get_MLDB_Descriptor_Subset(const KeyPoint&
}
}
*(values.ptr<float>(options.descriptor_channels*i)) = di;
float* pValues = &values[channels * i];
pValues[0] = di;
if (options.descriptor_channels == 2) {
*(values.ptr<float>(options.descriptor_channels*i + 1)) = dx;
if (channels == 2) {
pValues[1] = dx;
}
else if (options.descriptor_channels == 3) {
*(values.ptr<float>(options.descriptor_channels*i + 1)) = dx;
*(values.ptr<float>(options.descriptor_channels*i + 2)) = dy;
else if (channels == 3) {
pValues[1] = dx;
pValues[2] = dy;
}
}
// Do the comparisons
const float *vals = values.ptr<float>(0);
const int *comps = descriptorBits_.ptr<int>(0);
CV_Assert(divUp(descriptorBits_.rows, 8) == desc_size);
memset(desc, 0, desc_size);
for (int i = 0; i<descriptorBits_.rows; i++) {
if (vals[comps[2 * i]] > vals[comps[2 * i + 1]]) {
if (values[comps[2 * i]] > values[comps[2 * i + 1]]) {
desc[i / 8] |= (1 << (i % 8));
} else {
desc[i / 8] &= ~(1 << (i % 8));
}
}
}
@ -2002,7 +2037,7 @@ void MLDB_Descriptor_Subset_Invoker::Get_MLDB_Descriptor_Subset(const KeyPoint&
* @param kpt Input keypoint
* @param desc Descriptor vector
*/
void Upright_MLDB_Descriptor_Subset_Invoker::Get_Upright_MLDB_Descriptor_Subset(const KeyPoint& kpt, unsigned char *desc) const {
void Upright_MLDB_Descriptor_Subset_Invoker::Get_Upright_MLDB_Descriptor_Subset(const KeyPoint& kpt, unsigned char *desc, int desc_size) const {
float di = 0.0f, dx = 0.0f, dy = 0.0f;
float rx = 0.0f, ry = 0.0f;
@ -2014,7 +2049,7 @@ void Upright_MLDB_Descriptor_Subset_Invoker::Get_Upright_MLDB_Descriptor_Subset(
// Get the information from the keypoint
float ratio = (float)(1 << kpt.octave);
int scale = fRound(0.5f*kpt.size / ratio);
int scale = cvRound(0.5f*kpt.size / ratio);
const int level = kpt.class_id;
Mat Lx = evolution[level].Mx;
Mat Ly = evolution[level].My;
@ -2025,14 +2060,18 @@ 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++) {
@ -2042,8 +2081,8 @@ void Upright_MLDB_Descriptor_Subset_Invoker::Get_Upright_MLDB_Descriptor_Subset(
sample_y = yf + l*scale;
sample_x = xf + k*scale;
y1 = fRound(sample_y);
x1 = fRound(sample_x);
y1 = cvRound(sample_y);
x1 = cvRound(sample_x);
di += *(Lt.ptr<float>(y1)+x1);
if (options.descriptor_channels > 1) {
@ -2076,11 +2115,12 @@ void Upright_MLDB_Descriptor_Subset_Invoker::Get_Upright_MLDB_Descriptor_Subset(
const float *vals = values.ptr<float>(0);
const int *comps = descriptorBits_.ptr<int>(0);
CV_Assert(divUp(descriptorBits_.rows, 8) == desc_size);
memset(desc, 0, desc_size);
for (int i = 0; i<descriptorBits_.rows; i++) {
if (vals[comps[2 * i]] > vals[comps[2 * i + 1]]) {
desc[i / 8] |= (1 << (i % 8));
} else {
desc[i / 8] &= ~(1 << (i % 8));
}
}
}
@ -2120,7 +2160,7 @@ 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 +2174,12 @@ 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/AKAZEFeatures.h
View File

@ -25,6 +25,8 @@ struct Evolution
octave = 0;
sublevel = 0;
sigma_size = 0;
octave_ratio = 0.0f;
border = 0;
}
UMat Lx, Ly; ///< First order spatial derivatives

48
modules/features2d/src/kaze/KAZEFeatures.cpp
View File

@ -68,7 +68,7 @@ void KAZEFeatures::Allocate_Memory_Evolution(void) {
aux.Ldet = Mat::zeros(options_.img_height, options_.img_width, CV_32F);
aux.esigma = options_.soffset*pow((float)2.0f, (float)(j) / (float)(options_.nsublevels)+i);
aux.etime = 0.5f*(aux.esigma*aux.esigma);
aux.sigma_size = fRound(aux.esigma);
aux.sigma_size = cvRound(aux.esigma);
aux.octave = i;
aux.sublevel = j;
evolution_.push_back(aux);
@ -363,10 +363,10 @@ void KAZEFeatures::Determinant_Hessian(std::vector<KeyPoint>& kpts)
if (is_extremum == true) {
// Check that the point is under the image limits for the descriptor computation
left_x = fRound(kpts_par_[i][j].pt.x - smax*kpts_par_[i][j].size);
right_x = fRound(kpts_par_[i][j].pt.x + smax*kpts_par_[i][j].size);
up_y = fRound(kpts_par_[i][j].pt.y - smax*kpts_par_[i][j].size);
down_y = fRound(kpts_par_[i][j].pt.y + smax*kpts_par_[i][j].size);
left_x = cvRound(kpts_par_[i][j].pt.x - smax*kpts_par_[i][j].size);
right_x = cvRound(kpts_par_[i][j].pt.x + smax*kpts_par_[i][j].size);
up_y = cvRound(kpts_par_[i][j].pt.y - smax*kpts_par_[i][j].size);
down_y = cvRound(kpts_par_[i][j].pt.y + smax*kpts_par_[i][j].size);
if (left_x < 0 || right_x >= evolution_[level].Ldet.cols ||
up_y < 0 || down_y >= evolution_[level].Ldet.rows) {
@ -587,14 +587,14 @@ void KAZEFeatures::Compute_Main_Orientation(KeyPoint &kpt, const std::vector<TEv
xf = kpt.pt.x;
yf = kpt.pt.y;
level = kpt.class_id;
s = fRound(kpt.size / 2.0f);
s = cvRound(kpt.size / 2.0f);
// Calculate derivatives responses for points within radius of 6*scale
for (int i = -6; i <= 6; ++i) {
for (int j = -6; j <= 6; ++j) {
if (i*i + j*j < 36) {
iy = fRound(yf + j*s);
ix = fRound(xf + i*s);
iy = cvRound(yf + j*s);
ix = cvRound(xf + i*s);
if (iy >= 0 && iy < options.img_height && ix >= 0 && ix < options.img_width) {
gweight = gaussian(iy - yf, ix - xf, 2.5f*s);
@ -606,7 +606,7 @@ void KAZEFeatures::Compute_Main_Orientation(KeyPoint &kpt, const std::vector<TEv
resY[idx] = 0.0;
}
Ang[idx] = getAngle(resX[idx], resY[idx]);
Ang[idx] = fastAtan2(resX[idx], resY[idx]) * (float)(CV_PI / 180.0f);
++idx;
}
}
@ -638,7 +638,7 @@ void KAZEFeatures::Compute_Main_Orientation(KeyPoint &kpt, const std::vector<TEv
if (sumX*sumX + sumY*sumY > max) {
// store largest orientation
max = sumX*sumX + sumY*sumY;
kpt.angle = getAngle(sumX, sumY) * 180.f / static_cast<float>(CV_PI);
kpt.angle = fastAtan2(sumX, sumY);
}
}
}
@ -676,7 +676,7 @@ void KAZE_Descriptor_Invoker::Get_KAZE_Upright_Descriptor_64(const KeyPoint &kpt
// Get the information from the keypoint
yf = kpt.pt.y;
xf = kpt.pt.x;
scale = fRound(kpt.size / 2.0f);
scale = cvRound(kpt.size / 2.0f);
level = kpt.class_id;
i = -8;
@ -804,8 +804,8 @@ void KAZE_Descriptor_Invoker::Get_KAZE_Descriptor_64(const KeyPoint &kpt, float
// Get the information from the keypoint
yf = kpt.pt.y;
xf = kpt.pt.x;
scale = fRound(kpt.size / 2.0f);
angle = (kpt.angle * static_cast<float>(CV_PI)) / 180.f;
scale = cvRound(kpt.size / 2.0f);
angle = kpt.angle * static_cast<float>(CV_PI / 180.f);
level = kpt.class_id;
co = cos(angle);
si = sin(angle);
@ -843,13 +843,13 @@ void KAZE_Descriptor_Invoker::Get_KAZE_Descriptor_64(const KeyPoint &kpt, float
// Get the gaussian weighted x and y responses
gauss_s1 = gaussian(xs - sample_x, ys - sample_y, 2.5f*scale);
y1 = fRound(sample_y - 0.5f);
x1 = fRound(sample_x - 0.5f);
y1 = cvFloor(sample_y);
x1 = cvFloor(sample_x);
checkDescriptorLimits(x1, y1, options_.img_width, options_.img_height);
y2 = (int)(sample_y + 0.5f);
x2 = (int)(sample_x + 0.5f);
y2 = y1 + 1;
x2 = x1 + 1;
checkDescriptorLimits(x2, y2, options_.img_width, options_.img_height);
@ -935,7 +935,7 @@ void KAZE_Descriptor_Invoker::Get_KAZE_Upright_Descriptor_128(const KeyPoint &kp
// Get the information from the keypoint
yf = kpt.pt.y;
xf = kpt.pt.x;
scale = fRound(kpt.size / 2.0f);
scale = cvRound(kpt.size / 2.0f);
level = kpt.class_id;
i = -8;
@ -1087,8 +1087,8 @@ void KAZE_Descriptor_Invoker::Get_KAZE_Descriptor_128(const KeyPoint &kpt, float
// Get the information from the keypoint
yf = kpt.pt.y;
xf = kpt.pt.x;
scale = fRound(kpt.size / 2.0f);
angle = (kpt.angle * static_cast<float>(CV_PI)) / 180.f;
scale = cvRound(kpt.size / 2.0f);
angle = kpt.angle * static_cast<float>(CV_PI / 180.f);
level = kpt.class_id;
co = cos(angle);
si = sin(angle);
@ -1129,13 +1129,13 @@ void KAZE_Descriptor_Invoker::Get_KAZE_Descriptor_128(const KeyPoint &kpt, float
// Get the gaussian weighted x and y responses
gauss_s1 = gaussian(xs - sample_x, ys - sample_y, 2.5f*scale);
y1 = fRound(sample_y - 0.5f);
x1 = fRound(sample_x - 0.5f);
y1 = cvFloor(sample_y);
x1 = cvFloor(sample_x);
checkDescriptorLimits(x1, y1, options_.img_width, options_.img_height);
y2 = (int)(sample_y + 0.5f);
x2 = (int)(sample_x + 0.5f);
y2 = y1 + 1;
x2 = x1 + 1;
checkDescriptorLimits(x2, y2, options_.img_width, options_.img_height);

2
modules/features2d/src/kaze/fed.cpp
View File

@ -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
View File

@ -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_;
}

55
modules/features2d/src/kaze/utils.h
View File

@ -1,31 +1,6 @@
#ifndef __OPENCV_FEATURES_2D_KAZE_UTILS_H__
#define __OPENCV_FEATURES_2D_KAZE_UTILS_H__
/* ************************************************************************* */
/**
* @brief This function computes the angle from the vector given by (X Y). From 0 to 2*Pi
*/
inline float getAngle(float x, float y) {
if (x >= 0 && y >= 0) {
return atanf(y / x);
}
if (x < 0 && y >= 0) {
return static_cast<float>(CV_PI)-atanf(-y / x);
}
if (x < 0 && y < 0) {
return static_cast<float>(CV_PI)+atanf(y / x);
}
if (x >= 0 && y < 0) {
return static_cast<float>(2.0 * CV_PI) - atanf(-y / x);
}
return 0;
}
/* ************************************************************************* */
/**
* @brief This function computes the value of a 2D Gaussian function
@ -64,34 +39,4 @@ inline void checkDescriptorLimits(int &x, int &y, int width, int height) {
}
}
/* ************************************************************************* */
/**
* @brief This funtion rounds float to nearest integer
* @param flt Input float
* @return dst Nearest integer
*/
inline int fRound(float flt) {
return (int)(flt + 0.5f);
}
/* ************************************************************************* */
/**
* @brief Exponentiation by squaring
* @param flt Exponentiation base
* @return dst Exponentiation value
*/
inline int fastpow(int base, int exp) {
int res = 1;
while(exp > 0) {
if(exp & 1) {
exp--;
res *= base;
} else {
exp /= 2;
base *= base;
}
}
return res;
}
#endif

8
modules/features2d/test/test_descriptors_invariance.cpp
View File

@ -11,7 +11,7 @@ using std::tr1::make_tuple;
using std::tr1::get;
using namespace testing;
#define SHOW_DEBUG_LOG 0
#define SHOW_DEBUG_LOG 1
typedef std::tr1::tuple<std::string, Ptr<FeatureDetector>, Ptr<DescriptorExtractor>, float>
String_FeatureDetector_DescriptorExtractor_Float_t;
@ -72,7 +72,7 @@ TEST_P(DescriptorRotationInvariance, rotation)
vector<KeyPoint> keypoints0;
Mat descriptors0;
featureDetector->detect(image0, keypoints0, mask0);
std::cout << "Intial keypoints: " << keypoints0.size() << std::endl;
std::cout << "Keypoints: " << keypoints0.size() << std::endl;
EXPECT_GE(keypoints0.size(), 15u);
descriptorExtractor->compute(image0, keypoints0, descriptors0);
@ -109,7 +109,7 @@ TEST_P(DescriptorRotationInvariance, rotation)
#if SHOW_DEBUG_LOG
std::cout
<< "angle = " << angle
<< ", keypoints = " << keypoints1.size()
<< ", inliers = " << descInliersCount
<< ", descInliersRatio = " << static_cast<float>(descInliersCount) / keypoints0.size()
<< std::endl;
#endif
@ -121,6 +121,7 @@ TEST_P(DescriptorScaleInvariance, scale)
{
vector<KeyPoint> keypoints0;
featureDetector->detect(image0, keypoints0);
std::cout << "Keypoints: " << keypoints0.size() << std::endl;
EXPECT_GE(keypoints0.size(), 15u);
Mat descriptors0;
descriptorExtractor->compute(image0, keypoints0, descriptors0);
@ -159,6 +160,7 @@ TEST_P(DescriptorScaleInvariance, scale)
#if SHOW_DEBUG_LOG
std::cout
<< "scale = " << scale
<< ", inliers = " << descInliersCount
<< ", descInliersRatio = " << static_cast<float>(descInliersCount) / keypoints0.size()
<< std::endl;
#endif

117
modules/features2d/test/test_descriptors_regression.cpp
View File

@ -56,6 +56,7 @@ static void writeMatInBin( const Mat& mat, const string& filename )
FILE* f = fopen( filename.c_str(), "wb");
if( f )
{
CV_Assert(4 == sizeof(int));
int type = mat.type();
fwrite( (void*)&mat.rows, sizeof(int), 1, f );
fwrite( (void*)&mat.cols, sizeof(int), 1, f );
@ -72,6 +73,7 @@ static Mat readMatFromBin( const string& filename )
FILE* f = fopen( filename.c_str(), "rb" );
if( f )
{
CV_Assert(4 == sizeof(int));
int rows, cols, type, dataSize;
size_t elements_read1 = fread( (void*)&rows, sizeof(int), 1, f );
size_t elements_read2 = fread( (void*)&cols, sizeof(int), 1, f );
@ -123,24 +125,37 @@ protected:
CV_Assert( DataType<ValueType>::type == validDescriptors.type() );
int dimension = validDescriptors.cols;
DistanceType curMaxDist = std::numeric_limits<DistanceType>::min();
DistanceType curMaxDist = 0;
size_t exact_count = 0, failed_count = 0;
for( int y = 0; y < validDescriptors.rows; y++ )
{
DistanceType dist = distance( validDescriptors.ptr<ValueType>(y), calcDescriptors.ptr<ValueType>(y), dimension );
if (dist == 0)
exact_count++;
if( dist > curMaxDist )
{
if (dist > maxDist)
failed_count++;
curMaxDist = dist;
}
#if 0
if (dist > 0)
{
std::cout << "i=" << y << " fail_count=" << failed_count << " dist=" << dist << std::endl;
std::cout << "valid: " << validDescriptors.row(y) << std::endl;
std::cout << " calc: " << calcDescriptors.row(y) << std::endl;
}
#endif
}
float exact_percents = (100 * (float)exact_count / validDescriptors.rows);
float failed_percents = (100 * (float)failed_count / validDescriptors.rows);
stringstream ss;
ss << "Max distance between valid and computed descriptors " << curMaxDist;
if( curMaxDist <= maxDist )
ss << "." << endl;
else
{
ss << ">" << maxDist << " - bad accuracy!"<< endl;
ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
}
ts->printf(cvtest::TS::LOG, ss.str().c_str() );
ss << "Exact count (dist == 0): " << exact_count << " (" << (int)exact_percents << "%)" << std::endl
<< "Failed count (dist > " << maxDist << "): " << failed_count << " (" << (int)failed_percents << "%)" << std::endl
<< "Max distance between valid and computed descriptors (" << validDescriptors.size() << "): " << curMaxDist;
EXPECT_LE(failed_percents, 20.0f);
std::cout << ss.str() << std::endl;
}
void emptyDataTest()
@ -202,22 +217,57 @@ protected:
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
return;
}
const std::string keypoints_filename = string(ts->get_data_path()) +
(detector.empty()
? (FEATURES2D_DIR + "/" + std::string("keypoints.xml.gz"))
: (DESCRIPTOR_DIR + "/" + name + "_keypoints.xml.gz"));
FileStorage fs(keypoints_filename, FileStorage::READ);
vector<KeyPoint> keypoints;
FileStorage fs( string(ts->get_data_path()) + FEATURES2D_DIR + "/keypoints.xml.gz", FileStorage::READ );
if(!detector.empty()) {
detector->detect(img, keypoints);
} else {
read( fs.getFirstTopLevelNode(), keypoints );
EXPECT_TRUE(fs.isOpened()) << "Keypoint testdata is missing. Re-computing and re-writing keypoints testdata...";
if (!fs.isOpened())
{
fs.open(keypoints_filename, FileStorage::WRITE);
ASSERT_TRUE(fs.isOpened()) << "File for writting keypoints can not be opened.";
if (detector.empty())
{
Ptr<ORB> fd = ORB::create();
fd->detect(img, keypoints);
}
else
{
detector->detect(img, keypoints);
}
write(fs, "keypoints", keypoints);
fs.release();
}
if(!keypoints.empty())
else
{
read(fs.getFirstTopLevelNode(), keypoints);
fs.release();
}
if(!detector.empty())
{
vector<KeyPoint> calcKeypoints;
detector->detect(img, calcKeypoints);
// TODO validate received keypoints
int diff = abs((int)calcKeypoints.size() - (int)keypoints.size());
if (diff > 0)
{
std::cout << "Keypoints difference: " << diff << std::endl;
EXPECT_LE(diff, (int)(keypoints.size() * 0.03f));
}
}
ASSERT_FALSE(keypoints.empty());
{
Mat calcDescriptors;
double t = (double)getTickCount();
dextractor->compute( img, keypoints, calcDescriptors );
dextractor->compute(img, keypoints, calcDescriptors);
t = getTickCount() - t;
ts->printf(cvtest::TS::LOG, "\nAverage time of computing one descriptor = %g ms.\n", t/((double)getTickFrequency()*1000.)/calcDescriptors.rows);
if( calcDescriptors.rows != (int)keypoints.size() )
if (calcDescriptors.rows != (int)keypoints.size())
{
ts->printf( cvtest::TS::LOG, "Count of computed descriptors and keypoints count must be equal.\n" );
ts->printf( cvtest::TS::LOG, "Count of keypoints is %d.\n", (int)keypoints.size() );
@ -226,7 +276,7 @@ protected:
return;
}
if( calcDescriptors.cols != dextractor->descriptorSize() || calcDescriptors.type() != dextractor->descriptorType() )
if (calcDescriptors.cols != dextractor->descriptorSize() || calcDescriptors.type() != dextractor->descriptorType())
{
ts->printf( cvtest::TS::LOG, "Incorrect descriptor size or descriptor type.\n" );
ts->printf( cvtest::TS::LOG, "Expected size is %d.\n", dextractor->descriptorSize() );
@ -239,33 +289,14 @@ protected:
// TODO read and write descriptor extractor parameters and check them
Mat validDescriptors = readDescriptors();
if( !validDescriptors.empty() )
compareDescriptors( validDescriptors, calcDescriptors );
else
EXPECT_FALSE(validDescriptors.empty()) << "Descriptors testdata is missing. Re-writing descriptors testdata...";
if (!validDescriptors.empty())
{
if( !writeDescriptors( calcDescriptors ) )
{
ts->printf( cvtest::TS::LOG, "Descriptors can not be written.\n" );
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
return;
}
}
}
if(!fs.isOpened())
{
ts->printf( cvtest::TS::LOG, "Compute and write keypoints.\n" );
fs.open( string(ts->get_data_path()) + FEATURES2D_DIR + "/keypoints.xml.gz", FileStorage::WRITE );
if( fs.isOpened() )
{
Ptr<ORB> fd = ORB::create();
fd->detect(img, keypoints);
write( fs, "keypoints", keypoints );
compareDescriptors(validDescriptors, calcDescriptors);
}
else
{
ts->printf(cvtest::TS::LOG, "File for writting keypoints can not be opened.\n");
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
return;
ASSERT_TRUE(writeDescriptors(calcDescriptors)) << "Descriptors can not be written.";
}
}
}
@ -344,7 +375,7 @@ TEST( Features2d_DescriptorExtractor_KAZE, regression )
TEST( Features2d_DescriptorExtractor_AKAZE, regression )
{
CV_DescriptorExtractorTest<Hamming> test( "descriptor-akaze",
(CV_DescriptorExtractorTest<Hamming>::DistanceType)12.f,
(CV_DescriptorExtractorTest<Hamming>::DistanceType)(486*0.05f),
AKAZE::create(),
Hamming(), AKAZE::create());
test.safe_run();

2
modules/features2d/test/test_detectors_invariance.cpp
View File

@ -11,7 +11,7 @@ using std::tr1::make_tuple;
using std::tr1::get;
using namespace testing;
#define SHOW_DEBUG_LOG 0
#define SHOW_DEBUG_LOG 1
typedef std::tr1::tuple<std::string, Ptr<FeatureDetector>, float, float> String_FeatureDetector_Float_Float_t;
const static std::string IMAGE_TSUKUBA = "features2d/tsukuba.png";

5
modules/photo/src/fast_nlmeans_denoising_opencl.hpp
View File

@ -23,11 +23,6 @@ enum
CTA_SIZE_DEFAULT = 256
};
static int divUp(int a, int b)
{
return (a + b - 1) / b;
}
template <typename FT, typename ST, typename WT>
static bool ocl_calcAlmostDist2Weight(UMat & almostDist2Weight,
int searchWindowSize, int templateWindowSize,

4
modules/stitching/perf/perf_stich.cpp
View File

@ -102,7 +102,7 @@ PERF_TEST_P(stitchDatasets, affine, testing::Combine(AFFINE_DATASETS, TEST_DETEC
Mat pano;
vector<Mat> imgs;
int width, height, allowed_diff = 10;
int width, height, allowed_diff = 20;
Ptr<detail::FeaturesFinder> featuresFinder = getFeatureFinder(detector);
if(dataset == "budapest")
@ -117,7 +117,7 @@ PERF_TEST_P(stitchDatasets, affine, testing::Combine(AFFINE_DATASETS, TEST_DETEC
height = 1158;
// this dataset is big, the results between surf and orb differ slightly,
// but both are still good
allowed_diff = 27;
allowed_diff = 50;
}
else if (dataset == "newspaper")
{