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Several type of formal refactoring:

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1. someMatrix.data -> someMatrix.prt()
2. someMatrix.data + someMatrix.step * lineIndex -> someMatrix.ptr( lineIndex )
3. (SomeType*) someMatrix.data -> someMatrix.ptr<SomeType>()
4. someMatrix.data -> !someMatrix.empty() ( or !someMatrix.data -> someMatrix.empty() ) in logical expressions
This commit is contained in:
Adil Ibragimov
2014-08-13 15:08:27 +04:00
parent 30111a786a
commit 8a4a1bb018
134 changed files with 988 additions and 986 deletions
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18
modules/features2d/src/brisk.cpp
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@@ -427,7 +427,7 @@ BRISK::smoothedIntensity(const cv::Mat& image, const cv::Mat& integral, const fl
if (dx + dy > 2)
{
// now the calculation:
const uchar* ptr = image.data + x_left + imagecols * y_top;
const uchar* ptr = image.ptr() + x_left + imagecols * y_top;
// first the corners:
ret_val = A * int(*ptr);
ptr += dx + 1;
@@ -438,7 +438,7 @@ BRISK::smoothedIntensity(const cv::Mat& image, const cv::Mat& integral, const fl
ret_val += D * int(*ptr);
// next the edges:
int* ptr_integral = (int*) integral.data + x_left + integralcols * y_top + 1;
const int* ptr_integral = integral.ptr<int>() + x_left + integralcols * y_top + 1;
// find a simple path through the different surface corners
const int tmp1 = (*ptr_integral);
ptr_integral += dx;
@@ -475,7 +475,7 @@ BRISK::smoothedIntensity(const cv::Mat& image, const cv::Mat& integral, const fl
}
// now the calculation:
const uchar* ptr = image.data + x_left + imagecols * y_top;
const uchar* ptr = image.ptr() + x_left + imagecols * y_top;
// first row:
ret_val = A * int(*ptr);
ptr++;
@@ -607,7 +607,7 @@ BRISK::computeDescriptorsAndOrOrientation(InputArray _image, InputArray _mask, s
int t2;
// the feature orientation
const uchar* ptr = descriptors.data;
const uchar* ptr = descriptors.ptr();
for (size_t k = 0; k < ksize; k++)
{
cv::KeyPoint& kp = keypoints[k];
@@ -1070,7 +1070,7 @@ BriskScaleSpace::isMax2D(const int layer, const int x_layer, const int y_layer)
{
const cv::Mat& scores = pyramid_[layer].scores();
const int scorescols = scores.cols;
const uchar* data = scores.data + y_layer * scorescols + x_layer;
const uchar* data = scores.ptr() + y_layer * scorescols + x_layer;
// decision tree:
const uchar center = (*data);
data--;
@@ -1154,11 +1154,11 @@ BriskScaleSpace::isMax2D(const int layer, const int x_layer, const int y_layer)
{
// in this case, we have to analyze the situation more carefully:
// the values are gaussian blurred and then we really decide
data = scores.data + y_layer * scorescols + x_layer;
data = scores.ptr() + y_layer * scorescols + x_layer;
int smoothedcenter = 4 * center + 2 * (s_10 + s10 + s0_1 + s01) + s_1_1 + s1_1 + s_11 + s11;
for (unsigned int i = 0; i < deltasize; i += 2)
{
data = scores.data + (y_layer - 1 + delta[i + 1]) * scorescols + x_layer + delta[i] - 1;
data = scores.ptr() + (y_layer - 1 + delta[i + 1]) * scorescols + x_layer + delta[i] - 1;
int othercenter = *data;
data++;
othercenter += 2 * (*data);
@@ -2140,7 +2140,7 @@ BriskLayer::value(const cv::Mat& mat, float xf, float yf, float scale_in) const
const int r_y = (int)((yf - y) * 1024);
const int r_x_1 = (1024 - r_x);
const int r_y_1 = (1024 - r_y);
const uchar* ptr = image.data + x + y * imagecols;
const uchar* ptr = image.ptr() + x + y * imagecols;
// just interpolate:
ret_val = (r_x_1 * r_y_1 * int(*ptr));
ptr++;
@@ -2186,7 +2186,7 @@ BriskLayer::value(const cv::Mat& mat, float xf, float yf, float scale_in) const
const int r_y1_i = (int)(r_y1 * scaling);
// now the calculation:
const uchar* ptr = image.data + x_left + imagecols * y_top;
const uchar* ptr = image.ptr() + x_left + imagecols * y_top;
// first row:
ret_val = A * int(*ptr);
ptr++;