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Fix linux warns & more cosmetics.

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pull/229/head
cbalint13 10 years ago
parent 5785a6a58a
commit c1b2e23775
1 changed files with 33 additions and 33 deletions
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  1. 66
      modules/xfeatures2d/src/daisy.cpp

66
modules/xfeatures2d/src/daisy.cpp
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@ -286,7 +286,7 @@ protected:
void set_normalization( int nrm_type ) { m_nrm_type = nrm_type; }
// applies one of the normalizations (partial,full,sift) to the desciptors.
void normalize_descriptors(int nrm_type = DAISY::NRM_NONE);
void normalize_descriptors( int nrm_type = DAISY::NRM_NONE );
// normalizes histograms individually
void normalize_histograms();
@ -320,7 +320,7 @@ protected:
// EXPERIMENTAL: DO NOT USE IF YOU ARE NOT ENGIN TOLA: tells to compute the
// scales for every pixel so that the resulting descriptors are scale
// invariant.
void scale_invariant( bool state=true )
void scale_invariant( bool state = true )
{
g_scale_en = (int)( (log(g_sigma_2/g_sigma_0)) / log(g_sigma_step) ) - g_scale_st;
m_scale_invariant = state;
@ -329,7 +329,7 @@ protected:
// EXPERIMENTAL: DO NOT USE IF YOU ARE NOT ENGIN TOLA: tells to compute the
// orientations for every pixel so that the resulting descriptors are
// rotation invariant. orientation steps are 360/ori_resolution
void rotation_invariant(int ori_resolution=36, bool state=true)
void rotation_invariant( int ori_resolution = 36, bool state = true )
{
m_rotation_invariant = state;
m_orientation_resolution = ori_resolution;
@ -365,7 +365,7 @@ protected:
return (g_cube_number+1)* m_cube_size;
}
void normalize_descriptor(float* desc, int nrm_type = DAISY::NRM_NONE)
void normalize_descriptor( float* desc, int nrm_type = DAISY::NRM_NONE )
{
if( nrm_type == DAISY::NRM_NONE ) nrm_type = m_nrm_type;
else if( nrm_type == DAISY::NRM_PARTIAL ) normalize_partial(desc);
@ -391,17 +391,17 @@ private:
// precomputed operations meaning that you must call compute_descriptors()
// before calling this function. if you want normalized descriptors, call
// normalize_descriptors() before calling compute_descriptors()
inline void get_descriptor(int y, int x, float* &descriptor);
inline void get_descriptor( int y, int x, float* &descriptor );
// computes the descriptor and returns the result in 'descriptor' ( allocate
// 'descriptor' memory first ie: float descriptor = new
// float[m_descriptor_size]; -> the descriptor is normalized.
inline void get_descriptor(double y, double x, int orientation, float* descriptor );
inline void get_descriptor( double y, double x, int orientation, float* descriptor );
// computes the descriptor and returns the result in 'descriptor' ( allocate
// 'descriptor' memory first ie: float descriptor = new
// float[m_descriptor_size]; -> the descriptor is NOT normalized.
inline void get_unnormalized_descriptor(double y, double x, int orientation, float* descriptor );
inline void get_unnormalized_descriptor( double y, double x, int orientation, float* descriptor );
// computes the descriptor at homography-warped grid. (y,x) is not the
// coordinates of this image but the coordinates of the original grid where
@ -409,7 +409,7 @@ private:
// and we warp this grid with H and compute the descriptor on this warped
// grid; returns null/false if centers falls outside the image; allocate
// 'descriptor' memory first. descriptor is normalized.
inline bool get_descriptor(double y, double x, int orientation, double* H, float* descriptor );
inline bool get_descriptor( double y, double x, int orientation, double* H, float* descriptor);
// computes the descriptor at homography-warped grid. (y,x) is not the
// coordinates of this image but the coordinates of the original grid where
@ -417,7 +417,7 @@ private:
// and we warp this grid with H and compute the descriptor on this warped
// grid; returns null/false if centers falls outside the image; allocate
// 'descriptor' memory first. descriptor is NOT normalized.
inline bool get_unnormalized_descriptor(double y, double x, int orientation, double* H, float* descriptor );
inline bool get_unnormalized_descriptor( double y, double x, int orientation, double* H, float* descriptor );
// compute the smoothed gradient layers.
inline void compute_smoothed_gradient_layers();
@ -452,7 +452,7 @@ private:
inline bool ni_get_descriptor( double y, double x, int orientation, double* H, float* descriptor );
// creates a 1D gaussian filter with N(mean,sigma).
inline void gaussian_1d(float* fltr, int fsz, float sigma, float mean )
inline void gaussian_1d( float* fltr, int fsz, float sigma, float mean )
{
CV_Assert(fltr != NULL);
int sz = (fsz-1)/2;
@ -476,27 +476,27 @@ private:
inline void conv_horizontal( float* image, int h, int w, float* kernel, int ksize )
{
CvMat cvI; cvInitMatHeader(&cvI, h, w, CV_32FC1, (float*)image);
CvMat cvK; cvInitMatHeader(&cvK, 1, ksize, CV_32FC1, (float*)kernel );
CvMat cvK; cvInitMatHeader(&cvK, 1, ksize, CV_32FC1, (float*)kernel);
cvFilter2D( &cvI, &cvI, &cvK );
}
inline void conv_horizontal( double* image, int h, int w, double* kernel, int ksize )
{
CvMat cvI; cvInitMatHeader(&cvI, h, w, CV_64FC1, (double*)image);
CvMat cvK; cvInitMatHeader(&cvK, 1, ksize, CV_64FC1, (double*)kernel );
CvMat cvK; cvInitMatHeader(&cvK, 1, ksize, CV_64FC1, (double*)kernel);
cvFilter2D( &cvI, &cvI, &cvK );
}
inline void conv_vertical( float* image, int h, int w, float* kernel, int ksize )
{
CvMat cvI; cvInitMatHeader(&cvI, h, w, CV_32FC1, (float*)image);
CvMat cvK; cvInitMatHeader(&cvK, ksize, 1, CV_32FC1, (float*)kernel );
CvMat cvK; cvInitMatHeader(&cvK, ksize, 1, CV_32FC1, (float*)kernel);
cvFilter2D( &cvI, &cvI, &cvK );
}
inline void conv_vertical( double* image, int h, int w, double* kernel, int ksize )
{
CvMat cvI; cvInitMatHeader(&cvI, h, w, CV_64FC1, (double*)image);
CvMat cvK; cvInitMatHeader(&cvK, ksize, 1, CV_64FC1, (double*)kernel );
CvMat cvK; cvInitMatHeader(&cvK, ksize, 1, CV_64FC1, (double*)kernel);
cvFilter2D( &cvI, &cvI, &cvK );
}
@ -792,7 +792,7 @@ private:
T* layers = zeros<T>(layer_no * data_size);
// smooth the data matrix
T* bdata = blur_gaussian_2d<T,T>( data, h, w, 0.5, 5, false);
T* bdata = blur_gaussian_2d<T,T>( data, h, w, 0.5, 5, false );
T *dx = new T[data_size];
T *dy = new T[data_size];
@ -853,23 +853,23 @@ private:
// be careful, 'data' is destroyed afterwards
template<class T> inline
// original T* workspace=0 was removed
void layered_gradient( T* data, int h, int w, int layer_no, T* layers, int lwork=0 )
void layered_gradient( T* data, int h, int w, int layer_no, T* layers, int lwork = 0 )
{
int data_size = h * w;
CV_Assert(layers!=NULL);
memset(layers,0,sizeof(T)*data_size*layer_no);
bool empty=false;
bool was_empty = false;
T* work=NULL;
if( lwork < 3*data_size ) {
work = new T[3*data_size];
empty=true;
was_empty = true;
}
// // smooth the data matrix
// T* bdata = blur_gaussian_2d<T,T>( data, h, w, 0.5, 5, false);
float kernel[5]; gaussian_1d(kernel, 5, 0.5, 0);
memcpy( work, data, sizeof(T)*data_size);
memcpy( work, data, sizeof(T)*data_size );
convolve_sym( work, h, w, kernel, 5 );
T *dx = work+data_size;
@ -894,7 +894,7 @@ private:
else layer_l[index] = 0;
}
}
if( empty ) delete []work;
if( was_empty ) delete []work;
}
// casts a type T2 array into a type T1 array.
@ -915,7 +915,7 @@ private:
// to be an odd number. if in_place=true, then T1 must be equal
// to T2 naturally.
template<class T1, class T2> inline
T1* blur_gaussian_2d( T2* array, int rn, int cn, float sigma, int kernel_size=0, bool in_place=false )
T1* blur_gaussian_2d( T2* array, int rn, int cn, float sigma, int kernel_size = 0, bool in_place = false )
{
T1* out = NULL;
@ -1167,7 +1167,7 @@ void DAISY_Impl::set_cube_gaussians( double* sigma_array, int sz )
g_cube_number = sz;
if( m_cube_sigmas ) deallocate( m_cube_sigmas );
m_cube_sigmas = allocate<double>(g_cube_number);
m_cube_sigmas = allocate<double>( g_cube_number );
for( int r=0; r<g_cube_number; r++ )
{
@ -1243,7 +1243,7 @@ void DAISY_Impl::normalize_histograms()
{
float* hist = dst + (y*m_w+x)*m_hist_th_q_no;
float norm = l2norm( hist, m_hist_th_q_no );
if( norm != 0.0 ) divide( hist, m_hist_th_q_no, norm);
if( norm != 0.0 ) divide( hist, m_hist_th_q_no, norm );
}
}
}
@ -1284,7 +1284,7 @@ void DAISY_Impl::compute_smoothed_gradient_layers()
void DAISY_Impl::compute_oriented_grid_points()
{
m_oriented_grid_points = allocate<double>(g_grid_orientation_resolution, m_grid_point_number*2 );
m_oriented_grid_points = allocate<double>( g_grid_orientation_resolution, m_grid_point_number*2 );
for( int i=0; i<g_grid_orientation_resolution; i++ )
{
@ -1631,26 +1631,26 @@ inline void DAISY_Impl::ni_get_histogram( float* histogram, int y, int x, int sh
}
}
inline void DAISY_Impl::get_descriptor(int y, int x, float* &descriptor)
inline void DAISY_Impl::get_descriptor( int y, int x, float* &descriptor )
{
CV_Assert( m_dense_descriptors != NULL );
CV_Assert( y<m_h && x<m_w && y>=0 && x>=0 );
descriptor = &(m_dense_descriptors[(y*m_w+x)*m_descriptor_size]);
}
inline void DAISY_Impl::get_descriptor(double y, double x, int orientation, float* descriptor )
inline void DAISY_Impl::get_descriptor( double y, double x, int orientation, float* descriptor )
{
get_unnormalized_descriptor(y, x, orientation, descriptor );
normalize_descriptor(descriptor, m_nrm_type);
}
inline void DAISY_Impl::get_unnormalized_descriptor(double y, double x, int orientation, float* descriptor )
inline void DAISY_Impl::get_unnormalized_descriptor( double y, double x, int orientation, float* descriptor )
{
if( m_disable_interpolation ) ni_get_descriptor(y,x,orientation,descriptor);
else i_get_descriptor(y,x,orientation,descriptor);
}
inline void DAISY_Impl::i_get_descriptor(double y, double x, int orientation, float* descriptor )
inline void DAISY_Impl::i_get_descriptor( double y, double x, int orientation, float* descriptor )
{
// memset( descriptor, 0, sizeof(float)*m_descriptor_size );
//
@ -1689,7 +1689,7 @@ inline void DAISY_Impl::i_get_descriptor(double y, double x, int orientation, fl
}
}
inline void DAISY_Impl::ni_get_descriptor(double y, double x, int orientation, float* descriptor )
inline void DAISY_Impl::ni_get_descriptor( double y, double x, int orientation, float* descriptor )
{
// memset( descriptor, 0, sizeof(float)*m_descriptor_size );
//
@ -1738,20 +1738,20 @@ inline void DAISY_Impl::ni_get_descriptor(double y, double x, int orientation, f
}
// Warped get_descriptor's
inline bool DAISY_Impl::get_descriptor(double y, double x, int orientation, double* H, float* descriptor )
inline bool DAISY_Impl::get_descriptor( double y, double x, int orientation, double* H, float* descriptor )
{
bool rval = get_unnormalized_descriptor(y,x,orientation, H, descriptor);
if( rval ) normalize_descriptor(descriptor, m_nrm_type);
return rval;
}
inline bool DAISY_Impl::get_unnormalized_descriptor(double y, double x, int orientation, double* H, float* descriptor )
inline bool DAISY_Impl::get_unnormalized_descriptor( double y, double x, int orientation, double* H, float* descriptor )
{
if( m_disable_interpolation ) return ni_get_descriptor(y,x,orientation,H,descriptor);
else return i_get_descriptor(y,x,orientation,H,descriptor);
}
inline bool DAISY_Impl::i_get_descriptor(double y, double x, int orientation, double* H, float* descriptor )
inline bool DAISY_Impl::i_get_descriptor( double y, double x, int orientation, double* H, float* descriptor )
{
// memset( descriptor, 0, sizeof(float)*m_descriptor_size );
//
@ -1806,7 +1806,7 @@ inline bool DAISY_Impl::i_get_descriptor(double y, double x, int orientation, do
return true;
}
inline bool DAISY_Impl::ni_get_descriptor(double y, double x, int orientation, double* H, float* descriptor )
inline bool DAISY_Impl::ni_get_descriptor( double y, double x, int orientation, double* H, float* descriptor )
{
// memset( descriptor, 0, sizeof(float)*m_descriptor_size );
//

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