@ -48,9 +48,9 @@ using namespace std;
namespace cv
{
ERStat : : ERStat ( int init_level , int init_pixel , int init_x , int init_y ) : pixel ( init_pixel ) ,
level ( init_level ) , area ( 0 ) , perimeter ( 0 ) , euler ( 0 ) , probability ( 1.0 ) ,
parent ( 0 ) , child ( 0 ) , next ( 0 ) , prev ( 0 ) , local_maxima ( 0 ) ,
ERStat : : ERStat ( int init_level , int init_pixel , int init_x , int init_y ) : pixel ( init_pixel ) ,
level ( init_level ) , area ( 0 ) , perimeter ( 0 ) , euler ( 0 ) , probability ( 1.0 ) ,
parent ( 0 ) , child ( 0 ) , next ( 0 ) , prev ( 0 ) , local_maxima ( 0 ) ,
max_probability_ancestor ( 0 ) , min_probability_ancestor ( 0 )
{
rect = Rect ( init_x , init_y , 1 , 1 ) ;
@ -76,17 +76,17 @@ public:
//Destructor
~ ERFilterNM ( ) { } ;
float minProbability ;
float minProbability ;
bool nonMaxSuppression ;
float minProbabilityDiff ;
// the key method. Takes image on input, vector of ERStat is output for the first stage,
// the key method. Takes image on input, vector of ERStat is output for the first stage,
// input/output - for the second one.
void run ( InputArray image , std : : vector < ERStat > & regions ) ;
protected :
int thresholdDelta ;
float maxArea ;
float maxArea ;
float minArea ;
Ptr < ERFilter : : Callback > classifier ;
@ -116,8 +116,8 @@ private:
// extract the component tree and store all the ER regions
void er_tree_extract ( InputArray image ) ;
// accumulate a pixel into an ER
void er_add_pixel ( ERStat * parent , int x , int y , int non_boundary_neighbours ,
int non_boundary_neighbours_horiz ,
void er_add_pixel ( ERStat * parent , int x , int y , int non_boundary_neighbours ,
int non_boundary_neighbours_horiz ,
int d_C1 , int d_C2 , int d_C3 ) ;
// merge an ER with its nested parent
void er_merge ( ERStat * parent , ERStat * child ) ;
@ -133,7 +133,7 @@ private:
// default 1st stage classifier
class CV_EXPORTS ERClassifierNM1 : public ERFilter : : Callback
class CV_EXPORTS ERClassifierNM1 : public ERFilter : : Callback
{
public :
//Constructor
@ -142,14 +142,14 @@ public:
~ ERClassifierNM1 ( ) { } ;
// The classifier must return probability measure for the region.
double eval ( const ERStat & stat ) ;
double eval ( const ERStat & stat ) ;
private :
CvBoost boost ;
} ;
// default 2nd stage classifier
class CV_EXPORTS ERClassifierNM2 : public ERFilter : : Callback
class CV_EXPORTS ERClassifierNM2 : public ERFilter : : Callback
{
public :
//constructor
@ -158,7 +158,7 @@ public:
~ ERClassifierNM2 ( ) { } ;
// The classifier must return probability measure for the region.
double eval ( const ERStat & stat ) ;
double eval ( const ERStat & stat ) ;
private :
CvBoost boost ;
@ -182,7 +182,7 @@ ERFilterNM::ERFilterNM()
classifier = NULL ;
}
// the key method. Takes image on input, vector of ERStat is output for the first stage,
// the key method. Takes image on input, vector of ERStat is output for the first stage,
// input/output for the second one.
void ERFilterNM : : run ( InputArray image , std : : vector < ERStat > & _regions )
{
@ -192,7 +192,7 @@ void ERFilterNM::run( InputArray image, std::vector<ERStat>& _regions )
regions = & _regions ;
region_mask = Mat : : zeros ( image . getMat ( ) . rows + 2 , image . getMat ( ) . cols + 2 , CV_8UC1 ) ;
// if regions vector is empty we must extract the entire component tree
if ( regions - > size ( ) = = 0 )
{
@ -237,13 +237,13 @@ void ERFilterNM::er_tree_extract( InputArray image )
src = ( image . getMat ( ) / thresholdDelta ) - 1 ;
}
const unsigned char * image_data = src . data ;
int width = src . cols , height = src . rows ;
const unsigned char * image_data = src . data ;
int width = src . cols , height = src . rows ;
// the component stack
vector < ERStat * > er_stack ;
//the quads for euler number calculation
//the quads for euler number calculation
unsigned char quads [ 3 ] [ 4 ] ;
quads [ 0 ] [ 0 ] = 1 < < 3 ;
quads [ 0 ] [ 1 ] = 1 < < 2 ;
@ -271,32 +271,32 @@ void ERFilterNM::er_tree_extract( InputArray image )
// we'll look initially for all pixels with grey-level lower than a grey-level higher than any allowed in the image
int threshold_level = ( 255 / thresholdDelta ) + 1 ;
// starting from the first pixel (0,0)
int current_pixel = 0 ;
int current_edge = 0 ;
int current_level = image_data [ 0 ] ;
accessible_pixel_mask [ 0 ] = true ;
bool push_new_component = true ;
for ( ; ; ) {
int x = current_pixel % width ;
int y = current_pixel / width ;
// push a component with current level in the component stack
if ( push_new_component )
if ( push_new_component )
er_stack . push_back ( new ERStat ( current_level , current_pixel , x , y ) ) ;
push_new_component = false ;
// explore the (remaining) edges to the neighbors to the current pixel
for ( current_edge = current_edge ; current_edge < 4 ; current_edge + + )
for ( current_edge = current_edge ; current_edge < 4 ; current_edge + + )
{
int neighbour_pixel = current_pixel ;
switch ( current_edge )
switch ( current_edge )
{
case 0 : if ( x < width - 1 ) neighbour_pixel = current_pixel + 1 ; break ;
case 1 : if ( y < height - 1 ) neighbour_pixel = current_pixel + width ; break ;
@ -305,46 +305,46 @@ void ERFilterNM::er_tree_extract( InputArray image )
}
// if neighbour is not accessible, mark it accessible and retreive its grey-level value
if ( ! accessible_pixel_mask [ neighbour_pixel ] & & ( neighbour_pixel ! = current_pixel ) )
if ( ! accessible_pixel_mask [ neighbour_pixel ] & & ( neighbour_pixel ! = current_pixel ) )
{
int neighbour_level = image_data [ neighbour_pixel ] ;
accessible_pixel_mask [ neighbour_pixel ] = true ;
// if neighbour level is not lower than current level add neighbour to the boundary heap
if ( neighbour_level > = current_level )
// if neighbour level is not lower than current level add neighbour to the boundary heap
if ( neighbour_level > = current_level )
{
boundary_pixes [ neighbour_level ] . push_back ( neighbour_pixel ) ;
boundary_edges [ neighbour_level ] . push_back ( 0 ) ;
// if neighbour level is lower than our threshold_level set threshold_level to neighbour level
if ( neighbour_level < threshold_level )
threshold_level = neighbour_level ;
}
else // if neighbour level is lower than current add current_pixel (and next edge)
}
else // if neighbour level is lower than current add current_pixel (and next edge)
// to the boundary heap for later processing
{
boundary_pixes [ current_level ] . push_back ( current_pixel ) ;
boundary_edges [ current_level ] . push_back ( current_edge + 1 ) ;
// if neighbour level is lower than threshold_level set threshold_level to neighbour level
if ( current_level < threshold_level )
threshold_level = current_level ;
// consider the new pixel and its grey-level as current pixel
current_pixel = neighbour_pixel ;
current_edge = 0 ;
current_level = neighbour_level ;
// and push a new component
push_new_component = true ;
break ;
break ;
}
}
} // else neigbor was already accessible
if ( push_new_component ) continue ;
@ -363,12 +363,12 @@ void ERFilterNM::er_tree_extract( InputArray image )
quad_after [ 2 ] = 1 < < 2 ;
quad_after [ 3 ] = 1 ;
for ( int edge = 0 ; edge < 8 ; edge + + )
for ( int edge = 0 ; edge < 8 ; edge + + )
{
int neighbour4 = - 1 ;
int neighbour8 = - 1 ;
int cell = 0 ;
switch ( edge )
switch ( edge )
{
case 0 : if ( x < width - 1 ) { neighbour4 = neighbour8 = current_pixel + 1 ; } cell = 5 ; break ;
case 1 : if ( ( x < width - 1 ) & & ( y < height - 1 ) ) { neighbour8 = current_pixel + 1 + width ; } cell = 8 ; break ;
@ -391,7 +391,7 @@ void ERFilterNM::er_tree_extract( InputArray image )
{
if ( accumulated_pixel_mask [ neighbour8 ] )
pix_value = image_data [ neighbour8 ] ;
}
}
if ( pix_value < = image_data [ current_pixel ] )
{
@ -453,18 +453,18 @@ void ERFilterNM::er_tree_extract( InputArray image )
C_before [ p ] + + ;
if ( ( quad_before [ 1 ] = = quads [ p ] [ q ] ) & & ( ( p < 2 ) | | ( q < 2 ) ) )
C_before [ p ] + + ;
if ( ( quad_before [ 2 ] = = quads [ p ] [ q ] ) & & ( ( p < 2 ) | | ( q < 2 ) ) )
if ( ( quad_before [ 2 ] = = quads [ p ] [ q ] ) & & ( ( p < 2 ) | | ( q < 2 ) ) )
C_before [ p ] + + ;
if ( ( quad_before [ 3 ] = = quads [ p ] [ q ] ) & & ( ( p < 2 ) | | ( q < 2 ) ) )
C_before [ p ] + + ;
if ( ( quad_after [ 0 ] = = quads [ p ] [ q ] ) & & ( ( p < 2 ) | | ( q < 2 ) ) )
if ( ( quad_after [ 0 ] = = quads [ p ] [ q ] ) & & ( ( p < 2 ) | | ( q < 2 ) ) )
C_after [ p ] + + ;
if ( ( quad_after [ 1 ] = = quads [ p ] [ q ] ) & & ( ( p < 2 ) | | ( q < 2 ) ) )
if ( ( quad_after [ 1 ] = = quads [ p ] [ q ] ) & & ( ( p < 2 ) | | ( q < 2 ) ) )
C_after [ p ] + + ;
if ( ( quad_after [ 2 ] = = quads [ p ] [ q ] ) & & ( ( p < 2 ) | | ( q < 2 ) ) )
if ( ( quad_after [ 2 ] = = quads [ p ] [ q ] ) & & ( ( p < 2 ) | | ( q < 2 ) ) )
C_after [ p ] + + ;
if ( ( quad_after [ 3 ] = = quads [ p ] [ q ] ) & & ( ( p < 2 ) | | ( q < 2 ) ) )
if ( ( quad_after [ 3 ] = = quads [ p ] [ q ] ) & & ( ( p < 2 ) | | ( q < 2 ) ) )
C_after [ p ] + + ;
}
}
@ -475,9 +475,9 @@ void ERFilterNM::er_tree_extract( InputArray image )
er_add_pixel ( er_stack . back ( ) , x , y , non_boundary_neighbours , non_boundary_neighbours_horiz , d_C1 , d_C2 , d_C3 ) ;
accumulated_pixel_mask [ current_pixel ] = true ;
// if we have processed all the possible threshold levels (the hea is empty) we are done!
if ( threshold_level = = ( 255 / thresholdDelta ) + 1 )
if ( threshold_level = = ( 255 / thresholdDelta ) + 1 )
{
// save the extracted regions into the output vector
@ -490,18 +490,18 @@ void ERFilterNM::er_tree_extract( InputArray image )
return ;
}
// pop the heap of boundary pixels
current_pixel = boundary_pixes [ threshold_level ] . back ( ) ;
boundary_pixes [ threshold_level ] . erase ( boundary_pixes [ threshold_level ] . end ( ) - 1 ) ;
current_edge = boundary_edges [ threshold_level ] . back ( ) ;
boundary_edges [ threshold_level ] . erase ( boundary_edges [ threshold_level ] . end ( ) - 1 ) ;
while ( boundary_pixes [ threshold_level ] . empty ( ) & & ( threshold_level < ( 255 / thresholdDelta ) + 1 ) )
threshold_level + + ;
int new_level = image_data [ current_pixel ] ;
// if the new pixel has higher grey value than the current one
@ -514,11 +514,11 @@ void ERFilterNM::er_tree_extract( InputArray image )
{
ERStat * er = er_stack . back ( ) ;
er_stack . erase ( er_stack . end ( ) - 1 ) ;
if ( new_level < er_stack . back ( ) - > level )
if ( new_level < er_stack . back ( ) - > level )
{
er_stack . push_back ( new ERStat ( new_level , current_pixel , current_pixel % width , current_pixel / width ) ) ;
er_merge ( er_stack . back ( ) , er ) ;
er_merge ( er_stack . back ( ) , er ) ;
break ;
}
@ -531,8 +531,8 @@ void ERFilterNM::er_tree_extract( InputArray image )
}
// accumulate a pixel into an ER
void ERFilterNM : : er_add_pixel ( ERStat * parent , int x , int y , int non_border_neighbours ,
int non_border_neighbours_horiz ,
void ERFilterNM : : er_add_pixel ( ERStat * parent , int x , int y , int non_border_neighbours ,
int non_border_neighbours_horiz ,
int d_C1 , int d_C2 , int d_C3 )
{
parent - > area + + ;
@ -575,7 +575,7 @@ void ERFilterNM::er_merge(ERStat *parent, ERStat *child)
parent - > area + = child - > area ;
parent - > perimeter + = child - > perimeter ;
for ( int i = parent - > rect . y ; i < = min ( parent - > rect . br ( ) . y - 1 , child - > rect . br ( ) . y - 1 ) ; i + + )
if ( i - child - > rect . y > = 0 )
@ -584,12 +584,12 @@ void ERFilterNM::er_merge(ERStat *parent, ERStat *child)
for ( int i = parent - > rect . y - 1 ; i > = child - > rect . y ; i - - )
if ( i - child - > rect . y < ( int ) child - > crossings - > size ( ) )
parent - > crossings - > push_front ( child - > crossings - > at ( i - child - > rect . y ) ) ;
else
else
parent - > crossings - > push_front ( 0 ) ;
for ( int i = parent - > rect . br ( ) . y ; i < child - > rect . y ; i + + )
parent - > crossings - > push_back ( 0 ) ;
for ( int i = max ( parent - > rect . br ( ) . y , child - > rect . y ) ; i < = child - > rect . br ( ) . y - 1 ; i + + )
parent - > crossings - > push_back ( child - > crossings - > at ( i - child - > rect . y ) ) ;
@ -618,8 +618,8 @@ void ERFilterNM::er_merge(ERStat *parent, ERStat *child)
std : : sort ( m_crossings . begin ( ) , m_crossings . end ( ) ) ;
child - > med_crossings = ( float ) m_crossings . at ( 1 ) ;
// free unnecessary mem
child - > crossings - > clear ( ) ;
// free unnecessary mem
child - > crossings - > clear ( ) ;
delete ( child - > crossings ) ;
child - > crossings = NULL ;
@ -632,15 +632,15 @@ void ERFilterNM::er_merge(ERStat *parent, ERStat *child)
child - > probability = classifier - > eval ( * child ) ;
}
if ( ( ( classifier ! = NULL ) ? ( child - > probability > = minProbability ) : true ) & &
( ( child - > area > = ( minArea * region_mask . rows * region_mask . cols ) ) & &
if ( ( ( classifier ! = NULL ) ? ( child - > probability > = minProbability ) : true ) & &
( ( child - > area > = ( minArea * region_mask . rows * region_mask . cols ) ) & &
( child - > area < = ( maxArea * region_mask . rows * region_mask . cols ) ) ) )
{
num_accepted_regions + + ;
child - > next = parent - > child ;
if ( parent - > child )
if ( parent - > child )
parent - > child - > prev = child ;
parent - > child = child ;
child - > parent = parent ;
@ -658,7 +658,7 @@ void ERFilterNM::er_merge(ERStat *parent, ERStat *child)
while ( new_child - > next ! = NULL )
new_child = new_child - > next ;
new_child - > next = parent - > child ;
if ( parent - > child )
if ( parent - > child )
parent - > child - > prev = new_child ;
parent - > child = child - > child ;
child - > child - > parent = parent ;
@ -672,8 +672,8 @@ void ERFilterNM::er_merge(ERStat *parent, ERStat *child)
child - > crossings = NULL ;
}
delete ( child ) ;
}
}
}
// recursively walk the tree and clean memory
@ -691,11 +691,11 @@ void ERFilterNM::er_tree_clean( ERStat *stat )
}
delete stat ;
}
// copy extracted regions into the output vector
ERStat * ERFilterNM : : er_save ( ERStat * er , ERStat * parent , ERStat * prev )
{
regions - > push_back ( * er ) ;
regions - > back ( ) . parent = parent ;
@ -714,7 +714,7 @@ ERStat* ERFilterNM::er_save( ERStat *er, ERStat *parent, ERStat *prev )
this_er - > probability = 0 ; //TODO this makes sense in order to select at least one region in short tree's but is it really necessary?
this_er - > max_probability_ancestor = this_er ;
this_er - > min_probability_ancestor = this_er ;
}
}
else
{
this_er - > max_probability_ancestor = ( this_er - > probability > parent - > max_probability_ancestor - > probability ) ? this_er : parent - > max_probability_ancestor ;
@ -730,11 +730,11 @@ ERStat* ERFilterNM::er_save( ERStat *er, ERStat *parent, ERStat *prev )
// this_er->min_probability_ancestor->local_maxima = false;
this_er - > max_probability_ancestor = this_er ;
this_er - > min_probability_ancestor = this_er ;
this_er - > min_probability_ancestor = this_er ;
}
}
}
for ( ERStat * child = er - > child ; child ; child = child - > next )
{
old_prev = er_save ( child , this_er , old_prev ) ;
@ -749,16 +749,16 @@ ERStat* ERFilterNM::er_tree_filter ( InputArray image, ERStat * stat, ERStat *pa
Mat src = image . getMat ( ) ;
// assert correct image type
CV_Assert ( src . type ( ) = = CV_8UC1 ) ;
//Fill the region and calculate 2nd stage features
Mat region = region_mask ( Rect ( Point ( stat - > rect . x , stat - > rect . y ) , Point ( stat - > rect . br ( ) . x + 2 , stat - > rect . br ( ) . y + 2 ) ) ) ;
region = Scalar ( 0 ) ;
int newMaskVal = 255 ;
int flags = 4 + ( newMaskVal < < 8 ) + FLOODFILL_FIXED_RANGE + FLOODFILL_MASK_ONLY ;
Rect rect ;
floodFill ( src ( Rect ( Point ( stat - > rect . x , stat - > rect . y ) , Point ( stat - > rect . br ( ) . x , stat - > rect . br ( ) . y ) ) ) ,
region , Point ( stat - > pixel % src . cols - stat - > rect . x , stat - > pixel / src . cols - stat - > rect . y ) ,
floodFill ( src ( Rect ( Point ( stat - > rect . x , stat - > rect . y ) , Point ( stat - > rect . br ( ) . x , stat - > rect . br ( ) . y ) ) ) ,
region , Point ( stat - > pixel % src . cols - stat - > rect . x , stat - > pixel / src . cols - stat - > rect . y ) ,
Scalar ( 255 ) , & rect , Scalar ( stat - > level ) , Scalar ( 0 ) , flags ) ;
rect . width + = 2 ;
rect . height + = 2 ;
@ -768,9 +768,9 @@ ERStat* ERFilterNM::er_tree_filter ( InputArray image, ERStat * stat, ERStat *pa
vector < Point > contour_poly ;
vector < Vec4i > hierarchy ;
findContours ( region , contours , hierarchy , RETR_TREE , CHAIN_APPROX_NONE , Point ( 0 , 0 ) ) ;
//TODO check epsilon parameter of approxPolyDP (set empirically) : we want more precission
//TODO check epsilon parameter of approxPolyDP (set empirically) : we want more precission
// if the region is very small because otherwise we'll loose all the convexities
approxPolyDP ( Mat ( contours [ 0 ] ) , contour_poly , max ( rect . width , rect . height ) / 25 , true ) ;
approxPolyDP ( Mat ( contours [ 0 ] ) , contour_poly , max ( rect . width , rect . height ) / 25 , true ) ;
bool was_convex = false ;
@ -829,11 +829,11 @@ ERStat* ERFilterNM::er_tree_filter ( InputArray image, ERStat * stat, ERStat *pa
if ( ( classifier ! = NULL ) & & ( stat - > parent ! = NULL ) )
{
stat - > probability = classifier - > eval ( * stat ) ;
}
}
if ( ( ( ( classifier ! = NULL ) ? ( stat - > probability > = minProbability ) : true ) & &
( ( stat - > area > = minArea * region_mask . rows * region_mask . cols ) & &
( stat - > area < = maxArea * region_mask . rows * region_mask . cols ) ) ) | |
if ( ( ( ( classifier ! = NULL ) ? ( stat - > probability > = minProbability ) : true ) & &
( ( stat - > area > = minArea * region_mask . rows * region_mask . cols ) & &
( stat - > area < = maxArea * region_mask . rows * region_mask . cols ) ) ) | |
( stat - > parent = = NULL ) )
{
@ -979,19 +979,19 @@ int ERFilterNM::getNumRejected()
ERClassifierNM1 : : ERClassifierNM1 ( )
{
if ( ifstream ( " ./trained_classifierNM1.xml " ) )
if ( ifstream ( " ./trained_classifierNM1.xml " ) )
{
// The file with default classifier exists
boost . load ( " ./trained_classifierNM1.xml " , " boost " ) ;
}
else if ( ifstream ( " ./training/trained_classifierNM1.xml " ) )
}
else if ( ifstream ( " ./training/trained_classifierNM1.xml " ) )
{
// The file with default classifier exists
boost . load ( " ./training/trained_classifierNM1.xml " , " boost " ) ;
}
else
}
else
{
// File not found
// File not found
CV_Error ( CV_StsBadArg , " Default classifier ./trained_classifierNM1.xml not found! " ) ;
}
} ;
@ -1017,19 +1017,19 @@ double ERClassifierNM1::eval(const ERStat& stat)
ERClassifierNM2 : : ERClassifierNM2 ( )
{
if ( ifstream ( " ./trained_classifierNM2.xml " ) )
if ( ifstream ( " ./trained_classifierNM2.xml " ) )
{
// The file with default classifier exists
boost . load ( " ./trained_classifierNM2.xml " , " boost " ) ;
}
else if ( ifstream ( " ./training/trained_classifierNM2.xml " ) )
}
else if ( ifstream ( " ./training/trained_classifierNM2.xml " ) )
{
// The file with default classifier exists
boost . load ( " ./training/trained_classifierNM2.xml " , " boost " ) ;
}
else
}
else
{
// File not found
// File not found
CV_Error ( CV_StsBadArg , " Default classifier ./trained_classifierNM2.xml not found! " ) ;
}
} ;
@ -1040,7 +1040,7 @@ double ERClassifierNM2::eval(const ERStat& stat)
float arr [ ] = { 0 , ( float ) ( stat . rect . width ) / ( stat . rect . height ) , // aspect ratio
sqrt ( ( float ) ( stat . area ) ) / stat . perimeter , // compactness
( float ) ( 1 - stat . euler ) , //number of holes
stat . med_crossings , stat . hole_area_ratio ,
stat . med_crossings , stat . hole_area_ratio ,
stat . convex_hull_ratio , stat . num_inflexion_points } ;
vector < float > sample ( arr , arr + sizeof ( arr ) / sizeof ( arr [ 0 ] ) ) ;
@ -1055,15 +1055,15 @@ double ERClassifierNM2::eval(const ERStat& stat)
/*!
Create an Extremal Region Filter for the 1 st stage classifier of N & M algorithm
Neumann L . , Matas J . : Real - Time Scene Text Localization and Recognition , CVPR 2012
The component tree of the image is extracted by a threshold increased step by step
from 0 to 255 , incrementally computable descriptors ( aspect_ratio , compactness ,
number of holes , and number of horizontal crossings ) are computed for each ER
and used as features for a classifier which estimates the class - conditional
probability P ( er | character ) . The value of P ( er | character ) is tracked using the inclusion
relation of ER across all thresholds and only the ERs which correspond to local maximum
from 0 to 255 , incrementally computable descriptors ( aspect_ratio , compactness ,
number of holes , and number of horizontal crossings ) are computed for each ER
and used as features for a classifier which estimates the class - conditional
probability P ( er | character ) . The value of P ( er | character ) is tracked using the inclusion
relation of ER across all thresholds and only the ERs which correspond to local maximum
of the probability P ( er | character ) are selected ( if the local maximum of the
probability is above a global limit pmin and the difference between local maximum and
probability is above a global limit pmin and the difference between local maximum and
local minimum is greater than minProbabilityDiff ) .
\ param cb Callback with the classifier .
@ -1072,11 +1072,11 @@ double ERClassifierNM2::eval(const ERStat& stat)
\ param minArea The minimum area ( % of image size ) allowed for retreived ER ' s
\ param minArea The maximum area ( % of image size ) allowed for retreived ER ' s
\ param minProbability The minimum probability P ( er | character ) allowed for retreived ER ' s
\ param nonMaxSuppression Whenever non - maximum suppression is done over the branch probabilities
\ param nonMaxSuppression Whenever non - maximum suppression is done over the branch probabilities
\ param minProbability The minimum probability difference between local maxima and local minima ERs
*/
Ptr < ERFilter > createERFilterNM1 ( const Ptr < ERFilter : : Callback > & cb , int thresholdDelta ,
float minArea , float maxArea , float minProbability ,
Ptr < ERFilter > createERFilterNM1 ( const Ptr < ERFilter : : Callback > & cb , int thresholdDelta ,
float minArea , float maxArea , float minProbability ,
bool nonMaxSuppression , float minProbabilityDiff )
{
@ -1086,7 +1086,7 @@ Ptr<ERFilter> createERFilterNM1(const Ptr<ERFilter::Callback>& cb, int threshold
CV_Assert ( ( minProbabilityDiff > = 0. ) & & ( minProbabilityDiff < = 1. ) ) ;
Ptr < ERFilterNM > filter = new ERFilterNM ( ) ;
if ( cb = = NULL )
filter - > setCallback ( new ERClassifierNM1 ( ) ) ;
else
@ -1105,9 +1105,9 @@ Ptr<ERFilter> createERFilterNM1(const Ptr<ERFilter::Callback>& cb, int threshold
Create an Extremal Region Filter for the 2 nd stage classifier of N & M algorithm
Neumann L . , Matas J . : Real - Time Scene Text Localization and Recognition , CVPR 2012
In the second stage , the ERs that passed the first stage are classified into character
In the second stage , the ERs that passed the first stage are classified into character
and non - character classes using more informative but also more computationally expensive
features . The classifier uses all the features calculated in the first stage and the following
features . The classifier uses all the features calculated in the first stage and the following
additional features : hole area ratio , convex hull ratio , and number of outer inflexion points .
\ param cb Callback with the classifier
@ -1121,7 +1121,7 @@ Ptr<ERFilter> createERFilterNM2(const Ptr<ERFilter::Callback>& cb, float minProb
Ptr < ERFilterNM > filter = new ERFilterNM ( ) ;
if ( cb = = NULL )
filter - > setCallback ( new ERClassifierNM2 ( ) ) ;
else
Roman Donchenko
12 years ago