Skip to content

O
opencv_contrib
Commit 157e08f3 authored by lluis's avatar lluis
Browse files
Options

fix warnings from MSVC and remove trailing whitespaces

parent 786876c0
Hide whitespace changes
Inline Side-by-side
Showing with 108 additions and 106 deletions
modules/text/include/opencv2/text/erfilter.hpp
View file @ 157e08f3
...@@ -52,7 +52,7 @@ ...@@ -52,7 +52,7 @@
namespace cv namespace cv
{ {
namespace text namespace text
{ {
/*! /*!
Extremal Region Stat structure Extremal Region Stat structure
...@@ -266,11 +266,11 @@ enum { ERGROUPING_ORIENTATION_HORIZ = 0, ...@@ -266,11 +266,11 @@ enum { ERGROUPING_ORIENTATION_HORIZ = 0,
\param minProbability The minimum probability for accepting a group \param minProbability The minimum probability for accepting a group
\param groups The output of the algorithm are stored in this parameter as list of rectangles. \param groups The output of the algorithm are stored in this parameter as list of rectangles.
*/ */
CV_EXPORTS void erGrouping(InputArray img, InputArrayOfArrays channels, CV_EXPORTS void erGrouping(InputArray img, InputArrayOfArrays channels,
std::vector<std::vector<ERStat> > &regions, std::vector<std::vector<ERStat> > &regions,
std::vector<std::vector<Vec2i> > &groups, std::vector<std::vector<Vec2i> > &groups,
std::vector<Rect> &groups_rects, std::vector<Rect> &groups_rects,
int method = ERGROUPING_ORIENTATION_HORIZ, int method = ERGROUPING_ORIENTATION_HORIZ,
const std::string& filename = std::string(), const std::string& filename = std::string(),
float minProbablity = 0.5); float minProbablity = 0.5);
......
modules/text/src/erfilter.cpp
View file @ 157e08f3
...@@ -58,7 +58,7 @@ ...@@ -58,7 +58,7 @@
namespace cv namespace cv
{ {
namespace text namespace text
{ {
using namespace std; using namespace std;
...@@ -2182,6 +2182,8 @@ public: ...@@ -2182,6 +2182,8 @@ public:
void operator()(double *data, unsigned int num, int dim, unsigned char method, void operator()(double *data, unsigned int num, int dim, unsigned char method,
unsigned char metric, vector< vector<int> > *meaningful_clusters); unsigned char metric, vector< vector<int> > *meaningful_clusters);
MaxMeaningfulClustering & operator=(const MaxMeaningfulClustering &a);
private: private:
double minProbability; double minProbability;
CvBoost group_boost; CvBoost group_boost;
...@@ -2466,11 +2468,11 @@ static int getAngleABC( Point a, Point b, Point c ) ...@@ -2466,11 +2468,11 @@ static int getAngleABC( Point a, Point b, Point c )
Point cb = Point( b.x - c.x, b.y - c.y ); Point cb = Point( b.x - c.x, b.y - c.y );
// dot product // dot product
float dot = (ab.x * cb.x + ab.y * cb.y); float dot = (float)(ab.x * cb.x + ab.y * cb.y);
// length square of both vectors // length square of both vectors
float abSqr = ab.x * ab.x + ab.y * ab.y; float abSqr = (float)(ab.x * ab.x + ab.y * ab.y);
float cbSqr = cb.x * cb.x + cb.y * cb.y; float cbSqr = (float)(cb.x * cb.x + cb.y * cb.y);
// square of cosine of the needed angle // square of cosine of the needed angle
float cosSqr = dot * dot / abSqr / cbSqr; float cosSqr = dot * dot / abSqr / cbSqr;
...@@ -2491,7 +2493,7 @@ static int getAngleABC( Point a, Point b, Point c ) ...@@ -2491,7 +2493,7 @@ static int getAngleABC( Point a, Point b, Point c )
float rslt = alpha2 / 2; float rslt = alpha2 / 2;
float rs = rslt * 180. / pi; float rs = (float)(rslt * 180. / pi);
// Now revolve the ambiguities. // Now revolve the ambiguities.
...@@ -2505,7 +2507,7 @@ static int getAngleABC( Point a, Point b, Point c ) ...@@ -2505,7 +2507,7 @@ static int getAngleABC( Point a, Point b, Point c )
rs = 180 - rs; rs = 180 - rs;
// 2. Determine the sign. For this we'll use the Determinant of two vectors. // 2. Determine the sign. For this we'll use the Determinant of two vectors.
float det = (ab.x * cb.y - ab.y * cb.y); float det = (float)(ab.x * cb.y - ab.y * cb.y);
if (det < 0) if (det < 0)
rs = -rs; rs = -rs;
...@@ -2520,16 +2522,16 @@ double MaxMeaningfulClustering::probability(vector<int> &cluster) ...@@ -2520,16 +2522,16 @@ double MaxMeaningfulClustering::probability(vector<int> &cluster)
vector<float> sample; vector<float> sample;
sample.push_back(0); sample.push_back(0);
sample.push_back(cluster.size()); sample.push_back((float)cluster.size());
Mat diameters ( cluster.size(), 1, CV_32F, 1 ); Mat diameters ( (int)cluster.size(), 1, CV_32F, 1 );
Mat strokes ( cluster.size(), 1, CV_32F, 1 ); Mat strokes ( (int)cluster.size(), 1, CV_32F, 1 );
Mat gradients ( cluster.size(), 1, CV_32F, 1 ); Mat gradients ( (int)cluster.size(), 1, CV_32F, 1 );
Mat fg_intensities ( cluster.size(), 1, CV_32F, 1 ); Mat fg_intensities ( (int)cluster.size(), 1, CV_32F, 1 );
Mat bg_intensities ( cluster.size(), 1, CV_32F, 1 ); Mat bg_intensities ( (int)cluster.size(), 1, CV_32F, 1 );
Mat axial_ratios ( cluster.size(), 1, CV_32F, 1 ); Mat axial_ratios ( (int)cluster.size(), 1, CV_32F, 1 );
Mat chull_ratios ( cluster.size(), 1, CV_32F, 1 ); Mat chull_ratios ( (int)cluster.size(), 1, CV_32F, 1 );
Mat convexities ( cluster.size(), 1, CV_32F, 1 ); Mat convexities ( (int)cluster.size(), 1, CV_32F, 1 );
Subdiv2D subdiv(Rect(0,0,imsize.width,imsize.height)); Subdiv2D subdiv(Rect(0,0,imsize.width,imsize.height));
vector< vector<Point> > forest(cluster.size()); vector< vector<Point> > forest(cluster.size());
float maxAvgOverlap = 0; float maxAvgOverlap = 0;
...@@ -2573,15 +2575,15 @@ double MaxMeaningfulClustering::probability(vector<int> &cluster) ...@@ -2573,15 +2575,15 @@ double MaxMeaningfulClustering::probability(vector<int> &cluster)
Scalar mean,std; Scalar mean,std;
meanStdDev( diameters, mean, std ); meanStdDev( diameters, mean, std );
sample.push_back(std[0]/mean[0]); float diameter_mean = mean[0]; sample.push_back((float)(std[0]/mean[0])); float diameter_mean = (float)mean[0];
meanStdDev( strokes, mean, std ); meanStdDev( strokes, mean, std );
sample.push_back(std[0]/mean[0]); sample.push_back((float)(std[0]/mean[0]));
meanStdDev( gradients, mean, std ); meanStdDev( gradients, mean, std );
sample.push_back(std[0]); sample.push_back((float)std[0]);
meanStdDev( fg_intensities, mean, std ); meanStdDev( fg_intensities, mean, std );
sample.push_back(std[0]); sample.push_back((float)std[0]);
meanStdDev( bg_intensities, mean, std ); meanStdDev( bg_intensities, mean, std );
sample.push_back(std[0]); sample.push_back((float)std[0]);
/* begin Kruskal algorithm to find the MST */ /* begin Kruskal algorithm to find the MST */
vector<Vec4f> edgeList; vector<Vec4f> edgeList;
...@@ -2627,13 +2629,13 @@ double MaxMeaningfulClustering::probability(vector<int> &cluster) ...@@ -2627,13 +2629,13 @@ double MaxMeaningfulClustering::probability(vector<int> &cluster)
Point t_pt0 = Point(cvRound(t[0]), cvRound(t[1])); Point t_pt0 = Point(cvRound(t[0]), cvRound(t[1]));
Point t_pt1 = Point(cvRound(t[2]), cvRound(t[3])); Point t_pt1 = Point(cvRound(t[2]), cvRound(t[3]));
if(q_pt0 == t_pt0) if(q_pt0 == t_pt0)
angles.push_back(getAngleABC(q_pt1, q_pt0 , t_pt1)); angles.push_back((float)getAngleABC(q_pt1, q_pt0 , t_pt1));
if(q_pt0 == t_pt1) if(q_pt0 == t_pt1)
angles.push_back(getAngleABC(q_pt1, q_pt0 , t_pt0)); angles.push_back((float)getAngleABC(q_pt1, q_pt0 , t_pt0));
if(q_pt1 == t_pt0) if(q_pt1 == t_pt0)
angles.push_back(getAngleABC(q_pt0, q_pt1 , t_pt1)); angles.push_back((float)getAngleABC(q_pt0, q_pt1 , t_pt1));
if(q_pt1 == t_pt1) if(q_pt1 == t_pt1)
angles.push_back(getAngleABC(q_pt0, q_pt1 , t_pt0)); angles.push_back((float)getAngleABC(q_pt0, q_pt1 , t_pt0));
} }
} }
//cout << "we have " << angles.size() << " angles " << endl; //cout << "we have " << angles.size() << " angles " << endl;
...@@ -2642,15 +2644,15 @@ double MaxMeaningfulClustering::probability(vector<int> &cluster) ...@@ -2642,15 +2644,15 @@ double MaxMeaningfulClustering::probability(vector<int> &cluster)
//cout << endl; //cout << endl;
meanStdDev( angles, mean, std ); meanStdDev( angles, mean, std );
sample.push_back(std[0]); sample.push_back((float)std[0]);
sample.push_back(mean[0]); sample.push_back((float)mean[0]);
meanStdDev( edge_distances, mean, std ); meanStdDev( edge_distances, mean, std );
sample.push_back(std[0]/mean[0]); sample.push_back((float)(std[0]/mean[0]));
sample.push_back(mean[0]/diameter_mean); sample.push_back((float)(mean[0]/diameter_mean));
meanStdDev( axial_ratios, mean, std ); meanStdDev( axial_ratios, mean, std );
sample.push_back(mean[0]); sample.push_back((float)mean[0]);
sample.push_back(std[0]); sample.push_back((float)std[0]);
/// Calculate average shape self-similarity /// Calculate average shape self-similarity
double avg_shape_match = 0; double avg_shape_match = 0;
...@@ -2694,12 +2696,12 @@ double MaxMeaningfulClustering::probability(vector<int> &cluster) ...@@ -2694,12 +2696,12 @@ double MaxMeaningfulClustering::probability(vector<int> &cluster)
sample.push_back(maxAvgOverlap); sample.push_back(maxAvgOverlap);
meanStdDev( chull_ratios, mean, std ); meanStdDev( chull_ratios, mean, std );
sample.push_back(mean[0]); sample.push_back((float)mean[0]);
sample.push_back(std[0]); sample.push_back((float)std[0]);
meanStdDev( convexities, mean, std ); meanStdDev( convexities, mean, std );
sample.push_back(mean[0]); sample.push_back((float)mean[0]);
sample.push_back(std[0]); sample.push_back((float)std[0]);
float votes_group = group_boost.predict( Mat(sample), Mat(), Range::all(), false, true ); float votes_group = group_boost.predict( Mat(sample), Mat(), Range::all(), false, true );
...@@ -2764,14 +2766,14 @@ bool guo_hall_thinning(const Mat1b & img, Mat& skeleton) ...@@ -2764,14 +2766,14 @@ bool guo_hall_thinning(const Mat1b & img, Mat& skeleton)
if (*skeleton_ptr++ == 255) if (*skeleton_ptr++ == 255)
{ {
bool p2, p3, p4, p5, p6, p7, p8, p9; bool p2, p3, p4, p5, p6, p7, p8, p9;
p2 = skeleton.data[(row-1) * skeleton.cols + col]; p2 = (bool)(skeleton.data[(row-1) * skeleton.cols + col]);
p3 = skeleton.data[(row-1) * skeleton.cols + col+1]; p3 = (bool)(skeleton.data[(row-1) * skeleton.cols + col+1]);
p4 = skeleton.data[row * skeleton.cols + col+1]; p4 = (bool)(skeleton.data[row * skeleton.cols + col+1]);
p5 = skeleton.data[(row+1) * skeleton.cols + col+1]; p5 = (bool)(skeleton.data[(row+1) * skeleton.cols + col+1]);
p6 = skeleton.data[(row+1) * skeleton.cols + col]; p6 = (bool)(skeleton.data[(row+1) * skeleton.cols + col]);
p7 = skeleton.data[(row+1) * skeleton.cols + col-1]; p7 = (bool)(skeleton.data[(row+1) * skeleton.cols + col-1]);
p8 = skeleton.data[row * skeleton.cols + col-1]; p8 = (bool)(skeleton.data[row * skeleton.cols + col-1]);
p9 = skeleton.data[(row-1) * skeleton.cols + col-1]; p9 = (bool)(skeleton.data[(row-1) * skeleton.cols + col-1]);
int C = (!p2 & (p3 | p4)) + (!p4 & (p5 | p6)) + int C = (!p2 & (p3 | p4)) + (!p4 & (p5 | p6)) +
(!p6 & (p7 | p8)) + (!p8 & (p9 | p2)); (!p6 & (p7 | p8)) + (!p8 & (p9 | p2));
...@@ -2790,11 +2792,11 @@ bool guo_hall_thinning(const Mat1b & img, Mat& skeleton) ...@@ -2790,11 +2792,11 @@ bool guo_hall_thinning(const Mat1b & img, Mat& skeleton)
} }
// set all points in rows_to_set (of skel) // set all points in rows_to_set (of skel)
unsigned int rows_to_set_size = rows_to_set.size(); unsigned int rows_to_set_size = (unsigned int)rows_to_set.size();
for (unsigned int pt_idx = 0; pt_idx < rows_to_set_size; ++pt_idx) for (unsigned int pt_idx = 0; pt_idx < rows_to_set_size; ++pt_idx)
{ {
if (!changed) if (!changed)
changed = (skeleton.data[rows_to_set[pt_idx] * skeleton.cols + cols_to_set[pt_idx]]); changed = (bool)(skeleton.data[rows_to_set[pt_idx] * skeleton.cols + cols_to_set[pt_idx]]);
int key = rows_to_set[pt_idx] * skeleton.cols + cols_to_set[pt_idx]; int key = rows_to_set[pt_idx] * skeleton.cols + cols_to_set[pt_idx];
skeleton.data[key] = 0; skeleton.data[key] = 0;
...@@ -2930,7 +2932,7 @@ float extract_features(Mat &grey, Mat& channel, vector<ERStat> &regions, vector< ...@@ -2930,7 +2932,7 @@ float extract_features(Mat &grey, Mat& channel, vector<ERStat> &regions, vector<
if (hull_idx.size()>2) if (hull_idx.size()>2)
if (contours0[0].size()>3) if (contours0[0].size()>3)
convexityDefects(contours0[0],hull_idx,cx); convexityDefects(contours0[0],hull_idx,cx);
f.convexities = cx.size(); f.convexities = (int)cx.size();
rect_mask = Scalar(0); rect_mask = Scalar(0);
...@@ -2959,7 +2961,7 @@ float extract_features(Mat &grey, Mat& channel, vector<ERStat> &regions, vector< ...@@ -2959,7 +2961,7 @@ float extract_features(Mat &grey, Mat& channel, vector<ERStat> &regions, vector<
/*! /*!
Find groups of Extremal Regions that are organized as text blocks. This function implements Find groups of Extremal Regions that are organized as text blocks. This function implements
the grouping algorithm described in: the grouping algorithm described in:
Gomez L. and Karatzas D.: A Fast Hierarchical Method for Multi-script and Arbitrary Oriented Gomez L. and Karatzas D.: A Fast Hierarchical Method for Multi-script and Arbitrary Oriented
Scene Text Extraction, arXiv:1407.7504 [cs.CV]. Scene Text Extraction, arXiv:1407.7504 [cs.CV].
Gomez L. and Karatzas D.: Multi-script Text Extraction from Natural Scenes, ICDAR 2013. Gomez L. and Karatzas D.: Multi-script Text Extraction from Natural Scenes, ICDAR 2013.
...@@ -3079,7 +3081,7 @@ static void erGroupingGK(InputArray _image, InputArrayOfArrays _src, vector<vect ...@@ -3079,7 +3081,7 @@ static void erGroupingGK(InputArray _image, InputArrayOfArrays _src, vector<vect
#define PAIR_MIN_HEIGHT_RATIO 0.4 #define PAIR_MIN_HEIGHT_RATIO 0.4
#define PAIR_MIN_CENTROID_ANGLE - 0.85 #define PAIR_MIN_CENTROID_ANGLE - 0.85
#define PAIR_MAX_CENTROID_ANGLE 0.85 #define PAIR_MAX_CENTROID_ANGLE 0.85
#define PAIR_MIN_REGION_DIST - 0.4 #define PAIR_MIN_REGION_DIST - 0.4
#define PAIR_MAX_REGION_DIST 2.2 #define PAIR_MAX_REGION_DIST 2.2
#define PAIR_MAX_INTENSITY_DIST 111 #define PAIR_MAX_INTENSITY_DIST 111
#define PAIR_MAX_AB_DIST 54 #define PAIR_MAX_AB_DIST 54
...@@ -3132,7 +3134,7 @@ float distanceLinesEstimates(line_estimates &a, line_estimates &b) ...@@ -3132,7 +3134,7 @@ float distanceLinesEstimates(line_estimates &a, line_estimates &b)
int x_max = max(a.x_max, b.x_max); int x_max = max(a.x_max, b.x_max);
int h_max = max(a.h_max, b.h_max); int h_max = max(a.h_max, b.h_max);
float dist_top = INT_MAX, dist_bottom = INT_MAX; float dist_top = FLT_MAX, dist_bottom = FLT_MAX;
for (int i=0; i<2; i++) for (int i=0; i<2; i++)
{ {
float top_a0, top_a1, bottom_a0, bottom_a1; float top_a0, top_a1, bottom_a0, bottom_a1;
...@@ -3276,10 +3278,10 @@ void fitLine(Point p1, Point p2, float &a0, float &a1) ...@@ -3276,10 +3278,10 @@ void fitLine(Point p1, Point p2, float &a0, float &a1)
// out a1 is the slope // out a1 is the slope
void fitLineOLS(Point p1, Point p2, Point p3, float &a0, float &a1) void fitLineOLS(Point p1, Point p2, Point p3, float &a0, float &a1)
{ {
float sumx = p1.x + p2.x + p3.x; float sumx = (float)(p1.x + p2.x + p3.x);
float sumy = p1.y + p2.y + p3.y; float sumy = (float)(p1.y + p2.y + p3.y);
float sumxy = p1.x*p1.y + p2.x*p2.y + p3.x*p3.y; float sumxy = (float)(p1.x*p1.y + p2.x*p2.y + p3.x*p3.y);
float sumx2 = p1.x*p1.x + p2.x*p2.x + p3.x*p3.x; float sumx2 = (float)(p1.x*p1.x + p2.x*p2.x + p3.x*p3.x);
// line coefficients // line coefficients
a0=(float)(sumy*sumx2-sumx*sumxy) / (3*sumx2-sumx*sumx); a0=(float)(sumy*sumx2-sumx*sumxy) / (3*sumx2-sumx*sumx);
...@@ -3299,7 +3301,7 @@ float fitLineLMS(Point p1, Point p2, Point p3, float &a0, float &a1) ...@@ -3299,7 +3301,7 @@ float fitLineLMS(Point p1, Point p2, Point p3, float &a0, float &a1)
//Least-Median of Squares does not make sense with only three points //Least-Median of Squares does not make sense with only three points
//becuse any line passing by two of them has median_error = 0 //becuse any line passing by two of them has median_error = 0
//So we'll take the one with smaller slope //So we'll take the one with smaller slope
float l_a0, l_a1, best_slope=INT_MAX, err=0; float l_a0, l_a1, best_slope=FLT_MAX, err=0;
if (p1.x != p2.x) if (p1.x != p2.x)
{ {
...@@ -3448,13 +3450,13 @@ bool isValidPair(Mat &grey, Mat &lab, Mat &mask, vector<Mat> &channels, vector< ...@@ -3448,13 +3450,13 @@ bool isValidPair(Mat &grey, Mat &lab, Mat &mask, vector<Mat> &channels, vector<
if (j->rect.x == i->rect.x) if (j->rect.x == i->rect.x)
return false; return false;
float height_ratio = (float)min(i->rect.height,j->rect.height) / float height_ratio = (float)min(i->rect.height,j->rect.height) /
max(i->rect.height,j->rect.height); max(i->rect.height,j->rect.height);
Point center_i(i->rect.x+i->rect.width/2, i->rect.y+i->rect.height/2); Point center_i(i->rect.x+i->rect.width/2, i->rect.y+i->rect.height/2);
Point center_j(j->rect.x+j->rect.width/2, j->rect.y+j->rect.height/2); Point center_j(j->rect.x+j->rect.width/2, j->rect.y+j->rect.height/2);
float centroid_angle = atan2(center_j.y-center_i.y, center_j.x-center_i.x); float centroid_angle = (float)atan2(center_j.y-center_i.y, center_j.x-center_i.x);
int avg_width = (i->rect.width + j->rect.width) / 2; int avg_width = (i->rect.width + j->rect.width) / 2;
float norm_distance = (float)(j->rect.x-(i->rect.x+i->rect.width))/avg_width; float norm_distance = (float)(j->rect.x-(i->rect.x+i->rect.width))/avg_width;
...@@ -3489,10 +3491,10 @@ bool isValidPair(Mat &grey, Mat &lab, Mat &mask, vector<Mat> &channels, vector< ...@@ -3489,10 +3491,10 @@ bool isValidPair(Mat &grey, Mat &lab, Mat &mask, vector<Mat> &channels, vector<
Scalar mean,std; Scalar mean,std;
meanStdDev(grey(i->rect),mean,std,rect_mask); meanStdDev(grey(i->rect),mean,std,rect_mask);
int grey_mean1 = mean[0]; int grey_mean1 = (int)mean[0];
meanStdDev(lab(i->rect),mean,std,rect_mask); meanStdDev(lab(i->rect),mean,std,rect_mask);
float a_mean1 = mean[1]; float a_mean1 = (float)mean[1];
float b_mean1 = mean[2]; float b_mean1 = (float)mean[2];
region = mask(Rect(Point(j->rect.x,j->rect.y), region = mask(Rect(Point(j->rect.x,j->rect.y),
Point(j->rect.br().x+2,j->rect.br().y+2))); Point(j->rect.br().x+2,j->rect.br().y+2)));
...@@ -3506,10 +3508,10 @@ bool isValidPair(Mat &grey, Mat &lab, Mat &mask, vector<Mat> &channels, vector< ...@@ -3506,10 +3508,10 @@ bool isValidPair(Mat &grey, Mat &lab, Mat &mask, vector<Mat> &channels, vector<
rect_mask = mask(Rect(j->rect.x+1,j->rect.y+1,j->rect.width,j->rect.height)); rect_mask = mask(Rect(j->rect.x+1,j->rect.y+1,j->rect.width,j->rect.height));
meanStdDev(grey(j->rect),mean,std,rect_mask); meanStdDev(grey(j->rect),mean,std,rect_mask);
int grey_mean2 = mean[0]; int grey_mean2 = (int)mean[0];
meanStdDev(lab(j->rect),mean,std,rect_mask); meanStdDev(lab(j->rect),mean,std,rect_mask);
float a_mean2 = mean[1]; float a_mean2 = (float)mean[1];
float b_mean2 = mean[2]; float b_mean2 = (float)mean[2];
if (abs(grey_mean1-grey_mean2) > PAIR_MAX_INTENSITY_DIST) if (abs(grey_mean1-grey_mean2) > PAIR_MAX_INTENSITY_DIST)
return false; return false;
...@@ -3614,10 +3616,10 @@ bool isValidTriplet(vector< vector<ERStat> >& regions, region_pair pair1, region ...@@ -3614,10 +3616,10 @@ bool isValidTriplet(vector< vector<ERStat> >& regions, region_pair pair1, region
(triplet.estimates.bottom2_a0 < triplet.estimates.top2_a0) ) (triplet.estimates.bottom2_a0 < triplet.estimates.top2_a0) )
return false; return false;
int central_height = min(triplet.estimates.bottom1_a0, triplet.estimates.bottom2_a0) - int central_height = (int)min(triplet.estimates.bottom1_a0, triplet.estimates.bottom2_a0) -
max(triplet.estimates.top1_a0,triplet.estimates.top2_a0); (int)max(triplet.estimates.top1_a0,triplet.estimates.top2_a0);
int top_height = abs(triplet.estimates.top1_a0 - triplet.estimates.top2_a0); int top_height = (int)abs(triplet.estimates.top1_a0 - triplet.estimates.top2_a0);
int bottom_height = abs(triplet.estimates.bottom1_a0 - triplet.estimates.bottom2_a0); int bottom_height = (int)abs(triplet.estimates.bottom1_a0 - triplet.estimates.bottom2_a0);
if (central_height == 0) if (central_height == 0)
return false; return false;
...@@ -3660,9 +3662,9 @@ bool isValidSequence(region_sequence &sequence1, region_sequence &sequence2) ...@@ -3660,9 +3662,9 @@ bool isValidSequence(region_sequence &sequence1, region_sequence &sequence2)
// Check if two triplets share a region in common // Check if two triplets share a region in common
bool haveCommonRegion(region_triplet &t1, region_triplet &t2) bool haveCommonRegion(region_triplet &t1, region_triplet &t2)
{ {
if ((t1.a==t2.a) || (t1.a==t2.b) || (t1.a==t2.c) || if ((t1.a==t2.a) || (t1.a==t2.b) || (t1.a==t2.c) ||
(t1.b==t2.a) || (t1.b==t2.b) || (t1.b==t2.c) || (t1.b==t2.a) || (t1.b==t2.b) || (t1.b==t2.c) ||
(t1.c==t2.a) || (t1.c==t2.b) || (t1.c==t2.c)) (t1.c==t2.a) || (t1.c==t2.b) || (t1.c==t2.c))
return true; return true;
return false; return false;
...@@ -3690,7 +3692,7 @@ bool sort_couples (Vec3i i,Vec3i j) { return (i[0]<j[0]); } ...@@ -3690,7 +3692,7 @@ bool sort_couples (Vec3i i,Vec3i j) { return (i[0]<j[0]); }
Find groups of Extremal Regions that are organized as text lines. This function implements Find groups of Extremal Regions that are organized as text lines. This function implements
the grouping algorithm described in: the grouping algorithm described in:
Neumann L., Matas J.: Real-Time Scene Text Localization and Recognition, CVPR 2012 Neumann L., Matas J.: Real-Time Scene Text Localization and Recognition, CVPR 2012
Neumann L., Matas J.: A method for text localization and detection, ACCV 2010 Neumann L., Matas J.: A method for text localization and detection, ACCV 2010
\param _img Original RGB image from wich the regions were extracted. \param _img Original RGB image from wich the regions were extracted.
\param _src Vector of sinle channel images CV_8UC1 from wich the regions were extracted. \param _src Vector of sinle channel images CV_8UC1 from wich the regions were extracted.
...@@ -3720,7 +3722,7 @@ void erGroupingNM(InputArray _img, InputArrayOfArrays _src, vector< vector<ERSta ...@@ -3720,7 +3722,7 @@ void erGroupingNM(InputArray _img, InputArrayOfArrays _src, vector< vector<ERSta
vector< Vec2i > all_regions; vector< Vec2i > all_regions;
for(size_t r=0; r<regions[c].size(); r++) for(size_t r=0; r<regions[c].size(); r++)
{ {
all_regions.push_back(Vec2i(c,r)); all_regions.push_back(Vec2i((int)c,(int)r));
} }
vector< region_pair > valid_pairs; vector< region_pair > valid_pairs;
...@@ -3728,12 +3730,12 @@ void erGroupingNM(InputArray _img, InputArrayOfArrays _src, vector< vector<ERSta ...@@ -3728,12 +3730,12 @@ void erGroupingNM(InputArray _img, InputArrayOfArrays _src, vector< vector<ERSta
Mat grey,lab; Mat grey,lab;
cvtColor(img, lab, COLOR_RGB2Lab); cvtColor(img, lab, COLOR_RGB2Lab);
cvtColor(img, grey, COLOR_RGB2GRAY); cvtColor(img, grey, COLOR_RGB2GRAY);
//check every possible pair of regions //check every possible pair of regions
for (size_t i=0; i<all_regions.size(); i++) for (size_t i=0; i<all_regions.size(); i++)
{ {
vector<int> i_siblings; vector<int> i_siblings;
int first_i_sibling_idx = valid_pairs.size(); int first_i_sibling_idx = (int)valid_pairs.size();
for (size_t j=i+1; j<all_regions.size(); j++) for (size_t j=i+1; j<all_regions.size(); j++)
{ {
// check height ratio, centroid angle and region distance normalized by region width // check height ratio, centroid angle and region distance normalized by region width
...@@ -3758,11 +3760,11 @@ void erGroupingNM(InputArray _img, InputArrayOfArrays _src, vector< vector<ERSta ...@@ -3758,11 +3760,11 @@ void erGroupingNM(InputArray _img, InputArrayOfArrays _src, vector< vector<ERSta
regions[all_regions[i_siblings[k]][0]][all_regions[i_siblings[k]][1]].rect.width/2, regions[all_regions[i_siblings[k]][0]][all_regions[i_siblings[k]][1]].rect.width/2,
regions[all_regions[i_siblings[k]][0]][all_regions[i_siblings[k]][1]].rect.y + regions[all_regions[i_siblings[k]][0]][all_regions[i_siblings[k]][1]].rect.y +
regions[all_regions[i_siblings[k]][0]][all_regions[i_siblings[k]][1]].rect.height/2 ); regions[all_regions[i_siblings[k]][0]][all_regions[i_siblings[k]][1]].rect.height/2 );
if ( norm(i_center - j_center) < norm(i_center - k_center) ) if ( norm(i_center - j_center) < norm(i_center - k_center) )
{ {
valid_pairs[first_i_sibling_idx+k] = region_pair(all_regions[i],all_regions[j]); valid_pairs[first_i_sibling_idx+k] = region_pair(all_regions[i],all_regions[j]);
i_siblings[k] = j; i_siblings[k] = (int)j;
} }
isCycle = true; isCycle = true;
break; break;
...@@ -3771,17 +3773,17 @@ void erGroupingNM(InputArray _img, InputArrayOfArrays _src, vector< vector<ERSta ...@@ -3771,17 +3773,17 @@ void erGroupingNM(InputArray _img, InputArrayOfArrays _src, vector< vector<ERSta
if (!isCycle) if (!isCycle)
{ {
valid_pairs.push_back(region_pair(all_regions[i],all_regions[j])); valid_pairs.push_back(region_pair(all_regions[i],all_regions[j]));
i_siblings.push_back(j); i_siblings.push_back((int)j);
//cout << "Valid pair (" << all_regions[i][0] << "," << all_regions[i][1] << ") (" << all_regions[j][0] << "," << all_regions[j][1] << ")" << endl; //cout << "Valid pair (" << all_regions[i][0] << "," << all_regions[i][1] << ") (" << all_regions[j][0] << "," << all_regions[j][1] << ")" << endl;
} }
} }
} }
} }
//cout << "GroupingNM : detected " << valid_pairs.size() << " valid pairs" << endl; //cout << "GroupingNM : detected " << valid_pairs.size() << " valid pairs" << endl;
vector< region_triplet > valid_triplets; vector< region_triplet > valid_triplets;
//check every possible triplet of regions //check every possible triplet of regions
for (size_t i=0; i<valid_pairs.size(); i++) for (size_t i=0; i<valid_pairs.size(); i++)
{ {
...@@ -3796,18 +3798,18 @@ void erGroupingNM(InputArray _img, InputArrayOfArrays _src, vector< vector<ERSta ...@@ -3796,18 +3798,18 @@ void erGroupingNM(InputArray _img, InputArrayOfArrays _src, vector< vector<ERSta
} }
} }
} }
//cout << "GroupingNM : detected " << valid_triplets.size() << " valid triplets" << endl; //cout << "GroupingNM : detected " << valid_triplets.size() << " valid triplets" << endl;
vector<region_sequence> valid_sequences; vector<region_sequence> valid_sequences;
vector<region_sequence> pending_sequences; vector<region_sequence> pending_sequences;
for (size_t i=0; i<valid_triplets.size(); i++) for (size_t i=0; i<valid_triplets.size(); i++)
{ {
pending_sequences.push_back(region_sequence(valid_triplets[i])); pending_sequences.push_back(region_sequence(valid_triplets[i]));
} }
for (size_t i=0; i<pending_sequences.size(); i++) for (size_t i=0; i<pending_sequences.size(); i++)
{ {
bool expanded = false; bool expanded = false;
...@@ -3826,7 +3828,7 @@ void erGroupingNM(InputArray _img, InputArrayOfArrays _src, vector< vector<ERSta ...@@ -3826,7 +3828,7 @@ void erGroupingNM(InputArray _img, InputArrayOfArrays _src, vector< vector<ERSta
valid_sequences.push_back(pending_sequences[i]); valid_sequences.push_back(pending_sequences[i]);
} }
} }
// remove a sequence if one its regions is already grouped within a longer seq // remove a sequence if one its regions is already grouped within a longer seq
for (size_t i=0; i<valid_sequences.size(); i++) for (size_t i=0; i<valid_sequences.size(); i++)
{ {
...@@ -3848,15 +3850,15 @@ void erGroupingNM(InputArray _img, InputArrayOfArrays _src, vector< vector<ERSta ...@@ -3848,15 +3850,15 @@ void erGroupingNM(InputArray _img, InputArrayOfArrays _src, vector< vector<ERSta
} }
} }
} }
//cout << "GroupingNM : detected " << valid_sequences.size() << " sequences." << endl; //cout << "GroupingNM : detected " << valid_sequences.size() << " sequences." << endl;
if (do_feedback_loop) if (do_feedback_loop)
{ {
//Feedback loop of detected lines to region extraction ... tries to recover missmatches in the region decomposition step by extracting regions in the neighbourhood of a valid sequence and checking if they are consistent with its line estimates //Feedback loop of detected lines to region extraction ... tries to recover missmatches in the region decomposition step by extracting regions in the neighbourhood of a valid sequence and checking if they are consistent with its line estimates
Ptr<ERFilter> er_filter = createERFilterNM1(loadClassifierNM1("trained_classifierNM1.xml"),1,0.005,0.3,0.,true,0.1); Ptr<ERFilter> er_filter = createERFilterNM1(loadClassifierNM1("trained_classifierNM1.xml"),1,0.005f,0.3f,0.f,true,0.1f);
for (int i=0; i<(int)valid_sequences.size(); i++) for (int i=0; i<(int)valid_sequences.size(); i++)
{ {
vector<Point> bbox_points; vector<Point> bbox_points;
...@@ -3916,21 +3918,21 @@ void erGroupingNM(InputArray _img, InputArrayOfArrays _src, vector< vector<ERSta ...@@ -3916,21 +3918,21 @@ void erGroupingNM(InputArray _img, InputArrayOfArrays _src, vector< vector<ERSta
regions[c].push_back(aux_regions[r]); regions[c].push_back(aux_regions[r]);
for (size_t j=0; j<valid_sequences[i].triplets.size(); j++) for (size_t j=0; j<valid_sequences[i].triplets.size(); j++)
{ {
if (isValidPair(grey, lab, mask, src, regions, valid_sequences[i].triplets[j].a, Vec2i(c,regions[c].size()-1))) if (isValidPair(grey, lab, mask, src, regions, valid_sequences[i].triplets[j].a, Vec2i(c,(int)regions[c].size()-1)))
{ {
if (regions[valid_sequences[i].triplets[j].a[0]][valid_sequences[i].triplets[j].a[1]].rect.x > aux_regions[r].rect.x) if (regions[valid_sequences[i].triplets[j].a[0]][valid_sequences[i].triplets[j].a[1]].rect.x > aux_regions[r].rect.x)
right_couples.push_back(Vec3i(regions[valid_sequences[i].triplets[j].a[0]][valid_sequences[i].triplets[j].a[1]].rect.x - aux_regions[r].rect.x, valid_sequences[i].triplets[j].a[0],valid_sequences[i].triplets[j].a[1])); right_couples.push_back(Vec3i(regions[valid_sequences[i].triplets[j].a[0]][valid_sequences[i].triplets[j].a[1]].rect.x - aux_regions[r].rect.x, valid_sequences[i].triplets[j].a[0],valid_sequences[i].triplets[j].a[1]));
else else
left_couples.push_back(Vec3i(aux_regions[r].rect.x - regions[valid_sequences[i].triplets[j].a[0]][valid_sequences[i].triplets[j].a[1]].rect.x, valid_sequences[i].triplets[j].a[0],valid_sequences[i].triplets[j].a[1])); left_couples.push_back(Vec3i(aux_regions[r].rect.x - regions[valid_sequences[i].triplets[j].a[0]][valid_sequences[i].triplets[j].a[1]].rect.x, valid_sequences[i].triplets[j].a[0],valid_sequences[i].triplets[j].a[1]));
} }
if (isValidPair(grey, lab, mask, src, regions, valid_sequences[i].triplets[j].b, Vec2i(c,regions[c].size()-1))) if (isValidPair(grey, lab, mask, src, regions, valid_sequences[i].triplets[j].b, Vec2i(c,(int)regions[c].size()-1)))
{ {
if (regions[valid_sequences[i].triplets[j].b[0]][valid_sequences[i].triplets[j].b[1]].rect.x > aux_regions[r].rect.x) if (regions[valid_sequences[i].triplets[j].b[0]][valid_sequences[i].triplets[j].b[1]].rect.x > aux_regions[r].rect.x)
right_couples.push_back(Vec3i(regions[valid_sequences[i].triplets[j].b[0]][valid_sequences[i].triplets[j].b[1]].rect.x - aux_regions[r].rect.x, valid_sequences[i].triplets[j].b[0],valid_sequences[i].triplets[j].b[1])); right_couples.push_back(Vec3i(regions[valid_sequences[i].triplets[j].b[0]][valid_sequences[i].triplets[j].b[1]].rect.x - aux_regions[r].rect.x, valid_sequences[i].triplets[j].b[0],valid_sequences[i].triplets[j].b[1]));
else else
left_couples.push_back(Vec3i(aux_regions[r].rect.x - regions[valid_sequences[i].triplets[j].b[0]][valid_sequences[i].triplets[j].b[1]].rect.x, valid_sequences[i].triplets[j].b[0],valid_sequences[i].triplets[j].b[1])); left_couples.push_back(Vec3i(aux_regions[r].rect.x - regions[valid_sequences[i].triplets[j].b[0]][valid_sequences[i].triplets[j].b[1]].rect.x, valid_sequences[i].triplets[j].b[0],valid_sequences[i].triplets[j].b[1]));
} }
if (isValidPair(grey, lab, mask, src, regions, valid_sequences[i].triplets[j].c, Vec2i(c,regions[c].size()-1))) if (isValidPair(grey, lab, mask, src, regions, valid_sequences[i].triplets[j].c, Vec2i(c,(int)regions[c].size()-1)))
{ {
if (regions[valid_sequences[i].triplets[j].c[0]][valid_sequences[i].triplets[j].c[1]].rect.x > aux_regions[r].rect.x) if (regions[valid_sequences[i].triplets[j].c[0]][valid_sequences[i].triplets[j].c[1]].rect.x > aux_regions[r].rect.x)
right_couples.push_back(Vec3i(regions[valid_sequences[i].triplets[j].c[0]][valid_sequences[i].triplets[j].c[1]].rect.x - aux_regions[r].rect.x, valid_sequences[i].triplets[j].c[0],valid_sequences[i].triplets[j].c[1])); right_couples.push_back(Vec3i(regions[valid_sequences[i].triplets[j].c[0]][valid_sequences[i].triplets[j].c[1]].rect.x - aux_regions[r].rect.x, valid_sequences[i].triplets[j].c[0],valid_sequences[i].triplets[j].c[1]));
...@@ -3945,8 +3947,8 @@ void erGroupingNM(InputArray _img, InputArrayOfArrays _src, vector< vector<ERSta ...@@ -3945,8 +3947,8 @@ void erGroupingNM(InputArray _img, InputArrayOfArrays _src, vector< vector<ERSta
{ {
sort(left_couples.begin(), left_couples.end(), sort_couples); sort(left_couples.begin(), left_couples.end(), sort_couples);
sort(right_couples.begin(), right_couples.end(), sort_couples); sort(right_couples.begin(), right_couples.end(), sort_couples);
region_pair pair1(Vec2i(left_couples[0][1],left_couples[0][2]),Vec2i(c,regions[c].size()-1)); region_pair pair1(Vec2i(left_couples[0][1],left_couples[0][2]),Vec2i(c,(int)regions[c].size()-1));
region_pair pair2(Vec2i(c,regions[c].size()-1), Vec2i(right_couples[0][1],right_couples[0][2])); region_pair pair2(Vec2i(c,(int)regions[c].size()-1), Vec2i(right_couples[0][1],right_couples[0][2]));
region_triplet triplet(Vec2i(0,0),Vec2i(0,0),Vec2i(0,0)); region_triplet triplet(Vec2i(0,0),Vec2i(0,0),Vec2i(0,0));
if (isValidTriplet(regions, pair1, pair2, triplet)) if (isValidTriplet(regions, pair1, pair2, triplet))
{ {
...@@ -3956,7 +3958,7 @@ void erGroupingNM(InputArray _img, InputArrayOfArrays _src, vector< vector<ERSta ...@@ -3956,7 +3958,7 @@ void erGroupingNM(InputArray _img, InputArrayOfArrays _src, vector< vector<ERSta
else if (right_couples.size() >= 2) else if (right_couples.size() >= 2)
{ {
sort(right_couples.begin(), right_couples.end(), sort_couples); sort(right_couples.begin(), right_couples.end(), sort_couples);
region_pair pair1(Vec2i(c,regions[c].size()-1), Vec2i(right_couples[0][1],right_couples[0][2])); region_pair pair1(Vec2i(c,(int)regions[c].size()-1), Vec2i(right_couples[0][1],right_couples[0][2]));
region_pair pair2(Vec2i(right_couples[0][1],right_couples[0][2]), Vec2i(right_couples[1][1],right_couples[1][2])); region_pair pair2(Vec2i(right_couples[0][1],right_couples[0][2]), Vec2i(right_couples[1][1],right_couples[1][2]));
region_triplet triplet(Vec2i(0,0),Vec2i(0,0),Vec2i(0,0)); region_triplet triplet(Vec2i(0,0),Vec2i(0,0),Vec2i(0,0));
if (isValidTriplet(regions, pair1, pair2, triplet)) if (isValidTriplet(regions, pair1, pair2, triplet))
...@@ -3968,7 +3970,7 @@ void erGroupingNM(InputArray _img, InputArrayOfArrays _src, vector< vector<ERSta ...@@ -3968,7 +3970,7 @@ void erGroupingNM(InputArray _img, InputArrayOfArrays _src, vector< vector<ERSta
{ {
sort(left_couples.begin(), left_couples.end(), sort_couples); sort(left_couples.begin(), left_couples.end(), sort_couples);
region_pair pair1(Vec2i(left_couples[1][1],left_couples[1][2]), Vec2i(left_couples[0][1],left_couples[0][2])); region_pair pair1(Vec2i(left_couples[1][1],left_couples[1][2]), Vec2i(left_couples[0][1],left_couples[0][2]));
region_pair pair2(Vec2i(left_couples[0][1],left_couples[0][2]),Vec2i(c,regions[c].size()-1)); region_pair pair2(Vec2i(left_couples[0][1],left_couples[0][2]),Vec2i(c,(int)regions[c].size()-1));
region_triplet triplet(Vec2i(0,0),Vec2i(0,0),Vec2i(0,0)); region_triplet triplet(Vec2i(0,0),Vec2i(0,0),Vec2i(0,0));
if (isValidTriplet(regions, pair1, pair2, triplet)) if (isValidTriplet(regions, pair1, pair2, triplet))
{ {
...@@ -3997,13 +3999,13 @@ void erGroupingNM(InputArray _img, InputArrayOfArrays _src, vector< vector<ERSta ...@@ -3997,13 +3999,13 @@ void erGroupingNM(InputArray _img, InputArrayOfArrays _src, vector< vector<ERSta
} }
// Prepare the sequences for output // Prepare the sequences for output
for (size_t i=0; i<valid_sequences.size(); i++) for (size_t i=0; i<valid_sequences.size(); i++)
{ {
vector<Point> bbox_points; vector<Point> bbox_points;
vector<Vec2i> group_regions; vector<Vec2i> group_regions;
for (size_t j=0; j<valid_sequences[i].triplets.size(); j++) for (size_t j=0; j<valid_sequences[i].triplets.size(); j++)
{ {
size_t prev_size = group_regions.size(); size_t prev_size = group_regions.size();
...@@ -4013,17 +4015,17 @@ void erGroupingNM(InputArray _img, InputArrayOfArrays _src, vector< vector<ERSta ...@@ -4013,17 +4015,17 @@ void erGroupingNM(InputArray _img, InputArrayOfArrays _src, vector< vector<ERSta
group_regions.push_back(valid_sequences[i].triplets[j].b); group_regions.push_back(valid_sequences[i].triplets[j].b);
if(find(group_regions.begin(), group_regions.end(), valid_sequences[i].triplets[j].c) == group_regions.end()) if(find(group_regions.begin(), group_regions.end(), valid_sequences[i].triplets[j].c) == group_regions.end())
group_regions.push_back(valid_sequences[i].triplets[j].c); group_regions.push_back(valid_sequences[i].triplets[j].c);
for (size_t k=prev_size; k<group_regions.size(); k++) for (size_t k=prev_size; k<group_regions.size(); k++)
{ {
bbox_points.push_back(regions[group_regions[k][0]][group_regions[k][1]].rect.tl()); bbox_points.push_back(regions[group_regions[k][0]][group_regions[k][1]].rect.tl());
bbox_points.push_back(regions[group_regions[k][0]][group_regions[k][1]].rect.br()); bbox_points.push_back(regions[group_regions[k][0]][group_regions[k][1]].rect.br());
} }
} }
out_groups.push_back(group_regions); out_groups.push_back(group_regions);
out_boxes.push_back(boundingRect(bbox_points)); out_boxes.push_back(boundingRect(bbox_points));
} }
} }
......
Markdown is supported
0% or
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment