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Commit 157e08f3 authored by lluis's avatar lluis
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fix warnings from MSVC and remove trailing whitespaces

parent 786876c0
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Showing with 108 additions and 106 deletions
modules/text/include/opencv2/text/erfilter.hpp
View file @ 157e08f3
......@@ -52,7 +52,7 @@
namespace cv
{
namespace text
{
{
/*!
Extremal Region Stat structure
......@@ -266,11 +266,11 @@ enum { ERGROUPING_ORIENTATION_HORIZ = 0,
\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.
*/
CV_EXPORTS void erGrouping(InputArray img, InputArrayOfArrays channels,
std::vector<std::vector<ERStat> > &regions,
std::vector<std::vector<Vec2i> > &groups,
std::vector<Rect> &groups_rects,
int method = ERGROUPING_ORIENTATION_HORIZ,
CV_EXPORTS void erGrouping(InputArray img, InputArrayOfArrays channels,
std::vector<std::vector<ERStat> > &regions,
std::vector<std::vector<Vec2i> > &groups,
std::vector<Rect> &groups_rects,
int method = ERGROUPING_ORIENTATION_HORIZ,
const std::string& filename = std::string(),
float minProbablity = 0.5);
......
modules/text/src/erfilter.cpp
View file @ 157e08f3
......@@ -58,7 +58,7 @@
namespace cv
{
namespace text
{
{
using namespace std;
......@@ -2182,6 +2182,8 @@ public:
void operator()(double *data, unsigned int num, int dim, unsigned char method,
unsigned char metric, vector< vector<int> > *meaningful_clusters);
MaxMeaningfulClustering & operator=(const MaxMeaningfulClustering &a);
private:
double minProbability;
CvBoost group_boost;
......@@ -2466,11 +2468,11 @@ static int getAngleABC( Point a, Point b, Point c )
Point cb = Point( b.x - c.x, b.y - c.y );
// 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
float abSqr = ab.x * ab.x + ab.y * ab.y;
float cbSqr = cb.x * cb.x + cb.y * cb.y;
float abSqr = (float)(ab.x * ab.x + ab.y * ab.y);
float cbSqr = (float)(cb.x * cb.x + cb.y * cb.y);
// square of cosine of the needed angle
float cosSqr = dot * dot / abSqr / cbSqr;
......@@ -2491,7 +2493,7 @@ static int getAngleABC( Point a, Point b, Point c )
float rslt = alpha2 / 2;
float rs = rslt * 180. / pi;
float rs = (float)(rslt * 180. / pi);
// Now revolve the ambiguities.
......@@ -2505,7 +2507,7 @@ static int getAngleABC( Point a, Point b, Point c )
rs = 180 - rs;
// 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)
rs = -rs;
......@@ -2520,16 +2522,16 @@ double MaxMeaningfulClustering::probability(vector<int> &cluster)
vector<float> sample;
sample.push_back(0);
sample.push_back(cluster.size());
Mat diameters ( cluster.size(), 1, CV_32F, 1 );
Mat strokes ( cluster.size(), 1, CV_32F, 1 );
Mat gradients ( cluster.size(), 1, CV_32F, 1 );
Mat fg_intensities ( cluster.size(), 1, CV_32F, 1 );
Mat bg_intensities ( cluster.size(), 1, CV_32F, 1 );
Mat axial_ratios ( cluster.size(), 1, CV_32F, 1 );
Mat chull_ratios ( cluster.size(), 1, CV_32F, 1 );
Mat convexities ( cluster.size(), 1, CV_32F, 1 );
sample.push_back((float)cluster.size());
Mat diameters ( (int)cluster.size(), 1, CV_32F, 1 );
Mat strokes ( (int)cluster.size(), 1, CV_32F, 1 );
Mat gradients ( (int)cluster.size(), 1, CV_32F, 1 );
Mat fg_intensities ( (int)cluster.size(), 1, CV_32F, 1 );
Mat bg_intensities ( (int)cluster.size(), 1, CV_32F, 1 );
Mat axial_ratios ( (int)cluster.size(), 1, CV_32F, 1 );
Mat chull_ratios ( (int)cluster.size(), 1, CV_32F, 1 );
Mat convexities ( (int)cluster.size(), 1, CV_32F, 1 );
Subdiv2D subdiv(Rect(0,0,imsize.width,imsize.height));
vector< vector<Point> > forest(cluster.size());
float maxAvgOverlap = 0;
......@@ -2573,15 +2575,15 @@ double MaxMeaningfulClustering::probability(vector<int> &cluster)
Scalar 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 );
sample.push_back(std[0]/mean[0]);
sample.push_back((float)(std[0]/mean[0]));
meanStdDev( gradients, mean, std );
sample.push_back(std[0]);
sample.push_back((float)std[0]);
meanStdDev( fg_intensities, mean, std );
sample.push_back(std[0]);
sample.push_back((float)std[0]);
meanStdDev( bg_intensities, mean, std );
sample.push_back(std[0]);
sample.push_back((float)std[0]);
/* begin Kruskal algorithm to find the MST */
vector<Vec4f> edgeList;
......@@ -2627,13 +2629,13 @@ double MaxMeaningfulClustering::probability(vector<int> &cluster)
Point t_pt0 = Point(cvRound(t[0]), cvRound(t[1]));
Point t_pt1 = Point(cvRound(t[2]), cvRound(t[3]));
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)
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)
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)
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;
......@@ -2642,15 +2644,15 @@ double MaxMeaningfulClustering::probability(vector<int> &cluster)
//cout << endl;
meanStdDev( angles, mean, std );
sample.push_back(std[0]);
sample.push_back(mean[0]);
sample.push_back((float)std[0]);
sample.push_back((float)mean[0]);
meanStdDev( edge_distances, mean, std );
sample.push_back(std[0]/mean[0]);
sample.push_back(mean[0]/diameter_mean);
sample.push_back((float)(std[0]/mean[0]));
sample.push_back((float)(mean[0]/diameter_mean));
meanStdDev( axial_ratios, mean, std );
sample.push_back(mean[0]);
sample.push_back(std[0]);
sample.push_back((float)mean[0]);
sample.push_back((float)std[0]);
/// Calculate average shape self-similarity
double avg_shape_match = 0;
......@@ -2694,12 +2696,12 @@ double MaxMeaningfulClustering::probability(vector<int> &cluster)
sample.push_back(maxAvgOverlap);
meanStdDev( chull_ratios, mean, std );
sample.push_back(mean[0]);
sample.push_back(std[0]);
sample.push_back((float)mean[0]);
sample.push_back((float)std[0]);
meanStdDev( convexities, mean, std );
sample.push_back(mean[0]);
sample.push_back(std[0]);
sample.push_back((float)mean[0]);
sample.push_back((float)std[0]);
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)
if (*skeleton_ptr++ == 255)
{
bool p2, p3, p4, p5, p6, p7, p8, p9;
p2 = skeleton.data[(row-1) * skeleton.cols + col];
p3 = skeleton.data[(row-1) * skeleton.cols + col+1];
p4 = skeleton.data[row * skeleton.cols + col+1];
p5 = skeleton.data[(row+1) * skeleton.cols + col+1];
p6 = skeleton.data[(row+1) * skeleton.cols + col];
p7 = skeleton.data[(row+1) * skeleton.cols + col-1];
p8 = skeleton.data[row * skeleton.cols + col-1];
p9 = skeleton.data[(row-1) * skeleton.cols + col-1];
p2 = (bool)(skeleton.data[(row-1) * skeleton.cols + col]);
p3 = (bool)(skeleton.data[(row-1) * skeleton.cols + col+1]);
p4 = (bool)(skeleton.data[row * skeleton.cols + col+1]);
p5 = (bool)(skeleton.data[(row+1) * skeleton.cols + col+1]);
p6 = (bool)(skeleton.data[(row+1) * skeleton.cols + col]);
p7 = (bool)(skeleton.data[(row+1) * skeleton.cols + col-1]);
p8 = (bool)(skeleton.data[row * skeleton.cols + col-1]);
p9 = (bool)(skeleton.data[(row-1) * skeleton.cols + col-1]);
int C = (!p2 & (p3 | p4)) + (!p4 & (p5 | p6)) +
(!p6 & (p7 | p8)) + (!p8 & (p9 | p2));
......@@ -2790,11 +2792,11 @@ bool guo_hall_thinning(const Mat1b & img, Mat& skeleton)
}
// 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)
{
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];
skeleton.data[key] = 0;
......@@ -2930,7 +2932,7 @@ float extract_features(Mat &grey, Mat& channel, vector<ERStat> &regions, vector<
if (hull_idx.size()>2)
if (contours0[0].size()>3)
convexityDefects(contours0[0],hull_idx,cx);
f.convexities = cx.size();
f.convexities = (int)cx.size();
rect_mask = Scalar(0);
......@@ -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
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].
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
#define PAIR_MIN_HEIGHT_RATIO 0.4
#define PAIR_MIN_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_INTENSITY_DIST 111
#define PAIR_MAX_AB_DIST 54
......@@ -3132,7 +3134,7 @@ float distanceLinesEstimates(line_estimates &a, line_estimates &b)
int x_max = max(a.x_max, b.x_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++)
{
float top_a0, top_a1, bottom_a0, bottom_a1;
......@@ -3276,10 +3278,10 @@ void fitLine(Point p1, Point p2, float &a0, float &a1)
// out a1 is the slope
void fitLineOLS(Point p1, Point p2, Point p3, float &a0, float &a1)
{
float sumx = p1.x + p2.x + p3.x;
float sumy = p1.y + p2.y + p3.y;
float sumxy = 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 sumx = (float)(p1.x + p2.x + p3.x);
float sumy = (float)(p1.y + p2.y + p3.y);
float sumxy = (float)(p1.x*p1.y + p2.x*p2.y + p3.x*p3.y);
float sumx2 = (float)(p1.x*p1.x + p2.x*p2.x + p3.x*p3.x);
// line coefficients
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)
//Least-Median of Squares does not make sense with only three points
//becuse any line passing by two of them has median_error = 0
//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)
{
......@@ -3448,13 +3450,13 @@ bool isValidPair(Mat &grey, Mat &lab, Mat &mask, vector<Mat> &channels, vector<
if (j->rect.x == i->rect.x)
return false;
float height_ratio = (float)min(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_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;
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<
Scalar mean,std;
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);
float a_mean1 = mean[1];
float b_mean1 = mean[2];
float a_mean1 = (float)mean[1];
float b_mean1 = (float)mean[2];
region = mask(Rect(Point(j->rect.x,j->rect.y),
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<
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);
int grey_mean2 = mean[0];
int grey_mean2 = (int)mean[0];
meanStdDev(lab(j->rect),mean,std,rect_mask);
float a_mean2 = mean[1];
float b_mean2 = mean[2];
float a_mean2 = (float)mean[1];
float b_mean2 = (float)mean[2];
if (abs(grey_mean1-grey_mean2) > PAIR_MAX_INTENSITY_DIST)
return false;
......@@ -3614,10 +3616,10 @@ bool isValidTriplet(vector< vector<ERStat> >& regions, region_pair pair1, region
(triplet.estimates.bottom2_a0 < triplet.estimates.top2_a0) )
return false;
int central_height = min(triplet.estimates.bottom1_a0, triplet.estimates.bottom2_a0) -
max(triplet.estimates.top1_a0,triplet.estimates.top2_a0);
int top_height = abs(triplet.estimates.top1_a0 - triplet.estimates.top2_a0);
int bottom_height = abs(triplet.estimates.bottom1_a0 - triplet.estimates.bottom2_a0);
int central_height = (int)min(triplet.estimates.bottom1_a0, triplet.estimates.bottom2_a0) -
(int)max(triplet.estimates.top1_a0,triplet.estimates.top2_a0);
int top_height = (int)abs(triplet.estimates.top1_a0 - triplet.estimates.top2_a0);
int bottom_height = (int)abs(triplet.estimates.bottom1_a0 - triplet.estimates.bottom2_a0);
if (central_height == 0)
return false;
......@@ -3660,9 +3662,9 @@ bool isValidSequence(region_sequence &sequence1, region_sequence &sequence2)
// Check if two triplets share a region in common
bool haveCommonRegion(region_triplet &t1, region_triplet &t2)
{
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.c==t2.a) || (t1.c==t2.b) || (t1.c==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.c==t2.a) || (t1.c==t2.b) || (t1.c==t2.c))
return true;
return false;
......@@ -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
the grouping algorithm described in:
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 _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
vector< Vec2i > all_regions;
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;
......@@ -3728,12 +3730,12 @@ void erGroupingNM(InputArray _img, InputArrayOfArrays _src, vector< vector<ERSta
Mat grey,lab;
cvtColor(img, lab, COLOR_RGB2Lab);
cvtColor(img, grey, COLOR_RGB2GRAY);
//check every possible pair of regions
for (size_t i=0; i<all_regions.size(); i++)
{
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++)
{
// 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
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.height/2 );
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]);
i_siblings[k] = j;
i_siblings[k] = (int)j;
}
isCycle = true;
break;
......@@ -3771,17 +3773,17 @@ void erGroupingNM(InputArray _img, InputArrayOfArrays _src, vector< vector<ERSta
if (!isCycle)
{
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 << "GroupingNM : detected " << valid_pairs.size() << " valid pairs" << endl;
vector< region_triplet > valid_triplets;
//check every possible triplet of regions
for (size_t i=0; i<valid_pairs.size(); i++)
{
......@@ -3796,18 +3798,18 @@ void erGroupingNM(InputArray _img, InputArrayOfArrays _src, vector< vector<ERSta
}
}
}
//cout << "GroupingNM : detected " << valid_triplets.size() << " valid triplets" << endl;
vector<region_sequence> valid_sequences;
vector<region_sequence> pending_sequences;
for (size_t i=0; i<valid_triplets.size(); i++)
{
pending_sequences.push_back(region_sequence(valid_triplets[i]));
}
for (size_t i=0; i<pending_sequences.size(); i++)
{
bool expanded = false;
......@@ -3826,7 +3828,7 @@ void erGroupingNM(InputArray _img, InputArrayOfArrays _src, vector< vector<ERSta
valid_sequences.push_back(pending_sequences[i]);
}
}
// remove a sequence if one its regions is already grouped within a longer seq
for (size_t i=0; i<valid_sequences.size(); i++)
{
......@@ -3848,15 +3850,15 @@ void erGroupingNM(InputArray _img, InputArrayOfArrays _src, vector< vector<ERSta
}
}
}
//cout << "GroupingNM : detected " << valid_sequences.size() << " sequences." << endl;
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
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++)
{
vector<Point> bbox_points;
......@@ -3916,21 +3918,21 @@ void erGroupingNM(InputArray _img, InputArrayOfArrays _src, vector< vector<ERSta
regions[c].push_back(aux_regions[r]);
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)
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
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)
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
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)
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
{
sort(left_couples.begin(), left_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 pair2(Vec2i(c,regions[c].size()-1), Vec2i(right_couples[0][1],right_couples[0][2]));
region_pair pair1(Vec2i(left_couples[0][1],left_couples[0][2]),Vec2i(c,(int)regions[c].size()-1));
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));
if (isValidTriplet(regions, pair1, pair2, triplet))
{
......@@ -3956,7 +3958,7 @@ void erGroupingNM(InputArray _img, InputArrayOfArrays _src, vector< vector<ERSta
else if (right_couples.size() >= 2)
{
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_triplet triplet(Vec2i(0,0),Vec2i(0,0),Vec2i(0,0));
if (isValidTriplet(regions, pair1, pair2, triplet))
......@@ -3968,7 +3970,7 @@ void erGroupingNM(InputArray _img, InputArrayOfArrays _src, vector< vector<ERSta
{
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 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));
if (isValidTriplet(regions, pair1, pair2, triplet))
{
......@@ -3997,13 +3999,13 @@ void erGroupingNM(InputArray _img, InputArrayOfArrays _src, vector< vector<ERSta
}
// Prepare the sequences for output
for (size_t i=0; i<valid_sequences.size(); i++)
{
vector<Point> bbox_points;
vector<Vec2i> group_regions;
for (size_t j=0; j<valid_sequences[i].triplets.size(); j++)
{
size_t prev_size = group_regions.size();
......@@ -4013,17 +4015,17 @@ void erGroupingNM(InputArray _img, InputArrayOfArrays _src, vector< vector<ERSta
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())
group_regions.push_back(valid_sequences[i].triplets[j].c);
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.br());
}
}
out_groups.push_back(group_regions);
out_boxes.push_back(boundingRect(bbox_points));
}
}
......
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