/
Utils.cpp
852 lines (716 loc) · 27.5 KB
/
Utils.cpp
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#include "Utils.h"
std::string getexepath()
{
char the_path[256];
getcwd(the_path, 255);
strcat(the_path, "/");
return string(the_path);
}
std::string getAbsolutePath( string path) {
char actualpath [PATH_MAX];
char *ptr = realpath(path.c_str(), actualpath) ;
return std::string(actualpath);
}
string getbase(string csv_filename) {
std::string base_filename = string(csv_filename).substr(0,string(csv_filename).find_last_of("/\\") + 1);
if(base_filename.length()==0)
base_filename = getexepath();
// cout << "exe path " << getexepath() << endl;
return base_filename;
}
string getbasename(string csv_filename) {
return csv_filename.substr(csv_filename.find_last_of("/\\") + 1, csv_filename.length());
}
// read image
Mat readI ( string path ) {
Mat image = imread(path, CV_LOAD_IMAGE_COLOR); // Read the file
int height = image.rows, width = image.cols, area = height * width, channel = image.channels();
double factor = image.rows / 500;
if(factor > 1.5) {
factor = 1 / factor;
resize(image,image,Size(),factor,factor,INTER_AREA);
}
return image;
}
void readCSV( const char * csv_file, std::vector<std::vector<std::string> > & input) {
std::ifstream csv_f(csv_file, std::ios::in | std::ios::binary);
std::string str;
while (std::getline(csv_f, str))
{
// Process str
std::stringstream ss( str );
std::vector<string> result;
while( ss.good() )
{
std::string substr;
getline( ss, substr, ',' );
result.push_back( substr );
}
input.push_back( result );
}
}
void group_by_image( std::vector<std::vector<std::string> > input, bool rotated, bool correct,float ratio, std::vector<std::string> & input_path, std::vector<std::vector<string> > &input_labels, std::vector<std::vector<RotatedRect> > &input_rotatedrects ) {
for(int i = 0 ; i < input.size(); i++) {
// compute index
int index = -1;
for(int j = 0; j < input_path.size(); j++)
if( input_path[j] == input[i][0] )
index = j;
if( index == -1 ) {
input_path.push_back( input[i][0] );
std::vector<string> vec_int ;
std::vector<RotatedRect> vec_rotatedrect;
input_rotatedrects.push_back( vec_rotatedrect );
input_labels.push_back( vec_int );
index = input_path.size() -1;
}
// cout << "index : " << index << endl;
// take orientation into account or not
int orient = 0;
if( rotated ) orient = stoi(input[i][6]);
// cout << "Orientation : " << orient << endl;
Size2f s;
if( correct )
s = correct_ratio(stoi(input[i][4]), stoi(input[i][5]), ratio);
else
s = Size2f(stoi(input[i][4]), stoi(input[i][5]));
input_rotatedrects[index].push_back( RotatedRect ( Point2f(stoi(input[i][2]) , stoi(input[i][3]) ) , s, orient ) );
input_labels[index].push_back( input[i][1] );
}
}
void findRectangle( std::string image_path, std::string csvfile, vector<Rect> &outputRects) {
std::ifstream file( csvfile );
std::string str;
while (std::getline(file, str))
{
std::stringstream ss( str );
vector<string> result;
while( ss.good() )
{
string substr;
getline( ss, substr, ',' );
result.push_back( substr );
}
if( result[0] == image_path )
outputRects.push_back( Rect(stoi(result[2]) - stoi(result[4])/2.0 , stoi(result[3]) - stoi(result[5])/2.0, stoi(result[4]), stoi(result[5])) );
}
}
// returns orientation between 0 and 180
int compute_orientation(Mat full_image, string output_path)
{
Mat dst, cdst;
Canny(full_image, dst, 50, 200, 3);
cvtColor(dst, cdst, CV_GRAY2BGR);
vector<Vec4i> lines;
HoughLinesP(dst, lines, 1, CV_PI/180, 100, 50, 2 );
int hist [180] = { };
for( size_t i = 0; i < lines.size(); i++ )
{
Vec4i l = lines[i];
line( cdst, Point(l[0], l[1]), Point(l[2], l[3]), Scalar(0,0,255), 3, CV_AA);
int angle = ( int(round(atan2( l[3] - l[1] , l[2] - l[0] ) * 180/PI)) ) % 180 ;
if(angle < 0)
angle = 180 + angle;
hist[angle] ++;
}
if(output_path!="")
imwrite(output_path,cdst);
int max_rot = 0;
int max_rot_pond = 0;
for( int i = 0; i < 180 ; i ++ )
if( hist[i] > max_rot_pond) {
max_rot_pond = hist[i];
max_rot = i;
}
// int max_rot = distance(hist, max_element(hist, hist + 180)) ;
//cout << "Max element : " << max_rot << endl;
return max_rot;
}
Mat multiplyBy(Mat image, float factor) {
Mat res (image.rows, image.cols, CV_32FC1);
// for(int i = 0 ; i < 6; i ++)
// cout << "hey " << to_string(image.at<uchar>(0,i,0)) << endl;
for(int i = 0 ; i < res.cols; i ++)
for(int j = 0; j < res.rows ; j ++) {
// cout << "hey " << to_string(image.at<uchar>(j,i)* factor) << endl;
res.at<float>(j,i,0) = image.at<uchar>(j,i,0) * factor;
}
return res;
};
void detectRectsAndContours(CascadeClassifier * cc, WorkImage image, vector<cv::Rect> & plateZone, vector<vector<cv::Point> > & all_contours) {
//PLATE DETECTION
cc->detectMultiScale(image.gray_image,plateZone,1.05,5);
cout << "Nb plate zones : " << plateZone.size() << endl ;
if(plateZone.size()==0)
return;
//LETTER DETECTION
vector<Vec4i> hierarchy;
findContours(image.threshold_image.clone(),all_contours,hierarchy,CV_RETR_LIST,CV_CHAIN_APPROX_NONE);
cout << "Nb contours found : " << all_contours.size() << endl ;
// Mat canny_output;
// int thresh = 100;
// int max_thresh = 255;
// RNG rng(12345);
// Canny( image.threshold_image, canny_output, thresh, thresh*2, 3 );
// findContours( canny_output, all_contours, hierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE, cv::Point(0, 0) );
// imwrite(output_dir + "/" +path.substr(path.find_last_of("/\\") + 1) , threshold_image );
}
//process image
void extractRect( Mat image, Rect & plateZone,vector<vector<cv::Point> > & all_contours, vector<vector<cv::Point> > & orderedContours, vector<cv::Rect> & orderedRects) {
vector<vector<cv::Point> > contours_inside;
vector<cv::Rect> boundRects_inside;
for( int i = 0 ; i < all_contours.size() ; i ++) {
// filter on small contours
if( contourArea(all_contours[i]) > image.rows * image.cols / 1000000 ){
cv::Rect bR = boundingRect(all_contours[i]);
//filter countours in plate zone if platezone exists
if( inside( bR, plateZone ) ) {
contours_inside.push_back(all_contours[i]);
boundRects_inside.push_back(bR);
}
}
}
cout << " Nb big enough contours inside zone : " << contours_inside.size() << endl ;
// delete contours inside other
vector<vector<cv::Point> > contours;
vector<cv::Rect> boundRects;
for( int i = 0 ; i < contours_inside.size() ; i ++) {
bool sup = true;
for( int j = 0 ; j < contours_inside.size() ; j ++)
if( boundRects_inside[j].contains(boundRects_inside[i].tl() ) && boundRects_inside[j].contains(boundRects_inside[i].br() ) ) {
sup = false;
break;
}
if(sup) {
contours.push_back(contours_inside[i]);
boundRects.push_back(boundRects_inside[i]);
//getOrientation( contours_inside[i], image.image);
}
}
cout << " Nb big enough and deduplicated contours inside zone : " << contours.size() << endl ;
if(contours.size() == 0) return;
// distance de couleur entre les lettres
vector<vector<cv::Point> > contours2;
vector<cv::Rect> boundRects2;
Mat mask = Mat::zeros(image.rows, image.cols, CV_8UC1);
drawContours(mask, contours, -1, Scalar(255), CV_FILLED);
//imshow("mask", mask);
int channels[] = {0,1,2};
float sranges[] = { 0, 256 };
const float* ranges[] = { sranges, sranges, sranges };
int histSize[] = { 25, 25, 25 };
Mat b_hist;
calcHist(&image,1,channels,mask,b_hist,3,histSize,ranges,true,false);
normalize(b_hist, b_hist );
for( int j = 0 ; j < contours.size() ; j ++ ) {
Mat sub = image(boundRects[j]);
Mat sub_mask = mask(boundRects[j]);
// imshow("mask", sub_mask);
Mat b_hist_j;
calcHist(&sub,1,channels,sub_mask,b_hist_j,3,histSize,ranges,true,false);
normalize(b_hist_j, b_hist_j );
float distance = 0;
for( int x = 0 ; x < histSize[0] ; x ++ )
for( int y = 0 ; y < histSize[1] ; y ++ )
for( int z = 0 ; z < histSize[2] ; z ++ )
{
distance += min( b_hist_j.at<float>(x, y, z), b_hist.at<float>(x, y, z));
}
//cout << distance << endl;
if(distance > 1.7) //1.70
{
contours2.push_back(contours[j]);
boundRects2.push_back(boundRects[j]);
getOrientation( contours[j], image);
}
}
//order RECTS
int min_rect_abs = -1;
for( int j = 0 ; j < boundRects2.size() ; j ++) {
int argmin = 0;
int current_max = 1000000000;
for(int i = 0 ; i < boundRects2.size(); i ++ )
if( (boundRects2[i].tl().x < current_max) && (boundRects2[i].tl().x > min_rect_abs) ) {
argmin = i;
current_max = boundRects2[argmin].tl().x;
}
orderedRects.push_back( boundRects2[argmin] );
orderedContours.push_back( contours2[argmin] );
min_rect_abs = boundRects2[argmin].tl().x;
}
}
Mat resizeContains( Mat image, int cols, int rows, int & left, int & right, int & up, int & down, bool noise ) {
//resize
Mat resized_image;
double normalization_factor_width = ((float)cols) / ((float) image.size().width);
double normalization_factor_height = ((float)rows) / ((float)image.size().height);
double normalization_factor = std::min(normalization_factor_width, normalization_factor_height);
resize(image,resized_image,Size(), normalization_factor, normalization_factor,INTER_LINEAR);
//makeborder
RNG rng;
Mat last_image;
if(noise)
add_salt_pepper_noise(last_image,0.3,0.3,&rng);
left = round((cols - resized_image.size().width) / 2.0);
right = cols - resized_image.size().width - left;
up = round((rows - resized_image.size().height) / 2.0);
down = rows - resized_image.size().height - up;
if(noise)
copyMakeBorder(resized_image, last_image, up, down, left, right , BORDER_TRANSPARENT);
else
copyMakeBorder(resized_image, last_image, up, down, left, right , BORDER_CONSTANT, white);
resized_image.release();
return last_image;
}
// noise functions
void add_salt_pepper_noise(Mat &img, float pa, float pb, RNG *rng )
{
Mat saltpepper_noise = Mat::zeros(img.rows, img.cols,CV_8U);
randu(saltpepper_noise,0,255);
Mat black , white;
threshold(saltpepper_noise, black, 5.0, 255, cv::THRESH_BINARY_INV);
threshold(saltpepper_noise, white, 250.0, 255, cv::THRESH_BINARY);
img.setTo(255,white);
img.setTo(0,black);
}
void add_gaussian_noise(Mat &srcArr,double mean,double sigma, RNG *rng)
{
Mat NoiseArr = srcArr.clone();
rng->fill(NoiseArr, RNG::NORMAL, mean,sigma);
add(srcArr, NoiseArr, srcArr);
}
// display functions
void displayRects (Mat image, vector<cv::Rect> plateZone, Scalar color1) {
for(int i = 0 ; i < plateZone.size(); i ++) {
rectangle( image, plateZone[i].tl(), plateZone[i].br(), color1, 2, 8, 0 );
}
}
void displayRectangle (Mat image, cv::Rect r, Scalar color1) {
rectangle( image, r.tl(), r.br(), color1, 2, 8, 0 );
}
void displayRotatedRectangle (Mat image, RotatedRect rRect, Scalar color1 ) {
cv::Point2f vertices[4];
rRect.points(vertices);
for (int i = 0; i < 4; i++)
line(image, vertices[i], vertices[(i+1)%4], color1, 2, 8, 0 );
}
void displayCross (Mat image, Point2f p, Scalar color1) {
line(image, p - Point2f(3.0,0.0), p + Point2f(3.0,0.0), color1, 1, 8, 0 );
line(image, p - Point2f(0.0,3.0), p + Point2f(0.0,3.0), color1, 1, 8, 0 );
}
void displayText( Mat image, string text, cv::Point textOrg , double fontScale ) {
int fontFace = 0;
int thickness = max( (int)(3 * fontScale), 1);
int baseline=0;
Size textSize = getTextSize(text, fontFace,fontScale, thickness, &baseline);
baseline += thickness;
rectangle(image, textOrg + Point(0, baseline + textSize.height / 2),
textOrg + Point(textSize.width, - textSize.height / 2),
Scalar(0,0,255),CV_FILLED);
putText(image, text, textOrg + Point(0, textSize.height/2), fontFace, fontScale,Scalar::all(255), thickness, (int)(8.0 * fontScale));
}
//OCR
bool isLegal(char c)
{
int len = sizeof(legal)/sizeof(char);
for (int i = 0; i < len; i++)
if (c == legal[i])
return true;
return false;
}
int char2Class(char c)
{
int len = sizeof(legal)/sizeof(char);
for (int i = 0; i < len; i++)
if (c == legal[i])
return i;
return -1;
}
void most_probable_month(std::vector<float> output1, std::vector<float> output2, string &month, float & month_proba ) {
month_proba = 0.0;
char month_c [3] ;
month_c[2] = '\0';
// ensemble des chaines possibles
for(int i = 0; i < 2 ; i ++) { // 0 ou 1
int max_d = 10;
int min_d = 1 ;
if(i == 1) {max_d = 3; min_d = 0;};
for(int j = min_d; j < max_d ; j ++ ) {
float score = output1[i]+ output2[j];
if( score > month_proba) {
month_c[0] = driving_letters[i];
month_c[1] = driving_letters[j];
month = month_c;
month_proba = score;
}
}
}
}
void most_probable_year(std::vector<float> output1, std::vector<float> output2, std::vector<float> output3, std::vector<float> output4, string &year, float & year_proba) {
year_proba = 0.0;
char year_c [5];
year_c[4] = '\0';
for(int i = 0; i < 12; i ++) {// 190x, 191x, 192x, 193x, 194x, 195x, 196x ... 199x, 200x, 201x
float prob ;
if(i == 10) {
prob = output1[2] + output2[0] + output3[0];
year_c [0] = '2';
year_c [1] = '0';
year_c [2] = '0';
} else if(i == 11) {
prob = output1[2] + output2[0] + output3[1];
year_c [0] = '2';
year_c [1] = '0';
year_c [2] = '1';
} else {
prob = output1[1] + output2[9] + output3[i];
year_c [0] = '1';
year_c [1] = '9';
year_c [2] = driving_letters[i];
}
int max_d = 10;
if(i == 11) max_d = 7;
for(int j = 0; j < max_d ; j ++) {
float score = prob + output4[j];
if( score > year_proba) {
year_c [3] = driving_letters[j];
year = year_c ;
year_proba = score;
}
}
}
};
void compute_lines(std::vector<Point> letter_points, int margin, vector< vector<Point> > &ordered_lines, vector< float > &ordered_lines_y) {
// int margin = 5;
float height = 0.0;
for( int i = 0; i < letter_points.size(); i ++ )
height = std::max( (float)( letter_points[i].y), height );
std::vector<float> counts;
std::vector<std::vector<Point> > ordered_points;
for( int y = 0 ; y < height - margin ; y ++) {
int count = 0;
std::vector<Point> line_points;
for(int i = 0; i < letter_points.size(); i++ )
if( (letter_points[i].y >= y) && (letter_points[i].y <= y + margin) ) {
count ++ ;
line_points.push_back(letter_points[i]);
}
counts.push_back(count);
ordered_points.push_back( line_points );
}
// cout << "ok " << endl;
std::vector<int> maxY = Argmax(counts, 50000);
// cout << "max" <<endl;
std::vector<std::vector<Point> > line_points;
std::vector<int> maxYvalid ;
for(int i = 0; i < maxY.size(); i ++) {
int m = maxY[i]; // index de la ligne
std::vector<Point> o = ordered_points[m];
if(!o.size() || o.size()< 15) {
cout << " Less than 15 points in this line; skip." << endl;
break;
}
bool next = false;
for(int j = 0; j < maxYvalid.size() ; j ++ )
if( (m + margin >= maxYvalid[j] ) && (m - margin <= maxYvalid[j]) ) { next = true; break;}
if(next)
continue;
maxYvalid.push_back( m );
line_points.push_back( o );
}
float last_y = - 10.0;
for(int i = 0; i < line_points.size(); i ++) {
int min_y = 100000000;
int arg_min = 0;
for(int j = 0; j < line_points.size(); j ++) {
if( line_points[j][0].y > last_y and line_points[j][0].y < min_y ) {
min_y = line_points[j][0].y;
arg_min = j;
}
}
last_y = min_y ;
ordered_lines.push_back( line_points[arg_min] );
ordered_lines_y.push_back( min_y );
}
}
void compute_lines_knn(std::vector<Point> letter_points, int nb_lines, vector< vector<Point> > &ordered_lines, vector< float > &ordered_lines_y){
// calcul des lignes
if( letter_points.size() > nb_lines ) {
Mat mtx(letter_points.size(), 1, CV_32F), bestLabels, centers;
for(int i = 0; i < letter_points.size(); i ++)
mtx.at<float>(i,0) = letter_points[i].y;
cv::kmeans(mtx,nb_lines,bestLabels, cv::TermCriteria(CV_TERMCRIT_ITER, 10, 1.0),3, cv::KMEANS_PP_CENTERS, centers);
vector<float> line_y ;
for(int i = 0 ; i < centers.rows ; i ++){
//cout << "Center :" << centers.at<float>(i,0) << endl;
line_y.push_back( centers.at<float>(i,0) );
}
// création des lignes de points
vector<vector<Point> > lines;
for (int i = 0; i < nb_lines ; i++) {
vector<Point> line;
lines.push_back( line );
}
for (int i = 0; i < bestLabels.rows; i++)
lines[bestLabels.at<int>(i,0)].push_back( letter_points[i] );
// order lines
vector<int> order;
float last_min = -10.0;
for( int i = 0 ; i < centers.rows ; i ++) {
// le minimum courant et son index
float current_min = 100000000000.0;
int current_argmin;
// on cherche un minimum supérieur au dernier min
for( int j = 0; j < centers.rows ; j ++) {
float y_j = centers.at<float>(j,0);
if( y_j < current_min && y_j > last_min ) {
current_argmin = j;
current_min = y_j;
}
}
// on met à jour le dernier min
last_min = current_min;
order.push_back(current_argmin);
}
float last_y = - 10.0;
for(int i = 0; i < order.size(); i ++) {
int line_index = order[i];
if( centers.at<float>(line_index,0) > last_y +5.0 ) {
//création d'une nouvelle ligne
ordered_lines.push_back(lines[line_index]);
last_y = centers.at<float>(line_index,0);
ordered_lines_y.push_back( centers.at<float>(line_index,0) );
} else {
// on fait la moyenne pondérée des lignes comme y celle des deux lignes qui a le plus de points
//ajout à la ligne précédente
if( lines[line_index].size() > ordered_lines[ordered_lines.size()-1].size() )
ordered_lines_y[ordered_lines.size()-1] = centers.at<float>(line_index,0);
for(int j = 0; j < lines[line_index].size(); j ++)
ordered_lines[ordered_lines.size()-1].push_back(lines[line_index][j]);
}
}
mtx.release();
bestLabels.release();
centers.release();
}
}
// rect functions
int getCenterX(cv::Rect r) {
return r.tl().x + floor(((float)( r.br().x - r.tl().x ) ) / 2.0);
}
int getCenterY(cv::Rect r) {
return r.tl().y + floor(((float)( r.br().y - r.tl().y ) ) / 2.0);
}
bool inside(cv::Rect bR, cv::Rect plateZone) {
int bx = bR.tl().x, by = bR.tl().y, bh = bR.size().height, bw = bR.size().width;
//bR.area() > ((double) plateZone.area()) / 35
return bR.size().height < plateZone.size().height && bR.size().height * 3 > plateZone.size().height && bR.size().width * 5 < plateZone.size().width && bR.size().width *25 > plateZone.size().width && plateZone.contains( cv::Point2f( bx + bw/2, by + bh/2) ) ;
}
// compute if a rectangle is inside an image
bool is_in_image(Rect r, Mat img) {
return ( ( r.tl().x >= 0 ) && ( r.tl().y >= 0 ) && ( r.br().x < img.cols ) && ( r.br().y < img.rows ) );
}
bool is_in_image(RotatedRect r, Mat img) {
return is_in_image( r.boundingRect(), img);
}
Point2f change_ref ( Point2f p, float center_x, float center_y, float orientation) {
Point2f p_repositioned (p.x - center_x, p.y - center_y );
double orientation_radian = orientation * 3.14159265 / 180.0 ;
double hypothenuse = sqrt( p_repositioned.x * p_repositioned.x + p_repositioned.y * p_repositioned.y );
double angle = atan2( p_repositioned.y , p_repositioned.x ) ;
return Point2f( hypothenuse * cos( angle - orientation_radian ), hypothenuse * sin( angle - orientation_radian ) );
}
// compute if point is in rotated rectangle
bool is_in( Point p, RotatedRect rr ) {
Point2f p_new = change_ref ( p, rr.center.x, rr.center.y, rr.angle);
//cout << p_repositioned << endl;
//cout << " compare " << (new_y < rr.size.height / 2.0) << " - " << new_y << " - " << rr.size.height / 2.0 << endl;
//cout << "is in " << ( (new_x > - rr.size.width / 2.0) && (new_x < rr.size.width / 2.0) && (new_y > - rr.size.height / 2.0) && (new_y < rr.size.height / 2.0) ) ;
return ( (p_new.x >= - rr.size.width / 2.0) && (p_new.x <= rr.size.width / 2.0) && (p_new.y >= - rr.size.height / 2.0) && (p_new.y <= rr.size.height / 2.0) );
}
// compute brute-force Intersection Over Union
float intersectionOverUnion( RotatedRect r1, RotatedRect r2) {
Rect br1 = r1.boundingRect();
Rect br2 = r2.boundingRect();
Rect br ( Point( min(br1.tl().x , br2.tl().x ) , min(br1.tl().y , br2.tl().y ) ) , Point(max(br1.br().x , br2.br().x ) , max(br1.br().y , br2.br().y ) ) );
//cout << "Area : " << br.area() << endl;
int intersection = 0;
for(int i = 0 ; i <= br.size().width; i ++ )
for(int j = 0; j <= br.size().height ; j ++) {
Point p ( br.tl().x + i , br.tl().y + j );
if( is_in(p,r1) && is_in(p,r2) )
intersection ++;
}
//cout << "BR : " << br2.tl() << " -> " << br2.br() << endl;
//cout << "IOU " << ((float)intersection) << " - " << ((float) br.area()) << endl;
return ((float)intersection) / ( (float) br.area());
}
// others
Mat createOne(vector<Mat> & images, int cols, int rows, int gap_size, int dim)
{
cv::Mat result ( rows * dim + (rows+2) * gap_size, cols * dim + (cols+2) * gap_size, images[0].type(),white);
size_t i = 0;
int current_height = gap_size;
int current_width = gap_size;
for ( int y = 0; y < rows; y++ ) {
for ( int x = 0; x < cols; x++ ) {
if ( i >= images.size() )
return result;
// get the ROI in our result-image
cv::Mat to(result,
cv::Range(current_height, current_height + dim),
cv::Range(current_width, current_width + dim));
// copy the current image to the ROI
images[i++].copyTo(to);
current_width += dim + gap_size;
}
// next line - reset width and update height
current_width = gap_size;
current_height += dim + gap_size;
}
return result;
}
void standard_deviation(vector<int> data, double & mean, double & stdeviation,double & median)
{
std::sort (data.begin(), data.end());
mean=0.0;
stdeviation=0.0;
int i;
for(i=0; i<data.size();++i)
mean+=data[i];
mean = mean / data.size();
for(i=0; i< data.size();++i)
stdeviation += (data[i]-mean)*(data[i]-mean);
stdeviation = sqrt(stdeviation / data.size());
median = round(data.size() / 2);
return;
}
double getOrientation(vector<cv::Point> &pts, Mat &img)
{
//Construct a buffer used by the pca analysis
Mat data_pts = Mat(pts.size(), 2, CV_64FC1);
for (int i = 0; i < data_pts.rows; ++i)
{
data_pts.at<double>(i, 0) = pts[i].x;
data_pts.at<double>(i, 1) = pts[i].y;
}
//Perform PCA analysis
PCA pca_analysis(data_pts, Mat(), CV_PCA_DATA_AS_ROW);
//Store the position of the object
cv::Point pos = cv::Point(pca_analysis.mean.at<double>(0, 0),
pca_analysis.mean.at<double>(0, 1));
//Store the eigenvalues and eigenvectors
vector<cv::Point2d> eigen_vecs(2);
vector<double> eigen_val(2);
for (int i = 0; i < 2; ++i)
{
eigen_vecs[i] = cv::Point2d(pca_analysis.eigenvectors.at<double>(i, 0),
pca_analysis.eigenvectors.at<double>(i, 1));
eigen_val[i] = pca_analysis.eigenvalues.at<double>(0, i);
}
// Draw the principal components
circle(img, pos, 3, CV_RGB(255, 0, 255), 2);
if( eigen_vecs[0].x * eigen_val[0] * eigen_vecs[0].x * eigen_val[0] + eigen_vecs[0].y * eigen_val[0] * eigen_vecs[0].y * eigen_val[0] > eigen_vecs[1].x * eigen_val[1] * eigen_vecs[1].x * eigen_val[1] + eigen_vecs[1].y * eigen_val[1] * eigen_vecs[1].y * eigen_val[1] ) {
line(img, pos, pos + 0.2 * cv::Point(eigen_vecs[0].x * eigen_val[0], eigen_vecs[0].y * eigen_val[0]) , CV_RGB(255, 255, 0));
return atan2(eigen_vecs[0].y, eigen_vecs[0].x) * 180 / 3.1417 - 90;
} else {
line(img, pos, pos + 0.2 * cv::Point(eigen_vecs[1].x * eigen_val[1], eigen_vecs[1].y * eigen_val[1]) , CV_RGB(0, 255, 255));
return atan2(eigen_vecs[1].y, eigen_vecs[1].x) * 180 / 3.1417 - 90;
}
}
void myGetQuadrangleSubPix(const Mat& src, Mat& dst,Mat& m )
{
cv::Size win_size = dst.size();
double matrix[6];
cv::Mat M(2, 3, CV_64F, matrix);
m.convertTo(M, CV_64F);
double dx = (win_size.width - 1)*0.5;
double dy = (win_size.height - 1)*0.5;
matrix[2] -= matrix[0]*dx + matrix[1]*dy;
matrix[5] -= matrix[3]*dx + matrix[4]*dy;
// RNG rng;
// add_salt_pepper_noise(dst,0.3,0.3,&rng);
cv::warpAffine(src, dst, M, dst.size(),
cv::INTER_LINEAR + cv::WARP_INVERSE_MAP,
cv::BORDER_CONSTANT);
}
void getRotRectImg(cv::RotatedRect rr,Mat &img,Mat& dst)
{
Mat m(2,3,CV_64FC1);
float ang=rr.angle*CV_PI/180.0;
m.at<double>(0,0)=cos(ang);
m.at<double>(1,0)=sin(ang);
m.at<double>(0,1)=-sin(ang);
m.at<double>(1,1)=cos(ang);
m.at<double>(0,2)=rr.center.x;
m.at<double>(1,2)=rr.center.y;
myGetQuadrangleSubPix(img,dst,m);
}
Mat extractRotatedRect( Mat src, RotatedRect rect ) {
Mat dst(rect.size,CV_32FC3);
getRotRectImg(rect,src,dst);
return dst;
// // matrices we'll use
// Mat M, rotated, cropped;
// // get angle and size from the bounding box
// float angle = rect.angle;
// Size rect_size = rect.size;
// // thanks to http://felix.abecassis.me/2011/10/opencv-rotation-deskewing/
// if (rect.angle < -45.) {
// angle += 90.0;
// std::swap(rect_size.width, rect_size.height);
// }
// // get the rotation matrix
// M = getRotationMatrix2D(rect.center, angle, 1.0);
// // perform the affine transformation
// warpAffine(src, rotated, M, src.size(), INTER_CUBIC,cv::BORDER_CONSTANT);
// // crop the resulting image
// getRectSubPix(rotated, rect_size, rect.center, cropped);
// return cropped;
}
void correct_ratio ( Rect & r, double ratio ) {
float width = (float) r.size().width;
float height = (float) r.size().height;
float current_ratio = height / width;
if ( current_ratio > ratio ) {
// augment width
int new_width_delta = floor( ( height / ratio - width ) / 2.0 );
r.x -= new_width_delta;
r.width += new_width_delta * 2;
} else {
// augment height
int new_height_delta = floor( (width * ratio -height) / 2.0 );
r.y -= new_height_delta ;
r.height += new_height_delta * 2 ;
}
};
Size2f correct_ratio ( float width, float height, double ratio ) {
float current_ratio = height / width;
if ( current_ratio > ratio ) {
// augment width
int new_width = floor( height / ratio );
return Size( new_width, height );
// r.size.width = new_width;
} else {
// augment height
int new_height = floor( width * ratio );
return Size( width, new_height );
// r.size.height = new_height;
}
};
bool PairCompare(const std::pair<float, int>& lhs,
const std::pair<float, int>& rhs) {
return lhs.first > rhs.first;
}
/* Return the indices of the top N values of vector v. */
std::vector<int> Argmax(const std::vector<float>& v, int N) {
std::vector<std::pair<float, int> > pairs;
for (size_t i = 0; i < v.size(); ++i)
pairs.push_back(std::make_pair(v[i], i));
unsigned long max_N = std::min((unsigned long) N, pairs.size());
std::partial_sort(pairs.begin(), pairs.begin() + max_N, pairs.end(), PairCompare);
std::vector<int> result;
for (int i = 0; i < max_N; ++i)
result.push_back(pairs[i].second);
return result;
}