Distance transformation functions on most of the libraries (OpenCV, VXL etc.) only output the distance transformation. However, must of the time we also need the label/position of the shorted distance point along with the shortest distance for a given point, which is often referred as distance transformation feature in computer vision and machine learning. Following is the simplest implementation of distance transformation feature in C++ using OpenCV library.
//============================================================================
// Name : OpenCVTest.cpp
// Author : Rudra Poudel
// Description : Example of distance transformation features. To find the shorted distance label we travel shorted distance path i.e. kind of gradient descent.
//============================================================================
#include "opencv2/core/core.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/calib3d/calib3d.hpp"
#include "opencv2/highgui/highgui.hpp"
#include <stdio.h>
#include <iostream>
#define VIDEO_FRAME_HEIGHT 480
#define VIDEO_FRAME_WIDTH 640
void VerifyDistTransformLabels(cv::Mat& depth_map, cv::Mat& labels, int row_start, int row_end, int col_start, int col_end) {
for( int y = row_start; y < row_end; y++ ) {
//src_data_row = dist_trans.ptr<float>(y);
for( int x = col_start; x < col_end; x++ ) {
cv::Vec2i alabel = labels.at<cv::Vec2i>(y,x);
assert(alabel[0] != -1);
assert(alabel[1] != -1);
}
}
}
cv::Vec2i GetLowestDistNeighbour(cv::Mat& dist_trans, int x, int y){
int x_start = x>0 ? x-1 : 0;
int y_start = y>0 ? y-1 : 0;
int x_end = x<(dist_trans.cols -1) ? x+1 : x;
int y_end = y<(dist_trans.rows -1) ? y+1 : y;
cv::Vec2i p; //p[0] = x; p[1] = y; assignment not necessary
float min_dist = dist_trans.at<float>(y, x);
float dist;
for( int row = y_start; row <= y_end; row++ ) {
for( int col = x_start; col <= x_end; col++ ) {
dist = dist_trans.at<float>(row, col);
if(dist<min_dist) {
min_dist = dist;
p[0] = col;
p[1] = row;
}
}
}
return p;
}
cv::Vec2i GetDistTransformLabel(cv::Mat& dist_trans, cv::Mat& labels, int x, int y) {
if(dist_trans.at<float>(y,x) == 0) {
cv::Vec2i alabel(x,y);
return alabel;
} else {
cv::Vec2i p = GetLowestDistNeighbour(dist_trans, x, y);
cv::Vec2i& alabel = labels.at<cv::Vec2i>(p[1], p[0]); // (y, x)
if(alabel[0] == -1 )
alabel = GetDistTransformLabel(dist_trans, labels, p[0], p[1]);
return alabel;
}
}
void GetDistTransformLabels(cv::Mat& dist_trans, cv::Mat& labels, int row_start, int row_end, int col_start, int col_end) {
assert(dist_trans.type() == CV_32FC1);
assert(labels.type() == CV_32SC2);
labels.setTo(-1); //float* src_data_row;
for( int y = row_start; y < row_end; y++ ) {
//src_data_row = dist_trans.ptr<float>(y);
for( int x = col_start; x < col_end; x++ ) {
cv::Vec2i& alabel = labels.at<cv::Vec2i>(y,x);
if(alabel[0] == -1 ) {
alabel = GetDistTransformLabel(dist_trans, labels, x, y);
}
}
}
}
int main( int argc, char** argv )
{
cv::Mat depth_map(VIDEO_FRAME_HEIGHT, VIDEO_FRAME_WIDTH, CV_8UC1, 1);
cv::Mat dist_trans(VIDEO_FRAME_HEIGHT, VIDEO_FRAME_WIDTH, CV_32FC1);
cv::Mat labels(VIDEO_FRAME_HEIGHT, VIDEO_FRAME_WIDTH, CV_32SC2);
cv::circle(depth_map, cv::Point2i(320, 240), 40, cvScalar(255,255,255), 10);
cv::MatExpr mask_fg = depth_map > 1;
cv::MatExpr mask_bg = depth_map == 1;
depth_map.setTo(0, mask_fg);
depth_map.setTo(255, mask_bg);
cv::distanceTransform(depth_map, dist_trans, CV_DIST_L2, CV_DIST_MASK_PRECISE);
GetDistTransformLabels(dist_trans, labels, 0, dist_trans.rows, 0, dist_trans.cols);
VerifyDistTransformLabels(depth_map, labels, 0, labels.rows, 0, labels.cols);
double min_value, max_value;
cv::minMaxLoc(dist_trans, &min_value, &max_value);
dist_trans *=(1/max_value);
std::cout<<"\nmin_value: "<<min_value<<"\tmax_value: "<<max_value;
std::cout<<"\n";
//cv::imshow("Dist transform for", depth_map);
cv::imshow("Distance Transformation", dist_trans);
cv::waitKey(40000);
return 0;
}
