In this tutorial you will learn how to:
In the previous tutorial we learned how to use the Sobel Operator. It was based on the fact that in the edge area, the pixel intensity shows a “jump” or a high variation of intensity. Getting the first derivative of the intensity, we observed that an edge is characterized by a maximum, as it can be seen in the figure:
And...what happens if we take the second derivative?
You can observe that the second derivative is zero! So, we can also use this criterion to attempt to detect edges in an image. However, note that zeros will not only appear in edges (they can actually appear in other meaningless locations); this can be solved by applying filtering where needed.
#include "opencv2/imgproc.hpp"
#include "opencv2/highgui.hpp"
#include <stdlib.h>
#include <stdio.h>
using namespace cv;
/** @function main */
int main( int argc, char** argv )
{
Mat src, src_gray, dst;
int kernel_size = 3;
int scale = 1;
int delta = 0;
int ddepth = CV_16S;
char* window_name = "Laplace Demo";
int c;
/// Load an image
src = imread( argv[1] );
if( !src.data )
{ return -1; }
/// Remove noise by blurring with a Gaussian filter
GaussianBlur( src, src, Size(3,3), 0, 0, BORDER_DEFAULT );
/// Convert the image to grayscale
cvtColor( src, src_gray, COLOR_RGB2GRAY );
/// Create window
namedWindow( window_name, WINDOW_AUTOSIZE );
/// Apply Laplace function
Mat abs_dst;
Laplacian( src_gray, dst, ddepth, kernel_size, scale, delta, BORDER_DEFAULT );
convertScaleAbs( dst, abs_dst );
/// Show what you got
imshow( window_name, abs_dst );
waitKey(0);
return 0;
}
Create some needed variables:
Mat src, src_gray, dst;
int kernel_size = 3;
int scale = 1;
int delta = 0;
int ddepth = CV_16S;
char* window_name = "Laplace Demo";
Loads the source image:
src = imread( argv[1] );
if( !src.data )
{ return -1; }
Apply a Gaussian blur to reduce noise:
GaussianBlur( src, src, Size(3,3), 0, 0, BORDER_DEFAULT );
Convert the image to grayscale using cvtColor
cvtColor( src, src_gray, COLOR_RGB2GRAY );
Apply the Laplacian operator to the grayscale image:
Laplacian( src_gray, dst, ddepth, kernel_size, scale, delta, BORDER_DEFAULT );
where the arguments are:
Convert the output from the Laplacian operator to a CV_8U image:
convertScaleAbs( dst, abs_dst );
Display the result in a window:
imshow( window_name, abs_dst );
After compiling the code above, we can run it giving as argument the path to an image. For example, using as an input:
We obtain the following result. Notice how the trees and the silhouette of the cow are approximately well defined (except in areas in which the intensity are very similar, i.e. around the cow’s head). Also, note that the roof of the house behind the trees (right side) is notoriously marked. This is due to the fact that the contrast is higher in that region.