Opencv C++ Code For Detecting Lines Inclined at minus 45 degree

This OpenCV C++ Tutorial is about Slant Line Detection i.e. detecting lines inclined at minus 45 degrees
To detect slant lines inclined at -45 degress, we use the mask:

2	-1	-1
-1	2	-1
-1	-1	2

Thus sliding this mask over an Image we can detect Lines inclined at -45 degrees.
Here is the Opencv C++ Example of Slant Line (lines/edges at 45 degrees)Detection below:

Here is the Opencv C++ Example of Slant Line (lines/edges at 45 degrees)Detection below:

//Opencv C++ Code for detecting line inclined at minus 45 degree
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include "iostream"
 
using namespace cv;
using namespace std;
 
int main( )
{
    Mat src1,src2;
 int a;
 Scalar intensity1=0;
    src1 = imread("C:\\Users\\arjun\\Desktop\\opencv-test.jpg", CV_LOAD_IMAGE_GRAYSCALE);
 src2 = src1.clone();

 //If image not found 
 if (!src1.data)                                                                          
     {  
      cout << "No image data \n";  
      return -1;  
     } 

 //Take the Size of Mask
  cout<<"Enter the mask Size =";
  cin>>a;

 //for loop for counting the number of rows and columns and displaying the pixel value at each point
   for (int i = 0; i < src1.rows-a; i++) { 
    for (int j = 0; j < src1.cols-a; j++) { 
   Scalar intensity2=0;
   for (int p = 0; p<a; p++) { 
    for (int q = 0; q<a; q++) { 
    intensity1 = src1.at<uchar>(i+p,j+q); 
       if (p==q)
   {
    intensity2.val[0] +=(a-1)*intensity1.val[0];
   }
       else
   {
      intensity2.val[0] +=(-1)*intensity1.val[0];
   }
   
     }
  
  }
      src2.at<uchar>(i+(a-1)/2,j+(a-1)/2)=intensity2.val[0]/(a*a);
  } 
    
  }

  //Display the original image
  namedWindow("Display Image");                
  imshow("Display Image", src1);  
  
  //Display the Low Pass Filtered Image image
  namedWindow("Low Pass Filtered Image");     
  imshow("Low Pass Filtered Image", src2);  
  imwrite("C:\\Users\\arjun\\Desktop\\opencv-slant-line.jpg",src2);
  waitKey(0);
  return 0;
}

Input:

Output:

Opencv C++ Code For Detecting Lines Inclined at plus 45 degree

This OpenCV C++ Tutorial is about Slant Line Detection i.e. detecting lines inclined at plus 45 degrees
To detect slant lines inclined at +45 degress, we use the mask:

-1	-1	2
-1	2	-1
2	-1	-1

Thus sliding this mask over an Image we can detect Lines inclined at +45 degrees.
Here is the Opencv C++ Example of Slant Line (lines/edges at 45 degrees)Detection below:

//Opencv C++ Code for detecting line inclined at 45 degree
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include "iostream"
 
using namespace cv;
using namespace std;
 
int main( )
{
    Mat src1,src2;
 int a;
 Scalar intensity1=0;
    src1 = imread("C:\\Users\\arjun\\Desktop\\opencv-test.jpg", CV_LOAD_IMAGE_GRAYSCALE);
 src2 = src1.clone();

 //If image not found 
 if (!src1.data)                                                                          
     {  
      cout << "No image data \n";  
      return -1;  
     } 

  cout<<"Enter the mask Size =";
  cin>>a;

 //for loop for counting the number of rows and columns and displaying the pixel value at each point
   for (int i = 0; i < src1.rows-a; i++) { 
    for (int j = 0; j < src1.cols-a; j++) { 
   Scalar intensity2=0;
   for (int p = 0; p<a; p++) { 
     for (int q = 0; q<a; q++){ 
    intensity1 = src1.at<uchar>(i+p,j+q); 
    if( (p+q==(a-1)))
      {
    intensity2.val[0] +=(a-1)*intensity1.val[0];
      }
       else
      {
    intensity2.val[0] +=(-1)*intensity1.val[0];
      }
   
     }
  
  }
        src2.at<uchar>(i+(a-1)/2,j+(a-1)/2)=intensity2.val[0]/(a*a);
   } 
    
  }
 //Display the original image
 namedWindow("Display Image");                
 imshow("Display Image", src1); 

 //Display the Low Pass Filtered Image image
    namedWindow("Line Detection Image");     
 imshow("Line Detection Image", src2);  
    imwrite("C:\\Users\\arjun\\Desktop\\opencv-slant-line.jpg",src2);
 waitKey(0);
 return 0;
 }

Input:

Output:

OpenCV C++ Code For Horizontal Line Detection

This OpenCV C++ Tutorial is about Horizontal Line Detection i.e. How to Detect Horizontal Edges or Lines in an Image
What is a Line? A line is a straight one-dimensional figure having no thickness and extending infinitely in both directions.
In case of Image Processing it is a continuous set of pixels which have values distinctly different from that of the surrounding pixels.
Recall that for point detection ,we use a mask as show below:

-1	-1	-1
-1	8	-1
-1	-1	-1

Thus if the mask or Kernel is moved over uniform intensity the response due to the mask is zero.(Sum of all elements of a mask is zero.
Since a point, in image-processing refer to the pixel of distinct value compared to that of the surrounding pixels.
Hence for Horizontal Line detection, we would have to detect the set of horizontal pixels of distinct values from the surrounding pixels.
And if there are no such set of pixels,the output of it should be zero.
Hence we would be choosing the mask as shown below:

-1	-1	-1
2	2	2
-1	-1	-1

And we would slide this mask over the whole image to detect horizontal lines.
Here is the Opencv C++ Example of Horizontal Edge/Line Detection below:

//Opencv C++ Example for Detecting Horizontal Lines
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include "iostream"
 
using namespace cv;
using namespace std;
 
int main( )
{
    Mat src1,src2;
 int a;
 Scalar intensity1=0;
    src1 = imread("C:\\Users\\arjun\\Desktop\\opencv-test.png", CV_LOAD_IMAGE_GRAYSCALE);
 //If image not found 
 if (!src1.data)                                                                          
     { cout << "No image data \n";  return -1; } 

 src2 = src1.clone();
 cout<<"Enter the mask Size =";
 cin>>a;

 //for loop for counting the number of rows and columns and displaying the pixel value at each point
   for (int i = 0; i < src1.rows-a; i++)  { 
    for (int j = 0; j < src1.cols-a; j++) { 
   Scalar intensity2=0;
   for (int p = 0; p<a; p++) { 
     for (int q = 0; q<a; q++)  { 
       intensity1 = src1.at<uchar>(i+p,j+q); 
       if( (p==(a-1)/2))
       {
      intensity2.val[0] +=(a-1)*intensity1.val[0];
       }
       else
       {
            intensity2.val[0] +=(-1)*intensity1.val[0];
       }
     }
  }
    src2.at<uchar>(i+(a-1)/2,j+(a-1)/2)=intensity2.val[0]/(a*a);
   } 
   }
  //Display the original image
  namedWindow("Display Image");                
  imshow("Display Image", src1); 

  //Display the Low Pass Filtered Image image
  namedWindow("Low Pass Filtered Image");     
  imshow("Low Pass Filtered Image", src2);  
  waitKey(0);
  return 0;
 }

Input:

Output:

Note: This mask is only applicable for detecting light lines in dark background.
If you want to detect dark lines in light background we need to negate the values of mask(i.e. multiply all the elements of mask with -1.)
If we want to detect the lines irrespective of any background, we need to take the absolute value of the result obtained when mask if moved over the image.
More accurate result can be obtained if we smoothen the image before applying line detection.