Splitting the Image in to three channels with Open CV and C Language

In image processing some tines we need to separate the channels of image. This is needful to achieve the required contrast of the image. Because some times if we remove the one or more channels from the image we can get better understanding of the image under consideration. In some cases we have to process only few channels of the image rather than the entire image. Open cv provides the functions to achieve the same. We can do the same thing with out using the functions of the open cv.

Fallowing code snippet is useful for splitting the image channels:

#include "stdafx.h" 
#include "cv.h"
#include "highgui.h"
#include <conio.h>

void main()
{
    IplImage *Simg;
    IplImage *Rimg;
    IplImage *Gimg;
    IplImage *Bimg;

    int nrows,ncols;

    Simg=cvLoadImage("E:/test.jpg");
    Rimg=cvCreateImage(cvSize(Simg->width,Simg->height),Simg->depth,3);
    Gimg=cvCreateImage(cvSize(Simg->width,Simg->height),Simg->depth,3);
    Bimg=cvCreateImage(cvSize(Simg->width,Simg->height),Simg->depth,3);

    cvNamedWindow("Red",0);
    cvNamedWindow("Green",0);
    cvNamedWindow("Blue",0);
    cvResizeWindow("Red",320,320);
    cvResizeWindow("Green",320,320);
    cvResizeWindow("Blue",320,320);

    nrows=Simg->height;
    ncols=Simg->width;
    
     for(int i=0;i<nrows*ncols*3;i++)
    {
        Rimg->imageData[i]=0;
        Gimg->imageData[i]=0;
        Bimg->imageData[i]=0;
    }
    for(int i=0;i<nrows*ncols*3;i+=3)
    {   
            Rimg->imageData[i+2]=Simg->imageData[i+2];
            Gimg->imageData[i+1]=Simg->imageData[i+1];
            Bimg->imageData[i]=Simg->imageData[i];
     }
   
    cvShowImage("Red",Rimg);
    cvShowImage("Green",Gimg);
    cvShowImage("Blue",Bimg);
    cvWaitKey(0);
    _getch();
}

for example consider the image with three channels. Load that image into the memory with the help of Open CV. Next create the three images with the same size,depth and channels. Fill the entire image with the zeros means with black pixels. Now extract the individual channels from the original image and store them in the newly created images. Open cv changes the order of the pixels to BGR instead of RGB.
If we apply the ->height and ->width on the image in Open CV we will get the number of rows and columns. But actually if the image is color we have columns more than what we get. that is if we have width as 20 the actual columns on the disk is 60 because each channel will store in separate byte. In memory they will be stored in interleaved manner. So to get the red pixels we need to get the alternate pixels with the gap of two.
By running the above code on the below image:

  

we will get the fallowing images:

 

LED flashing simulation in multisim

In real world Applications LED's play important role. They are mainly used as indicators. In embedded industry they are used to indicate the events. Embedded world mainly depends on the two things. One is embeddable processor and the software top run it. Every processor or micro controller needs little bit of power to operate and some circuit connections to make it useful.
In this post I will explain how we can use 8051 micro controller to control the behavior of LED. The simulation is done in multisim.

We need to apply proper voltages to the controller so that it can operate. It needs one power source and sink. Any normal battery can be used as the power supply and the negative terminal of the same battery can be used as ground. Just connect the LED to the any port pin of the controller. But avoid to use port1 because for that port we need to supply external pull up resistors. Remaining all ports has inbuilt pull up resistors.
Now coming to the actual intention of the post we need to toggle the LED. For some time it will be on after that it is off for the same amount of time. It can be achieved by altering the output of the port pin. The software written in assembly or embedded C. The code snippet as fallows:
#include <reg52.h>
sbit pin = P1^5;
bit state;
void init(void);
void changeState(void);
void Wait(const unsigned int);
void main()
{
init();
while(1)
{
changeState();
Wait();
}
}
void init()
{
state = 0;
}
void changeState()
{
if (state == 1)
{
state = 0;
pin = 0;
}
else
{
state = 1;
pin = 1;
}
}
void Wait()
{
unsigned y;
{
for (y = 0; y <= 100; y++);
}
}
Before loading this code in the micro controller we need to convert this code into .hex file to dump in controller. We can use keil for the same. We need to include the header regx52.h it has all the ports and functions defined in it. Pin 5 is configured to operate and to control the LED.
Program has three functions. First one initializes the led state to zero. And the wait function creates the delay for some period of time. For that period LED remains in on/off state only. Change state function changes the global variable value so that in the next call to the wait function the status of the LED changes. As use val we have on main function to start and run the program.

Color Image Rotation in C++ and with Open CV

In previous post explains how we can apply rotation to gray scale image that is single channel images. In this post I will explain the same for multy channel images aka color images.

In real time scenarios some times we need to apply the geometric transformation on the images like rotation etc. This process includes rotating the entire image around the center of the image. It is better to map the rotated image on other image like blank image. In digital computers images are treated as the matrices or two dimensional vectors.

Int his process we find some image points falling on the outside of the boundaries. There are lot of procedures to deal with this type of problems. One is leaving those points. Second one is plotting the rotated image on the larger canvas.

In digital computer this process becomes simple matrix multiplication. In geometry as well this process is represented as the matrix operations. In image processing this type of transformations are called affine transforms.

Mathematically whole precess can be represented in two steps.

x2=cos(t)*(x1-x0)-sin(t)*(y1-y0)+x0
y2=sin(t)*(x1-x0)+cos(t)*(y1-y0)+y0

x2 is the new coordinate of the rotated image corresponding to x1 of original image similarly y2.
t is the required angle of rotation.  x0 and y0 or the center coordinates of the image.

The fallowing code snippet written in open cv and c++ does the exactly same.

 #include "stdafx.h"
#include "cv.h"
#include "highgui.h"
#include <conio.h>
#include <math.h>

using namespace cv;
using namespace std;

#define PI 3.14159265

void main()
{
    int angle;
    Mat img;
    img=imread("E:/test.jpg");
    Mat nimg((img.rows),(img.cols),CV_8UC3,Scalar(0));
    Mat tm(2,2,CV_32SC1);
    Mat nc(2,2,CV_32SC1);
    Mat oc(2,2,CV_32SC1);
    cout<<"Enter the Rotation angle\n";
    cin>>angle;
    float cosine=cos(angle*PI/180.0);
    float sine=sin(angle*PI/180.0);

    float cx=img.cols/2.0;
    float cy=img.rows/2.0;

    for(int i=0;i<img.rows;i++)
    {
        for(int j=0;j<img.cols;j++)
        {
            int nx=(cosine*(i-cx))-(sine*(j-cy))+cx;
            int ny=(sine*(i-cx))+(cosine*(j-cy))+cy;
            if((nx>=0)&&(ny>=0)&&(nx<=nimg.rows)&&(ny<=nimg.cols))
            {
                           for( int c = 0; c < 3; c++ )
                {
                nimg.at<Vec3b>(nx,ny)[c]=img.at<Vec3b>(i,j)[c];
                }
            }
        }
    }
    imshow("Image",nimg);
    waitKey(100);
     _getch();
}

The difference is just we iterate through the three channels. In Open CV we will get the pixel intensities with .at<Vec3b> array and by using index we can get the individual channel intensities.  

Image Rotation with Open cv and C++

In real time scenarios some times we need to apply the geometric transformation on the images like rotation etc. This process includes rotating the entire image around the center of the image. It is better to map the rotated image on other image like blank image. In digital computers images are treated as the matrices or two dimensional vectors.

Int his process we find some image points falling on the outside of the boundaries. There are lot of procedures to deal with this type of problems. One is leaving those points. Second one is plotting the rotated image on the larger canvas.

In digital computer this process becomes simple matrix multiplication. In geometry as well this process is represented as the matrix operations. In image processing this type of transformations are called affine transforms.

Mathematically whole precess can be represented in two steps.

x2=cos(t)*(x1-x0)-sin(t)*(y1-y0)+x0
y2=sin(t)*(x1-x0)+cos(t)*(y1-y0)+y0

x2 is the new coordinate of the rotated image corresponding to x1 of original image similarly y2.
t is the required angle of rotation.  x0 and y0 or the center coordinates of the image.

The fallowing code snippet written in open cv and c++ does the exactly same.

#include "stdafx.h" 
#include "cv.h"
#include "highgui.h"
#include <conio.h>
#include <math.h>

using namespace cv;
using namespace std;

#define PI 3.14159265

void main()
{
    int angle;
    Mat img;
    img=imread("E:/test.jpg",CV_LOAD_IMAGE_GRAYSCALE);
    Mat nimg(img.rows,img.cols,CV_8UC1,Scalar(0));
    Mat tm(2,2,CV_32SC1);
    Mat nc(2,2,CV_32SC1);
    Mat oc(2,2,CV_32SC1);
    cout<<"Enter the Rotation angle\n";
    cin>>angle;
    float cosine=cos(angle*PI/180.0);
    float sine=sin(angle*PI/180.0);

    float cx=img.cols/2.0;
    float cy=img.rows/2.0;


    for(int i=0;i<img.rows;i++)
    {
        for(int j=0;j<img.cols;j++)
        {
            int nx=(cosine*(i-cx))-(sine*(j-cy))+cx;
            int ny=(sine*(i-cx))+(cosine*(j-cy))+cy;
            if((nx>=0)&&(ny>=0)&&(nx<=img.rows)&&(ny<=img.cols))
            {
            nimg.at<uchar>(nx,ny)=img.at<uchar>(i,j);
            }
        }
    }
   imshow("Image",nimg);
   waitKey(100);
    _getch();
}