Converting RGB Image to HSI

Converting RGB Image to HSI

H stands for Hue, S for Saturation and I for Intensity.

MATLAB CODE:

Read a RGB Image

A=imread('peppers.png');

figure,imshow(A);title('RGB Image');

%Represent the RGB image in [0 1] range

I=double(A)/255;

R=I(:,:,1);

G=I(:,:,2);

B=I(:,:,3);

%Hue

numi=1/2*((R-G)+(R-B));

denom=((R-G).^2+((R-B).*(G-B))).^0.5;

%To avoid divide by zero exception add a small number in the denominator

H=acosd(numi./(denom+0.000001));

%If B>G then H= 360-Theta

H(B>G)=360-H(B>G);

%Normalize to the range [0 1]

H=H/360;

%Saturation

S=1- (3./(sum(I,3)+0.000001)).*min(I,[],3);

%Intensity

I=sum(I,3)./3;

%HSI

HSI=zeros(size(A));

HSI(:,:,1)=H;

HSI(:,:,2)=S;

HSI(:,:,3)=I;

figure,imshow(HSI);title('HSI Image');

Explanation:

1.    Read a RGB image using ‘imread’ function.

2.    Each RGB component will be in the range of [0 255].  Represent the image in [0 1] range by dividing the image by 255.

3.    Find the theta value. If B<=G then H= theta. If B>G then H= 360-theta

4.    Use ‘acosd’ function to find inverse cosine and obtain the result in degrees.

5.    Divide the hue component by 360 to represent in the range [0 1]

6.    Similarly, find the saturation and the intensity components.

7.    Display the image.

Table Tennis Ball Detection-MATLAB CODE:

Using the concept of roundness to detect a circular object, the table tennis ball in mid air or in palm is detected.

FLOW CHART:

1.     Read input image

·        Read a RGB image with ball in mid air or ball in palm.

                   MATLAB CODE:

                       %Read the input image

         Img=imread(Filename);

         axes('Position',[0 .1 .74 .8],'xtick',[],'ytick',[]);

         imshow(Img);title('Original Image');

2.     Image Pre-processing

·        Convert the RGB image to grayscale image.

·        Apply median filter

·        Adjust the brightness and contrast of the image using ‘imadjust’ function.

                      MATLAB CODE:

           I=rgb2gray(Img);     % Converting RGB Image to

                                % Gray Scale Image

           I=im2double(I);      % Converting Gray scale Image

                                % to Double type

           J = medfilt2(I,[3 3]); % Median Filter , 

                                  % 3x3 Convolution

                                  % on Image

           I2 = imadjust(J);     % Improve to quality of Image

                                 % and adjusting

                                 % contrast and brightness values

3.     Threshold the image

·        The pixel value greater than the threshold value is converted to one else zero.

                     MATLAB CODE:

           Ib = I2> 0.9627; 

4.     Image Labeling

·        Label the connected components using ‘bwlabel’ function

·        Remove components that are smaller in size.

                     MATLAB CODE:

           %Labelling

           [Label,total] = bwlabel(Ib,4); % Indexing segments by

                                          % binary label function

            %Remove components that is small and tiny

            for i=1:total

                if(sum(sum(Label==i)) < 500 )

                    Label(Label==i)=0;

  

                end

            end

5.     Find the image properties: Area, Perimeter and Centroid

·        Using ‘regionprops’ function, find the Area, Perimeter, Bounding Box and Centroid.

                     MATLAB CODE:

            %Find the properties of the image

             Sdata = regionprops(Label,'all'); 

6.     Calculate the Roundness

·        Roundness = 4*PI*A/P^2

           MATLAB CODE:

                %Find the components number

          Un=unique(Label);

          my_max=0.0;

            %Check the Roundness metrics

            %Roundness=4*PI*Area/Perimeter.^2

            for i=2:numel(Un)

               Roundness=(4*pi*Sdata(Un(i)).Area)/Sdata(Un(i)).Perimeter.^2;

               my_max=max(my_max,Roundness);

                if(Roundness==my_max)

                   ele=Un(i);

                end

            end

7.     Find the component with the maximum roundness value

·        Find the max of the Roundness value for all the labeled components

8.     Show the detected table tennis ball

·        Use the ‘BoundingBox’ values to plot rectangle around the ball

·        Mark the centroid of the ball

                     MATLAB CODE:

          %Draw the box around the ball

           box=Sdata(ele).BoundingBox;

           box(1,1:2)=box(1,1:2)-15;

           box(1,3:4)=box(1,3)+25;

          %Crop the image

           C=imcrop(Img,box);

          %Find the centroid

          cen=Sdata(ele).Centroid;

          %Display the image

           axes('Position',[0 .1 .74 .8],'xtick',[],'ytick',[])

           imshow(Img);

           hold on

           plot(cen(1,1),cen(1,2),'rx');%Mark the centroid

9.     Generate report

·        Find the radius using the Equidiameter obtained using ‘regionprops’ function.

·        Display the radius,Area,Perimeter and Centroid of the ball.

·        Show the Binary and Original image of the cropped ball.

MATLAB CODE:

        Rad=(sdata(ele).EquivDiameter)/2;

        Rad=strcat('Radius of the Ball :',num2str(Rad));

     

        Area=sdata(ele).Area;

        Area=strcat('Area of the ball:',num2str(Area));

       

        Pmt=sdata(ele).Perimeter;

        Pmt=strcat('Perimeter of the ball:',num2str(Pmt));

       

        Cen=sdata(ele).Centroid;

        Cent=strcat('Centroid:',num2str(Cen(1,1)),',',num2str(Cen(1,2)));

BALL IN MID AIR:

Min Filter - MATLAB CODE

MIN FILTER

•      To find the darkest points in an image.
•       Finds the minimum value in the area encompassed by the filter.
•       Reduces the salt noise as a result of the min operation.
•       The 0^th percentile filter is min filter.

The MIN Filtering is similar to MAX filter. Check MAX filter post to know how the formula is used to filter the pepper noise.

MATLAB CODE:

%READ AN IMAGE

A = imread('board.tif');

A = rgb2gray(A(1:300,1:300,:));

figure,imshow(A),title('ORIGINAL IMAGE');

%PREALLOCATE THE OUTPUT MATRIX

B=zeros(size(A));

%PAD THE MATRIX A WITH ZEROS

modifyA=padarray(A,[1 1]);

        x=[1:3]';

        y=[1:3]';

       

for i= 1:size(modifyA,1)-2

    for j=1:size(modifyA,2)-2

      

       %VECTORIZED METHOD 

       window=reshape(modifyA(i+x-1,j+y-1),[],1);

       %FIND THE MINIMUM VALUE IN THE SELECTED WINDOW

        B(i,j)=min(window);

    end

end

%CONVERT THE OUTPUT MATRIX TO 0-255 RANGE IMAGE TYPE

B=uint8(B);

figure,imshow(B),title('IMAGE AFTER MIN FILTERING');

Max Filter - MATLAB CODE

• To find the brightest points in an image.
•  Finds the maximum value in the area encompassed by the filter.
•  Reduces the pepper noise as a result of the max operation.
•  The 100^th percentile filter is max filter. Check the 50^th percentile filter i.e the median filter.

MATLAB CODE:

%READ AN IMAGE

A = imread('board.tif');

A = rgb2gray(A(1:300,1:300,:));

figure,imshow(A),title('ORIGINAL IMAGE');

%PREALLOCATE THE OUTPUT MATRIX

B=zeros(size(A));

%PAD THE MATRIX A WITH ZEROS

modifyA=padarray(A,[1 1]);

        x=[1:3]';

        y=[1:3]';

       

for i= 1:size(modifyA,1)-2

    for j=1:size(modifyA,2)-2

      

       %VECTORIZED METHOD

       window=reshape(modifyA(i+x-1,j+y-1),[],1);

       %FIND THE MAXIMUM VALUE IN THE SELECTED WINDOW

        

       B(i,j)=max(window);

   

    end

end

%CONVERT THE OUTPUT MATRIX TO 0-255 RANGE IMAGE TYPE

B=uint8(B);

figure,imshow(B),title('IMAGE AFTER MAX FILTERING');

2-D Inverse Discrete Cosine Transform

Consider the result obtained after DCT. (Check 2d-DCT )

Apply Inverse Discrete Cosine Transform to obtain the original Image.

MATLAB CODE:

%2-D INVERSE DISCRETE COSINE TRANSFORM

%PREALLOCATE THE MATRIX

A=zeros(size(B));

Temp=zeros(size(B));

[M N]=size(B);

x=1:M;

x=repmat(x',1,N);

y=repmat(1:N,M,1);

figure,

imshow(log(abs(B)),[]);colormap(jet);title('After DCT');

for i=1:M

    for j = 1: N

   

        if(i==1)

          AlphaP=sqrt(1/M);

        else

          AlphaP=sqrt(2/M);

        end

       

        if(j==1)

          AlphaQ=sqrt(1/N);

        else

          AlphaQ=sqrt(2/N);

        end

                 

        cs1=cos((pi*(2*x-1)*(i-1))/(2*M));

        cs2=cos((pi*(2*y-1)*(j-1))/(2*N));

        Temp=B.*cs1.*cs2*AlphaP*AlphaQ;

              

          A(i,j)=sum(sum(Temp));

    end

end

%OUTPUT

figure,

imshow(abs(A),[0 255]);title('Image after IDCT');