BALL DETECTION using MATLAB

TABLE TENNIS BALL DETECTION-MAT LAB 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:

 

MATLAB Function ‘regionprops’

Find Area, Perimeter, Centroid, Equivdiameter, Roundness and Bounding Box without Using MATLAB Function ‘regionprops’ 

MATLAB CODE: 

      %Measure Basic Image Properties without using 'regionprops' function

%Measure Area, Perimeter, Centroid , Equvidiameter, Roundness and Bounding Box

clc

%Read Original Image

I=imread('coins.png');

%Convert to Binary

B=im2bw(I);
                                                 
%Fill the holes

C=imfill(B,'holes');
                                                    
 %Label the image

[Label,Total]=bwlabel(C,8);

%Object Number

num=4;

[row, col] = find(Label==num);

%To find Bounding Box

sx=min(col)-0.5;

sy=min(row)-0.5;

breadth=max(col)-min(col)+1;

len=max(row)-min(row)+1;

BBox=[sx sy breadth len];

display(BBox);

figure,imshow(I);

hold on;

x=zeros([1 5]);

y=zeros([1 5]);

x(:)=BBox(1);

y(:)=BBox(2);

x(2:3)=BBox(1)+BBox(3);

y(3:4)=BBox(2)+BBox(4);

plot(x,y);

%Find Area

Obj_area=numel(row);

display(Obj_area);

%Find Centroid

X=mean(col);

Y=mean(row);

Centroid=[X Y];

display(Centroid);

plot(X,Y,'ro','color','r');

hold off;

%Find Perimeter

BW=bwboundaries(Label==num);

c=cell2mat(BW(1));

Perimeter=0;

for i=1:size(c,1)-1

Perimeter=Perimeter+sqrt((c(i,1)-c(i+1,1)).^2+(c(i,2)-c(i+1,2)).^2);

end

display(Perimeter);
                                

%Find Equivdiameter

EquivD=sqrt(4*(Obj_area)/pi);

display(EquivD);

%Find Roundness

Roundness=(4*Obj_area*pi)/Perimeter.^2;

display(Roundness);
                          

%Calculation with 'regionprops'(For verification Purpose);

%Sdata=regionprops(Label,'all');

%Sdata(num).BoundingBox

%Sdata(num).Area

%Sdata(num).Centroid

%Sdata(num).Perimeter

%Sdata(num).EquivDiameter

FACE DETECTION - MATLAB CODE

Lets see how to detect face, nose, mouth and eyes using the MATLAB built-in class and function. Based on Viola-Jones face detection algorithm, the computer vision system toolbox contains vision.CascadeObjectDetector System object which detects objects based on above mentioned algorithm.

   Prerequisite: Computer vision system toolbox

FACE DETECTION:

clear all

clc

%Detect objects using Viola-Jones Algorithm

%To detect Face

FDetect = vision.CascadeObjectDetector;

%Read the input image

I = imread('HarryPotter.jpg');

%Returns Bounding Box values based on number of objects

BB = step(FDetect,I);

figure,

imshow(I); hold on

for i = 1:size(BB,1)

    rectangle('Position',BB(i,:),'LineWidth',5,'LineStyle','-','EdgeColor','r');

end

title('Face Detection');

hold off;

 

The step(Detector,I) returns Bounding Box value that contains [x,y,Height,Width] of the objects of interest.

BB =

    52    38    73    73

   379    84    71    71

   198    57    72    72

NOSE DETECTION:

%To detect Nose

NoseDetect = vision.CascadeObjectDetector('Nose','MergeThreshold',16);

BB=step(NoseDetect,I);

figure,

imshow(I); hold on

for i = 1:size(BB,1)

    rectangle('Position',BB(i,:),'LineWidth',4,'LineStyle','-','EdgeColor','b');

end

title('Nose Detection');

hold off;

 

EXPLANATION:

To denote the object of interest as 'nose', the argument  'Nose' is passed.

vision.CascadeObjectDetector('Nose','MergeThreshold',16);

The default syntax for Nose detection :

vision.CascadeObjectDetector('Nose');

Based on the input image, we can modify the default values of the parameters passed to vision.CascaseObjectDetector. Here the default value for 'MergeThreshold' is 4.

When default value for 'MergeThreshold' is used, the result is not correct.

 

To avoid multiple detection around an object, the 'MergeThreshold' value can be overridden. 

MOUTH DETECTION:

%To detect Mouth

MouthDetect = vision.CascadeObjectDetector('Mouth','MergeThreshold',16);

BB=step(MouthDetect,I);

figure,

imshow(I); hold on

for i = 1:size(BB,1)

 rectangle('Position',BB(i,:),'LineWidth',4,'LineStyle','-','EdgeColor','r');

end

title('Mouth Detection');

hold off;

 

EYE DETECTION:

%To detect Eyes

EyeDetect = vision.CascadeObjectDetector('EyePairBig');

%Read the input Image

I = imread('harry_potter.jpg');

BB=step(EyeDetect,I);

figure,imshow(I);

rectangle('Position',BB,'LineWidth',4,'LineStyle','-','EdgeColor','b');

title('Eyes Detection');

Eyes=imcrop(I,BB);

figure,imshow(Eyes);

 

 

I will discuss more about object detection and how to train detectors to identify object of our interest in my upcoming posts. Keep reading for updates.