IMAGE PROCESSING

TABLE TENNIS BALL DETECTION-MATLAB CODE:

noreply@blogger.com (Aaron Angel) — Tue, 23 Apr 2013 11:59:12 PDT

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

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

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

noreply@blogger.com (Aaron Angel) — Mon, 22 Apr 2013 11:13:39 PDT

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. CheckMAX 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

%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

noreply@blogger.com (Aaron Angel) — Mon, 22 Apr 2013 10:56:39 PDT

MAX FILTER:

· 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

%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

noreply@blogger.com (Aaron Angel) — Fri, 12 Apr 2013 22:31:19 PDT

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

%OUTPUT

figure,

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

2-D Discrete Cosine Transform

noreply@blogger.com (Aaron Angel) — Thu, 14 Mar 2013 07:04:05 PDT

DCT is a technique for converting a signal into elementary frequency components.

It is widely used in image compression. Check Inverse discrete cosine transform for the reverse process.

MATLAB CODE:




%CONSIDER A MATRIX

A=[4 2 9 60; 7 10 5 77;88 66 44 3];



%PREALLOCATE THE MATRICES

B=zeros(size(A));

Temp=zeros(size(A));

display(A);



%FIND THE SIZE OF THE
MATRIX

[M N]=size(A);



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

For A(1,1), m=1 and n=1. If m= 1 then AlphaP=sqrt(1/M)=0.5774

If n=1 then AlphaQ=sqrt(1/N)=0.5000

For A(2,3),m=1 and n=3. AlphaP=sqrt(2/M)=0.8165
AlphaQ = sqrt(2/N) = 0.7071




Temp=0;

       for x=1:M

            for y=1:N

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

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

                    Temp=Temp+(A(x,y)*cs1*cs2);

             end

        end

        B(i,j)=AlphaP*AlphaQ*Temp;

    end

end

%2-D discrete Cosine Transform



display(B);

EXAMPLE 2

%2-D DISCRETE COSINE TRANSFORM FOR AN IMAGE

%READ THE IMAGE

I=imread('coins.png');

I=I(90:150,140:210);

A=double(I);

%PREALLOCATE THE MATRIX

B=zeros(size(A));

Temp=zeros(size(A));

[M N]=size(A);

x=1:M;

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

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

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=A.*cs1.*cs2;

B(i,j)=AlphaP*AlphaQ*sum(sum(Temp));

end

%OUTPUT

figure,

subplot(2,1,1);imshow(I);title('Original Image');

subplot(2,1,2),imshow(log(abs(B)),[]);
colormap(jet);title('After DCT');

Bit-Plane Slicing

noreply@blogger.com (Aaron Angel) — Mon, 05 Nov 2012 03:46:46 PST

Digitally, an image is represented in terms of pixels.

These pixels can be expressed further in terms of bits.

Consider the image ‘coins.png’ and the pixel representation of the image.

Consider the pixels that are bounded within the yellow line. The binary formats for those values are (8-bit representation)

The binary format for the pixel value 167 is 10100111

Similarly, for 144 it is 10010000

This 8-bit image is composed of eight 1-bit planes.

Plane 1 contains the lowest order bit of all the pixels in the image.

And plane 8 contains the highest order bit of all the pixels in the image.

Let’s see how we can do this using MATLAB

A=[167 133 111

144 140 135

159 154 148]

B=bitget(A,1); %Lowest order bit of all pixels

‘bitget’ is a MATLAB function used to fetch a bit from the specified position from all the pixels.

B=[1 1 1

0 0 1

1 0 0]

B=bitget(A,8);%Highest order bit of all pixels

B=[1 1 0

1 1 1

1 1 1]

MATLAB CODE:

%Bit Planes from 1 to 8. Output Format: Binary

A=imread('coins.png');

B=bitget(A,1);

figure,

subplot(2,2,1);imshow(logical(B));title('Bit plane 1');

B=bitget(A,2);

subplot(2,2,2);imshow(logical(B));title('Bit plane 2');

B=bitget(A,3);

subplot(2,2,3);imshow(logical(B));title('Bit plane 3');

B=bitget(A,4);

subplot(2,2,4);imshow(logical(B));title('Bit plane 4');

B=bitget(A,5);

figure,

subplot(2,2,1);imshow(logical(B));title('Bit plane 5');

B=bitget(A,6);

subplot(2,2,2);imshow(logical(B));title('Bit plane 6');

B=bitget(A,7);

subplot(2,2,3);imshow(logical(B));title('Bit plane 7');

B=bitget(A,8);

subplot(2,2,4);imshow(logical(B));title('Bit plane 8');

Image reconstruction using n bit planes.

1. The nth plane in the pixels are multiplied by the constant 2^n-1

2. For instance, consider the matrix

A= A=[167 133 111

144 140 135

159 154 148] and the respective bit format

3. Combine the 8 bit plane and 7 bit plane.

For 10100111, multiply the 8 bit plane with 128 and 7 bit plane with 64.

(1x128)+(0x64)+(1x0)+(0x0)+(0x0)+(1x0)+(1x0)+(1x0)=128

4. Repeat this process for all the values in the matrix and the final result will be

[128 128 64

128 128 128

128 128 128]

MATLAB CODE:

%Image reconstruction by combining 8 bit plane and 7 bit plane

A=imread('coins.png');

B=zeros(size(A));

B=bitset(B,7,bitget(A,7));

B=bitset(B,8,bitget(A,8));

B=uint8(B);

figure,imshow(B);

Image reconstructed using 8 and 7 bit planes

Explanation:

‘bitset’ is used to set a bit at a specified position. Use ‘bitget’ to get the bit at the positions 7 and 8 from all the pixels in matrix A and use ‘bitset’ to set these bit values at the positions 7 and 8 in the matrix B.

%Image reconstruction by combining 8,7,6 and 5 bit planes

A=imread('coins.png');

B=zeros(size(A));

B=bitset(B,8,bitget(A,8));

B=bitset(B,7,bitget(A,7));

B=bitset(B,6,bitget(A,6));

B=bitset(B,5,bitget(A,5));

B=uint8(B);

figure,imshow(B);

Image reconstructed using 5,6,7 and 8 bit planes

Read words in a file in reverse order

noreply@blogger.com (Aaron Angel) — Tue, 09 Oct 2012 08:08:26 PDT

Consider a file that contains the following text,

Brand New World

The result after reading the words in the reverse order

World New Brand

Steps to be performed:

1. Open a file and move the pointer to the end of the file.

2. Move the pointer to the current-1 position

3. Read the character and store it in an array.

4. Move the pointer to the current-2 position and read the character and store it in an array.

5. If the character is a blank space, then reverse the array. To reverse array use 'fliplr'.

6. Append the array to a string and initialize the array to zero.

7. Repeat this procedure till the pointer reaches the beginning of the file.

8. To learn more about file reverse: http://angeljohnsy.blogspot.in/2012/10/read-file-in-reverse-from-end-of-file.html

MATLAB CODE:

%Open a file to read

fp=fopen('rev.txt');

%Move the pointer to the end of the file

fseek(fp,0,'eof');

%Find the position

fsz=ftell(fp);

n=1;

%Move the pointer to the current-1 position

fseek(fp,-1,'cof');

%Read a character

c=fread(fp,1,'*char');

%Store in the matrix

M(1,n)=c;

Words=0;

%Check whether the file pointer has reached the beginning of the file

while(fseek(fp,-2,'cof')~=-1)

c=fread(fp,1,'*char');

%When a space is encountered reverse the character array and append

%it to a string

if(isspace(c))

if(Words==0)

%Intially, the string is empty.

%Append the array in the reverse order with a blank space

Words=[fliplr(M) blanks(1)];

else

%Append the reversed character array to the string

Words=[Words fliplr(M)];

end

n=1;

M='';

else

%The array is updated with the characters until blank space is

%encountered

n=n+1;

M(1,n)=c;

end

Words=[Words fliplr(M)];

display(Words);

fclose(fp);

Result:

Words =

World New Brand

Read a file in reverse -from end of file to Beginning of the file

noreply@blogger.com (Aaron Angel) — Tue, 09 Oct 2012 04:33:49 PDT

Consider a file that contains the following text,

Brand New World

After reading the file in the reverse order the result will be

dlroW weN dnarB

MATLAB CODE:

%Open a text file

fp=fopen('rev.txt');

%Move to the End Of the File

fseek(fp,0,'eof');

%Get the position

fsz=ftell(fp);

%Preallocate the character matrix

M=char(zeros([1 fsz]));

n=1;

%Move one position backward

fseek(fp,-1,'cof');

Here ‘cof’ is Current position of the file

%Read the character

c=fread(fp,1,'*char');

Here the ‘fread’ function reads one data in character format

%Store the character in the matrix

M(1,n)=c;

%Check whether the file pointer has reached the beginning of the file

while(fseek(fp,-2,'cof')~=-1)

n=n+1;

c=fread(fp,1,'*char');

M(1,n)=c;

end

fclose(fp);

display(M);

Result:

M =

dlroW weN dnarB

Image Erosion without using MATLAB function 'imerode'

noreply@blogger.com (Aaron Angel) — Sat, 15 Sep 2012 08:39:25 PDT

In MATLAB, ‘imerode’ is a function used to make the objects thin. MATLAB code without using 'imerode' function and explanation is provided here.

MATLAB CODE:

A=[1 0 1 1 1; 1 0 1 0 0; 1 1 1 0 0;0 0 1 1 1];

%Structuring element

B=[1 1 0];

%Pad array with ones on both sides

C=padarray(A,[0 1],1);

%Intialize the matrix D of size A with zeros

D=false(size(A));

for i=1:size(C,1)

for j=1:size(C,2)-2

In=C(i,j:j+2);

%Find the position of ones in the structuring element

In1=find(B==1);

%Check whether the elements in the window have the value one in the

%same positions of the structuring element

if(In(In1)==1)

D(i,j)=1;

end

display(D);

Explanation:

1. Consider a matrix A and a structuring element B.

2. Initialize a matrix D of size A with zeros.

3. Construct a window of size B with the elements of matrix A.

4. Check whether the ones in the structuring element B overlap the ones in the window.

5. If it overlaps, then update D with one else zero.

Example 2:

A=imread('circles.png');

figure,imshow(A);

Original Image

%Structuring element

B=getnhood(strel('disk',11));

m=size(B,1);

%Pad array on all the sides

n=floor(m/2);

C=padarray(A,[n n],1);

%Intialize a matrix with size of matrix A

D=false(size(A));

for i=1:size(C,1)-(m-1)

for j=1:size(C,2)-(m-1)

In=C(i:i+(m-1),j:j+(m-1));

In1=find(B==1);

if(In(In1)==1)

D(i,j)=1;

end

figure,imshow(D);

After Erosion

Image Dilation without using 'imdilate' function

noreply@blogger.com (Aaron Angel) — Thu, 13 Sep 2012 21:02:11 PDT

In MATLAB, ‘imdilate’is the function that dilates the image using a structuring element. Let’s learn how this function works using some examples and codes.

MATLAB CODE:

Example 1:

A=[1 0 0 0 1; 1 0 1 0 0; 1 1 1 0 0;0 0 1 1 1];

%Structuring element

B=[1 0 0; 0 1 0; 0 0 1];

%Pad zeros on all the sides

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

%Intialize a matrix of matrix size A with zeros

D=false(size(A));

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

for j=1:size(C,2)-2

%Perform logical AND operation

D(i,j)=sum(sum(B&C(i:i+2,j:j+2)));

end

display(D);

Example 2:

A=imread('text.png');

Original Image

A=im2bw(A);
%structuring element

B=[1;1;1;1;1;1;1;];

C=padarray(A,[3 0]);

D=false(size(A));

for i=1:size(C,1)-6

for j=1:size(C,2)

D(i,j)=sum(B&C(i:i+6,j));

end

figure,imshow(D);

Dilated Image

Example 3:

A=imread('text.png');

A=im2bw(A);
%Structuring element

B=[1 1 1 1 1 1 1;];
C=padarray(A,[0 3]);

D=false(size(A));

for i=1:size(C,1)

for j=1:size(C,2)-6

D(i,j)=sum(B&C(i,j:j+6));

end

figure,imshow(D);

Dilated image

Run Length Encoding

noreply@blogger.com (Aaron Angel) — Tue, 29 May 2012 22:19:16 PDT

Consider a matrix A with 15 elements,

A= [10 10 9 9 9 9 4 0 0 0 0 0 10 10 10]

In the given example,

10 has occurred 2 times, 9 has occurred 4 times, 4 has occurred once, 0 has occurred 5 times and 10 has occurred 3 times.

After Run length encoding, we obtain the matrix without any repetition in the adjacent elements, [10 9 4 0 10]. And the occurrences of each element [2 4 1 5 3]

Thus the matrix is reduced to 10 elements from 15 elements.

Let’s see how to code this reduction method.

Consider the above matrix A,

1. Find the difference between adjacent elements. Use the function ‘diff(A)’ to find the difference.
[0 -1 0 0 0 -5 -4 0 0 0 0 10 0 0]

2. Convert it to logical format. The elements without repetition are denoted with one and the repeated elements with zero.
[0 1 0 0 0 1 1 0 0 0 0 1 0 0 1]

3. Find the position of the elements that has the value one in the above step.
[2 6 7 12 15].

4. Find the unique element values using the positions obtained from the above step. In the matrix A, the element at the position 2 is 10, the element at the position 6 is 9, the element at the position 7 is 4, the element at the position 12 is 0 and the element at the position 15 is 10.
[10 9 4 0 10]

5. The first element in the matrix is 10, it has occurred 2 times. We obtained the occurrence of the first element alone from the matrix in the step 3. For the remaining elements, find the difference of the matrix in the step 3.
i.e. diff([2 6 7 12 15]); The result after concatenating the first element of the matrix obtained in step 3 with difference for the matrix in the step 3 is [2 4 1 5 3]

6. Thus in the step 4 we obtain the elements without repetition,
[10 9 4 0 10] and the occurrences in step 5, [2 4 1 5 3].

MATLAB CODE:

A=[5 2 2 2 3 3 3 3 4 4 1 1 1 1 1 1 1 6 6 4 4]

F=[logical(diff(A)) 1];

In=find(F~=0);

Ele=A(In);

C=[In(1) diff(In)];

Result=zeros([numel(Ele) 2]);

Result(:,1)=Ele;

Result(:,2)=C;

display(Result);

SAMPLE OUTPUT:

Result =

5 1

2 3

3 4

4 2

1 7

6 2

4 2

EXPLANATION:

5 has occurred 1 times, 2 has occurred 3 times, 3 has occurred 4 times, 4 has occurred 2 times, 1 has occurred 7 times, 6 has occurred 2 times and 4 has occurred 2 times.

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

noreply@blogger.com (Aaron Angel) — Thu, 07 Jun 2012 02:29:18 PDT

In MATLAB, the function ‘regionprops’ is used to measure the image properties. Here are some basic properties computed without using the function.

Read an image and find the connected components using ‘bwlabel’ function.

Using the Labeled matrix as an input, the properties can be measured.

Example:

A=[1 0 0 1

1 1 1 1

0 0 1 1]

To find Area:

· The total number of ‘ON’ pixels in the image.

The number of ones in the matrix is 8.

To find Centroid:

· Find the row and column having pixel value one. Eg.[row,column]=find(label==1)

Row=[ 1 2 2 2 3 1 2 3]

Column=[ 1 1 2 3 3 4 4 4]

· Find the mean of the row and column having pixel value one.

Mean of Row=2 and mean of column= 2.75

To find the Bounding Box:

· We need 4 points, starting position(x,y) , length and breadth.

· Minimum value of row and column minus 0.5 gives starting position(x,y) respectively

· Minimum value of row=1-0.5=0.5

· Minimum value of column=1-0.5=0.5

· Maximum value of column – minimum value of column+1 gives breadth of the box

· Maximum value of column=4

· Max value-min value of column=3+1

· Maximum value of row- minimum value of row +1gives length of the box

· maximum value of row=3

· Max value – Min value=2+1

· Bounding Box value for the given example:0.5000 0.5000 4.0000 3.0000

· For more details on how to draw a rectangle check here: http://angeljohnsy.blogspot.in/2011/06/how-to-draw-in-matlab.html

To find the Perimeter

· Find the boundary of the labeled component

Boundary pixels:

1 1

2 2

2 3

1 4

2 4

3 4

3 3

2 2

2 1

1 1

· Find the distance between the each adjoining pair of pixels around the border of the region.

· Use the distance formula:

· For instance, calculate the distance between the two points (1,1) and (2,2). distance=sqrt((2-1).^2+(2-1).^2)=1.41

· Similarly, the distance is computed for all the pixel positions.

· The perimeter for the given example is 10.2426

To find the Roundness:

· Roundness of an object can be determined using the formula:

Roundness=(4*Area*pi)/(Perimeter.^2)
If the Roundness is greater than 0.90 then, the object is circular in shape.

Result= (4*8*3.14)/10.2426.^2=0.9582

To find the Equivdiameter

· Formula: sqrt(4*Area/pi).

Equivdiameter for the given example:3.1915

Click here to get full MATLAB CODE

Auto Cropping- Based on labeling the connected components

noreply@blogger.com (Aaron Angel) — Tue, 01 May 2012 20:27:33 PDT

This post is about labeling the connected components in a binary image and crop the connected components based on the label. The main two functions used for this simple operation are ‘bwlabel’ and ‘regionprops’.

I used ‘bwlabel’ to label the connected components. After labeling, I used ‘regionprops’ function to find the rectangle containing the region. To find the rectangle points, I used ‘BoundingBox’ property.

Then the labeled components are automatically cropped and the image is displayed. To crop an image, ‘imcrop’ function is used.

MATLAB CODE:



A=imread('coins.png');
figure,imshow(A); title('Original Image');



%Convert the Image to binary
B=im2bw(A);
 
%Fill the holes
C=imfill(B,'holes');
 
%Label the connected components
[Label,Total]=bwlabel(C,8);
figure,imshow(C); title('Labelled Image');



%Rectangle containing the region
Sdata=regionprops(Label,'BoundingBox');
 
%Crop all the Coins 
for i=1:Total
    Img=imcrop(A,Sdata(i).BoundingBox);
    Name=strcat('Object Number:',num2str(i));
    figure,imshow(Img); title(Name);
end

Another Example:

'coins.png'

Labeled Image

Text On Sphere - MATLAB CODE

noreply@blogger.com (Aaron Angel) — Tue, 01 May 2012 21:32:42 PDT

Text on Sphere-MATLAB CODE

We can see how to wrap a text around a sphere. First obtain the x,y and z co-ordinates of the sphere.These co-ordinates can be used as an input for surface object.

Surface object generates sphere using the co-ordinates. Example:[x,y,z]=sphere(25); surface(x,y,z);

Now read a text image and convert it into binary. Label the components in the image. You can also check how to label components without BWLABELfunction in MATLAB. Now use the labeled image as a CDATA value in surface object.

Example: surface(x,y,z, ‘CDATA’,matrix);

MATLAB CODE:


%Input a text image
A=imread('quotes.png');



A=~im2bw(A);
B=bwlabel(A,8);
F=double(B);
mytext=flipud(F);


% Create the surface.
[x,y,z] = sphere(100);


figure,surface(x,y,z,'CData',mytext,'FaceColor','texturemap','FaceLighting','phong','EdgeLighting','phong','EdgeColor','none');

%set the colormap. Some built-in colormaps are pink, copper, jet, hot...

%colormap pink

%Here I created my own colormap, the colors are white[1 1 1], black[0 0 0] and %blue [0 0 1]. Initialize the map with zeros(black) for 256x3 matrix. %Add blue
%color to some components(words). Initialize the first row with [ 1 1 1](white).



map=zeros([253 3]);
map(1,:)=0;
map(50:80,3)=1;
colormap(map);



%Create light object
light('position',[1 2 0 ],'Style','infinite','color',[0.8 0.7 0.8]);
light('position',[-2 -3 0 ],'color',[0.8 0.7 0.8]);


%Specify the viewpoint
axis square off
view(12,0);

Another Example: To create a GLOBE from a WORLD MAP

INPUT IMAGE

To convert a RGB image into SPHERE:
MATLAB CODE:



A=imread('mountain.jpg');
[mytext,map]=rgb2ind(A,256);
mytext=flipud(mytext);

% Create the surface.
[x,y,z] = sphere(100);

figure,surface(x,y,z,'CData',mytext,'FaceColor','texturemap','FaceLighting','phong','EdgeLighting','phong','EdgeColor','none');
%set the colormap. 

colormap(map);


%Create light object
light('position',[1 2 0 ],'Style','infinite','color',[0.8 0.7 0.8]);
light('position',[-2 -3 0 ],'color',[0.8 0.7 0.8]);


%Specify the viewpoint
axis square off
%view(12,0);
view(180,0);

Hope you enjoyed this post. Check the link for more images:http://www.facebook.com/media/set/?set=a.352096201499476.75050.241768945865536&type=1

How to add caption/subtitles to Video in MATLAB

noreply@blogger.com (Aaron Angel) — Mon, 16 Apr 2012 21:45:51 PDT

In my previous post, I discussed about how to create videofrom still images and now we can add subtitles to the video. I used the ‘text’ function to add the subtitles.

The procedure to add subtitles to the Video:

· Read an video file

· Read each video frame data

· Add text to each frame using the syntax ‘text(x,y,string)’.

· Use imshow to display the image.

· Get the frame from the figure displayed.

MATLAB CODE:

%Input a Video file

obj=mmreader('Poem.avi');

A=read(obj);

j=1;

%Preallocate the frame structure

Frame=struct('cdata',1,'colormap',cell([1 100]));

INPUT VIDEO

%Get the first frame as image

%Add text

%Use 'getframe' to grab the figure

Caption={'How to add Caption/Subtitles';'to video in MATLAB';'GET CODE AT http://angeljohnsy.blogspot.com/';};

I=A(:,:,:,1);

imshow(I),hold on

text(80,100,Caption{1},'Color','w','FontWeight','Bold','FontSize',30);

text(150,200,Caption{2},'Color','w','FontWeight','Bold','FontSize',30);

text(300,480,Caption{3},'Color','r','FontWeight','Bold','FontSize',15);

Frame(j:j+9)=getframe;

j=j+10;

%Get the Second frame as an image i.eA(:,:,:,2);

%Add the Poem title and the author name

Title_={'THE ROAD NOT TAKEN';'-ROBERT FROST'};

I=A(:,:,:,2);

imshow(I), hold on

text(40,100,Title_{1},'Color','k','FontWeight','Bold','FontSize',40);

text(350,550,Title_{2},'Color','k','FontWeight','Bold','FontSize',30);

Frame(j:j+9)=getframe;

j=j+10;

%Create a cell array with the Poem versus

Txt={'TWO roads diverged in a yellow wood,';

'And sorry I could not travel both';

'And be one traveler, long I stood';

'And looked down one as far as I could';

'To where it bent in the undergrowth;';

'Then took the other, as just as fair,';

'And having perhaps the better claim,';

'Because it was grassy and wanted wear;';

'Though as for that the passing there';

'Had worn them really about the same,';

'And both that morning equally lay';

'In leaves no step had trodden black.';

'Oh, I kept the first for another day!';

'Yet knowing how way leads on to way,';

'I doubted if I should ever come back.';

'I shall be telling this with a sigh';

'Somewhere ages and ages hence:';

'Two roads diverged in a wood, and I—';

'I took the one less traveled by,';

'And that has made all the difference.';};

%display the image

%Add the poem lines

%Grab the frame

%There are 20 lines totally

%Add 3 and 2 lines alternatively in each frame

inc=0;

for i=3:size(A,4)

n=mod(i,2);

I=A(:,:,:,i);

imshow(I), hold on

for k=1:2+n

text(80,440+(k*40),Txt{k+inc},'Color','w','FontWeight','Bold','FontSize',25);

end

inc=inc+2+n;

Frame(j:j+19)=getframe;

j=j+20;

end

Create a Video File:

obj=avifile('Road Not Taken.avi','FPS',3);

obj=addframe(obj,Frame);

obj=close(obj);

VIDEO WITH SUBTITLE

Explanation:

text(40,100,Title_{1},'Color','k','FontWeight','Bold','FontSize',40);

This code will add the text ‘THE ROAD NOT TAKEN’ with color font size 40, font weight bold and color black (k).

Extraction of Connected components without using BWLABEL function

noreply@blogger.com (Aaron Angel) — Tue, 01 May 2012 21:57:28 PDT

Many of the visitors to this blog mailed me to post a MATLAB code for extracting the connected components. In MATLAB, a function called BWLABEL is available to label the connected components.

Based on the following iterative expression, the connected components are extracted.

where k=1,2,3…

Using the above expression, connected components are extracted without using the function BWLABEL

LABELLING:

MATLAB CODE:


I=imread('label3.jpg');



I=im2bw(I);

%Structuring element
B=strel('square',3);
A=I;
%Find a non-zero element's position.
p=find(A==1);
p=p(1);
Label=zeros([size(A,1) size(A,2)]);
N=0;

while(~isempty(p))
    N=N+1;%Label for each component
    p=p(1);
X=false([size(A,1) size(A,2)]);
X(p)=1;

Y=A&imdilate(X,B);
while(~isequal(X,Y))
    X=Y;
    Y=A&imdilate(X,B);
end

Pos=find(Y==1);

A(Pos)=0;
%Label the components
Label(Pos)=N;

p=find(A==1);

end
imtool(Label);

EXPLANATION:

Read an image (A) and convert it into binary image.
Define a structuring element (B).
Initialize the Label matrix with zeros.
Find a non-zero element position in the input matrix A.
Initialize a matrix X with zeros and place 1 in the non-zero element position found in the previous step.
Perform dilation using the structuring element B on matrix X. i.e. imdilate(X,B);
Perform intersection with the matrix A. Y= A&imdilate(X, B).
Check whether Y==X. If no, then X=Y and perform steps 6 and 7 again else stop the iteration.
Find the non-zero elements position in the Y. In matrix Label place a number N in those positions. N is for labeling the connected components.
Similarly, place zero in those positions in the input matrix A.
Again find a non-zero element position in the matrix A. If found, goto step 5 else stop the iteration.
Using the labels the connected components can be extracted.

EXTRACTION:

MATLAB CODE:



%Extracting the components
Im=zeros([size(A,1) size(A,2)]);
ele=find(Label==1);
Im(ele)=1;
figure,imshow(Im);title('Label:1');

To obtain the first component, find the positions with value=1 in the Label Matrix. Similarly, other components can be extracted.



%Extracting the characters 'I M A G E'
ele=find(Label==2|Label==3|Label==6|Label==7|Label==9);
Im1=zeros([size(A,1) size(A,2)]);
Im1(ele)=1;
figure,imshow(Im1);title('Specific components');

From the Label matrix, I extracted the characters I, M, A, G and E alone by specifying the label numbers 2,3,6,7 and 9.

%Total number of Letters



Total=sprintf('Total Number of Letters:%d',N);
display(Total);

The total number of components extracted in the above example Image:

Total =

Total Number of letters:14



%Differentiate each component with a specific color
RGBIm=zeros([size(Label,1) size(Label,2) 3]);
R=zeros([size(Label,1) size(Label,2)]);
G=zeros([size(Label,1) size(Label,2)]);
B=zeros([size(Label,1) size(Label,2)]);
U=64;
V=255;
W=128;
for i=1:N
    Pos=find(Label==i);
    R(Pos)=mod(i,2)*V;
    G(Pos)=mod(i,5)*U;
    B(Pos)=mod(i,3)*W;
   
   
 end
RGBIm(:,:,1)=R;
RGBIm(:,:,2)=G;
RGBIm(:,:,3)=B;
RGBIm=uint8(RGBIm);
figure,imshow(RGBIm);title('Labelled Components');

For each label, unique color is given. Here is another example for labeling and extracting the components. Read the image, ‘coins.png’ , convert it into binary and fill the holes. Then perform labeling and extraction.

Total =

Total Number of coins: 10