IMAGE PROCESSING

Auto Cropping- Based on labeling the connected components

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

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Identifying the objects based on count(bwlabel)

Identifying the objects based on labeling

Steps To Be Performed:

Convert the RGB image to binary image.
Fill the holes in the image.
Label the objects in the image based on connectivity 8
Display the images in a RGB format

4.1. Store the co-ordinates(x,y) of the object 1 which is labeled as 1.

4.2. Calculate the image size for object one. The length can be found by subtracting the maximum and the minimum of y values. Similary for x find the width by subtracting the maximum and minimum of x values.

4.3. Now map the pixel value to the new image.

Original Image

A=imread('num2.jpg');

figure,imshow(A);

title('Original image');

C=~im2bw(A);

B=imfill(C,'holes');

label=bwlabel(B,8);

for j=1:max(max(label))

[row, col] = find(label==j);

len=max(row)-min(row)+2;

breadth=max(col)-min(col)+2;

target=uint8(zeros([len breadth 3] ));

sy=min(col)-1;

sx=min(row)-1;

for i=1:size(row,1)

x=row(i,1)-sx;

y=col(i,1)-sy;

target(x,y,:)=A(row(i,1),col(i,1),:);

end

mytitle=strcat('Object Number:',num2str(j));

figure,imshow(target);title(mytitle);

end

The objects that are displayed in a RGB format.

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Identifying Objects based on color (RGB)

Here I used a bitmap image with different shapes filled with primary colors Red, Blue and Green.

The objects in the image are separated based on the colors. The image is a RGB image which is a 3 dimensional matrix.

Lets use (i,j) for getting the pixel position of the image A.

In the image, A (i, j, 1) represents the value of red color.

A (i, j, 2) represents the green color.

A (i, j, 3) represents the blue color.

To separate the objects of color red:

Check if A (i, j, 1) is positive. [In most cases the value will be 255];

A (i, j, 2) and A (i, j, 3) will be zero.

Similarly, other colors can be separated.

MATLAB CODE:

A=imread('shapes.bmp');

figure,imshow(A);

title('Original image');

%Preallocate the matrix with the size of A

Red=zeros(size(A));

Blue=zeros(size(A));

Green=zeros(size(A));

for i=1:size(A,1)

for j=1:size(A,2)

%The Objects with Red color

if(A(i,j,1) <= 0)

Red(i,j,1)=A(i,j,1);

Red(i,j,2)=A(i,j,2);

Red(i,j,3)=A(i,j,3);

end

%The Objects with Green color

if(A(i,j,2) <= 0)

Green(i,j,1)=A(i,j,1);

Green(i,j,2)=A(i,j,2);

Green(i,j,3)=A(i,j,3);

end

%The Objects with Blue color

if(A(i,j,3) <= 0)

Blue(i,j,1)=A(i,j,1);

Blue(i,j,2)=A(i,j,2);

Blue(i,j,3)=A(i,j,3);

end

Red=uint8(Red);

figure,imshow(Red);

title('Red color objects');

Blue=uint8(Blue);

figure,imshow(Blue);

title('Blue color objects');

Green=uint8(Green);

figure,imshow(Green);

title('Green color objects');

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