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    Pavan Kumar Sharma

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    Pavan Kumar Sharma

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    3 views19 pages

    Pavan Kumar Sharma

    Uploaded by

    varu.vairagya2

    Copyright:

    © All Rights Reserved
    Download as DOCX, PDF, TXT or read online from Scribd
    Download as docx, pdf, or txt
    of 19

    DEPARTMENT OF COMPUTER ENGINEERING &

    APPLICATIONS

    DIP LAB FILE


    Name: Pavan Kumar Sharma

    University Rollno. : 2115000702

    Subject Name: Digital Image Processing Lab


    Subject Code: BCSE 0101
    Course: B.Tech.
    Year: III Semester: V

    INDEX
    Exp. Page
    Program
    No. No.

    1 Introduction with the MATLAB Command. 1

    2 Read the image and display it. 1

    3 Create the horizontal and vertical strips. 1

    4 Create chessboard. 2

    5 Read the colored image and check attributes. 2

    6 Perform Log Transformation on Image. 3

    7 Perform Negative Transformation on Image. 3

    8 Perform Power Log Transformation on Image. 4

    9 Perform Gray Level Slicing with and without background. 4-5

    10 Perform Histogram Equalization on image. 5-6

    11 Find Four, Diagonal and Eight Neighbours of image. 6-7

    12 Perform Dilation on Image. 7-8

    13 Perform Erosion on Image. 8-9

    14 Perform Perforn Closing(Dilation Followed by Erosion) on Image. 9-10


    Experiment -1

    Objective:- Introduction with the MATLAB Command.

    Commands:-

    1. clc - Clear Command window

    2. clearall - clear the workspace

    3. date - displays current date

    4. linspace - creates regularly spaced rectory

    5. Mat – returns largest element

    6. Min - returns smallest element

    7. size – computes array size

    8. ones – creates an array of ones

    9. zeros – creates an array of zeros

    10. subplot – creates plot in subwindows.


    Experiment -2

    Objective:- Read the image and display it.

    Implement:-

    Img = imread(’cameraman.tif’);
    imshow(Img);

    Output:-
    Experiment -3

    Objective:- Create the horizontal and vertical strips.

    Implement:-

    zero_arr = zeros(10,20); %m*n zeros matrix

    %horizontal strips
    hor_strip = uint8(zero_arr);
    hor_strip(2:2:10,:) = 255;
    subplot(1,2,1),imshow(hor_strip)

    %vertical strip
    ver_strip = uint8(zero_arr);
    ver_strip(:,2:2:20) = 255; % array(st_row:diff:end_row,st_col:diff:end_col)
    subplot(1,2,2),imshow(ver_strip)

    Output:-
    Experiment -4

    Objective:- Create chessboard.

    Implement:-

    zero_arr = zeros(8,8); %m*n zeros matrix


    %chess
    hor_strip = uint8(zero_arr);
    hor_strip(2:2:8,:) = 255;
    hor_strip(:,2:2:8) = 255;
    hor_strip(2:2:8,2:2:8) = 0;
    subplot(1,2,1),imshow(hor_strip)

    Output:-

    Experiment -5
    Objective:- Read the colored image and check attributes.

    Implement:-

    i1 = imread ("peppers.png");
    r = i1 (:,:,1);
    g = i1(:,:,2);
    b = i1(:,:,3);

    subplot(2,2,1);
    imshow (i1);

    subplot(2,2,2);
    imshow (r);

    subplot(2,2,3);
    imshow (g);

    subplot(2,2,4);
    imshow (b);

    Output:-

    Experiment -6
    Objective:- Perform Log Transformation on Image.

    Implement:-

    img = imread('cameraman.tif');

    subplot(1,2,1), imshow(img)

    % Convert datatype to Double


    % (for allowing fractional values)
    r = double(img);

    % Constant determining the


    % nature of the log curve
    C = 40;

    % Performing log transformation


    S = C * log(1 + r);
    %T = 255/(C * log(256));

    % Converting the datatype back


    % to integer for displaying
    Log = uint8(S);
    subplot(1,2,2), imshow(Log)

    Output:-

    Experiment -7
    Objective:- Perform Negative Transformation on Image.

    Implement:-

    img = imread('cameraman.tif');
    %Negative Transformation - 1
    neg=255-img;

    subplot(1,2,1),imshow(img)

    %Negative Transformation - 2
    neg=img;
    for row=1:size(img,1)
    for col=1:size(img,2)
    neg(row,col,:)=255-img(row,col,:);
    end
    end

    subplot(1,2,2),imshow(neg)

    Output:-

    Experiment -8
    Objective:- Perform Power Log Transformation on Image.

    Implement:-

    img = imread('cameraman.tif');

    subplot(1,2,1), imshow(img)

    % Convert datatype to Double


    % (for allowing fractional values)
    r = double(img);

    % Constant determining the


    % nature of the log curve
    C = 100;
    gamma = 0.5;
    % Performing power log transformation
    S = C * log(1 + r)^gamma;
    T = 255/(C * log(256)^gamma);

    % Converting the datatype back


    % to integer for displaying
    Log = uint8(T * S);
    subplot(1,2,2), imshow(Log)

    Output:-

    Experiment -9
    Objective:- Perform Gray Level Slicing with and without background.

    Implement:-

    % Read the input image


    grayImage = imread('cameraman.tif');

    % Set the threshold values (adjust as needed)


    lowThreshold = 50;
    highThreshold = 150;

    % Perform gray level slicing without background reference


    outputImage = grayImage;
    outputImage(grayImage > lowThreshold & grayImage < highThreshold) =
    255;

    % Perform gray level slicing with background reference


    whback= grayImage;
    whback(grayImage < lowThreshold | grayImage > highThreshold) = 255;
    subplot(2,2,3), imshow(whback), title('With Background')
    % Display the original and processed images
    figure;
    subplot(2, 2, 1), imshow(grayImage), title('Original Image');
    subplot(2, 2, 2), imshow(outputImage), title('Gray Level Slicing
    without Background Reference');

    Output:-

    Experiment -10
    Objective:- Perform Histogram Equalization on image.

    Implement:-

    img=imread('cameraman.tif');

    [n,m]=size(img);
    %n-->row
    %m-->col

    % First Column --> rk


    for i=1:256
    t(i,1)=i-1;
    t(i,2)=0;
    end

    % Second Column --> nk


    %Frequency Count
    for i=1:n
    for j=1:m
    pix=img(i,j)+1;
    t(pix,2)=t(pix,2)+1;
    end
    end

    for i=1:256
    t(i,3)=t(i,2)/(256*256);
    end

    t(1,4)=t(1,3)
    for i=2:256
    t(i,4)=t(i-1,4)+t(i,3);
    end

    for i=1:256
    t(i,5)=t(i,4)*255;
    end

    for i=1:256
    t(i,6)=round(t(i,5));
    end

    for i=1:256s(i,1)=i-1;
    s(i,2)=0;
    end

    for i=1:256
    pix=t(i,6)+1;
    s(pix,2)=s(pix,2)+t(i,2);
    end
    img_2=img;
    for i=1:n
    for j=1:m
    img_2(i,j)=t(img(i,j)+1,6);
    end
    end

    subplot(3,2,1),imshow(img),title('Original');
    subplot(3,2,2),imshow(img_2),title('Manual Transform');
    subplot(3,2,3),bar(t(:,1),t(:,2)),title('Histogram');
    subplot(3,2,4),bar(s(:,1),s(:,2)),title('Histogram');
    subplot(3,2,5),imhist(img_2),title("Equalized");

    Output:-

    Experiment -11
    Objective:- Find Four, Diagonal and Eight Neighbours of image.

    Implement:-

    % Define the image matrix


    img = [1, 2, 3;
    4, 5, 6;
    7, 8, 9];

    % Input values for x and y


    x = input('X-axis: ');
    y = input('Y-axis: ');

    % Define height and width


    [height, width] = size(img);
    fprintf('Height: %d, Width: %d\n', height, width);

    % Initialize arrays for neighbors


    fourN = NaN(1, 4);
    eightN = NaN(1, 8);
    diagonalN = NaN(1, 4);

    % Four Neighbour
    if x > 1
    fourN(1) = img(x - 1, y);
    end

    if y > 1
    fourN(2) = img(x, y - 1);
    end

    if x < height
    fourN(3) = img(x + 1, y);
    end

    if y < width
    fourN(4) = img(x, y + 1);
    end

    % Diagonal Neighbour
    if x > 1 && y > 1
    diagonalN(1) = img(x - 1, y - 1);
    end

    if x < height && y < width


    diagonalN(2) = img(x + 1, y + 1);
    end

    if x < height && y > 1


    diagonalN(3) = img(x + 1, y - 1);
    end

    if x > 1 && y < width


    diagonalN(4) = img(x - 1, y + 1);
    end

    % Display the results


    fprintf('Four Neighbors: %s\n', mat2str(fourN));
    fprintf('Diagonal Neighbors: %s\n', mat2str(diagonalN));

    % Combine eight neighbors


    eightN(1:4) = fourN(1:4);
    eightN(5:8) = diagonalN(1:4);

    fprintf('Eight Neighbors: %s\n', mat2str(eightN));

    Output:-
    Experiment -12

    Objective:- Perform Dilation on Image.

    Implement:-

    % Create a binary image


    binaryImage = imread('cameraman.tif')
    binaryImage = im2bw(binaryImage);
    % Create a structuring element (a simple 3x3 square)
    se = logical(ones(3, 3));

    % Get the size of the binary image


    [rows, cols] = size(binaryImage);
    % Initialize the result image
    dilatedBinaryImage = false(rows, cols);

    % Iterate through the image and perform dilation


    for r = 2:rows-1
    for c = 2:cols-1
    % Check if any of the structuring element pixels overlap with
    the image
    if any(any(binaryImage(r-1:r+1, c-1:c+1) & se))
    dilatedBinaryImage(r, c) = true;
    end
    end
    end

    % Display the original and manually dilated binary images


    figure;
    subplot(2, 1, 1),imshow(binaryImage);
    title('Original Binary Image');
    subplot(2, 1, 2),imshow(dilatedBinaryImage);
    title('Manually Dilated Binary Image');

    Output:-
    Experiment -13

    Objective:- Perform Erosion on Image.


    Implement:-

    % Create a binary image


    binaryImage = imread('cameraman.tif');
    binaryImage = im2bw(binaryImage);
    % Create a structuring element (a simple 3x3 square)
    se = logical(ones(3, 3));

    [rows, cols] = size(binaryImage);


    erodedBinaryImage = true(rows, cols);

    % Iterate through the image and perform erosion


    for r = 2:rows-1
    for c = 2:cols-1
    % Check if all structuring element pixels overlap with the
    image
    if all(all(binaryImage(r-1:r+1, c-1:c+1) & se))
    erodedBinaryImage(r, c) = true;
    else
    erodedBinaryImage(r, c) = false;
    end
    end
    end

    % Display the original and manually eroded binary images


    figure;
    subplot(1, 2, 1),imshow(binaryImage);
    title('Original Binary Image');

    subplot(1, 2, 2),imshow(erodedBinaryImage);
    title('Manually Eroded Binary Image');

    Output:-
    Experiment -14

    Objective:- Perform Perforn Closing(Dilation Followed by Erosion) on Image.

    Implement:-

    %Closing
    % Create a binary image
    binaryImage = imread('cameraman.tif')
    binaryImage = im2bw(binaryImage);
    % Create a structuring element (a simple 3x3 square)
    se = logical(ones(3, 3));

    % Get the size of the binary image


    [rows, cols] = size(binaryImage);

    % Initialize the dilatedresult image


    dilatedBinaryImage = false(rows, cols);

    % Display the original image


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

    % Iterate through the image and perform dilation


    for r = 2:rows-1
    for c = 2:cols-1
    % Check if any of the structuring element pixels overlap with
    the image
    if any(any(binaryImage(r-1:r+1, c-1:c+1) & se))
    dilatedBinaryImage(r, c) = true;
    end
    end
    end

    % Display the dilated image


    figure;
    subplot(2, 2, 2), imshow(binaryImage), title('Dilated Image');

    % Initialize the result image


    erodedBinaryImage = true(rows, cols);

    % Iterate through the image and perform erosion


    for r = 2:rows-1
    for c = 2:cols-1
    % Check if all structuring element pixels overlap with the image
    if all(all(dilatedBinaryImage(r-1:r+1, c-1:c+1) & se))
    erodedBinaryImage(r, c) = true;
    else
    erodedBinaryImage(r, c) = false;
    end
    end
    end

    subplot(2, 2, 3), imshow(erodedBinaryImage), title('Erosion Binary


    Image');

    Output:-

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