1 image reading and rotation
a = imread(')； % read image
Subplots (2, 2, 1), im show (a), title ('original'); % output image
I = RGB 2 gray(A)；
Subplot (2,2,2), imshow(A), title ('gray image');
Subplot (2,2,3), imhist(I), title ('gray image histogram'); % output original histogram
θ= 30°； J = imrotate(I，theta)； % Try changing the angle θ.
Subplot (2, 2, 4), instant message display (j), title ("rotating image")
2 edge detection
I=imread('C:\Users\HP\Desktop\ usual summary \ luffy.jpg');
Subplots (2, 2, 1), im show (I), title ('original');
I 1=edge(I，' Sobel ')；
Subplot (2 2,2,2), im show (i 1), title ('Sobel edge detection');
I2=edge(I，' prewitt ')；
Subplot (2, 2, 3), im show (I2), title ('prewitt edge detection');
I3=edge(I，' log ')；
Subplots (2, 2, 4), im show (i3), Title ('log edge detection');
3 image inversion
MATLAB program is implemented as follows:
I = im read(' xian . BMP ')；
j = double(I)；
J =-J+(256- 1)； % image inversion linear transformation
h = uint 8(J)；
Subplots (1, 2, 1), im show (i);
Subplots (1, 2, 2), im show (h);
4. Gray linear transformation
MATLAB program is implemented as follows:
I = im read(' xian . BMP ')；
Subplots (2,2, 1), im show (i);
Title ("original");
axis()；
Axis open; % display coordinate system
I 1 = RGB 2 gray(I)；
Subplots (2, 2, 2), im show (I1);
Title ("grayscale image");
axis()；
Axis open; % display coordinate system
J=imadjust(I 1，[0. 1 0.5]，[])； % local stretching, stretching gray scale is between [0. 1.5] and [0. 1].
Subplots (2, 2, 3), im show (j);
Title ('Linear Transform Image [0.1.5]');
axis()；
Grid open; % show gridlines
Axis open; % display coordinate system
K=imadjust(I 1，[0.3 0.7]，[])； % local stretching, stretching gray scale is between [0.3-0.7] and [0. 1].
Subplots (2, 2, 4), im show (k);
Title ('Linear Transform Image [0.30.7]');
axis()；
Grid open; % show gridlines
Axis open; % display coordinate system
5. Nonlinear conversion
MATLAB program is implemented as follows:
I = im read(' xian . BMP ')；
I 1 = RGB 2 gray(I)；
Subplots (1, 2, 1), im show (I1);
Title ("grayscale image");
axis()；
Grid open; % show gridlines
Axis open; % display coordinate system
j = double(I 1)；
J = 40 *(log(J+ 1))；
h = uint 8(J)；
Subplots (1, 2, 2), im show (h);
Title ("Logarithmically transformed image");
axis()；
Grid open; % show gridlines
Axis open; % display coordinate system
4. Histogram equalization
MATLAB program is implemented as follows:
I = im read(' xian . BMP ')；
I = RGB 2 gray(I)；
Figure;
Subplots (2, 2,1);
Imshow (1);
Subplots (2, 2, 2);
Imhist (1);
I 1 = histeq(I)；
Figure;
Subplots (2, 2,1);
imshow(I 1)；
Subplots (2, 2, 2);
imhist(I 1)；
5. Linear smoothing filter
Using MATLAB to realize the program of suppressing noise by regional average method;
I = im read(' xian . BMP ')；
Subplot (23 1)
Imshow (1)
Title ("Original Image")
I = RGB 2 gray(I)；
I 1=imnoise(I,' salt & pepper', 0.02);
Subplots (232)
imshow(I 1)
Title ("Image with Salt and Pepper Noise")
k 1 = filter 2(f special(' average '，3)，I 1)/255； % 3*3 Template smoothing filtering.
k2=filter2(fspecial('average '，5)，I 1)/255； % for 5*5 template smoothing filter k3 = filter2 (fspecial ('average', 7), I1)/255; % 7*7 template smooth filtering.
k4=filter2(fspecial('average '，9)，I 1)/255； % 9*9 template smooth filtering.
Subplot (233), im show (k1); Title ('3*3 template smooth filtering');
Subplots (234), IM Show (K2); Title ('5*5 template smooth filtering');
Subplots (235), IM Show (K3); Title ('7*7 template smooth filtering');
Subplots (236), IM SHOW (K4); Title ('9*9 Template Smooth Filtering');
6. Median filter
The program of median filtering with MATLAB is as follows:
I = im read(' xian . BMP ')；
I = RGB 2 gray(I)；
J=imnoise(I,' salt & pepper', 0.02);
Subplot (23 1), im show (I); Title ("original");
Subplot (232), im show (j); Title ('Add salt and pepper noise image');
k 1 = medfilt 2(J)； % 3*3 template median filtering.
k2=medfilt2(J，)； % for 5*5 template median filtering.
k3=medfilt2(J，)； Median filtering of% 7*7 template.
k4=medfilt2(J，)； Median filtering of% 9*9 template.
Subplot (233), im show (k1); Title ('3*3 template median filtering');
Subplots (234), IM Show (K2); Title ('5*5 template median filtering');
Subplots (235), IM Show (K3); Title ('7*7 template median filtering');
Subplots (236), IM SHOW (K4); Title ('9*9 template median filtering');
7. Use Sobel operator and Laplacian operator to sharpen the image:
I = im read(' xian . BMP ')；
Subplots (2,2, 1), im show (i);
Title ("original");
axis()；
Grid open; % show gridlines
Axis open; % display coordinate system
I 1 = im2bw(I)；
Subplots (2, 2, 2), im show (I1);
Title ("binary image");
axis()；
Grid open; % show gridlines
Axis open; % display coordinate system
h = f special(' Sobel ')； % select sobel operator
J=filter2(H，I 1)； % convolution operation
Subplots (2, 2, 3), im show (j);
Title ("sobel operator sharpening image");
axis()；
Grid open; % show gridlines
Axis open; % display coordinate system
h=[0 1 0， 1 -4 1，0 1 0]； % Laplace operator
J 1=conv2(I 1, h,' same'); % convolution operation
Subplots (2, 2, 4), im show (j1);
Title ('Laplace operator sharpening image');
axis()；
Grid open; % show gridlines
Axis open; % display coordinate system
8. Gradient operator edge detection
MATLAB is used to realize the following functions:
I = im read(' xian . BMP ')；
Subplots (2, 3,1);
Imshow (1);
Title ("original");
axis()；
Grid open; % show gridlines
Axis open; % display coordinate system
I 1 = im2bw(I)；
Subplots (2, 3, 2);
imshow(I 1)；
Title ("binary image");
axis()；
Grid open; % show gridlines
Axis open; % display coordinate system
I2=edge(I 1，' Roberts ')；
Figure;
Subplots (2, 3, 3);
imshow(I2)；
Title ("roberts operator segmentation result");
axis()；
Grid open; % show gridlines
Axis open; % display coordinate system
I3=edge(I 1，' Sobel ')；
Subplots (2, 3, 4);
im show(I3)；
Title ('sobel operator segmentation result');
axis()；
Grid open; % show gridlines
Axis open; % display coordinate system
I4=edge(I 1，' Prewitt ')；
Subplots (2, 3, 5);
im show(I4)；
Title ("Prewitt operator segmentation result");
axis()；
Grid open; % show gridlines
Axis open; % display coordinate system
9. Logarithmic operator for edge detection
MATLAB program is used to realize the following functions:
I = im read(' xian . BMP ')；
Subplots (2, 2,1);
Imshow (1);
Title ("original");
I 1 = RGB 2 gray(I)；
Subplots (2, 2, 2);
imshow(I 1)；
Title ("grayscale image");
I2=edge(I 1，' log ')；
Subplots (2, 2, 3);
imshow(I2)；
Title ("Logarithmic Operator Division Result");
Edge detection of 10. Canny operator
MATLAB program is used to realize the following functions:
I = im read(' xian . BMP ')；
Subplots (2, 2,1);
Imshow (1);
Title ("Original Image")
I 1 = RGB 2 gray(I)；
Subplots (2, 2, 2);
imshow(I 1)；
Title ("grayscale image");
I2=edge(I 1，' canny ')；
Subplots (2, 2, 3);
imshow(I2)；
Title ("canny operator segmentation result");
1 1. boundary tracing (bwtraceboundary function)
Chromaticity control center
Clean up all
I = im read(' xian . BMP ')；
I guessed.
Imshow (1);
Title ("original");
I 1 = RGB 2 gray(I)； % Converts a color image to a grayscale image.
threshold = gray thresh(I 1)； % Calculate the threshold required to convert gray image into binary image.
BW=im2bw(I 1, threshold); % Converts a grayscale image to a binary image
I guessed.
im show(BW)；
Title ("binary image");
Dim = size (bw);
col = round(dim(2)/2)-90； % calculate starting point column coordinates
row=find(BW(:，col)， 1)； % Calculate the coordinates of the starting line
Connectivity = 8;
num _ points = 180；
Contour = bwtraceboundary, [row, col],' n', connectivity, num _ points);
% extract boundary
I guessed.
imshow(I 1)；
Hold on;
plot(contour(:，2)，contour(:， 1)，' g '，' LineWidth '，2)；
Title ("Boundary Tracking Image");
12. Hough transform
I = im read(' xian . BMP ')；
Pancake = rgb2gray (i);
Subplots (2, 2,1);
Imshow (pancake);
Title ("grayscale image");
axis()；
Grid open;
Axis open;
BW=edge (pancake, Prewitt);
Subplots (2, 2, 2);
im show(BW)；
Title ("image after edge detection by prewitt operator");
axis()；
Grid open;
Axis open;
[H，T，R]= Hough(BW)；
Subplots (2, 2, 3);
imshow(H，[]，' XData '，T，' YData '，R，' InitialMagnification '，' fit ')；
Title ('Hough Transform Diagram');
xlabel('\theta ')，ylabel(' \ rho ')；
Open the shaft, the shaft is normal, and keep it;
P=houghpeaks(H，5，' threshold '，ceil(0.3 * max(H(:)))；
x=T(P(:，2))； y=R(P(:， 1))；
Plot(x, y,' s',' color',' white');
lines=houghlines(BW，T，R，P，' FillGap '，5，' MinLength '，7)；
Subplots (2, 2, 4); , imshow (pancake);
Title ('Hough Transform Image Detection');
axis()；
Grid open;
Axis open;
Hold on;
max _ len = 0；
For k= 1: length (line)
xy =[lines(k). point 1； lines(k). point 2]；
Plot(xy (:,1), xy (:,2),' line width', 2,' color',' green');
Plot(xy( 1, 1), xy( 1, 2),' x',' line width', 2,' color',' yellow');
Plot(xy(2, 1), xy(2,2),' x',' line width', 2,' color',' red');
len = norm(lines(k). point 1-lines(k). point 2)；
if(len & gt； max_len)
max _ len = len
xy _ long = xy
end
end
Plot(xy_long (:,1), xy_long (:,2),' line width', 2,' color',' cyan');
13. histogram threshold method
Using MATLAB to realize histogram threshold method;
I = im read(' xian . BMP ')；
I 1 = RGB 2 gray(I)；
Figure;
Subplots (2, 2,1);
imshow(I 1)；
Title ("Gray Image")
axis()；
Grid open; % show gridlines
Axis open; % display coordinate system
[m，n]= size(I 1)； % Measure the image size parameter
GP = zero (1, 256); % Create a vector in advance to store the gray probability.
For k=0:255
GP(k+ 1)= length(find(I 1 = = k))/(m * n)； % Calculate the probability of each gray level and store it in the corresponding position in GP.
end
Plot (2 2,2,2), bar (0: 255, gp,' g')% plot histogram.
Title ("Gray Histogram")
Xlabel ('gray value')
Ylabel ("probability of occurrence")
I2=im2bw(I， 150/255)；
Subplots (2, 2, 3), IM Show (I2);
Title ("Segmentation Image with Threshold of 150")
axis()；
Grid open; % show gridlines
Axis open; % display coordinate system
I3=im2bw(I，200/255)； %
Subplots (2, 2, 4), IM Show (i3);
Title ("Segmentation Image with Threshold of 200")
axis()；
Grid open; % show gridlines
Axis open; % display coordinate system
14. Automatic threshold method: Otsu method
Realization of Otsu algorithm with MATLAB;
Chromaticity control center
Clean up all
I = im read(' xian . BMP ')；
Subplots (1, 2, 1), im show (i);
Title ("Original Image")
axis()；
Grid open; % show gridlines
Axis open; % display coordinate system
level = gray thresh(I)； % determine the gray threshold
BW=im2bw(I，level)；
Subplots (1, 2, 2), im show (bw);
Title ("Otsu Threshold Segmentation Image")
axis()；
Grid open; % show gridlines
Axis open; % display coordinate system
15. Expansion operation
I = im read(' xian . BMP ')； % load image
I 1 = RGB 2 gray(I)；
Subplots (1, 2,1);
imshow(I 1)；
Title ("Gray Image")
axis()；
Grid open; % show gridlines
Axis open; % display coordinate system
se=strel('disk '， 1)； % Generate circular structural elements
I2 = im dila(I 1，se)； % Expand the image with the generated structural elements
Subplots (1, 2, 2);
imshow(I2)；
Title ("Extended Image");
axis()；
Grid open; % show gridlines
Axis open; % display coordinate system
16. Corrosion operation
Realization of corrosion operation with MATLAB
I = im read(' xian . BMP ')； % load image
I 1 = RGB 2 gray(I)；
Subplots (1, 2,1);
imshow(I 1)；
Title ("Gray Image")
axis()；
Grid open; % show gridlines
Axis open; % display coordinate system
se=strel('disk '， 1)； % Generate circular structural elements
I2=imerode(I 1，se)； % Etch the image with the generated structural elements.
Subplots (1, 2, 2);
imshow(I2)；
Title ("Etched image");
axis()；
Grid open; % show gridlines
Axis open; % display coordinate system
Open and close operations.
Realization of opening and closing operation with MATLAB
I = im read(' xian . BMP ')； % load image
Subplots (2,2, 1), im show (i);
Title ("original");
axis()；
Axis open; % display coordinate system
I 1 = RGB 2 gray(I)；
Subplots (2, 2, 2), im show (I1);
Title ("grayscale image");
axis()；
Axis open; % display coordinate system
se=strel('disk '， 1)； % adopts a circle with a radius of 1 as the structural element.
I2=imopen(I 1，se)； % open operation
I3=imclose(I 1，se)； % close operation
Subplots (2, 2, 3), IM Show (I2);
Title ("Open image after calculation");
axis()；
Axis open; % display coordinate system
Subplots (2, 2, 4), IM Show (i3);
Title ("image after closing operation");
axis()；
Axis open; % display coordinate system
18. Combined opening and closing operations
I = im read(' xian . BMP ')； % load image
Subplots (3, 2, 1), im show (i);
Title ("original");
axis()；
Axis open; % display coordinate system
I 1 = RGB 2 gray(I)；
Subplots (3, 2, 2), im show (I1);
Title ("grayscale image");
axis()；
Axis open; % display coordinate system
se=strel('disk '， 1)；
I2=imopen(I 1，se)； % open operation
I3=imclose(I 1，se)； % close operation
Subplots (3, 2, 3), IM Show (I2);
Title ("Open image after calculation");
axis()；
Axis open; % display coordinate system
Subplots (3, 2, 4), IM Show (i3);
Title ("image after closing operation");
axis()；
Axis open; % display coordinate system
se=strel('disk '， 1)；
I4=imopen(I 1，se)；
I5=imclose(I4，se)；
Subplots (3, 2, 5), IM Show (i5); % switch operation image
Title ("Open-Close Operation Image");
axis()；
Axis open; % display coordinate system
I6=imclose(I 1，se)；
I7=imopen(I6，se)；
Subplots (3, 2, 6), IM Show (i7); % switch operation image
Title ("Close-Open Operation Image");
axis()；
Axis open; % display coordinate system
19. Morphological boundary extraction
Use MATLAB to realize the following functions:
I = im read(' xian . BMP ')； % load image
Subplots (1, 3, 1), im show (i);
Title ("original");
axis()；
Grid open; % show gridlines
Axis open; % display coordinate system
I 1 = im2bw(I)；
Subplot (1, 3, 2), im show (I1);
Title ("binary image");
axis()；
Grid open; % show gridlines
Axis open; % display coordinate system
I2 = bwperim(I 1)； % Get the perimeter of the area
Subplots (1, 3, 3), im show (I2);
Title ("Binary image of boundary perimeter");
axis()；
Grid open;
Axis open;
20. Morphological skeleton extraction
Use MATLAB to realize the following functions:
I = im read(' xian . BMP ')；
Subplots (2,2, 1), im show (i);
Title ("original");
axis()；
Axis open;
I 1 = im2bw(I)；
Subplots (2, 2, 2), im show (I1);
Title ("binary image");
axis()；
Axis open;
I2=bwmorph(I 1，' skel '， 1)；
Subplots (2, 2, 3), IM Show (I2);
Title ('1 skeleton extraction');
axis()；
Axis open;
I3=bwmorph(I 1，' skel '，2)；
Subplots (2, 2, 4), IM Show (i3);
Title ("Secondary Skeleton Extraction");
axis()；
Axis open;
2 1. Directly extract four vertex coordinates.
I = im read(' xian . BMP ')；
I = I(:，:， 1)；
BW = im2bw(I)；
I guessed.
imshow(~BW)
[x，y]=getpts
Smoothing filtering
h=fspecial('average '，9)；
I_gray=imfilter(I_gray，h，' replicate ')； % smooth filtering