how to solve image process error using MATLAB

Loading Images, converting color image to grayscale, writing the image into a file

A='D:\.. ..\img1.bmp';

B=imread(A,'bmp');

figure(1),imshow(B);

C=rgb2gray(B);

figure(2),imshow(C);

imwrite(C,'C:\Users\pc\Desktop\.. ..\imgwrite1.jpg','jpg');

A = im2double(B)

%{

I2 = im2double(I) converts the intensity image I to double precision,

rescaling the data if necessary.

I can be a grayscale intensity image, a truecolor image, or a binary image.

If the input image is of class double,

then the output image is identical.

%}

H=im2double(imread(A,'bmp'));

%{

Adjust image intensity values or colormap

= imadjust(I,[low_in; high_in],[low_out; high_out]) maps the values in I to new values in J

such that values between low_in and high_in map to values between low_out and high_out.

Note If high_out is less than low_out,

imadjust reverses the output image, as in a photographic negative.

%}

C=imadjust(B,[0 1],[1 0]);

%Enhance contrast using histogram equalization

E=histeq(B);

%{

level = graythresh(I) computes a global threshold (level) that can be used to convert an intensity image to a binary image

with im2bw.

level is a normalized intensity value that lies in the range [0, 1].

%}

F = graythresh(B);

%{

BW = im2bw(I, level)

converts the grayscale image I to a binary image.

The output image BW replaces all pixels in

the input image with luminance greater than level with the value 1 (white)

and replaces all other pixels with the value 0 (black)

%}

G= im2bw(B,F);

A='D:\.. .. \img1.bmp';

H=im2double(imread(A,'bmp'));

figure(1), imshow(H);

B= rgb2gray(H);

figure(2), imshow(B);

C=imadjust(B,[0 1],[1 0]);

figure(3),imshow(C);

E=histeq(B);

figure(4), imshow(E);

F = graythresh(B);

G= im2bw(B,F);

figure(5), imshow(G);

Adding salt and pepper noise to an image

II = imread('eight.tif'); I=rgb2gray(II);

J = imnoise(I,'salt & pepper');

figure, imshow(I)

figure, imshow(J)

Filtering noise using median filter

II = imread('eight.tif');I=rgb2gray(II);

J = imnoise(I,'salt & pepper');

figure, imshow(I)

figure, imshow(J)

k=medfilt2(J);figure, imshow(k);

I=imread('rice.tif'); % load MATLAB image

subplot(2,2,1);

imshow(I); title('original image');

h=fspecial('sobel');

A=imfilter(I,h);

subplot(2,2,2);

imshow(A);title('horizontal sobel');

B=edge(I,'sobel',[],'both');

subplot(2,2,3);imshow(B);title('sobel');

C=edge(I,'canny',[],1);

subplot(2,2,4);imshow(C);title('canny');

}ii=imread('eight.tif');

i=rgb2gray(ii);

h=fspecial('sobel'); % creat sobel hz edge detection

a=imfilter(i,h);% to get vertival direction use h’

b=edge(i,'sobel',[],'both');% filter sobel in both directions

c=edge(i,'canny',[],1);% canny edge detection and 1 is the standard deviation of gaussian filter

subplot(2,2,1),imshow(i);% original image

subplot(2,2,2),imshow(a);% hz sobel

subplot(2,2,3), imshow(b);% sobel both directions

subplot(2,2,4), imshow(c);% canny edge detection

[KALYAN ACHARJYA]

What is the Error & which line?

Answer 1

[DGM]

There are many errors. I'm just going to add comments. This needs a lot of cleanup.

A='peppers.png'; % <-- i'm using this instead
B=imread(A);
figure(1),imshow(B);
C=rgb2gray(B);
figure(2),imshow(C);
imwrite(C,'c.jpg');  % <-- i'm using this instead
A = im2double(B);

%{
I2 = im2double(I) converts the intensity image I to double precision, 
rescaling the data if necessary. 
I can be a grayscale intensity image, a truecolor image, or a binary image.
If the input image is of class double, 
then the output image is identical.
%}
%H=im2double(imread(A)); % <-- A is a numeric array, not a filename
H=im2double(A);

%{
Adjust image intensity values or colormap
 = imadjust(I,[low_in; high_in],[low_out; high_out]) maps the values in I to new values in J 
such that values between low_in and high_in map to values between low_out and high_out.
Note   If high_out is less than low_out, 
imadjust reverses the output image, as in a photographic negative.
%}
C=imadjust(B,[0 1],[1 0]);

%Enhance contrast using histogram equalization
E=histeq(B);

%{
level = graythresh(I) computes a global threshold (level)  that can be used to convert an intensity image to a binary image 
with im2bw. 
level is a normalized intensity value that lies in the range [0, 1].
%}
F = graythresh(B);

%{
BW = im2bw(I, level) 
converts the grayscale image I to a binary image. 
The output image BW replaces all pixels in 
the input image with luminance greater than level with the value 1 (white) 
and replaces all other pixels with the value 0 (black)
%}
G= im2bw(B,F);

%%
A='peppers.png';
H=im2double(imread(A));
figure(1), imshow(H);
B= rgb2gray(H);
figure(2), imshow(B);
C=imadjust(B,[0 1],[1 0]);
figure(3),imshow(C);
E=histeq(B);
figure(4), imshow(E);
F = graythresh(B);
G= im2bw(B,F);
figure(5), imshow(G);

%% these entire blocks are redundant; delete one
%Adding salt and pepper noise to an image
II = imread('eight.tif'); 
%I=rgb2gray(II); % <-- II is not an RGB image
I = II; % <-- i'm using this instead
J = imnoise(I,'salt & pepper');
figure, imshow(I)
figure, imshow(J)

%Filtering noise using median filter
II = imread('eight.tif');
%I=rgb2gray(II); % <-- II is not an RGB image
I = II; % <-- i'm using this instead
J = imnoise(I,'salt & pepper');
figure, imshow(I)
figure, imshow(J)

%%
k=medfilt2(J);figure, imshow(k);
%I=imread('rice.tif'); % <-- file does not exist
I=imread('rice.png'); % <-- i'm using this instead

subplot(2,2,1);
imshow(I); title('original image');
h=fspecial('sobel');
A=imfilter(I,h);
subplot(2,2,2);
imshow(A);title('horizontal sobel');
B=edge(I,'sobel',[],'both');
subplot(2,2,3);imshow(B);title('sobel');
C=edge(I,'canny',[],1);
subplot(2,2,4);imshow(C);title('canny');

%}ii=imread('eight.tif');  % <-- dangling }
ii=imread('eight.tif');  % removed
%i=rgb2gray(ii); % <-- again, ii is not an RGB image
i = ii; % <-- i'm using this instead

h=fspecial('sobel'); % creat sobel hz edge detection
a=imfilter(i,h);% to get vertival direction use h’
b=edge(i,'sobel',[],'both');% filter sobel in both directions
c=edge(i,'canny',[],1);% canny edge detection and 1 is the standard deviation of gaussian filter
subplot(2,2,1),imshow(i);% original image
subplot(2,2,2),imshow(a);% hz sobel
subplot(2,2,3), imshow(b);% sobel both directions
subplot(2,2,4), imshow(c);% canny edge detection