How to find the matrix value from this signature picture?
how to find the matrix value from:
RGB image to Gray Scale
Gray Scale to Binary Image
Binary Image to Inverted Binary Image
Inverted Binary Image with clean border
Inverted Binary Image with clean border to extract bounding box
I already know the code from RGB to extract bounding box:
%// Read in image and convert to binary
%// Also clear the borders
im = imread('http://postimg.org/image/qptg2jgsz/2a2705fb/');
im_bw = imclearborder(im2bw(rgb2gray(im)));
%// Find those non-zero pixel locations
[rows, cols] = find(im_bw);
min_row = min(rows);
max_row = max(rows);
min_col = min(cols);
max_col = max(cols);
%// Now extract the bounding box
bb = im_bw(min_row:max_row, min_col:max_col);
%// Show the image
imshow(bb);
Edit: Actually in your code you already have the binary image... BW stands for Black and white...
Have you tried the basic Matlab example?
BW = im2bw(I, level);
In case you want an automatic choice of threshold level try Otsu's method.
level = graythresh(I)
Related
Let say we have grayscale image im:
And use imagesc(im) to get:
With code:
im = rgb2gray(im2single(imread('tesla.jpg'))); % get image
h = imagesc(log(abs(fftshift(fft2(im))))); % imagesc handle
How can one convert the intensity graphic h (2nd image) to a standard RGB image (2x2 float matrix that one can manipulate, crop, etc) in matlab?
I don't need the axes, numbers or tics of the intensity image, I only need to maintain the color.
Thank you.
%turn off the axes
axis off
%save the image
saveas(h,'test.png')
%read the saved image
im_fft = imread('test.png');
%remove white border
sum_img = sum(im_fft,3); sum_img(sum_img(:) ~= 255*3) = 0; sum_img = logical(sum_img);
im_fft = im_fft(~all(sum_img,2), ~all(sum_img,1),:);
%Done!
figure, imshow(im_fft)
The resulting image can be used only for presentations/illustrations, not for analysis - quantization and sampling corrupts it significantly
what I am trying is to compare two gray scale images by ploting their intensity into graph. The code is bellow is for single image.
img11 = imread('img.bmp');
[rows cols ColorChannels] = size(img11);
for i=1:cols
for j=1:rows
intensityValue = img11(j,i);
end
end
% below trying different plot method
plot(intensityValue);
plot(1:length(img11),img11);
plot(img11(:))
My expected result for two images is like below pictures: here
not like
this here
Based on your code you should be able to do the following.
img11 = imread('img1.bmp');
img22 = imread('img2.bmp');
figure;
imagesc(img11); % verify you image
figure;
plot(img11(:)); hold on;
plot(img22(:));
Using the command (:) will flatten a matrix into a single vector starting at the top left and going down in columns. If that is not the orientation that you want you try to rotate/transpose the image (or try using reshape(), but it might be confusing at the start). Additionally, if your image has large variations in the pixel intensity moving average filter can be useful.
Len = 128;
smooth_vector = filter(ones(Len,1)/Len,1,double(img11(:)));
figure; plot(smooth_vector);
I'm implementing image mosaic in Matlab using SURF.the problem is
outputView = imref2d(size(img1)*2);
Ir = imwarp(img2,tform,'OutputView',outputView);
it produces
i want it something like this
if i change
outputView = imref2d(size(img1)*2);
to
outputView = imref2d(size(img1));
matlab crops the second image so it can fit in first image size after transforming.
Notice that when you warp the image with respect to the target plane, many of the pixels in this new plane are equal to 0. A very rudimentary algorithm is to simply threshold your image so that you find values above 0 then find the largest bounding box that encompasses the non-zero pixels... then crop:
[rows,cols] = find(Ir(:,:,1) > 0);
topLeftRow = min(rows);
topLeftCol = min(cols);
bottomRightRow = max(rows);
bottomRightCol = max(cols);
Ir_crop = Ir(topLeftRow:bottomRightRow, topLeftCol:bottomRightCol, :);
I am trying to find a way to crop from a circle object (Image A) the largest square that can fit inside it.
Can someone please explain/show me how to find the biggest square fit parameters of the white space inside the circle (Image I) and based on them crop the square in the original image (Image A).
Script:
A = imread('E:/CirTest/Test.jpg');
%imshow(A)
level = graythresh(A);
BW = im2bw(A,level);
%imshow(BW)
I = imfill(BW, 'holes');
imshow(I)
d = imdistline;
[centers, radii, metric] = imfindcircles(A,[1 500]);
imageCrop=imcrop(A, [BoxBottomX BoxBottomY NewX NewY]);
I have a solution for you but it requires a bit of extra work. What I would do first is use imfill but directly on the grayscale image. This way, noisy pixels in uniform areas get inpainted with the same intensities so that thresholding is easier. You can still use graythresh or Otsu's thresholding and do this on the inpainted image.
Here's some code to get you started:
figure; % Open up a new figure
% Read in image and convert to grayscale
A = rgb2gray(imread('http://i.stack.imgur.com/vNECg.jpg'));
subplot(1,3,1); imshow(A);
title('Original Image');
% Find the optimum threshold via Otsu
level = graythresh(A);
% Inpaint noisy areas
I = imfill(A, 'holes');
subplot(1,3,2); imshow(I);
title('Inpainted image');
% Threshold the image
BW = im2bw(I, level);
subplot(1,3,3); imshow(BW);
title('Thresholded Image');
The above code does the three operations that I mentioned, and we see this figure:
Notice that the thresholded image has border pixels that need to be removed so we can concentrate on the circular object. You can use the imclearborder function to remove the border pixels. When we do that:
% Clear off the border pixels and leave only the circular object
BW2 = imclearborder(BW);
figure; imshow(BW2);
... we now get this image:
Unfortunately, there are some noisy pixels, but we can very easily use morphology, specifically the opening operation with a small circular disk structuring element to remove these noisy pixels. Using strel with the appropriate structuring element in addition to imopen should help do the trick:
% Clear out noisy pixels
SE = strel('disk', 3, 0);
out = imopen(BW2, SE);
figure; imshow(out);
We now get:
This mask contains the locations of the circular object we now need to use to crop our original image. The last part is to determine the row and column locations using this mask to locate the top left and bottom right corner of the original image and we thus crop it:
% Find row and column locations of circular object
[row,col] = find(out);
% Find top left and bottom right corners
top_row = min(row);
top_col = min(col);
bottom_row = max(row);
bottom_col = max(col);
% Crop the image
crop = A(top_row:bottom_row, top_col:bottom_col);
% Show the cropped image
figure; imshow(crop);
We now get:
It's not perfect, but it will of course get you started. If you want to copy and paste this in its entirety and run this on your computer, here we are:
figure; % Open up a new figure
% Read in image and convert to grayscale
A = rgb2gray(imread('http://i.stack.imgur.com/vNECg.jpg'));
subplot(2,3,1); imshow(A);
title('Original Image');
% Find the optimum threshold via Otsu
level = graythresh(A);
% Inpaint noisy areas
I = imfill(A, 'holes');
subplot(2,3,2); imshow(I);
title('Inpainted image');
% Threshold the image
BW = im2bw(I, level);
subplot(2,3,3); imshow(BW);
title('Thresholded Image');
% Clear off the border pixels and leave only the circular object
BW2 = imclearborder(BW);
subplot(2,3,4); imshow(BW2);
title('Cleared Border Pixels');
% Clear out noisy pixels
SE = strel('disk', 3, 0);
out = imopen(BW2, SE);
% Show the final mask
subplot(2,3,5); imshow(out);
title('Final Mask');
% Find row and column locations of circular object
[row,col] = find(out);
% Find top left and bottom right corners
top_row = min(row);
top_col = min(col);
bottom_row = max(row);
bottom_col = max(col);
% Crop the image
crop = A(top_row:bottom_row, top_col:bottom_col);
% Show the cropped image
subplot(2,3,6);
imshow(crop);
title('Cropped Image');
... and our final figure is:
You can use bwdist with L_inf distance (aka 'chessboard') to get the axis-aligned distance to the edges of the region, thus concluding the dimensions of the largest bounded box:
bw = imread('http://i.stack.imgur.com/7yCaD.png');
lb = bwlabel(bw);
reg = lb==2; %// pick largest area
d = bwdist(~reg,'chessboard'); %// compute the axis aligned distance from boundary inward
r = max(d(:)); %// find the largest distance to boundary
[cy cx] = find(d==r,1); %// find the location most distant
boundedBox = [cx-r, cy-r, 2*r, 2*r];
And the result is
figure;
imshow(bw,'border','tight');
hold on;
rectangle('Position', boundedBox, 'EdgeColor','r');
Once you have the bounding box, you can use imcrop to crop the original image
imageCrop = imcrop(A, boundedBox);
Alternatively, you can
imageCrop = A(cy + (-r:r-1), cx + (-r:r-1) );
I can't find information online about the intensity rescaling of a 3D image made of several 2D images.
I'm looking for the same function as imadjust which only works for 2D images.
My 3D image is the combination of 2D images stacked together but I have to process the 3D image and not the 2D images one by one.
I can't loop imadjust because I want to process the images as one, to consider all the information available, in all directions.
For applying imadjust for set of 2D grayscale images taking the whole value into account, this trick might work
a = imread('pout.tif');
a = imresize(a,[256 256]); %// re-sizing to match image b's dimension
b = imread('cameraman.tif');
Im = cat(3,a,b);
%//where a,b are separate grayscale images of same dimensions
%// if you have the images separately you could edit this line to
%// Im = cat(2,a,b);
%// and also avoid the next step
%// reshaping into a 2D matrix to apply imadjust
Im = reshape(Im,size(Im,1),[]);
out = imadjust(Im); %// applying imadjust
%// finally reshaping back to its original shape
out = reshape(out,size(a,1),size(a,2),[]);
To check:
x = out(:,:,1);
y = out(:,:,2);
As you could see from the Workspace image, the first image (variable x) is not re-scaled to 0-255 as its previous range (variable a) was not near the 0 point.
WorkSpace:
Edit: You could do this as a one-step process like this: (as the other answer suggests)
%// reshaping to single column using colon operator and then using imadjust
%// then reshaping it back
out = reshape(imadjust(Image3D(:)),size(Image3D));
Edit2:
As you have image as cell arrays in I2, try this:
I2D = cat(2,I2{:})
The only way to do this for 3D image is to treat the data as a vector and then reshape back.
Something like this:
%create a random 3D image.
x = rand(10,20,30);
%adjust intensity range
x_adj = imadjust( x(:), size(x) );