I am having issue with cropping of image. My task includes an image. I have to crop the image at x,y coordinates, which I have tried and got success.
Now I want to show/save both images, the cropped one and also the image which is being cropped (which will have a subtracted area of cropped part just like subtracting the small cavity from the image).
My Code:
B = imread('B1.jpg');
% figure,imshow(B)
GimageB = rgb2gray(B);
% figure, imshow(GimageB)
J = imcrop(B,[284 235 95 80]);
figure, imshow(J)
To show the image without the "extracted" area, fill that area with zero!
img=rgb2gray(imread('http://weknowyourdreams.com/images/cat/cat-03.jpg'));
img2 = imcrop(img,[500 600 700 800]);
img3=img;
% fill area with zero (note the numbers, compare to imcrop)
img3(500:500+700, 600:600+800)=0;
figure()
imshow(img3)
Related
As part of a 'Capture The Flag' challenge the attached jpg was scrambled to obscure the content. The image ("flagpoles.jpg") is 1600 pixels by 1600 pixels. The concentric lines appear to have a blocksize of 10 pixels wide. (It resembles a Frank Stella painting). It appears the original image has been split into four portions which are arranged symmetrically around the center. I have been trying to write a python script to work through the pixels and unscramble the concentric squares. My efforts have resulted in two useless recurring conditions, either no change or increased scrambling. I think this might be because I am working on the entire image and it might be better to try and unscramble part of it. Here is the code I have. At the moment it only processes half of the pixels because I am trying to match up portions of the picture with each other. I tried sending the blocks to the other side of the image to try and match them up, but there is no improvement. Any assistance in getting a clear picture would be gratefully received.
from PIL import Image
import math
im = Image.open("flagpoles.jpg", "r")
pic = im.load()
def rot(A, r, x1, y1):
myArray = []
for i in range(r):
myArray.append([])
for j in range(r):
myArray[i].append(pic[x1+i, y1+j])
for i in range(r):
for j in range(r):
pic[x1+i,y1+j] = myArray[r-1-i][r-1-j]
xres = 800
yres = 800
blocksize = 10
for i in range(blocksize, blocksize+1):
for j in range(int(math.floor(float(xres)/float(blocksize+2+i)))):
for k in range(int(math.floor(float(yres)/float(blocksize+2+i)))):
rot(pic, blocksize+2+i, j*(blocksize+2+i), k*(blocksize+2+i))
im.save("hopeful.png")
print("Finished!")
The image seems to consist of concentric square boxes of width 10 pixels, each rotated by 90° relative to the previous one. After every four rotations, the pixels are once again oriented in the same direction.
You can easily undo this by making a copy of the image and repeatedly rotating by 270° while cropping away a 10 px border. Paste these rotated images back into the corresponding locations to retrieve the original image.
from PIL import Image
step_size = 10
angle_step = 270
img = Image.open("flagpoles.jpg", "r")
img.load()
w = img.width
assert img.height == w
img_tmp = img.copy() # Copy of the image that we're going to rotate
offset = 0 # Coordinate where the rotated images should be pasted
cropmax = w - step_size # Maximum coordinate of cropping region
while cropmax > step_size:
# Rotate the copy of the image
img_tmp = img_tmp.rotate(angle_step)
# Paste it into the original image
img.paste(img_tmp, (offset,offset))
# Crop a 10 px border away from the copy
img_tmp = img_tmp.crop((step_size, step_size, cropmax, cropmax))
# Update the crop position and width for the next iteration
cropmax -= step_size * 2
offset += step_size
img.save("fixed.jpg")
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
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 have two sets of images of different size for each set. The first set is images of 400x400 pixels with real picture objects.
The second set is 319x319, with image silhouettes of different scale than the real picture objects.
What I want to achieve, is basically to have the silhouettes replaced by the real picture objects (i.e. beaver) of the first set. So the end result will be 319x319 resolution images with real picture objects. Here is an example:
The first set images cannot simply be resized to 319x319, since the beaver will not match the silhouette. There are about 100 images with different "beaver size to beaver's silhouette size" relationships. Is there a way to automate this procedure?
So far, I've tried #cxw suggestion up to step 2. Here is the code of EllipseDirectFit I used. And here is my code to plot the images with the ellipse fits. I don't know how to proceed to steps 3-5.. I think from EllipseDirectFit function -> 2*abs(A(1)) should be the ellipsi's major axes. (NOTE: 'a1.bmp' is the real image and 'b1.bmp' is the silhouette).
In case anyone else has the same problem as me, I post the code that solved my problem. I actually followed cxw's suggestion and fitted an ellipse for both real and silhouette pictures, then resized the real picture based on the ratio of the silhouette-ellipse's major axis to the real-ellipse major axis. This made the image object match in size the silhouette image object (i.e. the beaver). Then I either cropped, or added border pixels to match the resolution I needed (i.e. 319x319).
% fetching the images
realList = getAllFiles('./real_images'); % getAllFiles => StackOverflow function
silhList = getAllFiles('./silhouettes');
for qq = 1:numel(realList)
% Name of the file to save
str = realList{qq}(15:end);
a = imread(realList{qq}); % assign real image
background_Ra = a(1,1,1); % getting the background colors
background_Ga = a(1,1,2);
background_Ba = a(1,1,3);
% finding the points (x,y) to pass to fit_ellipse
[x1,y1]=find(a(:,:,1)~=background_Ra | a(:,:,2)~=background_Ga | a(:,:,3)~=background_Ba);
% fitting an ellipse to these points
z1 = fit_ellipse(x1,y1); % Mathworks file exchange function
b = imread(silhList{qq}); % assign silhouette image
background_R2b = b(1,1,1); % getting the background colors
background_G2b = b(1,1,2);
background_B2b = b(1,1,3);
% finding the points (x,y) to pass to fit_ellipse
[x2,y2]=find(b(:,:,1)~=background_R2b & b(:,:,2)~=background_G2b & b(:,:,3)~=background_B2b);
% fitting an ellipse to these points
z2 = fit_ellipse(x2,y2);
% ratio of silhouette's ellipse major axis to real image's ellipse
% major axis
ellaxratio = z2.long_axis/z1.long_axis;
% resizing based on ellaxratio, so that the real image object size will
% now fit the silhouette's image object size
c = imresize(a,ellaxratio); c = rgb2gray(c);
bordercolor = c(end,end);
% if the resulting image is smaller, add pixels around it until they
% match with the silhouette image resolution
if size(c) < 319
while size(c) < 319
% 'addborder' is a Mathworks file exchange function
c = addborder(c(:,:,1),1, bordercolor ,'outer');
end
% if the resulting image is larger, crop pixels until they match
else size(c) > 319
while size(c) > 319
c = c(2:end-1,2:end-1);
end
end
% in a few cases, the resulting resolution is 318x318, instead of
% 319x319, so a small adjustment won't hurt.
if size(c) ~= 319
c = imresize(c,[319 319]);
end
% saving..
imwrite(c,['./good_fits/' str '.bmp'])
end
I don't have code for this, but here's how I would proceed, just off-hand. There's almost certainly a better way :) .
For each of the real image and the silhouette image:
Get the X, Y coordinates of the pixels that aren't the background. Edit Example tested in Octave:
background_R = img(1,1,1)
background_G = img(1,1,2)
background_B = img(1,1,3)
[xs,ys]=find(img(:,:,1)~=background_R | img(:,:,2)~=background_G | img(:,:,3)~=background_B)
The logical OR is because the image can differ from the background in any color component.
Fit an ellipse to the X, Y coordinate pairs you found. E.g., use this routine from File Exchange. (Actually, I suppose you could use a circle fit or any other shape fit you wanted, as long as size and position are the only differences between the non-background portions of the images.)
Now you have ellipse parameters for the real image and the silhouette image. Assuming the aspect ratios are the same, those ellipses should differ only in center and scale.
Resize the real image (imresize) based on the ratio of silhouette ellipse major axis length to real image ellipse major axis length. Now they should be the same size.
Find the centers. Using the above fit routine,
A=EllipseDirectFit(...)
% switch to Mathworld notation from http://mathworld.wolfram.com/Ellipse.html
ma=A(1); mb=A(2)/2; mc=A(3); md=A(4)/2; mf=A(5)/2; mg=A(6);
center_x = (mc*md-mb*mf)/(mb**2-ma*mc)
center_y = (ma*mf-mb*md)/(mb**2-ma*mc)
Move the real image data in a 3-d matrix so that the ellipse centers
coincide. For example,
cx_silhouette = ... (as above, for the silhouette image)
cy_silhouette = ...
cx_real = ... (as above, for the *resized* real image)
cy_real = ...
shifted = zeros(size(silhouette_image)) % where we're going to put the real image
deltax = cx_silhouette - cx_real
deltay = cy_silhouette - cy_real
% if deltax==deltay==0, you're done with this step. If not:
portion = resized_real_image(max(deltay,0):319-abs(deltay), max(deltax,0):319-abs(deltax), :); % or something like that - grab the overlapping part of the resized real image
shifted(max(deltay,0):min(deltay+319,319), max(deltax,0):min(deltax+319,319), :) = portion; % or something like that - slide the portion of the resized real image in x and y. Now _shifted_ should line up with the silhouette image.
Using the background color (or the black silhouette — same difference) as a mask, copy pixels from the resized, moved real image into the silhouette image.
Hope this helps!
I use following example codes to overlay an index image on a background image (in the example below it is RGB but for my problem the background is a gray sacle image). Then my questions are:
How to display the colorbar on the side of the overlaid image? The colorbar should be the colorbar of the overlaid index image NOT the colorbar of the background image.
How to fix the color map range of the overlaid index image? I have several pairs of gray scale background + overlaid index image (using 'jet' color map). I need them to be shown in the same scale. I try to use set(iim2,'caxis', [0 1]); but the 'image' in Matlab hasn't got the property of 'caxis'.
Please help and thanks very much!
% Create the background
% This example uses a blend of colors from left to right, converted to a TrueColor image
% Use repmat to replicate the pattern in the matrix
% Use the "jet" colormap to specify the color space
bg = ind2rgb(repmat(1:64,64,1),jet(64));
% Create an image and its corresponding transparency data
% This example uses a random set of pixels to create a TrueColor image
im = rand(100,100,3);
% Make the image fade in from left to right by designing its alphadata
% Use repmat to replicate the pattern in the transparency fading
imAlphaData = repmat(0:1/size(im,2):1-1/size(im,2),size(im,1),1);
% Display the images created in subplots
hf = figure('units','normalized','position',[.2 .2 .6 .6]);
ax1 = subplot(2,3,1);
ibg = image(bg);
axis off
title('Background')
ax2 = subplot(2,3,4);
iim = image(im);
axis off
title('Image without transparency yet')
% Now set up axes that overlay the background with the image
% Notice how the image is resized from specifying the spatial
% coordinates to locate it in the axes.
ax3 = subplot(2,3,[2:3, 5:6]);
ibg2 = image(bg);
axis off
hold on
% Overlay the image, and set the transparency previously calculated
iim2 = image(im,'XData',[30 50],'YData',[10 30]);
set(iim2,'AlphaData',imAlphaData);
title(sprintf('Using transparency while overlaying images:\nresult is multiple image objects.'))