how to separate objects in image using matlab
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Consider:
inpict = imread('toolspic.png'); % cropped from thumbnail
bpict = rgb2gray(inpict)<220; % pick a threshold to isolate from BG
S = regionprops(bpict,'boundingbox','filledimage');
C = cell(numel(S),1);
for n = 1:numel(S)
% get mask of object only
mk = S(n).FilledImage;
% get corresponding rectangular area of original image
bb = floor(S(n).BoundingBox);
samp = inpict(bb(2):bb(2)+bb(4)-1,bb(1):bb(1)+bb(3)-1,:);
% remove adjacent object fragments from rectangular sample
samp(repmat(~mk,[1 1 size(inpict,3)])) = 255;
% store this image
C{n} = samp;
end
% just plot the images for viewing
for n = 1:numel(S)
subplot(3,4,n)
imshow(C{n},'border','tight')
end
Of course, there are other things to consider. This uses a very tight binary mask to extract the objects from the image. If the objects had soft edges, they would inherit hard edges from the mask. If the purpose is visual, then it may be desired to use a dilated mask and/or a softened (numeric) masking approach. Either would have further complications, particularly the need to deal with objects near the image boundaries.
5 个评论
siraj hashem
2021-6-22
siraj hashem
2021-6-22
If I had to guess, I would suspect the problem is with objects that touch the image boundary.
You could try doing this after the first two lines:
inpict = padarray(inpict,[2 2],255,'both'); % pad image with bg color (white)
bpict = padarray(bpict,[2 2],0,'both'); % pad mask with bg color (black)
There are other ways to do the same thing, but this is simple enough.
If that doesn't help, consider attaching one of the problem images.
siraj hashem
2021-6-23
Thank you sir
This attached image has a problem 
DGM
2021-6-23
The issue was just with some speckles being picked up as objects. I guess I should've thought about that. Using bwareaopen on bpict will remove spots smaller than a given number of pixels.
bpict = bwareaopen(bpict,100);
Given that I'm using a masking approach with FilledImage, filling bpict itself shouldn't be necessary unless the interiors of certain objects are patterned in a way that makes it difficult to despeckle by connected group size. For sake of robustness, it might be a good thing to add prior to calling bwareaopen().
bpict = imfill(bpict,'holes');
更多回答（2 个）
Jonas
2021-6-21
if your input image has white background you can easily generate a binare image after conversion to grayscale and using thresholding
grayIm=rgb2gray(im);
binIm=grayIm(grayIm<0.9);
on the binary image you can use bwconvhull with the 'objects' option or/and finally bwlabel(); this gives you the positions in which you can find different objects. you can then cut the objects out of the original image and save them as separate image
Image Analyst
2021-6-23
What I'd do is to binarize the image then call imfill() to fill holes in the objects.
grayImage = rgb2gray(rgbImage);
binaryImage = grayImage < 220;
binaryImage = imfill(binaryImage, 'holes');
Then call regionprops to get the bounding box of each image.
props = regionprops(binaryImage, 'BoundingBox');
Then I'd crop out each sub-image. Then I'd use a double for loop with isequal() to compare every image with every other image to see which subimage is unique.
numObjects = length(props)
alreadyUsed = false(numObjects); % Keep track if we've already seen this object or not.
for k1 = 1 : numObjects
image1 = imcrop(grayImage, props(k1).BoundingBox);
foundIt = false;
for k2 = 1 : numObjects
if k1 == k2
% Don't bother comparing object to itself.
continue;
end
image2 = imcrop(grayImage, props(k2).BoundingBox);
foundIt = false;
if isequal(image1, image2)
foundIt = true;
break; % Don't bother checking against the others, unless you want a count.
end
end
if ~foundIt
% We have not seen this object yet. So paste it onto our
% output canvass.
% Code for you to complete to paste image1 onto canvass
end
end
If it's unique, paste it onto your output canvass. If it's not unique and it's the second instance of the object, then don't paste it onto the output image. It should work as long as it's a computer graphics image and all objects are identical, i.e. no varying compression artifacts. If there are slight differences, then make sure they're the same size and use corrcoeff() to see how similar the images are.
Some demos are included which may be informative.
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