How can I isolate some objects from a image?

(hot_air_balloons.jpg)

(canyon.jpg)

I try to isolate the 4 yellow hot air balloons from the image to paste them to canyon.jpg (second image). I aim to obtain a binary image that the pixesls of hot air ballons white and all other areas black to be able to isolate them from rest of the image. Could you help me to obtain that binary image?

[Umar]

Hi Ufuk,

You need to generate a binary image where the pixels of the hot air balloons are white and all other areas are black. This binary image will help you isolate the hot air balloons from the rest of the image. So, you already know how to read the original image containing the hot air balloons using the imread function. Next,convert the RGB image to grayscale using the rgb2gray function to simplify the processing. Now, to create a binary image, you need to apply a threshold to the grayscale image. So, choose set threshold value and adjust based on the specific image and requirements.The resulting binary image will have white pixels representing the hot air balloons and black pixels for the background. Finally, display the binary image using imshow and save it as your picture_binary.jpg for further use. Hopefully, this will help resolve your problem.

Please let me know if you have any further questions.

[DGM]

See also:

https://www.mathworks.com/matlabcentral/answers/1873142-replace-the-image-background

https://www.mathworks.com/matlabcentral/answers/1876692-filling-holes-in-the-masked-image-please-help-me-to-replace-blue-color-background-of-given-giraffe

https://www.mathworks.com/matlabcentral/answers/1615875-merging-two-images-with-background

Answer 1

[Kaustab Pal]

Hi @Ufuk Can,

From what I understand, you need a mask to segment out the balloons. While thresholding is one method to achieve this, it is most effective when the background color is uniform. In your case, the balloons are the foreground, and the background contains a wide range of colors.

A suitable approach would be to use the lazysnapping tool from the Image Processing Toolbox. With lazysnapping, you need to select both the foreground and background areas. To improve the accuracy of the results, you should adjust the number of foreground and background bounding boxes along with the number of the superpixels.

Below is a code snippet demonstrating how to use this tool for your example:

RGB = imread('Hot Air Balloons.jpeg');
imshow(RGB)
L = superpixels(RGB,5000); % vary the amount to make the results more accurate

% add more foreground bounding boxes to make the results more accurate
f1 = drawrectangle(gca,'Color','g');
f2 = drawrectangle(gca,'Color','g');
f3 = drawrectangle(gca,'Color','g');
foreground = createMask(f1,RGB) + createMask(f2,RGB) + createMask(f3,RGB);

% add more background bounding boxes to make the results more accurate
b1 = drawrectangle(gca,'Color','r');
b2 = drawrectangle(gca,'Color','r');
b3 = drawrectangle(gca,'Color','r');
b4 = drawrectangle(gca,'Color','r');
background = createMask(b1,RGB) + createMask(b2,RGB) + createMask(b3,RGB) + createMask(b4,RGB);

BW = lazysnapping(RGB,L,foreground,background);
imshow(BW);

You can read more about lazysnapping here: https://www.mathworks.com/help/images/ref/lazysnapping.html

Hope this helps.

Answer 2

[DGM]

As per usual with these assignments, the images are either microscopic, or they're low-quality JPGs or both. Besides the practical challenge of trying to get a good mask using automated magic on a pixel salad, we also have to contend with the usual issues of mismatched lighting and improper perspective making the composition conspicuously questionable.

How would I get a mask? I would use an image editing application to make a mask that's exactly as good or bad as I care to make it. I just threw it in GIMP, made a few lasso selections and tightened them up with brushwork. I'm only going to select two of the balloons because it's not my assignment and trying to cram all four into the output is just going to make everything look that much more ridiculous.

% inputs and parameters

% two low-quality mismatched JPGs
FG = imread('Hot Air Balloons.jpeg'); % 659x1200
BG = imread('Canyon.jpeg'); % 182x276

% an antialiased (numeric) mask
mk = imread('balloonmask.png'); % I, uint8

% amount to rescale objects
% this isn't necessarily the ratio of image sizes
scale = 0.25;

% object offsets in BG space (px, [x y])
% one row per object in the mask
% offsets must be non-negative integers
% offsets must keep the objects within BG
offset = [150 30; 40 10];

% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% rescale the FG and mask
FG = imresize(FG,scale);
mk = imresize(mk,scale);

% get blob bounding boxes
S = regionprops(mk > 0,'boundingbox');

% compose the output one blob at a time
outpict = im2double(BG);
for k = 1:numel(S)
    % selections from FG, mask
    xr = ceil(S(k).BoundingBox(1)) + (0:S(k).BoundingBox(3) - 1);
    yr = ceil(S(k).BoundingBox(2)) + (0:S(k).BoundingBox(4) - 1);
    thisFG = im2double(FG(yr,xr,:));
    thismk = im2double(mk(yr,xr,:));
    
    % selection from BG
    xr = offset(k,1) + (1:S(k).BoundingBox(3));
    yr = offset(k,2) + (1:S(k).BoundingBox(4));
    thisBG = outpict(yr,xr,:);
    
    % compose
    outpict(yr,xr,:) = thisFG.*thismk + thisBG.*(1-thismk);
end

% rescast and scale as desired
outpict = im2uint8(outpict);

% show it
imshow(outpict)

I've made no effort to ensure that the selections stay within the extents of BG either by constraining subscripts or by cropping the selections. If you select a scaling factor and/or offset which places the output bounding box outside of the area of BG, it will produce an indexing error.

There really isn't any arrangement where the lighting and perspective make much sense with these objects and background, but I suppose it's not as bad as a basketball hovering in an arctic wasteland.