I wonder if it's possible to set the color of a pixel in an image, where the coordinate of the pixel is randomly chosen within a defined color.
For example an image (100x100) that contains 3 colors, red(4000px), black(3000px) and purple(3000px). Now I want to change 100 random red pixels into yellow.
I've played around with imagesetpixel, but can't figure out if it's possible to set the $x and $y randomly within a color range.
imagesetpixel ($image , $x , $y , $color )
I'm assuming that we do not know how the different colored pixels are arranged in the image. In your case where you have many red pixels there is an easy method.
while(!exit):
pixel = select random pixel
if pixel.color = red:
pixel.color = yellow
exit = true
end if
end while
This will change one pixel so you need to do that 100 times.
If you only have a few pixels of the color to change you may be better off first finding all pixels of one color and storing their coordinates in a list or an array and then randomly choosing one element of that data structure.
Related
I have an binary word image with grid and i want to find the foreground pixels (number of black and white in each segments) in each segments
So I just wanted to get the total number of black pixels for an image and I didn't want to segment it. Didn't feel like having to learn how to segment a matrix.
Easy fix to get the sum of the black pixels:
sum(sum(bw == 0))
Conversely to get the white pixels
sum(sum(bw == 1))
The operation function sum(bw == 0) just gets everything to a [1xn] matrix, so you will need to perform sum and you will get the whole image matrix summed.
Thought that cumsum would do the trick, but sum(sum()) is just better for this case. Tells you how little I know about coding. Have fun with this info, note that I'm just using this to determine the area coverage of a binarized image, but for anything else particularly useful.
Assume your black and white image is bw
sum(bw==0) // number of black pixels
sum(bw==1) // number of white pixels
For each grid find the range in both directions, then apply the same idea.
For example, from x1 to x2 and y1 to y2 is one grid:
sum(bw(x1:x2, y1:y2) == 0) // will give you the black pixels you want
That is easy with logical indexing.
For each segment, do:
n_white=sum(segment(:)==1);
n_black=sum(segment(:)==0);
I'm trying to create a mask (or similar result) in order to erase pieces of a binary image that are not attached to the object surrounded by the boundary. I saw this thread (http://www.mathworks.com/matlabcentral/answers/120579-converting-boundary-to-mask) to do this from bwboundaries, but I'm having trouble making suitable changes to it. My goal is to use this code to isolate the part of this topography map that is connected, and get rid of the extra pieces. I need to retain the structure inside of the bounded area, as I was then going to use bwboundaries to create additional boundary lines of the main object's "interior" structure.
The following is my code to first create the single boundary line by searching for the bottom left pixel of the black area to begin the trace. It just looks for the first column of the image that isn't completely white and selects the last black pixel. The second section was then to create the inner boundary lines. Note that I am attempting this two step process, but if there is a way to do it with only one I'd like to hear that solution as well. Ultimately I just want boundaries for the main, large black area and the holes inside of it, while getting rid of the extra pieces hanging around.
figName='Images/BookTrace_1';
BW = imread([figName,'.png']);
BW=im2bw(BW);
imshow(BW,[]);
for j=1:size(BW,2)
if sum(BW(:,j))~=sum(BW(:,1))
corner=BW(:,j);
c=j-1;
break
end
end
r=find(corner==0);
r=r(end);
outline = bwtraceboundary(BW,[r c],'W',8,Inf,'counterclockwise');
hold on;
plot(outline(:,2),outline(:,1),'g','LineWidth',2);
[B,L] = bwboundaries(BW);
hold on
for k = 1:length(B)
boundary = B{k};
plot(boundary(:,2), boundary(:,1), 'g', 'LineWidth', 2)
end
Any suggestions or tips are greatly appreciated. If there are questions, please let me know and I'll update the post. Thank you!
EDIT: For clarification, my end goal is as in the below image. I need to trace all of the outer and inner boundaries attached to the main object, while eliminating any spare small pieces that are not attached to it.
It's very simple. I actually wouldn't use the code above and use the image processing toolbox instead. There's a built-in function to remove any white pixels that touch the border of the image. Use the imclearborder function.
The function will return a new binary image where any pixels that were touching the borders of the image will be removed. Given your code, it's very simply:
out = imclearborder(BW);
Using the above image as an example, I'm going to threshold it so that the green lines are removed... or rather merged with the other white pixels, and I'll call the above function:
BW = imread('http://i.stack.imgur.com/jhLOw.png'); %// Read from StackOverflow
BW = im2bw(BW); %// Convert to binary
out = imclearborder(BW); %// Remove pixels along border
imshow(out); %// Show image
We get:
If you want the opposite effect, where you want to retain the boundaries and remove everything else inside, simply create a new image by copying the original one and use the output from the above to null these pixel locations.
out2 = BW; %// Make copy
out2(out) = 0; %// Set pixels not belonging to boundary to 0
imshow(out2); %// Show image
We thus get:
Edit
Given the above desired output, I believe I know what you want now. You wish to fill in the holes for each group of pixels and trace along the boundary of the desired result. The fact that we have this split up into two categories is going to be useful. For those objects that are in the interior, use the imfill function and specify the holes option to fill in any of the black holes so that they're white. For the objects that exterior, this will need a bit of work. What I would do is invert the image so that pixels that are black become white and vice-versa, then use the bwareaopen function to clear away any pixels whose area is below a certain amount. This will remove those small isolated black regions that are along the border of the exterior regions. Once you're done, re-invert the image. The effect of this is that the small holes will be eliminated. I chose a threshold of 500 pixels for the area... seems to work well.
Therefore, using the above variables as reference, do this:
%// Fill holes for both regions separately
out_fill = imfill(out, 'holes');
out2_fill = ~bwareaopen(~out2, 500);
%// Merge together
final_out = out_fill | out2_fill;
This is what we get:
If you want a nice green border like in your example to illustrate this point, you can do this:
perim = bwperim(final_out);
red = final_out;
green = final_out;
blue = final_out;
red(perim) = 0;
blue(perim) = 0;
out_colour = 255*uint8(cat(3, red, green, blue));
imshow(out_colour);
The above code finds the perimeter of the objects, then we create a new image where the red and blue channels along the perimeter are set to 0, while setting the green channel to 255.
We get this:
You can ignore the green pixel border that surrounds the image. That's just a side effect with the way I'm finding the perimeter along the objects in the image. In fact, the image you supplied to me had a white pixel border that surrounds the whole region, so I'm not sure if that's intended or if that's part of the whole grand scheme of things.
To consolidate into a working example so that you can copy and paste into MATLAB, here's all of the code in one code block:
%// Pre-processing
BW = imread('http://i.stack.imgur.com/jhLOw.png'); %// Read from StackOverflow
BW = im2bw(BW); %// Convert to binary
out = imclearborder(BW); %// Remove pixels along border
%// Obtain pixels that are along border
out2 = BW; %// Make copy
out2(out) = 0; %// Set pixels not belonging to boundary to 0
%// Fill holes for both regions separately
out_fill = imfill(out, 'holes');
out2_fill = ~bwareaopen(~out2, 500);
%// Merge together
final_out = out_fill | out2_fill;
%// Show final output
figure;
imshow(final_out);
%// Bonus - Show perimeter of output in green
perim = bwperim(final_out);
red = final_out;
green = final_out;
blue = final_out;
red(perim) = 0;
blue(perim) = 0;
out_colour = 255*uint8(cat(3, red, green, blue));
figure;
imshow(out_colour);
I have to create an algorithm to determine a dark "grayscale image" in matlab, so I have to collect all pixels' intensity value then evaluate that if 65% of all pixels in that particular image is lesser than 100 then it is dark.
the question is how to collect/get these value to create an algorithm like this?
Assume your image is contained in an array Img (for instance, obtained with imread). Then:
% Define a threshold
th = 100;
% Get the percentage of pixels below the threshold
p = nnz(Img<th)/numel(Img)*100;
% Decide what to do
if p<65
...
else
...
end
Hope this helps,
I have a RGB image which has only black and white squares. I want to count number to non gray pixels in this image. I am new to matlab. I want to check the quality of image as it should only contain black and white pixels.Actually I have undistorted this image due that some colored fringes are appeared.I want to know the how many color are introduced to check the quality of the image.
using matlab to get counts of specific pixel values in an image.
Images are RGBA <512x512x4 uint8> when read into matlab (although we can disregard the alpha channel).
Something like this
count = sum(im(:, :, 1) == 255 & im(:, :, 3) == 255 & im(:, :, 3) == 255);
will give you the count of such pixels. Replace sum with find to get the indices of those pixels if you need that.
A pixel is said to be gray if its R,G,B components are all same.
Using this logic
%// checking for equality of R,G,B values
B = any(diff(im,[],3),3); %// selecting only non-gray pixels
count = sum(B(:)); %// Number of non-gray pixels
PS: This answer is tailored from this and this answer.
I've got two images, one 100x100 that I want to plot in grayscale and one 20x20 that I want to plot using another colormap. The latter should be superimposed on the former.
This is my current attempt:
A = randn(100);
B = ones(20);
imagesc(A);
colormap(gray);
hold on;
imagesc(B);
colormap(jet);
There are a couple of problems with this:
I can't change the offset of the smaller image. (They always share the upper-left pixel.)
They have the same colormap. (The second colormap changes the color of all pixels.)
The pixel values are normalised over the composite image, so that the first image changes if the second image introduces new extreme values. The scalings for the two images should be separate.
How can I fix this?
I want an effect similar to this, except that my coloured overlay is rectangular and not wibbly:
Just change it so that you pass in a full and proper color matrix for A (i.e. 100x100x3 matrix), rather than letting it decide:
A = rand(100); % Using rand not randn because image doesn't like numbers > 1
A = repmat(A, [1, 1, 3]);
B = rand(20); % Changed to rand to illustrate effect of colormap
imagesc(A);
hold on;
Bimg = imagesc(B);
colormap jet;
To set the position of B's image within its parent axes, you can use its XData and YData properties, which are both set to [1 20] when this code has completed. The first number specifies the coordinate of the leftmost/uppermost point in the image, and the second number the coordinate of the rightmost/lowest point in the image. It will stretch the image if it doesn't match the original size.
Example:
xpos = get(Bimg, 'XData');
xpos = xpos + 20; % shift right a bit
set(Bimg, 'XData', xpos);