I am writing some code to create an image from pixel data stored in a medical image file (DICOM). Essentially, I have a two dimensional array (rows x columns) where each element of the array contains the colour of the pixel at that region in the image. The element values vary between 0 (black) and 1 (white). Values between 0-1 are differing shades of grey.
The language I am using (Xojo) allows me to set the colour of an individual pixel on a canvas but only using the RGB or HSV colour space.
How would I, for instance, get the HSV value for my pixel value of 0.9 (which would be a reasonably "light" shade of grey)?
Use the HSV color value of
H = 0
S = 0
V = your pixel value of 0.9
g.FillColor=HSV(0.0, 0.0, 0.9) From Xojo Documentation
See the Image HSV#wikipedia.org in the top right corner.
Related
lets say one image is shaped (x,y,3) and another (x,y,4). Now depending on whether the value of pixel in the 4th layer is 0 or non zero. I need to replace pixel values in the first image using the following rule.
if pixel in the 4th layer is 0 return image has values= value in (x,y,3) at that pixel in the rgb image
if non zero return image has value = value in value at that pixel in the rgba image
the return image should have shape (x,y,3)
If I understand you correctly, you effectively want to use the alpha part of the RGBA image as a binary mask. Assuming A is the rgb base image and B the rgba top image, both represented as numpy arrays:
mask = B[:, :, 3] > 0.5 # you can also use another threshold here
A[mask] = B[mask, 0:3]
Both images must have the same shape for this.
Alternatively, you can also overlay the second image on top of the first one using the alpha channel instead of using a binary mask:
A = (1-B[:,:,3])*A + B[:,:,3]*B[:,:,0:3]
You can do:
mask = img2[...,3] > 0
new_img = np.where(mask[...,None], img2[...,:3], img1)
Also without need for broadcasting:
mask = img2[...,3:] > 0
new_img = np.where(mask, img2[...,:3], img1)
I have 2 co-located images, both created in a similar way and both have the size of 7,221 x 119 pixels.
I want to write a logic like this:
If the R,G,B values of a certain pixel (called it x) in image 1 = 0,0,0 (black) And the R,G,B values of pixel x in image 2 = 0,0,0 (black) then change the R,G,B values of pixel x in image 1 to 255,255,255 (white), Else no change.
How can I do this in either Matlab or Python?
You should be able to do this in python with the Pillow package. You need to load the two pixels, check if all the color channels are 0 and if so make them 255, then save the image again. In Python 0 is interpreted as False, so not any(vals) will be True when vals includes only zeros.
from PIL import Image
im1 = Image.open("image1.jpg")
im2 = Image.open("image2.jpg")
pixel = (0, 0)
newcolor = (255,)*3
if not any(im1.getpixel(pixel)) and not any(im2.getpixel(pixel)):
im1.putpixel(pixel, newcolor)
im1.save('image1conv.jpg')
Note that not any(im1.getpixel(pixel)) and not any(im2.getpixel(pixel)) could be rewritten as not any(im1.getpixel(pixel) + im2.getpixel(pixel)), but I think the first way has clearer logic.
I have an 8-bit grayscale image with different values (0,1,2,3,4,..., 255). What I want to do is color the grayscale image with colors like blue, red, etc. Until now, I have been doing this coloring but only in a greyscale. How can I do it with actual colors?
Here is the code I have written so far. This is where I am searching for all values that are white in an image and replacing them with a darkish gray:
for k = 1:length(tifFiles)
baseFileName = tifFiles(k).name;
fullFileName = fullfile(myFolder, baseFileName);
fprintf(1, 'Now reading %s\n', fullFileName);
imageArray = imread(fullFileName);
%// Logic to replace white grayscale values with darkish gray here
ind_plain = find(imageArray == 255);
imageArray(ind_plain) = 50;
imwrite(imageArray, fullFileName);
end
What you are asking is to perform a pseudo colouring of an image. Doing this in MATLAB is actually quite easy. You can use the grayscale intensities as an index into a colour map, and each intensity would generate a unique colour. First, what you need to do is create a colour map that is 256 elements long, then use ind2rgb to create your colour image given the grayscale intensities / indices of your image.
There are many different colour maps that are available to you in MATLAB. Here are the current available colour maps in MATLAB without the recently added Parula colour map that was introduced in R2014:
How the colour maps work is that lower indices / grayscale values have colours that move toward the left side of the spectrum and higher indices / grayscale values have colours that move toward the right side of the spectrum.
If you want to create a colour map with 256 elements, you simply use any one of those colour maps as a function and specify 256 as the input parameter to generate a 256 element colour map for you. For example, if you wanted to use the HSV colour map, you would do this in MATLAB:
cmap = hsv(256);
Now, given your grayscale image in your MATLAB workspace is stored in imageArray, simply use ind2rgb this way:
colourArray = ind2rgb(double(imageArray)+1, cmap);
The first argument is the grayscale image you want to pseudocolour, and the second input is the colour map produced by any one of MATLAB's colour mapping functions. colourArray will contain your pseudo coloured image. Take note that we offset the grayscale image by 1 and also cast to double. The reason for this is because MATLAB is a 1-indexed programming language, so we have to start indexing into arrays / matrices starting at 1. Because your intensities range from [0,255], and we want to use this to index into the colour map, we must make this go from [1,256] to allow the indexing. In addition, you are most likely using uint8 images, and so adding 1 to a uint8 will simply saturate any values that are already at 255 to 255. We won't be able to go to 256. Therefore, you need to cast the image temporarily to double so that we can increase the precision of the image and then add 1 to allow the image to go to 256 if merited.
Here's an example using the cameraman.tif image that's part of the image processing toolbox. This is what it looks like:
So we can load in that image in MATLAB like so:
imageArray = imread('cameraman.tif');
Next, we can use the above image, generate a HSV colour map then pseudocolour the image:
cmap = hsv(256);
colourArray = ind2rgb(imageArray+1, cmap);
We get:
Take note that you don't have to use any of the colour maps that MATLAB provides. In fact, you can create your own colour map. All you have to do is create a 256 x 3 matrix where each column denotes the proportion of red (first column), green (second column) and blue (third column) values per intensity. Therefore, the first row gives you the colour that is mapped to intensity 0, the second row gives you the colour that is mapped to intensity 1 and so on. Also, you need to make sure that the intensities are floating-point and range from [0,1]. For example, these are the first 10 rows of the HSV colour map generated above:
>> cmap(1:10,:)
ans =
1.0000 0 0
1.0000 0.0234 0
1.0000 0.0469 0
1.0000 0.0703 0
1.0000 0.0938 0
1.0000 0.1172 0
1.0000 0.1406 0
1.0000 0.1641 0
1.0000 0.1875 0
1.0000 0.2109 0
You can then use this custom colour map into ind2rgb to pseudocolour your image.
Good luck and have fun!
Explanation
I have a semi-transparent color of unknown value.
I have a sample of this unknown color composited over a black background and another sample over a white background.
How do I find the RGBA value of the unknown color?
Example
Note: RGB values of composites are calculated using formulas from the Wikipedia article on alpha compositing
Composite over black:
rgb(103.5, 32.5, 169.5)
Composite over white:
rgb(167.25, 96, 233.25)
Calculated value of unknown color will be:
rgba(138, 43, 226, 0.75)
What I've Read
Manually alpha blending an RGBA pixel with an RGB pixel
Calculate source RGBA value from overlay
It took some experimentation, but I think I figured it out.
Subtracting any of the color component values between the black and white composite should give you the inverse of the original color's alpha value, eg:
A_original = 1 - ((R_white_composite - R_black_composite) / 255) // in %, 0.0 to 1.0
It should yield the same value whether you use the R, G, or B component. Now that you have the original alpha, finding the new components is as easy as:
R_original = R_black_composite / A_original
G_original = G_black_composite / A_original
B_original = B_black_composite / A_original
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.