I have a table like this one: https://docs.google.com/spreadsheets/d/1Kn4vfbHwpif7u-6ZTznFpBJFNHhnStETPIQVyQq8xgY/edit#gid=0 with bottom / top color and the Red, Green, Blue (RGB) of the result (where it states 'Preparation' for the bottom color means the canvas so essentially its the RGB of the top color - those rows are the 'single colors').
I am looking for a relation between the double colors and the single colors. E.g. could I somehow subtract one color from their combination and get the other? Either by using the RGB values or using the images in some software?
Note that the type/formula/function/relation that I am looking for will be only for specific single colors and their combinations which I have already measured (do not care if it is valid beyond the dataset that I am working with)
I want to build a single stacked clustered column chart to simulate earth's substances with their respective image legend. (e.g. https://ibb.co/cqUvv9)
For example, I want from 0 meters to -10 to show sand, right beneath from -10 to -25 rock, etc. and expand their respective png images based on their values.
So far I managed to build the clustered column with matplotlib, I just want to know if there's a way of showing images instead of bars.
I'm fairly new to GIMP
I have some black-and-white images in RGB mode. I want to highlight some areas in plain Red (ie, zero Blue+Green), some in plain Blue (ie, zero Red+Green), and the rest in plain Green (ie, zero Red+Blue)
I will be selecting several areas using Paths, though a simple rectangle would be fine for now
The final image will be Green, with 2+ selections of Red, and 2+ selections of Blue
I have experimented with multiple layers & multiple images, but I always get problems with the selection areas
Thanks in advance !
If what you want is keep the value of the R (or G or B) channel in the selection, while setting the other two components to 0:
Create a layer group above your image layer
Add a transparent layer in the group, name it "Green", bucket-fill with green
Add a transparent layer in the group, above "Green", name it "Red+Blue"
Set the group to "Multiply" mode. You should see your image in green.
To add red/blue, make a selection, make sure "Red+Blue" is the active layer, and bucket fill the selection with Red or Blue.
Notes
With some selection tools, (path, ellipse...) you may have to use Select>Sharpen before the bucket-fill to make sure that there are no partially selected pixels
To see the original image when making selections, just make the group invisible.
You can also use two separate layers for Red and Blue (both above the Green one, in the layer group)
If what you want is just areas of #FF0000 and #0000FF over a #00FF00 background, then just hide the initial image layer when exporting.
Given the RGB color white #ffffff, how would one split this into N colors?
Imagine a Rainbow, it has 7 colors.
How would one programatically yield these 7 colors? If you can arrive at 7 colors in this known spectrum, how would one yield say 70 colors of this spectrum in the same relative order? Meaning that this rainbow would contain 10 "steps" between Orange and Yellow for example. The Orange and Yellow are no longer side by side but separated by an interpolation of color between them.
I have more then 1 week reading about selective color change of an image. It meand selcting a color from a color picker and then select a part of image in which I want to change the color and apply the changing of color form original color to color of color picker.
E.g. if I select a blue color in color picker and I also select a red part in the image I should be able to change red color to blue color in all the image.
Another example. If I have an image with red apples and oranges and if I select an apple on the image and a blue color in the color picket, then all apples should be changing the color from red to blue.
I have some ideas but of course I need something more concrete on how to do this
Thank you for reading
As a starting point, consider clustering the colors of your image. If you don't know how many clusters you want, then you will need methods to determine whether to merge or not two given clusters. For the moment, let us suppose that we know that number. For example, given the following image at left, I mapped its colors to 3 clusters, which have the mean colors as shown in the middle, and representing each cluster by its mean color gives the figure at right.
With the output at right, now what you need is a method to replace colors. Suppose the user clicks (a single point) somewhere in your image, then you know the positions in the original image that you will need to modify. For the next image, the user (me) clicked on a point that is contained by the "orange" cluster. Then he clicked on some blue hue. From that, you make a mask representing the points in the "orange" cluster and play with that. I considered a simple gaussian filter followed by a flat dilation 3x5. Then you replace the hues in the original image according to the produced mask (after the low pass filtering, the values on it are also considered as a alpha value for compositing the images).
Not perfect at all, but you could have a better clustering than me and also a much-less-primitive color replacement method. I intentionally skipped the details about clustering method, color space, and others, because I used only basic k-means on RGB without any pre-processing of the input. So you can consider the results above as a baseline for anything else you can do.
Given the image, a selected color, and a target new color - you can't do much that isn't ugly. You also need a range, some amount of variation in color, so you can say one pixel's color is "close enough" while another is clearly "different".
First step of processing: You create a mask image, which is grayscale and varying from 0.0 to 1.0 (or from zero to some maximum value we'll treat as 1.0), and the same size as the input image. For each input pixel, test if its color is sufficiently near the selected color. If it's "the same" or "close enough" put 1.0 in the mask. If it's different, put 0.0. If is sorta borderline, put an in-between value. Exactly how to do this depends on the details of the image.
This might work best in LAB space, and testing for sameness according to the angle of the A,B coordinates relative to their origin.
Once you have the mask, put it aside. Now color-transform the whole image. This might be best done in HSV space. Don't touch the V channel. Add a constant to S, modulo 360deg (or mod 256, if S is stored as bytes) and multiply S by a constant chosen so that the coordinates in HSV corresponding to the selected color is moved to the HSV coordinates for the target color. Convert the transformed S and H, with the unchanged L, back to RGB.
Finally, use the mask to blend the original image with the color-transformed one. Apply this to each channel - red, green, blue:
output = (1-mask)*original + mask*transformed
If you're doing it all in byte arrays, 0 is 0.0 and 255 is 1.0, and be careful of overflow and signed/unsigned problems.