I am trying to find a segment in color wheel which is dedicated to particular color only (For example, red or green or black or violet).
So far, i have tried following:
I used 12 colors and 20 shades of each color to narrow down a color range(https://digitalsynopsis.com/design/color-thesaurus-correct-names-of-shades/). But, i was clearly not able to define the range of particular color because i have not found any pattern which could say when i will enter into differen color segment.
For example, to define black color, what digit should be the minimum and maximum in RGB or any other module?
Related
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 have two images, one is a portion of an original image, the other is the whole of the original image covered by a uniform semi-transparent colour layer (in this case orange).
Can I use the colour difference between pixels from the first and second images to reverse engineer the colour and transparency of the covering, and if so can I then use it to find the original colour of a pixel without an uncovered equivalent? Is there just a nice single button solution within GIMP or do I need to do some actual programming/maths? I have basically no experience with image manipulation so any help would be appreciated.
Here are the uncovered and covered RGB values to help explain (and the missing value at the bottom).
Colour
Base
+Transparent
Colour 1
#179fb7
#f8b76f
Colour 2
#2fafc8
#f8bf6f
Colour 3
#3fc8d8
#f8c877
Colour 4
#578f08
#f8b73f
Colour 5
#6faf2f
#f8bf47
Colour 6
#87c847
#f8c84f
Colour 7
#9fd85f
#f8c857
Colour 8
#d0bf47
#f8bf4f
Colour 9
#8f9f1f
#f8b747
Colour 10
#6faf2f
#f8d077
Colour 11
?
#f8d06f
I assume you can find every possible colour-transparency pair that would cause the transformation for each row and the plot each as a line to find the intersection point, but I don't know enough about how colour works to do that.
My question is about color tracking... What is the color threshold value for white in python ? I need to track the white color alone in a group of pictures. So I need to separate the white color. In order to do so I need to know the threshold value of white color...
It depends on your pictures. Assuming that you're going to threshold using the RGB values, the RGB value for white is (255, 255, 255). But this value holds true for pure white color. If you have real-world pictures, you might have clearly white color at certain areas in your image but they wouldn't have the value (225, 255, 255). Factors like the shadow, lighting conditions, angle etc. contribute to the variance from pure white color value.
In order to threshold correctly, you need to check the range of values for your set of pictures. I recently worked on a similar problem and for my case, the range of values was as follows:
Red channel: 200-255
Green channel: 180-255
Blue channel: 140-255
But please note that this accepts a lot of variation of white like light yellow. It will highly depend on your case so make sure you check the range on your data.
One way of that can be by displaying/showing your image using skimage and then hovering over white areas, it will display the RGB value on the bottom right corner of the image. Here is the code for showing an image in skimage:
from skimage import io
def show(img):
io.imshow(img)
io.show()
You can create a range of values/threshold from the values you notice this way.
This question already has answers here:
Determine font color based on background color
(22 answers)
Closed 2 years ago.
I am currently developing an App. In this App I get some RGB color code from the server, that I want to display.
As the color I get should be the background color of a Label, there is some text, that should be displayed above it. The user defining the color is completely free in choosing a color, so I am in need of finding an algorithm, where the text color of the label is somehow contrasted to the color I get from the webservice.
Problem explained in an example:
I choose black 0x000000 to be the textcolor. If the user defines a color like dark gray 0x111111, the text's visibility would be very bad.
In addition, I want the text color to be black or white in order to not over-color the app.
So how can I determine, if white or black is the better color for the textcolor, based on a RGB code I get from the server?
You just should calculate contrast ratio for user color with black and white, then choose the color with higher ratio.
Here is description for calculating relative color contrast from RGB.
Note that relative luminance of white is 1.0 and for black is 0.0, so (if you are considering only black and white) you can just check if relative luminance of user color is <0.5 then use white, and use black otherwise.
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.