Suppose I have some data to be displayed on a bar chart, and each bar will be coloured according to its height.
Say the bars range in height from a to b.
What is the best way of calculating the numbers in a continuous colour map so that a is assigned a set of RGB values, a+1 is assigned a set of RGB values all the way through to b. The data is continuous.
The colours assigned need to be such that if data point 1 has value x and data point 2 has a value close to x, the colours assigned to data points 1 and 2 will be similar.
Pretty much, depending on what program you're using to do it in, it's fairly straight forward. Think of having 3 columns R G and B (they could be stored multiple different ways).
Pure red is 255 with G and B being 0. Same goes for pure green and blue with their respective colors. Then it's just a matter of covering every possible color you want.
A sample color chart is here
Another option is to use the HSV color system and then convert it to RGB if you really need to have RGB. I personally find working with HSV to be easier and more concise when generating a whole array of usable colors.
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.
I have an image and I am picking colors by RGB (data sampling). I select N points from a specific region in the image which has the "same" color. By "same" I mean, that part of the image belongs to an object, (let's say a yellow object). Each picked point in the RGB case has three values [R,G,B]. For example: [120,150,225]. And the maximum and minimum for each field are 255 and 0 respectively.
Let's assume that I picked N points from the region of the object in the image. The points obviously have different RGB values but from the same family (a gradient of the specific color).
Question:
I want to find a range for each RGB field that when I apply a color filter on the image the pixels related to that specific object remain (to be considered as inliers). Is it correct to find the maximum and minimum from the sampled points and consider them as the filter range? For example if the max and min of the field R are 120 ,170 respectively, can it be used as a the range that should be kept.
In my opinion, the idea is not true. Because when choosing the max and min of a set of sampled data some points will be out of that range and also there will be some point on the object that doesn't fit in this range.
What is a better solution to include more points as inliers?
If anybody needs to see collected data samples, please let me know.
I am not sure I fully grasp what you are asking for, but in my opinion filtering in RGB is not the way to go. You should use a different color space than RGB if you want to compare pixels of similar color. RGB is good for representing colors on a screen, but you actually want to look at the hue, saturation and intensity (lightness, or luminance) for analysing visible similarities in colors.
For example, you should convert your pixels to HSI or HSL color space first, then compare the different parameters you get. At that point, it is more natural to compare the resulting hue in a hue range, saturation in a saturation range, and so on.
Go here for further information on how to convert to and from RGB.
What happens here is that you implicitly try to reinvent either color indexing or histogram back-projection. You call it color filter but it is better to focus on probabilities than on colors and color spaces. Colors of course not super reliable and change with lighting (though hue tends to stay the same given non-colored illumination) that's why some color spaces are better than others. You can handle this separately but it seems that you are more interested in the principles of calculating "filtering operation" that will do segmentation of the foreground object from background. Hopefully.
In short, a histogram back-projection works by first creating a histogram for R, G, B within object area and then back-projecting them into the image in the following way. For each pixel in the image find its bin in the histogram, calculate its relative weight (probability) given overall sum of the bins and put this probability into the image. In such a way each pixel would have probability that it belongs to the object. You can improve it by dividing with probability of background if you want to model background too.
The result will be messy but somewhat resemble an object segment plus some background noise. It has to be cleaned and then reconnected into object using separate methods such as connected components, grab cut, morphological operation, blur, etc.
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.
I have a list of several different "random" colors values (no less than 1 and no more than 8 colors). (Random means that there is no telling of their mutual "contrast".)
Colors are given as RGB values (possible simplification: as H values in HSL model, or in some other color system of choice — I have some degree of control of how original colors are generated).
I need to compute a single one color value that is the most "contrast" (i.e. visually distinguishable) from all colors from the list.
A practical criteria for the contrast, for the case with 8 colors:
If we draw 9 squares, filled with our colors as follows:
[1][2][3]
[4][X][5]
[6][7][8]
Color of square X must be clearly distinguishable from all adjacent colors.
Possible simplification: reduce maximum number of colors from 8 to 4 (squares 2, 4, 5, 7 in the example, ignore diagonals).
I think the best solution could be:
maximize hue difference with all the colors (simple linear optimization)
maximize lighting
maximize saturation
http://www.colorsontheweb.com/colorcontrasts.asp
Edit: with linear programming, you could give lower significance to the diagonal colors.
Edit2: What maximization means:
You want to maximize the hue contrast, that means the sum of all |Hi - result|, where Hi stands for Hue of color i, is to be maximized. You can even create conditions for minimum difference, e.g. |Hi - result| > Hmin. The actual calculation can be done by giving the equations to the linear optimization algorithm or you can try all hue values between 0.0 and 1.0 stepping by 0.05 and saving the best result.
http://en.wikipedia.org/wiki/Linear_programming.