I want to get the most used color from image.
In most used color I dont mean specific pixel, I mean a most used color RANGE.
For example if there is an 2x3 pixels image and two pixels are f00(red) and the rest for are: 0b0, 0c0, 0d0, 0e0, 0f0 (Kind of green), I should get 0d0 (average of greens) and not the F00 (red, because there are exactly 2 pixels of this color).
I want to distinguish that kind of cases.
How am I supposed to do it?
Or where can I find materials to learn how it can be done?
Thanks.
Search about Color histogram using matlab.
There are a lot of recourse for this topic.
Related
Let's say I have an image of a ball like this one:
I want to separate the colors of the ball to the color groups. In this case I should have 2 main color groups - "brown" and "white". The "brown" group will have all the brown pixels and the "white" group will have all the white pixels.
I'm using matlab for this task. The way that I thought to do is:
to look at the RGB channels. I used scatter to look if I could clearly see some groups, but I didn't.
to look at the bayer vales. But couldn't see any groups either.
to run an edge detector. Then, in each enclosed area I'll find the mean of the pixels. The areas that will have similar mean values (within a certain threshold) will belong to the same group. It seemed to sort of to work but in many case it didn't
Any other ideas?
This task is called segmentation, in your case each color is a segment and segments are not always continuous.
Searching segmentation examples for Matlab should yield a lot of code examples and theorems.
Note one thing, there is no ground truth solution, you can't say how many segments there are for each image since it is subjective question. In a general case you can run clustering algorithm on the color values which will break the image to color segments, there are algorithms which will find the number of groups automatically - this can be a good start for the number of color groups in your image.
quick search yielded these works, they can get you started with ideas:
Image segmentation with matlab
Using EM for image segmentation
While image segmentation would be the correct way to treat color separation, if your image is simple, you can try to do it brute-force.
Here, converting to HSV would be easier to handle with the image.
For the white parts of the image:
I=imread('ball.jpg');
H=rgb2hsv(I);
% separate dimensions
h=H(:,:,1);
s=H(:,:,2);
v=H(:,:,3);
% color conditions
v(v<0.8 | s>0.7 | h>0.7 )=NaN;
h(isnan(v))=NaN;
s(isnan(v))=NaN;
% convert image back
W=cat(3,h,s,v);
White_image=hsv2rgb(W);
figure; imagesc(White_image);
And for the brown parts:
% separate dimensions
h=H(:,:,1);
s=H(:,:,2);
v=H(:,:,3);
% color conditions
v(s<0.6 | v>0.8 )=NaN;
h(isnan(v))=NaN;
s(isnan(v))=NaN;
% convert image back
B=cat(3,h,s,v);
Brown_image=hsv2rgb(B);
figure; imagesc(Brown_image); axis off
I want to make filters like shown here
these are my target filters but can you please guide me how to go for them
how i can make filters like these?
which algorithms i need to follow? and which step i need to take as beginner?
Which is the better and easiest way to get the values of RGB and shades of filters .
copy of image from link above by spektre:
the source image is the first after camera in the first line.
very hard to say from single non test-screen image.
the black and white filter
is easy just convert RGB to intensity i and then instead RGB write iii color. The simplest not precise conversion is
i=(R+G+B)/3
but better way is use of weights
i=w0*R+w1*G+w2*B
where w0+w1+w2=1 the values can be found by a little google search effort
the rest
some filters seem like over exponated colors or weighted colors like this:
r=w0*r; if (r>255) r=255;
g=w1*g; if (g>255) g=255;
b=w2*b; if (b>255) b=255;
write an app with 3 scrollbars for w0,w1,w2 in range <0-10> and redraw image with above formula. After little experimenting you should find w0,w1,w2 for most of the filters ... The rest can be mix of colors like this:
r=w00*r+w01*g+w02*b; if (r>255) r=255;
g=w10*r+w11*g+w12*b; if (g>255) g=255;
b=w20*r+w21*g+w22*b; if (b>255) b=255;
or:
i=(r+g+b)/3
r=w0*r+w3*i; if (r>255) r=255;
g=w1*g+w3*i; if (g>255) g=255;
b=w2*b+w3*i; if (b>255) b=255;
btw if you want the closest similarity you can:
find test colors in input image
like R shades, G shades , B shades , RG,RB,BG,RGB shades from 0-255. Then get colors from filtered image at the same position and draw depedency graphs for each shade draw R,G,B intensities.
One axis is input image color intensity and the other one is R,G,B intensity of filtered color. Then you should see which formula is used directly and can also compute the weights from it. This is how over-exponation works for Red color
if the lines are not lines but curves
then some kind of gamma correction is used so formulas use polynomial of higher order (power of 2,3,4...) mostly power of 2 suffice. In that case the weights can be also negative !!!
some filters could use different color spaces
for example transform RGB to HSV shift hue and convert back to RGB. That will shift colors a little.
i'm interested in some kind of charcoal-filters like the photoshop Photocopy-Filter or the note-paper.
Have someone a paper or some instructions how this filter works?
In best case i want to create the following:
input:
Output:
greetings
I think it's a process akin to pan-sharpening. I could get a quite similar image in gimp by:
Converting to gray
Duplicating into two layers
Lightly blurring one layer
Edge-detecting in the other layer with a DOG filter with large radius
Compositing the two layers, playing a bit with the transparency.
What this is doing is converting the color picture into a 0-1 bitmap picture.
They typically use a threshold function which returns 1 (white) for some values and 0 (black) for some other.
One simple function would be transform the image from color to gray-scale, and then select a shade of gray above which everything is white, and below it everything is black. The actual threshold you use could be made adaptive depending on the brightness of the picture (you want a certain percentage of pixels to be white).
It can also be adaptive based on the context within the picture (i.e. a dark area may still have some white pixels to show local contrast). The trees behind the house are not all black because the filtering is sensitive to the average darkness of the region.
Also note that the area close to the light gap in the tree has a cluster of dark pixels, because of its relative darkness. The edges of the home, the bench are also highlighted. There is an edge detection element at play.
I do not know exactly what effect you gave an example of but there are a variety that are similar to it. As VSOverFlow pointed out, thresholding an image would result in something very similar to that though I do not think it is what is being used. Open cv has a function for this, its documentation can be found here. You may also want to look into Otsu's method for thresholding.
Again as VSOverFlow pointed out, there is an edge detection element at play as well. You may want to investigate the Sobel and Prewitt filters. Those are 3 simple options that will give you something similar to the image you provided. Perhaps you could threshold the result from the Prewitt filter? I have no knowledge of how Photoshop implements its filters. If none of these options are close enough to what you are looking for I would recommend looking for information on the specific implementations of those filters in photoshop.
I have a bunch of sports team logos. What I want to do is find the color that is used for the highest percentage of pixels. So, for the patriots logo below, I would pick out the blue or #000f47 (white will not be an acceptable color), as this is used for the highest percentage of pixels. Obviously I can eyeball each image, use the color picker tool in Gimp/Photoshop, and determine the color. However, I would like to script this if possible.
I can use any format for the picture input. Would it be possible to read the raw bitmap file format and determine this way? What would be an easy format to read? Do any tools support this, like ImageMagick, etc?
Thanks
If you're up for it then it's fairly straight forward to write your own image processor in C#; just run through the pixels, grab the R, G and, B values and increment a counter for each unique combination.
Having said that, if the image is anti-aliased then what you or I would eye-ball as being blue will be variations of the RGB and the processor would count them seperately. You might want to build in some allowable tollerances into the processor.
Just to be picky, isn't the most frequent pixel value in the image above white not blue?
What is the best (result, not performance) algorithm to fetch dominant colors from an image. The algorithm should discard the background of the image.
I know I can build an array of colors and how many they appear in the image, but I need a way to determine what is the background and what is the foreground, and keep only the second (foreground) in mind while read the dominant colors.
The problem is very hard especially for gradient backgrounds or backrounds with patterns (not plain)
Isolating the foreground from the background is beyond the scope of this particular answer, but...
I've found that applying a pixelation filter to an image will draw out a really good set of 'average' colours.
Before
After
I sometimes use this approach to derive a pallete of colours with a particular mood. I first find a photograph with the general tones I'm after, pixelate and then sample from the resulting image.
(Thanks to Pietro De Grandi for the image, found on unsplash.com)
The colour summarizer is a pretty sweet spot for info on this subject, not to mention their seemingly free XML Web API that will produce descriptive colour statistics for an image of your choosing, reporting back the following formatted with swatches in HTML or as XML...
what is the average color hue, saturation and value in my image?
what is the RGB colour that is most representative of the image?
what do the RGB and HSV histograms look like?
what is the image's human readable colour description (e.g. dark pure blue)?
The purpose of this utility is to generate metadata that summarizes an
image's colour characteristics for inclusion in an image database,
such as Flickr. In particular this tool is being used to generate
metadata for Flickr's Color Fields group.
In my experience though.. this tool still misses the "human-readable" / obvious "main" color, A LOT of the time. Silly machines!
I would say this problem is closer to "impossible" than "very hard". The only approach to it that I can think of would be to make the assumption that the background of an image is likely to consist of solid blocks of similar colors, while the foreground is likely to consist of smaller blocks of dissimilar colors.
If this assumption is generally true, then you could scan through the whole image and weight pixels according to how similar or dissimilar they are to neighboring pixels. In other words, if a pixel's neighbors (within some arbitrary radius, perhaps) were all similar colors, you would not incorporate that pixel into the overall estimate. If the neighbors tend to be very different colors, you would weight the pixel heavily, perhaps in proportion to the degree of difference.
This may not work perfectly, but it would definitely at least tend to exclude large swaths of similar colors.
As far as my knowledge of image processing algorithms extends , there is no certain way to get the "foreground"; it is only possible to get the borders between objects. You'll probably have to make do with an average, or your proposed array count method. In that, you'll want to give colours with higher saturation a higher "score" as they're much more prominent.