I reduced an image to 12 colors, annotated with some text (here it's color saturation, sorted) :
The text color is %[pixel:p{10,10}*2] (background *2) (I made a little script that I can share if you're interested).
As you can see, text is not very readable (contrast) in all cases (colors). Is there a smarter formula than a simple linear scaling to make text pop in all/most cases ?
As per Fred's suggestion, using luminosity is much better. Using black & white text depending on luminosity > 56 or not :
And for a not colorful image :
The text represents L component of HSL value. Notice the change from black to white when value crosses 56.
Related
I have sRGB images with color casts. To remove it manually I usually use Photoshop Level Adjustments. Photoshop also have tools for that: Auto Contrast or even better Auto Tone which also takes shadows, midtones & highlights into account.
If I remove the cast manually I adjust each of the RGB channels individually so that the darkest pixels are set to pure black and the lightest to pure white and then redistribute all other values (spreading the histogram). This is a simple approach but shows good results for my images.
In my node.js app I'm using sharp for image processing which uses libvips as its processing engine. I tried to remove the cast with .normalize() but this command works on all channels together and not individual for each of the RGB channels. So it doesn't work for me.
I also asked this question on the sharp project page. I tested the suggestion from lovell to try it with hist_local but the results are not useable for me.
Now I would like to find out how this could be done using the native libvips. I've played around with nip2 GUI and different commands but could not figure out how it could be achieved:
Histogram > Equalise Histogram > Global => Picture looks over saturated
Image > Levels > Scale to 0 - 255 => Channels ar not all spreading from 0 - 255 (I don't understand exactly what this command does?)
Thanks for every hint!
Addition
Here is a example with pictures from Photoshop to show what I want.
The source image is a picture of a frame from a film negative.
Image before processing
Step1 Invert image
Image after inversion
Step2 using Auto tone in Photoshop (works the same way as my description above about manually remove the color cast)
Image after Auto Tone
This last picture is ok for me.
nip2 has a menu item for this.
Load your image and mark a region on it containing the area you'd like to be neutral. It can be any lightness, it doesn't need to be white.
Use File / Open to get the file dialog and you should see the image loaded in your workspace as a thumbnail.
Doubleclick on the thumbnail to open an image view window.
In the view window, zoom and pan to the right spot. The user guide (press F1) has a section on image navigation.
Hold down CTRL and click and drag down and right to mark a rectangular region.
Back in the main window, click Toolkits / Tasks / Capture / White balance. You should see something like:
You can drag an resize your region to change the neutral point. Use the colour picker to set what white means. You can make other whites with (for example) Colour / New / Colour from CCT and link them together.
Click Colour / New / Colour from CCT to make a colour picker from CCT (correlated colour temperature) -- the temperature in Kelvin of that white.
Set it to something interesting, like 4800 for warm white.
Click on the formula for A5.white to edit it, and enter the cell of your CCT widget (A7 in this case).
Now you can drag the region to adjust the pixels to set the neutral from, and drag the CCT slider to set the temperature.
It can be annoying to find things in the toolkit menu. There's a thing for searching toolkits: in the main window, click View / Toolkit browser. You can enter something like "white" and it'll show related toolkit entries.
Here's another answer, but using pyvips and responding to the previous comments. I didn't want to delete the first answer as it still seemed useful.
This version finds the image histogram, searches for thresholds which will select 0.5% and 99.5% of pixels in each image band, then rescales the image so that those pixel values become 0 and 255.
import sys
import pyvips
# trim off this percentage of pixels from the top and bottom
trim_percent = 0.5
def percent(hist, percentage):
"""From a histogram, find the threshold above which lie
#percentage of pixels."""
# normalised cumulative histogram
norm = hist.hist_cum().hist_norm()
# column and row profile over percentage
c, r = (norm > norm.width * percentage / 100).profile()
return r.avg()
image = pyvips.Image.new_from_file(sys.argv[1])
# photographic negative
image = image.invert()
# find image histogram, split to set of separate bands
bands = image.hist_find().bandsplit()
# for each band, the low and high thresholds
low = [percent(band, trim_percent) for band in bands]
high = [percent(band, 100 - trim_percent) for band in bands]
# rescale image
scale = [255.0 / (h - l) for h, l in zip(high, low)]
image = (image - low) * scale
image.write_to_file(sys.argv[2])
It seems to give roughly similar results to the PS button. If I run:
$ ./autolevel.py ~/pics/before.jpg x.jpg
I see:
In the meantime I've found the Simplest Color Balance Algorithm which exactly describes the problem with color casts and there you can also find a C source code.
It is exactly the same solution as John describes in his second answer but as a small piece of c-code.
I'm now trying to use it as C/C++ addon with N-API under node.js.
Some details:
I'm making a small prototype in Framer, some kind a wallpaper app. I use vibrant.js to automatically pick colors from the images to add a bit of a tint to my interface. I use two vibrant color profiles: "DarkMuted" - for the backgrounds and "Vibrant" - for active controls / accents etc.
Unfortunately, color combintation looks dull and desaturated sometimes, active elements don't stand out as much as I want it.
So my first decision was to
Blindly edit colors.
I convert them to hsl and explicitly set s and l values.
s: .2, l: .2 # DarkMuted
s: .6, l: .8 # Vibrant
This creates enough contrast between the two, but also has a drawback: sometimes colors look a bit oversaturated and distorted (compared to the input).
By this link you can find pairs of screenshots to show you the difference between "original" color pair returned by "vibrant.js" and colors with adjusted s and l values.
I've already asked on another forum if it's possible to apply automated adjustments to the color, to normalize percieved bias for some color ranges. The answer was "almost impossible".
I would say that subjectively acceptable color rate is ~ 65% but the result is too unpredictable. Since it's an automatic solution I can't rely on that too much.
So I decided to approach it another way:
Generate a bunch of colors and filter one
The problem here is:
I've not found how to generate more than one color per profile with vibrant.js
Also, I've tried the color-thief.js library to generate a palette of dominant colors and then filter, what I call, a "vibrant" color.
# Threshold values I used
thr = {minL: .4, maxL: .8, minS: .6, maxS: .8}
But here the another problem occurs - not every image has a set of colors that fall under my threshold. Some images have a pastel gamma or b/w and don't return anything.
So,
Can I overcome the vibrant.js limitation of 1 color per profile to have a bunch of "Vibrant" colors and then pick one that suits my requirements?
Or, maybe, there is another / better solution of doing it?
There is a specification about minimum contrast between colors (WCAG) you can find it here. So a possible strategie would be extracting the colors with vibrant.js and after that you could check contrast with a function. You can find a guide to build a function to check color constrast here. The last step probably would be generate colors variations with good contrast based on the results from the color contrast function. You can generate variations using this lib.
There are 3 leather pieces of Brown color.Among them one is dark brown in color.I have to highlight that odd piece(dark brown piece).The procedure which I followed in my code is:
converted given image to HSV.
checked the saturation range(S value) as well as the brightness range (V value) of the odd piece and other two light intensity pieces.
But the problem is, The odd piece values(both s and v value ranges) overlaps with the other two pieces(light intensity) values.
So,which color model best suits for this problem ?
If, Illumination changes the values gets changed again,How should I tackle this problem?
What type of Camera should I use ?
I am trying to change the white point/white balance programmatically. This is what I want to accomplish:
- Choose a (random) pixel from the image
- Get color of that pixel
- Transform the image so that all pixels of that color will be transformed to white and all other colors shifted to match
I have accomplished the first two steps but the third step is not really working out.
At first I thought that, as per Apples documentation CIWhitePointAdjust should be the thing to accomplish exactly that but, although it does change the image it is not doing what I would like/expect it to do.
Then it seemed that CIColorMatrix should be something that would help me to shift the colors but I was (and still am) at a loss of what to input to it with those pesky vectors.
I have tried almost everything (same RGB values on all vectors, corresponding values (R for R, etc.) on each vector, 1 - corresponding value, 1 + corresponding value, 1/corresponding value. RGB values and different (1 - x, 1 + x, 1 / x).
I have also come across CITemperatureAndTint that, as per Apples documentation should also help, but I have not yet figured out how to convert from RGB to temperature and tint. I have seen algorithms and formulas about converting from RGB to Temperatur, but nothing regarding tint. I will continue experimenting with this a little though.
Any help much appreciated!
After a lot of experimenting and mathematics I finally got my app to work almost the way I want.
If anyone else will find themselves facing a similar problem then here is what I did.
I ended up using CITemperatureAndTint filter supplying a color in Kelvins calculated from the selected pixels RGB value and user suppliable tint value.
To get to Kelvins I:
- firstly converted RGB to XYZ using the D65 illuminant (ie Daylight).
- then converted from XYZ to Yxy. Both of these conversions were made using the algorithms found from EasyRGB.
- I then calculated Kelvins from Yxy using the McCamry's formula I found in a paper here.
These steps got the image in the ballpark but not quite there, so I added a UISlider for the user to supply the tint value ranging from -100 to 100.
With selecting a point that should be white and choosing values from the positive side of the tint scale (all the images I on my phone tend to be more yellow) an image can now be converted to (more) neutral colors. Yey!
I supplyed the calculated temperature and user chosen tint as inputNeutral vector values.
6500 (D65 daylight) and 0 as inputTargetNeutral vector values to CITTemperatureAndTint filter.
I have a bunch of uncompressed bitonal TIF document images. All of them have a watermark in the middle. When I run them through OCR, the text that overlaps with the watermark does not get recognized. I am trying to see if I can apply some type of cleanup to remove those watermarks to be able to recognize the missing text.
Again, the images are black and white, but when you look at the watermark it appears grey since it has a pattern of black and white pixels that makes the letters in the watermark less "dense" than regular text. At the same time, the watermark letters are very big, much bigger than the regular text.
An example of a somewhat similar image is this (except this one is color and the watermark characters in my case are a lot thicker and bigger; my watermarks are also a lot shorter: only 3 to 4 letters long)
It seems that there might be some sort of clean up filter that would be similar to removing large black borders from an image except borders are ually "denser" than a watermark so they appear "more black".
I have 3 tools at my disposal: GIMP, ImageMagick and IrfanView. Can you recommend any specific features of any subset of these tools that might help me?
Playing with contrast etc did not help, but I found a different way. As stated above, the regular text is a lot "denser" than the watermark text meaning that a regular black pixel has more surrounding black pixels than a watermark black pixel. So I devised a simple window-based filtering and thresholding algorithm.
Here's how I did it in Matlab, using a 5X5 window:
im=imread('imageWithWmark.tif');
imInv = ~im;
nr=size(imInv,1);
nc=size(imInv,2);
d = 2; % for 5X5 window
counts = zeros(nr,nc);
for rr = d+1 : nr-d-1
for cc = d+1 : nc-d-1
counts(rr,cc) = nnz(imInv(rr-d:rr+d,cc-d:cc+d));
end
end
thresh=10; % 10 out of 25 -- the larger the thresh the thinner the resulting letters are
imThresh = (counts>=thresh) & imInv;
imwrite(~imThresh,sprintf('Thresh_%d.tif',thresh),'Compression','none','Resolution',300);
Of course, the size of the window, the threshold and other parameters depend on the parameters of the regular text on the page (letter bigger/smaller, thicker/thinner etc) but even this initial version worked pretty well