According to Colour Structure Descriptor (CSD) definition it says, CSD can be use to express the structure of colours in an image using HMMD colour space. But how?
Since it gives number of times a colour appear, how can we derive the position from it?
If it is impossible, what could I do to extract the structure of colours in an image.
(Basically I want an output to express some thing similar. Example: Blue on top part, Green on middle part and Brown on bottom part.)
Related
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.
I have a couple of questions, which get tied back to a simple need - I want to use the quality histogram as a colorbar in my publication. To export it along with labels for publication, I tried just taking a snapshot with the appropriate tool, but if I use alpha/ solid white background the text/ colorbars is not visible. If I use the solid black or meshlab background, the text is white, or can not be used directly in publication.
My questions are as follows:
I know how to change the text color on meshlab window. Is there a similar function to change the text font size on meshlab window?
As a more demanding question, is there a way I can import the quality map file into matlab or some other software, and plot a custom colorbar. I will append my .qmap file here, but it seems that the color field is empty, and I can not reproduce the colors without them.
%%%%%QMAP FILE TO FOLLOW%%%%%
// COLOR BAND FILE STRUCTURE - first row: RED CHANNEL DATA - second row GREEN CHANNEL DATA - third row: BLUE CHANNEL DATA
// CHANNEL DATA STRUCTURE - the channel structure is grouped in many triples. The items of each triple represent respectively: X VALUE, Y_LOWER VALUE, Y_UPPER VALUE of each node-key of the transfer function
0;0.5;0.125;1;0.375;1;0.625;0;0.875;0;1;0;
0;0;0.125;0;0.375;1;0.625;1;0.875;0;1;0;
0;0;0.125;0;0.375;0;0.625;1;0.875;1;1;0.5;
//THE FOLLOWING 4 VALUES REPRESENT EQUALIZER SETTINGS - the first and the third values represent respectively the minimum and the maximum quality values used in histogram, the second one represent the position (in percentage) of the middle quality, and the last one represent the level of brightness as a floating point number (0 copletely dark, 1 original brightness, 2 completely white)
-0.001;0.714286;0.0004;1;
Example Image
I want to remove the lines (shown in RED color) as they are out of order. Lines shown in black color are repeating at same period (approximately). Period is not known beforehand. Is there any way of deleting non-periodic lines( shown in red color) automatically?
NOTE: Image is binary ( back & while).. lines shown in red color only for illustration.
Of course there is any way. There is almost always some way to do something.
Infortunately you have not provided any particular problem. The entire thing is too broad to be answered here.
To help you getting started: (I highly recommend you start with pen, paper and your brain)
Detect the lines -> google or think, there are many standard ways to detect lines in an image. if you don't have noise in your binary image its trivial.
find any aequidistant sets -> think
delete the rest -> think ( you know what is good so everything else has to go away)
I assume, your lines are (almost) vertical.
The following should work
turn the image to a column sum histogram
try a Fourier transformation on the signal (potentially padding the image appropriately)
pick the maximum/peak from the Fourier spectrum as your base period
If you need the lines rather than the position of the lines, generate a mask with lines at appropriate intervals (as determined by your analysis before) and apply to the image.
Is ShapeRenderer class has the ability to do this,I would like to create boundaries for a custom object (such as a rectangle with rounded corners) and then fill it.And after that some text on that filled rounded box.
shapeRenderer.begin(ShapeType.Filled);
shapeRenderer.setColor(Color.RED);
//shapeRenderer.line(...);
shapeRenderer.curve(x1, y1, cx1, cy1, cx2, cy2, x2, y2, segments);
shapeRenderer.line(...);
shapeRenderer.curve(...);
shapeRenderer.line(...);
shapeRenderer.curve(...);
shapeRenderer.line(...);
shapeRenderer.curve(...);
shapeRenderer.setColor(Color.BLACK);
shapeRenderer.fill();
shapeRenderer.end();
Any suggestion to do it in libgdx.
As Lestat said, you can use Scene2d.
Whether you are using Scene2d or not, a NinePatch would probably be suitable if you want to draw a rounded rectangle that scales well (check this link).
If you want to be able to set the color of your image/control, you can use NinePatch.setColor().
Here are two example scenarios regarding colors:
You will always have a black stroke/outline, and an arbitrary fill. In this case make the original image have black stroke/outline and white interior. When you 'tint' the image using NinePatch.setColor(), the stroke/outline will be unaffected and will remain black, while the interior (fill) will be the same as the color provide to the mentioned method.
You have arbitrary fill and arbitrary stroke. In this case you need 2 separate original images. First one would contain the 'fill' and would be completely white. Second one would contain the 'stroke' in white color and would be transparent inside. When drawing you would draw first the fill with its tint and then the stroke with its tint and that's all.
If for some reason you needed different corner curve radii, you would probably need separate images (or image pairs) for each radius to get the best result.
I am using pandas and matplotlib to generate bar-graphs with lots of bars.
I know how to cycle through a list of selected colors (How to give a pandas/matplotlib bar graph custom colors).
The question is what colors to select so that my graph prints nicely on a paper (it is for a research paper). What I am most interested in is sufficient contrast between the columns and a selection of colors that looks pleasant. I would like to have multiple colors instead of gray-scale or single-hue colorschemes.
Are there any predetermined schemes to select from that people use?
So your requirements are "lots of colors" and "no two colors should map to the same grayscale value when printed", right? The second criteria should be met by any "sequential" colormaps (which increase or decrease monotically in luminance). I think out of all the choices in matplotlib, you are left with cubehelix (already mentioned), gnuplot, and gnuplot2:
The white line is the luminance of each color, so you can see that each color will map to a different grayscale value when printed. The black line is hue, showing they cycle through a variety of colors.
Note that cubehelix is actually a function (from matplotlib._cm import cubehelix), and you can adjust the parameters of the helix to produce more widely-varying colors, as shown here. In other words, cubehelix is not a colormap, it's a family of colormaps. Here are 2 variations:
For less wildly-varying colors (more pleasant for many things, but maybe not for your bar graphs), maybe try the ColorBrewer 3-color maps, YlOrRd, PuBuGn, YlGnBu:
https://www.flickr.com/photos/omegatron/7298887952/
I wouldn't recommend using only this color to identify bar graphs, though. You should always use text labels as the primary identifier. Also note that some of these produce white bars that completely blend in with the background, since they are intended for heatmaps, not chart colors:
from matplotlib import pyplot as plt
import pandas, numpy as np # I find np.random.randint to be better
# Make the data
x = [{i:np.random.randint(1,5)} for i in range(10)]
df = pandas.DataFrame(x)
# Make a list by cycling through the colors you care about
# to match the length of your data.
cmap = plt.get_cmap('cubehelix')
indices = np.linspace(0, cmap.N, len(x))
my_colors = [cmap(int(i)) for i in indices]
# Specify this list of colors as the `color` option to `plot`.
df.plot(kind='bar', stacked=True, color=my_colors)
And these are the new guys:
In 1.5 matplotlib will ship with 4 new rationally designed color maps:
'viridis' (default color map as of 2.0)
'magma'
'plasma'
'inferno'.
The process of designing these color maps is presented in A Better Default Colormap for Matplotlib | SciPy 2015 .
The tool developed for this process can be installed by pip install viscm.
I would suggest the cubehelix color map. It is designed to have correct luminosity ordering in both color and gray-scale.
I am not aware of predetermined schemes. I usually use a few colours for publication plots. I mostly take two things into consideration when choosing colours:
Colour-blindness: this page on wikipedia has lots of good info about choosing colours that are distinguishable to most color-blind people. If you notice on the "tips for editors" section, once you take the guidelines into account there are only a few sets of colours available. (A good rule of thumb is to never mix red and green!) You can also use the linked colour-blind simulators to see if your plot would be well visible.
Luminance: most of the journals in my field will publish in B&W by default. Even though most people read the papers online, I still like to make sure that the plots can be understood when printed in grayscale. So I take care to use colours that have different luminances. To test, a good way is to just desaturate the image produced, and you'll have a good idea of how it looks when printed in grayscale. In many cases (particularly line or scatter plots), I also use other things than colour to distinguish between sets (eg. line styles, different markers).
If no colours are specified in matplotlib plots, it has a default set of colours that it cycles through. This answer has a good explanation on how to change that default set of colours. You can customise that to your preferred set of colours, so the plots would use them in turn.