I have created my own color using the following command:
colorpalette Red, luminate(0(10)100, level) nograph
return list
foreach x of numlist 1/10 {
local color`x' `r(p`x')'
}
I can access and use these colors just fine, and the second color for example is
di "`color2'"
156 0 0
However, problems arise when I want to add transparency. Say I am overlaying two histograms and want to add transparency, if I do something like
twoway hist somevar, color("156 0 0"%30)
and here, it just ignores the %30 part. color(red%30) works fine, but color("`color2'"%30) does not.
How can I add transparency with self-created colors after the creation? I would prefer not to recreate the whole colorpalette everytime that I want to change the transparency.
Everything within quotes is treated as one object, so you need to include %30 within the quotes.
twoway hist price, color("156 0 0%30")
Related
I have a QColor value and I need to break it down into its RGB components between 0 and 1 with only one value after decimal point.
For example: Orange color is
QColor color = QColor(255,128,0)
qreal green = color.greenF();
qDebug() << green; //0.501960784
Whereas the green component must be 0.6. That is, it's rgb value is (255,128,0) or (1,0.6,0).
How to get 0.6 instead of 0.501960784?
But Orange color is 255,128,0
There is no such thing as "the" orange color. Everyone calls something else using the same word. Orange isn't a color, it's a range of hues. Those hues become colors once you assign them some saturation and brightness. There's a whole lot of colors that can be represented using an 8-bit-per-componet R,G,B triple that all have a hue that is orange, and that thus qualify as an orange. There's no the orange,
Whereas the green component must be 0.6. That is, it's rgb value is (255,128,0) or (1,0.6,0).
It's not. QColor tells you so, and basic math tells you so. The color clearly is 1/0.6/0, or 1*255, 6/10*255, 0*255, or 255, 1530/10, 0 or 255, 153, 0 exactly. It won't ever be 255,128,0 and I have no idea who told you that, but they were wrong.
So it's really simple: forget it all. Just use QColor::redF, greenF and blueF. They work the way they should.
Oh, and you didn't even mention the elephants in the room that are color spaces. An RGB triple has no physical meaning - it's entirely abstract - until you map it to a physical color space. And you better use calibrated output devices to interface your color choice with the user, otherwise it'll be endless silliness all around.
How to implement connected component labeling in python with open cv?
This is an image example:
I need connected component labeling to separate objects on a black and white image.
The OpenCV 3.0 docs for connectedComponents() don't mention Python but it actually is implemented. See for e.g. this SO question. On OpenCV 3.4.0 and above, the docs do include the Python signatures, as can be seen on the current master docs.
The function call is simple: num_labels, labels_im = cv2.connectedComponents(img) and you can specify a parameter connectivity to check for 4- or 8-way (default) connectivity. The difference is that 4-way connectivity just checks the top, bottom, left, and right pixels and sees if they connect; 8-way checks if any of the eight neighboring pixels connect. If you have diagonal connections (like you do here) you should specify connectivity=8. Note that it just numbers each component and gives them increasing integer labels starting at 0. So all the zeros are connected, all the ones are connected, etc. If you want to visualize them, you can map those numbers to specific colors. I like to map them to different hues, combine them into an HSV image, and then convert to BGR to display. Here's an example with your image:
import cv2
import numpy as np
img = cv2.imread('eGaIy.jpg', 0)
img = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY)[1] # ensure binary
num_labels, labels_im = cv2.connectedComponents(img)
def imshow_components(labels):
# Map component labels to hue val
label_hue = np.uint8(179*labels/np.max(labels))
blank_ch = 255*np.ones_like(label_hue)
labeled_img = cv2.merge([label_hue, blank_ch, blank_ch])
# cvt to BGR for display
labeled_img = cv2.cvtColor(labeled_img, cv2.COLOR_HSV2BGR)
# set bg label to black
labeled_img[label_hue==0] = 0
cv2.imshow('labeled.png', labeled_img)
cv2.waitKey()
imshow_components(labels_im)
My adaptation of the CCL in 2D is:
1) Convert the image into a 1/0 image, with 1 being the object pixels and 0 being the background pixels.
2) Make a 2 pass CCL algorithm by implementing the Union-Find algorithm with pass compression. You can see more here.
In the First pass in this CCL implementation, you check the neighbor pixels, in the case your target pixel is an object pixel, and compare their label between them so that you can generate equivalences between them. You assign the least label, of those neighbor pixels which are objects pixels (label>0) to your target pixel. In this way, you are not only assigning an object label to your target pixesl (label>0) but also creating a list of equivalences.
2) In the second pass, you go through all the pixels, and change their previous label by the label of its parent label by just looking into the equivalent table stored in your Union-Find class.
3)I implemented an additional pass to make the labels follow a sequential order (1,2,3,4....) instead of a random order (23,45,1,...). That involves changing the labels "name" just for aesthetic purposes.
I have a RRD database with data:
"DS:pkts_transmitted:GAUGE:120:0:U",
"DS:pkts_received:GAUGE:120:0:U",
"DS:pkts_lost:GAUGE:120:0:U",
"DS:rtt_min:GAUGE:120:0:U",
"DS:rtt_avg:GAUGE:120:0:U",
"DS:rtt_max:GAUGE:120:0:U",
And I want that the Avg line change colour if I lose any package.
For example, if I lose 5 packets make the line blue, if I lose 10 make it red.
I see people doing it but I read the documentation and I can't find how to do this.
The way to do this is to actually have multiple lines defined (one of each colour) and hide the ones you don't want to see at any time, using calculations.
For example, say we have an RRD with two DSs:
DS:x:GAUGE:60:0:U
DS:y:GAUGE:60:0:1
Now, we want to show the line for x in red if y is 0, and blue if it is 1. To do this, we create two calculated values, x1 and x2.
CDEF:x1=y,0,EQ,x,UNKN,IF
CDEF:x2=y,1,EQ,x,UNKN,IF
Thus, x1 is active if y=0 and x2 if y=1. Yes, this could be simplified, but I'm showing it like this for the example.
Now, we can make lines using these:
LINE:x1#ff0000:MyLine
LINE:x2#0000ff
Note that the second line doesn't need a legend. Now, the line will appear to change colour depending on the value of the y metric, since at any time the other line will be UNKN and therefore not displayed.
You can extend this, of course, to have multiple colours and more complex thresholds.
I am working on a project to losslessly compress a specific style of BMP images that look like this
I have thought about doing pattern recognition, to find repetitive blocks of N x N pixels but I feel like it wont be fast enough execution time.
Any suggestions?
EDIT: I have access to the dataset that created these images too, I just use the image to visualize my data.
Optical illusions make it hard to tell for sure but are the colors only black/blue/red/green? If so, the most straightforward compression would be to simply make more efficient use of pixels. I'm thinking pixels use a fixed amount of space regardless of what color they are. Thus, chances are you are using 12x as many pixels as you really need to be. Since a pixel can be a lot more colors than just those four.
A simple way to do that would be to do label the pixels with the following base 4 numbers:
Black = 0
Red = 1
Green = 2
Blue = 3
Example:
The first four colors of the image seems to be Blue-Red-Blue-Blue. This is equal to 3233 in base 4, which is simply EF in base 16 or 239 in base 10. This is enough to define what the red color of the new pixel should be. The next 4 would define the green color and the final 4 define what the blue color is. Thus turning 12 pixels into a single pixel.
Beyond that you'll probably want to look into more conventional compression software.
I'm drawing a color selection button and I'm looking for a nice and simple formula to get a good text color (foreground) for a given background color in RGB.
A simple try would be to just take the complement color but this will produce an odd looking button for colors like pure blue or pure red.
Is there something well known that does this?
If it matters at all, I'm using QT.
For maximum legibility, you want maximum brightness contrast without getting into hues which don't work together. The most consistent way to do this is to stick with black or white for the text color. You might be able to come up with more aesthetically pleasing schemes, but none of them will be more legible.
To pick between black or white, you need to know the brightness of the background. This gets a little more complicated, due to two factors:
The perceived brightness of the individual primaries red, green, and blue are not identical. The quickest advice I can give is to use the traditional formula to convert RGB to gray - R*0.299 + G*0.587 + B*0.114. There are lots of other formulas.
The gamma curve applied to displays makes the middle gray value higher than you'd expect. This is easily solved by using 186 as the middle value rather than 128. Anything less than 186 should use white text, anything greater than 186 should use black text.
I'm no expert on programming things related to RGB, but from a designer's perspective, often the most readable color will be just a much lighter (if the background color is dark) or darker (if the background color is light) version of the same shade.
Basically you'd take your RGB values and if they're closer to 0 (dark) you'd push them each up by an equal amount for your foreground color, or vice versa if it's a light BG.
Complement colors can actually be really painful on the eyes for readability.
Leverage an outline for legibility
If by "good text color (foreground)" you intend it for legibility purposes when the user chooses any background colour, you can always produce white text having a black outline. It will be legible on any solid, patterned or gradient background, from black through white and anything in between.
Even if this doesn't hit the mark of your intention, I think it worthwhile posted here because I came looking for similar solutions.
Building on top of Mark's response, here's some Ruby code that'll do the work
rgbval = "8A23C0".hex
r = rgbval >> 16
g = (rgbval & 65280) >> 8
b = rgbval & 255
brightness = r*0.299 + g*0.587 + b*0.114
return (brightness > 160) ? "#000" : "#fff"
You are better off with a high difference in luminosity. In general, colored backgrounds with colored text suck for readability, hurting the eyes over time. Lightly tinted colors (e.g. in HSB, S~10%, B>90%) with black text work fine, or lightly tinted text over a black background. I'd stay away from coloring both. Dark text (b~30%, s>50%) with a subtle coloration over a white background can also be fine. Yellow (amber) text on a deep blue background has excellent readability, as does amber or green on black. This is why old dumbterms (vt100, vt52, etc.) went for these colors.
If you really need to do color-on-color for the 'look', you could reverse both H and B, while pinning saturation at a moderate to low level.
And one last note: if you have a 50% gray background, rethink your interface. You're robbing yourself of half your dynamic range! You're alienating low-visibility users, including anyone over 35...
Color combinations often look terrible when not carefully chosen. Why not use either white or black for the text, depending on the Brightness of the color. (Will need to convert to HSB first.)
Or let the user choose either black or white text.
Or use pre-defined combinations. This is what Google does in their calendar product.
I've been looking for a simailr answer and came across this post and some others that I thought I'd share. According to http://juicystudio.com/services/luminositycontrastratio.php#specify the "Success Criterion 1.4.3 of WCAG 2.0 requires the visual presentation of text and images of text has a contrast ratio of at least 4.5:1" with some exceptions. That site lets you put in foreground and background colors to compute their contrast, although it would be helpful if it would suggest alternatives or ranges.
One of the best sites I've found for visualizing color contrast is http://colorizer.org/ It lets you adjust almost all manner of color scales (RGB, CMYK, etc.) at the same time and then shows you the result on the screen, such as white text on a yellow background.
I usually look at color complements, they also have color complement wheels to help
http://www.makart.com/resources/artclass/cwheel.html
If your color is HSL, flip the Hue by 180 degrees for a decent calculation
I wanted to put #MarkRansom's answer into use and managed to create this snippet:
I got the values From seeing how sRGB converts to CIE XYZ and built upon that.
The script simply tracks the position of the foreground item and it's position regarding the colored background items.
Then based on background luminosity it gradually changes the foreground text color to either black or white.
Open the codepen for full example
https://codepen.io/AndrewKnife/pen/XWBggQq
const calculateLight = (colorItem: number) => {
let c = colorItem / 255.0;
if (c <= 0.03928) {
c /= 12.92;
} else {
c = Math.pow((c + 0.055) / 1.055, 2.4);
}
return c;
};
const calculateLuminosity = (color: RGBColor) => {
return (
0.2126 * calculateLight(color.r) +
0.7152 * calculateLight(color.g) +
0.0722 * calculateLight(color.b)
);
};
const getContrastColor = (color: RGBColor) => {
if (calculateLuminosity(color) > LUMINOSITY_LIMIT) {
return FONT_COLOR_DARK;
}
return FONT_COLOR_LIGHT;
};
I thing that converting to HSV might be the way, but IMO changing hue would look weird. I'd try keeping the hue and fiddling with value and maybe saturation (light red buttons with dark red text ... hm sounds scary :-) ).