I don't understand why $ff008000 is pure green and not $ff00FF00 (with is lime) ? I was thinking that TAlphaColor is formed of four channels (ARGB) specified as 4-byte hexadecimal number. so how $ff008000 with have 80 in the g channel could be pure green and $ff00FF00 with have ff in the g channel can be lime ?
Green = TColor($008000);
Lime = TColor($00FF00);
The naming of the colors is what is giving you a difficulty.
Instead of the RGB color model, think of the HSB (Hue, Saturation, Brighness - also known as HSL - Hue Saturation - luminance) color model.
In that model the amount of color is given by the Saturation, and the 'color' is given by the Hue, so any color with the same value for Hue and Saturation can be said to be the same color even though they won't look the same if the brightness is different. We could say that a fully saturated color is 'pure' but we still have colors that look different because of the different brightness levels.
Pure is just a label here, they could have called it 'only' to have the same meaning.
Related
I want to generate a rainbow of colors, with the same perceived brightness, and same perceived saturation.
In essence, I am looking for a formula that takes three parameters: getRgbColor(hue, perceived_brightness, perceived_saturation) and returns the corresponding color, or some sort of error if no color with these constraints exists.
By "same perceived brightness" I mean: an average person seeing these colors on their average monitor would say that these colors appear to be about as bright as one particular shade of gray.
By "same perceived saturation" I mean: an average person seeing these colors on their average monitor would say that these colors appear to be equally colorful, when compared to that shade of gray.
Everyone will perceive colors a bit differently, so I am seeking an average consensus.
According to my understanding, to generate colors of the same "perceived brightness", I could use the CIELAB color space, and set the [L]uminosity. But then I do not know what values to use for a and b, and how to set the saturation, or the hue.
To generate colors of the same "perceived saturation", I think I could use the HSV or HSL color space, and set the [S]aturation. But in those color spaces, the "perceived brightness" does not seem to correspond to the [V]alue or [L]ightness. A shade of blue appears much darker than a shade of yellow with the same value, or the same lightness.
I am using opencv for the graphics output, and I am looking for either a way to calculate these colors in opencv, or a general formula.
With colour values represented in CIE L*a*b*, it is possible to perform a conversion to cylindrical coordinates, i.e. CIE L*C*Hab*, to separate hue and saturation components: https://en.wikipedia.org/wiki/CIELAB_color_space#Cylindrical_representation:_CIELCh_or_CIEHLC
i have not found an article to explain why by transofrmation RGB Color model to HSI we have undefinde Saturation when the color is black?
Qeustion1: what is the explation of this (the mathematic reason)
Question2: when the singularity is in the black color, that means we could not define what is the Saturation. but the Question then why the HSI is sensitiv to error also when the Saturation is small(not Zero but in the near from Zero). i have read that it is better not allow the saturation to be very small?
The mathematical reason is an indeterminate form 0/0. It should be intuitive that "there is nothing to see" in black.
The saturation of RGB 0,0,0 cannot be calculated and is therefor defined as zero.
S = (max(r,g,b) - min(r,g,b)) / max(r,g,b)
You see that r=g=b=0 would result in a problem as we cannot devide by 0.
The formula also shows that very small saturation values can only occur if we have very similar RGB values. If a pixel has a low saturation it is "more gray" or more achromatic. It doesn't make much sense to apply colour based rules to non-colours.
Given an image, how do I go about determining if a certain pixel is "white" ? Based on Wikipedia, I understand that if the RGB values are at (255,255,255), the pixel is considered white and that a lower similar set of values for eg. (200,200,200) would mean that it is a "darker shade of white" i.e. gray.
Should I just set a threshold of example 80% for each channel and if the RGB at a certain pixel passes that condition then it is marked as gray/white ? Are there any papers that I can read up for help ?
Regards,
Haziq
The solution is to convert your color space from RGB to HSV. Here is sample algorithm thread. Finally apply threshold in Value (Lightness) Channel to filter bright region.
If you simply threshold all channels at, say 200, you are allowing the Red to differ from the Green and that to differ from the Blue, which means you are allowing colour into your images and all the following colours would be permitted:
You need to ensure that, not only are Red, Green and Blue above 200, but further that they are equal. That way you only permit this range:
In the HSL model, you need Lightness to be above say 80%, but also the Saturation to be zero to ensure white/gray.
I have a image and i want to detect a blue rectange in it. My teacher told me that:
convert it to HSV color model
define a thresh hold to make it become a binary image with the color we want to detect
So why do we do that ? why don't we direct thresh hold the rgb image ?
thanks for answer
You can find the answer to your question here
the basic summary is that HSV is better for object detection,
OpenCV usually captures images and videos in 8-bit, unsigned integer, BGR format. In other words, captured images can be considered as 3 matrices, BLUE,RED and GREEN with integer values ranges from 0 to 255.
How BGR image is formed
In the above image, each small box represents a pixel of the image. In real images, these pixels are so small that human eye cannot differentiate.
Usually, one can think that BGR color space is more suitable for color based segmentation. But HSV color space is the most suitable color space for color based image segmentation. So, in the above application, I have converted the color space of original image of the video from BGR to HSV image.
HSV color space is consists of 3 matrices, 'hue', 'saturation' and 'value'. In OpenCV, value range for 'hue', 'saturation' and 'value' are respectively 0-179, 0-255 and 0-255. 'Hue' represents the color, 'saturation' represents the amount to which that respective color is mixed with white and 'value' represents the amount to which that respective color is mixed with black.
According to http://en.wikipedia.org/wiki/HSL_and_HSV#Use_in_image_analysis :
Because the R, G, and B components of an object’s color in a digital image are all correlated with the amount of light hitting the object, and therefore with each other, image descriptions in terms of those components make object discrimination difficult. Descriptions in terms of hue/lightness/chroma or hue/lightness/saturation are often more relevant.
Also some good info here
The HSV color space abstracts color (hue) by separating it from saturation and pseudo-illumination. This makes it practical for real-world applications such as the one you have provided.
R, G, B in RGB are all co-related to the color luminance( what we loosely call intensity),i.e., We cannot separate color information from luminance. HSV or Hue Saturation Value is used to separate image luminance from color information. This makes it easier when we are working on or need luminance of the image/frame. HSV also used in situations where color description plays an integral role.
Cheers
I'd like design a chart and set the colors
from a single exemplar. Same way as in Excel's:
Is there some sort of a formula or algorithm to
generate the next shade of color from a given
shade or color?
That looks to me like they just took the same hue (basic color) and turned the brightness up and down. That can be done easily enough with a HSL or HSV transformations. Check Wikipedia for HSL and HSV color spaces to get some understanding of the theory involved.
Basic idea: Computers represent color with a mixture of red intensity, green intensity and blue intensity, called RGB, because that's the way the screen displays color. HSL (Hue, Saturation, Lightness) and HSV (Hue, Saturation, Value) are two alternative models for representing color that are more intuitive and closer to the way human beings tend to think about how colors look.
Hue is the basic color, represented (more or less) as an angle on a color wheel. Saturation is a linear value, from 0 (gray) to 255 (bright, vibrant color). And Lightness/Value represent brightness, from 0 (black) to 100 (white).
The algorithms to transform from RGB -> HSL and HSL -> RGB (or HSV instead of HSL) are pretty straightforward. Try transforming your color to HS*, adjusting the brightness, and transforming back. By taking several different brightness values from low to high, and arranging them as wedges in a pie chart, you can duplicate that picture pretty easily.
Look into the HSV colour space. Using it you can produce different shades or tints starting from a given colour. There is a page with Pascal / Delphi code for conversion between RGB and HSV at efg's Computer Lab.
Roderick , the #mghie links are great to start, additionally try out the Colorlib Delphi Library , wich lets you convert between color models as well as HTML color conversion utilities. is very complete, full source code included and freeware ;).
check the demo application , in this image you can see a blue pallete generated using this library.