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Note that the inputs and outputs, except for the angle in degrees, are in the range of 0 to 1. Implementation and Algorithms p.
Here it is with some values converted to int to prevent that. All the other methods I see here work by casing everything into parts of a hexagon, and approximating "angles" from that.
By instead starting with a different equation using cosines, and solving for h s and v, you get a lot nicer relationship between hsv and rgb, and tweening becomes smoother at the cost of it being way slower.
Assume everything is floating point. This link has formulas for what you want. I created a possibly faster implementation by using range for RGBS and V and range for Hue avoiding the division , and grouping the cases into two categories:.
But I can provide simple hsv2rgb algorithm rgb2hsv here which I currently discover - I update wiki with description: Main improvement is that I carefully observe r,g,b as hue functions and introduce simpler shape function to describe them without loosing accuracy.
Make your voice heard. Take the Developer Survey now. David H David H Gerard out is in degrees. This is not radians.
Floating point arithmetic and floating point storage means that two values can be equal in approximate value, but not equal in stored value even though formulaically they are the same.
For rgb2hsv, where in. Comparing two floating-point values for equality is completely well-defined and a legal thing to do. The problem is that you may not get the answer you actually wanted, and likely intended to perform a looser comparison.
You can also try this code without floats faster but less accurate: Leszek Szary Leszek Szary 5, 1 33 To solve problems such as these, the HSL and HSV models scale the chroma so that it always fits into the range [0, 1] for every combination of hue and lightness or value, calling the new attribute saturation in both cases fig.
To calculate either, simply divide the chroma by the maximum chroma for that value or lightness. The HSI model commonly used for computer vision, which takes H 2 as a hue dimension and the component average I "intensity" as a lightness dimension, does not attempt to "fill" a cylinder by its definition of saturation.
Instead of presenting color choice or modification interfaces to end users, the goal of HSI is to facilitate separation of shapes in an image. Saturation is therefore defined in line with the psychometric definition: See the Use in image analysis section of this article.
Using the same name for these three different definitions of saturation leads to some confusion, as the three attributes describe substantially different color relationships; in HSV and HSI, the term roughly matches the psychometric definition, of a chroma of a color relative to its own lightness, but in HSL it does not come close.
Even worse, the word saturation is also often used for one of the measurements we call chroma above C or C 2.
The original purpose of HSL and HSV and similar models, and their most common current application, is in color selection tools.
At their simplest, some such color pickers provide three sliders, one for each attribute. Most, however, show a two-dimensional slice through the model, along with a slider controlling which particular slice is shown.
Several color choosers from the s are shown to the right, most of which have remained nearly unchanged in the intervening time: Some more sophisticated variants are designed for choosing whole sets of colors, basing their suggestions of compatible colors on the HSL or HSV relationships between them.
Most web applications needing color selection also base their tools on HSL or HSV, and pre-packaged open source color choosers exist for most major web front-end frameworks.
HSL and HSV are sometimes used to define gradients for data visualization , as in maps or medical images. Image editing software also commonly includes tools for adjusting colors with reference to HSL or HSV coordinates, or to coordinates in a model based on the "intensity" or luma defined above.
In particular, tools with a pair of "hue" and "saturation" sliders are commonplace, dating to at least the lates, but various more complicated color tools have also been implemented.
For instance, the Unix image viewer and color editor xv allowed six user-definable hue H ranges to be rotated and resized, included a dial -like control for saturation S HSV , and a curves -like interface for controlling value V —see fig.
Video editors also use these models. These have been copied widely, but several imitators use the HSL e. The applications of such tools include object detection, for instance in robot vision ; object recognition , for instance of faces , text , or license plates ; content-based image retrieval ; and analysis of medical images.
For the most part, computer vision algorithms used on color images are straightforward extensions to algorithms designed for grayscale images, for instance k-means or fuzzy clustering of pixel colors, or canny edge detection.
At the simplest, each color component is separately passed through the same algorithm. It is important, therefore, that the features of interest can be distinguished in the color dimensions used.
Starting in the late s, transformations like HSV or HSI were used as a compromise between effectiveness for segmentation and computational complexity.
They can be thought of as similar in approach and intent to the neural processing used by human color vision, without agreeing in particulars: In recent years, such models have continued to see wide use, as their performance compares favorably with more complex models, and their computational simplicity remains compelling.
While HSL, HSV, and related spaces serve well enough to, for instance, choose a single color, they ignore much of the complexity of color appearance.
Essentially, they trade off perceptual relevance for computation speed, from a time in computing history high-end s graphics workstations, or mids consumer desktops when more sophisticated models would have been too computationally expensive.
If we plot the RGB gamut in a more perceptually-uniform space, such as CIELAB see below , it becomes immediately clear that the red, green, and blue primaries do not have the same lightness or chroma, or evenly spaced hues.
Furthermore, different RGB displays use different primaries, and so have different gamuts. If we take an image and extract the hue, saturation, and lightness or value components, and then compare these to the components of the same name as defined by color scientists, we can quickly see the difference, perceptually.
For example, examine the following images of a fire breather fig. The original is in the sRGB colorspace. Though none of the dimensions in these spaces match their perceptual analogs, the value of HSV and the saturation of HSL are particular offenders.
Such perversities led Cynthia Brewer, expert in color scheme choices for maps and information displays, to tell the American Statistical Association:.
Computer science offers a few poorer cousins to these perceptual spaces that may also turn up in your software interface, such as HSV and HLS. Perceptual color dimensions are poorly scaled by the color specifications that are provided in these and some other systems.
For example, saturation and lightness are confounded, so a saturation scale may also contain a wide range of lightnesses for example, it may progress from white to green which is a combination of both lightness and saturation.
These flaws make the systems difficult to use to control the look of a color scheme in a systematic manner. If much tweaking is required to achieve the desired effect, the system offers little benefit over grappling with raw specifications in RGB or CMY.
If these problems make HSL and HSV problematic for choosing colors or color schemes, they make them much worse for image adjustment.
HSL and HSV, as Brewer mentioned, confound perceptual color-making attributes, so that changing any dimension results in non-uniform changes to all three perceptual dimensions, and distorts all of the color relationships in the image.
In the example below fig. Notice how the hue-shifted middle version without such a correction dramatically changes the perceived lightness relationships between colors in the image.
The creators of HSL and HSV were far from the first to imagine colors fitting into conic or spherical shapes, with neutrals running from black to white in a central axis, and hues corresponding to angles around that axis.
Similar arrangements date back to the 18th century, and continue to be developed in the most modern and scientific models.
First, we compute chroma, by multiplying saturation by the maximum chroma for a given lightness or value. Next, we find the point on one of the bottom three faces of the RGB cube which has the same hue and chroma as our color and therefore projects onto the same point in the chromaticity plane.
Finally, we add equal amounts of R , G , and B to reach the proper lightness or value. Then we can find a point R 1 , G 1 , B 1 along the bottom three faces of the RGB cube, with the same hue and chroma as our color using the intermediate value X for the second largest component of this color:.
At this point we get something similar to red shape from fig. We observe following shape properties Fig. First, we find chroma:. Then we can, again, find a point R 1 , G 1 , B 1 along the bottom three faces of the RGB cube, with the same hue and chroma as our color using the intermediate value X for the second largest component of this color:.
Then we can find R , G , and B by adding the same amount to each component, to match luma:. Mouse over the swatches below to see the R , G , and B values for each swatch in a tooltip.
From Wikipedia, the free encyclopedia. Alternative representations of the RGB color model. If we plot hue and a HSL lightness or b HSV value against chroma range of RGB values rather than saturation chroma over maximum chroma for that slice , the resulting solid is a bicone or cone , respectively, not a cylinder.
Such diagrams often claim to represent HSL or HSV directly, with the chroma dimension confusingly labelled "saturation". Painters long mixed colors by combining relatively bright pigments with black and white.
Mixtures with white are called tints , mixtures with black are called shades , and mixtures with both are called tones. See Tints and shades.
This color model by German chemist Wilhelm Ostwald exemplifies the "mixtures with white and black" approach, organizing 24 "pure" colors into a hue circle , and colors of each hue into a triangle.
The model thus takes the shape of a bicone. The same image, with a portion removed for clarity. Tektronix graphics terminals used the earliest commercial implementation of HSL, in This diagram, from a patent filed in , shows the bicone geometry underlying the model.
Color picker and Image editing. Computer vision and Image analysis.