I was really confuse between intensity slicing and color map implementation in OpenCV. Is the color maps implementation in OpenCV the same with the concept of intensity slicing? Can anyone clarify this to me. Your help will be very much appreciated. Thank you.
Intensity slicing is more like a thresholding action. You have 2 kinds, one is without background, so black, and the selected greyscale colors are white. In OpenCV this can be achieved with threshold or inRange. The second one is with background, which you turn certain greyscale values white and the rest you leave them as they are... I do not know any OpenCV function that do this... but it can be easily achieve with inRange to get the binary mask and then setTo with the mask and to color white.
Now, the color mapping is actually as its name says, mapping colors :) This means that for each "colormap" it has a color value for each 8 bit greyscale value, i.e. 256 colors. Then it creates a new colored image by putting a color value that mapped the value of the greyscale pixel intensity. In the "Jet" colormap, 0 in greyscale will be mapped to a dark blue. And 255 in greyscale will mapped to a dark red.
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I would like to use GPUImage's Histogram Equalization filter (link to .h) (link to .m) for a camera app. I'd like to use it in real time and present it as an option to be applied on the live camera feed. I understand this may be an expensive operation and cause some latency.
I'm confused about how this filter works. When selected in GPUImage's example project (Filter Showcase) the filter shows a very dark image that is biased toward red and blue which does not seem to be the way equalization should work.
Also what is the difference between the histogram types kGPUImageHistogramLuminance and kGPUImageHistogramRGB? Filter Showcase uses kGPUImageHistogramLuminance but the default in the init is kGPUImageHistogramRGB. If I switch Filter Showcase to kGPUImageHistogramRGB, I just get a black screen. My goal is an overall contrast optimization.
Does anyone have experience using this filter? Or are there current limitations with this filter that are documented somewhere?
Histogram equalization of RGB images is done using the Luminance as equalizing the RGB channels separately would render the colour information useless.
You basically convert RGB to a colour space that separates colour from intensity information. Then equalize the intensity image and finally reconvert it to RGB.
According to the documentation: http://oss.io/p/BradLarson/GPUImage
GPUImageHistogramFilter: This analyzes the incoming image and creates
an output histogram with the frequency at which each color value
occurs. The output of this filter is a 3-pixel-high, 256-pixel-wide
image with the center (vertical) pixels containing pixels that
correspond to the frequency at which various color values occurred.
Each color value occupies one of the 256 width positions, from 0 on
the left to 255 on the right. This histogram can be generated for
individual color channels (kGPUImageHistogramRed,
kGPUImageHistogramGreen, kGPUImageHistogramBlue), the luminance of the
image (kGPUImageHistogramLuminance), or for all three color channels
at once (kGPUImageHistogramRGB).
I'm not very familiar with the programming language used so I can't tell if the implementation is correct. But in the end, colours should not change too much. Pixels should just become brighter or darker.
I'm writing a C++ code which can identify color black. I want to use HSV color space, but I cannot find the value range for black. Can anybody provide me the value range for color black in HSV color space?
In the HSV color space black color is represented by any point (H,S,V) having V = 0. To visualise this see the cylindrical 3D models in the HSV wiki which make it very easy to understand.
Black hairs filter
hair_color_low=[0,0,0]
hair_color_high=[360,255,50]
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
Anyone know any algorithm to non-linearly change lightness using HSI model?
I am currently doing something like this.
new intensity = old intensity^(1/4)
It increases lightness of dark color more than lightness of bright color.
The problem is that before enhancement, if I have some pixels look like black color because of very low lightness, their lightness increase after enhancement and their actual colors appear which make black area of photo has different colors(eg: grey,blue). I have tried quite a few ways to solve it by lowering new lightness of black spot but I have no luck so far.
Is there anyway to solve it or is there better algorithm? The problem is only with color which appear to be black before enhancement.
Please help. Thank a lot.
The HSI values of dark pixels are usually degenerate. This is because, for example, a fully saturated maximally-dark blue = black, is identical in appearance to a completely de-saturated (grey) pixel at its darkest = black (this is the reason the 3D space shape usually has a pointed tip at the degenerate/singular colors).
You should not enhance pixels under a certain threshold value, or alternatively, use some weighting function that inhibits enhancement at the very dark values.
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.