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I’m trying to calculate an average value of one color over the whole image in order to determine how color, saturation or intencity or eny other value describing this changes between frmaes of the video. However i would like to get just one value that will describe whole frame (and sigle, chosen color in it). Calculating simple average value of color in frame gives me very small differences between video frames, just 2-3 on a 0..255 space.

Is there any other method to determine color of the image other than histogram which as i understand will give me more than one value describing single frame.

michael
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  • Are you trying to extract a single color from each frame, and then use that color to distinguish between frames? – mpenkov Oct 26 '11 at 14:25

2 Answers2

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Which library are you using for image processing? If it's OpenCV (or Matlab) then the steps here will be quite easy. Otherwise you'd need to look around and experiment a bit.

  1. Use a Mean Shift filter on RGB (or gray, whichever) to cluster the colors in the image - nearly similar colors are clustered together. This lessens the number of colors to deal with.

  2. Change to gray-level and compute a frequency histogram with bins [0...255] of pixel values that are present in the image

  3. The highest frequency - the median - will correspond to the bin (color) that is present the most. The frequency of each bin will give you the no. of pixels of the color that is present in the frame.

  4. Take the median value as the single color to describe your frame - the color present in the largest amount in the frame.

    The key point here is if the above steps are fast enough for realtime video. You'd have to try to find out I guess.

Community
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AruniRC
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Worst case scenario, you could loop over all the pixels in the image and do a count. Not sure what you are using programming wise but I use Python with Numpy something similar to this. Where pb is a gtk pixbuf with my image in it.

def pull_color_out(self, pb, *args):
    counter = 0
    dat = pb.get_pixels_array().copy()
    for y in range(0,pb.get_width()):
        for x in range(0,pb.get_height()):
            p = dat[x][y]
            #counts pure red pixels
            if p[1] = 255 and p[2] = 0 and p[3] = 0:
               counter += 1

    return counter

Other than that, I would normally use a histogram and get the data I need from that. Mainly, this will not be your fastest option, especially for a video, but if you have time or just a few frames then hack away :P

bsktball11ch
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