![]() P.S.: What I actually wanted to ask is, whether there is a similar function for such a calculation in OpenCV or SimpleCV. As explained here, dilation is performed by convoluting the input image with some kernel. ![]() The approach works quite well, as different cell-patterns with same titration produce similar brightness. You need to perform a dilation.A dilation is a morphological operation which causes brighter regions within the input image to grow. The ratio 2/3 could be also increased to 3/4 (which reduces the range of pixels considered as bright). Calculate mean values of all pixel between HMax * 2/3 and HMax.Find histogram maximum (HMax) using threshold for removing hot pixels.Sometimes they are small bright dots on very black background, sometimes less bright bigger areas on not so dark background. There are different methods of illuminating images, first simply with the help of Python Imaging Library (PIL) which has solid image processing capabilities, one of them is illuminating images, with inbuilt predefined method ImageEnhance.Brightness we can perform image illumination in one line. The images I use are grey-scale cell-images produced by a microscope. A star in a dark sky may look more bright than a cloudy sky by day, while the average pixel value of the first image will be much smaller. This article gives an in-depth knowledge about how can an image brightness be changed using OpenCV. All answers I've found on SO deal with human observation of a single pixel RGB vs HSV.įrom my observations, the subjective brightness of an image also depends strongly on the pattern. ![]() I was about to ask the same, but then found out, that similar question gave no satisfactory answers.
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