![]() The OP asked for 90 degree rotation but I'll change to 45 degrees because when you use an angle that isn't zero or 90, you should change the horizontal alignment as well otherwise your labels will be off-center and a bit misleading (and I'm guessing many people who come here want to rotate axes to something other than 90). See how noise filtering improves the result.Many "correct" answers here but I'll add one more since I think some details are left out of several. In the third case, the image is first filtered with a 5x5 gaussian kernel to remove the noise, then Otsu thresholding is applied. In the second case, Otsu's thresholding is applied directly. In the first case, global thresholding with a value of 127 is applied. The algorithm then finds the optimal threshold value which is returned as the first output.Ĭheck out the example below. The threshold value can be chosen arbitrary. In order to do so, the cv.threshold() function is used, where cv.THRESH_OTSU is passed as an extra flag. Similarly, Otsu's method determines an optimal global threshold value from the image histogram. A good threshold would be in the middle of those two values. In contrast, Otsu's method avoids having to choose a value and determines it automatically.Ĭonsider an image with only two distinct image values ( bimodal image), where the histogram would only consist of two peaks. ![]() In global thresholding, we used an arbitrary chosen value as a threshold. ![]() The code below compares global thresholding and adaptive thresholding for an image with varying illumination: The blockSize determines the size of the neighbourhood area and C is a constant that is subtracted from the mean or weighted sum of the neighbourhood pixels.
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