Adaptive threshold opencv python

Содержание

Adaptive threshold opencv python
Adaptive threshold opencv python
Adaptive Thresholding
Otsu’s Binarization

Adaptive threshold opencv python

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Adaptive threshold opencv python

Note To plot multiple images, we have used the plt.subplot() function. Please checkout the matplotlib docs for more details.

The code yields this result:

Adaptive Thresholding

In the previous section, we used one global value as a threshold. But this might not be good in all cases, e.g. if an image has different lighting conditions in different areas. In that case, adaptive thresholding can help. Here, the algorithm determines the threshold for a pixel based on a small region around it. So we get different thresholds for different regions of the same image which gives better results for images with varying illumination.

In addition to the parameters described above, the method cv.adaptiveThreshold takes three input parameters:

The adaptiveMethod decides how the threshold value is calculated:

cv.ADAPTIVE_THRESH_MEAN_C: The threshold value is the mean of the neighbourhood area minus the constant C.
cv.ADAPTIVE_THRESH_GAUSSIAN_C: The threshold value is a gaussian-weighted sum of the neighbourhood values minus the constant C.

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.

The code below compares global thresholding and adaptive thresholding for an image with varying illumination:

Otsu’s Binarization

In global thresholding, we used an arbitrary chosen value as a threshold. In contrast, Otsu’s method avoids having to choose a value and determines it automatically.

Consider an image with only two distinct image values (bimodal image), where the histogram would only consist of two peaks. A good threshold would be in the middle of those two values. Similarly, Otsu’s method determines an optimal global threshold value from the image histogram.

In order to do so, the cv.threshold() function is used, where cv.THRESH_OTSU is passed as an extra flag. The threshold value can be chosen arbitrary. The algorithm then finds the optimal threshold value which is returned as the first output.

Check out the example below. The input image is a noisy image. In the first case, global thresholding with a value of 127 is applied. In the second case, Otsu’s thresholding is applied directly. In the third case, the image is first filtered with a 5×5 gaussian kernel to remove the noise, then Otsu thresholding is applied. See how noise filtering improves the result.

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