threshold-adaptive

threshold_adaptive

skimage.filters.threshold_adaptive(image, block_size, method='gaussian', offset=0, mode='reflect', param=None) [source]

Applies an adaptive threshold to an array.

Also known as local or dynamic thresholding where the threshold value is the weighted mean for the local neighborhood of a pixel subtracted by a constant. Alternatively the threshold can be determined dynamically by a a given function using the ‘generic’ method.

Parameters:

image : (N, M) ndarray Input image. block_size : int Odd size of pixel neighborhood which is used to calculate the threshold value (e.g. 3, 5, 7, ..., 21, ...). method : {‘generic’, ‘gaussian’, ‘mean’, ‘median’}, optional Method used to determine adaptive threshold for local neighbourhood in weighted mean image. ‘generic’: use custom function (see param parameter) ‘gaussian’: apply gaussian filter (see param parameter for custom sigma value) ‘mean’: apply arithmetic mean filter ‘median’: apply median rank filter By default the ‘gaussian’ method is used. offset : float, optional Constant subtracted from weighted mean of neighborhood to calculate the local threshold value. Default offset is 0. mode : {‘reflect’, ‘constant’, ‘nearest’, ‘mirror’, ‘wrap’}, optional The mode parameter determines how the array borders are handled, where cval is the value when mode is equal to ‘constant’. Default is ‘reflect’. param : {int, function}, optional Either specify sigma for ‘gaussian’ method or function object for ‘generic’ method. This functions takes the flat array of local neighbourhood as a single argument and returns the calculated threshold for the centre pixel.

Returns:

threshold : (N, M) ndarray Thresholded binary image

References

[R197]

http://docs.opencv.org/modules/imgproc/doc/miscellaneous_transformations.html?highlight=threshold#adaptivethreshold

Examples

>>> from skimage.data import camera
>>> image = camera()[:50, :50]
>>> binary_image1 = threshold_adaptive(image, 15, 'mean')
>>> func = lambda arr: arr.mean()
>>> binary_image2 = threshold_adaptive(image, 15, 'generic', param=func)