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improved doc string of adaptive threshold with more detailed description and example

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This commit is contained in:
Johannes Schönberger
2012-04-27 10:00:10 +02:00
parent 829f6ad770
commit 59376a7c20
2 changed files with 29 additions and 12 deletions
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skimage/filter/_thresholding.pyx
+2 -2
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@@ -9,14 +9,14 @@ cimport cython
def _threshold_adaptive(np.ndarray[np.double_t, ndim=2] image, int block_size,
method, double offset, mode, param):
cdef int r, c
cdef np.ndarray[np.float64_t, ndim=2] thres_image
cdef np.ndarray[np.double_t, ndim=2] thres_image
if method == 'generic':
thres_image = scipy.ndimage.generic_filter(image, param, block_size,
mode=mode)
elif method == 'gaussian':
if param is None:
# automatically determine sigme which covers > 99% of distribution
# automatically determine sigma which covers > 99% of distribution
sigma = (block_size - 1) / 6.0
thres_image = scipy.ndimage.gaussian_filter(image, sigma, mode=mode)
elif method == 'mean':
skimage/filter/thresholding.py
+27 -10
View File
@@ -21,30 +21,47 @@ def threshold_adaptive(image, block_size, method='gaussian', offset=0,
image : NxM ndarray
Input image.
block_size : int
uneven size of pixel neighborhood which is used to calculate the
threshold value (e.g. 3, 5, 7, ..., 21, ...)
Uneven 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. By default the 'gaussian'
method is used.
Method used to determine adaptive threshold fpr 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
Constant subtracted from weighted mean of neighborhood to calculate
the local threshold value. Default offset is 0.
mode : {'reflect','constant','nearest','mirror', 'wrap'}, optional
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'
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.
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 : NxM ndarray
thresholded binary image
Thresholded binary image
References
----------
http://docs.opencv.org/modules/imgproc/doc/miscellaneous_transformations
.html?highlight=threshold#adaptivethreshold
Examples
--------
>>> from skimage.data import camera
>>> image = camera()
>>> binary_image1 = threshold_adaptive(image, 15, 'mean')
>>> func = lambda arr: arr.mean()
>>> binary_image2 = threshold_adaptive(image, 15, 'generic', param=func)
"""
# not using img_as_float because offset parameter wouldn't work
image = image.astype('double')