black_tophat skimage.morphology.black_tophat(image, selem=None, *args, **kwargs) [source] Return black top hat of an image. The black top hat of an image is defined as its morphological closing minus the original image. This operation returns the dark spots of the image that are smaller than the structuring element. Note that dark spots in the original image are bright spots after the black top hat. Parameters: image : ndarray Image array. selem : ndarray, optional The neighborhood expre

moments_central skimage.measure.moments_central(image, cr, cc, order=3) [source] Calculate all central image moments up to a certain order. The center coordinates (cr, cc) can be calculated from the raw moments as: {m[0, 1] / m[0, 0], m[1, 0] / m[0, 0]}. Note that central moments are translation invariant but not scale and rotation invariant. Parameters: image : 2D double or uint8 array Rasterized shape as image. cr : double Center row coordinate. cc : double Center column coordinate.

moments_normalized skimage.measure.moments_normalized(mu, order=3) [source] Calculate all normalized central image moments up to a certain order. Note that normalized central moments are translation and scale invariant but not rotation invariant. Parameters: mu : (M, M) array Central image moments, where M must be > order. order : int, optional Maximum order of moments. Default is 3. Returns: nu : (order + 1, order + 1) array Normalized central image moments. References [R278] W

mean_bilateral skimage.filters.rank.mean_bilateral(image, selem, out=None, mask=None, shift_x=False, shift_y=False, s0=10, s1=10) [source] Apply a flat kernel bilateral filter. This is an edge-preserving and noise reducing denoising filter. It averages pixels based on their spatial closeness and radiometric similarity. Spatial closeness is measured by considering only the local pixel neighborhood given by a structuring element. Radiometric similarity is defined by the greylevel interval [g-s

sum_percentile skimage.filters.rank.sum_percentile(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=0, p1=1) [source] Return the local sum of pixels. Only greyvalues between percentiles [p0, p1] are considered in the filter. Note that the sum may overflow depending on the data type of the input array. Parameters: image : 2-D array (uint8, uint16) Input image. selem : 2-D array The neighborhood expressed as a 2-D array of 1’s and 0’s. out : 2-D array (same dtype as inp

approximate_polygon skimage.measure.approximate_polygon(coords, tolerance) [source] Approximate a polygonal chain with the specified tolerance. It is based on the Douglas-Peucker algorithm. Note that the approximated polygon is always within the convex hull of the original polygon. Parameters: coords : (N, 2) array Coordinate array. tolerance : float Maximum distance from original points of polygon to approximated polygonal chain. If tolerance is 0, the original coordinate array is retur

match_descriptors skimage.feature.match_descriptors(descriptors1, descriptors2, metric=None, p=2, max_distance=inf, cross_check=True) [source] Brute-force matching of descriptors. For each descriptor in the first set this matcher finds the closest descriptor in the second set (and vice-versa in the case of enabled cross-checking). Parameters: descriptors1 : (M, P) array Binary descriptors of size P about M keypoints in the first image. descriptors2 : (N, P) array Binary descriptors of si

lab2lch skimage.color.lab2lch(lab) [source] CIE-LAB to CIE-LCH color space conversion. LCH is the cylindrical representation of the LAB (Cartesian) colorspace Parameters: lab : array_like The N-D image in CIE-LAB format. The last (N+1-th) dimension must have at least 3 elements, corresponding to the L, a, and b color channels. Subsequent elements are copied. Returns: out : ndarray The image in LCH format, in a N-D array with same shape as input lab. Raises: ValueError If lch does n

entropy skimage.filters.rank.entropy(image, selem, out=None, mask=None, shift_x=False, shift_y=False) [source] Local entropy. The entropy is computed using base 2 logarithm i.e. the filter returns the minimum number of bits needed to encode the local greylevel distribution. Parameters: image : 2-D array (uint8, uint16) Input image. selem : 2-D array The neighborhood expressed as a 2-D array of 1’s and 0’s. out : 2-D array (same dtype as input) If None, a new array is allocated. mask :

threshold skimage.filters.rank.threshold(image, selem, out=None, mask=None, shift_x=False, shift_y=False) [source] Local threshold of an image. The resulting binary mask is True if the greyvalue of the center pixel is greater than the local mean. Parameters: image : 2-D array (uint8, uint16) Input image. selem : 2-D array The neighborhood expressed as a 2-D array of 1’s and 0’s. out : 2-D array (same dtype as input) If None, a new array is allocated. mask : ndarray Mask array that de