perimeter skimage.measure.perimeter(image, neighbourhood=4) [source] Calculate total perimeter of all objects in binary image. Parameters: image : array Binary image. neighbourhood : 4 or 8, optional Neighborhood connectivity for border pixel determination. Returns: perimeter : float Total perimeter of all objects in binary image. References [R282] K. Benkrid, D. Crookes. Design and FPGA Implementation of a Perimeter Estimator. The Queen’s University of Belfast. http://www.cs.qub.a

star skimage.morphology.star(a, dtype=) [source] Generates a star shaped structuring element. Start has 8 vertices and is an overlap of square of size 2*a + 1 with its 45 degree rotated version. The slanted sides are 45 or 135 degrees to the horizontal axis. Parameters: a : int Parameter deciding the size of the star structural element. The side of the square array returned is 2*a + 1 + 2*floor(a / 2). Returns: selem : ndarray The structuring element where elements of the neighborhood

Module: viewer skimage.viewer.warn(message[, category, ...]) A version of warnings.warn with a default stacklevel of 2. skimage.viewer.CollectionViewer(image_collection) Viewer for displaying image collections. skimage.viewer.ImageViewer(image[, useblit]) Viewer for displaying images.

vprewitt skimage.filters.vprewitt(*args, **kwargs) [source] Deprecated function. Use skimage.filters.prewitt_v instead. Find the vertical edges of an image using the Prewitt transform. Parameters: image : 2-D array Image to process. mask : 2-D array, optional An optional mask to limit the application to a certain area. Note that pixels surrounding masked regions are also masked to prevent masked regions from affecting the result. Returns: output : 2-D array The absolute Prewitt edge

Module: future.graph skimage.future.graph.cut_normalized(labels, rag) Perform Normalized Graph cut on the Region Adjacency Graph. skimage.future.graph.cut_threshold(labels, ...) Combine regions separated by weight less than threshold. skimage.future.graph.draw_rag(labels, rag, img) Draw a Region Adjacency Graph on an image. skimage.future.graph.merge_hierarchical(...) Perform hierarchical merging of a RAG. skimage.future.graph.ncut(labels, rag[, ...]) Perform Normalized Graph cut on the Re

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

gradient_percentile skimage.filters.rank.gradient_percentile(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=0, p1=1) [source] Return local gradient of an image (i.e. local maximum - local minimum). Only greyvalues between percentiles [p0, p1] are considered in the filter. 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

img_as_uint skimage.img_as_uint(image, force_copy=False) [source] Convert an image to 16-bit unsigned integer format. Parameters: image : ndarray Input image. force_copy : bool Force a copy of the data, irrespective of its current dtype. Returns: out : ndarray of uint16 Output image. Notes Negative input values will be clipped. Positive values are scaled between 0 and 65535.

wiener skimage.filters.wiener(data, impulse_response=None, filter_params={}, K=0.25, predefined_filter=None) [source] Minimum Mean Square Error (Wiener) inverse filter. Parameters: data : (M,N) ndarray Input data. K : float or (M,N) ndarray Ratio between power spectrum of noise and undegraded image. impulse_response : callable f(r, c, **filter_params) Impulse response of the filter. See LPIFilter2D.__init__. filter_params : dict Additional keyword parameters to the impulse_response f

gray2rgb skimage.color.gray2rgb(image, alpha=None) [source] Create an RGB representation of a gray-level image. Parameters: image : array_like Input image of shape (M, N [, P]). alpha : bool, optional Ensure that the output image has an alpha layer. If None, alpha layers are passed through but not created. Returns: rgb : ndarray RGB image of shape (M, N, [, P], 3). Raises: ValueError If the input is not a 2- or 3-dimensional image.