mark_boundaries skimage.segmentation.mark_boundaries(image, label_img, color=(1, 1, 0), outline_color=None, mode='outer', background_label=0) [source] Return image with boundaries between labeled regions highlighted. Parameters: image : (M, N[, 3]) array Grayscale or RGB image. label_img : (M, N) array of int Label array where regions are marked by different integer values. color : length-3 sequence, optional RGB color of boundaries in the output image. outline_color : length-3 sequen

imshow_collection skimage.io.imshow_collection(ic, plugin=None, **plugin_args) [source] Display a collection of images. Parameters: ic : ImageCollection Collection to display. plugin : str Name of plugin to use. By default, the different plugins are tried until a suitable candidate is found. Other Parameters: plugin_args : keywords Passed to the given plugin.

hough_line_peaks skimage.transform.hough_line_peaks(hspace, angles, dists, min_distance=9, min_angle=10, threshold=None, num_peaks=inf) [source] Return peaks in hough transform. Identifies most prominent lines separated by a certain angle and distance in a hough transform. Non-maximum suppression with different sizes is applied separately in the first (distances) and second (angles) dimension of the hough space to identify peaks. Parameters: hspace : (N, M) array Hough space returned by th

convex_hull_object skimage.morphology.convex_hull_object(image, neighbors=8) [source] Compute the convex hull image of individual objects in a binary image. The convex hull is the set of pixels included in the smallest convex polygon that surround all white pixels in the input image. Parameters: image : (M, N) array Binary input image. neighbors : {4, 8}, int Whether to use 4- or 8-connectivity. Returns: hull : ndarray of bool Binary image with pixels in convex hull set to True. No

binary_erosion skimage.morphology.binary_erosion(image, selem=None, *args, **kwargs) [source] Return fast binary morphological erosion of an image. This function returns the same result as greyscale erosion but performs faster for binary images. Morphological erosion sets a pixel at (i,j) to the minimum over all pixels in the neighborhood centered at (i,j). Erosion shrinks bright regions and enlarges dark regions. Parameters: image : ndarray Binary input image. selem : ndarray, optional

label skimage.measure.label(input, neighbors=None, background=None, return_num=False, connectivity=None) [source] Label connected regions of an integer array. Two pixels are connected when they are neighbors and have the same value. In 2D, they can be neighbors either in a 1- or 2-connected sense. The value refers to the maximum number of orthogonal hops to consider a pixel/voxel a neighbor: 1-connectivity 2-connectivity diagonal connection close-up [ ] [ ] [ ] [ ]

Module: util skimage.util.apply_parallel(function, array) Map a function in parallel across an array. skimage.util.crop(ar, crop_width[, copy, order]) Crop array ar by crop_width along each dimension. skimage.util.dtype_limits(image[, clip_negative]) Return intensity limits, i.e. skimage.util.img_as_bool(image[, force_copy]) Convert an image to boolean format. skimage.util.img_as_float(image[, force_copy]) Convert an image to double-precision floating point format. skimage.util.img_as_int

integral_image skimage.transform.integral_image(img) [source] Integral image / summed area table. The integral image contains the sum of all elements above and to the left of it, i.e.: Parameters: img : ndarray Input image. Returns: S : ndarray Integral image/summed area table of same shape as input image. References [R369] F.C. Crow, “Summed-area tables for texture mapping,” ACM SIGGRAPH Computer Graphics, vol. 18, 1984, pp. 207-212.

combine_stains skimage.color.combine_stains(stains, conv_matrix) [source] Stain to RGB color space conversion. Parameters: stains : array_like The image in stain color space, in a 3-D array of shape (.., .., 3). conv_matrix: ndarray The stain separation matrix as described by G. Landini [R31]. Returns: out : ndarray The image in RGB format, in a 3-D array of shape (.., .., 3). Raises: ValueError If stains is not a 3-D array of shape (.., .., 3). Notes Stain combination matrices

percentile skimage.filters.rank.percentile(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=0) [source] Return local percentile of an image. Returns the value of the p0 lower percentile of the local greyvalue distribution. 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)