threshold_percentile skimage.filters.rank.threshold_percentile(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=0) [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. 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 :

compare_nrmse skimage.measure.compare_nrmse(im_true, im_test, norm_type='Euclidean') [source] Compute the normalized root mean-squared error (NRMSE) between two images. Parameters: im_true : ndarray Ground-truth image. im_test : ndarray Test image. norm_type : {‘Euclidean’, ‘min-max’, ‘mean’} Controls the normalization method to use in the denominator of the NRMSE. There is no standard method of normalization across the literature [R259]. The methods available here are as follows: ‘Euc

match_template skimage.feature.match_template(image, template, pad_input=False, mode='constant', constant_values=0) [source] Match a template to a 2-D or 3-D image using normalized correlation. The output is an array with values between -1.0 and 1.0. The value at a given position corresponds to the correlation coefficient between the image and the template. For pad_input=True matches correspond to the center and otherwise to the top-left corner of the template. To find the best match you mus

corner_fast skimage.feature.corner_fast(image, n=12, threshold=0.15) [source] Extract FAST corners for a given image. Parameters: image : 2D ndarray Input image. n : int Minimum number of consecutive pixels out of 16 pixels on the circle that should all be either brighter or darker w.r.t testpixel. A point c on the circle is darker w.r.t test pixel p if Ic < Ip - threshold and brighter if Ic > Ip + threshold. Also stands for the n in FAST-n corner detector. threshold : float Thre

hprewitt skimage.filters.hprewitt(*args, **kwargs) [source] Deprecated function. Use skimage.filters.prewitt_h instead. Find the horizontal 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 edg

multiblock_lbp skimage.feature.multiblock_lbp(int_image, r, c, width, height) [source] Multi-block local binary pattern (MB-LBP). The features are calculated similarly to local binary patterns (LBPs), (See local_binary_pattern()) except that summed blocks are used instead of individual pixel values. MB-LBP is an extension of LBP that can be computed on multiple scales in constant time using the integral image. Nine equally-sized rectangles are used to compute a feature. For each rectangle, t

rag_mean_color skimage.future.graph.rag_mean_color(image, labels, connectivity=2, mode='distance', sigma=255.0) [source] Compute the Region Adjacency Graph using mean colors. Given an image and its initial segmentation, this method constructs the corresponding Region Adjacency Graph (RAG). Each node in the RAG represents a set of pixels within image with the same label in labels. The weight between two adjacent regions represents how similar or dissimilar two regions are depending on the mod

regular_grid skimage.util.regular_grid(ar_shape, n_points) [source] Find n_points regularly spaced along ar_shape. The returned points (as slices) should be as close to cubically-spaced as possible. Essentially, the points are spaced by the Nth root of the input array size, where N is the number of dimensions. However, if an array dimension cannot fit a full step size, it is “discarded”, and the computation is done for only the remaining dimensions. Parameters: ar_shape : array-like of ints

ThickLineTool class skimage.viewer.canvastools.ThickLineTool(manager, on_move=None, on_enter=None, on_release=None, on_change=None, maxdist=10, line_props=None, handle_props=None) [source] Bases: skimage.viewer.canvastools.linetool.LineTool Widget for line selection in a plot. The thickness of the line can be varied using the mouse scroll wheel, or with the ‘+’ and ‘-‘ keys. Parameters: manager : Viewer or PlotPlugin. Skimage viewer or plot plugin object. on_move : function Function call

LineTool class skimage.viewer.canvastools.LineTool(manager, on_move=None, on_release=None, on_enter=None, maxdist=10, line_props=None, handle_props=None, **kwargs) [source] Bases: skimage.viewer.canvastools.base.CanvasToolBase Widget for line selection in a plot. Parameters: manager : Viewer or PlotPlugin. Skimage viewer or plot plugin object. on_move : function Function called whenever a control handle is moved. This function must accept the end points of line as the only argument. on_