line_aa skimage.draw.line_aa() Generate anti-aliased line pixel coordinates. Parameters: y0, x0 : int Starting position (row, column). y1, x1 : int End position (row, column). Returns: rr, cc, val : (N,) ndarray (int, int, float) Indices of pixels (rr, cc) and intensity values (val). img[rr, cc] = val. References [R72] A Rasterizing Algorithm for Drawing Curves, A. Zingl, 2012 http://members.chello.at/easyfilter/Bresenham.pdf Examples >>> from skimage.draw import line_aa

random_walker skimage.segmentation.random_walker(data, labels, beta=130, mode='bf', tol=0.001, copy=True, multichannel=False, return_full_prob=False, spacing=None) [source] Random walker algorithm for segmentation from markers. Random walker algorithm is implemented for gray-level or multichannel images. Parameters: data : array_like Image to be segmented in phases. Gray-level data can be two- or three-dimensional; multichannel data can be three- or four- dimensional (multichannel=True) wi

cube skimage.morphology.cube(width, dtype=) [source] Generates a cube-shaped structuring element. This is the 3D equivalent of a square. Every pixel along the perimeter has a chessboard distance no greater than radius (radius=floor(width/2)) pixels. Parameters: width : int The width, height and depth of the cube. Returns: selem : ndarray A structuring element consisting only of ones, i.e. every pixel belongs to the neighborhood. Other Parameters: dtype : data-type The data type of

white_tophat skimage.morphology.white_tophat(image, selem=None, *args, **kwargs) [source] Return white top hat of an image. The white top hat of an image is defined as the image minus its morphological opening. This operation returns the bright spots of the image that are smaller than the structuring element. Parameters: image : ndarray Image array. selem : ndarray, optional The neighborhood expressed as an array of 1’s and 0’s. If None, use cross-shaped structuring element (connectivity

roberts_pos_diag skimage.filters.roberts_pos_diag(image, mask=None) [source] Find the cross edges of an image using Roberts’ cross operator. The kernel is applied to the input image to produce separate measurements of the gradient component one orientation. 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 affecti

BRIEF class skimage.feature.BRIEF(descriptor_size=256, patch_size=49, mode='normal', sigma=1, sample_seed=1) [source] Bases: skimage.feature.util.DescriptorExtractor BRIEF binary descriptor extractor. BRIEF (Binary Robust Independent Elementary Features) is an efficient feature point descriptor. It is highly discriminative even when using relatively few bits and is computed using simple intensity difference tests. For each keypoint, intensity comparisons are carried out for a specifically di

otsu skimage.filters.rank.otsu(image, selem, out=None, mask=None, shift_x=False, shift_y=False) [source] Local Otsu’s threshold value for each pixel. Parameters: image : ndarray Image array (uint8 array). selem : 2-D array The neighborhood expressed as a 2-D array of 1’s and 0’s. out : ndarray If None, a new array will be allocated. mask : ndarray Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). shif

threshold_adaptive skimage.filters.threshold_adaptive(image, block_size, method='gaussian', offset=0, mode='reflect', param=None) [source] Applies an adaptive threshold to an array. Also known as local or dynamic thresholding where the threshold value is the weighted mean for the local neighborhood of a pixel subtracted by a constant. Alternatively the threshold can be determined dynamically by a a given function using the ‘generic’ method. Parameters: image : (N, M) ndarray Input image.

EllipseModel class skimage.measure.EllipseModel [source] Bases: skimage.measure.fit.BaseModel Total least squares estimator for 2D ellipses. The functional model of the ellipse is: xt = xc + a*cos(theta)*cos(t) - b*sin(theta)*sin(t) yt = yc + a*sin(theta)*cos(t) + b*cos(theta)*sin(t) d = sqrt((x - xt)**2 + (y - yt)**2) where (xt, yt) is the closest point on the ellipse to (x, y). Thus d is the shortest distance from the point to the ellipse. This estimator minimizes the squared distances fr

img_as_ubyte skimage.img_as_ubyte(image, force_copy=False) [source] Convert an image to 8-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 ubyte (uint8) Output image. Notes Negative input values will be clipped. Positive values are scaled between 0 and 255.