structural_similarity skimage.measure.structural_similarity(*args, **kwargs) [source] Deprecated function. Use compare_ssim instead.

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

start_qtapp skimage.viewer.utils.start_qtapp(app=None) [source] Start Qt mainloop

sobel_v skimage.filters.sobel_v(image, mask=None) [source] Find the vertical edges of an image using the Sobel 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 Sobel edge map. Notes We use the following kernel: 1 0 -1 2 0 -2 1 0 -1

square skimage.morphology.square(width, dtype=) [source] Generates a flat, square-shaped structuring element. Every pixel along the perimeter has a chessboard distance no greater than radius (radius=floor(width/2)) pixels. Parameters: width : int The width and height of the square. 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 the structuring element.

sobel_h skimage.filters.sobel_h(image, mask=None) [source] Find the horizontal edges of an image using the Sobel 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 Sobel edge map. Notes We use the following kernel: 1 2 1 0 0 0 -1 -2

sobel skimage.filters.sobel(image, mask=None) [source] Find the edge magnitude using the Sobel 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 Sobel edge map. See also scharr, prewitt, roberts, feature.canny Notes Take the square root of th

Slider class skimage.viewer.widgets.Slider(name, low=0.0, high=1.0, value=None, value_type='float', ptype='kwarg', callback=None, max_edit_width=60, orientation='horizontal', update_on='release') [source] Bases: skimage.viewer.widgets.core.BaseWidget Slider widget for adjusting numeric parameters. Parameters: name : str Name of slider parameter. If this parameter is passed as a keyword argument, it must match the name of that keyword argument (spaces are replaced with underscores). In addi

slic skimage.segmentation.slic(image, n_segments=100, compactness=10.0, max_iter=10, sigma=0, spacing=None, multichannel=True, convert2lab=None, enforce_connectivity=True, min_size_factor=0.5, max_size_factor=3, slic_zero=False) [source] Segments image using k-means clustering in Color-(x,y,z) space. Parameters: image : 2D, 3D or 4D ndarray Input image, which can be 2D or 3D, and grayscale or multichannel (see multichannel parameter). n_segments : int, optional The (approximate) number o

Module: novice