scharr_h skimage.filters.scharr_h(image, mask=None) [source] Find the horizontal edges of an image using the Scharr 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 Scharr edge map. Notes We use the following kernel: 3 10 3 0 0 0 -3

route_through_array skimage.graph.route_through_array(array, start, end, fully_connected=True, geometric=True) [source] Simple example of how to use the MCP and MCP_Geometric classes. See the MCP and MCP_Geometric class documentation for explanation of the path-finding algorithm. Parameters: array : ndarray Array of costs. start : iterable n-d index into array defining the starting point end : iterable n-d index into array defining the end point fully_connected : bool (optional) If T

SaveButtons class skimage.viewer.widgets.SaveButtons(name='Save to:', default_format='png') [source] Bases: skimage.viewer.widgets.core.BaseWidget Buttons to save image to io.stack or to a file. __init__(name='Save to:', default_format='png') [source] save_to_file(filename=None) [source] save_to_stack() [source]

rotate skimage.transform.rotate(image, angle, resize=False, center=None, order=1, mode='constant', cval=0, clip=True, preserve_range=False) [source] Rotate image by a certain angle around its center. Parameters: image : ndarray Input image. angle : float Rotation angle in degrees in counter-clockwise direction. resize : bool, optional Determine whether the shape of the output image will be automatically calculated, so the complete rotated image exactly fits. Default is False. center :

rocket skimage.data.rocket() [source] Launch photo of DSCOVR on Falcon 9 by SpaceX. This is the launch photo of Falcon 9 carrying DSCOVR lifted off from SpaceX’s Launch Complex 40 at Cape Canaveral Air Force Station, FL. Notes This image was downloaded from SpaceX Photos. The image was captured by SpaceX and released in the public domain.

roberts_positive_diagonal skimage.filters.roberts_positive_diagonal(*args, **kwargs) [source] Deprecated function. Use skimage.filters.roberts_pos_diag instead. 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 pixe

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

roberts_negative_diagonal skimage.filters.roberts_negative_diagonal(*args, **kwargs) [source] Deprecated function. Use skimage.filters.roberts_neg_diag instead. Find the cross edges of an image using the 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

roberts_neg_diag skimage.filters.roberts_neg_diag(image, mask=None) [source] Find the cross edges of an image using the 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 aff

richardson_lucy skimage.restoration.richardson_lucy(image, psf, iterations=50, clip=True) [source] Richardson-Lucy deconvolution. Parameters: image : ndarray Input degraded image (can be N dimensional). psf : ndarray The point spread function. iterations : int Number of iterations. This parameter plays the role of regularisation. clip : boolean, optional True by default. If true, pixel value of the result above 1 or under -1 are thresholded for skimage pipeline compatibility. Retur