compare_ssim skimage.measure.compare_ssim(X, Y, win_size=None, gradient=False, dynamic_range=None, multichannel=False, gaussian_weights=False, full=False, **kwargs) [source] Compute the mean structural similarity index between two images. Parameters: X, Y : ndarray Image. Any dimensionality. win_size : int or None The side-length of the sliding window used in comparison. Must be an odd value. If gaussian_weights is True, this is ignored and the window size will depend on sigma. gradient

compare_psnr skimage.measure.compare_psnr(im_true, im_test, dynamic_range=None) [source] Compute the peak signal to noise ratio (PSNR) for an image. Parameters: im_true : ndarray Ground-truth image. im_test : ndarray Test image. dynamic_range : int The dynamic range of the input image (distance between minimum and maximum possible values). By default, this is estimated from the image data-type. Returns: psnr : float The PSNR metric. References [R260] https://en.wikipedia.org/wiki

compare_mse skimage.measure.compare_mse(im1, im2) [source] Compute the mean-squared error between two images. Parameters: im1, im2 : ndarray Image. Any dimensionality. Returns: mse : float The mean-squared error (MSE) metric.

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

ComboBox class skimage.viewer.widgets.ComboBox(name, items, ptype='kwarg', callback=None) [source] Bases: skimage.viewer.widgets.core.BaseWidget ComboBox widget for selecting among a list of choices. Parameters: name : str Name of ComboBox 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 addition, this name is displayed as the name of the ComboBox. items: list of str Allowed parameter

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

CollectionViewer class skimage.viewer.viewers.CollectionViewer(image_collection, update_on='move', **kwargs) [source] Bases: skimage.viewer.viewers.core.ImageViewer Viewer for displaying image collections. Select the displayed frame of the image collection using the slider or with the following keyboard shortcuts: left/right arrows Previous/next image in collection. number keys, 0–9 0% to 90% of collection. For example, “5” goes to the image in the middle (i.e. 50%) of the collection. home/

CollectionViewer class skimage.viewer.CollectionViewer(image_collection, update_on='move', **kwargs) [source] Bases: skimage.viewer.viewers.core.ImageViewer Viewer for displaying image collections. Select the displayed frame of the image collection using the slider or with the following keyboard shortcuts: left/right arrows Previous/next image in collection. number keys, 0–9 0% to 90% of collection. For example, “5” goes to the image in the middle (i.e. 50%) of the collection. home/end keys

closing skimage.morphology.closing(image, selem=None, *args, **kwargs) [source] Return greyscale morphological closing of an image. The morphological closing on an image is defined as a dilation followed by an erosion. Closing can remove small dark spots (i.e. “pepper”) and connect small bright cracks. This tends to “close” up (dark) gaps between (bright) features. Parameters: image : ndarray Image array. selem : ndarray, optional The neighborhood expressed as an array of 1’s and 0’s. If

coins skimage.data.coins() [source] Greek coins from Pompeii. This image shows several coins outlined against a gray background. It is especially useful in, e.g. segmentation tests, where individual objects need to be identified against a background. The background shares enough grey levels with the coins that a simple segmentation is not sufficient. Notes This image was downloaded from the Brooklyn Museum Collection. No known copyright restrictions.