update_axes_image skimage.viewer.utils.update_axes_image(image_axes, image) [source] Update the image displayed by an image plot. This sets the image plot’s array and updates its shape appropriately Parameters: image_axes : matplotlib.image.AxesImage Image axes to update. image : array Image array.

unwrap_phase skimage.restoration.unwrap_phase(image, wrap_around=False, seed=None) [source] Recover the original from a wrapped phase image. From an image wrapped to lie in the interval [-pi, pi), recover the original, unwrapped image. Parameters: image : 1D, 2D or 3D ndarray of floats, optionally a masked array The values should be in the range [-pi, pi). If a masked array is provided, the masked entries will not be changed, and their values will not be used to guide the unwrapping of nei

unsupervised_wiener skimage.restoration.unsupervised_wiener(image, psf, reg=None, user_params=None, is_real=True, clip=True) [source] Unsupervised Wiener-Hunt deconvolution. Return the deconvolution with a Wiener-Hunt approach, where the hyperparameters are automatically estimated. The algorithm is a stochastic iterative process (Gibbs sampler) described in the reference below. See also wiener function. Parameters: image : (M, N) ndarray The input degraded image. psf : ndarray The impuls

unique_rows skimage.util.unique_rows(ar) [source] Remove repeated rows from a 2D array. In particular, if given an array of coordinates of shape (Npoints, Ndim), it will remove repeated points. Parameters: ar : 2-D ndarray The input array. Returns: ar_out : 2-D ndarray A copy of the input array with repeated rows removed. Raises: ValueError : if ar is not two-dimensional. Notes The function will generate a copy of ar if it is not C-contiguous, which will negatively affect performan

Tutorials Image Segmentation How to parallelize loops

TiffWriter class skimage.external.tifffile.TiffWriter(filename, bigtiff=False, byteorder=None, software='tifffile.py') [source] Bases: object Write image data to TIFF file. TiffWriter instances must be closed using the close method, which is automatically called when using the ‘with’ statement. Examples >>> data = numpy.random.rand(2, 5, 3, 301, 219) >>> with TiffWriter('temp.tif', bigtiff=True) as tif: ... for i in range(data.shape[0]): ... tif.save(data[i], co

tophat skimage.filters.rank.tophat(image, selem, out=None, mask=None, shift_x=False, shift_y=False) [source] Local top-hat of an image. This filter computes the morphological opening of the image and then subtracts the result from the original image. 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 : 2-D array (same dtype as input) If None, a new array is allocated. mask : ndarray Mask array tha

TiffSequence class skimage.external.tifffile.TiffSequence(files, imread=, pattern='axes', *args, **kwargs) [source] Bases: object Sequence of image files. The data shape and dtype of all files must match. Examples >>> tifs = TiffSequence("test.oif.files/*.tif") >>> tifs.shape, tifs.axes ((2, 100), 'CT') >>> data = tifs.asarray() >>> data.shape (2, 100, 256, 256) Attributes files (list) List of file names. shape (tuple) Shape of image sequence. axe

threshold_yen skimage.filters.threshold_yen(image, nbins=256) [source] Return threshold value based on Yen’s method. Parameters: image : array Input image. nbins : int, optional Number of bins used to calculate histogram. This value is ignored for integer arrays. Returns: threshold : float Upper threshold value. All pixels intensities that less or equal of this value assumed as foreground. References [R207] Yen J.C., Chang F.J., and Chang S. (1995) “A New Criterion for Automatic Mu

TiffFile class skimage.external.tifffile.TiffFile(arg, name=None, offset=None, size=None, multifile=True, multifile_close=True) [source] Bases: object Read image and metadata from TIFF, STK, LSM, and FluoView files. TiffFile instances must be closed using the close method, which is automatically called when using the ‘with’ statement. Examples >>> with TiffFile('test.tif') as tif: ... data = tif.asarray() ... data.shape (256, 256, 4) Attributes pages (list) All TIFF pages