join_segmentations skimage.segmentation.join_segmentations(s1, s2) [source] Return the join of the two input segmentations. The join J of S1 and S2 is defined as the segmentation in which two voxels are in the same segment if and only if they are in the same segment in both S1 and S2. Parameters: s1, s2 : numpy arrays s1 and s2 are label fields of the same shape. Returns: j : numpy array The join segmentation of s1 and s2. Examples >>> from skimage.segmentation import join_s

iradon_sart skimage.transform.iradon_sart(radon_image, theta=None, image=None, projection_shifts=None, clip=None, relaxation=0.15) [source] Inverse radon transform Reconstruct an image from the radon transform, using a single iteration of the Simultaneous Algebraic Reconstruction Technique (SART) algorithm. Parameters: radon_image : 2D array, dtype=float Image containing radon transform (sinogram). Each column of the image corresponds to a projection along a different angle. The tomography

iradon skimage.transform.iradon(radon_image, theta=None, output_size=None, filter='ramp', interpolation='linear', circle=False) [source] Inverse radon transform. Reconstruct an image from the radon transform, using the filtered back projection algorithm. Parameters: radon_image : array_like, dtype=float Image containing radon transform (sinogram). Each column of the image corresponds to a projection along a different angle. The tomography rotation axis should lie at the pixel index radon_i

inverse skimage.filters.inverse(data, impulse_response=None, filter_params={}, max_gain=2, predefined_filter=None) [source] Apply the filter in reverse to the given data. Parameters: data : (M,N) ndarray Input data. impulse_response : callable f(r, c, **filter_params) Impulse response of the filter. See LPIFilter2D.__init__. filter_params : dict Additional keyword parameters to the impulse_response function. max_gain : float Limit the filter gain. Often, the filter contains zeros, wh

integral_image skimage.transform.integral_image(img) [source] Integral image / summed area table. The integral image contains the sum of all elements above and to the left of it, i.e.: Parameters: img : ndarray Input image. Returns: S : ndarray Integral image/summed area table of same shape as input image. References [R369] F.C. Crow, “Summed-area tables for texture mapping,” ACM SIGGRAPH Computer Graphics, vol. 18, 1984, pp. 207-212.

integrate skimage.transform.integrate(ii, start, end, *args) [source] Use an integral image to integrate over a given window. Parameters: ii : ndarray Integral image. start : List of tuples, each tuple of length equal to dimension of ii Coordinates of top left corner of window(s). Each tuple in the list contains the starting row, col, ... index i.e [(row_win1, col_win1, ...), (row_win2, col_win2,...), ...]. end : List of tuples, each tuple of length equal to dimension of ii Coordinates

init_qtapp skimage.viewer.utils.init_qtapp() [source] Initialize QAppliction. The QApplication needs to be initialized before creating any QWidgets

imshow_collection skimage.io.imshow_collection(ic, plugin=None, **plugin_args) [source] Display a collection of images. Parameters: ic : ImageCollection Collection to display. plugin : str Name of plugin to use. By default, the different plugins are tried until a suitable candidate is found. Other Parameters: plugin_args : keywords Passed to the given plugin.

imshow skimage.io.imshow(arr, plugin=None, **plugin_args) [source] Display an image. Parameters: arr : ndarray or str Image data or name of image file. plugin : str Name of plugin to use. By default, the different plugins are tried (starting with the Python Imaging Library) until a suitable candidate is found. Other Parameters: plugin_args : keywords Passed to the given plugin.

imshow skimage.external.tifffile.imshow(data, title=None, vmin=0, vmax=None, cmap=None, bitspersample=None, photometric='rgb', interpolation='nearest', dpi=96, figure=None, subplot=111, maxdim=8192, **kwargs) [source] Plot n-dimensional images using matplotlib.pyplot. Return figure, subplot and plot axis. Requires pyplot already imported from matplotlib import pyplot. Parameters: bitspersample : int or None Number of bits per channel in integer RGB images. photometric : {‘miniswhite’, ‘mi