hessian_matrix skimage.feature.hessian_matrix(image, sigma=1, mode='constant', cval=0) [source] Compute Hessian matrix. The Hessian matrix is defined as: H = [Hxx Hxy] [Hxy Hyy] which is computed by convolving the image with the second derivatives of the Gaussian kernel in the respective x- and y-directions. Parameters: image : ndarray Input image. sigma : float Standard deviation used for the Gaussian kernel, which is used as weighting function for the auto-correlation matrix. mod

remove_small_objects skimage.morphology.remove_small_objects(ar, min_size=64, connectivity=1, in_place=False) [source] Remove connected components smaller than the specified size. Parameters: ar : ndarray (arbitrary shape, int or bool type) The array containing the connected components of interest. If the array type is int, it is assumed that it contains already-labeled objects. The ints must be non-negative. min_size : int, optional (default: 64) The smallest allowable connected compone

Module: viewer.widgets Widgets for interacting with ImageViewer. These widgets should be added to a Plugin subclass using its add_widget method or calling: plugin += Widget(...) on a Plugin instance. The Plugin will delegate action based on the widget’s parameter type specified by its ptype attribute, which can be: 'arg' : positional argument passed to Plugin's `filter_image` method. 'kwarg' : keyword argument passed to Plugin's `filter_image` method. 'plugin' : attribute of Plugin. You'll pro

is_low_contrast skimage.exposure.is_low_contrast(image, fraction_threshold=0.05, lower_percentile=1, upper_percentile=99, method='linear') [source] Detemine if an image is low contrast. Parameters: image : array-like The image under test. fraction_threshold : float, optional The low contrast fraction threshold. An image is considered low- contrast when its range of brightness spans less than this fraction of its data type’s full range. [R90] lower_bound : float, optional Disregard valu

sum_percentile skimage.filters.rank.sum_percentile(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=0, p1=1) [source] Return the local sum of pixels. Only greyvalues between percentiles [p0, p1] are considered in the filter. Note that the sum may overflow depending on the data type of the input array. 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 inp

imread skimage.io.imread(fname, as_grey=False, plugin=None, flatten=None, **plugin_args) [source] Load an image from file. Parameters: fname : string Image file name, e.g. test.jpg or URL. as_grey : bool If True, convert color images to grey-scale (32-bit floats). Images that are already in grey-scale format are not converted. 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. I

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

autolevel_percentile skimage.filters.rank.autolevel_percentile(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=0, p1=1) [source] Return greyscale local autolevel of an image. This filter locally stretches the histogram of greyvalues to cover the entire range of values from “white” to “black”. Only greyvalues between percentiles [p0, p1] are considered in the filter. Parameters: image : 2-D array (uint8, uint16) Input image. selem : 2-D array The neighborhood expressed

dilation skimage.morphology.dilation(image, selem=None, *args, **kwargs) [source] Return greyscale morphological dilation of an image. Morphological dilation sets a pixel at (i,j) to the maximum over all pixels in the neighborhood centered at (i,j). Dilation enlarges bright regions and shrinks dark regions. Parameters: image : ndarray Image array. selem : ndarray, optional The neighborhood expressed as a 2-D array of 1’s and 0’s. If None, use cross-shaped structuring element (connectivit

new_plot skimage.viewer.utils.new_plot(parent=None, subplot_kw=None, **fig_kw) [source] Return new figure and axes. Parameters: parent : QtWidget Qt widget that displays the plot objects. If None, you must manually call canvas.setParent and pass the parent widget. subplot_kw : dict Keyword arguments passed matplotlib.figure.Figure.add_subplot. fig_kw : dict Keyword arguments passed matplotlib.figure.Figure.