img_as_ubyte skimage.img_as_ubyte(image, force_copy=False) [source] Convert an image to 8-bit unsigned integer format. Parameters: image : ndarray Input image. force_copy : bool Force a copy of the data, irrespective of its current dtype. Returns: out : ndarray of ubyte (uint8) Output image. Notes Negative input values will be clipped. Positive values are scaled between 0 and 255.

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

figimage skimage.viewer.utils.figimage(image, scale=1, dpi=None, **kwargs) [source] Return figure and axes with figure tightly surrounding image. Unlike pyplot.figimage, this actually plots onto an axes object, which fills the figure. Plotting the image onto an axes allows for subsequent overlays of axes artists. Parameters: image : array image to plot scale : float If scale is 1, the figure and axes have the same dimension as the image. Smaller values of scale will shrink the figure. d

gabor_kernel skimage.filters.gabor_kernel(frequency, theta=0, bandwidth=1, sigma_x=None, sigma_y=None, n_stds=3, offset=0) [source] Return complex 2D Gabor filter kernel. Gabor kernel is a Gaussian kernel modulated by a complex harmonic function. Harmonic function consists of an imaginary sine function and a real cosine function. Spatial frequency is inversely proportional to the wavelength of the harmonic and to the standard deviation of a Gaussian kernel. The bandwidth is also inversely pr

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.

swirl skimage.transform.swirl(image, center=None, strength=1, radius=100, rotation=0, output_shape=None, order=1, mode='constant', cval=0, clip=True, preserve_range=False) [source] Perform a swirl transformation. Parameters: image : ndarray Input image. center : (row, column) tuple or (2,) ndarray, optional Center coordinate of transformation. strength : float, optional The amount of swirling applied. radius : float, optional The extent of the swirl in pixels. The effect dies out rap

dtype_limits skimage.util.dtype_limits(image, clip_negative=True) [source] Return intensity limits, i.e. (min, max) tuple, of the image’s dtype. Parameters: image : ndarray Input image. clip_negative : bool If True, clip the negative range (i.e. return 0 for min intensity) even if the image dtype allows negative values.

radon skimage.transform.radon(image, theta=None, circle=False) [source] Calculates the radon transform of an image given specified projection angles. Parameters: image : array_like, dtype=float Input image. The rotation axis will be located in the pixel with indices (image.shape[0] // 2, image.shape[1] // 2). theta : array_like, dtype=float, optional (default np.arange(180)) Projection angles (in degrees). circle : boolean, optional Assume image is zero outside the inscribed circle, ma

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.

pad skimage.util.pad(array, pad_width, mode=None, **kwargs) [source] Pads an array. Parameters: array : array_like of rank N Input array pad_width : {sequence, array_like, int} Number of values padded to the edges of each axis. ((before_1, after_1), ... (before_N, after_N)) unique pad widths for each axis. ((before, after),) yields same before and after pad for each axis. (pad,) or int is a shortcut for before = after = pad width for all axes. mode : str or function One of the followin