moments_hu skimage.measure.moments_hu(nu) [source] Calculate Hu’s set of image moments. Note that this set of moments is proofed to be translation, scale and rotation invariant. Parameters: nu : (M, M) array Normalized central image moments, where M must be > 4. Returns: nu : (7, 1) array Hu’s set of image moments. References [R273] M. K. Hu, “Visual Pattern Recognition by Moment Invariants”, IRE Trans. Info. Theory, vol. IT-8, pp. 179-187, 1962 [R274] Wilhelm Burger, Mark Burge.

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

Module: draw skimage.draw.bezier_curve Generate Bezier curve coordinates. skimage.draw.circle(r, c, radius[, shape]) Generate coordinates of pixels within circle. skimage.draw.circle_perimeter Generate circle perimeter coordinates. skimage.draw.circle_perimeter_aa Generate anti-aliased circle perimeter coordinates. skimage.draw.ellipse(r, c, yradius, xradius) Generate coordinates of pixels within ellipse. skimage.draw.ellipse_perimeter Generate ellipse perimeter coordinates. skimage.draw.ell

resize skimage.transform.resize(image, output_shape, order=1, mode='constant', cval=0, clip=True, preserve_range=False) [source] Resize image to match a certain size. Performs interpolation to up-size or down-size images. For down-sampling N-dimensional images by applying the arithmetic sum or mean, see skimage.measure.local_sum and skimage.transform.downscale_local_mean, respectively. Parameters: image : ndarray Input image. output_shape : tuple or ndarray Size of the generated output i

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

line skimage.draw.line() Generate line pixel coordinates. Parameters: y0, x0 : int Starting position (row, column). y1, x1 : int End position (row, column). Returns: rr, cc : (N,) ndarray of int Indices of pixels that belong to the line. May be used to directly index into an array, e.g. img[rr, cc] = 1. See also line_aa Anti-aliased line generator Examples >>> from skimage.draw import line >>> img = np.zeros((10, 10), dtype=np.uint8) >>> rr, cc = lin

ThickLineTool class skimage.viewer.canvastools.ThickLineTool(manager, on_move=None, on_enter=None, on_release=None, on_change=None, maxdist=10, line_props=None, handle_props=None) [source] Bases: skimage.viewer.canvastools.linetool.LineTool Widget for line selection in a plot. The thickness of the line can be varied using the mouse scroll wheel, or with the ‘+’ and ‘-‘ keys. Parameters: manager : Viewer or PlotPlugin. Skimage viewer or plot plugin object. on_move : function Function call

Module: segmentation skimage.segmentation.active_contour(image, snake) Active contour model. skimage.segmentation.clear_border(labels[, ...]) Clear objects connected to the label image border. skimage.segmentation.felzenszwalb(image[, ...]) Computes Felsenszwalb’s efficient graph based image segmentation. skimage.segmentation.find_boundaries(label_img) Return bool array where boundaries between labeled regions are True. skimage.segmentation.join_segmentations(s1, s2) Return the join of the

adjust_gamma skimage.exposure.adjust_gamma(image, gamma=1, gain=1) [source] Performs Gamma Correction on the input image. Also known as Power Law Transform. This function transforms the input image pixelwise according to the equation O = I**gamma after scaling each pixel to the range 0 to 1. Parameters: image : ndarray Input image. gamma : float Non negative real number. Default value is 1. gain : float The constant multiplier. Default value is 1. Returns: out : ndarray Gamma corre

pop_bilateral skimage.filters.rank.pop_bilateral(image, selem, out=None, mask=None, shift_x=False, shift_y=False, s0=10, s1=10) [source] Return the local number (population) of pixels. The number of pixels is defined as the number of pixels which are included in the structuring element and the mask. Additionally pixels must have a greylevel inside the interval [g-s0, g+s1] where g is the greyvalue of the center pixel. Parameters: image : 2-D array (uint8, uint16) Input image. selem : 2-D