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.

circle_perimeter skimage.draw.circle_perimeter() Generate circle perimeter coordinates. Parameters: cy, cx : int Centre coordinate of circle. radius: int Radius of circle. method : {‘bresenham’, ‘andres’}, optional bresenham : Bresenham method (default) andres : Andres method shape : tuple, optional Image shape which is used to determine the maximum extent of output pixel coordinates. This is useful for circles which exceed the image size. By default the full extent of the circle are

rank_order skimage.filters.rank_order(image) [source] Return an image of the same shape where each pixel is the index of the pixel value in the ascending order of the unique values of image, aka the rank-order value. Parameters: image: ndarray Returns: labels: ndarray of type np.uint32, of shape image.shape New array where each pixel has the rank-order value of the corresponding pixel in image. Pixel values are between 0 and n - 1, where n is the number of distinct unique values in image

CheckBox class skimage.viewer.widgets.CheckBox(name, value=False, alignment='center', ptype='kwarg', callback=None) [source] Bases: skimage.viewer.widgets.core.BaseWidget CheckBox widget Parameters: name : str Name of CheckBox parameter. If this parameter is passed as a keyword argument, it must match the name of that keyword argument (spaces are replaced with underscores). In addition, this name is displayed as the name of the CheckBox. value: {False, True}, optional Initial state of th

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

guess_spatial_dimensions skimage.color.guess_spatial_dimensions(image) [source] Make an educated guess about whether an image has a channels dimension. Parameters: image : ndarray The input image. Returns: spatial_dims : int or None The number of spatial dimensions of image. If ambiguous, the value is None. Raises: ValueError If the image array has less than two or more than four dimensions.

immunohistochemistry skimage.data.immunohistochemistry() [source] Immunohistochemical (IHC) staining with hematoxylin counterstaining. This picture shows colonic glands where the IHC expression of FHL2 protein is revealed with DAB. Hematoxylin counterstaining is applied to enhance the negative parts of the tissue. This image was acquired at the Center for Microscopy And Molecular Imaging (CMMI). No known copyright restrictions.

opening skimage.morphology.opening(image, selem=None, *args, **kwargs) [source] Return greyscale morphological opening of an image. The morphological opening on an image is defined as an erosion followed by a dilation. Opening can remove small bright spots (i.e. “salt”) and connect small dark cracks. This tends to “open” up (dark) gaps between (bright) features. Parameters: image : ndarray Image array. selem : ndarray, optional The neighborhood expressed as an array of 1’s and 0’s. If No

LPIFilter2D class skimage.filters.LPIFilter2D(impulse_response, **filter_params) [source] Bases: object Linear Position-Invariant Filter (2-dimensional) __init__(impulse_response, **filter_params) [source] Parameters: impulse_response : callable f(r, c, **filter_params) Function that yields the impulse response. r and c are 1-dimensional vectors that represent row and column positions, in other words coordinates are (r[0],c[0]),(r[0],c[1]) etc. **filter_params are passed through. In oth

MCP class skimage.graph.MCP(costs, offsets=None, fully_connected=True, sampling=None) Bases: object A class for finding the minimum cost path through a given n-d costs array. Given an n-d costs array, this class can be used to find the minimum-cost path through that array from any set of points to any other set of points. Basic usage is to initialize the class and call find_costs() with a one or more starting indices (and an optional list of end indices). After that, call traceback() one or