ellipsoid skimage.draw.ellipsoid(a, b, c, spacing=(1.0, 1.0, 1.0), levelset=False) [source] Generates ellipsoid with semimajor axes aligned with grid dimensions on grid with specified spacing. Parameters: a : float Length of semimajor axis aligned with x-axis. b : float Length of semimajor axis aligned with y-axis. c : float Length of semimajor axis aligned with z-axis. spacing : tuple of floats, length 3 Spacing in (x, y, z) spatial dimensions. levelset : bool If True, returns the

tophat skimage.filters.rank.tophat(image, selem, out=None, mask=None, shift_x=False, shift_y=False) [source] Local top-hat of an image. This filter computes the morphological opening of the image and then subtracts the result from the original image. 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 input) If None, a new array is allocated. mask : ndarray Mask array tha

FigureCanvas class skimage.viewer.utils.FigureCanvas(figure, **kwargs) [source] Bases: matplotlib.backends.backend_qt4agg.FigureCanvasQTAgg Canvas for displaying images. __init__(figure, **kwargs) [source] resizeEvent(event) [source]

compare_mse skimage.measure.compare_mse(im1, im2) [source] Compute the mean-squared error between two images. Parameters: im1, im2 : ndarray Image. Any dimensionality. Returns: mse : float The mean-squared error (MSE) metric.

draw_multiblock_lbp skimage.feature.draw_multiblock_lbp(img, r, c, width, height, lbp_code=0, color_greater_block=[1, 1, 1], color_less_block=[0, 0.69, 0.96], alpha=0.5) [source] Multi-block local binary pattern visualization. Blocks with higher sums are colored with alpha-blended white rectangles, whereas blocks with lower sums are colored alpha-blended cyan. Colors and the alpha parameter can be changed. Parameters: img : ndarray of float or uint Image on which to visualize the pattern.

minimum skimage.filters.rank.minimum(image, selem, out=None, mask=None, shift_x=False, shift_y=False) [source] Return local minimum of an image. 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 input) If None, a new array is allocated. mask : ndarray Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is use

compare_psnr skimage.measure.compare_psnr(im_true, im_test, dynamic_range=None) [source] Compute the peak signal to noise ratio (PSNR) for an image. Parameters: im_true : ndarray Ground-truth image. im_test : ndarray Test image. dynamic_range : int The dynamic range of the input image (distance between minimum and maximum possible values). By default, this is estimated from the image data-type. Returns: psnr : float The PSNR metric. References [R260] https://en.wikipedia.org/wiki

corner_peaks skimage.feature.corner_peaks(image, min_distance=1, threshold_abs=None, threshold_rel=0.1, exclude_border=True, indices=True, num_peaks=inf, footprint=None, labels=None) [source] Find corners in corner measure response image. This differs from skimage.feature.peak_local_max in that it suppresses multiple connected peaks with the same accumulator value. Parameters: * : * See skimage.feature.peak_local_max(). Examples >>> from skimage.feature import peak_local_max >

AffineTransform class skimage.transform.AffineTransform(matrix=None, scale=None, rotation=None, shear=None, translation=None) [source] Bases: skimage.transform._geometric.ProjectiveTransform 2D affine transformation of the form: ..:math: X = a0*x + a1*y + a2 = = sx*x*cos(rotation) - sy*y*sin(rotation + shear) + a2 Y = b0*x + b1*y + b2 = = sx*x*sin(rotation) + sy*y*cos(rotation + shear) + b2 where sx and sy are zoom factors in the x and y directions, and the homogeneous transformation matrix

Utility Functions img_as_float Convert an image to floating point format, with values in [0, 1]. img_as_uint Convert an image to unsigned integer format, with values in [0, 65535]. img_as_int Convert an image to signed integer format, with values in [-32768, 32767]. img_as_ubyte Convert an image to unsigned byte format, with values in [0, 255]. skimage.dtype_limits(image[, clip_negative]) Return intensity limits, i.e. skimage.img_as_bool(image[, force_copy]) Convert an image to boolean form