plot_matches skimage.feature.plot_matches(ax, image1, image2, keypoints1, keypoints2, matches, keypoints_color='k', matches_color=None, only_matches=False) [source] Plot matched features. Parameters: ax : matplotlib.axes.Axes Matches and image are drawn in this ax. image1 : (N, M [, 3]) array First grayscale or color image. image2 : (N, M [, 3]) array Second grayscale or color image. keypoints1 : (K1, 2) array First keypoint coordinates as (row, col). keypoints2 : (K2, 2) array Sec

OKCancelButtons class skimage.viewer.widgets.OKCancelButtons(button_width=80) [source] Bases: skimage.viewer.widgets.core.BaseWidget Buttons that close the parent plugin. OK will replace the original image with the current (filtered) image. Cancel will just close the plugin. __init__(button_width=80) [source] close_plugin() [source] update_original_image() [source]

threshold_percentile skimage.filters.rank.threshold_percentile(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=0) [source] Local threshold of an image. The resulting binary mask is True if the greyvalue of the center pixel is greater than the local mean. 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 as a 2-D array of 1’s and 0’s. out :

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

matrix_transform skimage.transform.matrix_transform(coords, matrix) [source] Apply 2D matrix transform. Parameters: coords : (N, 2) array x, y coordinates to transform matrix : (3, 3) array Homogeneous transformation matrix. Returns: coords : (N, 2) array Transformed coordinates.

rgb2hsv skimage.color.rgb2hsv(rgb) [source] RGB to HSV color space conversion. Parameters: rgb : array_like The image in RGB format, in a 3-D array of shape (.., .., 3). Returns: out : ndarray The image in HSV format, in a 3-D array of shape (.., .., 3). Raises: ValueError If rgb is not a 3-D array of shape (.., .., 3). Notes The conversion assumes an input data range of [0, 1] for all color components. Conversion between RGB and HSV color spaces results in some loss of precision

img_as_bool skimage.util.img_as_bool(image, force_copy=False) [source] Convert an image to boolean format. Parameters: image : ndarray Input image. force_copy : bool Force a copy of the data, irrespective of its current dtype. Returns: out : ndarray of bool (bool_) Output image. Notes The upper half of the input dtype’s positive range is True, and the lower half is False. All negative values (if present) are False.

maximum skimage.filters.rank.maximum(image, selem, out=None, mask=None, shift_x=False, shift_y=False) [source] Return local maximum 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

lab2rgb skimage.color.lab2rgb(lab) [source] Lab to RGB color space conversion. Parameters: lab : array_like The image in Lab format, in a 3-D array of shape (.., .., 3). Returns: out : ndarray The image in RGB format, in a 3-D array of shape (.., .., 3). Raises: ValueError If lab is not a 3-D array of shape (.., .., 3). Notes This function uses lab2xyz and xyz2rgb.

corner_foerstner skimage.feature.corner_foerstner(image, sigma=1) [source] Compute Foerstner corner measure response image. This corner detector uses information from the auto-correlation matrix A: A = [(imx**2) (imx*imy)] = [Axx Axy] [(imx*imy) (imy**2)] [Axy Ayy] Where imx and imy are first derivatives, averaged with a gaussian filter. The corner measure is then defined as: w = det(A) / trace(A) (size of error ellipse) q = 4 * det(A) / trace(A)**2 (roundness of erro