Button class skimage.viewer.widgets.Button(name, callback) [source] Bases: skimage.viewer.widgets.core.BaseWidget Button which calls callback upon click. Parameters: name : str Name of button. callback : callable f() Function to call when button is clicked. __init__(name, callback) [source]

erosion skimage.morphology.erosion(image, selem=None, *args, **kwargs) [source] Return greyscale morphological erosion of an image. Morphological erosion sets a pixel at (i,j) to the minimum over all pixels in the neighborhood centered at (i,j). Erosion shrinks bright regions and enlarges dark regions. Parameters: image : ndarray Image array. selem : ndarray, optional The neighborhood expressed as an array of 1’s and 0’s. If None, use cross-shaped structuring element (connectivity=1). o

hough_line skimage.transform.hough_line(img, theta=None) [source] Perform a straight line Hough transform. Parameters: img : (M, N) ndarray Input image with nonzero values representing edges. theta : 1D ndarray of double Angles at which to compute the transform, in radians. Defaults to -pi/2 .. pi/2 Returns: H : 2-D ndarray of uint64 Hough transform accumulator. theta : ndarray Angles at which the transform was computed, in radians. distances : ndarray Distance values. Notes Th

is_low_contrast skimage.exposure.is_low_contrast(image, fraction_threshold=0.05, lower_percentile=1, upper_percentile=99, method='linear') [source] Detemine if an image is low contrast. Parameters: image : array-like The image under test. fraction_threshold : float, optional The low contrast fraction threshold. An image is considered low- contrast when its range of brightness spans less than this fraction of its data type’s full range. [R90] lower_bound : float, optional Disregard valu

canny skimage.feature.canny(image, sigma=1.0, low_threshold=None, high_threshold=None, mask=None, use_quantiles=False) [source] Edge filter an image using the Canny algorithm. Parameters: image : 2D array Greyscale input image to detect edges on; can be of any dtype. sigma : float Standard deviation of the Gaussian filter. low_threshold : float Lower bound for hysteresis thresholding (linking edges). If None, low_threshold is set to 10% of dtype’s max. high_threshold : float Upper bo

lch2lab skimage.color.lch2lab(lch) [source] CIE-LCH to CIE-LAB color space conversion. LCH is the cylindrical representation of the LAB (Cartesian) colorspace Parameters: lch : array_like The N-D image in CIE-LCH format. The last (N+1-th) dimension must have at least 3 elements, corresponding to the L, a, and b color channels. Subsequent elements are copied. Returns: out : ndarray The image in LAB format, with same shape as input lch. Raises: ValueError If lch does not have at leas

Module: novice

scharr skimage.filters.scharr(image, mask=None) [source] Find the edge magnitude using the Scharr transform. Parameters: image : 2-D array Image to process. mask : 2-D array, optional An optional mask to limit the application to a certain area. Note that pixels surrounding masked regions are also masked to prevent masked regions from affecting the result. Returns: output : 2-D array The Scharr edge map. See also sobel, prewitt, canny Notes Take the square root of the sum of the s

CircleModel class skimage.measure.CircleModel [source] Bases: skimage.measure.fit.BaseModel Total least squares estimator for 2D circles. The functional model of the circle is: r**2 = (x - xc)**2 + (y - yc)**2 This estimator minimizes the squared distances from all points to the circle: min{ sum((r - sqrt((x_i - xc)**2 + (y_i - yc)**2))**2) } A minimum number of 3 points is required to solve for the parameters. Attributes params (tuple) Circle model parameters in the following order xc, yc

cumulative_distribution skimage.exposure.cumulative_distribution(image, nbins=256) [source] Return cumulative distribution function (cdf) for the given image. Parameters: image : array Image array. nbins : int Number of bins for image histogram. Returns: img_cdf : array Values of cumulative distribution function. bin_centers : array Centers of bins. See also histogram References [R85] http://en.wikipedia.org/wiki/Cumulative_distribution_function Examples >>> from skima