felzenszwalb skimage.segmentation.felzenszwalb(image, scale=1, sigma=0.8, min_size=20) [source] Computes Felsenszwalb’s efficient graph based image segmentation. Produces an oversegmentation of a multichannel (i.e. RGB) image using a fast, minimum spanning tree based clustering on the image grid. The parameter scale sets an observation level. Higher scale means less and larger segments. sigma is the diameter of a Gaussian kernel, used for smoothing the image prior to segmentation. The number

probabilistic_hough_line skimage.transform.probabilistic_hough_line(img, threshold=10, line_length=50, line_gap=10, theta=None) [source] Return lines from a progressive probabilistic line Hough transform. Parameters: img : (M, N) ndarray Input image with nonzero values representing edges. threshold : int, optional (default 10) Threshold line_length : int, optional (default 50) Minimum accepted length of detected lines. Increase the parameter to extract longer lines. line_gap : int, op

pop_percentile skimage.filters.rank.pop_percentile(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=0, p1=1) [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. 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 arr

Module: transform skimage.transform.downscale_local_mean(...) Down-sample N-dimensional image by local averaging. skimage.transform.estimate_transform(ttype, ...) Estimate 2D geometric transformation parameters. skimage.transform.frt2(a) Compute the 2-dimensional finite radon transform (FRT) for an n x n integer array. skimage.transform.hough_circle(image, radius) Perform a circular Hough transform. skimage.transform.hough_ellipse(img[, ...]) Perform an elliptical Hough transform. skimage

sobel_v skimage.filters.sobel_v(image, mask=None) [source] Find the vertical edges of an image using the Sobel 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 Sobel edge map. Notes We use the following kernel: 1 0 -1 2 0 -2 1 0 -1

equalize skimage.filters.rank.equalize(image, selem, out=None, mask=None, shift_x=False, shift_y=False) [source] Equalize image using local histogram. 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

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

imread skimage.data.imread(fname, as_grey=False, plugin=None, flatten=None, **plugin_args) [source] Load an image from file. Parameters: fname : string Image file name, e.g. test.jpg or URL. as_grey : bool If True, convert color images to grey-scale (32-bit floats). Images that are already in grey-scale format are not converted. plugin : str Name of plugin to use. By default, the different plugins are tried (starting with the Python Imaging Library) until a suitable candidate is found.

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

threshold_yen skimage.filters.threshold_yen(image, nbins=256) [source] Return threshold value based on Yen’s method. Parameters: image : array Input image. nbins : int, optional Number of bins used to calculate histogram. This value is ignored for integer arrays. Returns: threshold : float Upper threshold value. All pixels intensities that less or equal of this value assumed as foreground. References [R207] Yen J.C., Chang F.J., and Chang S. (1995) “A New Criterion for Automatic Mu