hough_ellipse skimage.transform.hough_ellipse(img, threshold=4, accuracy=1, min_size=4, max_size=None) [source] Perform an elliptical Hough transform. Parameters: img : (M, N) ndarray Input image with nonzero values representing edges. threshold: int, optional (default 4) Accumulator threshold value. accuracy : double, optional (default 1) Bin size on the minor axis used in the accumulator. min_size : int, optional (default 4) Minimal major axis length. max_size : int, optional Max

blob_dog skimage.feature.blob_dog(image, min_sigma=1, max_sigma=50, sigma_ratio=1.6, threshold=2.0, overlap=0.5) [source] Finds blobs in the given grayscale image. Blobs are found using the Difference of Gaussian (DoG) method [R126]. For each blob found, the method returns its coordinates and the standard deviation of the Gaussian kernel that detected the blob. Parameters: image : ndarray Input grayscale image, blobs are assumed to be light on dark background (white on black). min_sigma :

roberts_neg_diag skimage.filters.roberts_neg_diag(image, mask=None) [source] Find the cross edges of an image using the Roberts’ Cross operator. The kernel is applied to the input image to produce separate measurements of the gradient component one orientation. 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 aff

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

img_as_ubyte skimage.util.img_as_ubyte(image, force_copy=False) [source] Convert an image to 8-bit unsigned integer format. Parameters: image : ndarray Input image. force_copy : bool Force a copy of the data, irrespective of its current dtype. Returns: out : ndarray of ubyte (uint8) Output image. Notes Negative input values will be clipped. Positive values are scaled between 0 and 255.

corner_moravec skimage.feature.corner_moravec() Compute Moravec corner measure response image. This is one of the simplest corner detectors and is comparatively fast but has several limitations (e.g. not rotation invariant). Parameters: image : ndarray Input image. window_size : int, optional (default 1) Window size. Returns: response : ndarray Moravec response image. References [R136] http://kiwi.cs.dal.ca/~dparks/CornerDetection/moravec.htm [R137] http://en.wikipedia.org/wiki/Cor

Module: filters.rank skimage.filters.rank.autolevel(image, selem) Auto-level image using local histogram. skimage.filters.rank.autolevel_percentile(...) Return greyscale local autolevel of an image. skimage.filters.rank.bottomhat(image, selem) Local bottom-hat of an image. skimage.filters.rank.enhance_contrast(image, ...) Enhance contrast of an image. skimage.filters.rank.enhance_contrast_percentile(...) Enhance contrast of an image. skimage.filters.rank.entropy(image, selem[, ...]) Local

TiffFile class skimage.external.tifffile.TiffFile(arg, name=None, offset=None, size=None, multifile=True, multifile_close=True) [source] Bases: object Read image and metadata from TIFF, STK, LSM, and FluoView files. TiffFile instances must be closed using the close method, which is automatically called when using the ‘with’ statement. Examples >>> with TiffFile('test.tif') as tif: ... data = tif.asarray() ... data.shape (256, 256, 4) Attributes pages (list) All TIFF pages

warn skimage.viewer.warn(message, category=None, stacklevel=2) [source] A version of warnings.warn with a default stacklevel of 2.

Getting started scikit-image is an image processing Python package that works with numpy arrays. The package is imported as skimage: >>> import skimage Most functions of skimage are found within submodules: >>> from skimage import data >>> camera = data.camera() A list of submodules and functions is found on the API reference webpage. Within scikit-image, images are represented as NumPy arrays, for example 2-D arrays for grayscale 2-D images >>> type(camera