daisy skimage.feature.daisy(img, step=4, radius=15, rings=3, histograms=8, orientations=8, normalization='l1', sigmas=None, ring_radii=None, visualize=False) [source] Extract DAISY feature descriptors densely for the given image. DAISY is a feature descriptor similar to SIFT formulated in a way that allows for fast dense extraction. Typically, this is practical for bag-of-features image representations. The implementation follows Tola et al. [R144] but deviate on the following points: Histo

init_qtapp skimage.viewer.utils.init_qtapp() [source] Initialize QAppliction. The QApplication needs to be initialized before creating any QWidgets

rescale_intensity skimage.exposure.rescale_intensity(image, in_range='image', out_range='dtype') [source] Return image after stretching or shrinking its intensity levels. The desired intensity range of the input and output, in_range and out_range respectively, are used to stretch or shrink the intensity range of the input image. See examples below. Parameters: image : array Image array. in_range, out_range : str or 2-tuple Min and max intensity values of input and output image. The possi

find_contours skimage.measure.find_contours(array, level, fully_connected='low', positive_orientation='low') [source] Find iso-valued contours in a 2D array for a given level value. Uses the “marching squares” method to compute a the iso-valued contours of the input 2D array for a particular level value. Array values are linearly interpolated to provide better precision for the output contours. Parameters: array : 2D ndarray of double Input data in which to find contours. level : float V

denoise_bilateral skimage.restoration.denoise_bilateral(image, win_size=None, sigma_color=None, sigma_spatial=1, bins=10000, mode='constant', cval=0, multichannel=True, sigma_range=None) [source] Denoise image using bilateral filter. This is an edge-preserving and noise reducing denoising filter. It averages pixels based on their spatial closeness and radiometric similarity. Spatial closeness is measured by the gaussian function of the euclidian distance between two pixels and a certain stan

ImageCollection class skimage.io.ImageCollection(load_pattern, conserve_memory=True, load_func=None, **load_func_kwargs) [source] Bases: object Load and manage a collection of image files. Note that files are always stored in alphabetical order. Also note that slicing returns a new ImageCollection, not a view into the data. Parameters: load_pattern : str or list Pattern glob or filenames to load. The path can be absolute or relative. Multiple patterns should be separated by os.pathsep, e.g

horse skimage.data.horse() [source] Black and white silhouette of a horse. This image was downloaded from openclipart <http://openclipart.org/detail/158377/horse-by-marauder> Released into public domain and drawn and uploaded by Andreas Preuss (marauder).

ImageViewer class skimage.viewer.viewers.ImageViewer(image, useblit=True) [source] Bases: PyQt4.QtGui.QMainWindow Viewer for displaying images. This viewer is a simple container object that holds a Matplotlib axes for showing images. ImageViewer doesn’t subclass the Matplotlib axes (or figure) because of the high probability of name collisions. Subclasses and plugins will likely extend the update_image method to add custom overlays or filter the displayed image. Parameters: image : array I

checkerboard skimage.data.checkerboard() [source] Checkerboard image. Checkerboards are often used in image calibration, since the corner-points are easy to locate. Because of the many parallel edges, they also visualise distortions particularly well.

reconstruction skimage.morphology.reconstruction(seed, mask, method='dilation', selem=None, offset=None) [source] Perform a morphological reconstruction of an image. Morphological reconstruction by dilation is similar to basic morphological dilation: high-intensity values will replace nearby low-intensity values. The basic dilation operator, however, uses a structuring element to determine how far a value in the input image can spread. In contrast, reconstruction uses two images: a “seed” im