Image adjustment: transforming image content

unwrap_phase skimage.restoration.unwrap_phase(image, wrap_around=False, seed=None) [source] Recover the original from a wrapped phase image. From an image wrapped to lie in the interval [-pi, pi), recover the original, unwrapped image. Parameters: image : 1D, 2D or 3D ndarray of floats, optionally a masked array The values should be in the range [-pi, pi). If a masked array is provided, the masked entries will not be changed, and their values will not be used to guide the unwrapping of nei

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

BRIEF class skimage.feature.BRIEF(descriptor_size=256, patch_size=49, mode='normal', sigma=1, sample_seed=1) [source] Bases: skimage.feature.util.DescriptorExtractor BRIEF binary descriptor extractor. BRIEF (Binary Robust Independent Elementary Features) is an efficient feature point descriptor. It is highly discriminative even when using relatively few bits and is computed using simple intensity difference tests. For each keypoint, intensity comparisons are carried out for a specifically di

PiecewiseAffineTransform class skimage.transform.PiecewiseAffineTransform [source] Bases: skimage.transform._geometric.GeometricTransform 2D piecewise affine transformation. Control points are used to define the mapping. The transform is based on a Delaunay triangulation of the points to form a mesh. Each triangle is used to find a local affine transform. Attributes affines (list of AffineTransform objects) Affine transformations for each triangle in the mesh. inverse_affines (list of Affine

threshold_adaptive skimage.filters.threshold_adaptive(image, block_size, method='gaussian', offset=0, mode='reflect', param=None) [source] Applies an adaptive threshold to an array. Also known as local or dynamic thresholding where the threshold value is the weighted mean for the local neighborhood of a pixel subtracted by a constant. Alternatively the threshold can be determined dynamically by a a given function using the ‘generic’ method. Parameters: image : (N, M) ndarray Input image.

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: io Utilities to read and write images in various formats. The following plug-ins are available: Plugin Description pil Image reading via the Python Imaging Library qt Fast image display using the Qt library freeimage Load images using the FreeImage library gtk Fast image display using the GTK library matplotlib Display or save images using Matplotlib simpleitk Image reading and writing via SimpleITK imread Image reading and writing via imread imageio Image reading via the ImageIO Librar

convex_hull_image skimage.morphology.convex_hull_image(image) [source] Compute the convex hull image of a binary image. The convex hull is the set of pixels included in the smallest convex polygon that surround all white pixels in the input image. Parameters: image : (M, N) array Binary input image. This array is cast to bool before processing. Returns: hull : (M, N) array of bool Binary image with pixels in convex hull set to True. References [R296] http://blogs.mathworks.com/steve/

RAG class skimage.future.graph.RAG(label_image=None, connectivity=1, data=None, **attr) [source] Bases: networkx.classes.graph.Graph The Region Adjacency Graph (RAG) of an image, subclasses networx.Graph Parameters: label_image : array of int An initial segmentation, with each region labeled as a different integer. Every unique value in label_image will correspond to a node in the graph. connectivity : int in {1, ..., label_image.ndim}, optional The connectivity between pixels in label_i