lena skimage.data.lena(*args, **kwargs) [source] Deprecated function. Use skimage.data.astronaut instead. Colour “Lena” image. This image has been removed from scikit-image due to copyright concerns. The standard, yet sometimes controversial Lena test image was scanned from the November 1972 edition of Playboy magazine. From an image processing perspective, this image is useful because it contains smooth, textured, shaded as well as detail areas.

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

laplace skimage.filters.laplace(image, ksize=3, mask=None) [source] Find the edges of an image using the Laplace operator. Parameters: image : ndarray Image to process. ksize : int, optional Define the size of the discrete Laplacian operator such that it will have a size of (ksize,) * image.ndim. mask : ndarray, 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 r

label2rgb skimage.color.label2rgb(label, image=None, colors=None, alpha=0.3, bg_label=-1, bg_color=(0, 0, 0), image_alpha=1, kind='overlay') [source] Return an RGB image where color-coded labels are painted over the image. Parameters: label : array, shape (M, N) Integer array of labels with the same shape as image. image : array, shape (M, N, 3), optional Image used as underlay for labels. If the input is an RGB image, it’s converted to grayscale before coloring. colors : list, optional

label skimage.morphology.label(input, neighbors=None, background=None, return_num=False, connectivity=None) [source] Label connected regions of an integer array. Two pixels are connected when they are neighbors and have the same value. In 2D, they can be neighbors either in a 1- or 2-connected sense. The value refers to the maximum number of orthogonal hops to consider a pixel/voxel a neighbor: 1-connectivity 2-connectivity diagonal connection close-up [ ] [ ] [ ]

label skimage.measure.label(input, neighbors=None, background=None, return_num=False, connectivity=None) [source] Label connected regions of an integer array. Two pixels are connected when they are neighbors and have the same value. In 2D, they can be neighbors either in a 1- or 2-connected sense. The value refers to the maximum number of orthogonal hops to consider a pixel/voxel a neighbor: 1-connectivity 2-connectivity diagonal connection close-up [ ] [ ] [ ] [ ]

lab2xyz skimage.color.lab2xyz(lab, illuminant='D65', observer='2') [source] CIE-LAB to XYZcolor space conversion. Parameters: lab : array_like The image in lab format, in a 3-D array of shape (.., .., 3). illuminant : {“A”, “D50”, “D55”, “D65”, “D75”, “E”}, optional The name of the illuminant (the function is NOT case sensitive). observer : {“2”, “10”}, optional The aperture angle of the observer. Returns: out : ndarray The image in XYZ format, in a 3-D array of shape (.., .., 3).

lab2rgb skimage.color.lab2rgb(lab) [source] Lab to RGB color space conversion. Parameters: lab : array_like The image in Lab format, in a 3-D array of shape (.., .., 3). Returns: out : ndarray The image in RGB format, in a 3-D array of shape (.., .., 3). Raises: ValueError If lab is not a 3-D array of shape (.., .., 3). Notes This function uses lab2xyz and xyz2rgb.

lab2lch skimage.color.lab2lch(lab) [source] CIE-LAB to CIE-LCH color space conversion. LCH is the cylindrical representation of the LAB (Cartesian) colorspace Parameters: lab : array_like The N-D image in CIE-LAB 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 LCH format, in a N-D array with same shape as input lab. Raises: ValueError If lch does n

join_segmentations skimage.segmentation.join_segmentations(s1, s2) [source] Return the join of the two input segmentations. The join J of S1 and S2 is defined as the segmentation in which two voxels are in the same segment if and only if they are in the same segment in both S1 and S2. Parameters: s1, s2 : numpy arrays s1 and s2 are label fields of the same shape. Returns: j : numpy array The join segmentation of s1 and s2. Examples >>> from skimage.segmentation import join_s