MCP class skimage.graph.MCP(costs, offsets=None, fully_connected=True, sampling=None) Bases: object A class for finding the minimum cost path through a given n-d costs array. Given an n-d costs array, this class can be used to find the minimum-cost path through that array from any set of points to any other set of points. Basic usage is to initialize the class and call find_costs() with a one or more starting indices (and an optional list of end indices). After that, call traceback() one or

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

matrix_transform skimage.transform.matrix_transform(coords, matrix) [source] Apply 2D matrix transform. Parameters: coords : (N, 2) array x, y coordinates to transform matrix : (3, 3) array Homogeneous transformation matrix. Returns: coords : (N, 2) array Transformed coordinates.

match_template skimage.feature.match_template(image, template, pad_input=False, mode='constant', constant_values=0) [source] Match a template to a 2-D or 3-D image using normalized correlation. The output is an array with values between -1.0 and 1.0. The value at a given position corresponds to the correlation coefficient between the image and the template. For pad_input=True matches correspond to the center and otherwise to the top-left corner of the template. To find the best match you mus

match_descriptors skimage.feature.match_descriptors(descriptors1, descriptors2, metric=None, p=2, max_distance=inf, cross_check=True) [source] Brute-force matching of descriptors. For each descriptor in the first set this matcher finds the closest descriptor in the second set (and vice-versa in the case of enabled cross-checking). Parameters: descriptors1 : (M, P) array Binary descriptors of size P about M keypoints in the first image. descriptors2 : (N, P) array Binary descriptors of si

mark_boundaries skimage.segmentation.mark_boundaries(image, label_img, color=(1, 1, 0), outline_color=None, mode='outer', background_label=0) [source] Return image with boundaries between labeled regions highlighted. Parameters: image : (M, N[, 3]) array Grayscale or RGB image. label_img : (M, N) array of int Label array where regions are marked by different integer values. color : length-3 sequence, optional RGB color of boundaries in the output image. outline_color : length-3 sequen

marching_cubes skimage.measure.marching_cubes(volume, level, spacing=(1.0, 1.0, 1.0), gradient_direction='descent') [source] Marching cubes algorithm to find iso-valued surfaces in 3d volumetric data Parameters: volume : (M, N, P) array of doubles Input data volume to find isosurfaces. Will be cast to np.float64. level : float Contour value to search for isosurfaces in volume. spacing : length-3 tuple of floats Voxel spacing in spatial dimensions corresponding to numpy array indexing d

luv2xyz skimage.color.luv2xyz(luv, illuminant='D65', observer='2') [source] CIE-Luv to XYZ color space conversion. Parameters: luv : (M, N, [P,] 3) array_like The 3 or 4 dimensional image in CIE-Luv format. Final dimension denotes channels. 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 : (M, N, [P,] 3) ndarray The image i

luv2rgb skimage.color.luv2rgb(luv) [source] Luv to RGB color space conversion. Parameters: luv : (M, N, [P,] 3) array_like The 3 or 4 dimensional image in CIE Luv format. Final dimension denotes channels. Returns: out : (M, N, [P,] 3) ndarray The image in RGB format. Same dimensions as input. Raises: ValueError If luv is not a 3-D or 4-D array of shape (M, N, [P,] 3). Notes This function uses luv2xyz and xyz2rgb.

LPIFilter2D class skimage.filters.LPIFilter2D(impulse_response, **filter_params) [source] Bases: object Linear Position-Invariant Filter (2-dimensional) __init__(impulse_response, **filter_params) [source] Parameters: impulse_response : callable f(r, c, **filter_params) Function that yields the impulse response. r and c are 1-dimensional vectors that represent row and column positions, in other words coordinates are (r[0],c[0]),(r[0],c[1]) etc. **filter_params are passed through. In oth