felzenszwalb skimage.segmentation.felzenszwalb(image, scale=1, sigma=0.8, min_size=20) [source] Computes Felsenszwalb’s efficient graph based image segmentation. Produces an oversegmentation of a multichannel (i.e. RGB) image using a fast, minimum spanning tree based clustering on the image grid. The parameter scale sets an observation level. Higher scale means less and larger segments. sigma is the diameter of a Gaussian kernel, used for smoothing the image prior to segmentation. The number

inverse skimage.filters.inverse(data, impulse_response=None, filter_params={}, max_gain=2, predefined_filter=None) [source] Apply the filter in reverse to the given data. Parameters: data : (M,N) ndarray Input data. impulse_response : callable f(r, c, **filter_params) Impulse response of the filter. See LPIFilter2D.__init__. filter_params : dict Additional keyword parameters to the impulse_response function. max_gain : float Limit the filter gain. Often, the filter contains zeros, wh

LineModel class skimage.measure.LineModel [source] Bases: skimage.measure.fit.BaseModel Total least squares estimator for 2D lines. Lines are parameterized using polar coordinates as functional model: dist = x * cos(theta) + y * sin(theta) This parameterization is able to model vertical lines in contrast to the standard line model y = a*x + b. This estimator minimizes the squared distances from all points to the line: min{ sum((dist - x_i * cos(theta) + y_i * sin(theta))**2) } A minimum nu

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 [ ] [ ] [ ]

combine_stains skimage.color.combine_stains(stains, conv_matrix) [source] Stain to RGB color space conversion. Parameters: stains : array_like The image in stain color space, in a 3-D array of shape (.., .., 3). conv_matrix: ndarray The stain separation matrix as described by G. Landini [R31]. Returns: out : ndarray The image in RGB format, in a 3-D array of shape (.., .., 3). Raises: ValueError If stains is not a 3-D array of shape (.., .., 3). Notes Stain combination matrices

hsobel skimage.filters.hsobel(*args, **kwargs) [source] Deprecated function. Use skimage.filters.sobel_h instead. Find the horizontal edges of an image using the Sobel transform. 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 affecting the result. Returns: output : 2-D array The absolute Sobel edge map. N

cut_normalized skimage.future.graph.cut_normalized(labels, rag, thresh=0.001, num_cuts=10, in_place=True, max_edge=1.0) [source] Perform Normalized Graph cut on the Region Adjacency Graph. Given an image’s labels and its similarity RAG, recursively perform a 2-way normalized cut on it. All nodes belonging to a subgraph that cannot be cut further are assigned a unique label in the output. Parameters: labels : ndarray The array of labels. rag : RAG The region adjacency graph. thresh : flo

greycoprops skimage.feature.greycoprops(P, prop='contrast') [source] Calculate texture properties of a GLCM. Compute a feature of a grey level co-occurrence matrix to serve as a compact summary of the matrix. The properties are computed as follows: ‘contrast’: ‘dissimilarity’: ‘homogeneity’: ‘ASM’: ‘energy’: ‘correlation’: Parameters: P : ndarray Input array. P is the grey-level co-occurrence histogram for which to compute the specified property. The value P[i,j,d,theta] is the n

adjust_sigmoid skimage.exposure.adjust_sigmoid(image, cutoff=0.5, gain=10, inv=False) [source] Performs Sigmoid Correction on the input image. Also known as Contrast Adjustment. This function transforms the input image pixelwise according to the equation O = 1/(1 + exp*(gain*(cutoff - I))) after scaling each pixel to the range 0 to 1. Parameters: image : ndarray Input image. cutoff : float Cutoff of the sigmoid function that shifts the characteristic curve in horizontal direction. Defaul

crop skimage.util.crop(ar, crop_width, copy=False, order='K') [source] Crop array ar by crop_width along each dimension. Parameters: ar : array-like of rank N Input array. crop_width : {sequence, int} Number of values to remove from the edges of each axis. ((before_1, after_1), ... (before_N, after_N)) specifies unique crop widths at the start and end of each axis. ((before, after),) specifies a fixed start and end crop for every axis. (n,) or n for integer n is a shortcut for before = a