corner_peaks skimage.feature.corner_peaks(image, min_distance=1, threshold_abs=None, threshold_rel=0.1, exclude_border=True, indices=True, num_peaks=inf, footprint=None, labels=None) [source] Find corners in corner measure response image. This differs from skimage.feature.peak_local_max in that it suppresses multiple connected peaks with the same accumulator value. Parameters: * : * See skimage.feature.peak_local_max(). Examples >>> from skimage.feature import peak_local_max >

AffineTransform class skimage.transform.AffineTransform(matrix=None, scale=None, rotation=None, shear=None, translation=None) [source] Bases: skimage.transform._geometric.ProjectiveTransform 2D affine transformation of the form: ..:math: X = a0*x + a1*y + a2 = = sx*x*cos(rotation) - sy*y*sin(rotation + shear) + a2 Y = b0*x + b1*y + b2 = = sx*x*sin(rotation) + sy*y*cos(rotation + shear) + b2 where sx and sy are zoom factors in the x and y directions, and the homogeneous transformation matrix

Utility Functions img_as_float Convert an image to floating point format, with values in [0, 1]. img_as_uint Convert an image to unsigned integer format, with values in [0, 65535]. img_as_int Convert an image to signed integer format, with values in [-32768, 32767]. img_as_ubyte Convert an image to unsigned byte format, with values in [0, 255]. skimage.dtype_limits(image[, clip_negative]) Return intensity limits, i.e. skimage.img_as_bool(image[, force_copy]) Convert an image to boolean form

profile_line skimage.measure.profile_line(img, src, dst, linewidth=1, order=1, mode='constant', cval=0.0) [source] Return the intensity profile of an image measured along a scan line. Parameters: img : numeric array, shape (M, N[, C]) The image, either grayscale (2D array) or multichannel (3D array, where the final axis contains the channel information). src : 2-tuple of numeric scalar (float or int) The start point of the scan line. dst : 2-tuple of numeric scalar (float or int) The e

moments skimage.measure.moments(image, order=3) [source] Calculate all raw image moments up to a certain order. The following properties can be calculated from raw image moments: Area as: m[0, 0]. Centroid as: {m[0, 1] / m[0, 0], m[1, 0] / m[0, 0]}. Note that raw moments are neither translation, scale nor rotation invariant. Parameters: image : 2D double or uint8 array Rasterized shape as image. order : int, optional Maximum order of moments. Default is 3. Returns: m : (order + 1, o

concatenate_images skimage.io.concatenate_images(ic) [source] Concatenate all images in the image collection into an array. Parameters: ic: an iterable of images (including ImageCollection and MultiImage) The images to be concatenated. Returns: ar : np.ndarray An array having one more dimension than the images in ic. Raises: ValueError If images in ic don’t have identical shapes. See also ImageCollection.concatenate, MultiImage.concatenate

block_reduce skimage.measure.block_reduce(image, block_size, func=, cval=0) [source] Down-sample image by applying function to local blocks. Parameters: image : ndarray N-dimensional input image. block_size : array_like Array containing down-sampling integer factor along each axis. func : callable Function object which is used to calculate the return value for each local block. This function must implement an axis parameter such as numpy.sum or numpy.min. cval : float Constant paddin

merge_hierarchical skimage.future.graph.merge_hierarchical(labels, rag, thresh, rag_copy, in_place_merge, merge_func, weight_func) [source] Perform hierarchical merging of a RAG. Greedily merges the most similar pair of nodes until no edges lower than thresh remain. Parameters: labels : ndarray The array of labels. rag : RAG The Region Adjacency Graph. thresh : float Regions connected by an edge with weight smaller than thresh are merged. rag_copy : bool If set, the RAG copied before

structure_tensor skimage.feature.structure_tensor(image, sigma=1, mode='constant', cval=0) [source] Compute structure tensor using sum of squared differences. The structure tensor A is defined as: A = [Axx Axy] [Axy Ayy] which is approximated by the weighted sum of squared differences in a local window around each pixel in the image. Parameters: image : ndarray Input image. sigma : float Standard deviation used for the Gaussian kernel, which is used as a weighting function for the l

frt2 skimage.transform.frt2(a) [source] Compute the 2-dimensional finite radon transform (FRT) for an n x n integer array. Parameters: a : array_like A 2-D square n x n integer array. Returns: FRT : 2-D ndarray Finite Radon Transform array of (n+1) x n integer coefficients. See also ifrt2 The two-dimensional inverse FRT. Notes The FRT has a unique inverse if and only if n is prime. [FRT] The idea for this algorithm is due to Vlad Negnevitski. References [FRT] A. Kingston and I.