corner_orientations skimage.feature.corner_orientations() Compute the orientation of corners. The orientation of corners is computed using the first order central moment i.e. the center of mass approach. The corner orientation is the angle of the vector from the corner coordinate to the intensity centroid in the local neighborhood around the corner calculated using first order central moment. Parameters: image : 2D array Input grayscale image. corners : (N, 2) array Corner coordinates as

corner_kitchen_rosenfeld skimage.feature.corner_kitchen_rosenfeld(image, mode='constant', cval=0) [source] Compute Kitchen and Rosenfeld corner measure response image. The corner measure is calculated as follows: (imxx * imy**2 + imyy * imx**2 - 2 * imxy * imx * imy) / (imx**2 + imy**2) Where imx and imy are the first and imxx, imxy, imyy the second derivatives. Parameters: image : ndarray Input image. mode : {‘constant’, ‘reflect’, ‘wrap’, ‘nearest’, ‘mirror’}, optional How to hand

corner_foerstner skimage.feature.corner_foerstner(image, sigma=1) [source] Compute Foerstner corner measure response image. This corner detector uses information from the auto-correlation matrix A: A = [(imx**2) (imx*imy)] = [Axx Axy] [(imx*imy) (imy**2)] [Axy Ayy] Where imx and imy are first derivatives, averaged with a gaussian filter. The corner measure is then defined as: w = det(A) / trace(A) (size of error ellipse) q = 4 * det(A) / trace(A)**2 (roundness of erro

corner_harris skimage.feature.corner_harris(image, method='k', k=0.05, eps=1e-06, sigma=1) [source] Compute Harris corner measure response image. This corner detector uses information from the auto-correlation matrix A: A = [(imx**2) (imx*imy)] = [Axx Axy] [(imx*imy) (imy**2)] [Axy Ayy] Where imx and imy are first derivatives, averaged with a gaussian filter. The corner measure is then defined as: det(A) - k * trace(A)**2 or: 2 * det(A) / (trace(A) + eps) Parameters: image : nd

corner_fast skimage.feature.corner_fast(image, n=12, threshold=0.15) [source] Extract FAST corners for a given image. Parameters: image : 2D ndarray Input image. n : int Minimum number of consecutive pixels out of 16 pixels on the circle that should all be either brighter or darker w.r.t testpixel. A point c on the circle is darker w.r.t test pixel p if Ic < Ip - threshold and brighter if Ic > Ip + threshold. Also stands for the n in FAST-n corner detector. threshold : float Thre

copy_func skimage.filters.copy_func(f, name=None) [source] Create a copy of a function. Parameters: f : function Function to copy. name : str, optional Name of new function.

convert_colorspace skimage.color.convert_colorspace(arr, fromspace, tospace) [source] Convert an image array to a new color space. Parameters: arr : array_like The image to convert. fromspace : str The color space to convert from. Valid color space strings are ['RGB', 'HSV', 'RGB CIE', 'XYZ']. Value may also be specified as lower case. tospace : str The color space to convert to. Valid color space strings are ['RGB', 'HSV', 'RGB CIE', 'XYZ']. Value may also be specified as lower case.

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/

convex_hull_object skimage.morphology.convex_hull_object(image, neighbors=8) [source] Compute the convex hull image of individual objects in 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. neighbors : {4, 8}, int Whether to use 4- or 8-connectivity. Returns: hull : ndarray of bool Binary image with pixels in convex hull set to True. No

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