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.

A crash course on NumPy for images Images manipulated by scikit-image are simply NumPy arrays. Hence, a large fraction of operations on images will just consist in using NumPy: >>> from skimage import data >>> camera = data.camera() >>> type(camera) <type 'numpy.ndarray'> Retrieving the geometry of the image and the number of pixels: >>> camera.shape (512, 512) >>> camera.size 262144 Retrieving statistical information about gray values: >&

Picture class skimage.novice.Picture(path=None, array=None, xy_array=None) [source] Bases: object A 2-D picture made up of pixels. Examples Load an image from a file: >>> from skimage import novice >>> from skimage import data >>> picture = novice.open(data.data_dir + '/chelsea.png') Load an image from a URL (the URL must start with http(s):// or ftp(s)://): >>> picture = novice.open('http://scikit-image.org/_static/img/logo.png') Create a blank 100 pixe

gaussian skimage.filters.gaussian(image, sigma, output=None, mode='nearest', cval=0, multichannel=None) [source] Multi-dimensional Gaussian filter Parameters: image : array-like input image (grayscale or color) to filter. sigma : scalar or sequence of scalars standard deviation for Gaussian kernel. The standard deviations of the Gaussian filter are given for each axis as a sequence, or as a single number, in which case it is equal for all axes. output : array, optional The output param

view_as_windows skimage.util.view_as_windows(arr_in, window_shape, step=1) [source] Rolling window view of the input n-dimensional array. Windows are overlapping views of the input array, with adjacent windows shifted by a single row or column (or an index of a higher dimension). Parameters: arr_in : ndarray N-d input array. window_shape : integer or tuple of length arr_in.ndim Defines the shape of the elementary n-dimensional orthotope (better know as hyperrectangle [R383]) of the rolli

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.

radon skimage.transform.radon(image, theta=None, circle=False) [source] Calculates the radon transform of an image given specified projection angles. Parameters: image : array_like, dtype=float Input image. The rotation axis will be located in the pixel with indices (image.shape[0] // 2, image.shape[1] // 2). theta : array_like, dtype=float, optional (default np.arange(180)) Projection angles (in degrees). circle : boolean, optional Assume image is zero outside the inscribed circle, ma

threshold_percentile skimage.filters.rank.threshold_percentile(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=0) [source] Local threshold of an image. The resulting binary mask is True if the greyvalue of the center pixel is greater than the local mean. Only greyvalues between percentiles [p0, p1] are considered in the filter. 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 :

coffee skimage.data.coffee() [source] Coffee cup. This photograph is courtesy of Pikolo Espresso Bar. It contains several elliptical shapes as well as varying texture (smooth porcelain to course wood grain). Notes No copyright restrictions. CC0 by the photographer (Rachel Michetti).

Module: feature skimage.feature.blob_dog(image[, min_sigma, ...]) Finds blobs in the given grayscale image. skimage.feature.blob_doh(image[, min_sigma, ...]) Finds blobs in the given grayscale image. skimage.feature.blob_log(image[, min_sigma, ...]) Finds blobs in the given grayscale image. skimage.feature.canny(image[, sigma, ...]) Edge filter an image using the Canny algorithm. skimage.feature.corner_fast(image[, n, ...]) Extract FAST corners for a given image. skimage.feature.corner_fo