TiffSequence class skimage.external.tifffile.TiffSequence(files, imread=, pattern='axes', *args, **kwargs) [source] Bases: object Sequence of image files. The data shape and dtype of all files must match. Examples >>> tifs = TiffSequence("test.oif.files/*.tif") >>> tifs.shape, tifs.axes ((2, 100), 'CT') >>> data = tifs.asarray() >>> data.shape (2, 100, 256, 256) Attributes files (list) List of file names. shape (tuple) Shape of image sequence. axe

sobel skimage.filters.sobel(image, mask=None) [source] Find the edge magnitude 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 Sobel edge map. See also scharr, prewitt, roberts, feature.canny Notes Take the square root of th

enhance_contrast_percentile skimage.filters.rank.enhance_contrast_percentile(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=0, p1=1) [source] Enhance contrast of an image. This replaces each pixel by the local maximum if the pixel greyvalue is closer to the local maximum than the local minimum. Otherwise it is replaced by the local minimum. Only greyvalues between percentiles [p0, p1] are considered in the filter. Parameters: image : 2-D array (uint8, uint16) Input ima

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

subtract_mean_percentile skimage.filters.rank.subtract_mean_percentile(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=0, p1=1) [source] Return image subtracted from its 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 : 2-D array (same dtype as input) If None, a new array is allocated. mask

rgb2rgbcie skimage.color.rgb2rgbcie(rgb) [source] RGB to RGB CIE color space conversion. Parameters: rgb : array_like The image in RGB format, in a 3-D array of shape (.., .., 3). Returns: out : ndarray The image in RGB CIE format, in a 3-D array of shape (.., .., 3). Raises: ValueError If rgb is not a 3-D array of shape (.., .., 3). References [R52] http://en.wikipedia.org/wiki/CIE_1931_color_space Examples >>> from skimage import data >>> from skimage.color im

coins skimage.data.coins() [source] Greek coins from Pompeii. This image shows several coins outlined against a gray background. It is especially useful in, e.g. segmentation tests, where individual objects need to be identified against a background. The background shares enough grey levels with the coins that a simple segmentation is not sufficient. Notes This image was downloaded from the Brooklyn Museum Collection. No known copyright restrictions.

open skimage.novice.open(path) [source] Return Picture object from the given image path.

img_as_int skimage.img_as_int(image, force_copy=False) [source] Convert an image to 16-bit signed integer format. Parameters: image : ndarray Input image. force_copy : bool Force a copy of the data, irrespective of its current dtype. Returns: out : ndarray of uint16 Output image. Notes The values are scaled between -32768 and 32767. If the input data-type is positive-only (e.g., uint8), then the output image will still only have positive values.

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