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

Module: exposure skimage.exposure.adjust_gamma(image[, ...]) Performs Gamma Correction on the input image. skimage.exposure.adjust_log(image[, gain, inv]) Performs Logarithmic correction on the input image. skimage.exposure.adjust_sigmoid(image[, ...]) Performs Sigmoid Correction on the input image. skimage.exposure.cumulative_distribution(image) Return cumulative distribution function (cdf) for the given image. skimage.exposure.equalize_adapthist(image, ...) Contrast Limited Adaptive Hist

Module: external.tifffile skimage.external.tifffile.imread(files, **kwargs) Return image data from TIFF file(s) as numpy array. skimage.external.tifffile.imsave(filename, ...) Write image data to TIFF file. skimage.external.tifffile.imshow(data[, ...]) Plot n-dimensional images using matplotlib.pyplot. skimage.external.tifffile.TiffFile(arg[, ...]) Read image and metadata from TIFF, STK, LSM, and FluoView files. skimage.external.tifffile.TiffSequence(files) Sequence of image files. skimag

Module: draw skimage.draw.bezier_curve Generate Bezier curve coordinates. skimage.draw.circle(r, c, radius[, shape]) Generate coordinates of pixels within circle. skimage.draw.circle_perimeter Generate circle perimeter coordinates. skimage.draw.circle_perimeter_aa Generate anti-aliased circle perimeter coordinates. skimage.draw.ellipse(r, c, yradius, xradius) Generate coordinates of pixels within ellipse. skimage.draw.ellipse_perimeter Generate ellipse perimeter coordinates. skimage.draw.ell

Module: data Standard test images. For more images, see http://sipi.usc.edu/database/database.php skimage.data.astronaut() Colour image of the astronaut Eileen Collins. skimage.data.binary_blobs([length, ...]) Generate synthetic binary image with several rounded blob-like objects. skimage.data.camera() Gray-level “camera” image. skimage.data.checkerboard() Checkerboard image. skimage.data.chelsea() Chelsea the cat. skimage.data.clock() Motion blurred clock. skimage.data.coffee() Coffee

Module: color skimage.color.combine_stains(stains, conv_matrix) Stain to RGB color space conversion. skimage.color.convert_colorspace(arr, ...) Convert an image array to a new color space. skimage.color.deltaE_cie76(lab1, lab2) Euclidean distance between two points in Lab color space skimage.color.deltaE_ciede2000(lab1, lab2[, ...]) Color difference as given by the CIEDE 2000 standard. skimage.color.deltaE_ciede94(lab1, lab2[, ...]) Color difference according to CIEDE 94 standard skimage.

modal skimage.filters.rank.modal(image, selem, out=None, mask=None, shift_x=False, shift_y=False) [source] Return local mode of an image. The mode is the value that appears most often in the local histogram. 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 : ndarray Mask array that defines (>0) area of the image include

skimage Image Processing SciKit (Toolbox for SciPy) scikit-image (a.k.a. skimage) is a collection of algorithms for image processing and computer vision. The main package of skimage only provides a few utilities for converting between image data types; for most features, you need to import one of the following subpackages:

minimum skimage.filters.rank.minimum(image, selem, out=None, mask=None, shift_x=False, shift_y=False) [source] Return local minimum of an image. 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 : ndarray Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is use

mesh_surface_area skimage.measure.mesh_surface_area(verts, faces) [source] Compute surface area, given vertices & triangular faces Parameters: verts : (V, 3) array of floats Array containing (x, y, z) coordinates for V unique mesh vertices. faces : (F, 3) array of ints List of length-3 lists of integers, referencing vertex coordinates as provided in verts Returns: area : float Surface area of mesh. Units now [coordinate units] ** 2. See also skimage.measure.marching_cubes, ski