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

gabor_kernel skimage.filters.gabor_kernel(frequency, theta=0, bandwidth=1, sigma_x=None, sigma_y=None, n_stds=3, offset=0) [source] Return complex 2D Gabor filter kernel. Gabor kernel is a Gaussian kernel modulated by a complex harmonic function. Harmonic function consists of an imaginary sine function and a real cosine function. Spatial frequency is inversely proportional to the wavelength of the harmonic and to the standard deviation of a Gaussian kernel. The bandwidth is also inversely pr

mean_bilateral skimage.filters.rank.mean_bilateral(image, selem, out=None, mask=None, shift_x=False, shift_y=False, s0=10, s1=10) [source] Apply a flat kernel bilateral filter. This is an edge-preserving and noise reducing denoising filter. It averages pixels based on their spatial closeness and radiometric similarity. Spatial closeness is measured by considering only the local pixel neighborhood given by a structuring element. Radiometric similarity is defined by the greylevel interval [g-s

hough_ellipse skimage.transform.hough_ellipse(img, threshold=4, accuracy=1, min_size=4, max_size=None) [source] Perform an elliptical Hough transform. Parameters: img : (M, N) ndarray Input image with nonzero values representing edges. threshold: int, optional (default 4) Accumulator threshold value. accuracy : double, optional (default 1) Bin size on the minor axis used in the accumulator. min_size : int, optional (default 4) Minimal major axis length. max_size : int, optional Max

adjust_log skimage.exposure.adjust_log(image, gain=1, inv=False) [source] Performs Logarithmic correction on the input image. This function transforms the input image pixelwise according to the equation O = gain*log(1 + I) after scaling each pixel to the range 0 to 1. For inverse logarithmic correction, the equation is O = gain*(2**I - 1). Parameters: image : ndarray Input image. gain : float The constant multiplier. Default value is 1. inv : float If True, it performs inverse logarith

prewitt skimage.filters.prewitt(image, mask=None) [source] Find the edge magnitude using the Prewitt 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 Prewitt edge map. See also sobel, scharr Notes Return the square root of the sum of squares

scharr skimage.filters.scharr(image, mask=None) [source] Find the edge magnitude using the Scharr 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 Scharr edge map. See also sobel, prewitt, canny Notes Take the square root of the sum of the s

apply_parallel skimage.util.apply_parallel(function, array, chunks=None, depth=0, mode=None, extra_arguments=(), extra_keywords={}) [source] Map a function in parallel across an array. Split an array into possibly overlapping chunks of a given depth and boundary type, call the given function in parallel on the chunks, combine the chunks and return the resulting array. Parameters: function : function Function to be mapped which takes an array as an argument. array : numpy array Array whic

rgb2lab skimage.color.rgb2lab(rgb) [source] RGB to lab color space conversion. Parameters: rgb : array_like The image in RGB format, in a 3- or 4-D array of shape (.., ..,[ ..,] 3). Returns: out : ndarray The image in Lab format, in a 3- or 4-D array of shape (.., ..,[ ..,] 3). Raises: ValueError If rgb is not a 3- or 4-D array of shape (.., ..,[ ..,] 3). Notes This function uses rgb2xyz and xyz2lab.

pop_bilateral skimage.filters.rank.pop_bilateral(image, selem, out=None, mask=None, shift_x=False, shift_y=False, s0=10, s1=10) [source] Return the local number (population) of pixels. The number of pixels is defined as the number of pixels which are included in the structuring element and the mask. Additionally pixels must have a greylevel inside the interval [g-s0, g+s1] where g is the greyvalue of the center pixel. Parameters: image : 2-D array (uint8, uint16) Input image. selem : 2-D