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

adjust_gamma skimage.exposure.adjust_gamma(image, gamma=1, gain=1) [source] Performs Gamma Correction on the input image. Also known as Power Law Transform. This function transforms the input image pixelwise according to the equation O = I**gamma after scaling each pixel to the range 0 to 1. Parameters: image : ndarray Input image. gamma : float Non negative real number. Default value is 1. gain : float The constant multiplier. Default value is 1. Returns: out : ndarray Gamma corre

active_contour skimage.segmentation.active_contour(image, snake, alpha=0.01, beta=0.1, w_line=0, w_edge=1, gamma=0.01, bc='periodic', max_px_move=1.0, max_iterations=2500, convergence=0.1) [source] Active contour model. Active contours by fitting snakes to features of images. Supports single and multichannel 2D images. Snakes can be periodic (for segmentation) or have fixed and/or free ends. Parameters: image : (N, M) or (N, M, 3) ndarray Input image. snake : (N, 2) ndarray Initialisatio

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: >&