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from skimage import io as sio
import skimage

import numpy as np
import matplotlib
import matplotlib.pyplot as plt


img = skimage.img_as_float(sio.imread('im2.tif'))

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# Stefan van der Walt <stefan@sun.ac.za>, 2013
# License: CC0

from __future__ import division

import numpy as np

import matplotlib.pyplot as plt
import matplotlib.animation as animation
from matplotlib.patches import Circle

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import numpy as np
import os
from skimage.io import ImageCollection
from skimage.io.video import CvVideo

import cv


class WebcamVideo(CvVideo):
    """

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"""
==========================
Random walker segmentation
==========================

The random walker algorithm [1]_  determines the segmentation of an image from
a set of markers labeling several phases (2 or more). An anisotropic diffusion
equation is solved with tracers initiated at the markers' position. The local
diffusivity coefficient is greater if neighboring pixels have similar values,
so that diffusion is difficult across high gradients. The label of each unknown

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{
 "metadata": {
  "name": "maximum_amplitude"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {

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{
 "metadata": {
  "name": "cat"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {

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    @magic_arguments.argument(
        'filename', type=unicode,
        help='file to write'
    )
    @cell_magic
    def cat(self, line, cell):
        """Write the contents of the cell to a file.

        """
        nr_pipes = line.count('>')

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from skimage.viewer import ImageViewer
from skimage import data, img_as_float

from skimage.viewer.plugins.plotplugin import PlotPlugin
from skimage.viewer.widgets import Slider
from skimage.exposure import rescale_intensity
from skimage.transform import rotate

import numpy as np
import matplotlib.pyplot as plt