title: ImportError: cannot import name 'disk' from 'skimage.draw'
body: <p>**</p>
  <pre><code>I am new to the python interface. I am importing the numpy, scipy , gradient descend libraries . And By using the skimage I am importing imread and rgb2gray .By using the scipy library I am using the signal function .With gradient descent I am converting the image into imgray .Then I am applying the gaussian filter and when I am mapping corner function using harris response function I am getting the error:
  </code></pre>
  <p>It will be really helpful for me if anyone can help me.I have tried to install skimage multiple of time I have installed it and uninstalled it but I couldn't able to find any solution to it.I have also imported all the libraries required but I don't know what is the issue . Please provide me accurate result for the code. Thank you in advance.
  Below is my error :<br />
  **</p>
  <pre><code>**NameError                                 Traceback (most recent call last)
  &lt;ipython-input-7-76c3bdecb24d&gt; in &lt;module&gt;
  ----&gt; 1 corners = corner_peaks(harris_response)
        2 fig, ax = plt.subplots()
        3 ax.imshow(img, interpolation='nearest', cmap=plt.cm.gray)
        4 ax.plot(corners[:, 1], corners[:, 0], '.r', markersize=3)
  
  NameError: name 'corner_peaks' is not defined**
  
  
  
  
  Below is my code :
  
      from skimage.io import imread
      from skimage.color import rgb2gray
      img = imread('box.jpg')
      imggray = rgb2gray(img)
          
      from scipy import signal as sig
      import numpy as np
      def gradient_x(imggray):
           ##Sobel operator kernels.
           kernel_x = np.array([[-1, 0, 1],[-2, 0, 2],[-1, 0, 1]])
           return sig.convolve2d(imggray, kernel_x, mode='same')
      def gradient_y(imggray):
           kernel_y = np.array([[1, 2, 1], [0, 0, 0], [-1, -2, -1]])
      return sig.convolve2d(imggray, kernel_y, mode='same')
      I_x = gradient_x(imggray)
      I_y = gradient_y(imggray)
          
      from scipy.ndimage import gaussian_filter
          
      Ixx = gaussian_filter(I_x**2, sigma=1)
      Ixy = gaussian_filter(I_y*I_x, sigma=1)
      Iyy = gaussian_filter(I_y**2, sigma=1)
          
      k = 0.05
      # determinant
      detA = Ixx * Iyy - Ixy ** 2
      # trace
      traceA = Ixx + Iyy
           
      harris_response = detA - k * traceA ** 2
          
      img_copy_for_corners = np.copy(img)
      img_copy_for_edges = np.copy(img)
      for rowindex, response in enumerate(harris_response):
           for colindex, r in enumerate(response):
               if r &gt; 0:
             # this is a corner
                   img_copy_for_corners[rowindex, colindex] = [255,0,0]
               elif r &lt; 0:
             # this is an edge
                   img_copy_for_edges[rowindex, colindex] = [0,255,0]
          
      corners = corner_peaks(harris_response)
      fig, ax = plt.subplots()
      ax.imshow(img, interpolation='nearest', cmap=plt.cm.gray)
      ax.plot(corners[:, 1], corners[:, 0], '.r', markersize=3)
  </code></pre>