title: Python ValueError in this code
body: <p>Traceback (most recent call last):</p>
  
  <pre><code>  File "project.py", line 35, in &lt;module&gt;
      cells = [np.hsplit(row,100) for row in np.vsplit(img,50)]
    File "/usr/local/lib/python2.7/site-packages/numpy/lib/shape_base.py", line 623, in vsplit
      return split(ary, indices_or_sections, 0)
    File "/usr/local/lib/python2.7/site-packages/numpy/lib/shape_base.py", line 508, in split
      'array split does not result in an equal division')
  ValueError: array split does not result in an equal division
  </code></pre>
  
  <p>code</p>
  
  <pre><code>import cv2
  import numpy as np
  
  SZ=20
  bin_n = 16 # Number of bins
  
  svm_params = dict( kernel_type = cv2.SVM_LINEAR,
                      svm_type = cv2.SVM_C_SVC,
                      C=2.67, gamma=5.383 )
  
  affine_flags = cv2.WARP_INVERSE_MAP|cv2.INTER_LINEAR
  
  def deskew(img):
      m = cv2.moments(img)
      if abs(m['mu02']) &lt; 1e-2:
          return img.copy()
      skew = m['mu11']/m['mu02']
      M = np.float32([[1, skew, -0.5*SZ*skew], [0, 1, 0]])
      img = cv2.warpAffine(img,M,(SZ, SZ),flags=affine_flags)
      return img
  
  def hog(img):
      gx = cv2.Sobel(img, cv2.CV_32F, 1, 0)
      gy = cv2.Sobel(img, cv2.CV_32F, 0, 1)
      mag, ang = cv2.cartToPolar(gx, gy)
      bins = np.int32(bin_n*ang/(2*np.pi))    # quantizing binvalues in (0...16)
      bin_cells = bins[:10,:10], bins[10:,:10], bins[:10,10:], bins[10:,10:]
      mag_cells = mag[:10,:10], mag[10:,:10], mag[:10,10:], mag[10:,10:]
      hists = [np.bincount(b.ravel(), m.ravel(), bin_n) for b, m in zip(bin_cells, mag_cells)]
      hist = np.hstack(hists)     # hist is a 64 bit vector
      return hist
  
  img = cv2.imread('digits.png',0)
  
  cells = [np.hsplit(row,100) for row in np.vsplit(img,50)]
  
  # First half is trainData, remaining is testData
  train_cells = [ i[:50] for i in cells ]
  test_cells = [ i[50:] for i in cells]
  
  ######     Now training      ########################
  
  deskewed = [map(deskew,row) for row in train_cells]
  hogdata = [map(hog,row) for row in deskewed]
  trainData = np.float32(hogdata).reshape(-1,64)
  responses = np.float32(np.repeat(np.arange(10),250)[:,np.newaxis])
  
  svm = cv2.SVM()
  svm.train(trainData,responses, params=svm_params)
  svm.save('svm_data.dat')
  
  ######     Now testing      ########################
  
  deskewed = [map(deskew,row) for row in test_cells]
  hogdata = [map(hog,row) for row in deskewed]
  testData = np.float32(hogdata).reshape(-1,bin_n*4)
  result = svm.predict_all(testData)
  
  #######   Check Accuracy   ########################
  mask = result==responses
  correct = np.count_nonzero(mask)
  print correct*100.0/result.size
  </code></pre>