old_contents: from pylab import *
  from numpy import *
  from PIL import Image
  
  # If you have PCV installed, these imports should work
  from PCV.geometry import homography, warp
  from PCV.localdescriptors import sift
  
  """
  This is the panorama example from section 3.3.
  """
  
  # set paths to data folder
  featname = ['../data/Univ'+str(i+1)+'.sift' for i in range(5)] 
  imname = ['../data/Univ'+str(i+1)+'.jpg' for i in range(5)]
  
  # extract features and match
  l = {}
  d = {}
  for i in range(5): 
      # sift.process_image(imname[i],featname[i])
      l[i],d[i] = sift.read_features_from_file(featname[i])
  
  matches = {}
  for i in range(4):
      matches[i] = sift.match(d[i+1],d[i])
  
  # visualize the matches (Figure 3-11 in the book)
  for i in range(4):
      im1 = array(Image.open(imname[i]))
      im2 = array(Image.open(imname[i+1]))
      figure()
      sift.plot_matches(im2,im1,l[i+1],l[i],matches[i],show_below=True)
  
  
  # function to convert the matches to hom. points
  def convert_points(j):
      ndx = matches[j].nonzero()[0]
      fp = homography.make_homog(l[j+1][ndx,:2].T) 
      ndx2 = [int(matches[j][i]) for i in ndx]
      tp = homography.make_homog(l[j][ndx2,:2].T) 
      
      # switch x and y - TODO this should move elsewhere
      fp = vstack([fp[1],fp[0],fp[2]])
      tp = vstack([tp[1],tp[0],tp[2]])
      return fp,tp
  
  
  # estimate the homographies
  model = homography.RansacModel() 
  
  fp,tp = convert_points(1)
  H_12 = homography.H_from_ransac(fp,tp,model)[0] #im 1 to 2 
  
  fp,tp = convert_points(0)
  H_01 = homography.H_from_ransac(fp,tp,model)[0] #im 0 to 1 
  
  tp,fp = convert_points(2) #NB: reverse order
  H_32 = homography.H_from_ransac(fp,tp,model)[0] #im 3 to 2 
  
  tp,fp = convert_points(3) #NB: reverse order
  H_43 = homography.H_from_ransac(fp,tp,model)[0] #im 4 to 3    
  
  
  # warp the images
  delta = 2000 # for padding and translation
  
  im1 = array(Image.open(imname[1]), "uint8")
  im2 = array(Image.open(imname[2]), "uint8")
  im_12 = warp.panorama(H_12,im1,im2,delta,delta)
  
  im1 = array(Image.open(imname[0]), "f")
  im_02 = warp.panorama(dot(H_12,H_01),im1,im_12,delta,delta)
  
  im1 = array(Image.open(imname[3]), "f")
  im_32 = warp.panorama(H_32,im1,im_02,delta,delta)
  
  im1 = array(Image.open(imname[4]), "f")
  im_42 = warp.panorama(dot(H_32,H_43),im1,im_32,delta,2*delta)
  
  
  figure()
  imshow(array(im_42, "uint8"))
  axis('off')
  show()
  
  
new_contents: from pylab import *
  from numpy import *
  from PIL import Image
  
  # If you have PCV installed, these imports should work
  from PCV.geometry import homography, warp
  from PCV.localdescriptors import sift
  
  """
  This is the panorama example from section 3.3.
  """
  
  # set paths to data folder
  featname = ['../data/Univ'+str(i+1)+'.sift' for i in range(5)] 
  imname = ['../data/Univ'+str(i+1)+'.jpg' for i in range(5)]
  
  # extract features and match
  l = {}
  d = {}
  for i in range(5): 
      sift.process_image(imname[i],featname[i])
      l[i],d[i] = sift.read_features_from_file(featname[i])
  
  matches = {}
  for i in range(4):
      matches[i] = sift.match(d[i+1],d[i])
  
  # visualize the matches (Figure 3-11 in the book)
  for i in range(4):
      im1 = array(Image.open(imname[i]))
      im2 = array(Image.open(imname[i+1]))
      figure()
      sift.plot_matches(im2,im1,l[i+1],l[i],matches[i],show_below=True)
  
  
  # function to convert the matches to hom. points
  def convert_points(j):
      ndx = matches[j].nonzero()[0]
      fp = homography.make_homog(l[j+1][ndx,:2].T) 
      ndx2 = [int(matches[j][i]) for i in ndx]
      tp = homography.make_homog(l[j][ndx2,:2].T) 
      
      # switch x and y - TODO this should move elsewhere
      fp = vstack([fp[1],fp[0],fp[2]])
      tp = vstack([tp[1],tp[0],tp[2]])
      return fp,tp
  
  
  # estimate the homographies
  model = homography.RansacModel() 
  
  fp,tp = convert_points(1)
  H_12 = homography.H_from_ransac(fp,tp,model)[0] #im 1 to 2 
  
  fp,tp = convert_points(0)
  H_01 = homography.H_from_ransac(fp,tp,model)[0] #im 0 to 1 
  
  tp,fp = convert_points(2) #NB: reverse order
  H_32 = homography.H_from_ransac(fp,tp,model)[0] #im 3 to 2 
  
  tp,fp = convert_points(3) #NB: reverse order
  H_43 = homography.H_from_ransac(fp,tp,model)[0] #im 4 to 3    
  
  
  # warp the images
  delta = 2000 # for padding and translation
  
  im1 = array(Image.open(imname[1]), "uint8")
  im2 = array(Image.open(imname[2]), "uint8")
  im_12 = warp.panorama(H_12,im1,im2,delta,delta)
  
  im1 = array(Image.open(imname[0]), "f")
  im_02 = warp.panorama(dot(H_12,H_01),im1,im_12,delta,delta)
  
  im1 = array(Image.open(imname[3]), "f")
  im_32 = warp.panorama(H_32,im1,im_02,delta,delta)
  
  im1 = array(Image.open(imname[4]), "f")
  im_42 = warp.panorama(dot(H_32,H_43),im1,im_32,delta,2*delta)
  
  
  figure()
  imshow(array(im_42, "uint8"))
  axis('off')
  show()
  
  
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