Fix print statement for Python 3

doc/examples/applications/plot_geometric.py

@@ -7,6 +7,8 @@ In this example, we will see how to use geometric transformations in the context
 of image processing.
 """
 
+from __future__ import print_function
+
 import math
 import numpy as np
 import matplotlib.pyplot as plt
@@ -31,7 +33,7 @@ First we create a transformation using explicit parameters:
 
 tform = tf.SimilarityTransform(scale=1, rotation=math.pi / 2,
                                translation=(0, 1))
-print tform._matrix
+print(tform._matrix)
 
 """
 Alternatively you can define a transformation by the transformation matrix
@@ -49,8 +51,8 @@ systems:
 """
 
 coord = [1, 0]
-print tform2(coord)
-print tform2.inverse(tform(coord))
+print(tform2(coord))
+print(tform2.inverse(tform(coord)))
 
 """
 Image warping

doc/examples/plot_gabor.py

@@ -12,6 +12,8 @@ kernels. The mean and variance of the filtered images are then used as features
 for classification, which is based on the least squared error for simplicity.
 
 """
+from __future__ import print_function
+
 import matplotlib
 import matplotlib.pyplot as plt
 import numpy as np
@@ -69,19 +71,19 @@ ref_feats[0, :, :] = compute_feats(brick, kernels)
 ref_feats[1, :, :] = compute_feats(grass, kernels)
 ref_feats[2, :, :] = compute_feats(wall, kernels)
 
-print 'Rotated images matched against references using Gabor filter banks:'
+print('Rotated images matched against references using Gabor filter banks:')
 
-print 'original: brick, rotated: 30deg, match result:',
+print('original: brick, rotated: 30deg, match result:', end='')
 feats = compute_feats(nd.rotate(brick, angle=190, reshape=False), kernels)
-print image_names[match(feats, ref_feats)]
+print(image_names[match(feats, ref_feats)])
 
-print 'original: brick, rotated: 70deg, match result:',
+print('original: brick, rotated: 70deg, match result:', end='')
 feats = compute_feats(nd.rotate(brick, angle=70, reshape=False), kernels)
-print image_names[match(feats, ref_feats)]
+print(image_names[match(feats, ref_feats)])
 
-print 'original: grass, rotated: 145deg, match result:',
+print('original: grass, rotated: 145deg, match result:', end='')
 feats = compute_feats(nd.rotate(grass, angle=145, reshape=False), kernels)
-print image_names[match(feats, ref_feats)]
+print(image_names[match(feats, ref_feats)])
 
 
 def power(image, kernel):

doc/examples/plot_local_binary_pattern.py

@@ -9,9 +9,12 @@ textures. For simplicity the histogram distributions are then tested against
 each other using the Kullback-Leibler-Divergence.
 
 """
+from __future__ import print_function
+
 import numpy as np
 import matplotlib
 import matplotlib.pyplot as plt
+
 from skimage.transform import rotate
 from skimage.feature import local_binary_pattern
 from skimage import data
@@ -57,13 +60,13 @@ refs = {
 }
 
 # classify rotated textures
-print 'Rotated images matched against references using LBP:'
-print 'original: brick, rotated: 30deg, match result:',
-print match(refs, rotate(brick, angle=30, resize=False))
-print 'original: brick, rotated: 70deg, match result:',
-print match(refs, rotate(brick, angle=70, resize=False))
-print 'original: grass, rotated: 145deg, match result:',
-print match(refs, rotate(grass, angle=145, resize=False))
+print('Rotated images matched against references using LBP:')
+print('original: brick, rotated: 30deg, match result:', end='')
+print(match(refs, rotate(brick, angle=30, resize=False)))
+print('original: brick, rotated: 70deg, match result:', end='')
+print(match(refs, rotate(brick, angle=70, resize=False)))
+print('original: grass, rotated: 145deg, match result:', end='')
+print(match(refs, rotate(grass, angle=145, resize=False)))
 
 # plot histograms of LBP of textures
 fig, ((ax1, ax2, ax3), (ax4, ax5, ax6)) = plt.subplots(nrows=2, ncols=3,

doc/examples/plot_polygon.py

@@ -45,7 +45,7 @@ for _ in range(5):
 # approximate subdivided polygon with Douglas-Peucker algorithm
 appr_hand = approximate_polygon(new_hand, tolerance=0.02)
 
-print "Number of coordinates:", len(hand), len(new_hand), len(appr_hand)
+print("Number of coordinates:", len(hand), len(new_hand), len(appr_hand))
 
 fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(9, 4))
 
@@ -70,7 +70,7 @@ for contour in find_contours(img, 0):
     ax2.plot(coords[:, 1], coords[:, 0], '-r', linewidth=2)
     coords2 = approximate_polygon(contour, tolerance=39.5)
     ax2.plot(coords2[:, 1], coords2[:, 0], '-g', linewidth=2)
-    print "Number of coordinates:", len(contour), len(coords), len(coords2)
+    print("Number of coordinates:", len(contour), len(coords), len(coords2))
 
 ax2.axis((0, 800, 0, 800))
 

doc/examples/plot_segmentations.py

@@ -58,6 +58,7 @@ of Quickshift, while ``n_segments`` chooses the number of centers for kmeans.
     Pascal Fua, and Sabine Suesstrunk, SLIC Superpixels Compared to
     State-of-the-art Superpixel Methods, TPAMI, May 2012.
 """
+from __future__ import print_function
 
 import matplotlib.pyplot as plt
 import numpy as np