Python samples adapted for Python3 compatibility

Common fixes:
- print function
- int / float division
- map, zip iterators in py3 but lists in py2

Known bugs with opencv 3.0.0
- digits.py, called via digits_video.py: opencv#4969
- gaussian_mix.py: opencv#4232
- video_v4l2.py: opencv#5474

Not working:
- letter_recog.py due to changed ml_StatModel.train() signature

samples/python2/asift.py

@@ -19,6 +19,9 @@
   Press left mouse button on a feature point to see its matching point.
 '''
 
+# Python 2/3 compatibility
+from __future__ import print_function
+
 import numpy as np
 import cv2
 
@@ -96,15 +99,15 @@ def f(p):
         ires = pool.imap(f, params)
 
     for i, (k, d) in enumerate(ires):
-        print 'affine sampling: %d / %d\r' % (i+1, len(params)),
+        print('affine sampling: %d / %d\r' % (i+1, len(params)), end='')
         keypoints.extend(k)
         descrs.extend(d)
 
-    print
+    print()
     return keypoints, np.array(descrs)
 
 if __name__ == '__main__':
-    print __doc__
+    print(__doc__)
 
     import sys, getopt
     opts, args = getopt.getopt(sys.argv[1:], '', ['feature='])
@@ -121,36 +124,36 @@ def f(p):
     detector, matcher = init_feature(feature_name)
 
     if img1 is None:
-        print 'Failed to load fn1:', fn1
+        print('Failed to load fn1:', fn1)
         sys.exit(1)
 
     if img2 is None:
-        print 'Failed to load fn2:', fn2
+        print('Failed to load fn2:', fn2)
         sys.exit(1)
 
     if detector is None:
-        print 'unknown feature:', feature_name
+        print('unknown feature:', feature_name)
         sys.exit(1)
 
-    print 'using', feature_name
+    print('using', feature_name)
 
     pool=ThreadPool(processes = cv2.getNumberOfCPUs())
     kp1, desc1 = affine_detect(detector, img1, pool=pool)
     kp2, desc2 = affine_detect(detector, img2, pool=pool)
-    print 'img1 - %d features, img2 - %d features' % (len(kp1), len(kp2))
+    print('img1 - %d features, img2 - %d features' % (len(kp1), len(kp2)))
 
     def match_and_draw(win):
         with Timer('matching'):
             raw_matches = matcher.knnMatch(desc1, trainDescriptors = desc2, k = 2) #2
         p1, p2, kp_pairs = filter_matches(kp1, kp2, raw_matches)
         if len(p1) >= 4:
             H, status = cv2.findHomography(p1, p2, cv2.RANSAC, 5.0)
-            print '%d / %d  inliers/matched' % (np.sum(status), len(status))
+            print('%d / %d  inliers/matched' % (np.sum(status), len(status)))
             # do not draw outliers (there will be a lot of them)
             kp_pairs = [kpp for kpp, flag in zip(kp_pairs, status) if flag]
         else:
             H, status = None, None
-            print '%d matches found, not enough for homography estimation' % len(p1)
+            print('%d matches found, not enough for homography estimation' % len(p1))
 
         vis = explore_match(win, img1, img2, kp_pairs, None, H)
 

samples/python2/contours.py

@@ -9,15 +9,23 @@
 A trackbar is put up which controls the contour level from -3 to 3
 '''
 
+# Python 2/3 compatibility
+from __future__ import print_function
+import sys
+PY3 = sys.version_info[0] == 3
+
+if PY3:
+    xrange = range
+
 import numpy as np
 import cv2
 
 def make_image():
     img = np.zeros((500, 500), np.uint8)
     black, white = 0, 255
     for i in xrange(6):
-        dx = (i%2)*250 - 30
-        dy = (i/2)*150
+        dx = int((i%2)*250 - 30)
+        dy = int((i/2.)*150)
 
         if i == 0:
             for j in xrange(11):
@@ -41,7 +49,7 @@ def make_image():
     return img
 
 if __name__ == '__main__':
-    print __doc__
+    print(__doc__)
 
     img = make_image()
     h, w = img.shape[:2]
@@ -52,7 +60,7 @@ def make_image():
     def update(levels):
         vis = np.zeros((h, w, 3), np.uint8)
         levels = levels - 3
-        cv2.drawContours( vis, contours, (-1, 3)[levels <= 0], (128,255,255),
+        cv2.drawContours( vis, contours, (-1, 2)[levels <= 0], (128,255,255),
             3, cv2.LINE_AA, hierarchy, abs(levels) )
         cv2.imshow('contours', vis)
     update(3)

samples/python2/digits.py

@@ -23,6 +23,10 @@
    digits.py
 '''
 
+
+# Python 2/3 compatibility
+from __future__ import print_function
+
 # built-in modules
 from multiprocessing.pool import ThreadPool
 
@@ -50,7 +54,7 @@ def split2d(img, cell_size, flatten=True):
     return cells
 
 def load_digits(fn):
-    print 'loading "%s" ...' % fn
+    print('loading "%s" ...' % fn)
     digits_img = cv2.imread(fn, 0)
     digits = split2d(digits_img, (SZ, SZ))
     labels = np.repeat(np.arange(CLASS_N), len(digits)/CLASS_N)
@@ -67,7 +71,7 @@ def deskew(img):
 
 class StatModel(object):
     def load(self, fn):
-        self.model.load(fn)
+        self.model.load(fn)  # Known bug: https://github.com/Itseez/opencv/issues/4969
     def save(self, fn):
         self.model.save(fn)
 
@@ -101,14 +105,14 @@ def predict(self, samples):
 def evaluate_model(model, digits, samples, labels):
     resp = model.predict(samples)
     err = (labels != resp).mean()
-    print 'error: %.2f %%' % (err*100)
+    print('error: %.2f %%' % (err*100))
 
     confusion = np.zeros((10, 10), np.int32)
     for i, j in zip(labels, resp):
         confusion[i, j] += 1
-    print 'confusion matrix:'
-    print confusion
-    print
+    print('confusion matrix:')
+    print(confusion)
+    print()
 
     vis = []
     for img, flag in zip(digits, resp == labels):
@@ -145,17 +149,17 @@ def preprocess_hog(digits):
 
 
 if __name__ == '__main__':
-    print __doc__
+    print(__doc__)
 
     digits, labels = load_digits(DIGITS_FN)
 
-    print 'preprocessing...'
+    print('preprocessing...')
     # shuffle digits
     rand = np.random.RandomState(321)
     shuffle = rand.permutation(len(digits))
     digits, labels = digits[shuffle], labels[shuffle]
 
-    digits2 = map(deskew, digits)
+    digits2 = list(map(deskew, digits))
     samples = preprocess_hog(digits2)
 
     train_n = int(0.9*len(samples))
@@ -165,18 +169,18 @@ def preprocess_hog(digits):
     labels_train, labels_test = np.split(labels, [train_n])
 
 
-    print 'training KNearest...'
+    print('training KNearest...')
     model = KNearest(k=4)
     model.train(samples_train, labels_train)
     vis = evaluate_model(model, digits_test, samples_test, labels_test)
     cv2.imshow('KNearest test', vis)
 
-    print 'training SVM...'
+    print('training SVM...')
     model = SVM(C=2.67, gamma=5.383)
     model.train(samples_train, labels_train)
     vis = evaluate_model(model, digits_test, samples_test, labels_test)
     cv2.imshow('SVM test', vis)
-    print 'saving SVM as "digits_svm.dat"...'
+    print('saving SVM as "digits_svm.dat"...')
     model.save('digits_svm.dat')
 
     cv2.waitKey(0)

samples/python2/digits_adjust.py

@@ -13,6 +13,14 @@
 
 '''
 
+# Python 2/3 compatibility
+from __future__ import print_function
+import sys
+PY3 = sys.version_info[0] == 3
+
+if PY3:
+    xrange = range
+
 import numpy as np
 import cv2
 from multiprocessing.pool import ThreadPool
@@ -33,10 +41,10 @@ def f(i):
         model.train(train_samples, train_labels)
         resp = model.predict(test_samples)
         score = (resp != test_labels).mean()
-        print ".",
+        print(".", end='')
         return score
     if pool is None:
-        scores = map(f, xrange(kfold))
+        scores = list(map(f, xrange(kfold)))
     else:
         scores = pool.map(f, xrange(kfold))
     return np.mean(scores)
@@ -50,7 +58,7 @@ def preprocess(self):
         digits, labels = load_digits(DIGITS_FN)
         shuffle = np.random.permutation(len(digits))
         digits, labels = digits[shuffle], labels[shuffle]
-        digits2 = map(deskew, digits)
+        digits2 = list(map(deskew, digits))
         samples = preprocess_hog(digits2)
         return samples, labels
 
@@ -68,7 +76,7 @@ def adjust_SVM(self):
         scores = np.zeros((len(Cs), len(gammas)))
         scores[:] = np.nan
 
-        print 'adjusting SVM (may take a long time) ...'
+        print('adjusting SVM (may take a long time) ...')
         def f(job):
             i, j = job
             samples, labels = self.get_dataset()
@@ -79,20 +87,21 @@ def f(job):
         ires = self.run_jobs(f, np.ndindex(*scores.shape))
         for count, (i, j, score) in enumerate(ires):
             scores[i, j] = score
-            print '%d / %d (best error: %.2f %%, last: %.2f %%)' % (count+1, scores.size, np.nanmin(scores)*100, score*100)
-        print scores
+            print('%d / %d (best error: %.2f %%, last: %.2f %%)' %
+                  (count+1, scores.size, np.nanmin(scores)*100, score*100))
+        print(scores)
 
-        print 'writing score table to "svm_scores.npz"'
+        print('writing score table to "svm_scores.npz"')
         np.savez('svm_scores.npz', scores=scores, Cs=Cs, gammas=gammas)
 
         i, j = np.unravel_index(scores.argmin(), scores.shape)
         best_params = dict(C = Cs[i], gamma=gammas[j])
-        print 'best params:', best_params
-        print 'best error: %.2f %%' % (scores.min()*100)
+        print('best params:', best_params)
+        print('best error: %.2f %%' % (scores.min()*100))
         return best_params
 
     def adjust_KNearest(self):
-        print 'adjusting KNearest ...'
+        print('adjusting KNearest ...')
         def f(k):
             samples, labels = self.get_dataset()
             err = cross_validate(KNearest, dict(k=k), samples, labels)
@@ -101,24 +110,24 @@ def f(k):
         for k, err in self.run_jobs(f, xrange(1, 9)):
             if err < best_err:
                 best_err, best_k = err, k
-            print 'k = %d, error: %.2f %%' % (k, err*100)
+            print('k = %d, error: %.2f %%' % (k, err*100))
         best_params = dict(k=best_k)
-        print 'best params:', best_params, 'err: %.2f' % (best_err*100)
+        print('best params:', best_params, 'err: %.2f' % (best_err*100))
         return best_params
 
 
 if __name__ == '__main__':
     import getopt
     import sys
 
-    print __doc__
+    print(__doc__)
 
     args, _ = getopt.getopt(sys.argv[1:], '', ['model='])
     args = dict(args)
     args.setdefault('--model', 'svm')
     args.setdefault('--env', '')
     if args['--model'] not in ['svm', 'knearest']:
-        print 'unknown model "%s"' % args['--model']
+        print('unknown model "%s"' % args['--model'])
         sys.exit(1)
 
     t = clock()
@@ -127,4 +136,4 @@ def f(k):
         app.adjust_KNearest()
     else:
         app.adjust_SVM()
-    print 'work time: %f s' % (clock() - t)
+    print('work time: %f s' % (clock() - t))

samples/python2/digits_video.py

@@ -1,5 +1,8 @@
 #!/usr/bin/env python
 
+# Python 2/3 compatibility
+from __future__ import print_function
+
 import numpy as np
 import cv2
 
@@ -22,7 +25,7 @@ def main():
 
     classifier_fn = 'digits_svm.dat'
     if not os.path.exists(classifier_fn):
-        print '"%s" not found, run digits.py first' % classifier_fn
+        print('"%s" not found, run digits.py first' % classifier_fn)
         return
     model = SVM()
     model.load(classifier_fn)

samples/python2/edge.py

@@ -14,6 +14,7 @@
 from __future__ import print_function
 
 import cv2
+import numpy as np
 
 # relative module
 import video
@@ -45,7 +46,7 @@ def nothing(*arg):
         thrs2 = cv2.getTrackbarPos('thrs2', 'edge')
         edge = cv2.Canny(gray, thrs1, thrs2, apertureSize=5)
         vis = img.copy()
-        vis /= 2
+        vis = np.uint8(vis/2.)
         vis[edge != 0] = (0, 255, 0)
         cv2.imshow('edge', vis)
         ch = cv2.waitKey(5) & 0xFF

samples/python2/feature_homography.py

@@ -22,6 +22,9 @@
 Select a textured planar object to track by drawing a box with a mouse.
 '''
 
+# Python 2/3 compatibility
+from __future__ import print_function
+
 import numpy as np
 import cv2
 
@@ -84,7 +87,7 @@ def run(self):
 
 
 if __name__ == '__main__':
-    print __doc__
+    print(__doc__)
 
     import sys
     try: