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: https://github.com/Itseez/opencv/issues/4969
- gaussian_mix.py: https://github.com/Itseez/opencv/pull/4232
- video_v4l2.py: https://github.com/Itseez/opencv/pull/5474

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

samples/python2/asift.py

@@ -19,6 +19,9 @@ USAGE
   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 affine_detect(detector, img, mask=None, pool=None):
         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='])
@@ -121,23 +124,23 @@ if __name__ == '__main__':
     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'):
@@ -145,12 +148,12 @@ if __name__ == '__main__':
         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

@@ -9,6 +9,14 @@ Usage:
 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
 
@@ -16,8 +24,8 @@ 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 @@ if __name__ == '__main__':
     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 @@ Usage:
    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 @@ class SVM(StatModel):
 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 @@ if __name__ == '__main__':
     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 @@ Usage:
 
 '''
 
+# 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 cross_validate(model_class, params, samples, labels, kfold = 3, pool = None)
         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 @@ class App(object):
         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 @@ class App(object):
         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 @@ class App(object):
         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)
@@ -101,9 +110,9 @@ class App(object):
         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
 
 
@@ -111,14 +120,14 @@ 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 @@ if __name__ == '__main__':
         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

 #!/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 @@ Usage:
 from __future__ import print_function
 
 import cv2
+import numpy as np
 
 # relative module
 import video
@@ -45,7 +46,7 @@ if __name__ == '__main__':
         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 @@ Keys:
 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 @@ class App:
 
 
 if __name__ == '__main__':
-    print __doc__
+    print(__doc__)
 
     import sys
     try:

samples/python2/find_obj.py

@@ -14,6 +14,9 @@ USAGE
   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
 from common import anorm, getsize
@@ -82,8 +85,10 @@ def explore_match(win, img1, img2, kp_pairs, status = None, H = None):
 
     if status is None:
         status = np.ones(len(kp_pairs), np.bool_)
-    p1 = np.int32([kpp[0].pt for kpp in kp_pairs])
-    p2 = np.int32([kpp[1].pt for kpp in kp_pairs]) + (w1, 0)
+    p1, p2 = [], []  # python 2 / python 3 change of zip unpacking
+    for kpp in kp_pairs:
+        p1.append(np.int32(kpp[0].pt))
+        p2.append(np.int32(np.array(kpp[1].pt) + [w1, 0]))
 
     green = (0, 255, 0)
     red = (0, 0, 255)
@@ -133,7 +138,7 @@ def explore_match(win, img1, img2, kp_pairs, status = None, H = None):
 
 
 if __name__ == '__main__':
-    print __doc__
+    print(__doc__)
 
     import sys, getopt
     opts, args = getopt.getopt(sys.argv[1:], '', ['feature='])
@@ -150,33 +155,33 @@ if __name__ == '__main__':
     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)
 
     kp1, desc1 = detector.detectAndCompute(img1, None)
     kp2, desc2 = detector.detectAndCompute(img2, None)
-    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):
-        print 'matching...'
+        print('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)))
         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, status, H)
 

samples/python2/gaussian_mix.py

 #!/usr/bin/env python
 
+# 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
 from numpy import random
 import cv2
@@ -30,19 +38,21 @@ if __name__ == '__main__':
     cluster_n = 5
     img_size = 512
 
-    print 'press any key to update distributions, ESC - exit\n'
+    print('press any key to update distributions, ESC - exit\n')
 
     while True:
-        print 'sampling distributions...'
+        print('sampling distributions...')
         points, ref_distrs = make_gaussians(cluster_n, img_size)
 
-        print 'EM (opencv) ...'
-        em = cv2.EM(cluster_n, cv2.EM_COV_MAT_GENERIC)
-        em.train(points)
-        means = em.getMat('means')
-        covs = em.getMatVector('covs')
+        print('EM (opencv) ...')
+        em = cv2.ml.EM_create()
+        em.setClustersNumber(cluster_n)
+        em.setCovarianceMatrixType(cv2.ml.EM_COV_MAT_GENERIC)
+        em.trainEM(points)
+        means = em.getMeans()
+        covs = em.getCovs()  # Known bug: https://github.com/Itseez/opencv/pull/4232
         found_distrs = zip(means, covs)
-        print 'ready!\n'
+        print('ready!\n')
 
         img = np.zeros((img_size, img_size, 3), np.uint8)
         for x, y in np.int32(points):

samples/python2/hist.py

@@ -15,6 +15,9 @@ Usage : python hist.py <image_file>
 Abid Rahman 3/14/12 debug Gary Bradski
 '''
 
+# Python 2/3 compatibility
+from __future__ import print_function
+
 import cv2
 import numpy as np
 
@@ -38,8 +41,8 @@ def hist_curve(im):
 def hist_lines(im):
     h = np.zeros((300,256,3))
     if len(im.shape)!=2:
-        print "hist_lines applicable only for grayscale images"
-        #print "so converting image to grayscale for representation"
+        print("hist_lines applicable only for grayscale images")
+        #print("so converting image to grayscale for representation"
         im = cv2.cvtColor(im,cv2.COLOR_BGR2GRAY)
     hist_item = cv2.calcHist([im],[0],None,[256],[0,256])
     cv2.normalize(hist_item,hist_item,0,255,cv2.NORM_MINMAX)
@@ -58,18 +61,18 @@ if __name__ == '__main__':
         fname = sys.argv[1]
     else :
         fname = '../data/lena.jpg'
-        print "usage : python hist.py <image_file>"
+        print("usage : python hist.py <image_file>")
 
     im = cv2.imread(fname)
 
     if im is None:
-        print 'Failed to load image file:', fname
+        print('Failed to load image file:', fname)
         sys.exit(1)
 
     gray = cv2.cvtColor(im,cv2.COLOR_BGR2GRAY)
 
 
-    print ''' Histogram plotting \n
+    print(''' Histogram plotting \n
     Keymap :\n
     a - show histogram for color image in curve mode \n
     b - show histogram in bin mode \n
@@ -77,7 +80,7 @@ if __name__ == '__main__':
     d - show histogram for color image in curve mode \n
     e - show histogram for a normalized image in curve mode \n
     Esc - exit \n
-    '''
+    ''')
 
     cv2.imshow('image',im)
     while True:
@@ -86,31 +89,31 @@ if __name__ == '__main__':
             curve = hist_curve(im)
             cv2.imshow('histogram',curve)
             cv2.imshow('image',im)
-            print 'a'
+            print('a')
         elif k == ord('b'):
-            print 'b'
+            print('b')
             lines = hist_lines(im)
             cv2.imshow('histogram',lines)
             cv2.imshow('image',gray)
         elif k == ord('c'):
-            print 'c'
+            print('c')
             equ = cv2.equalizeHist(gray)
             lines = hist_lines(equ)
             cv2.imshow('histogram',lines)
             cv2.imshow('image',equ)
         elif k == ord('d'):
-            print 'd'
+            print('d')
             curve = hist_curve(gray)
             cv2.imshow('histogram',curve)
             cv2.imshow('image',gray)
         elif k == ord('e'):
-            print 'e'
-            norm = cv2.normalize(gray,alpha = 0,beta = 255,norm_type = cv2.NORM_MINMAX)
+            print('e')
+            norm = cv2.normalize(gray, gray, alpha = 0,beta = 255,norm_type = cv2.NORM_MINMAX)
             lines = hist_lines(norm)
             cv2.imshow('histogram',lines)
             cv2.imshow('image',norm)
         elif k == 27:
-            print 'ESC'
+            print('ESC')
             cv2.destroyAllWindows()
             break
     cv2.destroyAllWindows()

samples/python2/kmeans.py

@@ -10,6 +10,9 @@ Keyboard shortcuts:
    space - generate new distribution
 '''
 
+# Python 2/3 compatibility
+from __future__ import print_function
+
 import numpy as np
 import cv2
 
@@ -19,7 +22,7 @@ if __name__ == '__main__':
     cluster_n = 5
     img_size = 512
 
-    print __doc__
+    print(__doc__)
 
     # generating bright palette
     colors = np.zeros((1, cluster_n, 3), np.uint8)
@@ -28,7 +31,7 @@ if __name__ == '__main__':
     colors = cv2.cvtColor(colors, cv2.COLOR_HSV2BGR)[0]
 
     while True:
-        print 'sampling distributions...'
+        print('sampling distributions...')
         points, _ = make_gaussians(cluster_n, img_size)
 
         term_crit = (cv2.TERM_CRITERIA_EPS, 30, 0.1)
@@ -36,7 +39,8 @@ if __name__ == '__main__':
 
         img = np.zeros((img_size, img_size, 3), np.uint8)
         for (x, y), label in zip(np.int32(points), labels.ravel()):
-            c = map(int, colors[label])
+            c = list(map(int, colors[label]))
+
             cv2.circle(img, (x, y), 1, c, -1)
 
         cv2.imshow('gaussian mixture', img)

samples/python2/lappyr.py

@@ -64,7 +64,7 @@ if __name__ == '__main__':
 
         pyr = build_lappyr(frame, leveln)
         for i in xrange(leveln):
-            v = cv2.getTrackbarPos('%d'%i, 'level control') / 5
+            v = int(cv2.getTrackbarPos('%d'%i, 'level control') / 5)
             pyr[i] *= v
         res = merge_lappyr(pyr)
 

samples/python2/letter_recog.py

@@ -25,6 +25,9 @@ USAGE:
   Models: RTrees, KNearest, Boost, SVM, MLP
 '''
 
+# Python 2/3 compatibility
+from __future__ import print_function
+
 import numpy as np
 import cv2
 
@@ -58,22 +61,22 @@ class LetterStatModel(object):
 
 class RTrees(LetterStatModel):
     def __init__(self):
-        self.model = cv2.RTrees()
+        self.model = cv2.ml.RTrees_create()
 
     def train(self, samples, responses):
         sample_n, var_n = samples.shape
-        var_types = np.array([cv2.CV_VAR_NUMERICAL] * var_n + [cv2.CV_VAR_CATEGORICAL], np.uint8)
+        var_types = np.array([cv2.ml.VAR_NUMERICAL] * var_n + [cv2.ml.VAR_CATEGORICAL], np.uint8)
         #CvRTParams(10,10,0,false,15,0,true,4,100,0.01f,CV_TERMCRIT_ITER));
         params = dict(max_depth=10 )
-        self.model.train(samples, cv2.CV_ROW_SAMPLE, responses, varType = var_types, params = params)
+        self.model.train(samples, cv2.ml.ROW_SAMPLE, responses, varType = var_types, params = params)
 
     def predict(self, samples):
-        return np.float32( [self.model.predict(s) for s in samples] )
+        return [self.model.predict(s) for s in samples]
 
 
 class KNearest(LetterStatModel):
     def __init__(self):
-        self.model = cv2.KNearest()
+        self.model = cv2.ml.KNearest_create()
 
     def train(self, samples, responses):
         self.model.train(samples, responses)
@@ -85,16 +88,16 @@ class KNearest(LetterStatModel):
 
 class Boost(LetterStatModel):
     def __init__(self):
-        self.model = cv2.Boost()
+        self.model = cv2.ml.Boost_create()
 
     def train(self, samples, responses):
         sample_n, var_n = samples.shape
         new_samples = self.unroll_samples(samples)
         new_responses = self.unroll_responses(responses)
-        var_types = np.array([cv2.CV_VAR_NUMERICAL] * var_n + [cv2.CV_VAR_CATEGORICAL, cv2.CV_VAR_CATEGORICAL], np.uint8)
+        var_types = np.array([cv2.ml.VAR_NUMERICAL] * var_n + [cv2.ml.VAR_CATEGORICAL, cv2.ml.VAR_CATEGORICAL], np.uint8)
         #CvBoostParams(CvBoost::REAL, 100, 0.95, 5, false, 0 )
         params = dict(max_depth=5) #, use_surrogates=False)
-        self.model.train(new_samples, cv2.CV_ROW_SAMPLE, new_responses, varType = var_types, params=params)
+        self.model.train(new_samples, cv2.ml.ROW_SAMPLE, new_responses, varType = var_types, params=params)
 
     def predict(self, samples):
         new_samples = self.unroll_samples(samples)
@@ -105,11 +108,11 @@ class Boost(LetterStatModel):
 
 class SVM(LetterStatModel):
     def __init__(self):
-        self.model = cv2.SVM()
+        self.model = cv2.ml.SVM_create()
 
     def train(self, samples, responses):
-        params = dict( kernel_type = cv2.SVM_LINEAR,
-                       svm_type = cv2.SVM_C_SVC,
+        params = dict( kernel_type = cv2.ml.SVM_LINEAR,
+                       svm_type = cv2.ml.SVM_C_SVC,
                        C = 1 )
         self.model.train(samples, responses, params = params)
 
@@ -119,7 +122,7 @@ class SVM(LetterStatModel):
 
 class MLP(LetterStatModel):
     def __init__(self):
-        self.model = cv2.ANN_MLP()
+        self.model = cv2.ml.ANN_MLP_create()
 
     def train(self, samples, responses):
         sample_n, var_n = samples.shape
@@ -130,7 +133,7 @@ class MLP(LetterStatModel):
 
         # CvANN_MLP_TrainParams::BACKPROP,0.001
         params = dict( term_crit = (cv2.TERM_CRITERIA_COUNT, 300, 0.01),
-                       train_method = cv2.ANN_MLP_TRAIN_PARAMS_BACKPROP,
+                       train_method = cv2.ml.ANN_MLP_TRAIN_PARAMS_BACKPROP,
                        bp_dw_scale = 0.001,
                        bp_moment_scale = 0.0 )
         self.model.train(samples, np.float32(new_responses), None, params = params)
@@ -144,7 +147,7 @@ if __name__ == '__main__':
     import getopt
     import sys
 
-    print  __doc__
+    print(__doc__)
 
     models = [RTrees, KNearest, Boost, SVM, MLP] # NBayes
     models = dict( [(cls.__name__.lower(), cls) for cls in models] )
@@ -155,7 +158,7 @@ if __name__ == '__main__':
     args.setdefault('--model', 'rtrees')
     args.setdefault('--data', '../data/letter-recognition.data')
 
-    print 'loading data %s ...' % args['--data']
+    print('loading data %s ...' % args['--data'])
     samples, responses = load_base(args['--data'])
     Model = models[args['--model']]
     model = Model()
@@ -163,20 +166,20 @@ if __name__ == '__main__':
     train_n = int(len(samples)*model.train_ratio)
     if '--load' in args:
         fn = args['--load']
-        print 'loading model from %s ...' % fn
+        print('loading model from %s ...' % fn)
         model.load(fn)
     else:
-        print 'training %s ...' % Model.__name__
+        print('training %s ...' % Model.__name__)
         model.train(samples[:train_n], responses[:train_n])
 
-    print 'testing...'
+    print('testing...')
     train_rate = np.mean(model.predict(samples[:train_n]) == responses[:train_n])
     test_rate  = np.mean(model.predict(samples[train_n:]) == responses[train_n:])
 
-    print 'train rate: %f  test rate: %f' % (train_rate*100, test_rate*100)
+    print('train rate: %f  test rate: %f' % (train_rate*100, test_rate*100))
 
     if '--save' in args:
         fn = args['--save']
-        print 'saving model to %s ...' % fn
+        print('saving model to %s ...' % fn)
         model.save(fn)
     cv2.destroyAllWindows()

samples/python2/mouse_and_match.py

 #!/usr/bin/env python
 '''
-mouse_and_match.py [-i path | --input path: default ./]
+mouse_and_match.py [-i path | --input path: default ../data/]
 
 Demonstrate using a mouse to interact with an image:
  Read in the images in a directory one by one
  Allow the user to select parts of an image with a mouse
  When they let go of the mouse, it correlates (using matchTemplate) that patch with the image.
+
+ SPACE for next image
  ESC to exit
 '''
 
@@ -54,8 +56,10 @@ def onmouse(event, x, y, flags, param):
             drag_start = None
 
 if __name__ == '__main__':
+    print(__doc__)
+
     parser = argparse.ArgumentParser(description='Demonstrate mouse interaction with images')
-    parser.add_argument("-i","--input", default='./', help="Input directory.")
+    parser.add_argument("-i","--input", default='../data/', help="Input directory.")
     args = parser.parse_args()
     path = args.input
 
@@ -68,7 +72,7 @@ if __name__ == '__main__':
             print(infile)
 
             img=cv2.imread(infile,1)
-            if img == None:
+            if img is None:
                 continue
             sel = (0,0,0,0)
             drag_start = None

samples/python2/opt_flow.py

 #!/usr/bin/env python
 
+# Python 2/3 compatibility
+from __future__ import print_function
+
 import numpy as np
 import cv2
 import video
@@ -15,7 +18,7 @@ Keys:
 
 def draw_flow(img, flow, step=16):
     h, w = img.shape[:2]
-    y, x = np.mgrid[step/2:h:step, step/2:w:step].reshape(2,-1)
+    y, x = np.mgrid[step/2:h:step, step/2:w:step].reshape(2,-1).astype(int)
     fx, fy = flow[y,x].T
     lines = np.vstack([x, y, x+fx, y+fy]).T.reshape(-1, 2, 2)
     lines = np.int32(lines + 0.5)
@@ -47,7 +50,7 @@ def warp_flow(img, flow):
 
 if __name__ == '__main__':
     import sys
-    print help_message
+    print(help_message)
     try:
         fn = sys.argv[1]
     except:
@@ -78,10 +81,10 @@ if __name__ == '__main__':
             break
         if ch == ord('1'):
             show_hsv = not show_hsv
-            print 'HSV flow visualization is', ['off', 'on'][show_hsv]
+            print('HSV flow visualization is', ['off', 'on'][show_hsv])
         if ch == ord('2'):
             show_glitch = not show_glitch
             if show_glitch:
                 cur_glitch = img.copy()
-            print 'glitch is', ['off', 'on'][show_glitch]
+            print('glitch is', ['off', 'on'][show_glitch])
     cv2.destroyAllWindows()

samples/python2/peopledetect.py

@@ -35,7 +35,9 @@ if __name__ == '__main__':
     hog = cv2.HOGDescriptor()
     hog.setSVMDetector( cv2.HOGDescriptor_getDefaultPeopleDetector() )
 
-    for fn in it.chain(*map(glob, sys.argv[1:])):
+
+    default = ['../data/basketball2.png '] if len(sys.argv[1:]) == 0 else []
+    for fn in it.chain(*map(glob, default + sys.argv[1:])):
         print(fn, ' - ',)
         try:
             img = cv2.imread(fn)

samples/python2/plane_ar.py

@@ -22,6 +22,9 @@ Select a textured planar object to track by drawing a box with a mouse.
 Use 'focal' slider to adjust to camera focal length for proper video augmentation.
 '''
 
+# Python 2/3 compatibility
+from __future__ import print_function
+
 import numpy as np
 import cv2
 import video
@@ -97,7 +100,7 @@ class App:
 
 
 if __name__ == '__main__':
-    print __doc__
+    print(__doc__)
 
     import sys
     try:

samples/python2/plane_tracker.py

@@ -21,6 +21,14 @@ Keys:
 Select a textured planar object to track by drawing a box with a mouse.
 '''
 
+# 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
 
@@ -64,6 +72,7 @@ class PlaneTracker:
         self.detector = cv2.ORB_create( nfeatures = 1000 )
         self.matcher = cv2.FlannBasedMatcher(flann_params, {})  # bug : need to pass empty dict (#1329)
         self.targets = []
+        self.frame_points = []
 
     def add_target(self, image, rect, data=None):
         '''Add a new tracking target.'''
@@ -87,8 +96,8 @@ class PlaneTracker:
 
     def track(self, frame):
         '''Returns a list of detected TrackedTarget objects'''
-        frame_points, frame_descrs = self.detect_features(frame)
-        if len(frame_points) < MIN_MATCH_COUNT:
+        self.frame_points, frame_descrs = self.detect_features(frame)
+        if len(self.frame_points) < MIN_MATCH_COUNT:
             return []
         matches = self.matcher.knnMatch(frame_descrs, k = 2)
         matches = [m[0] for m in matches if len(m) == 2 and m[0].distance < m[1].distance * 0.75]
@@ -103,7 +112,7 @@ class PlaneTracker:
                 continue
             target = self.targets[imgIdx]
             p0 = [target.keypoints[m.trainIdx].pt for m in matches]
-            p1 = [frame_points[m.queryIdx].pt for m in matches]
+            p1 = [self.frame_points[m.queryIdx].pt for m in matches]
             p0, p1 = np.float32((p0, p1))
             H, status = cv2.findHomography(p0, p1, cv2.RANSAC, 3.0)
             status = status.ravel() != 0
@@ -169,7 +178,7 @@ class App:
                 break
 
 if __name__ == '__main__':
-    print __doc__
+    print(__doc__)
 
     import sys
     try:

samples/python2/stereo_match.py

@@ -6,6 +6,9 @@ Simple example of stereo image matching and point cloud generation.
 Resulting .ply file cam be easily viewed using MeshLab ( http://meshlab.sourceforge.net/ )
 '''
 
+# Python 2/3 compatibility
+from __future__ import print_function
+
 import numpy as np
 import cv2
 
@@ -25,13 +28,13 @@ def write_ply(fn, verts, colors):
     verts = verts.reshape(-1, 3)
     colors = colors.reshape(-1, 3)
     verts = np.hstack([verts, colors])
-    with open(fn, 'w') as f:
-        f.write(ply_header % dict(vert_num=len(verts)))
-        np.savetxt(f, verts, '%f %f %f %d %d %d')
+    with open(fn, 'wb') as f:
+        f.write((ply_header % dict(vert_num=len(verts))).encode('utf-8'))
+        np.savetxt(f, verts, fmt='%f %f %f %d %d %d ')
 
 
 if __name__ == '__main__':
-    print 'loading images...'
+    print('loading images...')
     imgL = cv2.pyrDown( cv2.imread('../data/aloeL.jpg') )  # downscale images for faster processing
     imgR = cv2.pyrDown( cv2.imread('../data/aloeR.jpg') )
 
@@ -50,10 +53,10 @@ if __name__ == '__main__':
         speckleRange = 32
     )
 
-    print 'computing disparity...'
+    print('computing disparity...')
     disp = stereo.compute(imgL, imgR).astype(np.float32) / 16.0
 
-    print 'generating 3d point cloud...',
+    print('generating 3d point cloud...',)
     h, w = imgL.shape[:2]
     f = 0.8*w                          # guess for focal length
     Q = np.float32([[1, 0, 0, -0.5*w],
@@ -67,7 +70,7 @@ if __name__ == '__main__':
     out_colors = colors[mask]
     out_fn = 'out.ply'
     write_ply('out.ply', out_points, out_colors)
-    print '%s saved' % 'out.ply'
+    print('%s saved' % 'out.ply')
 
     cv2.imshow('left', imgL)
     cv2.imshow('disparity', (disp-min_disp)/num_disp)

samples/python2/texture_flow.py

@@ -10,6 +10,9 @@ Usage:
     texture_flow.py [<image>]
 '''
 
+# Python 2/3 compatibility
+from __future__ import print_function
+
 import numpy as np
 import cv2
 
@@ -22,7 +25,7 @@ if __name__ == '__main__':
 
     img = cv2.imread(fn)
     if img is None:
-        print 'Failed to load image file:', fn
+        print('Failed to load image file:', fn)
         sys.exit(1)
 
     gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
@@ -36,7 +39,7 @@ if __name__ == '__main__':
     vis[:] = (192 + np.uint32(vis)) / 2
     d = 12
     points =  np.dstack( np.mgrid[d/2:w:d, d/2:h:d] ).reshape(-1, 2)
-    for x, y in points:
+    for x, y in np.int32(points):
        vx, vy = np.int32(flow[y, x]*d)
        cv2.line(vis, (x-vx, y-vy), (x+vx, y+vy), (0, 0, 0), 1, cv2.LINE_AA)
     cv2.imshow('input', img)

samples/python2/video.py

@@ -182,7 +182,7 @@ if __name__ == '__main__':
     if len(sources) == 0:
         sources = [ 0 ]
 
-    caps = map(create_capture, sources)
+    caps = list(map(create_capture, sources))
     shot_idx = 0
     while True:
         imgs = []

samples/python2/video_v4l2.py

@@ -14,6 +14,9 @@ Keys:
 
 '''
 
+# Python 2/3 compatibility
+from __future__ import print_function
+
 import cv2
 
 def decode_fourcc(v):
@@ -24,13 +27,13 @@ font = cv2.FONT_HERSHEY_SIMPLEX
 color = (0, 255, 0)
 
 cap = cv2.VideoCapture(0)
-cap.set(cv2.CAP_PROP_AUTOFOCUS, False)
+cap.set(cv2.CAP_PROP_AUTOFOCUS, False)  # Known bug: https://github.com/Itseez/opencv/pull/5474
 
 cv2.namedWindow("Video")
 
 convert_rgb = True
 fps = int(cap.get(cv2.CAP_PROP_FPS))
-focus = int(cap.get(cv2.CAP_PROP_FOCUS)) * 100
+focus = int(min(cap.get(cv2.CAP_PROP_FOCUS) * 100, 2**31-1))  # ceil focus to C_LONG as Python3 int can go to +inf
 
 cv2.createTrackbar("FPS", "Video", fps, 30, lambda v: cap.set(cv2.CAP_PROP_FPS, v))
 cv2.createTrackbar("Focus", "Video", focus, 100, lambda v: cap.set(cv2.CAP_PROP_FOCUS, v / 100))
@@ -55,7 +58,7 @@ while True:
     cv2.putText(img, "FPS: {}".format(fps), (15, 80), font, 1.0, color)
     cv2.imshow("Video", img)
 
-    k = cv2.waitKey(1)
+    k = 0xFF & cv2.waitKey(1)
 
     if k == 27:
         break

samples/python2/watershed.py

@@ -22,7 +22,8 @@ Keys
 '''
 
 
-
+# Python 2/3 compatibility
+from __future__ import print_function
 
 import numpy as np
 import cv2
@@ -44,7 +45,7 @@ class App:
         self.sketch = Sketcher('img', [self.markers_vis, self.markers], self.get_colors)
 
     def get_colors(self):
-        return map(int, self.colors[self.cur_marker]), self.cur_marker
+        return list(map(int, self.colors[self.cur_marker])), self.cur_marker
 
     def watershed(self):
         m = self.markers.copy()
@@ -60,13 +61,13 @@ class App:
                 break
             if ch >= ord('1') and ch <= ord('7'):
                 self.cur_marker = ch - ord('0')
-                print 'marker: ', self.cur_marker
+                print('marker: ', self.cur_marker)
             if ch == ord(' ') or (self.sketch.dirty and self.auto_update):
                 self.watershed()
                 self.sketch.dirty = False
             if ch in [ord('a'), ord('A')]:
                 self.auto_update = not self.auto_update
-                print 'auto_update if', ['off', 'on'][self.auto_update]
+                print('auto_update if', ['off', 'on'][self.auto_update])
             if ch in [ord('r'), ord('R')]:
                 self.markers[:] = 0
                 self.markers_vis[:] = self.img
@@ -80,5 +81,5 @@ if __name__ == '__main__':
         fn = sys.argv[1]
     except:
         fn = '../data/fruits.jpg'
-    print __doc__
+    print(__doc__)
     App(fn).run()