Python3 friendly printing in examples

python_examples/face_detector.py

@@ -35,15 +35,15 @@ detector = dlib.get_frontal_face_detector()
 win = dlib.image_window()
 
 for f in sys.argv[1:]:
-    print "processing file: ", f
+    print("processing file: ", f)
     img = io.imread(f)
     # The 1 in the second argument indicates that we should upsample the image
     # 1 time.  This will make everything bigger and allow us to detect more
     # faces.
     dets = detector(img,1)
-    print "number of faces detected: ", len(dets)
+    print("number of faces detected: ", len(dets))
     for d in dets:
-        print "  detection position left,top,right,bottom:", d.left(), d.top(), d.right(), d.bottom()
+        print("  detection position left,top,right,bottom:", d.left(), d.top(), d.right(), d.bottom())
 
     win.clear_overlay()
     win.set_image(img)

python_examples/max_cost_assignment.py

@@ -40,11 +40,11 @@ assignment = dlib.max_cost_assignment(cost)
 # This prints optimal assignments:  [2, 0, 1]
 # which indicates that we should assign the person from the first row of the cost matrix to
 # job 2, the middle row person to job 0, and the bottom row person to job 1.
-print "optimal assignments: ", assignment
+print("optimal assignments: ", assignment)
 
 
 # This prints optimal cost:  16.0
 # which is correct since our optimal assignment is 6+5+5.
-print "optimal cost: ", dlib.assignment_cost(cost, assignment)
+print("optimal cost: ", dlib.assignment_cost(cost, assignment))
 
 

python_examples/sequence_segmenter.py

@@ -176,9 +176,9 @@ else:
 # We can also measure the accuracy of a model relative to some labeled data.  This
 # statement prints the precision, recall, and F1-score of the model relative to the data in
 # training_sequences/segments.
-print "Test on training data:", dlib.test_sequence_segmenter(model, training_sequences, segments)
+print("Test on training data:", dlib.test_sequence_segmenter(model, training_sequences, segments))
 
 # We can also do 5-fold cross-validation and print the resulting precision, recall, and F1-score.
-print "cross validation:", dlib.cross_validate_sequence_segmenter(training_sequences, segments, 5, params)
+print("cross validation:", dlib.cross_validate_sequence_segmenter(training_sequences, segments, 5, params))
 
 

python_examples/svm_rank.py

@@ -53,8 +53,8 @@ rank = trainer.train(data)
 # Now if you call rank on a vector it will output a ranking score.  In
 # particular, the ranking score for relevant vectors should be larger than the
 # score for non-relevant vectors.  
-print "ranking score for a relevant vector:     ", rank(data.relevant[0])
-print "ranking score for a non-relevant vector: ", rank(data.nonrelevant[0])
+print("ranking score for a relevant vector:     ", rank(data.relevant[0]))
+print("ranking score for a non-relevant vector: ", rank(data.nonrelevant[0]))
 # The output is the following:
 #    ranking score for a relevant vector:     0.5
 #    ranking score for a non-relevant vector: -0.5
@@ -65,12 +65,12 @@ print "ranking score for a non-relevant vector: ", rank(data.nonrelevant[0])
 # In this case, the ordering accuracy tells us how often a non-relevant vector
 # was ranked ahead of a relevant vector.  In this case, it returns 1 for both
 # metrics, indicating that the rank function outputs a perfect ranking.
-print dlib.test_ranking_function(rank, data)
+print(dlib.test_ranking_function(rank, data))
 
 # The ranking scores are computed by taking the dot product between a learned
 # weight vector and a data vector.  If you want to see the learned weight vector
 # you can display it like so:
-print "weights: \n", rank.weights
+print("weights: \n", rank.weights)
 # In this case the weights are:
 #  0.5 
 # -0.5 
@@ -112,7 +112,7 @@ rank = trainer.train(queries)
 # splits and returns the overall ranking accuracy based on the held out data.
 # Just like test_ranking_function(), it reports both the ordering accuracy and
 # mean average precision.
-print "cross validation results: ", dlib.cross_validate_ranking_trainer(trainer, queries, 4)
+print("cross validation results: ", dlib.cross_validate_ranking_trainer(trainer, queries, 4))
 
 
 
@@ -141,8 +141,8 @@ data.nonrelevant.append(samp)
 
 trainer = dlib.svm_rank_trainer_sparse()
 rank = trainer.train(data)
-print "ranking score for a relevant vector:     ", rank(data.relevant[0])
-print "ranking score for a non-relevant vector: ", rank(data.nonrelevant[0])
+print("ranking score for a relevant vector:     ", rank(data.relevant[0]))
+print("ranking score for a non-relevant vector: ", rank(data.nonrelevant[0]))
 # Just as before, the output is the following:
 #    ranking score for a relevant vector:     0.5
 #    ranking score for a non-relevant vector: -0.5

python_examples/svm_struct.py

@@ -46,9 +46,9 @@ def main():
 
     # Print the weights and then evaluate predict_label() on each of our training samples.
     # Note that the correct label is predicted for each sample.
-    print weights
+    print(weights)
     for i in range(len(samples)):
-        print "predicted label for sample[{0}]: {1}".format(i, predict_label(weights, samples[i]))
+        print("predicted label for sample[{0}]: {1}".format(i, predict_label(weights, samples[i])))
 
 def predict_label(weights, sample):
     """Given the 9-dimensional weight vector which defines a 3 class classifier, predict the

python_examples/train_object_detector.py

@@ -24,10 +24,10 @@ from skimage import io
 # the path to this faces folder as a command line argument so we will know
 # where it is.
 if (len(sys.argv) != 2):
-    print "Give the path to the examples/faces directory as the argument to this"
-    print "program.  For example, if you are in the python_examples folder then "
-    print "execute this program by running:"
-    print "  ./train_object_detector.py ../examples/faces"
+    print("Give the path to the examples/faces directory as the argument to this")
+    print("program.  For example, if you are in the python_examples folder then ")
+    print("execute this program by running:")
+    print("  ./train_object_detector.py ../examples/faces")
     exit()
 faces_folder = sys.argv[1]
 
@@ -59,18 +59,18 @@ options.be_verbose = True
 # images with boxes.  To see how to use it read the tools/imglab/README.txt
 # file.  But for this example, we just use the training.xml file included with
 # dlib.
-dlib.train_simple_object_detector(faces_folder+"/training.xml","detector.svm", options)
+dlib.train_simple_object_detector(faces_folder+"/training.xml", "detector.svm", options)
 
 
 
 # Now that we have a face detector we can test it.  The first statement tests
-# it on the training data.  It will print the precision, recall, and then
+# it on the training data.  It will print(the precision, recall, and then)
 # average precision.
-print "\ntraining accuracy:", dlib.test_simple_object_detector(faces_folder+"/training.xml", "detector.svm")
+print("\ntraining accuracy:", dlib.test_simple_object_detector(faces_folder+"/training.xml", "detector.svm"))
 # However, to get an idea if it really worked without overfitting we need to
 # run it on images it wasn't trained on.  The next line does this.  Happily, we
 # see that the object detector works perfectly on the testing images.
-print "testing accuracy: ", dlib.test_simple_object_detector(faces_folder+"/testing.xml", "detector.svm")
+print("testing accuracy: ", dlib.test_simple_object_detector(faces_folder+"/testing.xml", "detector.svm"))
 
 
 
@@ -84,15 +84,15 @@ win_det.set_image(detector)
 
 # Now let's run the detector over the images in the faces folder and display the
 # results.
-print "\nShowing detections on the images in the faces folder..."
+print("\nShowing detections on the images in the faces folder...")
 win = dlib.image_window()
 for f in glob.glob(faces_folder+"/*.jpg"):
-    print "processing file:", f
+    print("processing file:", f)
     img = io.imread(f)
     dets = detector(img)
-    print "number of faces detected:", len(dets)
+    print("number of faces detected:", len(dets))
     for d in dets:
-        print "  detection position left,top,right,bottom:", d.left(), d.top(), d.right(), d.bottom()
+        print("  detection position left,top,right,bottom:", d.left(), d.top(), d.right(), d.bottom())
 
     win.clear_overlay()
     win.set_image(img)