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Python3 friendly printing in examples

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This commit is contained in:
Patrick Snape 2014-12-10 17:32:32 +00:00
parent e3aee32f34
commit cc3bb4993c
6 changed files with 28 additions and 28 deletions
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6
python_examples/face_detector.py
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@ -35,15 +35,15 @@ detector = dlib.get_frontal_face_detector()
win = dlib.image_window() win = dlib.image_window()
for f in sys.argv[1:]: for f in sys.argv[1:]:
print "processing file: ", f print("processing file: ", f)
img = io.imread(f) img = io.imread(f)
# The 1 in the second argument indicates that we should upsample the image # 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 # 1 time. This will make everything bigger and allow us to detect more
# faces. # faces.
dets = detector(img,1) dets = detector(img,1)
print "number of faces detected: ", len(dets) print("number of faces detected: ", len(dets))
for d in 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.clear_overlay()
win.set_image(img) win.set_image(img)

4
python_examples/max_cost_assignment.py
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@ -40,11 +40,11 @@ assignment = dlib.max_cost_assignment(cost)
# This prints optimal assignments: [2, 0, 1] # This prints optimal assignments: [2, 0, 1]
# which indicates that we should assign the person from the first row of the cost matrix to # 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. # 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 # This prints optimal cost: 16.0
# which is correct since our optimal assignment is 6+5+5. # 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))

4
python_examples/sequence_segmenter.py
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@ -176,9 +176,9 @@ else:
# We can also measure the accuracy of a model relative to some labeled data. This # 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 # statement prints the precision, recall, and F1-score of the model relative to the data in
# training_sequences/segments. # 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. # 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))

14
python_examples/svm_rank.py
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@ -53,8 +53,8 @@ rank = trainer.train(data)
# Now if you call rank on a vector it will output a ranking score. In # 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 # particular, the ranking score for relevant vectors should be larger than the
# score for non-relevant vectors. # score for non-relevant vectors.
print "ranking score for a relevant vector: ", rank(data.relevant[0]) 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 non-relevant vector: ", rank(data.nonrelevant[0]))
# The output is the following: # The output is the following:
# ranking score for a relevant vector: 0.5 # ranking score for a relevant vector: 0.5
# ranking score for a non-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 # 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 # 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. # 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 # 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 # weight vector and a data vector. If you want to see the learned weight vector
# you can display it like so: # you can display it like so:
print "weights: \n", rank.weights print("weights: \n", rank.weights)
# In this case the weights are: # In this case the weights are:
# 0.5 # 0.5
# -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. # 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 # Just like test_ranking_function(), it reports both the ordering accuracy and
# mean average precision. # 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() trainer = dlib.svm_rank_trainer_sparse()
rank = trainer.train(data) rank = trainer.train(data)
print "ranking score for a relevant vector: ", rank(data.relevant[0]) 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 non-relevant vector: ", rank(data.nonrelevant[0]))
# Just as before, the output is the following: # Just as before, the output is the following:
# ranking score for a relevant vector: 0.5 # ranking score for a relevant vector: 0.5
# ranking score for a non-relevant vector: -0.5 # ranking score for a non-relevant vector: -0.5

4
python_examples/svm_struct.py
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@ -46,9 +46,9 @@ def main():
# Print the weights and then evaluate predict_label() on each of our training samples. # Print the weights and then evaluate predict_label() on each of our training samples.
# Note that the correct label is predicted for each sample. # Note that the correct label is predicted for each sample.
print weights print(weights)
for i in range(len(samples)): 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): def predict_label(weights, sample):
"""Given the 9-dimensional weight vector which defines a 3 class classifier, predict the """Given the 9-dimensional weight vector which defines a 3 class classifier, predict the

22
python_examples/train_object_detector.py
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@ -24,10 +24,10 @@ from skimage import io
# the path to this faces folder as a command line argument so we will know # the path to this faces folder as a command line argument so we will know
# where it is. # where it is.
if (len(sys.argv) != 2): if (len(sys.argv) != 2):
print "Give the path to the examples/faces directory as the argument to this" 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("program. For example, if you are in the python_examples folder then ")
print "execute this program by running:" print("execute this program by running:")
print " ./train_object_detector.py ../examples/faces" print(" ./train_object_detector.py ../examples/faces")
exit() exit()
faces_folder = sys.argv[1] faces_folder = sys.argv[1]
@ -64,13 +64,13 @@ dlib.train_simple_object_detector(faces_folder+"/training.xml","detector.svm", o
# Now that we have a face detector we can test it. The first statement tests # 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. # 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 # 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 # 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. # 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 # Now let's run the detector over the images in the faces folder and display the
# results. # 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() win = dlib.image_window()
for f in glob.glob(faces_folder+"/*.jpg"): for f in glob.glob(faces_folder+"/*.jpg"):
print "processing file:", f print("processing file:", f)
img = io.imread(f) img = io.imread(f)
dets = detector(img) dets = detector(img)
print "number of faces detected:", len(dets) print("number of faces detected:", len(dets))
for d in 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.clear_overlay()
win.set_image(img) win.set_image(img)