OrgLion-ML
/
dlib
mirror of https://github.com/davisking/dlib.git
1
0
Fork 0
Code Issues Releases Wiki Activity

Add example of testing detector using existing data

This commit is contained in:
Patrick Snape 2014-12-10 17:42:05 +00:00
parent cc3bb4993c
commit 60318cb308
1 changed files with 7 additions and 3 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

10
python_examples/train_object_detector.py
View File

@ -66,11 +66,11 @@ dlib.train_simple_object_detector(faces_folder+"/training.xml", "detector.svm",
# 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)
# average precision.
print("\ntraining accuracy:", dlib.test_simple_object_detector(faces_folder+"/training.xml", "detector.svm"))
print("\ntraining accuracy: {}".format(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: {}".format(dlib.test_simple_object_detector(faces_folder+"/testing.xml", "detector.svm")))
@ -128,4 +128,8 @@ detector2 = dlib.simple_object_detector("detector2.svm")
win_det.set_image(detector2)
raw_input("Hit enter to continue")
# Note that you don't have to use the XML based input to
# test_simple_object_detector(). If you have already loaded your training
# images and bounding boxes for the objects then you can call it as shown
# below.
print("Training accuracy: {}".format(dlib.test_simple_object_detector(images, boxes, "detector.svm")))