def TrainManyTestOne():
"""
Training:
Train with many images of a subject.
Many subjects will be in model.
Testing:
Test with one image of each subject.
:return:
"""
os.remove(learnedData)
faceRecognizer = FaceRecognizer.Recognizer(learnedData)
train_images, test_images = [], []
for subject in os.listdir(path):
images = os.listdir(posixpath.join(path, subject))
mappedId = int(subject[-2:])
train_images = images[1:]
test_images.append(
posixpath.join(posixpath.join(path, subject), images[0]))
for image in train_images:
print "train", image, mappedId
faces = getFaces(
posixpath.join(posixpath.join(path, subject), image))
faceRecognizer.update(faces, [mappedId])
print "Prediction starts: "
for image in test_images:
print image
faces = getFaces(image)
faceRecognizer.predict(faces)
def TrainAllSubjectsExceptOneAndPredictThatSubject():
os.remove(learnedData)
faceRecognizer = FaceRecognizer.Recognizer(learnedData)
subjects = os.listdir(path)
train_subjects = subjects[:-1]
test_subjec = subjects[-1]
print train_subjects
print test_subjec
for subject in train_subjects:
# print subject
mappedId = int(subject[-2:])
for image in os.listdir(posixpath.join(path, subject)):
print image
faces = getFaces(
posixpath.join(posixpath.join(path, subject), image))
faceRecognizer.update(faces, [mappedId])
print "Prediction of images of ", test_subjec, "starts"
for image in os.listdir(posixpath.join(path, test_subjec)):
print image
faces = getFaces(
posixpath.join(posixpath.join(path, test_subjec), image))
faceRecognizer.predict(faces)
def TrainAllTestAll():
os.remove(learnedData)
faceRecognizer = FaceRecognizer.Recognizer(learnedData)
# Training starts
subjectId = 1
for subject in os.listdir(path):
# print subject
for images in os.listdir(posixpath.join(path, subject)):
# print images
mappedIds = [subjectId]
faces = getFaces(
posixpath.join(posixpath.join(path, subject), images))
faceRecognizer.update(faces, mappedIds)
subjectId += 1
# Testing starts
for subject in os.listdir(path):
# print subject
for images in os.listdir(posixpath.join(path, subject)):
faces = getFaces(
posixpath.join(posixpath.join(path, subject), images))
faceRecognizer.predict(faces)
