Skip to content

moved and generalized augmentation method in FacialKeypointRecognition #12

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
egolus wants to merge 1 commit into
base: master
Choose a base branch
from
Open

moved and generalized augmentation method in FacialKeypointRecognition #12

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
26 changes: 20 additions & 6 deletions FacialKeypointRecognition.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -39,6 +39,8 @@ class FacialKeypointRecognition:
X_train, y_train = [], []
X_test = []

X_train_augmented, X_test_augmented = [], []

prediction = []

def __init__(self, network=networks.simpleNetwork):
Expand Down Expand Up @@ -77,9 +79,6 @@ def loadData(self, *args, **kwargs):
kwargs.pop("cols", None)
self.X_test, _ = self.load(test=True, *args, **kwargs)

self.X_train = histogrammEqualization(self.X_train)
self.X_test = histogrammEqualization(self.X_test)

def load(self,
test=False,
cols=None,
Expand Down Expand Up @@ -145,12 +144,28 @@ def load(self,

return X, y

def augment(self, methods=[histogrammEqualization]):
"""
Use a list of augmentation algorythms
The methods are used in the order given.
As first input the unchanged sets X_train and X_test are used.
The output is saved in X_train_augmented and X_test_augmented.
"""

self.X_train_augmented = self.X_train
self.X_test_augmented = self.X_test

for method in methods:
self.X_train_augmented = method(self.X_train)
self.X_test_augmented = method(self.X_test)

def fit(self):
"""train the network with the training data"""

self.network.fit(self.X_train, self.y_train)

if self.X_train_augmented and self.X_test_augmented:
self.network.fit(self.X_train_augmented, self.y_train_augmented)
else:
self.network.fit(self.X_train, self.y_train)

def predict(self, X=None):
"""predict the target values for the testset"""
Expand All @@ -159,7 +174,6 @@ def predict(self, X=None):
X = self.X_test
self.prediction = self.network.predict(X)


def savePrediction(self):
"""save the predicted coordinates into a csv file to upload"""

Expand Down