VisageSnap 0.1

Face Classification package

VisageSnap

Recognizes faces and trains models, brings in the pictures and provides identification predictions and face classification. It also performs semi-supervised learning.

Feature

• Recognize faces.
• Train the model through semi-supervised learning with labeled or unlabeled pictures.
• It provides identification predictions and face classification for pictures.

Usage

Assign a label to the face

You should assign a NameLabel to the face you want to classify first. (NumberLabel MUST NOT BE -1)

vs = VisageSnap.Core()
people = ['Tom', 'Jerry']
# ['NameLabel1', 'NameLabel2', 'NameLabel3'...]

vs.set_label(people)

You can also do it like this so that assign a NameLabel and NumberLabel:

vs = VisageSnap.Core()
people = {
    # 'NameLabel': NumberLabel
    'Tom': 0,
    'Jerry': 1
}

vs.set_label(people)

Put the picture files to be used during training in the directory. In this case, the file name follows the following rules:

(NameLabel)-(Any character).extension

Tom-123.png
Tom-124.jpg
Tom-126.jpeg
Jerry-2.png
Jerry-3.png
Jerry-4.png

---

Recognize faces and train the model

Train with the picture files in the directory.

vs.train_labeled_data()

If you want to train with unlabeled data, you can also try to like this:

vs.train_unlabeled_data()

---

Identification predictions

Put the picture files you want to predict into the directory.

result = vs.predict_all()
print(result)

# Result:
#{
#   "target1.png": "Tom",
#   "target2.jpeg": "Jerry",
#   "target3.jpeg": "Jerry"
#}

To change the directory you work with

You should put the picture files into configured directory, and also model file is stored in model directory.

vs.set_directory({
    "labeled": "labeled_pic",
    "unlabeled": "unlabeled_pic",
    "model": "my_model.d"
})

Default Directory:

{
    "labeled": "labeled",
    "unlabeled": "unlabeled",
    "model": "model"
}