auto_face_recognition is Tensorflow based python library for fast face recognition
Project description
auto_face_recognition
Last Upadted: 19 November, 2020
- What is auto_face_recognition?
- Prerequisite
- Getting Started- How to use it?
- Future?
1. What is auto_face_recognition?
It is a python library for the Face Recognition. This library make face recognition easy and simple. This library uses Tensorflow 2.0+ for the face recognition and model training.
2. Prerequisite-
- To use it only Python (> 3.6) is required.
- Recommended Python < 3.9
3. Getting Started (How to use it)-
Install the latest version-
pip install auto_face_recognition
It will install all the required package automatically, including Tensorflow Latest.
Usage and Features-
After installing the library you can import the module-
-
Object Creation-
import auto_face_recognition obj = auto_face_recognition.AutoFaceRecognition()
-
Dataset Creation-
obj.datasetcreate(haarcascade_path='haarcascade/haarcascade_frontalface_default.xml', eyecascade_path='haarcascade/haarcascade_eye.xml')
Note: You need to pass the 'haarcascade_frontalface_default.xml' and 'haarcascade_eye.xml' path.
-
Model Training-
obj.face_recognition_train()
-
Predict Faces-
# Real Time obj.predict_faces() # Single Face Recofnition obj.predict_face()
Parameters You Can Choose-
datasetcreate
datasetcreate(dataset_path='datasets', class_name='Demo',
haarcascade_path='haarcascade/haarcascade_frontalface_default.xml',
eyecascade_path='haarcascade/haarcascade_eye.xml', eye_detect=False,
save_face_only=True, no_of_samples=100,
width=128, height=128, color_mode=False)
""""
Dataset Create by face detection
:param dataset_path: str (example: 'folder_of_dataset')
:param class_name: str (example: 'folder_of_dataset')
:param haarcascade_path: str (example: 'haarcascade_frontalface_default.xml)
:param eyecascade_path: str (example: 'haarcascade_eye.xml):param eye_detect: bool (example:True)
:param save_face_only: bool (example:True)
:param no_of_samples: int (example: 100)
:param width: int (example: 128)
:param height: int (example: 128)
:param color_mode: bool (example:False):return: None
"""
face_recognition_train
face_recognition_train(data_dir='datasets', batch_size=32, img_height=128, img_width=128, epochs=10, model_path='model', pretrained=None, base_model_trainable=False):
"""
Train TF Keras model according to dataset path
:param data_dir: str (example: 'folder_of_dataset')
:param batch_size: int (example:32)
:param img_height: int (example:128)
:param img_width: int (example:128)
:param epochs: int (example:10)
:param model_path: str (example: 'model')
:param pretrained: str (example: None, 'VGG16', 'ResNet50' or 'InceptionV3')
:param base_model_trainable: bool (example: False (Enable if you want to train the pretrained model's layer))
:return: None
"""
predict_faces
predict_faces(self, class_name=None, img_height=128, img_width=128,
haarcascade_path='haarcascade/haarcascade_frontalface_default.xml',
eyecascade_path='haarcascade/haarcascade_eye.xml', model_path='model',
color_mode=False):
"""
Predict Face
:param class_name: Type-List (example: ['class1', 'class2'] )
:param img_height: int (example:128)
:param img_width: int (example:128)
:param haarcascade_path: str (example: 'haarcascade_frontalface_default.xml)
:param eyecascade_path: str (example: 'haarcascade_eye.xml)
:param model_path: str (example: 'model')
:param color_mode: bool (example: False)
:return: None
"""
predict_face
predict_face(self, class_name=None, img_height=128, img_width=128,
haarcascade_path='haarcascade/haarcascade_frontalface_default.xml',
eyecascade_path='haarcascade/haarcascade_eye.xml', model_path='model',
color_mode=False, image_path='tmp.png'):
"""
Predict Face
:param class_name: Type-List (example: ['class1', 'class2'] )
:param img_height: int (example:128)
:param img_width: int (example:128)
:param haarcascade_path: str (example: 'haarcascade_frontalface_default.xml)
:param eyecascade_path: str (example: 'haarcascade_eye.xml)
:param model_path: str (example: 'model')
:param color_mode: bool (example: False)
:param image_path: str (example: 'src/image_predict.png'
:return: None
"""
4. Future?
Finetuning with Resnet and others.
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