Full Code of chen0040/keras-face for AI

Repository: chen0040/keras-face
Branch: master
Commit: f5971cdb1ff5
Files: 251
Total size: 78.6 MB

Directory structure:
gitextract_t_3dlf0s/

├── .gitignore
├── LICENSE
├── README.md
├── demo/
│   ├── __init__.py
│   ├── datasets/
│   │   ├── test_happy.h5
│   │   ├── train_face.h5
│   │   └── train_happy.h5
│   ├── face_net_demo.py
│   ├── models/
│   │   ├── siamese-face-net-architecture.h5
│   │   ├── siamese-face-net-config.npy
│   │   ├── siamese-face-net-weights.h5
│   │   └── weights/
│   │       ├── bn1_b.csv
│   │       ├── bn1_m.csv
│   │       ├── bn1_v.csv
│   │       ├── bn1_w.csv
│   │       ├── bn2_b.csv
│   │       ├── bn2_m.csv
│   │       ├── bn2_v.csv
│   │       ├── bn2_w.csv
│   │       ├── bn3_b.csv
│   │       ├── bn3_m.csv
│   │       ├── bn3_v.csv
│   │       ├── bn3_w.csv
│   │       ├── conv1_b.csv
│   │       ├── conv1_w.csv
│   │       ├── conv2_b.csv
│   │       ├── conv2_w.csv
│   │       ├── conv3_b.csv
│   │       ├── conv3_w.csv
│   │       ├── dense_b.csv
│   │       ├── dense_w.csv
│   │       ├── inception_3a_1x1_bn_b.csv
│   │       ├── inception_3a_1x1_bn_m.csv
│   │       ├── inception_3a_1x1_bn_v.csv
│   │       ├── inception_3a_1x1_bn_w.csv
│   │       ├── inception_3a_1x1_conv_b.csv
│   │       ├── inception_3a_1x1_conv_w.csv
│   │       ├── inception_3a_3x3_bn1_b.csv
│   │       ├── inception_3a_3x3_bn1_m.csv
│   │       ├── inception_3a_3x3_bn1_v.csv
│   │       ├── inception_3a_3x3_bn1_w.csv
│   │       ├── inception_3a_3x3_bn2_b.csv
│   │       ├── inception_3a_3x3_bn2_m.csv
│   │       ├── inception_3a_3x3_bn2_v.csv
│   │       ├── inception_3a_3x3_bn2_w.csv
│   │       ├── inception_3a_3x3_conv1_b.csv
│   │       ├── inception_3a_3x3_conv1_w.csv
│   │       ├── inception_3a_3x3_conv2_b.csv
│   │       ├── inception_3a_3x3_conv2_w.csv
│   │       ├── inception_3a_5x5_bn1_b.csv
│   │       ├── inception_3a_5x5_bn1_m.csv
│   │       ├── inception_3a_5x5_bn1_v.csv
│   │       ├── inception_3a_5x5_bn1_w.csv
│   │       ├── inception_3a_5x5_bn2_b.csv
│   │       ├── inception_3a_5x5_bn2_m.csv
│   │       ├── inception_3a_5x5_bn2_v.csv
│   │       ├── inception_3a_5x5_bn2_w.csv
│   │       ├── inception_3a_5x5_conv1_b.csv
│   │       ├── inception_3a_5x5_conv1_w.csv
│   │       ├── inception_3a_5x5_conv2_b.csv
│   │       ├── inception_3a_5x5_conv2_w.csv
│   │       ├── inception_3a_pool_bn_b.csv
│   │       ├── inception_3a_pool_bn_m.csv
│   │       ├── inception_3a_pool_bn_v.csv
│   │       ├── inception_3a_pool_bn_w.csv
│   │       ├── inception_3a_pool_conv_b.csv
│   │       ├── inception_3a_pool_conv_w.csv
│   │       ├── inception_3b_1x1_bn_b.csv
│   │       ├── inception_3b_1x1_bn_m.csv
│   │       ├── inception_3b_1x1_bn_v.csv
│   │       ├── inception_3b_1x1_bn_w.csv
│   │       ├── inception_3b_1x1_conv_b.csv
│   │       ├── inception_3b_1x1_conv_w.csv
│   │       ├── inception_3b_3x3_bn1_b.csv
│   │       ├── inception_3b_3x3_bn1_m.csv
│   │       ├── inception_3b_3x3_bn1_v.csv
│   │       ├── inception_3b_3x3_bn1_w.csv
│   │       ├── inception_3b_3x3_bn2_b.csv
│   │       ├── inception_3b_3x3_bn2_m.csv
│   │       ├── inception_3b_3x3_bn2_v.csv
│   │       ├── inception_3b_3x3_bn2_w.csv
│   │       ├── inception_3b_3x3_conv1_b.csv
│   │       ├── inception_3b_3x3_conv1_w.csv
│   │       ├── inception_3b_3x3_conv2_b.csv
│   │       ├── inception_3b_3x3_conv2_w.csv
│   │       ├── inception_3b_5x5_bn1_b.csv
│   │       ├── inception_3b_5x5_bn1_m.csv
│   │       ├── inception_3b_5x5_bn1_v.csv
│   │       ├── inception_3b_5x5_bn1_w.csv
│   │       ├── inception_3b_5x5_bn2_b.csv
│   │       ├── inception_3b_5x5_bn2_m.csv
│   │       ├── inception_3b_5x5_bn2_v.csv
│   │       ├── inception_3b_5x5_bn2_w.csv
│   │       ├── inception_3b_5x5_conv1_b.csv
│   │       ├── inception_3b_5x5_conv1_w.csv
│   │       ├── inception_3b_5x5_conv2_b.csv
│   │       ├── inception_3b_5x5_conv2_w.csv
│   │       ├── inception_3b_pool_bn_b.csv
│   │       ├── inception_3b_pool_bn_m.csv
│   │       ├── inception_3b_pool_bn_v.csv
│   │       ├── inception_3b_pool_bn_w.csv
│   │       ├── inception_3b_pool_conv_b.csv
│   │       ├── inception_3b_pool_conv_w.csv
│   │       ├── inception_3c_3x3_bn1_b.csv
│   │       ├── inception_3c_3x3_bn1_m.csv
│   │       ├── inception_3c_3x3_bn1_v.csv
│   │       ├── inception_3c_3x3_bn1_w.csv
│   │       ├── inception_3c_3x3_bn2_b.csv
│   │       ├── inception_3c_3x3_bn2_m.csv
│   │       ├── inception_3c_3x3_bn2_v.csv
│   │       ├── inception_3c_3x3_bn2_w.csv
│   │       ├── inception_3c_3x3_conv1_b.csv
│   │       ├── inception_3c_3x3_conv1_w.csv
│   │       ├── inception_3c_3x3_conv2_b.csv
│   │       ├── inception_3c_3x3_conv2_w.csv
│   │       ├── inception_3c_5x5_bn1_b.csv
│   │       ├── inception_3c_5x5_bn1_m.csv
│   │       ├── inception_3c_5x5_bn1_v.csv
│   │       ├── inception_3c_5x5_bn1_w.csv
│   │       ├── inception_3c_5x5_bn2_b.csv
│   │       ├── inception_3c_5x5_bn2_m.csv
│   │       ├── inception_3c_5x5_bn2_v.csv
│   │       ├── inception_3c_5x5_bn2_w.csv
│   │       ├── inception_3c_5x5_conv1_b.csv
│   │       ├── inception_3c_5x5_conv1_w.csv
│   │       ├── inception_3c_5x5_conv2_b.csv
│   │       ├── inception_3c_5x5_conv2_w.csv
│   │       ├── inception_4a_1x1_bn_b.csv
│   │       ├── inception_4a_1x1_bn_m.csv
│   │       ├── inception_4a_1x1_bn_v.csv
│   │       ├── inception_4a_1x1_bn_w.csv
│   │       ├── inception_4a_1x1_conv_b.csv
│   │       ├── inception_4a_1x1_conv_w.csv
│   │       ├── inception_4a_3x3_bn1_b.csv
│   │       ├── inception_4a_3x3_bn1_m.csv
│   │       ├── inception_4a_3x3_bn1_v.csv
│   │       ├── inception_4a_3x3_bn1_w.csv
│   │       ├── inception_4a_3x3_bn2_b.csv
│   │       ├── inception_4a_3x3_bn2_m.csv
│   │       ├── inception_4a_3x3_bn2_v.csv
│   │       ├── inception_4a_3x3_bn2_w.csv
│   │       ├── inception_4a_3x3_conv1_b.csv
│   │       ├── inception_4a_3x3_conv1_w.csv
│   │       ├── inception_4a_3x3_conv2_b.csv
│   │       ├── inception_4a_3x3_conv2_w.csv
│   │       ├── inception_4a_5x5_bn1_b.csv
│   │       ├── inception_4a_5x5_bn1_m.csv
│   │       ├── inception_4a_5x5_bn1_v.csv
│   │       ├── inception_4a_5x5_bn1_w.csv
│   │       ├── inception_4a_5x5_bn2_b.csv
│   │       ├── inception_4a_5x5_bn2_m.csv
│   │       ├── inception_4a_5x5_bn2_v.csv
│   │       ├── inception_4a_5x5_bn2_w.csv
│   │       ├── inception_4a_5x5_conv1_b.csv
│   │       ├── inception_4a_5x5_conv1_w.csv
│   │       ├── inception_4a_5x5_conv2_b.csv
│   │       ├── inception_4a_5x5_conv2_w.csv
│   │       ├── inception_4a_pool_bn_b.csv
│   │       ├── inception_4a_pool_bn_m.csv
│   │       ├── inception_4a_pool_bn_v.csv
│   │       ├── inception_4a_pool_bn_w.csv
│   │       ├── inception_4a_pool_conv_b.csv
│   │       ├── inception_4a_pool_conv_w.csv
│   │       ├── inception_4e_3x3_bn1_b.csv
│   │       ├── inception_4e_3x3_bn1_m.csv
│   │       ├── inception_4e_3x3_bn1_v.csv
│   │       ├── inception_4e_3x3_bn1_w.csv
│   │       ├── inception_4e_3x3_bn2_b.csv
│   │       ├── inception_4e_3x3_bn2_m.csv
│   │       ├── inception_4e_3x3_bn2_v.csv
│   │       ├── inception_4e_3x3_bn2_w.csv
│   │       ├── inception_4e_3x3_conv1_b.csv
│   │       ├── inception_4e_3x3_conv1_w.csv
│   │       ├── inception_4e_3x3_conv2_b.csv
│   │       ├── inception_4e_3x3_conv2_w.csv
│   │       ├── inception_4e_5x5_bn1_b.csv
│   │       ├── inception_4e_5x5_bn1_m.csv
│   │       ├── inception_4e_5x5_bn1_v.csv
│   │       ├── inception_4e_5x5_bn1_w.csv
│   │       ├── inception_4e_5x5_bn2_b.csv
│   │       ├── inception_4e_5x5_bn2_m.csv
│   │       ├── inception_4e_5x5_bn2_v.csv
│   │       ├── inception_4e_5x5_bn2_w.csv
│   │       ├── inception_4e_5x5_conv1_b.csv
│   │       ├── inception_4e_5x5_conv1_w.csv
│   │       ├── inception_4e_5x5_conv2_b.csv
│   │       ├── inception_4e_5x5_conv2_w.csv
│   │       ├── inception_5a_1x1_bn_b.csv
│   │       ├── inception_5a_1x1_bn_m.csv
│   │       ├── inception_5a_1x1_bn_v.csv
│   │       ├── inception_5a_1x1_bn_w.csv
│   │       ├── inception_5a_1x1_conv_b.csv
│   │       ├── inception_5a_1x1_conv_w.csv
│   │       ├── inception_5a_3x3_bn1_b.csv
│   │       ├── inception_5a_3x3_bn1_m.csv
│   │       ├── inception_5a_3x3_bn1_v.csv
│   │       ├── inception_5a_3x3_bn1_w.csv
│   │       ├── inception_5a_3x3_bn2_b.csv
│   │       ├── inception_5a_3x3_bn2_m.csv
│   │       ├── inception_5a_3x3_bn2_v.csv
│   │       ├── inception_5a_3x3_bn2_w.csv
│   │       ├── inception_5a_3x3_conv1_b.csv
│   │       ├── inception_5a_3x3_conv1_w.csv
│   │       ├── inception_5a_3x3_conv2_b.csv
│   │       ├── inception_5a_3x3_conv2_m.csv
│   │       ├── inception_5a_3x3_conv2_v.csv
│   │       ├── inception_5a_3x3_conv2_w.csv
│   │       ├── inception_5a_pool_bn_b.csv
│   │       ├── inception_5a_pool_bn_m.csv
│   │       ├── inception_5a_pool_bn_v.csv
│   │       ├── inception_5a_pool_bn_w.csv
│   │       ├── inception_5a_pool_conv_b.csv
│   │       ├── inception_5a_pool_conv_w.csv
│   │       ├── inception_5b_1x1_bn_b.csv
│   │       ├── inception_5b_1x1_bn_m.csv
│   │       ├── inception_5b_1x1_bn_v.csv
│   │       ├── inception_5b_1x1_bn_w.csv
│   │       ├── inception_5b_1x1_conv_b.csv
│   │       ├── inception_5b_1x1_conv_w.csv
│   │       ├── inception_5b_3x3_bn1_b.csv
│   │       ├── inception_5b_3x3_bn1_m.csv
│   │       ├── inception_5b_3x3_bn1_v.csv
│   │       ├── inception_5b_3x3_bn1_w.csv
│   │       ├── inception_5b_3x3_bn2_b.csv
│   │       ├── inception_5b_3x3_bn2_m.csv
│   │       ├── inception_5b_3x3_bn2_v.csv
│   │       ├── inception_5b_3x3_bn2_w.csv
│   │       ├── inception_5b_3x3_conv1_b.csv
│   │       ├── inception_5b_3x3_conv1_w.csv
│   │       ├── inception_5b_3x3_conv2_b.csv
│   │       ├── inception_5b_3x3_conv2_w.csv
│   │       ├── inception_5b_pool_bn_b.csv
│   │       ├── inception_5b_pool_bn_m.csv
│   │       ├── inception_5b_pool_bn_v.csv
│   │       ├── inception_5b_pool_bn_w.csv
│   │       ├── inception_5b_pool_conv_b.csv
│   │       └── inception_5b_pool_conv_w.csv
│   ├── opencv-files/
│   │   ├── haarcascade_frontalface_alt.xml
│   │   └── lbpcascade_frontalface.xml
│   ├── opencv_detect_faces.py
│   ├── siamese_demo_predict.py
│   └── siamese_demo_train.py
├── keras_face/
│   ├── __init__.py
│   └── library/
│       ├── __init__.py
│       ├── face_net.py
│       ├── fr_utils.py
│       ├── inception_blocks.py
│       ├── inception_blocks_v2.py
│       ├── opencv_utils.py
│       └── siamese.py
└── requirements.txt

================================================
FILE CONTENTS
================================================

================================================
FILE: .gitignore
================================================
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# Unit test / coverage reports
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# Translations
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# Django stuff:
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# pyenv
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# mypy
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================================================
FILE: LICENSE
================================================
MIT License

Copyright (c) 2018 Xianshun Chen

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.


================================================
FILE: README.md
================================================
# keras-face

face verification and recognition using Keras

The project contains two implementations: [DeepFace](keras_face/library/face_net.py) and 
[VGG16 + Siamese](keras_face/library/siamese.py)

* DeepFace: [keras_face/library/face_net.py](keras_face/library/face_net.py) contains the deep face implementation tought in the
coursea course deeplearning.ai
* VGG16 + Siamese: [keras_face/library/siamese.py](keras_face/library/siamese.py) is my own implementation of siamese network + VGG16 + 
contrastive loss function (image similarity function)

# Usage

### DeepFace 

Below shows the [sample codes](demo/face_net_demo.py) which verifies whether a particular camera image is a person in an image database or
whether a particular camera image is which person in the image database (or not at all)

```python
from keras_face.library.face_net import FaceNet


def main():
    model_dir_path = './models'
    image_dir_path = "./data/images"

    fnet = FaceNet()
    fnet.load_model(model_dir_path)

    database = {}
    database["danielle"] = fnet.img_to_encoding(image_dir_path + "/danielle.png")
    database["younes"] = fnet.img_to_encoding(image_dir_path + "/younes.jpg")
    database["tian"] = fnet.img_to_encoding(image_dir_path + "/tian.jpg")
    database["andrew"] = fnet.img_to_encoding(image_dir_path + "/andrew.jpg")
    database["kian"] = fnet.img_to_encoding(image_dir_path + "/kian.jpg")
    database["dan"] = fnet.img_to_encoding(image_dir_path + "/dan.jpg")
    database["sebastiano"] = fnet.img_to_encoding(image_dir_path + "/sebastiano.jpg")
    database["bertrand"] = fnet.img_to_encoding(image_dir_path + "/bertrand.jpg")
    database["kevin"] = fnet.img_to_encoding(image_dir_path + "/kevin.jpg")
    database["felix"] = fnet.img_to_encoding(image_dir_path + "/felix.jpg")
    database["benoit"] = fnet.img_to_encoding(image_dir_path + "/benoit.jpg")
    database["arnaud"] = fnet.img_to_encoding(image_dir_path + "/arnaud.jpg")

    # verifies whether a particular camera image is a person in the image database
    dist, is_valid = fnet.verify(image_dir_path + "/camera_0.jpg", "younes", database)
    print('camera_0.jpg is' + (' ' if is_valid else ' not ') + 'yournes')
    dist, is_valid = fnet.verify(image_dir_path + "/camera_2.jpg", "kian", database)
    print('camera_0.jpg is' + (' ' if is_valid else ' not ') + 'yournes')
    
    # whether a particular camera image is which person in the image database (or not at all)
    dist, identity = fnet.who_is_it(image_dir_path + "/camera_0.jpg", database)
    if identity is None:
        print('camera_0.jpg is not found in database')
    else:
        print('camera_0.jpg is ' + str(identity))


if __name__ == '__main__':
    main()
```

### VGG16 + Siamese

Below shows [sample codes](demo/siamese_demo_train.py) how to train the V166+Siamese network:

```python
from keras_face.library.siamese import SiameseFaceNet


def main():
    fnet = SiameseFaceNet()
    fnet.vgg16_include_top = True # default is False

    model_dir_path = './models'
    image_dir_path = "./data/images"

    database = dict()
    database["danielle"] = [fnet.img_to_encoding(image_dir_path + "/danielle.png")]
    database["younes"] = [fnet.img_to_encoding(image_dir_path + "/younes.jpg")]
    database["tian"] = [fnet.img_to_encoding(image_dir_path + "/tian.jpg")]
    database["andrew"] = [fnet.img_to_encoding(image_dir_path + "/andrew.jpg")]
    database["kian"] = [fnet.img_to_encoding(image_dir_path + "/kian.jpg")]
    database["dan"] = [fnet.img_to_encoding(image_dir_path + "/dan.jpg")]
    database["sebastiano"] = [fnet.img_to_encoding(image_dir_path + "/sebastiano.jpg")]
    database["bertrand"] = [fnet.img_to_encoding(image_dir_path + "/bertrand.jpg")]
    database["kevin"] = [fnet.img_to_encoding(image_dir_path + "/kevin.jpg")]
    database["felix"] = [fnet.img_to_encoding(image_dir_path + "/felix.jpg")]
    database["benoit"] = [fnet.img_to_encoding(image_dir_path + "/benoit.jpg")]
    database["arnaud"] = [fnet.img_to_encoding(image_dir_path + "/arnaud.jpg")]

    fnet.fit(database=database, model_dir_path=model_dir_path)

if __name__ == '__main__':
    main()

```

Below shows the [sample codes](demo/siamese_demo_train.py) which verifies whether a particular camera image is a person in an image database or
whether a particular camera image is which person in the image database (or not at all)

```python
from keras_face.library.siamese import SiameseFaceNet


def main():
    fnet = SiameseFaceNet()

    model_dir_path = './models'
    image_dir_path = "./data/images"
    fnet.load_model(model_dir_path)

    database = dict()
    database["danielle"] = [fnet.img_to_encoding(image_dir_path + "/danielle.png")]
    database["younes"] = [fnet.img_to_encoding(image_dir_path + "/younes.jpg")]
    database["tian"] = [fnet.img_to_encoding(image_dir_path + "/tian.jpg")]
    database["andrew"] = [fnet.img_to_encoding(image_dir_path + "/andrew.jpg")]
    database["kian"] = [fnet.img_to_encoding(image_dir_path + "/kian.jpg")]
    database["dan"] = [fnet.img_to_encoding(image_dir_path + "/dan.jpg")]
    database["sebastiano"] = [fnet.img_to_encoding(image_dir_path + "/sebastiano.jpg")]
    database["bertrand"] = [fnet.img_to_encoding(image_dir_path + "/bertrand.jpg")]
    database["kevin"] = [fnet.img_to_encoding(image_dir_path + "/kevin.jpg")]
    database["felix"] = [fnet.img_to_encoding(image_dir_path + "/felix.jpg")]
    database["benoit"] = [fnet.img_to_encoding(image_dir_path + "/benoit.jpg")]
    database["arnaud"] = [fnet.img_to_encoding(image_dir_path + "/arnaud.jpg")]

    fnet.verify(image_dir_path + "/camera_0.jpg", "younes", database)
    fnet.verify(image_dir_path + "/camera_2.jpg", "kian", database)
    fnet.who_is_it(image_dir_path + "/camera_0.jpg", database)


if __name__ == '__main__':
    main()
```

# Configure to run on GPU on Windows

* Step 1: Change tensorflow to tensorflow-gpu in requirements.txt and install tensorflow-gpu
* Step 2: Download and install the [CUDA® Toolkit 9.0](https://developer.nvidia.com/cuda-90-download-archive) (Please note that
currently CUDA® Toolkit 9.1 is not yet supported by tensorflow, therefore you should download CUDA® Toolkit 9.0)
* Step 3: Download and unzip the [cuDNN 7.4 for CUDA@ Toolkit 9.0](https://developer.nvidia.com/cudnn) and add the
bin folder of the unzipped directory to the $PATH of your Windows environment 

# Todo


For VGG16 + Siamese, the training was not well-done as there are currently very limited number of sample images used for
training (only 12 images for 12 persons). Ideally, need to train using 100,000 images for 10,000 persons. Will need
to add in larger dataset for the training

# Note
For DeepFace (namely [keras_face/library/face_net.py](keras_face/library/face_net.py)), some utility classes 
and weights are taken from [https://github.com/shahariarrabby/deeplearning.ai](https://github.com/shahariarrabby/deeplearning.ai)
, also it contains only the prediction part


================================================
FILE: demo/__init__.py
================================================


================================================
FILE: demo/datasets/train_face.h5
================================================
[File too large to display: 11.2 MB]

================================================
FILE: demo/face_net_demo.py
================================================
from keras_face.library.face_net import FaceNet


def main():
    model_dir_path = './models'
    image_dir_path = "./data/images"

    fnet = FaceNet()
    fnet.load_model(model_dir_path)

    database = dict()
    database["danielle"] = fnet.img_to_encoding(image_dir_path + "/danielle.png")
    database["younes"] = fnet.img_to_encoding(image_dir_path + "/younes.jpg")
    database["tian"] = fnet.img_to_encoding(image_dir_path + "/tian.jpg")
    database["andrew"] = fnet.img_to_encoding(image_dir_path + "/andrew.jpg")
    database["kian"] = fnet.img_to_encoding(image_dir_path + "/kian.jpg")
    database["dan"] = fnet.img_to_encoding(image_dir_path + "/dan.jpg")
    database["sebastiano"] = fnet.img_to_encoding(image_dir_path + "/sebastiano.jpg")
    database["bertrand"] = fnet.img_to_encoding(image_dir_path + "/bertrand.jpg")
    database["kevin"] = fnet.img_to_encoding(image_dir_path + "/kevin.jpg")
    database["felix"] = fnet.img_to_encoding(image_dir_path + "/felix.jpg")
    database["benoit"] = fnet.img_to_encoding(image_dir_path + "/benoit.jpg")
    database["arnaud"] = fnet.img_to_encoding(image_dir_path + "/arnaud.jpg")

    dist, is_valid = fnet.verify(image_dir_path + "/camera_0.jpg", "younes", database)
    print('camera_0.jpg is' + (' ' if is_valid else ' not ') + 'yournes')
    dist, is_valid = fnet.verify(image_dir_path + "/camera_2.jpg", "kian", database)
    print('camera_0.jpg is' + (' ' if is_valid else ' not ') + 'yournes')
    dist, identity = fnet.who_is_it(image_dir_path + "/camera_0.jpg", database)
    if identity is None:
        print('camera_0.jpg is not found in database')
    else:
        print('camera_0.jpg is ' + str(identity))


if __name__ == '__main__':
    main()

================================================
FILE: demo/models/siamese-face-net-architecture.h5
================================================
{"class_name": "Model", "config": {"name": "model_2", "layers": [{"name": "input_4", "class_name": "InputLayer", "config": {"batch_input_shape": [null, 1, 1000], "dtype": "float32", "sparse": false, "name": "input_4"}, "inbound_nodes": []}, {"name": "input_5", "class_name": "InputLayer", "config": {"batch_input_shape": [null, 1, 1000], "dtype": "float32", "sparse": false, "name": "input_5"}, "inbound_nodes": []}, {"name": "model_1", "class_name": "Model", "config": {"name": "model_1", "layers": [{"name": "input_3", "class_name": "InputLayer", "config": {"batch_input_shape": [null, 1, 1000], "dtype": "float32", "sparse": false, "name": "input_3"}, "inbound_nodes": []}, {"name": "flatten_1", "class_name": "Flatten", "config": {"name": "flatten_1", "trainable": true}, "inbound_nodes": [[["input_3", 0, 0, {}]]]}, {"name": "dense_1", "class_name": "Dense", "config": {"name": "dense_1", "trainable": true, "units": 128, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["flatten_1", 0, 0, {}]]]}, {"name": "dropout_1", "class_name": "Dropout", "config": {"name": "dropout_1", "trainable": true, "rate": 0.1, "noise_shape": null, "seed": null}, "inbound_nodes": [[["dense_1", 0, 0, {}]]]}, {"name": "dense_2", "class_name": "Dense", "config": {"name": "dense_2", "trainable": true, "units": 128, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["dropout_1", 0, 0, {}]]]}, {"name": "dropout_2", "class_name": "Dropout", "config": {"name": "dropout_2", "trainable": true, "rate": 0.1, "noise_shape": null, "seed": null}, "inbound_nodes": [[["dense_2", 0, 0, {}]]]}, {"name": "dense_3", "class_name": "Dense", "config": {"name": "dense_3", "trainable": true, "units": 128, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["dropout_2", 0, 0, {}]]]}], "input_layers": [["input_3", 0, 0]], "output_layers": [["dense_3", 0, 0]]}, "inbound_nodes": [[["input_4", 0, 0, {}]], [["input_5", 0, 0, {}]]]}, {"name": "lambda_1", "class_name": "Lambda", "config": {"name": "lambda_1", "trainable": true, "function": ["4wEAAAAAAAAAAwAAAAcAAABDAAAAczQAAAB8AFwCfQF9AnQAagF0AGoCdABqA3QAagR8AXwCGACD\nAWQBZAJkA40DdABqBYMAgwKDAVMAKQRO6QEAAABUKQLaBGF4aXPaCGtlZXBkaW1zKQbaAUvaBHNx\ncnTaB21heGltdW3aA3N1bdoGc3F1YXJl2gdlcHNpbG9uKQPaBXZlY3Rz2gF42gF5qQByDQAAAPo7\nQzovVXNlcnMvY2hlbjAvZ2l0L2tlcmFzLWZhY2Uva2VyYXNfZmFjZS9saWJyYXJ5L3NpYW1lc2Uu\ncHnaEmV1Y2xpZGVhbl9kaXN0YW5jZREAAABzBAAAAAABCAE=\n", null, null], "function_type": "lambda", "output_shape": ["4wEAAAAAAAAAAwAAAAIAAABDAAAAcxQAAAB8AFwCfQF9AnwBZAEZAGQCZgJTACkDTukAAAAA6QEA\nAACpACkD2gZzaGFwZXPaBnNoYXBlMdoGc2hhcGUycgMAAAByAwAAAPo7QzovVXNlcnMvY2hlbjAv\nZ2l0L2tlcmFzLWZhY2Uva2VyYXNfZmFjZS9saWJyYXJ5L3NpYW1lc2UucHnaFmV1Y2xfZGlzdF9v\ndXRwdXRfc2hhcGUWAAAAcwQAAAAAAQgB\n", null, null], "output_shape_type": "lambda", "arguments": {}}, "inbound_nodes": [[["model_1", 1, 0, {}], ["model_1", 2, 0, {}]]]}], "input_layers": [["input_4", 0, 0], ["input_5", 0, 0]], "output_layers": [["lambda_1", 0, 0]]}, "keras_version": "2.1.2", "backend": "tensorflow"}

================================================
FILE: demo/models/weights/bn1_b.csv
================================================
0.44082018733025,0.46729332208633,0.30322399735451,0.25438579916954,0.14921997487545,-0.0634535998106,-0.0087375286966562,0.087883867323399,0.16671289503574,0.24108871817589,0.47102379798889,0.19574452936649,0.71433508396149,0.37009617686272,0.33681431412697,-0.43196699023247,0.40930795669556,-0.37930861115456,0.33314296603203,0.23987257480621,0.076872996985912,0.22440548241138,0.1588883548975,-0.57838326692581,0.53369057178497,-0.07314345240593,0.30506387352943,0.68640786409378,-0.014614156447351,-0.033059660345316,0.44868624210358,0.18839684128761,0.22564433515072,0.34911566972733,-0.36880820989609,0.15457643568516,0.12065568566322,0.13494712114334,0.26464408636093,0.12797358632088,-0.31054544448853,-0.65417903661728,0.15118357539177,0.3181779384613,0.19564405083656,0.29291039705276,0.10827668011189,0.14236836135387,0.3719394505024,-0.85279095172882,0.53291606903076,0.06343125551939,0.074712976813316,0.19050298631191,0.22311590611935,0.75255787372589,-0.0094652092084289,0.25958821177483,0.070107035338879,0.425137758255,0.26559129357338,0.38788056373596,0.19769689440727,0.22403760254383


================================================
FILE: demo/models/weights/bn1_m.csv
================================================
-0.065506957471371,0.22722293436527,-0.578941822052,-0.14345446228981,0.66680991649628,-1.5636328458786,0.16514182090759,-1.2245861291885,-0.33862826228142,0.29384651780128,0.65359896421432,-1.0146083831787,-0.63312292098999,-0.10630244761705,0.043717667460442,1.0194334983826,0.38585233688354,1.8948419094086,-0.25858378410339,-0.43867608904839,-0.14805756509304,-0.10971843451262,0.063247673213482,0.81110084056854,-0.049584161490202,-0.076637499034405,-1.3350341320038,-0.1760409027338,-0.2630997300148,0.26457947492599,-0.39757370948792,-0.022711671888828,0.070588193833828,0.50485640764236,1.7002086639404,-0.16955614089966,-0.23769243061543,-0.067097716033459,0.020806768909097,-0.19647659361362,2.2399952411652,-1.4373537302017,-0.15111850202084,0.35617941617966,0.22343611717224,0.16994704306126,0.84415984153748,0.1205222979188,-0.88202464580536,-2.0284471511841,-0.68921321630478,-0.19959637522697,-0.031295742839575,0.095686532557011,0.16916450858116,-0.40878468751907,-0.053212083876133,-0.13638387620449,-0.77771198749542,-0.81007432937622,-0.13235683739185,0.94929683208466,-0.5961104631424,0.28273570537567


================================================
FILE: demo/models/weights/bn1_v.csv
================================================
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================================================
FILE: demo/models/weights/bn1_w.csv
================================================
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================================================
FILE: demo/models/weights/bn2_b.csv
================================================
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================================================
FILE: demo/models/weights/bn2_m.csv
================================================
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================================================
FILE: demo/models/weights/bn2_v.csv
================================================
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================================================
FILE: demo/models/weights/bn2_w.csv
================================================
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FILE: demo/models/weights/bn3_m.csv
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FILE: demo/models/weights/bn3_v.csv
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FILE: demo/models/weights/bn3_w.csv
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FILE: demo/models/weights/conv1_b.csv
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FILE: demo/models/weights/conv1_w.csv
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