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• keras-video-classifier-master
  • demo
    • vgg16_bidirectional_lstm_hi_dim_predict.py
    • reports
      • UCF-101
    • vgg16_lstm_hi_dim_predict.py
    • vgg16_lstm_predict.py
    • vgg16_lstm_train.py
    • very_large_data
      • .gitignore
    • models
      • UCF-101
        • conv3d-architecture.json
        • vgg16-bidirectional-lstm-architecture.json
        • cnn-architecture.json
        • cnn-config.npy
        • vgg16-bidirectional-lstm-config.npy
        • vgg16-lstm-architecture.json
        • vgg16-lstm-config.npy
        • vgg16-lstm-hi-dim-architecture.json
        • vgg16-lstm-hi-dim-config.npy
        • .gitignore
        • vgg16-bidirectional-lstm-hi-dim-config.npy
        • vgg16-bidirectional-lstm-hi-dim-architecture.json
    • cnn_train.py
    • vgg16_bidirectional_lstm_train.py
    • vgg16_bidirectional_lstm_hi_dim_train.py
    • vgg16_lstm_hi_dim_train.py
    • __init__.py
    • vgg16_bidirectional_lstm_predict.py
    • cnn_predict.py
  • LICENSE
  • requirements-on-my-python-env.txt
  • setup.py
  • keras_video_classifier
    • library
      • convolutional.py
      • recurrent_networks.py
      • utility
        • __init__.py
        • device_utils.py
        • ucf
          • UCF101_loader.py
          • __init__.py
        • plot_utils.py
        • frame_extractors
          • vgg16_feature_extractor.py
          • frame_extractor.py
          • __init__.py
      • __init__.py
    • __init__.py
  • notes
    • ReadMe.md
  • setup.cfg
  • README.md
  • requirements.txt
  • .gitignore

import numpy as np
from keras import backend as K
import os
import sys


def main():
    K.set_image_dim_ordering('tf')
    sys.path.append(os.path.join(os.path.dirname(__file__), '..'))

    from keras_video_classifier.library.utility.plot_utils import plot_and_save_history
    from keras_video_classifier.library.recurrent_networks import VGG16LSTMVideoClassifier
    from keras_video_classifier.library.utility.ucf.UCF101_loader import load_ucf

    data_set_name = 'UCF-101'
    input_dir_path = os.path.join(os.path.dirname(__file__), 'very_large_data')
    output_dir_path = os.path.join(os.path.dirname(__file__), 'models', data_set_name)
    report_dir_path = os.path.join(os.path.dirname(__file__), 'reports', data_set_name)

    np.random.seed(42)

    # this line downloads the video files of UCF-101 dataset if they are not available in the very_large_data folder
    load_ucf(input_dir_path)

    classifier = VGG16LSTMVideoClassifier()

    history = classifier.fit(data_dir_path=input_dir_path, model_dir_path=output_dir_path, vgg16_include_top=False,
                             data_set_name=data_set_name)

    plot_and_save_history(history, VGG16LSTMVideoClassifier.model_name,
                          report_dir_path + '/' + VGG16LSTMVideoClassifier.model_name + '-hi-dim-history.png')


if __name__ == '__main__':
    main()