Merge branch 'sound_dl_models' into 'master'

Add sound detection models

See merge request !19

User/views.py

@@ -429,6 +429,66 @@ def Customer_Frequency(request):
 def AdminHome(request):
     return render(request,'Admin/AdminHome.html')
 
+# tensorflow sound detection model
+# import tensorflow as tf
+# import tensorflow_io as tfio
+#
+# import tensorflow as tf
+# import tensorflow_io as tfio
+#
+# audio = tfio.audio.AudioIOTensor('gs://clouds-sample-tests/speech/brooklyn.flac')
+#
+# print(audio)
+#
+#
+# # remove last dimension
+# audio_tensor = tf.squeeze(audio_slice, axis=[-1])
+#
+# print(audio_tensor)
+#
+# from IPython.display import Audio
+#
+# Audio(audio_tensor.numpy(), rate=audio.rate.numpy())
+#
+#
+# import matplotlib.pyplot as plt
+#
+#
+# tensor = tf.cast(audio_tensor, tf.float32) / 32768.0
+#
+# plt.figure()
+# plt.plot(tensor.numpy())
+
+
+# pyaudio sound detection model
+# import numpy as np
+#
+# import warnings
+# warnings.filterwarnings("ignore")
+#
+# from pyaudioclassification import feature_extraction, train, predict, print_leaderboard
+#
+# parent_dir = '.'
+#
+#
+# if np.DataSource().exists("./feat.npy") and np.DataSource().exists("./label.npy"):
+#     features, labels = np.load('./feat.npy'), np.load('./label.npy')
+# else:
+#     features, labels = feature_extraction('./data/')
+#     np.save('./feat.npy', features)
+#     np.save('./label.npy', labels)
+#
+#
+# if np.DataSource().exists("./model.h5"):
+#     from keras.models import load_model
+#     model = load_model('./model.h5')
+# else:
+#     model = train(features, labels, epochs=100)
+#     model.save('./model.h5')
+
+
+
+