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# hop_len=int(win_len/4) # default
# fft_len=pow(2, int(np.log2(win_len)+1))
fft_len = 2048
win_len = fft_len
hop_len = int(win_len/4)

def spectrogramOp(X):
  spectrogram_out = librosa.core.stft(X, n_fft=fft_len, hop_length=hop_len, win_length=win_len, center=True)
  return np.absolute(spectrogram_out)

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onehot_labels = np.zeros((input_labels.size, n_classes)).astype(np.int32)
onehot_labels[np.arange(input_labels.size), input_labels] = 1

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add_noise = False # add different words, null samples and random noise
n_classes = len(hotwords) + int(add_noise) 

class_nSamples = 1000 # number of samples in the hotword classes
other_nSamples = float(class_nSamples)/(len(word_dirs) - n_classes) # number of samples to be picked from each of the non-hotword classes

def nLabel(word):
    return n_classes-1 if ( word not in hotwords ) else hotwords.index(word)

def textLabel(index):

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#Processing the data
dataset_min = 0.0
dataset_max = 1.0

def denormalize_dataset(input_val):
  global dataset_min, dataset_max
  return input_val * (dataset_max - dataset_min)

#Function to normalize input values
def normalize_dataset(input_val):

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max_length = 4 # length (in seconds) of input
desired_sr = 16000 # sampling rate to use
mic_sr = 16000 # rate supported by Sampling library like PDM
desired_samples = max_length*desired_sr # total number of samples in input

tf.random.set_seed(0)
np.random.seed(0)
os.environ['PYTHONHASHSEED'] = '0'

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word_dirs = list(set(df['class'].to_list()))
hotwords = ['jackhammer', 'dog_bark', 'siren', 'gun_shot']

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# pick random test data sample from one batch
x = random.randint(0, 41) # test set has 42 samples

for i in test_ds.as_numpy_iterator():
    img, label = i    
    plt.axis('off')   # remove axes
    #print(img.shape, x)
    plt.imshow(img[x])    # shape from (64, 256, 256, 3) --> (256, 256, 3)
    output = model.predict(np.expand_dims(img[x],0))    # getting output; input shape (256, 256, 3) --> (1, 256, 256, 3)
    pred = np.argmax(output[0])    # finding max

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base_model = keras.applications.DenseNet121(weights='imagenet', input_shape=input_shape, include_top=False)    # False, do not include the classification layer of the model
base_model.trainable = False

inputs = tf.keras.Input(shape=input_shape)

x = base_model(inputs, training=False)
x = keras.layers.GlobalAveragePooling2D()(x)
outputs = keras.layers.Dense(len(class_names), activation = 'softmax')(x)    # Add own classififcation layer

model = keras.Model(inputs, outputs)

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# Normalizing the pixel values for faster convergence

normalization_layer = layers.experimental.preprocessing.Rescaling(1./255)

train_ds = train_ds.map(lambda x, y: (normalization_layer(x), y))
test_ds = test_ds.map(lambda x, y: (normalization_layer(x), y))

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# Loading the dataset

path = 'currency/'
input_shape = (256, 256, 3)    # default input shape while loading the images

batch = 64

# The train and test datasets
print("Train dataset")
train_ds = preprocessing.image_dataset_from_directory(path+'Train', batch_size=batch, label_mode='categorical')