/
data_generator.py
72 lines (57 loc) · 2.49 KB
/
data_generator.py
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import numpy as np
import scipy.io
from tensorflow.keras.utils import to_categorical ,Sequence
from unwrap import unwrap
def normalize_real(x_source):
a_oo = x_source - x_source.real.min() - 1j * x_source.imag.min() # origin offsetted
return a_oo / np.abs(a_oo).max()
def normalize_angle(audio):
xaudio = (audio - np.min(audio)) / (np.max(audio) - np.min(audio))
# def normalize_angle (audio):
# audio= [item.flatten() for item in audio]
# audio = min_max_scaler.fit_transform(audio)
# audio= [item.reshape(256,256) for item in audio]
return xaudio
class DataGenerator(Sequence):
'Generates data for Keras'
def __init__(self, pair, class_map, batch_size=16, dim=(256, 256, 1), shuffle=True):
'Initialization'
self.dim = dim
self.pair = pair
self.class_map = class_map
self.batch_size = batch_size
self.shuffle = shuffle
self.on_epoch_end()
def __len__(self):
'Denotes the number of batches per epoch'
return int(np.floor(len(self.pair) / self.batch_size))
def __getitem__(self, index):
'Generate one batch of data'
# Generate indexes of the batch
indexes = self.indexes[index * self.batch_size:(index + 1) * self.batch_size]
# Find list of IDs
list_IDs_temp = [k for k in indexes]
# Generate data
X, y = self.__data_generation(list_IDs_temp)
return X, y
def on_epoch_end(self):
'Updates indexes after each epoch'
self.indexes = np.arange(len(self.pair))
if self.shuffle == True:
np.random.shuffle(self.indexes)
def __data_generation(self, list_IDs_temp):
'Generates data containing batch_size samples' # X : (n_samples, *dim, n_channels)
# Initialization
batch_imgs = list()
batch_labels = list()
# Generate data
for i in list_IDs_temp:
# Store sample
# print (self.pair[i][0])
img = scipy.io.loadmat(self.pair[i][0])['wrap']
img_normalized = normalize_angle(img)
batch_imgs.append(img_normalized)
label = unwrap(img, wrap_around_axis_0=False, wrap_around_axis_1=False, wrap_around_axis_2=False)
label_normalized = normalize_angle(label)
batch_labels.append(label_normalized)
return np.array(np.expand_dims(batch_imgs, axis=-1)), np.array(np.expand_dims(batch_labels, axis=-1))