def augmentation(self, X, Y):
print('Augmentation model...')
total = len(X)
x_train, y_train = [], []
for i in range(total):
if i % 100 == 0:
print('Aug', i)
x, y = X[i], Y[i]
#standart
x_train.append(x)
y_train.append(y)
for _ in range(2):
_x, _y = elastic_transform(x[0], y[0], 100, 20)
x_train.append(_x.reshape((1, ) + _x.shape))
y_train.append(_y.reshape((1, ) + _y.shape))
#flip x
x_train.append(flip_axis(x, 2))
y_train.append(flip_axis(y, 2))
#flip y
x_train.append(flip_axis(x, 1))
y_train.append(flip_axis(y, 1))
continue
#zoom
for _ in range(1):
_x, _y = random_zoom(x, y, (0.9, 1.1))
x_train.append(_x)
y_train.append(_y)
#intentsity
for _ in range(1):
_x = random_channel_shift(x, 5.0)
x_train.append(_x)
y_train.append(y)
# for j in range(5):
# xs, ys = load_aug(j)
# ys = self.norm_mask(ys)
# (xn, yn), _ = self.split_train_and_valid_by_patient(xs, ys, validation_split=self.validation_split, shuffle=False)
# for i in range(len(xn)):
# x_train.append(xn[i])
# y_train.append(yn[i])
x_train = np.array(x_train)
y_train = np.array(y_train)
return x_train, y_train
def augmentation(self, X, Y):
print('Augmentation model...')
total = len(X)
x_train, y_train = [], []
for i in xrange(total):
x, y = X[i], Y[i]
#standart
x_train.append(x)
y_train.append(y)
# for _ in xrange(1):
# _x, _y = elastic_transform(x[0], y[0], 100, 20)
# x_train.append(_x.reshape((1,) + _x.shape))
# y_train.append(_y.reshape((1,) + _y.shape))
#flip x
x_train.append(flip_axis(x, 2))
y_train.append(flip_axis(y, 2))
#flip y
x_train.append(flip_axis(x, 1))
y_train.append(flip_axis(y, 1))
#continue
#zoom
for _ in xrange(1):
_x, _y = random_zoom(x, y, (0.9, 1.1))
x_train.append(_x)
y_train.append(_y)
for _ in xrange(0):
_x, _y = random_rotation(x, y, 5)
x_train.append(_x)
y_train.append(_y)
#intentsity
for _ in xrange(1):
_x = random_channel_shift(x, 5.0)
x_train.append(_x)
y_train.append(y)
x_train = np.array(x_train)
y_train = np.array(y_train)
print('x_trian: ', x_train.shape)
return x_train, y_train
