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Generator.py
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from keras.preprocessing import image
import numpy as np
import keras.backend as K
import os
import cv2
class DriveDataGenerator(image.ImageDataGenerator):
def __init__(self,
featurewise_center=False,
samplewise_center=False,
featurewise_std_normalization=False,
samplewise_std_normalization=False,
zca_whitening=False,
zca_epsilon=1e-6,
rotation_range=0.,
width_shift_range=0.,
height_shift_range=0.,
shear_range=0.,
zoom_range=0.,
channel_shift_range=0.,
fill_mode='nearest',
cval=0.,
horizontal_flip=False,
vertical_flip=False,
rescale=None,
preprocessing_function=None,
data_format=None,
brighten_range=0):
super(DriveDataGenerator, self).__init__(featurewise_center,
samplewise_center,
featurewise_std_normalization,
samplewise_std_normalization,
zca_whitening,
zca_epsilon,
rotation_range,
width_shift_range,
height_shift_range,
shear_range,
zoom_range,
channel_shift_range,
fill_mode,
cval,
horizontal_flip,
vertical_flip,
rescale,
preprocessing_function,
data_format)
self.brighten_range = brighten_range
def flow(self, x_images, x_prev_states = None, y=None, batch_size=32, shuffle=True, seed=None,
save_to_dir=None, save_prefix='', save_format='png', zero_drop_percentage=0.5, roi=None):
return DriveIterator(
x_images, x_prev_states, y, self,
batch_size=batch_size,
shuffle=shuffle,
seed=seed,
data_format=self.data_format,
save_to_dir=save_to_dir,
save_prefix=save_prefix,
save_format=save_format,
zero_drop_percentage=zero_drop_percentage,
roi=roi)
def random_transform_with_states(self, x, seed=None):
"""Randomly augment a single image tensor.
# Arguments
x: 3D tensor, single image.
seed: random seed.
# Returns
A tuple. 0 -> randomly transformed version of the input (same shape). 1 -> true if image was horizontally flipped, false otherwise
"""
# x is a single image, so it doesn't have image number at index 0
img_row_axis = self.row_axis
img_col_axis = self.col_axis
img_channel_axis = self.channel_axis
is_image_horizontally_flipped = False
# use composition of homographies
# to generate final transform that needs to be applied
if self.rotation_range:
theta = np.pi / 180 * np.random.uniform(-self.rotation_range, self.rotation_range)
else:
theta = 0
if self.height_shift_range:
tx = np.random.uniform(-self.height_shift_range, self.height_shift_range) * x.shape[img_row_axis]
else:
tx = 0
if self.width_shift_range:
ty = np.random.uniform(-self.width_shift_range, self.width_shift_range) * x.shape[img_col_axis]
else:
ty = 0
if self.shear_range:
shear = np.random.uniform(-self.shear_range, self.shear_range)
else:
shear = 0
if self.zoom_range[0] == 1 and self.zoom_range[1] == 1:
zx, zy = 1, 1
else:
zx, zy = np.random.uniform(self.zoom_range[0], self.zoom_range[1], 2)
transform_matrix = None
if theta != 0:
rotation_matrix = np.array([[np.cos(theta), -np.sin(theta), 0],
[np.sin(theta), np.cos(theta), 0],
[0, 0, 1]])
transform_matrix = rotation_matrix
if tx != 0 or ty != 0:
shift_matrix = np.array([[1, 0, tx],
[0, 1, ty],
[0, 0, 1]])
transform_matrix = shift_matrix if transform_matrix is None else np.dot(transform_matrix, shift_matrix)
if shear != 0:
shear_matrix = np.array([[1, -np.sin(shear), 0],
[0, np.cos(shear), 0],
[0, 0, 1]])
transform_matrix = shear_matrix if transform_matrix is None else np.dot(transform_matrix, shear_matrix)
if zx != 1 or zy != 1:
zoom_matrix = np.array([[zx, 0, 0],
[0, zy, 0],
[0, 0, 1]])
transform_matrix = zoom_matrix if transform_matrix is None else np.dot(transform_matrix, zoom_matrix)
if transform_matrix is not None:
h, w = x.shape[img_row_axis], x.shape[img_col_axis]
transform_matrix = image.transform_matrix_offset_center(transform_matrix, h, w)
x = image.apply_transform(x, transform_matrix, img_channel_axis,
fill_mode=self.fill_mode, cval=self.cval)
if self.channel_shift_range != 0:
x = image.random_channel_shift(x,
self.channel_shift_range,
img_channel_axis)
if self.horizontal_flip:
if np.random.random() < 0.5:
x = image.flip_axis(x, img_col_axis)
is_image_horizontally_flipped = True
if self.vertical_flip:
if np.random.random() < 0.5:
x = image.flip_axis(x, img_row_axis)
if self.brighten_range != 0:
random_bright = np.random.uniform(low = 1.0-self.brighten_range, high=1.0+self.brighten_range)
#TODO: Write this as an apply to push operations into C for performance
img = cv2.cvtColor(x, cv2.COLOR_RGB2HSV)
img[:, :, 2] = np.clip(img[:, :, 2] * random_bright, 0, 255)
x = cv2.cvtColor(img, cv2.COLOR_HSV2RGB)
return (x, is_image_horizontally_flipped)
class DriveIterator(image.Iterator):
"""Iterator yielding data from a Numpy array.
# Arguments
x: Numpy array of input data.
y: Numpy array of targets data.
image_data_generator: Instance of `ImageDataGenerator`
to use for random transformations and normalization.
batch_size: Integer, size of a batch.
shuffle: Boolean, whether to shuffle the data between epochs.
seed: Random seed for data shuffling.
data_format: String, one of `channels_first`, `channels_last`.
save_to_dir: Optional directory where to save the pictures
being yielded, in a viewable format. This is useful
for visualizing the random transformations being
applied, for debugging purposes.
save_prefix: String prefix to use for saving sample
images (if `save_to_dir` is set).
save_format: Format to use for saving sample images
(if `save_to_dir` is set).
"""
def __init__(self, x_images, x_prev_states, y, image_data_generator,
batch_size=32, shuffle=False, seed=None,
data_format=None,
save_to_dir=None, save_prefix='', save_format='png', zero_drop_percentage = 0.5, roi = None):
if y is not None and len(x_images) != len(y):
raise ValueError('X (images tensor) and y (labels) '
'should have the same length. '
'Found: X.shape = %s, y.shape = %s' %
(np.asarray(x_images).shape, np.asarray(y).shape))
if data_format is None:
data_format = K.image_data_format()
self.x_images = x_images
self.zero_drop_percentage = zero_drop_percentage
self.roi = roi
if self.x_images.ndim != 4:
raise ValueError('Input data in `NumpyArrayIterator` '
'should ave rank 4. You passed an array '
'with shape', self.x_images.shape)
channels_axis = 3 if data_format == 'channels_last' else 1
if self.x_images.shape[channels_axis] not in {1, 3, 4}:
raise ValueError('NumpyArrayIterator is set to use the '
'data format convention "' + data_format + '" '
'(channels on axis ' + str(channels_axis) + '), i.e. expected '
'either 1, 3 or 4 channels on axis ' + str(channels_axis) + '. '
'However, it was passed an array with shape ' + str(self.x_images.shape) +
' (' + str(self.x_images.shape[channels_axis]) + ' channels).')
if x_prev_states is not None:
self.x_prev_states = x_prev_states
else:
self.x_prev_states = None
if y is not None:
self.y = y
else:
self.y = None
self.image_data_generator = image_data_generator
self.data_format = data_format
self.save_to_dir = save_to_dir
self.save_prefix = save_prefix
self.save_format = save_format
self.batch_size = batch_size
super(DriveIterator, self).__init__(x_images.shape[0], batch_size, shuffle, seed)
def next(self):
"""For python 2.x.
# Returns
The next batch.
"""
# Keeps under lock only the mechanism which advances
# the indexing of each batch.
with self.lock:
index_array = next(self.index_generator)
# The transformation of images is not under thread lock
# so it can be done in parallel
return self.__get_indexes(index_array)
def __get_indexes(self, index_array):
index_array = sorted(index_array)
if self.x_prev_states is not None:
batch_x_images = np.zeros(tuple([self.batch_size]+ list(self.x_images.shape)[1:]),
dtype=K.floatx())
batch_x_prev_states = np.zeros(tuple([self.batch_size]+list(self.x_prev_states.shape)[1:]), dtype=K.floatx())
else:
batch_x_images = np.zeros(tuple([self.batch_size] + list(self.x_images.shape)[1:]), dtype=K.floatx())
if self.roi is not None:
batch_x_images = batch_x_images[:, self.roi[0]:self.roi[1], self.roi[2]:self.roi[3], :]
used_indexes = []
is_horiz_flipped = []
for i, j in enumerate(index_array):
x_images = self.x_images[j]
if self.roi is not None:
x_images = x_images[self.roi[0]:self.roi[1], self.roi[2]:self.roi[3], :]
transformed = self.image_data_generator.random_transform_with_states(x_images.astype(K.floatx()))
x_images = transformed[0]
is_horiz_flipped.append(transformed[1])
x_images = self.image_data_generator.standardize(x_images)
batch_x_images[i] = x_images
if self.x_prev_states is not None:
x_prev_states = self.x_prev_states[j]
if (transformed[1]):
x_prev_states[0] *= -1.0
batch_x_prev_states[i] = x_prev_states
used_indexes.append(j)
if self.x_prev_states is not None:
batch_x = [np.asarray(batch_x_images), np.asarray(batch_x_prev_states)]
else:
batch_x = np.asarray(batch_x_images)
if self.save_to_dir:
for i in range(0, self.batch_size, 1):
hash = np.random.randint(1e4)
img = image.array_to_img(batch_x_images[i], self.data_format, scale=True)
fname = '{prefix}_{index}_{hash}.{format}'.format(prefix=self.save_prefix,
index=1,
hash=hash,
format=self.save_format)
img.save(os.path.join(self.save_to_dir, fname))
batch_y = self.y[list(sorted(used_indexes))]
idx = []
for i in range(0, len(is_horiz_flipped), 1):
if batch_y.shape[1] == 1:
if (is_horiz_flipped[i]):
batch_y[i] *= -1
if (np.isclose(batch_y[i], 0)):
if (np.random.uniform(low=0, high=1) > self.zero_drop_percentage):
idx.append(True)
else:
idx.append(False)
else:
idx.append(True)
else:
if (batch_y[i][int(len(batch_y[i])/2)] == 1):
if (np.random.uniform(low=0, high=1) > self.zero_drop_percentage):
idx.append(True)
else:
idx.append(False)
else:
idx.append(True)
if (is_horiz_flipped[i]):
batch_y[i] = batch_y[i][::-1]
batch_y = batch_y[idx]
batch_x[0] = batch_x[0][idx]
batch_x[1] = batch_x[1][idx]
return batch_x, batch_y
def _get_batches_of_transformed_samples(self, index_array):
return self.__get_indexes(index_array)