/
generator.py
469 lines (393 loc) · 19.1 KB
/
generator.py
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'''
Credit to https://github.com/pkern90/behavioral-cloning/blob/master/utils.py
No license provided
'''
import scipy.misc as spm
from keras.preprocessing.image import *
def normalize(images, new_max, new_min, old_max=None, old_min=None):
if old_min is None:
old_min = np.min(images)
if old_max is None:
old_max = np.max(images)
return (images - old_min) * ((new_max - new_min) / (old_max - old_min)) + new_min
def crop_image(img, cropping):
return img[cropping[0]:img.shape[0] - cropping[1], cropping[2]:img.shape[1] - cropping[3], :]
def get_cropped_shape(img_shape, cropping):
return (img_shape[0] - cropping[0] - cropping[1],
img_shape[1] - cropping[2] - cropping[3],
img_shape[2])
def resize_image(img, size):
return spm.imresize(img, size)
def extract_filename(path):
return path.split('/')[-1]
def adjust_path(path, new_location):
return '%s/%s' % (new_location, extract_filename(path))
def load_images(paths, target_size):
images = np.zeros((len(paths), *target_size, 3))
for i, p in enumerate(paths):
img = load_img(p, target_size=target_size)
img = img_to_array(img, dim_ordering='tf')
images[i] = img
return images
class RegressionImageDataGenerator(object):
"""Generate minibatches with
real-time data augmentation.
This implementation is a modified version of the ImageDataGenerator from Keras
(https://github.com/fchollet/keras/blob/master/keras/preprocessing/image.py).
# Arguments
featurewise_center: set input mean to 0 over the dataset.
samplewise_center: set each sample mean to 0.
featurewise_std_normalization: divide inputs by std of the dataset.
samplewise_std_normalization: divide each input by its std.
zca_whitening: apply ZCA whitening.
rotation_range: degrees (0 to 180).
rotation_value_transform: function to modify the label based on the rotation.
width_shift_range: fraction of total width.
width_shift_value_transform: function to modify the label based on the width_shift.
height_shift_range: fraction of total height.
height_shift_value_transform: function to modify the label based on the height_shift.
shear_range: shear intensity (shear angle in radians).
shear_value_transform: function to modify the label based on the shear.
zoom_range: amount of zoom. if scalar z, zoom will be randomly picked
in the range [1-z, 1+z]. A sequence of two can be passed instead
to select this range.
zoom_value_transform: function to modify the label based on the zoom.
channel_shift_range: shift range for each channels.
fill_mode: points outside the boundaries are filled according to the
given mode ('constant', 'nearest', 'reflect' or 'wrap'). Default
is 'nearest'.
cval: value used for points outside the boundaries when fill_mode is
'constant'. Default is 0.
horizontal_flip: whether to randomly flip images horizontally.
horizontal_flip_value_transform: function to modify the label based on the horizontal_flip.
vertical_flip: whether to randomly flip images vertically.
vertical_flip_value_transform: function to modify the label based on the vertical_flip.
rescale: rescaling factor. If None or 0, no rescaling is applied,
otherwise we multiply the data by the value provided (before applying
any other transformation).
dim_ordering: 'th' or 'tf'. In 'th' mode, the channels dimension
(the depth) is at index 1, in 'tf' mode it is at index 3.
It defaults to the `image_dim_ordering` value found in your
Keras config file at `~/.keras/keras.json`.
If you never set it, then it will be "th".
"""
def __init__(self,
featurewise_center=False,
samplewise_center=False,
featurewise_std_normalization=False,
samplewise_std_normalization=False,
zca_whitening=False,
rotation_range=0.,
rotation_value_transform=None,
width_shift_range=0.,
width_shift_value_transform=None,
height_shift_range=0.,
height_shift_value_transform=None,
shear_range=0.,
shear_value_transform=None,
zoom_range=0.,
zoom_value_transform=None,
channel_shift_range=0.,
fill_mode='nearest',
cval=0.,
horizontal_flip=False,
horizontal_flip_value_transform=None,
vertical_flip=False,
vertical_flip_value_transform=None,
rescale=None,
dim_ordering='default',
cropping=(0, 0, 0, 0)):
if dim_ordering == 'default':
dim_ordering = K.image_dim_ordering()
self.__dict__.update(locals())
self.mean = None
self.std = None
self.principal_components = None
self.rescale = rescale
if dim_ordering not in {'tf', 'th'}:
raise Exception('dim_ordering should be "tf" (channel after row and '
'column) or "th" (channel before row and column). '
'Received arg: ', dim_ordering)
self.dim_ordering = dim_ordering
if dim_ordering == 'th':
self.channel_index = 1
self.row_index = 2
self.col_index = 3
if dim_ordering == 'tf':
self.channel_index = 3
self.row_index = 1
self.col_index = 2
if np.isscalar(zoom_range):
self.zoom_range = [1 - zoom_range, 1 + zoom_range]
elif len(zoom_range) == 2:
self.zoom_range = [zoom_range[0], zoom_range[1]]
else:
raise Exception('zoom_range should be a float or '
'a tuple or list of two floats. '
'Received arg: ', zoom_range)
self.cropping = cropping
def flow(self, X, y=None, batch_size=32, shuffle=True, seed=None,
save_to_dir=None, save_prefix='', save_format='jpeg'):
return RegressionNumpyArrayIterator(
X, y, self,
batch_size=batch_size, shuffle=shuffle, seed=seed,
dim_ordering=self.dim_ordering,
save_to_dir=save_to_dir, save_prefix=save_prefix, save_format=save_format)
def flow_from_directory(self, directory, values,
target_size=(256, 256), color_mode='rgb',
batch_size=32, shuffle=True, seed=None,
save_to_dir=None, save_prefix='', save_format='jpeg'):
return RegressionDirectoryIterator(
directory, values, self,
target_size=target_size, color_mode=color_mode,
dim_ordering=self.dim_ordering,
batch_size=batch_size, shuffle=shuffle, seed=seed,
save_to_dir=save_to_dir, save_prefix=save_prefix, save_format=save_format)
def crop(self, x):
return crop_image(x, self.cropping)
def standardize(self, x):
if self.rescale:
if callable(self.rescale):
x = self.rescale(x)
else:
x *= self.rescale
# x is a single image, so it doesn't have image number at index 0
img_channel_index = self.channel_index - 1
if self.samplewise_center:
x -= np.mean(x, axis=img_channel_index, keepdims=True)
if self.samplewise_std_normalization:
x /= (np.std(x, axis=img_channel_index, keepdims=True) + 1e-7)
if self.featurewise_center:
x -= self.mean
if self.featurewise_std_normalization:
x /= (self.std + 1e-7)
if self.zca_whitening:
flatx = np.reshape(x, (x.size))
whitex = np.dot(flatx, self.principal_components)
x = np.reshape(whitex, (x.shape[0], x.shape[1], x.shape[2]))
return x
def random_transform(self, x, y):
# x is a single image, so it doesn't have image number at index 0
img_row_index = self.row_index - 1
img_col_index = self.col_index - 1
img_channel_index = self.channel_index - 1
# 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
rotation_matrix = np.array([[np.cos(theta), -np.sin(theta), 0],
[np.sin(theta), np.cos(theta), 0],
[0, 0, 1]])
if self.rotation_value_transform:
y = self.rotation_value_transform(y, theta)
if self.height_shift_range:
px = np.random.uniform(-self.height_shift_range, self.height_shift_range)
tx = px * x.shape[img_row_index]
else:
tx = 0
if self.height_shift_value_transform:
y = self.height_shift_value_transform(y, px)
if self.width_shift_range:
py = np.random.uniform(-self.width_shift_range, self.width_shift_range)
ty = py * x.shape[img_col_index]
else:
ty = 0
if self.width_shift_value_transform:
y = self.width_shift_value_transform(y, py)
translation_matrix = np.array([[1, 0, tx],
[0, 1, ty],
[0, 0, 1]])
if self.shear_range:
shear = np.random.uniform(-self.shear_range, self.shear_range)
else:
shear = 0
shear_matrix = np.array([[1, -np.sin(shear), 0],
[0, np.cos(shear), 0],
[0, 0, 1]])
if self.shear_value_transform:
y = self.shear_value_transform(y, shear)
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)
zoom_matrix = np.array([[zx, 0, 0],
[0, zy, 0],
[0, 0, 1]])
if self.zoom_value_transform:
y = self.zoom_value_transform(y, zx, zy)
transform_matrix = np.dot(np.dot(np.dot(rotation_matrix, translation_matrix), shear_matrix), zoom_matrix)
h, w = x.shape[img_row_index], x.shape[img_col_index]
transform_matrix = transform_matrix_offset_center(transform_matrix, h, w)
x = apply_transform(x, transform_matrix, img_channel_index,
fill_mode=self.fill_mode, cval=self.cval)
if self.channel_shift_range != 0:
x = random_channel_shift(x, self.channel_shift_range, img_channel_index)
if self.horizontal_flip:
if np.random.random() < 0.5:
x = flip_axis(x, img_col_index)
if self.horizontal_flip_value_transform:
y = self.horizontal_flip_value_transform(y)
if self.vertical_flip:
if np.random.random() < 0.5:
x = flip_axis(x, img_row_index)
if self.vertical_flip_value_transform:
y = self.vertical_flip_value_transform(y)
return x, y
def fit(self, X,
augment=False,
rounds=1,
seed=None):
'''Required for featurewise_center, featurewise_std_normalization
and zca_whitening.
# Arguments
X: Numpy array, the data to fit on.
augment: whether to fit on randomly augmented samples
rounds: if `augment`,
how many augmentation passes to do over the data
seed: random seed.
'''
if seed is not None:
np.random.seed(seed)
X = np.copy(X)
cropped = np.zeros((X.shape[0], *get_cropped_shape(X.shape[1:], self.cropping)))
for i, img in enumerate(X):
cropped[i] = self.crop(img)
X = cropped
if augment:
aX = np.zeros(tuple([rounds * X.shape[0]] + list(X.shape)[1:]))
for r in range(rounds):
for i in range(X.shape[0]):
aX[i + r * X.shape[0]] = self.random_transform(X[i])
X = aX
if self.featurewise_center:
self.mean = np.mean(X, axis=0)
X -= self.mean
if self.featurewise_std_normalization:
self.std = np.std(X, axis=0)
X /= (self.std + 1e-7)
if self.zca_whitening:
flatX = np.reshape(X, (X.shape[0], X.shape[1] * X.shape[2] * X.shape[3]))
sigma = np.dot(flatX.T, flatX) / flatX.shape[0]
U, S, V = linalg.svd(sigma)
self.principal_components = np.dot(np.dot(U, np.diag(1. / np.sqrt(S + 10e-7))), U.T)
class RegressionNumpyArrayIterator(Iterator):
"""
This implementation is a modified version of the NumpyArrayIterator from Keras
(https://github.com/fchollet/keras/blob/master/keras/preprocessing/image.py).
"""
def __init__(self, X, y, image_data_generator,
batch_size=32, shuffle=False, seed=None,
dim_ordering='default',
save_to_dir=None, save_prefix='', save_format='jpeg'):
if y is not None and len(X) != len(y):
raise Exception('X (images tensor) and y (labels) '
'should have the same length. '
'Found: X.shape = %s, y.shape = %s' % (np.asarray(X).shape, np.asarray(y).shape))
if dim_ordering == 'default':
dim_ordering = K.image_dim_ordering()
self.X = X
self.y = y
self.image_data_generator = image_data_generator
self.dim_ordering = dim_ordering
self.save_to_dir = save_to_dir
self.save_prefix = save_prefix
self.save_format = save_format
super(RegressionNumpyArrayIterator, self).__init__(X.shape[0], batch_size, shuffle, seed)
def next(self):
# for python 2.x.
# Keeps under lock only the mechanism which advances
# the indexing of each batch
# see http://anandology.com/blog/using-iterators-and-generators/
with self.lock:
index_array, current_index, current_batch_size = next(self.index_generator)
# The transformation of images is not under thread lock so it can be done in parallel
output_shape = get_cropped_shape(self.X[0].shape, self.image_data_generator.cropping)
batch_x = np.zeros((current_batch_size,) + output_shape)
batch_y = np.zeros(current_batch_size)
for i, j in enumerate(index_array):
x = self.X[j]
y = self.y[j]
x = self.image_data_generator.crop(x)
x, y = self.image_data_generator.random_transform(x, y)
x = self.image_data_generator.standardize(x)
batch_x[i] = x
batch_y[i] = y
if self.save_to_dir:
for i in range(current_batch_size):
img = array_to_img(batch_x[i], self.dim_ordering, scale=True)
fname = '{prefix}_{index}_{hash}.{format}'.format(prefix=self.save_prefix,
index=current_index + i,
hash=np.random.randint(1e4),
format=self.save_format)
img.save(os.path.join(self.save_to_dir, fname))
return batch_x, batch_y
class RegressionDirectoryIterator(Iterator):
"""
This implementation is a modified version of the DirectoryIterator from Keras
(https://github.com/fchollet/keras/blob/master/keras/preprocessing/image.py).
"""
def __init__(self, paths, values, image_data_generator,
target_size=(256, 256), color_mode='rgb',
dim_ordering='default',
batch_size=32, shuffle=True, seed=None,
save_to_dir=None, save_prefix='', save_format='jpeg'):
if dim_ordering == 'default':
dim_ordering = K.image_dim_ordering()
self.paths = paths
self.values = values
self.image_data_generator = image_data_generator
self.target_size = tuple(target_size)
if color_mode not in {'rgb', 'grayscale'}:
raise ValueError('Invalid color mode:', color_mode,
'; expected "rgb" or "grayscale".')
self.color_mode = color_mode
self.dim_ordering = dim_ordering
if self.color_mode == 'rgb':
if self.dim_ordering == 'tf':
self.image_shape = self.target_size + (3,)
else:
self.image_shape = (3,) + self.target_size
else:
if self.dim_ordering == 'tf':
self.image_shape = self.target_size + (1,)
else:
self.image_shape = (1,) + self.target_size
self.save_to_dir = save_to_dir
self.save_prefix = save_prefix
self.save_format = save_format
self.nb_sample = len(paths)
self.nb_values = len(values)
if self.nb_sample != self.nb_values:
raise ValueError("Number of values=%d does not match "
"number of samples=%d" % (self.nb_values, self.nb_sample))
super(RegressionDirectoryIterator, self).__init__(self.nb_sample, batch_size, shuffle, seed)
def next(self):
with self.lock:
index_array, current_index, current_batch_size = next(self.index_generator)
# The transformation of images is not under thread lock so it can be done in parallel
output_shape = get_cropped_shape(self.image_shape, self.image_data_generator.cropping)
batch_x = np.zeros((current_batch_size,) + output_shape)
batch_y = np.zeros(current_batch_size)
grayscale = self.color_mode == 'grayscale'
# build batch of image data
for i, j in enumerate(index_array):
path = self.paths[j]
img = load_img(path, grayscale=grayscale, target_size=self.target_size)
y = self.values[j]
x = img_to_array(img, dim_ordering=self.dim_ordering)
x = self.image_data_generator.crop(x)
x, y = self.image_data_generator.random_transform(x, y)
x = self.image_data_generator.standardize(x)
batch_x[i] = x
batch_y[i] = y
# optionally save augmented images to disk for debugging purposes
if self.save_to_dir:
for i in range(current_batch_size):
img = array_to_img(batch_x[i], self.dim_ordering, scale=True)
fname = '{prefix}_{index}_{hash}.{format}'.format(prefix=self.save_prefix,
index=current_index + i,
hash=np.random.randint(1e4),
format=self.save_format)
img.save(os.path.join(self.save_to_dir, fname))
return batch_x, batch_y