def __init__(self,
featurewise_center=False,
samplewise_center=False,
featurewise_std_normalization=False,
samplewise_std_normalization=False,
zca_whitening=False,
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):
ImageDataGenerator.__init__(
self, featurewise_center, samplewise_center,
featurewise_std_normalization, samplewise_std_normalization,
zca_whitening, 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)
def __init__(self, pre_process):
ImageDataGenerator.__init__(self,
height_shift_range=0.2,
width_shift_range=0.2,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True,
validation_split=0.1,
rotation_range=30,
preprocessing_function=pre_process)
def __init__(self):
ImageDataGenerator.__init__(self,
height_shift_range=0.3,
width_shift_range=0.3,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True,
validation_split=0.1,
rotation_range=45,
preprocessing_function=keras.applications.
densenet.preprocess_input)
def __init__(self,
featurewise_center=False,
samplewise_center=False,
featurewise_std_normalization=False,
samplewise_std_normalization=False,
zca_whitening=False,
zca_epsilon=1e-6,
crop_size=(299, 299),
random_crop=False,
rotation_range=(0, 0),
rotation_offset=(0, 0),
translate_range=(0, 0),
zoom_range=(1, 1),
isotropic_zoom=True,
horizontal_flip=False,
vertical_flip=False,
rescale=None,
contrast_range=(1, 1),
brightness_range=(0, 0),
blurring_radius=0,
fillval=0,
preprocessing_function=None):
ImageDataGenerator.__init__(
self,
featurewise_center,
samplewise_center,
featurewise_std_normalization,
samplewise_std_normalization,
zca_whitening,
zca_epsilon,
rotation_range,
zoom_range=zoom_range,
rescale=rescale,
horizontal_flip=horizontal_flip,
vertical_flip=vertical_flip,
preprocessing_function=preprocessing_function)
self.fillval = fillval
self.crop_size = crop_size
self.random_crop = random_crop
self.rotation_offset = rotation_offset
self.isotropic_zoom = isotropic_zoom
self.translate_range = translate_range
self.contrast_range = contrast_range
self.brightness_range = brightness_range
self.blurring_radius = blurring_radius
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,
crop_to=-1):
ImageDataGenerator.__init__(
self,
featurewise_center=featurewise_center,
samplewise_center=samplewise_center,
featurewise_std_normalization=featurewise_std_normalization,
samplewise_std_normalization=samplewise_std_normalization,
zca_whitening=zca_whitening,
zca_epsilon=zca_epsilon,
rotation_range=rotation_range,
width_shift_range=width_shift_range,
height_shift_range=height_shift_range,
shear_range=shear_range,
zoom_range=zoom_range,
channel_shift_range=channel_shift_range,
fill_mode=fill_mode,
cval=cval,
horizontal_flip=horizontal_flip,
vertical_flip=vertical_flip,
rescale=rescale,
preprocessing_function=preprocessing_function,
data_format=data_format)
self.crop_to = crop_to
def __init__(self, ImageDataGenerator, config):
""" Set DataGenerator parameters.
Args:
ImageDataGenerator: ImageDataGenerator object from Keras
config: dict
"""
self.config = config
ImageDataGenerator.__init__(self, ImageDataGenerator)
self.xSamples = self.config['dataset']['xSamples']
self.ySamples = self.config['dataset']['ySamples']
self.num_freq = len(util.get_frequencies())
self.factor = self.config['dataset']['factor']
self.mask_generator = MaskGenerator(int(self.xSamples / self.factor),
int(self.ySamples / self.factor),
self.num_freq,
rand_seed=7)
def __init__(self,
rot90=False,
gaussian_blur_range=None,
color_shift=None,
contrast_stretching=False,
histogram_equalization=False,
adaptive_equalization=False,
cut_out=None,
*args,
**kwargs):
self.rot90 = rot90
self.gaussian_blur_range = gaussian_blur_range
self.color_shift = color_shift
self.contrast_stretching = contrast_stretching
self.histogram_equalization = histogram_equalization
self.adaptive_equalization = adaptive_equalization
self.cut_out = cut_out
ImageDataGenerator.__init__(self, *args, **kwargs)
def __init__ (self,images, targets,
batch_size, keys=None,
rescale = 1./255,
horizontal_flip = True,
fill_mode = "nearest",
zoom_range = 0.05,
width_shift_range = 0,
height_shift_range=0,
rotation_range=10):
ImageDataGenerator.__init__(self,
rescale = rescale,
horizontal_flip = horizontal_flip,
fill_mode = fill_mode,
zoom_range = zoom_range,
width_shift_range = width_shift_range,
height_shift_range=height_shift_range,
rotation_range=rotation_range)
self.batch_size = batch_size
if keys is None:
self.keys = list(images.keys())
else:
self.keys=keys
self.n_samples = len(self.keys)
self.n = 0
self.images=images
self.targets={}
self.fract = np.divide(input_shape,output_shape)
for i,k in enumerate(images.keys()):
self.targets[k] = np.zeros(output_shape+(len(targets[k]),))
for j,l in enumerate(targets[k].keys()):
for m in range(len(targets[k][l][0])):
val_x = int(targets[k][l][1][m]/self.fract[0])
val_y = int(targets[k][l][0][m]/self.fract[0])
self.targets[k][val_x-1:val_x+2,val_y-1:val_y+2,j]=1
self.X = np.empty((self.batch_size,)+self.images[k].shape)
self.Y = np.zeros((self.batch_size,)+output_shape+(len(targets[k]),))
def __init__(self,args):
ImageDataGenerator.__init__(self,
args['featurewise_center'],
args['samplewise_center'],
args['featurewise_std_normalization'],
args['samplewise_std_normalization'],
args['zca_whitening'],
args['rotation_range'],
args['width_shift_range'],
args['height_shift_range'],
args['shear_range'],
args['zoom_range'],
args['channel_shift_range'],
args['fill_mode'],
args['cval'],
args['horizontal_flip'],
args['vertical_flip'],
args['rescale' ],
args['preprocessing_function'],
args['data_format'])
#Additional Params for this DataGenerator
self.elastic_deformation=args['elastic_deformation']
def __init__(self, **kwargs):
ImageDataGenerator.__init__(self, **kwargs)
def __init__(self):
ImageDataGenerator.__init__(self, rescale=1./255, shear_range= 0.2, zoom_range= 0.2, horizontal_flip=True, validation_split=0.1)
def __init__(self):
ImageDataGenerator.__init__(self, rescale=1./255, height_shift_range=0.25, width_shift_range=0.25, shear_range= 0.2, zoom_range= 0.2,
horizontal_flip=True, rotation_range = 30, validation_split=0.03)
