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)