import numpy
from image_augmentation import random_transform
from image_augmentation import random_warp

random_transform_args = {
    'rotation_range': 10,
    'zoom_range': 0.05,
    'shift_range': 0.05,
    'random_flip': 0.4,
    }

def get_training_data( images, batch_size ):
    indices = numpy.random.randint( len(images), size=batch_size )
    for i,index in enumerate(indices):
        image = images[index]
        image = random_transform( image, **random_transform_args )
        warped_img, target_img = random_warp( image )

        if i == 0:
            warped_images = numpy.empty( (batch_size,) + warped_img.shape, warped_img.dtype )
            target_images = numpy.empty( (batch_size,) + target_img.shape, warped_img.dtype )

        warped_images[i] = warped_img
        target_images[i] = target_img

    return warped_images, target_images