<reponame>SiddhantKapil/tefla
# Original code from: https://github.com/sveitser/kaggle_diabetic
# Original MIT license: https://github.com/sveitser/kaggle_diabetic/blob/master/LICENSE
"""data augmentation.

The code for data augmentation originally comes from
https://github.com/benanne/kaggle-ndsb/blob/master/data.py
"""
from __future__ import division, print_function

from PIL import Image

import skimage
import skimage.transform
from skimage.transform._warps_cy import _warp_fast

from tefla.core.data_load_ops import *

no_augmentation_params = {
    'zoom_range': (1.0, 1.0),
    'rotation_range': (0, 0),
    'shear_range': (0, 0),
    'translation_range': (0, 0),
    'do_flip': False,
    'allow_stretch': False,
}


def load_augmented_images(fnames, preprocessor, w, h, is_training, aug_params=no_augmentation_params, transform=None,
                          bbox=None, fill_mode='constant', fill_mode_cval=0, standardizer=None, save_to_dir=None):
    return np.array(
        [load_augment(f, preprocessor, w, h, is_training, aug_params, transform, bbox, fill_mode, fill_mode_cval,
                      standardizer, save_to_dir) for f in fnames])


def load_augment(fname, preprocessor, w, h, is_training, aug_params=no_augmentation_params, transform=None, bbox=None,
                 fill_mode='constant', fill_mode_cval=0, standardizer=None, save_to_dir=None):
    """Load augmented image with output shape (h, w, c)

    Default arguments return non augmented image of shape (h, w, c).
    To apply a fixed transform (and color augmentation) specify transform (and color_vec in standardizer).
    To generate a random augmentation specify aug_params (and sigma in standardizer).
    """
    img = _load_image_th(fname, preprocessor)
    # img shape - (c, h, w)

    if bbox is not None:
        img = _definite_crop(img, bbox)
        # print(img.shape)
        # import cv2
        # cv2.imshow("test", np.asarray(img[1,:,:], dtype=np.uint8))
        # cv2.waitKey(0)
        if bbox[4] == 1:
            img = img[:, :, ::-1]
    elif transform is not None:
        img = _perturb_fixed(img, tform_augment=transform, target_shape=(w, h), mode=fill_mode,
                             mode_cval=fill_mode_cval)
    else:
        img = _perturb(img, augmentation_params=aug_params, target_shape=(w, h), mode=fill_mode,
                       mode_cval=fill_mode_cval)

    if save_to_dir is not None:
        file_full_name = os.path.basename(fname)
        file_name, file_ext = os.path.splitext(file_full_name)
        fname2 = "%s/%s_DA_%d%s" % (save_to_dir, file_name, np.random.randint(1e4), file_ext)
        _save_image_th(img, fname2)

    if standardizer is not None:
        img = standardizer(img, is_training)

    # convert to shape (h, w, c)
    return img.transpose(1, 2, 0)


def image_no_preprocessing(fname):
    return Image.open(fname)


def load_images(imgs, preprocessor=image_no_preprocessing):
    """Loads and returns images in (h, w, c) format"""
    return np.array([_load_image_th(f, preprocessor).transpose(1, 2, 0) for f in imgs])


def balance_per_class_indices(y, weights):
    y = np.array(y)
    weights = np.array(weights, dtype=float)
    p = np.zeros(len(y))
    for i, weight in enumerate(weights):
        p[y == i] = weight
    return np.random.choice(np.arange(len(y)), size=len(y), replace=True,
                            p=np.array(p) / p.sum())


def build_augmentation_transform(zoom=(1.0, 1.0), rotation=0, shear=0, translation=(0, 0), flip=False):
    if flip:
        shear += 180
        rotation += 180
        # shear by 180 degrees is equivalent to rotation by 180 degrees + flip.
        # So after that we rotate it another 180 degrees to get just the flip.

    tform_augment = skimage.transform.AffineTransform(scale=(1 / zoom[0], 1 / zoom[1]), rotation=np.deg2rad(rotation),
                                                      shear=np.deg2rad(shear), translation=translation)
    return tform_augment


# internal stuff below

def _load_image_th(img, preprocessor=image_no_preprocessing):
    """Laad an image and return it in (c, h, w) format"""
    if isinstance(img, basestring):
        p_img = preprocessor(img)
        # PIL loaded image, size = (w, h)
        # numpy image from PIL image, shape = (h, w, c)
        # after transpose, shape = (c, h, w)
        np_img = np.array(p_img, dtype=np.float32)
        if len(np_img.shape) == 2:
            np_img = np.expand_dims(np_img, axis=2)
        return np_img.transpose(2, 0, 1)
    elif isinstance(img, np.ndarray):
        return preprocessor(img)
    else:
        raise AssertionError("Unknown image type")


def _save_image_th(x, fname):
    """Save an image supplied in (c, h, w) format"""
    x = x.transpose(1, 2, 0)
    img = Image.fromarray(x.astype('uint8'), 'RGB')
    print("Saving file: %s" % fname)
    img.save(fname)


def _fast_warp(img, tf, output_shape, mode='constant', mode_cval=0, order=0):
    """This wrapper function is faster than skimage.transform.warp """
    m = tf.params
    t_img = np.zeros((img.shape[0],) + output_shape, img.dtype)
    for i in range(t_img.shape[0]):
        t_img[i] = _warp_fast(img[i], m, output_shape=output_shape,
                              mode=mode, cval=mode_cval, order=order)
    return t_img


def _build_centering_transform(image_shape, target_shape):
    cols, rows = image_shape
    tcols, trows = target_shape
    shift_x = (cols - tcols) / 2.0
    shift_y = (rows - trows) / 2.0
    return skimage.transform.SimilarityTransform(translation=(shift_x, shift_y))


def _build_center_uncenter_transforms(image_shape):
    """
    These are used to ensure that zooming and rotation happens around the center of the image.
    Use these transforms to center and uncenter the image around such a transform.
    """
    center_shift = np.array(
        [image_shape[0], image_shape[1]]) / 2.0 - 0.5
    tform_uncenter = skimage.transform.SimilarityTransform(translation=-center_shift)
    tform_center = skimage.transform.SimilarityTransform(translation=center_shift)
    return tform_center, tform_uncenter


def _random_perturbation_transform(zoom_range, rotation_range, shear_range, translation_range, do_flip=True,
                                   allow_stretch=False, rng=np.random):
    shift_x = rng.uniform(*translation_range)
    shift_y = rng.uniform(*translation_range)
    translation = (shift_x, shift_y)

    rotation = rng.uniform(*rotation_range)
    shear = rng.uniform(*shear_range)

    if do_flip:
        flip = (rng.randint(2) > 0)  # flip half of the time
    else:
        flip = False

    # random zoom
    log_zoom_range = [np.log(z) for z in zoom_range]
    if isinstance(allow_stretch, float):
        log_stretch_range = [-np.log(allow_stretch), np.log(allow_stretch)]
        zoom = np.exp(rng.uniform(*log_zoom_range))
        stretch = np.exp(rng.uniform(*log_stretch_range))
        zoom_x = zoom * stretch
        zoom_y = zoom / stretch
    elif allow_stretch is True:  # avoid bugs, f.e. when it is an integer
        zoom_x = np.exp(rng.uniform(*log_zoom_range))
        zoom_y = np.exp(rng.uniform(*log_zoom_range))
    else:
        zoom_x = zoom_y = np.exp(rng.uniform(*log_zoom_range))
    # the range should be multiplicatively symmetric, so [1/1.1, 1.1] instead of [0.9, 1.1] makes more sense.

    return build_augmentation_transform((zoom_x, zoom_y), rotation, shear, translation, flip)


def _definite_crop(img, bbox):
    img = img[:, bbox[0]:bbox[2], bbox[1]:bbox[3]]
    return img


def _perturb(img, augmentation_params, target_shape, rng=np.random, mode='constant', mode_cval=0):
    # # DEBUG: draw a border to see where the image ends up
    # img[0, :] = 0.5
    # img[-1, :] = 0.5
    # img[:, 0] = 0.5
    # img[:, -1] = 0.5
    shape = img.shape[1:]
    tform_centering = _build_centering_transform(shape, target_shape)
    tform_center, tform_uncenter = _build_center_uncenter_transforms(shape)
    tform_augment = _random_perturbation_transform(rng=rng, **augmentation_params)
    # shift to center, augment, shift back (for the rotation/shearing)
    tform_augment = tform_uncenter + tform_augment + tform_center
    return _fast_warp(img, tform_centering + tform_augment,
                      output_shape=target_shape,
                      mode=mode, mode_cval=mode_cval)


# for test-time augmentation
def _perturb_fixed(img, tform_augment, target_shape=(50, 50), mode='constant', mode_cval=0):
    shape = img.shape[1:]
    tform_centering = _build_centering_transform(shape, target_shape)
    tform_center, tform_uncenter = _build_center_uncenter_transforms(shape)
    # shift to center, augment, shift back (for the rotation/shearing)
    tform_augment = tform_uncenter + tform_augment + tform_center
    return _fast_warp(img, tform_centering + tform_augment,
                      output_shape=target_shape, mode=mode, mode_cval=mode_cval)

# Original code from: https://github.com/sveitser/kaggle_diabetic
# Original MIT license: https://github.com/sveitser/kaggle_diabetic/blob/master/LICENSE
"""data augmentation.

The code for data augmentation originally comes from
https://github.com/benanne/kaggle-ndsb/blob/master/data.py
"""
from __future__ import division, print_function

from PIL import Image

import skimage
import skimage.transform
from skimage.transform._warps_cy import _warp_fast

from tefla.core.data_load_ops import *

no_augmentation_params = {
    'zoom_range': (1.0, 1.0),
    'rotation_range': (0, 0),
    'shear_range': (0, 0),
    'translation_range': (0, 0),
    'do_flip': False,
    'allow_stretch': False,
}


def load_augmented_images(fnames, preprocessor, w, h, is_training, aug_params=no_augmentation_params, transform=None,
                          bbox=None, fill_mode='constant', fill_mode_cval=0, standardizer=None, save_to_dir=None):
    return np.array(
        [load_augment(f, preprocessor, w, h, is_training, aug_params, transform, bbox, fill_mode, fill_mode_cval,
                      standardizer, save_to_dir) for f in fnames])


def load_augment(fname, preprocessor, w, h, is_training, aug_params=no_augmentation_params, transform=None, bbox=None,
                 fill_mode='constant', fill_mode_cval=0, standardizer=None, save_to_dir=None):
    """Load augmented image with output shape (h, w, c)

    Default arguments return non augmented image of shape (h, w, c).
    To apply a fixed transform (and color augmentation) specify transform (and color_vec in standardizer).
    To generate a random augmentation specify aug_params (and sigma in standardizer).
    """
    img = _load_image_th(fname, preprocessor)
    # img shape - (c, h, w)

    if bbox is not None:
        img = _definite_crop(img, bbox)
        # print(img.shape)
        # import cv2
        # cv2.imshow("test", np.asarray(img[1,:,:], dtype=np.uint8))
        # cv2.waitKey(0)
        if bbox[4] == 1:
            img = img[:, :, ::-1]
    elif transform is not None:
        img = _perturb_fixed(img, tform_augment=transform, target_shape=(w, h), mode=fill_mode,
                             mode_cval=fill_mode_cval)
    else:
        img = _perturb(img, augmentation_params=aug_params, target_shape=(w, h), mode=fill_mode,
                       mode_cval=fill_mode_cval)

    if save_to_dir is not None:
        file_full_name = os.path.basename(fname)
        file_name, file_ext = os.path.splitext(file_full_name)
        fname2 = "%s/%s_DA_%d%s" % (save_to_dir, file_name, np.random.randint(1e4), file_ext)
        _save_image_th(img, fname2)

    if standardizer is not None:
        img = standardizer(img, is_training)

    # convert to shape (h, w, c)
    return img.transpose(1, 2, 0)


def image_no_preprocessing(fname):
    return Image.open(fname)


def load_images(imgs, preprocessor=image_no_preprocessing):
    """Loads and returns images in (h, w, c) format"""
    return np.array([_load_image_th(f, preprocessor).transpose(1, 2, 0) for f in imgs])


def balance_per_class_indices(y, weights):
    y = np.array(y)
    weights = np.array(weights, dtype=float)
    p = np.zeros(len(y))
    for i, weight in enumerate(weights):
        p[y == i] = weight
    return np.random.choice(np.arange(len(y)), size=len(y), replace=True,
                            p=np.array(p) / p.sum())


def build_augmentation_transform(zoom=(1.0, 1.0), rotation=0, shear=0, translation=(0, 0), flip=False):
    if flip:
        shear += 180
        rotation += 180
        # shear by 180 degrees is equivalent to rotation by 180 degrees + flip.
        # So after that we rotate it another 180 degrees to get just the flip.

    tform_augment = skimage.transform.AffineTransform(scale=(1 / zoom[0], 1 / zoom[1]), rotation=np.deg2rad(rotation),
                                                      shear=np.deg2rad(shear), translation=translation)
    return tform_augment


# internal stuff below

def _load_image_th(img, preprocessor=image_no_preprocessing):
    """Laad an image and return it in (c, h, w) format"""
    if isinstance(img, basestring):
        p_img = preprocessor(img)
        # PIL loaded image, size = (w, h)
        # numpy image from PIL image, shape = (h, w, c)
        # after transpose, shape = (c, h, w)
        np_img = np.array(p_img, dtype=np.float32)
        if len(np_img.shape) == 2:
            np_img = np.expand_dims(np_img, axis=2)
        return np_img.transpose(2, 0, 1)
    elif isinstance(img, np.ndarray):
        return preprocessor(img)
    else:
        raise AssertionError("Unknown image type")


def _save_image_th(x, fname):
    """Save an image supplied in (c, h, w) format"""
    x = x.transpose(1, 2, 0)
    img = Image.fromarray(x.astype('uint8'), 'RGB')
    print("Saving file: %s" % fname)
    img.save(fname)


def _fast_warp(img, tf, output_shape, mode='constant', mode_cval=0, order=0):
    """This wrapper function is faster than skimage.transform.warp """
    m = tf.params
    t_img = np.zeros((img.shape[0],) + output_shape, img.dtype)
    for i in range(t_img.shape[0]):
        t_img[i] = _warp_fast(img[i], m, output_shape=output_shape,
                              mode=mode, cval=mode_cval, order=order)
    return t_img


def _build_centering_transform(image_shape, target_shape):
    cols, rows = image_shape
    tcols, trows = target_shape
    shift_x = (cols - tcols) / 2.0
    shift_y = (rows - trows) / 2.0
    return skimage.transform.SimilarityTransform(translation=(shift_x, shift_y))


def _build_center_uncenter_transforms(image_shape):
    """
    These are used to ensure that zooming and rotation happens around the center of the image.
    Use these transforms to center and uncenter the image around such a transform.
    """
    center_shift = np.array(
        [image_shape[0], image_shape[1]]) / 2.0 - 0.5
    tform_uncenter = skimage.transform.SimilarityTransform(translation=-center_shift)
    tform_center = skimage.transform.SimilarityTransform(translation=center_shift)
    return tform_center, tform_uncenter


def _random_perturbation_transform(zoom_range, rotation_range, shear_range, translation_range, do_flip=True,
                                   allow_stretch=False, rng=np.random):
    shift_x = rng.uniform(*translation_range)
    shift_y = rng.uniform(*translation_range)
    translation = (shift_x, shift_y)

    rotation = rng.uniform(*rotation_range)
    shear = rng.uniform(*shear_range)

    if do_flip:
        flip = (rng.randint(2) > 0)  # flip half of the time
    else:
        flip = False

    # random zoom
    log_zoom_range = [np.log(z) for z in zoom_range]
    if isinstance(allow_stretch, float):
        log_stretch_range = [-np.log(allow_stretch), np.log(allow_stretch)]
        zoom = np.exp(rng.uniform(*log_zoom_range))
        stretch = np.exp(rng.uniform(*log_stretch_range))
        zoom_x = zoom * stretch
        zoom_y = zoom / stretch
    elif allow_stretch is True:  # avoid bugs, f.e. when it is an integer
        zoom_x = np.exp(rng.uniform(*log_zoom_range))
        zoom_y = np.exp(rng.uniform(*log_zoom_range))
    else:
        zoom_x = zoom_y = np.exp(rng.uniform(*log_zoom_range))
    # the range should be multiplicatively symmetric, so [1/1.1, 1.1] instead of [0.9, 1.1] makes more sense.

    return build_augmentation_transform((zoom_x, zoom_y), rotation, shear, translation, flip)


def _definite_crop(img, bbox):
    img = img[:, bbox[0]:bbox[2], bbox[1]:bbox[3]]
    return img


def _perturb(img, augmentation_params, target_shape, rng=np.random, mode='constant', mode_cval=0):
    # # DEBUG: draw a border to see where the image ends up
    # img[0, :] = 0.5
    # img[-1, :] = 0.5
    # img[:, 0] = 0.5
    # img[:, -1] = 0.5
    shape = img.shape[1:]
    tform_centering = _build_centering_transform(shape, target_shape)
    tform_center, tform_uncenter = _build_center_uncenter_transforms(shape)
    tform_augment = _random_perturbation_transform(rng=rng, **augmentation_params)
    # shift to center, augment, shift back (for the rotation/shearing)
    tform_augment = tform_uncenter + tform_augment + tform_center
    return _fast_warp(img, tform_centering + tform_augment,
                      output_shape=target_shape,
                      mode=mode, mode_cval=mode_cval)


# for test-time augmentation
def _perturb_fixed(img, tform_augment, target_shape=(50, 50), mode='constant', mode_cval=0):
    shape = img.shape[1:]
    tform_centering = _build_centering_transform(shape, target_shape)
    tform_center, tform_uncenter = _build_center_uncenter_transforms(shape)
    # shift to center, augment, shift back (for the rotation/shearing)
    tform_augment = tform_uncenter + tform_augment + tform_center
    return _fast_warp(img, tform_centering + tform_augment,
                      output_shape=target_shape, mode=mode, mode_cval=mode_cval)
# Original code from: https://github.com/sveitser/kaggle_diabetic # Original MIT license: https://github.com/sveitser/kaggle_diabetic/blob/master/LICENSE data augmentation.  The code for data augmentation originally comes from https://github.com/benanne/kaggle-ndsb/blob/master/data.py Load augmented image with output shape (h, w, c)      Default arguments return non augmented image of shape (h, w, c).     To apply a fixed transform (and color augmentation) specify transform (and color_vec in standardizer).     To generate a random augmentation specify aug_params (and sigma in standardizer). # img shape - (c, h, w) # print(img.shape) # import cv2 # cv2.imshow("test", np.asarray(img[1,:,:], dtype=np.uint8)) # cv2.waitKey(0) # convert to shape (h, w, c) Loads and returns images in (h, w, c) format # shear by 180 degrees is equivalent to rotation by 180 degrees + flip. # So after that we rotate it another 180 degrees to get just the flip. # internal stuff below Laad an image and return it in (c, h, w) format # PIL loaded image, size = (w, h) # numpy image from PIL image, shape = (h, w, c) # after transpose, shape = (c, h, w) Save an image supplied in (c, h, w) format This wrapper function is faster than skimage.transform.warp These are used to ensure that zooming and rotation happens around the center of the image.     Use these transforms to center and uncenter the image around such a transform. # flip half of the time # random zoom # avoid bugs, f.e. when it is an integer # the range should be multiplicatively symmetric, so [1/1.1, 1.1] instead of [0.9, 1.1] makes more sense. # # DEBUG: draw a border to see where the image ends up # img[0, :] = 0.5 # img[-1, :] = 0.5 # img[:, 0] = 0.5 # img[:, -1] = 0.5 # shift to center, augment, shift back (for the rotation/shearing) # for test-time augmentation # shift to center, augment, shift back (for the rotation/shearing)