Source code for tensorpack.dataflow.imgaug.base

# -*- coding: utf-8 -*-
# File:

import inspect
import pprint
from abc import abstractmethod, ABCMeta
import six
from six.moves import zip

from ...utils.utils import get_rng
from ...utils.argtools import log_once
from ..image import check_dtype

__all__ = ['Augmentor', 'ImageAugmentor', 'AugmentorList']

[docs]@six.add_metaclass(ABCMeta) class Augmentor(object): """ Base class for an augmentor""" def __init__(self): self.reset_state() def _init(self, params=None): if params: for k, v in params.items(): if k != 'self' and not k.startswith('_'): setattr(self, k, v)
[docs] def reset_state(self): """ reset rng and other state """ self.rng = get_rng(self)
[docs] def augment(self, d): """ Perform augmentation on the data. Returns: augmented data """ d, params = self._augment_return_params(d) return d
[docs] def augment_return_params(self, d): """ Augment the data and return the augmentation parameters. The returned parameters can be used to augment another data with identical transformation. This can be used in, e.g. augmentation for image, masks, keypoints altogether. Returns: (augmented data, augmentation params) """ return self._augment_return_params(d)
def _augment_return_params(self, d): """ Augment the image and return both image and params """ prms = self._get_augment_params(d) return (self._augment(d, prms), prms)
[docs] def augment_with_params(self, d, param): """ Augment the data with the given param. Returns: augmented data """ return self._augment(d, param)
@abstractmethod def _augment(self, d, param): """ Augment with the given param and return the new data. The augmentor is allowed to modify data in-place. """ def _get_augment_params(self, d): """ Get the augmentor parameters. """ return None def _rand_range(self, low=1.0, high=None, size=None): """ Uniform float random number between low and high. """ if high is None: low, high = 0, low if size is None: size = [] return self.rng.uniform(low, high, size)
[docs] def __repr__(self): """ Produce something like: "imgaug.MyAugmentor(field1={self.field1}, field2={self.field2})" """ try: argspec = inspect.getargspec(self.__init__) assert argspec.varargs is None, "The default __repr__ doesn't work for varargs!" assert argspec.keywords is None, "The default __repr__ doesn't work for kwargs!" fields = argspec.args[1:] index_field_has_default = len(fields) - (0 if argspec.defaults is None else len(argspec.defaults)) classname = type(self).__name__ argstr = [] for idx, f in enumerate(fields): assert hasattr(self, f), \ "Attribute {} not found! Default __repr__ only works if attributes match the constructor.".format(f) attr = getattr(self, f) if idx >= index_field_has_default: if attr is argspec.defaults[idx - index_field_has_default]: continue argstr.append("{}={}".format(f, pprint.pformat(attr))) return "imgaug.{}({})".format(classname, ', '.join(argstr)) except AssertionError as e: log_once(e.args[0], 'warn') return super(Augmentor, self).__repr__()
__str__ = __repr__
[docs]class ImageAugmentor(Augmentor): """ ImageAugmentor should take images of type uint8 in range [0, 255], or floating point images in range [0, 1] or [0, 255]. """
[docs] def augment_coords(self, coords, param): """ Augment the coordinates given the param. By default, an augmentor keeps coordinates unchanged. If a subclass of :class:`ImageAugmentor` changes coordinates but couldn't implement this method, it should ``raise NotImplementedError()``. Args: coords: Nx2 floating point numpy array where each row is (x, y) Returns: new coords """ return self._augment_coords(coords, param)
def _augment_coords(self, coords, param): return coords
[docs]class AugmentorList(ImageAugmentor): """ Augment an image by a list of augmentors """
[docs] def __init__(self, augmentors): """ Args: augmentors (list): list of :class:`ImageAugmentor` instance to be applied. """ self.augmentors = augmentors super(AugmentorList, self).__init__()
def _get_augment_params(self, img): # the next augmentor requires the previous one to finish raise RuntimeError("Cannot simply get all parameters of a AugmentorList without running the augmentation!") def _augment_return_params(self, img): check_dtype(img) assert img.ndim in [2, 3], img.ndim prms = [] for a in self.augmentors: img, prm = a._augment_return_params(img) prms.append(prm) return img, prms def _augment(self, img, param): check_dtype(img) assert img.ndim in [2, 3], img.ndim for aug, prm in zip(self.augmentors, param): img = aug._augment(img, prm) return img def _augment_coords(self, coords, param): for aug, prm in zip(self.augmentors, param): coords = aug._augment_coords(coords, prm) return coords
[docs] def reset_state(self): """ Will reset state of each augmentor """ for a in self.augmentors: a.reset_state()