# Copyright (C) 2021-2024, Mindee.
# This program is licensed under the Apache License 2.0.
# See LICENSE or go to <https://opensource.org/licenses/Apache-2.0> for full license details.
import math
import random
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import numpy as np
from doctr.utils.repr import NestedObject
from .. import functional as F
__all__ = ["SampleCompose", "ImageTransform", "ColorInversion", "OneOf", "RandomApply", "RandomRotate", "RandomCrop"]
class SampleCompose(NestedObject):
"""Implements a wrapper that will apply transformations sequentially on both image and target
.. tabs::
.. tab:: TensorFlow
.. code:: python
>>> import numpy as np
>>> import tensorflow as tf
>>> from doctr.transforms import SampleCompose, ImageTransform, ColorInversion, RandomRotate
>>> transfo = SampleCompose([ImageTransform(ColorInversion((32, 32))), RandomRotate(30)])
>>> out, out_boxes = transfo(tf.random.uniform(shape=[64, 64, 3], minval=0, maxval=1), np.zeros((2, 4)))
.. tab:: PyTorch
.. code:: python
>>> import numpy as np
>>> import torch
>>> from doctr.transforms import SampleCompose, ImageTransform, ColorInversion, RandomRotate
>>> transfos = SampleCompose([ImageTransform(ColorInversion((32, 32))), RandomRotate(30)])
>>> out, out_boxes = transfos(torch.rand(8, 64, 64, 3), np.zeros((2, 4)))
Args:
----
transforms: list of transformation modules
"""
_children_names: List[str] = ["sample_transforms"]
def __init__(self, transforms: List[Callable[[Any, Any], Tuple[Any, Any]]]) -> None:
self.sample_transforms = transforms
def __call__(self, x: Any, target: Any) -> Tuple[Any, Any]:
for t in self.sample_transforms:
x, target = t(x, target)
return x, target
class ImageTransform(NestedObject):
"""Implements a transform wrapper to turn an image-only transformation into an image+target transform
.. tabs::
.. tab:: TensorFlow
.. code:: python
>>> import tensorflow as tf
>>> from doctr.transforms import ImageTransform, ColorInversion
>>> transfo = ImageTransform(ColorInversion((32, 32)))
>>> out, _ = transfo(tf.random.uniform(shape=[64, 64, 3], minval=0, maxval=1), None)
.. tab:: PyTorch
.. code:: python
>>> import torch
>>> from doctr.transforms import ImageTransform, ColorInversion
>>> transfo = ImageTransform(ColorInversion((32, 32)))
>>> out, _ = transfo(torch.rand(8, 64, 64, 3), None)
Args:
----
transform: the image transformation module to wrap
"""
_children_names: List[str] = ["img_transform"]
def __init__(self, transform: Callable[[Any], Any]) -> None:
self.img_transform = transform
def __call__(self, img: Any, target: Any) -> Tuple[Any, Any]:
img = self.img_transform(img)
return img, target
[docs]
class ColorInversion(NestedObject):
"""Applies the following tranformation to a tensor (image or batch of images):
convert to grayscale, colorize (shift 0-values randomly), and then invert colors
.. tabs::
.. tab:: TensorFlow
.. code:: python
>>> import tensorflow as tf
>>> from doctr.transforms import ColorInversion
>>> transfo = ColorInversion(min_val=0.6)
>>> out = transfo(tf.random.uniform(shape=[8, 64, 64, 3], minval=0, maxval=1))
.. tab:: PyTorch
.. code:: python
>>> import torch
>>> from doctr.transforms import ColorInversion
>>> transfo = ColorInversion(min_val=0.6)
>>> out = transfo(torch.rand(8, 64, 64, 3))
Args:
----
min_val: range [min_val, 1] to colorize RGB pixels
"""
def __init__(self, min_val: float = 0.5) -> None:
self.min_val = min_val
def extra_repr(self) -> str:
return f"min_val={self.min_val}"
def __call__(self, img: Any) -> Any:
return F.invert_colors(img, self.min_val)
[docs]
class OneOf(NestedObject):
"""Randomly apply one of the input transformations
.. tabs::
.. tab:: TensorFlow
.. code:: python
>>> import tensorflow as tf
>>> from doctr.transforms import OneOf
>>> transfo = OneOf([JpegQuality(), Gamma()])
>>> out = transfo(tf.random.uniform(shape=[64, 64, 3], minval=0, maxval=1))
.. tab:: PyTorch
.. code:: python
>>> import torch
>>> from doctr.transforms import OneOf
>>> transfo = OneOf([JpegQuality(), Gamma()])
>>> out = transfo(torch.rand(1, 64, 64, 3))
Args:
----
transforms: list of transformations, one only will be picked
"""
_children_names: List[str] = ["transforms"]
def __init__(self, transforms: List[Callable[[Any], Any]]) -> None:
self.transforms = transforms
def __call__(self, img: Any) -> Any:
# Pick transformation
transfo = self.transforms[int(random.random() * len(self.transforms))]
# Apply
return transfo(img)
[docs]
class RandomApply(NestedObject):
"""Apply with a probability p the input transformation
.. tabs::
.. tab:: TensorFlow
.. code:: python
>>> import tensorflow as tf
>>> from doctr.transforms import RandomApply
>>> transfo = RandomApply(Gamma(), p=.5)
>>> out = transfo(tf.random.uniform(shape=[64, 64, 3], minval=0, maxval=1))
.. tab:: PyTorch
.. code:: python
>>> import torch
>>> from doctr.transforms import RandomApply
>>> transfo = RandomApply(Gamma(), p=.5)
>>> out = transfo(torch.rand(1, 64, 64, 3))
Args:
----
transform: transformation to apply
p: probability to apply
"""
def __init__(self, transform: Callable[[Any], Any], p: float = 0.5) -> None:
self.transform = transform
self.p = p
def extra_repr(self) -> str:
return f"transform={self.transform}, p={self.p}"
def __call__(self, img: Any, target: Optional[np.ndarray] = None) -> Union[Any, Tuple[Any, np.ndarray]]:
if random.random() < self.p:
return self.transform(img) if target is None else self.transform(img, target) # type: ignore[call-arg]
return img if target is None else (img, target)
[docs]
class RandomRotate(NestedObject):
"""Randomly rotate a tensor image and its boxes
.. image:: https://doctr-static.mindee.com/models?id=v0.4.0/rotation_illustration.png&src=0
:align: center
Args:
----
max_angle: maximum angle for rotation, in degrees. Angles will be uniformly picked in
[-max_angle, max_angle]
expand: whether the image should be padded before the rotation
"""
def __init__(self, max_angle: float = 5.0, expand: bool = False) -> None:
self.max_angle = max_angle
self.expand = expand
def extra_repr(self) -> str:
return f"max_angle={self.max_angle}, expand={self.expand}"
def __call__(self, img: Any, target: np.ndarray) -> Tuple[Any, np.ndarray]:
angle = random.uniform(-self.max_angle, self.max_angle)
r_img, r_polys = F.rotate_sample(img, target, angle, self.expand)
# Removes deleted boxes
is_kept = (r_polys.max(1) > r_polys.min(1)).sum(1) == 2
return r_img, r_polys[is_kept]
[docs]
class RandomCrop(NestedObject):
"""Randomly crop a tensor image and its boxes
Args:
----
scale: tuple of floats, relative (min_area, max_area) of the crop
ratio: tuple of float, relative (min_ratio, max_ratio) where ratio = h/w
"""
def __init__(self, scale: Tuple[float, float] = (0.08, 1.0), ratio: Tuple[float, float] = (0.75, 1.33)) -> None:
self.scale = scale
self.ratio = ratio
def extra_repr(self) -> str:
return f"scale={self.scale}, ratio={self.ratio}"
def __call__(self, img: Any, target: Dict[str, np.ndarray]) -> Tuple[Any, Dict[str, np.ndarray]]:
scale = random.uniform(self.scale[0], self.scale[1])
ratio = random.uniform(self.ratio[0], self.ratio[1])
# Those might overflow
crop_h = math.sqrt(scale * ratio)
crop_w = math.sqrt(scale / ratio)
xmin, ymin = random.uniform(0, 1 - crop_w), random.uniform(0, 1 - crop_h)
xmax, ymax = xmin + crop_w, ymin + crop_h
# Clip them
xmin, ymin = max(xmin, 0), max(ymin, 0)
xmax, ymax = min(xmax, 1), min(ymax, 1)
croped_img, crop_boxes = F.crop_detection(img, target["boxes"], (xmin, ymin, xmax, ymax))
return croped_img, dict(boxes=crop_boxes)