mmclassification/mmcls/datasets/pipelines/auto_augment.py

336 lines
14 KiB
Python

import mmcv
import numpy as np
from ..builder import PIPELINES
def random_negative(value, random_negative_prob):
"""Randomly negate value based on random_negative_prob."""
return -value if np.random.rand() < random_negative_prob else value
@PIPELINES.register_module()
class Shear(object):
"""Shear images.
Args:
magnitude (int | float): The magnitude used for shear.
pad_val (int, tuple[int]): Pixel pad_val value for constant fill. If a
tuple of length 3, it is used to pad_val R, G, B channels
respectively. Defaults to 128.
prob (float): The probability for performing Shear therefore should be
in range [0, 1]. Defaults to 0.5.
direction (str): The shearing direction. Options are 'horizontal' and
'vertical'. Defaults to 'horizontal'.
random_negative_prob (float): The probability that turns the magnitude
negative, which should be in range [0,1]. Defaults to 0.5.
interpolation (str): Interpolation method. Options are 'nearest',
'bilinear', 'bicubic', 'area', 'lanczos'. Defaults to 'bicubic'.
"""
def __init__(self,
magnitude,
pad_val=128,
prob=0.5,
direction='horizontal',
random_negative_prob=0.5,
interpolation='bicubic'):
assert isinstance(magnitude, (int, float)), 'The magnitude type must '\
f'be int or float, but got {type(magnitude)} instead.'
if isinstance(pad_val, int):
pad_val = tuple([pad_val] * 3)
elif isinstance(pad_val, tuple):
assert len(pad_val) == 3, 'pad_val as a tuple must have 3 ' \
f'elements, got {len(pad_val)} instead.'
assert all(isinstance(i, int) for i in pad_val), 'pad_val as a '\
'tuple must got elements of int type.'
else:
raise TypeError('pad_val must be int or tuple with 3 elements.')
assert 0 <= prob <= 1.0, 'The prob should be in range [0,1], ' \
f'got {prob} instead.'
assert direction in ('horizontal', 'vertical'), 'direction must be ' \
f'either "horizontal" or "vertical", got {direction} instead.'
assert 0 <= random_negative_prob <= 1.0, 'The random_negative_prob ' \
f'should be in range [0,1], got {random_negative_prob} instead.'
self.magnitude = magnitude
self.pad_val = pad_val
self.prob = prob
self.direction = direction
self.random_negative_prob = random_negative_prob
self.interpolation = interpolation
def __call__(self, results):
if np.random.rand() > self.prob:
return results
magnitude = random_negative(self.magnitude, self.random_negative_prob)
for key in results.get('img_fields', ['img']):
img = results[key]
img_sheared = mmcv.imshear(
img,
magnitude,
direction=self.direction,
border_value=self.pad_val,
interpolation=self.interpolation)
results[key] = img_sheared.astype(img.dtype)
return results
def __repr__(self):
repr_str = self.__class__.__name__
repr_str += f'(magnitude={self.magnitude}, '
repr_str += f'pad_val={self.pad_val}, '
repr_str += f'prob={self.prob}, '
repr_str += f'direction={self.direction}, '
repr_str += f'random_negative_prob={self.random_negative_prob}, '
repr_str += f'interpolation={self.interpolation})'
return repr_str
@PIPELINES.register_module()
class Translate(object):
"""Translate images.
Args:
magnitude (int | float): The magnitude used for translate. Note that
the offset is calculated by magnitude * size in the corresponding
direction. With a magnitude of 1, the whole image will be moved out
of the range.
pad_val (int, tuple[int]): Pixel pad_val value for constant fill. If a
tuple of length 3, it is used to pad_val R, G, B channels
respectively. Defaults to 128.
prob (float): The probability for performing translate therefore should
be in range [0, 1]. Defaults to 0.5.
direction (str): The translating direction. Options are 'horizontal'
and 'vertical'. Defaults to 'horizontal'.
random_negative_prob (float): The probability that turns the magnitude
negative, which should be in range [0,1]. Defaults to 0.5.
interpolation (str): Interpolation method. Options are 'nearest',
'bilinear', 'bicubic', 'area', 'lanczos'. Defaults to 'nearest'.
"""
def __init__(self,
magnitude,
pad_val=128,
prob=0.5,
direction='horizontal',
random_negative_prob=0.5,
interpolation='nearest'):
assert isinstance(magnitude, (int, float)), 'The magnitude type must '\
f'be int or float, but got {type(magnitude)} instead.'
if isinstance(pad_val, int):
pad_val = tuple([pad_val] * 3)
elif isinstance(pad_val, tuple):
assert len(pad_val) == 3, 'pad_val as a tuple must have 3 ' \
f'elements, got {len(pad_val)} instead.'
assert all(isinstance(i, int) for i in pad_val), 'pad_val as a '\
'tuple must got elements of int type.'
else:
raise TypeError('pad_val must be int or tuple with 3 elements.')
assert 0 <= prob <= 1.0, 'The prob should be in range [0,1], ' \
f'got {prob} instead.'
assert direction in ('horizontal', 'vertical'), 'direction must be ' \
f'either "horizontal" or "vertical", got {direction} instead.'
assert 0 <= random_negative_prob <= 1.0, 'The random_negative_prob ' \
f'should be in range [0,1], got {random_negative_prob} instead.'
self.magnitude = magnitude
self.pad_val = pad_val
self.prob = prob
self.direction = direction
self.random_negative_prob = random_negative_prob
self.interpolation = interpolation
def __call__(self, results):
if np.random.rand() > self.prob:
return results
magnitude = random_negative(self.magnitude, self.random_negative_prob)
for key in results.get('img_fields', ['img']):
img = results[key]
height, width = img.shape[:2]
if self.direction == 'horizontal':
offset = magnitude * width
else:
offset = magnitude * height
img_translated = mmcv.imtranslate(
img,
offset,
direction=self.direction,
border_value=self.pad_val,
interpolation=self.interpolation)
results[key] = img_translated.astype(img.dtype)
return results
def __repr__(self):
repr_str = self.__class__.__name__
repr_str += f'(magnitude={self.magnitude}, '
repr_str += f'pad_val={self.pad_val}, '
repr_str += f'prob={self.prob}, '
repr_str += f'direction={self.direction}, '
repr_str += f'random_negative_prob={self.random_negative_prob}, '
repr_str += f'interpolation={self.interpolation})'
return repr_str
@PIPELINES.register_module()
class Rotate(object):
"""Rotate images.
Args:
angle (float): The angle used for rotate. Positive values stand for
clockwise rotation.
center (tuple[float], optional): Center point (w, h) of the rotation in
the source image. If None, the center of the image will be used.
defaults to None.
scale (float): Isotropic scale factor. Defaults to 1.0.
pad_val (int, tuple[int]): Pixel pad_val value for constant fill. If a
tuple of length 3, it is used to pad_val R, G, B channels
respectively. Defaults to 128.
prob (float): The probability for performing Rotate therefore should be
in range [0, 1]. Defaults to 0.5.
random_negative_prob (float): The probability that turns the angle
negative, which should be in range [0,1]. Defaults to 0.5.
interpolation (str): Interpolation method. Options are 'nearest',
'bilinear', 'bicubic', 'area', 'lanczos'. Defaults to 'nearest'.
"""
def __init__(self,
angle,
center=None,
scale=1.0,
pad_val=128,
prob=0.5,
random_negative_prob=0.5,
interpolation='nearest'):
assert isinstance(angle, float), 'The angle type must be float, but ' \
f'got {type(angle)} instead.'
if isinstance(center, tuple):
assert len(center) == 2, 'center as a tuple must have 2 ' \
f'elements, got {len(center)} elements instead.'
else:
assert center is None, 'The center type' \
f'must be tuple or None, got {type(center)} instead.'
assert isinstance(scale, float), 'the scale type must be float, but ' \
f'got {type(scale)} instead.'
if isinstance(pad_val, int):
pad_val = tuple([pad_val] * 3)
elif isinstance(pad_val, tuple):
assert len(pad_val) == 3, 'pad_val as a tuple must have 3 ' \
f'elements, got {len(pad_val)} instead.'
assert all(isinstance(i, int) for i in pad_val), 'pad_val as a '\
'tuple must got elements of int type.'
else:
raise TypeError('pad_val must be int or tuple with 3 elements.')
assert 0 <= prob <= 1.0, 'The prob should be in range [0,1], ' \
f'got {prob} instead.'
assert 0 <= random_negative_prob <= 1.0, 'The random_negative_prob ' \
f'should be in range [0,1], got {random_negative_prob} instead.'
self.angle = angle
self.center = center
self.scale = scale
self.pad_val = pad_val
self.prob = prob
self.random_negative_prob = random_negative_prob
self.interpolation = interpolation
def __call__(self, results):
if np.random.rand() > self.prob:
return results
angle = random_negative(self.angle, self.random_negative_prob)
for key in results.get('img_fields', ['img']):
img = results[key]
img_rotated = mmcv.imrotate(
img,
angle,
center=self.center,
scale=self.scale,
border_value=self.pad_val,
interpolation=self.interpolation)
results[key] = img_rotated.astype(img.dtype)
return results
def __repr__(self):
repr_str = self.__class__.__name__
repr_str += f'(angle={self.angle}, '
repr_str += f'center={self.center}, '
repr_str += f'scale={self.scale}, '
repr_str += f'pad_val={self.pad_val}, '
repr_str += f'prob={self.prob}, '
repr_str += f'random_negative_prob={self.random_negative_prob}, '
repr_str += f'interpolation={self.interpolation})'
return repr_str
@PIPELINES.register_module()
class Invert(object):
"""Invert images.
Args:
prob (float): The probability for performing invert therefore should
be in range [0, 1]. Defaults to 0.5.
"""
def __init__(self, prob=0.5):
assert 0 <= prob <= 1.0, 'The prob should be in range [0,1], ' \
f'got {prob} instead.'
self.prob = prob
def __call__(self, results):
if np.random.rand() > self.prob:
return results
for key in results.get('img_fields', ['img']):
img = results[key]
img_inverted = mmcv.iminvert(img)
results[key] = img_inverted.astype(img.dtype)
return results
def __repr__(self):
repr_str = self.__class__.__name__
repr_str += f'(prob={self.prob})'
return repr_str
@PIPELINES.register_module()
class ColorTransform(object):
"""Adjust the color balance of images.
Args:
magnitude (int | float): The magnitude used for color transform. A
positive magnitude would enhance the color and a negative magnitude
would make the image grayer. A magnitude=0 gives the origin img.
prob (float): The probability for performing ColorTransform therefore
should be in range [0, 1]. Defaults to 0.5.
random_negative_prob (float): The probability that turns the magnitude
negative, which should be in range [0,1]. Defaults to 0.5.
"""
def __init__(self, magnitude, prob=0.5, random_negative_prob=0.5):
assert isinstance(magnitude, (int, float)), 'The magnitude type must '\
f'be int or float, but got {type(magnitude)} instead.'
assert 0 <= prob <= 1.0, 'The prob should be in range [0,1], ' \
f'got {prob} instead.'
assert 0 <= random_negative_prob <= 1.0, 'The random_negative_prob ' \
f'should be in range [0,1], got {random_negative_prob} instead.'
self.magnitude = magnitude
self.prob = prob
self.random_negative_prob = random_negative_prob
def __call__(self, results):
if np.random.rand() > self.prob:
return results
magnitude = random_negative(self.magnitude, self.random_negative_prob)
for key in results.get('img_fields', ['img']):
img = results[key]
img_color_adjusted = mmcv.adjust_color(img, alpha=1 + magnitude)
results[key] = img_color_adjusted.astype(img.dtype)
return results
def __repr__(self):
repr_str = self.__class__.__name__
repr_str += f'(magnitude={self.magnitude}, '
repr_str += f'prob={self.prob}, '
repr_str += f'random_negative_prob={self.random_negative_prob})'
return repr_str