# Copyright (c) OpenMMLab. All rights reserved.
import copy
import cv2
import mmcv
import numpy as np
from ..builder import PIPELINES
from .compose import Compose
_MAX_LEVEL = 10
def level_to_value(level, max_value):
"""Map from level to values based on max_value."""
return (level / _MAX_LEVEL) * max_value
def enhance_level_to_value(level, a=1.8, b=0.1):
"""Map from level to values."""
return (level / _MAX_LEVEL) * a + b
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
def bbox2fields():
"""The key correspondence from bboxes to labels, masks and
segmentations."""
bbox2label = {
'gt_bboxes': 'gt_labels',
'gt_bboxes_ignore': 'gt_labels_ignore'
}
bbox2mask = {
'gt_bboxes': 'gt_masks',
'gt_bboxes_ignore': 'gt_masks_ignore'
}
bbox2seg = {
'gt_bboxes': 'gt_semantic_seg',
}
return bbox2label, bbox2mask, bbox2seg
@PIPELINES.register_module()
class AutoAugment:
"""Auto augmentation.
This data augmentation is proposed in `Learning Data Augmentation
Strategies for Object Detection <https://arxiv.org/pdf/1906.11172>`_.
TODO: Implement 'Shear', 'Sharpness' and 'Rotate' transforms
Args:
policies (list[list[dict]]): The policies of auto augmentation. Each
policy in ``policies`` is a specific augmentation policy, and is
composed by several augmentations (dict). When AutoAugment is
called, a random policy in ``policies`` will be selected to
augment images.
Examples:
>>> replace = (104, 116, 124)
>>> policies = [
>>> [
>>> dict(type='Sharpness', prob=0.0, level=8),
>>> dict(
>>> type='Shear',
>>> prob=0.4,
>>> level=0,
>>> replace=replace,
>>> axis='x')
>>> ],
>>> [
>>> dict(
>>> type='Rotate',
>>> prob=0.6,
>>> level=10,
>>> replace=replace),
>>> dict(type='Color', prob=1.0, level=6)
>>> ]
>>> ]
>>> augmentation = AutoAugment(policies)
>>> img = np.ones(100, 100, 3)
>>> gt_bboxes = np.ones(10, 4)
>>> results = dict(img=img, gt_bboxes=gt_bboxes)
>>> results = augmentation(results)
"""
def __init__(self, policies):
assert isinstance(policies, list) and len(policies) > 0, \
'Policies must be a non-empty list.'
for policy in policies:
assert isinstance(policy, list) and len(policy) > 0, \
'Each policy in policies must be a non-empty list.'
for augment in policy:
assert isinstance(augment, dict) and 'type' in augment, \
'Each specific augmentation must be a dict with key' \
' "type".'
self.policies = copy.deepcopy(policies)
self.transforms = [Compose(policy) for policy in self.policies]
def __call__(self, results):
transform = np.random.choice(self.transforms)
return transform(results)
def __repr__(self):
return f'{self.__class__.__name__}(policies={self.policies})'
@PIPELINES.register_module()
class Shear:
"""Apply Shear Transformation to image (and its corresponding bbox, mask,
segmentation).
Args:
level (int | float): The level should be in range [0,_MAX_LEVEL].
img_fill_val (int | float | tuple): The filled values for image border.
If float, the same fill value will be used for all the three
channels of image. If tuple, the should be 3 elements.
seg_ignore_label (int): The fill value used for segmentation map.
Note this value must equals ``ignore_label`` in ``semantic_head``
of the corresponding config. Default 255.
prob (float): The probability for performing Shear and should be in
range [0, 1].
direction (str): The direction for shear, either "horizontal"
or "vertical".
max_shear_magnitude (float): The maximum magnitude for Shear
transformation.
random_negative_prob (float): The probability that turns the
offset negative. Should be in range [0,1]
interpolation (str): Same as in :func:`mmcv.imshear`.
"""
def __init__(self,
level,
img_fill_val=128,
seg_ignore_label=255,
prob=0.5,
direction='horizontal',
max_shear_magnitude=0.3,
random_negative_prob=0.5,
interpolation='bilinear'):
assert isinstance(level, (int, float)), 'The level must be type ' \
f'int or float, got {type(level)}.'
assert 0 <= level <= _MAX_LEVEL, 'The level should be in range ' \
f'[0,{_MAX_LEVEL}], got {level}.'
if isinstance(img_fill_val, (float, int)):
img_fill_val = tuple([float(img_fill_val)] * 3)
elif isinstance(img_fill_val, tuple):
assert len(img_fill_val) == 3, 'img_fill_val as tuple must ' \
f'have 3 elements. got {len(img_fill_val)}.'
img_fill_val = tuple([float(val) for val in img_fill_val])
else:
raise ValueError(
'img_fill_val must be float or tuple with 3 elements.')
assert np.all([0 <= val <= 255 for val in img_fill_val]), 'all ' \
'elements of img_fill_val should between range [0,255].' \
f'got {img_fill_val}.'
assert 0 <= prob <= 1.0, 'The probability of shear should be in ' \
f'range [0,1]. got {prob}.'
assert direction in ('horizontal', 'vertical'), 'direction must ' \
f'in be either "horizontal" or "vertical". got {direction}.'
assert isinstance(max_shear_magnitude, float), 'max_shear_magnitude ' \
f'should be type float. got {type(max_shear_magnitude)}.'
assert 0. <= max_shear_magnitude <= 1., 'Defaultly ' \
'max_shear_magnitude should be in range [0,1]. ' \
f'got {max_shear_magnitude}.'
self.level = level
self.magnitude = level_to_value(level, max_shear_magnitude)
self.img_fill_val = img_fill_val
self.seg_ignore_label = seg_ignore_label
self.prob = prob
self.direction = direction
self.max_shear_magnitude = max_shear_magnitude
self.random_negative_prob = random_negative_prob
self.interpolation = interpolation
def _shear_img(self,
results,
magnitude,
direction='horizontal',
interpolation='bilinear'):
"""Shear the image.
Args:
results (dict): Result dict from loading pipeline.
magnitude (int | float): The magnitude used for shear.
direction (str): The direction for shear, either "horizontal"
or "vertical".
interpolation (str): Same as in :func:`mmcv.imshear`.
"""
for key in results.get('img_fields', ['img']):
img = results[key]
img_sheared = mmcv.imshear(
img,
magnitude,
direction,
border_value=self.img_fill_val,
interpolation=interpolation)
results[key] = img_sheared.astype(img.dtype)
results['img_shape'] = results[key].shape
def _shear_bboxes(self, results, magnitude):
"""Shear the bboxes."""
h, w, c = results['img_shape']
if self.direction == 'horizontal':
shear_matrix = np.stack([[1, magnitude],
[0, 1]]).astype(np.float32) # [2, 2]
else:
shear_matrix = np.stack([[1, 0], [magnitude,
1]]).astype(np.float32)
for key in results.get('bbox_fields', []):
min_x, min_y, max_x, max_y = np.split(
results[key], results[key].shape[-1], axis=-1)
coordinates = np.stack([[min_x, min_y], [max_x, min_y],
[min_x, max_y],
[max_x, max_y]]) # [4, 2, nb_box, 1]
coordinates = coordinates[..., 0].transpose(
(2, 1, 0)).astype(np.float32) # [nb_box, 2, 4]
new_coords = np.matmul(shear_matrix[None, :, :],
coordinates) # [nb_box, 2, 4]
min_x = np.min(new_coords[:, 0, :], axis=-1)
min_y = np.min(new_coords[:, 1, :], axis=-1)
max_x = np.max(new_coords[:, 0, :], axis=-1)
max_y = np.max(new_coords[:, 1, :], axis=-1)
min_x = np.clip(min_x, a_min=0, a_max=w)
min_y = np.clip(min_y, a_min=0, a_max=h)
max_x = np.clip(max_x, a_min=min_x, a_max=w)
max_y = np.clip(max_y, a_min=min_y, a_max=h)
results[key] = np.stack([min_x, min_y, max_x, max_y],
axis=-1).astype(results[key].dtype)
def _shear_masks(self,
results,
magnitude,
direction='horizontal',
fill_val=0,
interpolation='bilinear'):
"""Shear the masks."""
h, w, c = results['img_shape']
for key in results.get('mask_fields', []):
masks = results[key]
results[key] = masks.shear((h, w),
magnitude,
direction,
border_value=fill_val,
interpolation=interpolation)
def _shear_seg(self,
results,
magnitude,
direction='horizontal',
fill_val=255,
interpolation='bilinear'):
"""Shear the segmentation maps."""
for key in results.get('seg_fields', []):
seg = results[key]
results[key] = mmcv.imshear(
seg,
magnitude,
direction,
border_value=fill_val,
interpolation=interpolation).astype(seg.dtype)
def _filter_invalid(self, results, min_bbox_size=0):
"""Filter bboxes and corresponding masks too small after shear
augmentation."""
bbox2label, bbox2mask, _ = bbox2fields()
for key in results.get('bbox_fields', []):
bbox_w = results[key][:, 2] - results[key][:, 0]
bbox_h = results[key][:, 3] - results[key][:, 1]
valid_inds = (bbox_w > min_bbox_size) & (bbox_h > min_bbox_size)
valid_inds = np.nonzero(valid_inds)[0]
results[key] = results[key][valid_inds]
# label fields. e.g. gt_labels and gt_labels_ignore
label_key = bbox2label.get(key)
if label_key in results:
results[label_key] = results[label_key][valid_inds]
# mask fields, e.g. gt_masks and gt_masks_ignore
mask_key = bbox2mask.get(key)
if mask_key in results:
results[mask_key] = results[mask_key][valid_inds]
def __call__(self, results):
"""Call function to shear images, bounding boxes, masks and semantic
segmentation maps.
Args:
results (dict): Result dict from loading pipeline.
Returns:
dict: Sheared results.
"""
if np.random.rand() > self.prob:
return results
magnitude = random_negative(self.magnitude, self.random_negative_prob)
self._shear_img(results, magnitude, self.direction, self.interpolation)
self._shear_bboxes(results, magnitude)
# fill_val set to 0 for background of mask.
self._shear_masks(
results,
magnitude,
self.direction,
fill_val=0,
interpolation=self.interpolation)
self._shear_seg(
results,
magnitude,
self.direction,
fill_val=self.seg_ignore_label,
interpolation=self.interpolation)
self._filter_invalid(results)
return results
def __repr__(self):
repr_str = self.__class__.__name__
repr_str += f'(level={self.level}, '
repr_str += f'img_fill_val={self.img_fill_val}, '
repr_str += f'seg_ignore_label={self.seg_ignore_label}, '
repr_str += f'prob={self.prob}, '
repr_str += f'direction={self.direction}, '
repr_str += f'max_shear_magnitude={self.max_shear_magnitude}, '
repr_str += f'random_negative_prob={self.random_negative_prob}, '
repr_str += f'interpolation={self.interpolation})'
return repr_str
@PIPELINES.register_module()
class Rotate:
"""Apply Rotate Transformation to image (and its corresponding bbox, mask,
segmentation).
Args:
level (int | float): The level should be in range (0,_MAX_LEVEL].
scale (int | float): Isotropic scale factor. Same in
``mmcv.imrotate``.
center (int | float | tuple[float]): Center point (w, h) of the
rotation in the source image. If None, the center of the
image will be used. Same in ``mmcv.imrotate``.
img_fill_val (int | float | tuple): The fill value for image border.
If float, the same value will be used for all the three
channels of image. If tuple, the should be 3 elements (e.g.
equals the number of channels for image).
seg_ignore_label (int): The fill value used for segmentation map.
Note this value must equals ``ignore_label`` in ``semantic_head``
of the corresponding config. Default 255.
prob (float): The probability for perform transformation and
should be in range 0 to 1.
max_rotate_angle (int | float): The maximum angles for rotate
transformation.
random_negative_prob (float): The probability that turns the
offset negative.
"""
def __init__(self,
level,
scale=1,
center=None,
img_fill_val=128,
seg_ignore_label=255,
prob=0.5,
max_rotate_angle=30,
random_negative_prob=0.5):
assert isinstance(level, (int, float)), \
f'The level must be type int or float. got {type(level)}.'
assert 0 <= level <= _MAX_LEVEL, \
f'The level should be in range (0,{_MAX_LEVEL}]. got {level}.'
assert isinstance(scale, (int, float)), \
f'The scale must be type int or float. got type {type(scale)}.'
if isinstance(center, (int, float)):
center = (center, center)
elif isinstance(center, tuple):
assert len(center) == 2, 'center with type tuple must have '\
f'2 elements. got {len(center)} elements.'
else:
assert center is None, 'center must be None or type int, '\
f'float or tuple, got type {type(center)}.'
if isinstance(img_fill_val, (float, int)):
img_fill_val = tuple([float(img_fill_val)] * 3)
elif isinstance(img_fill_val, tuple):
assert len(img_fill_val) == 3, 'img_fill_val as tuple must '\
f'have 3 elements. got {len(img_fill_val)}.'
img_fill_val = tuple([float(val) for val in img_fill_val])
else:
raise ValueError(
'img_fill_val must be float or tuple with 3 elements.')
assert np.all([0 <= val <= 255 for val in img_fill_val]), \
'all elements of img_fill_val should between range [0,255]. '\
f'got {img_fill_val}.'
assert 0 <= prob <= 1.0, 'The probability should be in range [0,1]. '\
f'got {prob}.'
assert isinstance(max_rotate_angle, (int, float)), 'max_rotate_angle '\
f'should be type int or float. got type {type(max_rotate_angle)}.'
self.level = level
self.scale = scale
# Rotation angle in degrees. Positive values mean
# clockwise rotation.
self.angle = level_to_value(level, max_rotate_angle)
self.center = center
self.img_fill_val = img_fill_val
self.seg_ignore_label = seg_ignore_label
self.prob = prob
self.max_rotate_angle = max_rotate_angle
self.random_negative_prob = random_negative_prob
def _rotate_img(self, results, angle, center=None, scale=1.0):
"""Rotate the image.
Args:
results (dict): Result dict from loading pipeline.
angle (float): Rotation angle in degrees, positive values
mean clockwise rotation. Same in ``mmcv.imrotate``.
center (tuple[float], optional): Center point (w, h) of the
rotation. Same in ``mmcv.imrotate``.
scale (int | float): Isotropic scale factor. Same in
``mmcv.imrotate``.
"""
for key in results.get('img_fields', ['img']):
img = results[key].copy()
img_rotated = mmcv.imrotate(
img, angle, center, scale, border_value=self.img_fill_val)
results[key] = img_rotated.astype(img.dtype)
results['img_shape'] = results[key].shape
def _rotate_bboxes(self, results, rotate_matrix):
"""Rotate the bboxes."""
h, w, c = results['img_shape']
for key in results.get('bbox_fields', []):
min_x, min_y, max_x, max_y = np.split(
results[key], results[key].shape[-1], axis=-1)
coordinates = np.stack([[min_x, min_y], [max_x, min_y],
[min_x, max_y],
[max_x, max_y]]) # [4, 2, nb_bbox, 1]
# pad 1 to convert from format [x, y] to homogeneous
# coordinates format [x, y, 1]
coordinates = np.concatenate(
(coordinates,
np.ones((4, 1, coordinates.shape[2], 1), coordinates.dtype)),
axis=1) # [4, 3, nb_bbox, 1]
coordinates = coordinates.transpose(
(2, 0, 1, 3)) # [nb_bbox, 4, 3, 1]
rotated_coords = np.matmul(rotate_matrix,
coordinates) # [nb_bbox, 4, 2, 1]
rotated_coords = rotated_coords[..., 0] # [nb_bbox, 4, 2]
min_x, min_y = np.min(
rotated_coords[:, :, 0], axis=1), np.min(
rotated_coords[:, :, 1], axis=1)
max_x, max_y = np.max(
rotated_coords[:, :, 0], axis=1), np.max(
rotated_coords[:, :, 1], axis=1)
min_x, min_y = np.clip(
min_x, a_min=0, a_max=w), np.clip(
min_y, a_min=0, a_max=h)
max_x, max_y = np.clip(
max_x, a_min=min_x, a_max=w), np.clip(
max_y, a_min=min_y, a_max=h)
results[key] = np.stack([min_x, min_y, max_x, max_y],
axis=-1).astype(results[key].dtype)
def _rotate_masks(self,
results,
angle,
center=None,
scale=1.0,
fill_val=0):
"""Rotate the masks."""
h, w, c = results['img_shape']
for key in results.get('mask_fields', []):
masks = results[key]
results[key] = masks.rotate((h, w), angle, center, scale, fill_val)
def _rotate_seg(self,
results,
angle,
center=None,
scale=1.0,
fill_val=255):
"""Rotate the segmentation map."""
for key in results.get('seg_fields', []):
seg = results[key].copy()
results[key] = mmcv.imrotate(
seg, angle, center, scale,
border_value=fill_val).astype(seg.dtype)
def _filter_invalid(self, results, min_bbox_size=0):
"""Filter bboxes and corresponding masks too small after rotate
augmentation."""
bbox2label, bbox2mask, _ = bbox2fields()
for key in results.get('bbox_fields', []):
bbox_w = results[key][:, 2] - results[key][:, 0]
bbox_h = results[key][:, 3] - results[key][:, 1]
valid_inds = (bbox_w > min_bbox_size) & (bbox_h > min_bbox_size)
valid_inds = np.nonzero(valid_inds)[0]
results[key] = results[key][valid_inds]
# label fields. e.g. gt_labels and gt_labels_ignore
label_key = bbox2label.get(key)
if label_key in results:
results[label_key] = results[label_key][valid_inds]
# mask fields, e.g. gt_masks and gt_masks_ignore
mask_key = bbox2mask.get(key)
if mask_key in results:
results[mask_key] = results[mask_key][valid_inds]
def __call__(self, results):
"""Call function to rotate images, bounding boxes, masks and semantic
segmentation maps.
Args:
results (dict): Result dict from loading pipeline.
Returns:
dict: Rotated results.
"""
if np.random.rand() > self.prob:
return results
h, w = results['img'].shape[:2]
center = self.center
if center is None:
center = ((w - 1) * 0.5, (h - 1) * 0.5)
angle = random_negative(self.angle, self.random_negative_prob)
self._rotate_img(results, angle, center, self.scale)
rotate_matrix = cv2.getRotationMatrix2D(center, -angle, self.scale)
self._rotate_bboxes(results, rotate_matrix)
self._rotate_masks(results, angle, center, self.scale, fill_val=0)
self._rotate_seg(
results, angle, center, self.scale, fill_val=self.seg_ignore_label)
self._filter_invalid(results)
return results
def __repr__(self):
repr_str = self.__class__.__name__
repr_str += f'(level={self.level}, '
repr_str += f'scale={self.scale}, '
repr_str += f'center={self.center}, '
repr_str += f'img_fill_val={self.img_fill_val}, '
repr_str += f'seg_ignore_label={self.seg_ignore_label}, '
repr_str += f'prob={self.prob}, '
repr_str += f'max_rotate_angle={self.max_rotate_angle}, '
repr_str += f'random_negative_prob={self.random_negative_prob})'
return repr_str
@PIPELINES.register_module()
class Translate:
"""Translate the images, bboxes, masks and segmentation maps horizontally
or vertically.
Args:
level (int | float): The level for Translate and should be in
range [0,_MAX_LEVEL].
prob (float): The probability for performing translation and
should be in range [0, 1].
img_fill_val (int | float | tuple): The filled value for image
border. If float, the same fill value will be used for all
the three channels of image. If tuple, the should be 3
elements (e.g. equals the number of channels for image).
seg_ignore_label (int): The fill value used for segmentation map.
Note this value must equals ``ignore_label`` in ``semantic_head``
of the corresponding config. Default 255.
direction (str): The translate direction, either "horizontal"
or "vertical".
max_translate_offset (int | float): The maximum pixel's offset for
Translate.
random_negative_prob (float): The probability that turns the
offset negative.
min_size (int | float): The minimum pixel for filtering
invalid bboxes after the translation.
"""
def __init__(self,
level,
prob=0.5,
img_fill_val=128,
seg_ignore_label=255,
direction='horizontal',
max_translate_offset=250.,
random_negative_prob=0.5,
min_size=0):
assert isinstance(level, (int, float)), \
'The level must be type int or float.'
assert 0 <= level <= _MAX_LEVEL, \
'The level used for calculating Translate\'s offset should be ' \
'in range [0,_MAX_LEVEL]'
assert 0 <= prob <= 1.0, \
'The probability of translation should be in range [0, 1].'
if isinstance(img_fill_val, (float, int)):
img_fill_val = tuple([float(img_fill_val)] * 3)
elif isinstance(img_fill_val, tuple):
assert len(img_fill_val) == 3, \
'img_fill_val as tuple must have 3 elements.'
img_fill_val = tuple([float(val) for val in img_fill_val])
else:
raise ValueError('img_fill_val must be type float or tuple.')
assert np.all([0 <= val <= 255 for val in img_fill_val]), \
'all elements of img_fill_val should between range [0,255].'
assert direction in ('horizontal', 'vertical'), \
'direction should be "horizontal" or "vertical".'
assert isinstance(max_translate_offset, (int, float)), \
'The max_translate_offset must be type int or float.'
# the offset used for translation
self.offset = int(level_to_value(level, max_translate_offset))
self.level = level
self.prob = prob
self.img_fill_val = img_fill_val
self.seg_ignore_label = seg_ignore_label
self.direction = direction
self.max_translate_offset = max_translate_offset
self.random_negative_prob = random_negative_prob
self.min_size = min_size
def _translate_img(self, results, offset, direction='horizontal'):
"""Translate the image.
Args:
results (dict): Result dict from loading pipeline.
offset (int | float): The offset for translate.
direction (str): The translate direction, either "horizontal"
or "vertical".
"""
for key in results.get('img_fields', ['img']):
img = results[key].copy()
results[key] = mmcv.imtranslate(
img, offset, direction, self.img_fill_val).astype(img.dtype)
results['img_shape'] = results[key].shape
def _translate_bboxes(self, results, offset):
"""Shift bboxes horizontally or vertically, according to offset."""
h, w, c = results['img_shape']
for key in results.get('bbox_fields', []):
min_x, min_y, max_x, max_y = np.split(
results[key], results[key].shape[-1], axis=-1)
if self.direction == 'horizontal':
min_x = np.maximum(0, min_x + offset)
max_x = np.minimum(w, max_x + offset)
elif self.direction == 'vertical':
min_y = np.maximum(0, min_y + offset)
max_y = np.minimum(h, max_y + offset)
# the boxes translated outside of image will be filtered along with
# the corresponding masks, by invoking ``_filter_invalid``.
results[key] = np.concatenate([min_x, min_y, max_x, max_y],
axis=-1)
def _translate_masks(self,
results,
offset,
direction='horizontal',
fill_val=0):
"""Translate masks horizontally or vertically."""
h, w, c = results['img_shape']
for key in results.get('mask_fields', []):
masks = results[key]
results[key] = masks.translate((h, w), offset, direction, fill_val)
def _translate_seg(self,
results,
offset,
direction='horizontal',
fill_val=255):
"""Translate segmentation maps horizontally or vertically."""
for key in results.get('seg_fields', []):
seg = results[key].copy()
results[key] = mmcv.imtranslate(seg, offset, direction,
fill_val).astype(seg.dtype)
def _filter_invalid(self, results, min_size=0):
"""Filter bboxes and masks too small or translated out of image."""
bbox2label, bbox2mask, _ = bbox2fields()
for key in results.get('bbox_fields', []):
bbox_w = results[key][:, 2] - results[key][:, 0]
bbox_h = results[key][:, 3] - results[key][:, 1]
valid_inds = (bbox_w > min_size) & (bbox_h > min_size)
valid_inds = np.nonzero(valid_inds)[0]
results[key] = results[key][valid_inds]
# label fields. e.g. gt_labels and gt_labels_ignore
label_key = bbox2label.get(key)
if label_key in results:
results[label_key] = results[label_key][valid_inds]
# mask fields, e.g. gt_masks and gt_masks_ignore
mask_key = bbox2mask.get(key)
if mask_key in results:
results[mask_key] = results[mask_key][valid_inds]
return results
def __call__(self, results):
"""Call function to translate images, bounding boxes, masks and
semantic segmentation maps.
Args:
results (dict): Result dict from loading pipeline.
Returns:
dict: Translated results.
"""
if np.random.rand() > self.prob:
return results
offset = random_negative(self.offset, self.random_negative_prob)
self._translate_img(results, offset, self.direction)
self._translate_bboxes(results, offset)
# fill_val defaultly 0 for BitmapMasks and None for PolygonMasks.
self._translate_masks(results, offset, self.direction)
# fill_val set to ``seg_ignore_label`` for the ignored value
# of segmentation map.
self._translate_seg(
results, offset, self.direction, fill_val=self.seg_ignore_label)
self._filter_invalid(results, min_size=self.min_size)
return results
@PIPELINES.register_module()
class ColorTransform:
"""Apply Color transformation to image. The bboxes, masks, and
segmentations are not modified.
Args:
level (int | float): Should be in range [0,_MAX_LEVEL].
prob (float): The probability for performing Color transformation.
"""
def __init__(self, level, prob=0.5):
assert isinstance(level, (int, float)), \
'The level must be type int or float.'
assert 0 <= level <= _MAX_LEVEL, \
'The level should be in range [0,_MAX_LEVEL].'
assert 0 <= prob <= 1.0, \
'The probability should be in range [0,1].'
self.level = level
self.prob = prob
self.factor = enhance_level_to_value(level)
def _adjust_color_img(self, results, factor=1.0):
"""Apply Color transformation to image."""
for key in results.get('img_fields', ['img']):
# NOTE defaultly the image should be BGR format
img = results[key]
results[key] = mmcv.adjust_color(img, factor).astype(img.dtype)
def __call__(self, results):
"""Call function for Color transformation.
Args:
results (dict): Result dict from loading pipeline.
Returns:
dict: Colored results.
"""
if np.random.rand() > self.prob:
return results
self._adjust_color_img(results, self.factor)
return results
def __repr__(self):
repr_str = self.__class__.__name__
repr_str += f'(level={self.level}, '
repr_str += f'prob={self.prob})'
return repr_str
@PIPELINES.register_module()
class EqualizeTransform:
"""Apply Equalize transformation to image. The bboxes, masks and
segmentations are not modified.
Args:
prob (float): The probability for performing Equalize transformation.
"""
def __init__(self, prob=0.5):
assert 0 <= prob <= 1.0, \
'The probability should be in range [0,1].'
self.prob = prob
def _imequalize(self, results):
"""Equalizes the histogram of one image."""
for key in results.get('img_fields', ['img']):
img = results[key]
results[key] = mmcv.imequalize(img).astype(img.dtype)
def __call__(self, results):
"""Call function for Equalize transformation.
Args:
results (dict): Results dict from loading pipeline.
Returns:
dict: Results after the transformation.
"""
if np.random.rand() > self.prob:
return results
self._imequalize(results)
return results
def __repr__(self):
repr_str = self.__class__.__name__
repr_str += f'(prob={self.prob})'
@PIPELINES.register_module()
class BrightnessTransform:
"""Apply Brightness transformation to image. The bboxes, masks and
segmentations are not modified.
Args:
level (int | float): Should be in range [0,_MAX_LEVEL].
prob (float): The probability for performing Brightness transformation.
"""
def __init__(self, level, prob=0.5):
assert isinstance(level, (int, float)), \
'The level must be type int or float.'
assert 0 <= level <= _MAX_LEVEL, \
'The level should be in range [0,_MAX_LEVEL].'
assert 0 <= prob <= 1.0, \
'The probability should be in range [0,1].'
self.level = level
self.prob = prob
self.factor = enhance_level_to_value(level)
def _adjust_brightness_img(self, results, factor=1.0):
"""Adjust the brightness of image."""
for key in results.get('img_fields', ['img']):
img = results[key]
results[key] = mmcv.adjust_brightness(img,
factor).astype(img.dtype)
def __call__(self, results):
"""Call function for Brightness transformation.
Args:
results (dict): Results dict from loading pipeline.
Returns:
dict: Results after the transformation.
"""
if np.random.rand() > self.prob:
return results
self._adjust_brightness_img(results, self.factor)
return results
def __repr__(self):
repr_str = self.__class__.__name__
repr_str += f'(level={self.level}, '
repr_str += f'prob={self.prob})'
return repr_str
@PIPELINES.register_module()
class ContrastTransform:
"""Apply Contrast transformation to image. The bboxes, masks and
segmentations are not modified.
Args:
level (int | float): Should be in range [0,_MAX_LEVEL].
prob (float): The probability for performing Contrast transformation.
"""
def __init__(self, level, prob=0.5):
assert isinstance(level, (int, float)), \
'The level must be type int or float.'
assert 0 <= level <= _MAX_LEVEL, \
'The level should be in range [0,_MAX_LEVEL].'
assert 0 <= prob <= 1.0, \
'The probability should be in range [0,1].'
self.level = level
self.prob = prob
self.factor = enhance_level_to_value(level)
def _adjust_contrast_img(self, results, factor=1.0):
"""Adjust the image contrast."""
for key in results.get('img_fields', ['img']):
img = results[key]
results[key] = mmcv.adjust_contrast(img, factor).astype(img.dtype)
def __call__(self, results):
"""Call function for Contrast transformation.
Args:
results (dict): Results dict from loading pipeline.
Returns:
dict: Results after the transformation.
"""
if np.random.rand() > self.prob:
return results
self._adjust_contrast_img(results, self.factor)
return results
def __repr__(self):
repr_str = self.__class__.__name__
repr_str += f'(level={self.level}, '
repr_str += f'prob={self.prob})'
return repr_str