# Copyright (c) OpenMMLab. All rights reserved.
import random
import warnings
from collections.abc import Sequence
import cv2
import mmcv
import numpy as np
from mmcv.utils import digit_version
from torch.nn.modules.utils import _pair
from ..builder import PIPELINES
from .formatting import to_tensor
def _combine_quadruple(a, b):
return (a[0] + a[2] * b[0], a[1] + a[3] * b[1], a[2] * b[2], a[3] * b[3])
def _flip_quadruple(a):
return (1 - a[0] - a[2], a[1], a[2], a[3])
def _init_lazy_if_proper(results, lazy):
"""Initialize lazy operation properly.
Make sure that a lazy operation is properly initialized,
and avoid a non-lazy operation accidentally getting mixed in.
Required keys in results are "imgs" if "img_shape" not in results,
otherwise, Required keys in results are "img_shape", add or modified keys
are "img_shape", "lazy".
Add or modified keys in "lazy" are "original_shape", "crop_bbox", "flip",
"flip_direction", "interpolation".
Args:
results (dict): A dict stores data pipeline result.
lazy (bool): Determine whether to apply lazy operation. Default: False.
"""
if 'img_shape' not in results:
results['img_shape'] = results['imgs'][0].shape[:2]
if lazy:
if 'lazy' not in results:
img_h, img_w = results['img_shape']
lazyop = dict()
lazyop['original_shape'] = results['img_shape']
lazyop['crop_bbox'] = np.array([0, 0, img_w, img_h],
dtype=np.float32)
lazyop['flip'] = False
lazyop['flip_direction'] = None
lazyop['interpolation'] = None
results['lazy'] = lazyop
else:
assert 'lazy' not in results, 'Use Fuse after lazy operations'
@PIPELINES.register_module()
class TorchvisionTrans:
"""Torchvision Augmentations, under torchvision.transforms.
Args:
type (str): The name of the torchvision transformation.
"""
def __init__(self, type, **kwargs):
try:
import torchvision
import torchvision.transforms as tv_trans
except ImportError:
raise RuntimeError('Install torchvision to use TorchvisionTrans')
if digit_version(torchvision.__version__) < digit_version('0.8.0'):
raise RuntimeError('The version of torchvision should be at least '
'0.8.0')
trans = getattr(tv_trans, type, None)
assert trans, f'Transform {type} not in torchvision'
self.trans = trans(**kwargs)
def __call__(self, results):
assert 'imgs' in results
imgs = [x.transpose(2, 0, 1) for x in results['imgs']]
imgs = to_tensor(np.stack(imgs))
imgs = self.trans(imgs).data.numpy()
imgs[imgs > 255] = 255
imgs[imgs < 0] = 0
imgs = imgs.astype(np.uint8)
imgs = [x.transpose(1, 2, 0) for x in imgs]
results['imgs'] = imgs
return results
@PIPELINES.register_module()
class PytorchVideoTrans:
"""PytorchVideoTrans Augmentations, under pytorchvideo.transforms.
Args:
type (str): The name of the pytorchvideo transformation.
"""
def __init__(self, type, **kwargs):
try:
import torch
import pytorchvideo.transforms as ptv_trans
except ImportError:
raise RuntimeError('Install pytorchvideo to use PytorchVideoTrans')
if digit_version(torch.__version__) < digit_version('1.8.0'):
raise RuntimeError(
'The version of PyTorch should be at least 1.8.0')
trans = getattr(ptv_trans, type, None)
assert trans, f'Transform {type} not in pytorchvideo'
supported_pytorchvideo_trans = ('AugMix', 'RandAugment',
'RandomResizedCrop', 'ShortSideScale',
'RandomShortSideScale')
assert type in supported_pytorchvideo_trans,\
f'PytorchVideo Transform {type} is not supported in MMAction2'
self.trans = trans(**kwargs)
self.type = type
def __call__(self, results):
assert 'imgs' in results
assert 'gt_bboxes' not in results,\
f'PytorchVideo {self.type} doesn\'t support bboxes yet.'
assert 'proposals' not in results,\
f'PytorchVideo {self.type} doesn\'t support bboxes yet.'
if self.type in ('AugMix', 'RandAugment'):
# list[ndarray(h, w, 3)] -> torch.tensor(t, c, h, w)
imgs = [x.transpose(2, 0, 1) for x in results['imgs']]
imgs = to_tensor(np.stack(imgs))
else:
# list[ndarray(h, w, 3)] -> torch.tensor(c, t, h, w)
# uint8 -> float32
imgs = to_tensor((np.stack(results['imgs']).transpose(3, 0, 1, 2) /
255.).astype(np.float32))
imgs = self.trans(imgs).data.numpy()
if self.type in ('AugMix', 'RandAugment'):
imgs[imgs > 255] = 255
imgs[imgs < 0] = 0
imgs = imgs.astype(np.uint8)
# torch.tensor(t, c, h, w) -> list[ndarray(h, w, 3)]
imgs = [x.transpose(1, 2, 0) for x in imgs]
else:
# float32 -> uint8
imgs = imgs * 255
imgs[imgs > 255] = 255
imgs[imgs < 0] = 0
imgs = imgs.astype(np.uint8)
# torch.tensor(c, t, h, w) -> list[ndarray(h, w, 3)]
imgs = [x for x in imgs.transpose(1, 2, 3, 0)]
results['imgs'] = imgs
return results
@PIPELINES.register_module()
class PoseCompact:
"""Convert the coordinates of keypoints to make it more compact.
Specifically, it first find a tight bounding box that surrounds all joints
in each frame, then we expand the tight box by a given padding ratio. For
example, if 'padding == 0.25', then the expanded box has unchanged center,
and 1.25x width and height.
Required keys in results are "img_shape", "keypoint", add or modified keys
are "img_shape", "keypoint", "crop_quadruple".
Args:
padding (float): The padding size. Default: 0.25.
threshold (int): The threshold for the tight bounding box. If the width
or height of the tight bounding box is smaller than the threshold,
we do not perform the compact operation. Default: 10.
hw_ratio (float | tuple[float] | None): The hw_ratio of the expanded
box. Float indicates the specific ratio and tuple indicates a
ratio range. If set as None, it means there is no requirement on
hw_ratio. Default: None.
allow_imgpad (bool): Whether to allow expanding the box outside the
image to meet the hw_ratio requirement. Default: True.
Returns:
type: Description of returned object.
"""
def __init__(self,
padding=0.25,
threshold=10,
hw_ratio=None,
allow_imgpad=True):
self.padding = padding
self.threshold = threshold
if hw_ratio is not None:
hw_ratio = _pair(hw_ratio)
self.hw_ratio = hw_ratio
self.allow_imgpad = allow_imgpad
assert self.padding >= 0
def __call__(self, results):
img_shape = results['img_shape']
h, w = img_shape
kp = results['keypoint']
# Make NaN zero
kp[np.isnan(kp)] = 0.
kp_x = kp[..., 0]
kp_y = kp[..., 1]
min_x = np.min(kp_x[kp_x != 0], initial=np.Inf)
min_y = np.min(kp_y[kp_y != 0], initial=np.Inf)
max_x = np.max(kp_x[kp_x != 0], initial=-np.Inf)
max_y = np.max(kp_y[kp_y != 0], initial=-np.Inf)
# The compact area is too small
if max_x - min_x < self.threshold or max_y - min_y < self.threshold:
return results
center = ((max_x + min_x) / 2, (max_y + min_y) / 2)
half_width = (max_x - min_x) / 2 * (1 + self.padding)
half_height = (max_y - min_y) / 2 * (1 + self.padding)
if self.hw_ratio is not None:
half_height = max(self.hw_ratio[0] * half_width, half_height)
half_width = max(1 / self.hw_ratio[1] * half_height, half_width)
min_x, max_x = center[0] - half_width, center[0] + half_width
min_y, max_y = center[1] - half_height, center[1] + half_height
# hot update
if not self.allow_imgpad:
min_x, min_y = int(max(0, min_x)), int(max(0, min_y))
max_x, max_y = int(min(w, max_x)), int(min(h, max_y))
else:
min_x, min_y = int(min_x), int(min_y)
max_x, max_y = int(max_x), int(max_y)
kp_x[kp_x != 0] -= min_x
kp_y[kp_y != 0] -= min_y
new_shape = (max_y - min_y, max_x - min_x)
results['img_shape'] = new_shape
# the order is x, y, w, h (in [0, 1]), a tuple
crop_quadruple = results.get('crop_quadruple', (0., 0., 1., 1.))
new_crop_quadruple = (min_x / w, min_y / h, (max_x - min_x) / w,
(max_y - min_y) / h)
crop_quadruple = _combine_quadruple(crop_quadruple, new_crop_quadruple)
results['crop_quadruple'] = crop_quadruple
return results
def __repr__(self):
repr_str = (f'{self.__class__.__name__}(padding={self.padding}, '
f'threshold={self.threshold}, '
f'hw_ratio={self.hw_ratio}, '
f'allow_imgpad={self.allow_imgpad})')
return repr_str
@PIPELINES.register_module()
class Imgaug:
"""Imgaug augmentation.
Adds custom transformations from imgaug library.
Please visit `https://imgaug.readthedocs.io/en/latest/index.html`
to get more information. Two demo configs could be found in tsn and i3d
config folder.
It's better to use uint8 images as inputs since imgaug works best with
numpy dtype uint8 and isn't well tested with other dtypes. It should be
noted that not all of the augmenters have the same input and output dtype,
which may cause unexpected results.
Required keys are "imgs", "img_shape"(if "gt_bboxes" is not None) and
"modality", added or modified keys are "imgs", "img_shape", "gt_bboxes"
and "proposals".
It is worth mentioning that `Imgaug` will NOT create custom keys like
"interpolation", "crop_bbox", "flip_direction", etc. So when using
`Imgaug` along with other mmaction2 pipelines, we should pay more attention
to required keys.
Two steps to use `Imgaug` pipeline:
1. Create initialization parameter `transforms`. There are three ways
to create `transforms`.
1) string: only support `default` for now.
e.g. `transforms='default'`
2) list[dict]: create a list of augmenters by a list of dicts, each
dict corresponds to one augmenter. Every dict MUST contain a key
named `type`. `type` should be a string(iaa.Augmenter's name) or
an iaa.Augmenter subclass.
e.g. `transforms=[dict(type='Rotate', rotate=(-20, 20))]`
e.g. `transforms=[dict(type=iaa.Rotate, rotate=(-20, 20))]`
3) iaa.Augmenter: create an imgaug.Augmenter object.
e.g. `transforms=iaa.Rotate(rotate=(-20, 20))`
2. Add `Imgaug` in dataset pipeline. It is recommended to insert imgaug
pipeline before `Normalize`. A demo pipeline is listed as follows.
```
pipeline = [
dict(
type='SampleFrames',
clip_len=1,
frame_interval=1,
num_clips=16,
),
dict(type='RawFrameDecode'),
dict(type='Resize', scale=(-1, 256)),
dict(
type='MultiScaleCrop',
input_size=224,
scales=(1, 0.875, 0.75, 0.66),
random_crop=False,
max_wh_scale_gap=1,
num_fixed_crops=13),
dict(type='Resize', scale=(224, 224), keep_ratio=False),
dict(type='Flip', flip_ratio=0.5),
dict(type='Imgaug', transforms='default'),
# dict(type='Imgaug', transforms=[
# dict(type='Rotate', rotate=(-20, 20))
# ]),
dict(type='Normalize', **img_norm_cfg),
dict(type='FormatShape', input_format='NCHW'),
dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
dict(type='ToTensor', keys=['imgs', 'label'])
]
```
Args:
transforms (str | list[dict] | :obj:`iaa.Augmenter`): Three different
ways to create imgaug augmenter.
"""
def __init__(self, transforms):
import imgaug.augmenters as iaa
if transforms == 'default':
self.transforms = self.default_transforms()
elif isinstance(transforms, list):
assert all(isinstance(trans, dict) for trans in transforms)
self.transforms = transforms
elif isinstance(transforms, iaa.Augmenter):
self.aug = self.transforms = transforms
else:
raise ValueError('transforms must be `default` or a list of dicts'
' or iaa.Augmenter object')
if not isinstance(transforms, iaa.Augmenter):
self.aug = iaa.Sequential(
[self.imgaug_builder(t) for t in self.transforms])
@staticmethod
def default_transforms():
"""Default transforms for imgaug.
Implement RandAugment by imgaug.
Please visit `https://arxiv.org/abs/1909.13719` for more information.
Augmenters and hyper parameters are borrowed from the following repo:
https://github.com/tensorflow/tpu/blob/master/models/official/efficientnet/autoaugment.py # noqa
Miss one augmenter ``SolarizeAdd`` since imgaug doesn't support this.
Returns:
dict: The constructed RandAugment transforms.
"""
# RandAugment hyper params
num_augmenters = 2
cur_magnitude, max_magnitude = 9, 10
cur_level = 1.0 * cur_magnitude / max_magnitude
return [
dict(
type='SomeOf',
n=num_augmenters,
children=[
dict(
type='ShearX',
shear=17.19 * cur_level * random.choice([-1, 1])),
dict(
type='ShearY',
shear=17.19 * cur_level * random.choice([-1, 1])),
dict(
type='TranslateX',
percent=.2 * cur_level * random.choice([-1, 1])),
dict(
type='TranslateY',
percent=.2 * cur_level * random.choice([-1, 1])),
dict(
type='Rotate',
rotate=30 * cur_level * random.choice([-1, 1])),
dict(type='Posterize', nb_bits=max(1, int(4 * cur_level))),
dict(type='Solarize', threshold=256 * cur_level),
dict(type='EnhanceColor', factor=1.8 * cur_level + .1),
dict(type='EnhanceContrast', factor=1.8 * cur_level + .1),
dict(
type='EnhanceBrightness', factor=1.8 * cur_level + .1),
dict(type='EnhanceSharpness', factor=1.8 * cur_level + .1),
dict(type='Autocontrast', cutoff=0),
dict(type='Equalize'),
dict(type='Invert', p=1.),
dict(
type='Cutout',
nb_iterations=1,
size=0.2 * cur_level,
squared=True)
])
]
def imgaug_builder(self, cfg):
"""Import a module from imgaug.
It follows the logic of :func:`build_from_cfg`. Use a dict object to
create an iaa.Augmenter object.
Args:
cfg (dict): Config dict. It should at least contain the key "type".
Returns:
obj:`iaa.Augmenter`: The constructed imgaug augmenter.
"""
import imgaug.augmenters as iaa
assert isinstance(cfg, dict) and 'type' in cfg
args = cfg.copy()
obj_type = args.pop('type')
if mmcv.is_str(obj_type):
obj_cls = getattr(iaa, obj_type) if hasattr(iaa, obj_type) \
else getattr(iaa.pillike, obj_type)
elif issubclass(obj_type, iaa.Augmenter):
obj_cls = obj_type
else:
raise TypeError(
f'type must be a str or valid type, but got {type(obj_type)}')
if 'children' in args:
args['children'] = [
self.imgaug_builder(child) for child in args['children']
]
return obj_cls(**args)
def __repr__(self):
repr_str = self.__class__.__name__ + f'(transforms={self.aug})'
return repr_str
def __call__(self, results):
assert results['modality'] == 'RGB', 'Imgaug only support RGB images.'
in_type = results['imgs'][0].dtype.type
cur_aug = self.aug.to_deterministic()
results['imgs'] = [
cur_aug.augment_image(frame) for frame in results['imgs']
]
img_h, img_w, _ = results['imgs'][0].shape
out_type = results['imgs'][0].dtype.type
assert in_type == out_type, \
('Imgaug input dtype and output dtype are not the same. ',
f'Convert from {in_type} to {out_type}')
if 'gt_bboxes' in results:
from imgaug.augmentables import bbs
bbox_list = [
bbs.BoundingBox(
x1=bbox[0], y1=bbox[1], x2=bbox[2], y2=bbox[3])
for bbox in results['gt_bboxes']
]
bboxes = bbs.BoundingBoxesOnImage(
bbox_list, shape=results['img_shape'])
bbox_aug, *_ = cur_aug.augment_bounding_boxes([bboxes])
results['gt_bboxes'] = [[
max(bbox.x1, 0),
max(bbox.y1, 0),
min(bbox.x2, img_w),
min(bbox.y2, img_h)
] for bbox in bbox_aug.items]
if 'proposals' in results:
bbox_list = [
bbs.BoundingBox(
x1=bbox[0], y1=bbox[1], x2=bbox[2], y2=bbox[3])
for bbox in results['proposals']
]
bboxes = bbs.BoundingBoxesOnImage(
bbox_list, shape=results['img_shape'])
bbox_aug, *_ = cur_aug.augment_bounding_boxes([bboxes])
results['proposals'] = [[
max(bbox.x1, 0),
max(bbox.y1, 0),
min(bbox.x2, img_w),
min(bbox.y2, img_h)
] for bbox in bbox_aug.items]
results['img_shape'] = (img_h, img_w)
return results
@PIPELINES.register_module()
class Fuse:
"""Fuse lazy operations.
Fusion order:
crop -> resize -> flip
Required keys are "imgs", "img_shape" and "lazy", added or modified keys
are "imgs", "lazy".
Required keys in "lazy" are "crop_bbox", "interpolation", "flip_direction".
"""
def __call__(self, results):
if 'lazy' not in results:
raise ValueError('No lazy operation detected')
lazyop = results['lazy']
imgs = results['imgs']
# crop
left, top, right, bottom = lazyop['crop_bbox'].round().astype(int)
imgs = [img[top:bottom, left:right] for img in imgs]
# resize
img_h, img_w = results['img_shape']
if lazyop['interpolation'] is None:
interpolation = 'bilinear'
else:
interpolation = lazyop['interpolation']
imgs = [
mmcv.imresize(img, (img_w, img_h), interpolation=interpolation)
for img in imgs
]
# flip
if lazyop['flip']:
for img in imgs:
mmcv.imflip_(img, lazyop['flip_direction'])
results['imgs'] = imgs
del results['lazy']
return results
@PIPELINES.register_module()
class RandomCrop:
"""Vanilla square random crop that specifics the output size.
Required keys in results are "img_shape", "keypoint" (optional), "imgs"
(optional), added or modified keys are "keypoint", "imgs", "lazy"; Required
keys in "lazy" are "flip", "crop_bbox", added or modified key is
"crop_bbox".
Args:
size (int): The output size of the images.
lazy (bool): Determine whether to apply lazy operation. Default: False.
"""
def __init__(self, size, lazy=False):
if not isinstance(size, int):
raise TypeError(f'Size must be an int, but got {type(size)}')
self.size = size
self.lazy = lazy
@staticmethod
def _crop_kps(kps, crop_bbox):
return kps - crop_bbox[:2]
@staticmethod
def _crop_imgs(imgs, crop_bbox):
x1, y1, x2, y2 = crop_bbox
return [img[y1:y2, x1:x2] for img in imgs]
@staticmethod
def _box_crop(box, crop_bbox):
"""Crop the bounding boxes according to the crop_bbox.
Args:
box (np.ndarray): The bounding boxes.
crop_bbox(np.ndarray): The bbox used to crop the original image.
"""
x1, y1, x2, y2 = crop_bbox
img_w, img_h = x2 - x1, y2 - y1
box_ = box.copy()
box_[..., 0::2] = np.clip(box[..., 0::2] - x1, 0, img_w - 1)
box_[..., 1::2] = np.clip(box[..., 1::2] - y1, 0, img_h - 1)
return box_
def _all_box_crop(self, results, crop_bbox):
"""Crop the gt_bboxes and proposals in results according to crop_bbox.
Args:
results (dict): All information about the sample, which contain
'gt_bboxes' and 'proposals' (optional).
crop_bbox(np.ndarray): The bbox used to crop the original image.
"""
results['gt_bboxes'] = self._box_crop(results['gt_bboxes'], crop_bbox)
if 'proposals' in results and results['proposals'] is not None:
assert results['proposals'].shape[1] == 4
results['proposals'] = self._box_crop(results['proposals'],
crop_bbox)
return results
def __call__(self, results):
"""Performs the RandomCrop augmentation.
Args:
results (dict): The resulting dict to be modified and passed
to the next transform in pipeline.
"""
_init_lazy_if_proper(results, self.lazy)
if 'keypoint' in results:
assert not self.lazy, ('Keypoint Augmentations are not compatible '
'with lazy == True')
img_h, img_w = results['img_shape']
assert self.size <= img_h and self.size <= img_w
y_offset = 0
x_offset = 0
if img_h > self.size:
y_offset = int(np.random.randint(0, img_h - self.size))
if img_w > self.size:
x_offset = int(np.random.randint(0, img_w - self.size))
if 'crop_quadruple' not in results:
results['crop_quadruple'] = np.array(
[0, 0, 1, 1], # x, y, w, h
dtype=np.float32)
x_ratio, y_ratio = x_offset / img_w, y_offset / img_h
w_ratio, h_ratio = self.size / img_w, self.size / img_h
old_crop_quadruple = results['crop_quadruple']
old_x_ratio, old_y_ratio = old_crop_quadruple[0], old_crop_quadruple[1]
old_w_ratio, old_h_ratio = old_crop_quadruple[2], old_crop_quadruple[3]
new_crop_quadruple = [
old_x_ratio + x_ratio * old_w_ratio,
old_y_ratio + y_ratio * old_h_ratio, w_ratio * old_w_ratio,
h_ratio * old_h_ratio
]
results['crop_quadruple'] = np.array(
new_crop_quadruple, dtype=np.float32)
new_h, new_w = self.size, self.size
crop_bbox = np.array(
[x_offset, y_offset, x_offset + new_w, y_offset + new_h])
results['crop_bbox'] = crop_bbox
results['img_shape'] = (new_h, new_w)
if not self.lazy:
if 'keypoint' in results:
results['keypoint'] = self._crop_kps(results['keypoint'],
crop_bbox)
if 'imgs' in results:
results['imgs'] = self._crop_imgs(results['imgs'], crop_bbox)
else:
lazyop = results['lazy']
if lazyop['flip']:
raise NotImplementedError('Put Flip at last for now')
# record crop_bbox in lazyop dict to ensure only crop once in Fuse
lazy_left, lazy_top, lazy_right, lazy_bottom = lazyop['crop_bbox']
left = x_offset * (lazy_right - lazy_left) / img_w
right = (x_offset + new_w) * (lazy_right - lazy_left) / img_w
top = y_offset * (lazy_bottom - lazy_top) / img_h
bottom = (y_offset + new_h) * (lazy_bottom - lazy_top) / img_h
lazyop['crop_bbox'] = np.array([(lazy_left + left),
(lazy_top + top),
(lazy_left + right),
(lazy_top + bottom)],
dtype=np.float32)
# Process entity boxes
if 'gt_bboxes' in results:
assert not self.lazy
results = self._all_box_crop(results, results['crop_bbox'])
return results
def __repr__(self):
repr_str = (f'{self.__class__.__name__}(size={self.size}, '
f'lazy={self.lazy})')
return repr_str
@PIPELINES.register_module()
class RandomResizedCrop(RandomCrop):
"""Random crop that specifics the area and height-weight ratio range.
Required keys in results are "img_shape", "crop_bbox", "imgs" (optional),
"keypoint" (optional), added or modified keys are "imgs", "keypoint",
"crop_bbox" and "lazy"; Required keys in "lazy" are "flip", "crop_bbox",
added or modified key is "crop_bbox".
Args:
area_range (Tuple[float]): The candidate area scales range of
output cropped images. Default: (0.08, 1.0).
aspect_ratio_range (Tuple[float]): The candidate aspect ratio range of
output cropped images. Default: (3 / 4, 4 / 3).
lazy (bool): Determine whether to apply lazy operation. Default: False.
"""
def __init__(self,
area_range=(0.08, 1.0),
aspect_ratio_range=(3 / 4, 4 / 3),
lazy=False):
self.area_range = area_range
self.aspect_ratio_range = aspect_ratio_range
self.lazy = lazy
if not mmcv.is_tuple_of(self.area_range, float):
raise TypeError(f'Area_range must be a tuple of float, '
f'but got {type(area_range)}')
if not mmcv.is_tuple_of(self.aspect_ratio_range, float):
raise TypeError(f'Aspect_ratio_range must be a tuple of float, '
f'but got {type(aspect_ratio_range)}')
@staticmethod
def get_crop_bbox(img_shape,
area_range,
aspect_ratio_range,
max_attempts=10):
"""Get a crop bbox given the area range and aspect ratio range.
Args:
img_shape (Tuple[int]): Image shape
area_range (Tuple[float]): The candidate area scales range of
output cropped images. Default: (0.08, 1.0).
aspect_ratio_range (Tuple[float]): The candidate aspect
ratio range of output cropped images. Default: (3 / 4, 4 / 3).
max_attempts (int): The maximum of attempts. Default: 10.
max_attempts (int): Max attempts times to generate random candidate
bounding box. If it doesn't qualified one, the center bounding
box will be used.
Returns:
(list[int]) A random crop bbox within the area range and aspect
ratio range.
"""
assert 0 < area_range[0] <= area_range[1] <= 1
assert 0 < aspect_ratio_range[0] <= aspect_ratio_range[1]
img_h, img_w = img_shape
area = img_h * img_w
min_ar, max_ar = aspect_ratio_range
aspect_ratios = np.exp(
np.random.uniform(
np.log(min_ar), np.log(max_ar), size=max_attempts))
target_areas = np.random.uniform(*area_range, size=max_attempts) * area
candidate_crop_w = np.round(np.sqrt(target_areas *
aspect_ratios)).astype(np.int32)
candidate_crop_h = np.round(np.sqrt(target_areas /
aspect_ratios)).astype(np.int32)
for i in range(max_attempts):
crop_w = candidate_crop_w[i]
crop_h = candidate_crop_h[i]
if crop_h <= img_h and crop_w <= img_w:
x_offset = random.randint(0, img_w - crop_w)
y_offset = random.randint(0, img_h - crop_h)
return x_offset, y_offset, x_offset + crop_w, y_offset + crop_h
# Fallback
crop_size = min(img_h, img_w)
x_offset = (img_w - crop_size) // 2
y_offset = (img_h - crop_size) // 2
return x_offset, y_offset, x_offset + crop_size, y_offset + crop_size
def __call__(self, results):
"""Performs the RandomResizeCrop augmentation.
Args:
results (dict): The resulting dict to be modified and passed
to the next transform in pipeline.
"""
_init_lazy_if_proper(results, self.lazy)
if 'keypoint' in results:
assert not self.lazy, ('Keypoint Augmentations are not compatible '
'with lazy == True')
img_h, img_w = results['img_shape']
left, top, right, bottom = self.get_crop_bbox(
(img_h, img_w), self.area_range, self.aspect_ratio_range)
new_h, new_w = bottom - top, right - left
if 'crop_quadruple' not in results:
results['crop_quadruple'] = np.array(
[0, 0, 1, 1], # x, y, w, h
dtype=np.float32)
x_ratio, y_ratio = left / img_w, top / img_h
w_ratio, h_ratio = new_w / img_w, new_h / img_h
old_crop_quadruple = results['crop_quadruple']
old_x_ratio, old_y_ratio = old_crop_quadruple[0], old_crop_quadruple[1]
old_w_ratio, old_h_ratio = old_crop_quadruple[2], old_crop_quadruple[3]
new_crop_quadruple = [
old_x_ratio + x_ratio * old_w_ratio,
old_y_ratio + y_ratio * old_h_ratio, w_ratio * old_w_ratio,
h_ratio * old_h_ratio
]
results['crop_quadruple'] = np.array(
new_crop_quadruple, dtype=np.float32)
crop_bbox = np.array([left, top, right, bottom])
results['crop_bbox'] = crop_bbox
results['img_shape'] = (new_h, new_w)
if not self.lazy:
if 'keypoint' in results:
results['keypoint'] = self._crop_kps(results['keypoint'],
crop_bbox)
if 'imgs' in results:
results['imgs'] = self._crop_imgs(results['imgs'], crop_bbox)
else:
lazyop = results['lazy']
if lazyop['flip']:
raise NotImplementedError('Put Flip at last for now')
# record crop_bbox in lazyop dict to ensure only crop once in Fuse
lazy_left, lazy_top, lazy_right, lazy_bottom = lazyop['crop_bbox']
left = left * (lazy_right - lazy_left) / img_w
right = right * (lazy_right - lazy_left) / img_w
top = top * (lazy_bottom - lazy_top) / img_h
bottom = bottom * (lazy_bottom - lazy_top) / img_h
lazyop['crop_bbox'] = np.array([(lazy_left + left),
(lazy_top + top),
(lazy_left + right),
(lazy_top + bottom)],
dtype=np.float32)
if 'gt_bboxes' in results:
assert not self.lazy
results = self._all_box_crop(results, results['crop_bbox'])
return results
def __repr__(self):
repr_str = (f'{self.__class__.__name__}('
f'area_range={self.area_range}, '
f'aspect_ratio_range={self.aspect_ratio_range}, '
f'lazy={self.lazy})')
return repr_str
@PIPELINES.register_module()
class MultiScaleCrop(RandomCrop):
"""Crop images with a list of randomly selected scales.
Randomly select the w and h scales from a list of scales. Scale of 1 means
the base size, which is the minimal of image width and height. The scale
level of w and h is controlled to be smaller than a certain value to
prevent too large or small aspect ratio.
Required keys are "img_shape", "imgs" (optional), "keypoint" (optional),
added or modified keys are "imgs", "crop_bbox", "img_shape", "lazy" and
"scales". Required keys in "lazy" are "crop_bbox", added or modified key is
"crop_bbox".
Args:
input_size (int | tuple[int]): (w, h) of network input.
scales (tuple[float]): width and height scales to be selected.
max_wh_scale_gap (int): Maximum gap of w and h scale levels.
Default: 1.
random_crop (bool): If set to True, the cropping bbox will be randomly
sampled, otherwise it will be sampler from fixed regions.
Default: False.
num_fixed_crops (int): If set to 5, the cropping bbox will keep 5
basic fixed regions: "upper left", "upper right", "lower left",
"lower right", "center". If set to 13, the cropping bbox will
append another 8 fix regions: "center left", "center right",
"lower center", "upper center", "upper left quarter",
"upper right quarter", "lower left quarter", "lower right quarter".
Default: 5.
lazy (bool): Determine whether to apply lazy operation. Default: False.
"""
def __init__(self,
input_size,
scales=(1, ),
max_wh_scale_gap=1,
random_crop=False,
num_fixed_crops=5,
lazy=False):
self.input_size = _pair(input_size)
if not mmcv.is_tuple_of(self.input_size, int):
raise TypeError(f'Input_size must be int or tuple of int, '
f'but got {type(input_size)}')
if not isinstance(scales, tuple):
raise TypeError(f'Scales must be tuple, but got {type(scales)}')
if num_fixed_crops not in [5, 13]:
raise ValueError(f'Num_fix_crops must be in {[5, 13]}, '
f'but got {num_fixed_crops}')
self.scales = scales
self.max_wh_scale_gap = max_wh_scale_gap
self.random_crop = random_crop
self.num_fixed_crops = num_fixed_crops
self.lazy = lazy
def __call__(self, results):
"""Performs the MultiScaleCrop augmentation.
Args:
results (dict): The resulting dict to be modified and passed
to the next transform in pipeline.
"""
_init_lazy_if_proper(results, self.lazy)
if 'keypoint' in results:
assert not self.lazy, ('Keypoint Augmentations are not compatible '
'with lazy == True')
img_h, img_w = results['img_shape']
base_size = min(img_h, img_w)
crop_sizes = [int(base_size * s) for s in self.scales]
candidate_sizes = []
for i, h in enumerate(crop_sizes):
for j, w in enumerate(crop_sizes):
if abs(i - j) <= self.max_wh_scale_gap:
candidate_sizes.append([w, h])
crop_size = random.choice(candidate_sizes)
for i in range(2):
if abs(crop_size[i] - self.input_size[i]) < 3:
crop_size[i] = self.input_size[i]
crop_w, crop_h = crop_size
if self.random_crop:
x_offset = random.randint(0, img_w - crop_w)
y_offset = random.randint(0, img_h - crop_h)
else:
w_step = (img_w - crop_w) // 4
h_step = (img_h - crop_h) // 4
candidate_offsets = [
(0, 0), # upper left
(4 * w_step, 0), # upper right
(0, 4 * h_step), # lower left
(4 * w_step, 4 * h_step), # lower right
(2 * w_step, 2 * h_step), # center
]
if self.num_fixed_crops == 13:
extra_candidate_offsets = [
(0, 2 * h_step), # center left
(4 * w_step, 2 * h_step), # center right
(2 * w_step, 4 * h_step), # lower center
(2 * w_step, 0 * h_step), # upper center
(1 * w_step, 1 * h_step), # upper left quarter
(3 * w_step, 1 * h_step), # upper right quarter
(1 * w_step, 3 * h_step), # lower left quarter
(3 * w_step, 3 * h_step) # lower right quarter
]
candidate_offsets.extend(extra_candidate_offsets)
x_offset, y_offset = random.choice(candidate_offsets)
new_h, new_w = crop_h, crop_w
crop_bbox = np.array(
[x_offset, y_offset, x_offset + new_w, y_offset + new_h])
results['crop_bbox'] = crop_bbox
results['img_shape'] = (new_h, new_w)
results['scales'] = self.scales
if 'crop_quadruple' not in results:
results['crop_quadruple'] = np.array(
[0, 0, 1, 1], # x, y, w, h
dtype=np.float32)
x_ratio, y_ratio = x_offset / img_w, y_offset / img_h
w_ratio, h_ratio = new_w / img_w, new_h / img_h
old_crop_quadruple = results['crop_quadruple']
old_x_ratio, old_y_ratio = old_crop_quadruple[0], old_crop_quadruple[1]
old_w_ratio, old_h_ratio = old_crop_quadruple[2], old_crop_quadruple[3]
new_crop_quadruple = [
old_x_ratio + x_ratio * old_w_ratio,
old_y_ratio + y_ratio * old_h_ratio, w_ratio * old_w_ratio,
h_ratio * old_h_ratio
]
results['crop_quadruple'] = np.array(
new_crop_quadruple, dtype=np.float32)
if not self.lazy:
if 'keypoint' in results:
results['keypoint'] = self._crop_kps(results['keypoint'],
crop_bbox)
if 'imgs' in results:
results['imgs'] = self._crop_imgs(results['imgs'], crop_bbox)
else:
lazyop = results['lazy']
if lazyop['flip']:
raise NotImplementedError('Put Flip at last for now')
# record crop_bbox in lazyop dict to ensure only crop once in Fuse
lazy_left, lazy_top, lazy_right, lazy_bottom = lazyop['crop_bbox']
left = x_offset * (lazy_right - lazy_left) / img_w
right = (x_offset + new_w) * (lazy_right - lazy_left) / img_w
top = y_offset * (lazy_bottom - lazy_top) / img_h
bottom = (y_offset + new_h) * (lazy_bottom - lazy_top) / img_h
lazyop['crop_bbox'] = np.array([(lazy_left + left),
(lazy_top + top),
(lazy_left + right),
(lazy_top + bottom)],
dtype=np.float32)
if 'gt_bboxes' in results:
assert not self.lazy
results = self._all_box_crop(results, results['crop_bbox'])
return results
def __repr__(self):
repr_str = (f'{self.__class__.__name__}('
f'input_size={self.input_size}, scales={self.scales}, '
f'max_wh_scale_gap={self.max_wh_scale_gap}, '
f'random_crop={self.random_crop}, '
f'num_fixed_crops={self.num_fixed_crops}, '
f'lazy={self.lazy})')
return repr_str
@PIPELINES.register_module()
class Resize:
"""Resize images to a specific size.
Required keys are "img_shape", "modality", "imgs" (optional), "keypoint"
(optional), added or modified keys are "imgs", "img_shape", "keep_ratio",
"scale_factor", "lazy", "resize_size". Required keys in "lazy" is None,
added or modified key is "interpolation".
Args:
scale (float | Tuple[int]): If keep_ratio is True, it serves as scaling
factor or maximum size:
If it is a float number, the image will be rescaled by this
factor, else if it is a tuple of 2 integers, the image will
be rescaled as large as possible within the scale.
Otherwise, it serves as (w, h) of output size.
keep_ratio (bool): If set to True, Images will be resized without
changing the aspect ratio. Otherwise, it will resize images to a
given size. Default: True.
interpolation (str): Algorithm used for interpolation:
"nearest" | "bilinear". Default: "bilinear".
lazy (bool): Determine whether to apply lazy operation. Default: False.
"""
def __init__(self,
scale,
keep_ratio=True,
interpolation='bilinear',
lazy=False):
if isinstance(scale, float):
if scale <= 0:
raise ValueError(f'Invalid scale {scale}, must be positive.')
elif isinstance(scale, tuple):
max_long_edge = max(scale)
max_short_edge = min(scale)
if max_short_edge == -1:
# assign np.inf to long edge for rescaling short edge later.
scale = (np.inf, max_long_edge)
else:
raise TypeError(
f'Scale must be float or tuple of int, but got {type(scale)}')
self.scale = scale
self.keep_ratio = keep_ratio
self.interpolation = interpolation
self.lazy = lazy
def _resize_imgs(self, imgs, new_w, new_h):
return [
mmcv.imresize(
img, (new_w, new_h), interpolation=self.interpolation)
for img in imgs
]
@staticmethod
def _resize_kps(kps, scale_factor):
return kps * scale_factor
@staticmethod
def _box_resize(box, scale_factor):
"""Rescale the bounding boxes according to the scale_factor.
Args:
box (np.ndarray): The bounding boxes.
scale_factor (np.ndarray): The scale factor used for rescaling.
"""
assert len(scale_factor) == 2
scale_factor = np.concatenate([scale_factor, scale_factor])
return box * scale_factor
def __call__(self, results):
"""Performs the Resize augmentation.
Args:
results (dict): The resulting dict to be modified and passed
to the next transform in pipeline.
"""
_init_lazy_if_proper(results, self.lazy)
if 'keypoint' in results:
assert not self.lazy, ('Keypoint Augmentations are not compatible '
'with lazy == True')
if 'scale_factor' not in results:
results['scale_factor'] = np.array([1, 1], dtype=np.float32)
img_h, img_w = results['img_shape']
if self.keep_ratio:
new_w, new_h = mmcv.rescale_size((img_w, img_h), self.scale)
else:
new_w, new_h = self.scale
self.scale_factor = np.array([new_w / img_w, new_h / img_h],
dtype=np.float32)
results['img_shape'] = (new_h, new_w)
results['keep_ratio'] = self.keep_ratio
results['scale_factor'] = results['scale_factor'] * self.scale_factor
if not self.lazy:
if 'imgs' in results:
results['imgs'] = self._resize_imgs(results['imgs'], new_w,
new_h)
if 'keypoint' in results:
results['keypoint'] = self._resize_kps(results['keypoint'],
self.scale_factor)
else:
lazyop = results['lazy']
if lazyop['flip']:
raise NotImplementedError('Put Flip at last for now')
lazyop['interpolation'] = self.interpolation
if 'gt_bboxes' in results:
assert not self.lazy
results['gt_bboxes'] = self._box_resize(results['gt_bboxes'],
self.scale_factor)
if 'proposals' in results and results['proposals'] is not None:
assert results['proposals'].shape[1] == 4
results['proposals'] = self._box_resize(
results['proposals'], self.scale_factor)
return results
def __repr__(self):
repr_str = (f'{self.__class__.__name__}('
f'scale={self.scale}, keep_ratio={self.keep_ratio}, '
f'interpolation={self.interpolation}, '
f'lazy={self.lazy})')
return repr_str
@PIPELINES.register_module()
class RandomRescale:
"""Randomly resize images so that the short_edge is resized to a specific
size in a given range. The scale ratio is unchanged after resizing.
Required keys are "imgs", "img_shape", "modality", added or modified
keys are "imgs", "img_shape", "keep_ratio", "scale_factor", "resize_size",
"short_edge".
Args:
scale_range (tuple[int]): The range of short edge length. A closed
interval.
interpolation (str): Algorithm used for interpolation:
"nearest" | "bilinear". Default: "bilinear".
"""
def __init__(self, scale_range, interpolation='bilinear'):
self.scale_range = scale_range
# make sure scale_range is legal, first make sure the type is OK
assert mmcv.is_tuple_of(scale_range, int)
assert len(scale_range) == 2
assert scale_range[0] < scale_range[1]
assert np.all([x > 0 for x in scale_range])
self.keep_ratio = True
self.interpolation = interpolation
def __call__(self, results):
"""Performs the Resize augmentation.
Args:
results (dict): The resulting dict to be modified and passed
to the next transform in pipeline.
"""
short_edge = np.random.randint(self.scale_range[0],
self.scale_range[1] + 1)
resize = Resize((-1, short_edge),
keep_ratio=True,
interpolation=self.interpolation,
lazy=False)
results = resize(results)
results['short_edge'] = short_edge
return results
def __repr__(self):
scale_range = self.scale_range
repr_str = (f'{self.__class__.__name__}('
f'scale_range=({scale_range[0]}, {scale_range[1]}), '
f'interpolation={self.interpolation})')
return repr_str
@PIPELINES.register_module()
class Flip:
"""Flip the input images with a probability.
Reverse the order of elements in the given imgs with a specific direction.
The shape of the imgs is preserved, but the elements are reordered.
Required keys are "img_shape", "modality", "imgs" (optional), "keypoint"
(optional), added or modified keys are "imgs", "keypoint", "lazy" and
"flip_direction". Required keys in "lazy" is None, added or modified key
are "flip" and "flip_direction". The Flip augmentation should be placed
after any cropping / reshaping augmentations, to make sure crop_quadruple
is calculated properly.
Args:
flip_ratio (float): Probability of implementing flip. Default: 0.5.
direction (str): Flip imgs horizontally or vertically. Options are
"horizontal" | "vertical". Default: "horizontal".
flip_label_map (Dict[int, int] | None): Transform the label of the
flipped image with the specific label. Default: None.
left_kp (list[int]): Indexes of left keypoints, used to flip keypoints.
Default: None.
right_kp (list[ind]): Indexes of right keypoints, used to flip
keypoints. Default: None.
lazy (bool): Determine whether to apply lazy operation. Default: False.
"""
_directions = ['horizontal', 'vertical']
def __init__(self,
flip_ratio=0.5,
direction='horizontal',
flip_label_map=None,
left_kp=None,
right_kp=None,
lazy=False):
if direction not in self._directions:
raise ValueError(f'Direction {direction} is not supported. '
f'Currently support ones are {self._directions}')
self.flip_ratio = flip_ratio
self.direction = direction
self.flip_label_map = flip_label_map
self.left_kp = left_kp
self.right_kp = right_kp
self.lazy = lazy
def _flip_imgs(self, imgs, modality):
_ = [mmcv.imflip_(img, self.direction) for img in imgs]
lt = len(imgs)
if modality == 'Flow':
# The 1st frame of each 2 frames is flow-x
for i in range(0, lt, 2):
imgs[i] = mmcv.iminvert(imgs[i])
return imgs
def _flip_kps(self, kps, kpscores, img_width):
kp_x = kps[..., 0]
kp_x[kp_x != 0] = img_width - kp_x[kp_x != 0]
new_order = list(range(kps.shape[2]))
if self.left_kp is not None and self.right_kp is not None:
for left, right in zip(self.left_kp, self.right_kp):
new_order[left] = right
new_order[right] = left
kps = kps[:, :, new_order]
if kpscores is not None:
kpscores = kpscores[:, :, new_order]
return kps, kpscores
@staticmethod
def _box_flip(box, img_width):
"""Flip the bounding boxes given the width of the image.
Args:
box (np.ndarray): The bounding boxes.
img_width (int): The img width.
"""
box_ = box.copy()
box_[..., 0::4] = img_width - box[..., 2::4]
box_[..., 2::4] = img_width - box[..., 0::4]
return box_
def __call__(self, results):
"""Performs the Flip augmentation.
Args:
results (dict): The resulting dict to be modified and passed
to the next transform in pipeline.
"""
_init_lazy_if_proper(results, self.lazy)
if 'keypoint' in results:
assert not self.lazy, ('Keypoint Augmentations are not compatible '
'with lazy == True')
assert self.direction == 'horizontal', (
'Only horizontal flips are'
'supported for human keypoints')
modality = results['modality']
if modality == 'Flow':
assert self.direction == 'horizontal'
flip = np.random.rand() < self.flip_ratio
results['flip'] = flip
results['flip_direction'] = self.direction
img_width = results['img_shape'][1]
if self.flip_label_map is not None and flip:
results['label'] = self.flip_label_map.get(results['label'],
results['label'])
if not self.lazy:
if flip:
if 'imgs' in results:
results['imgs'] = self._flip_imgs(results['imgs'],
modality)
if 'keypoint' in results:
kp = results['keypoint']
kpscore = results.get('keypoint_score', None)
kp, kpscore = self._flip_kps(kp, kpscore, img_width)
results['keypoint'] = kp
if 'keypoint_score' in results:
results['keypoint_score'] = kpscore
else:
lazyop = results['lazy']
if lazyop['flip']:
raise NotImplementedError('Use one Flip please')
lazyop['flip'] = flip
lazyop['flip_direction'] = self.direction
if 'gt_bboxes' in results and flip:
assert not self.lazy and self.direction == 'horizontal'
width = results['img_shape'][1]
results['gt_bboxes'] = self._box_flip(results['gt_bboxes'], width)
if 'proposals' in results and results['proposals'] is not None:
assert results['proposals'].shape[1] == 4
results['proposals'] = self._box_flip(results['proposals'],
width)
return results
def __repr__(self):
repr_str = (
f'{self.__class__.__name__}('
f'flip_ratio={self.flip_ratio}, direction={self.direction}, '
f'flip_label_map={self.flip_label_map}, lazy={self.lazy})')
return repr_str
@PIPELINES.register_module()
class Normalize:
"""Normalize images with the given mean and std value.
Required keys are "imgs", "img_shape", "modality", added or modified
keys are "imgs" and "img_norm_cfg". If modality is 'Flow', additional
keys "scale_factor" is required
Args:
mean (Sequence[float]): Mean values of different channels.
std (Sequence[float]): Std values of different channels.
to_bgr (bool): Whether to convert channels from RGB to BGR.
Default: False.
adjust_magnitude (bool): Indicate whether to adjust the flow magnitude
on 'scale_factor' when modality is 'Flow'. Default: False.
"""
def __init__(self, mean, std, to_bgr=False, adjust_magnitude=False):
if not isinstance(mean, Sequence):
raise TypeError(
f'Mean must be list, tuple or np.ndarray, but got {type(mean)}'
)
if not isinstance(std, Sequence):
raise TypeError(
f'Std must be list, tuple or np.ndarray, but got {type(std)}')
self.mean = np.array(mean, dtype=np.float32)
self.std = np.array(std, dtype=np.float32)
self.to_bgr = to_bgr
self.adjust_magnitude = adjust_magnitude
def __call__(self, results):
modality = results['modality']
if modality == 'RGB':
n = len(results['imgs'])
h, w, c = results['imgs'][0].shape
imgs = np.empty((n, h, w, c), dtype=np.float32)
for i, img in enumerate(results['imgs']):
imgs[i] = img
for img in imgs:
mmcv.imnormalize_(img, self.mean, self.std, self.to_bgr)
results['imgs'] = imgs
results['img_norm_cfg'] = dict(
mean=self.mean, std=self.std, to_bgr=self.to_bgr)
return results
if modality == 'Flow':
num_imgs = len(results['imgs'])
assert num_imgs % 2 == 0
assert self.mean.shape[0] == 2
assert self.std.shape[0] == 2
n = num_imgs // 2
h, w = results['imgs'][0].shape
x_flow = np.empty((n, h, w), dtype=np.float32)
y_flow = np.empty((n, h, w), dtype=np.float32)
for i in range(n):
x_flow[i] = results['imgs'][2 * i]
y_flow[i] = results['imgs'][2 * i + 1]
x_flow = (x_flow - self.mean[0]) / self.std[0]
y_flow = (y_flow - self.mean[1]) / self.std[1]
if self.adjust_magnitude:
x_flow = x_flow * results['scale_factor'][0]
y_flow = y_flow * results['scale_factor'][1]
imgs = np.stack([x_flow, y_flow], axis=-1)
results['imgs'] = imgs
args = dict(
mean=self.mean,
std=self.std,
to_bgr=self.to_bgr,
adjust_magnitude=self.adjust_magnitude)
results['img_norm_cfg'] = args
return results
raise NotImplementedError
def __repr__(self):
repr_str = (f'{self.__class__.__name__}('
f'mean={self.mean}, '
f'std={self.std}, '
f'to_bgr={self.to_bgr}, '
f'adjust_magnitude={self.adjust_magnitude})')
return repr_str
@PIPELINES.register_module()
class ColorJitter:
"""Perform ColorJitter to each img.
Required keys are "imgs", added or modified keys are "imgs".
Args:
brightness (float | tuple[float]): The jitter range for brightness, if
set as a float, the range will be (1 - brightness, 1 + brightness).
Default: 0.5.
contrast (float | tuple[float]): The jitter range for contrast, if set
as a float, the range will be (1 - contrast, 1 + contrast).
Default: 0.5.
saturation (float | tuple[float]): The jitter range for saturation, if
set as a float, the range will be (1 - saturation, 1 + saturation).
Default: 0.5.
hue (float | tuple[float]): The jitter range for hue, if set as a
float, the range will be (-hue, hue). Default: 0.1.
"""
@staticmethod
def check_input(val, max, base):
if isinstance(val, tuple):
assert base - max <= val[0] <= val[1] <= base + max
return val
assert val <= max
return (base - val, base + val)
@staticmethod
def rgb_to_grayscale(img):
return 0.2989 * img[..., 0] + 0.587 * img[..., 1] + 0.114 * img[..., 2]
@staticmethod
def adjust_contrast(img, factor):
val = np.mean(ColorJitter.rgb_to_grayscale(img))
return factor * img + (1 - factor) * val
@staticmethod
def adjust_saturation(img, factor):
gray = np.stack([ColorJitter.rgb_to_grayscale(img)] * 3, axis=-1)
return factor * img + (1 - factor) * gray
@staticmethod
def adjust_hue(img, factor):
img = np.clip(img, 0, 255).astype(np.uint8)
hsv = cv2.cvtColor(img, cv2.COLOR_RGB2HSV)
offset = int(factor * 255)
hsv[..., 0] = (hsv[..., 0] + offset) % 180
img = cv2.cvtColor(hsv, cv2.COLOR_HSV2RGB)
return img.astype(np.float32)
def __init__(self, brightness=0.5, contrast=0.5, saturation=0.5, hue=0.1):
self.brightness = self.check_input(brightness, 1, 1)
self.contrast = self.check_input(contrast, 1, 1)
self.saturation = self.check_input(saturation, 1, 1)
self.hue = self.check_input(hue, 0.5, 0)
self.fn_idx = np.random.permutation(4)
def __call__(self, results):
imgs = results['imgs']
num_clips, clip_len = 1, len(imgs)
new_imgs = []
for i in range(num_clips):
b = np.random.uniform(
low=self.brightness[0], high=self.brightness[1])
c = np.random.uniform(low=self.contrast[0], high=self.contrast[1])
s = np.random.uniform(
low=self.saturation[0], high=self.saturation[1])
h = np.random.uniform(low=self.hue[0], high=self.hue[1])
start, end = i * clip_len, (i + 1) * clip_len
for img in imgs[start:end]:
img = img.astype(np.float32)
for fn_id in self.fn_idx:
if fn_id == 0 and b != 1:
img *= b
if fn_id == 1 and c != 1:
img = self.adjust_contrast(img, c)
if fn_id == 2 and s != 1:
img = self.adjust_saturation(img, s)
if fn_id == 3 and h != 0:
img = self.adjust_hue(img, h)
img = np.clip(img, 0, 255).astype(np.uint8)
new_imgs.append(img)
results['imgs'] = new_imgs
return results
def __repr__(self):
repr_str = (f'{self.__class__.__name__}('
f'brightness={self.brightness}, '
f'contrast={self.contrast}, '
f'saturation={self.saturation}, '
f'hue={self.hue})')
return repr_str
@PIPELINES.register_module()
class CenterCrop(RandomCrop):
"""Crop the center area from images.
Required keys are "img_shape", "imgs" (optional), "keypoint" (optional),
added or modified keys are "imgs", "keypoint", "crop_bbox", "lazy" and
"img_shape". Required keys in "lazy" is "crop_bbox", added or modified key
is "crop_bbox".
Args:
crop_size (int | tuple[int]): (w, h) of crop size.
lazy (bool): Determine whether to apply lazy operation. Default: False.
"""
def __init__(self, crop_size, lazy=False):
self.crop_size = _pair(crop_size)
self.lazy = lazy
if not mmcv.is_tuple_of(self.crop_size, int):
raise TypeError(f'Crop_size must be int or tuple of int, '
f'but got {type(crop_size)}')
def __call__(self, results):
"""Performs the CenterCrop augmentation.
Args:
results (dict): The resulting dict to be modified and passed
to the next transform in pipeline.
"""
_init_lazy_if_proper(results, self.lazy)
if 'keypoint' in results:
assert not self.lazy, ('Keypoint Augmentations are not compatible '
'with lazy == True')
img_h, img_w = results['img_shape']
crop_w, crop_h = self.crop_size
left = (img_w - crop_w) // 2
top = (img_h - crop_h) // 2
right = left + crop_w
bottom = top + crop_h
new_h, new_w = bottom - top, right - left
crop_bbox = np.array([left, top, right, bottom])
results['crop_bbox'] = crop_bbox
results['img_shape'] = (new_h, new_w)
if 'crop_quadruple' not in results:
results['crop_quadruple'] = np.array(
[0, 0, 1, 1], # x, y, w, h
dtype=np.float32)
x_ratio, y_ratio = left / img_w, top / img_h
w_ratio, h_ratio = new_w / img_w, new_h / img_h
old_crop_quadruple = results['crop_quadruple']
old_x_ratio, old_y_ratio = old_crop_quadruple[0], old_crop_quadruple[1]
old_w_ratio, old_h_ratio = old_crop_quadruple[2], old_crop_quadruple[3]
new_crop_quadruple = [
old_x_ratio + x_ratio * old_w_ratio,
old_y_ratio + y_ratio * old_h_ratio, w_ratio * old_w_ratio,
h_ratio * old_h_ratio
]
results['crop_quadruple'] = np.array(
new_crop_quadruple, dtype=np.float32)
if not self.lazy:
if 'keypoint' in results:
results['keypoint'] = self._crop_kps(results['keypoint'],
crop_bbox)
if 'imgs' in results:
results['imgs'] = self._crop_imgs(results['imgs'], crop_bbox)
else:
lazyop = results['lazy']
if lazyop['flip']:
raise NotImplementedError('Put Flip at last for now')
# record crop_bbox in lazyop dict to ensure only crop once in Fuse
lazy_left, lazy_top, lazy_right, lazy_bottom = lazyop['crop_bbox']
left = left * (lazy_right - lazy_left) / img_w
right = right * (lazy_right - lazy_left) / img_w
top = top * (lazy_bottom - lazy_top) / img_h
bottom = bottom * (lazy_bottom - lazy_top) / img_h
lazyop['crop_bbox'] = np.array([(lazy_left + left),
(lazy_top + top),
(lazy_left + right),
(lazy_top + bottom)],
dtype=np.float32)
if 'gt_bboxes' in results:
assert not self.lazy
results = self._all_box_crop(results, results['crop_bbox'])
return results
def __repr__(self):
repr_str = (f'{self.__class__.__name__}(crop_size={self.crop_size}, '
f'lazy={self.lazy})')
return repr_str
@PIPELINES.register_module()
class ThreeCrop:
"""Crop images into three crops.
Crop the images equally into three crops with equal intervals along the
shorter side.
Required keys are "imgs", "img_shape", added or modified keys are "imgs",
"crop_bbox" and "img_shape".
Args:
crop_size(int | tuple[int]): (w, h) of crop size.
"""
def __init__(self, crop_size):
self.crop_size = _pair(crop_size)
if not mmcv.is_tuple_of(self.crop_size, int):
raise TypeError(f'Crop_size must be int or tuple of int, '
f'but got {type(crop_size)}')
def __call__(self, results):
"""Performs the ThreeCrop augmentation.
Args:
results (dict): The resulting dict to be modified and passed
to the next transform in pipeline.
"""
_init_lazy_if_proper(results, False)
if 'gt_bboxes' in results or 'proposals' in results:
warnings.warn('ThreeCrop cannot process bounding boxes')
imgs = results['imgs']
img_h, img_w = results['imgs'][0].shape[:2]
crop_w, crop_h = self.crop_size
assert crop_h == img_h or crop_w == img_w
if crop_h == img_h:
w_step = (img_w - crop_w) // 2
offsets = [
(0, 0), # left
(2 * w_step, 0), # right
(w_step, 0), # middle
]
elif crop_w == img_w:
h_step = (img_h - crop_h) // 2
offsets = [
(0, 0), # top
(0, 2 * h_step), # down
(0, h_step), # middle
]
cropped = []
crop_bboxes = []
for x_offset, y_offset in offsets:
bbox = [x_offset, y_offset, x_offset + crop_w, y_offset + crop_h]
crop = [
img[y_offset:y_offset + crop_h, x_offset:x_offset + crop_w]
for img in imgs
]
cropped.extend(crop)
crop_bboxes.extend([bbox for _ in range(len(imgs))])
crop_bboxes = np.array(crop_bboxes)
results['imgs'] = cropped
results['crop_bbox'] = crop_bboxes
results['img_shape'] = results['imgs'][0].shape[:2]
return results
def __repr__(self):
repr_str = f'{self.__class__.__name__}(crop_size={self.crop_size})'
return repr_str
@PIPELINES.register_module()
class TenCrop:
"""Crop the images into 10 crops (corner + center + flip).
Crop the four corners and the center part of the image with the same
given crop_size, and flip it horizontally.
Required keys are "imgs", "img_shape", added or modified keys are "imgs",
"crop_bbox" and "img_shape".
Args:
crop_size(int | tuple[int]): (w, h) of crop size.
"""
def __init__(self, crop_size):
self.crop_size = _pair(crop_size)
if not mmcv.is_tuple_of(self.crop_size, int):
raise TypeError(f'Crop_size must be int or tuple of int, '
f'but got {type(crop_size)}')
def __call__(self, results):
"""Performs the TenCrop augmentation.
Args:
results (dict): The resulting dict to be modified and passed
to the next transform in pipeline.
"""
_init_lazy_if_proper(results, False)
if 'gt_bboxes' in results or 'proposals' in results:
warnings.warn('TenCrop cannot process bounding boxes')
imgs = results['imgs']
img_h, img_w = results['imgs'][0].shape[:2]
crop_w, crop_h = self.crop_size
w_step = (img_w - crop_w) // 4
h_step = (img_h - crop_h) // 4
offsets = [
(0, 0), # upper left
(4 * w_step, 0), # upper right
(0, 4 * h_step), # lower left
(4 * w_step, 4 * h_step), # lower right
(2 * w_step, 2 * h_step), # center
]
img_crops = list()
crop_bboxes = list()
for x_offset, y_offsets in offsets:
crop = [
img[y_offsets:y_offsets + crop_h, x_offset:x_offset + crop_w]
for img in imgs
]
flip_crop = [np.flip(c, axis=1).copy() for c in crop]
bbox = [x_offset, y_offsets, x_offset + crop_w, y_offsets + crop_h]
img_crops.extend(crop)
img_crops.extend(flip_crop)
crop_bboxes.extend([bbox for _ in range(len(imgs) * 2)])
crop_bboxes = np.array(crop_bboxes)
results['imgs'] = img_crops
results['crop_bbox'] = crop_bboxes
results['img_shape'] = results['imgs'][0].shape[:2]
return results
def __repr__(self):
repr_str = f'{self.__class__.__name__}(crop_size={self.crop_size})'
return repr_str
@PIPELINES.register_module()
class AudioAmplify:
"""Amplify the waveform.
Required keys are "audios", added or modified keys are "audios",
"amplify_ratio".
Args:
ratio (float): The ratio used to amplify the audio waveform.
"""
def __init__(self, ratio):
if isinstance(ratio, float):
self.ratio = ratio
else:
raise TypeError('Amplification ratio should be float.')
def __call__(self, results):
"""Perform the audio amplification.
Args:
results (dict): The resulting dict to be modified and passed
to the next transform in pipeline.
"""
assert 'audios' in results
results['audios'] *= self.ratio
results['amplify_ratio'] = self.ratio
return results
def __repr__(self):
repr_str = f'{self.__class__.__name__}(ratio={self.ratio})'
return repr_str
@PIPELINES.register_module()
class MelSpectrogram:
"""MelSpectrogram. Transfer an audio wave into a melspectogram figure.
Required keys are "audios", "sample_rate", "num_clips", added or modified
keys are "audios".
Args:
window_size (int): The window size in millisecond. Default: 32.
step_size (int): The step size in millisecond. Default: 16.
n_mels (int): Number of mels. Default: 80.
fixed_length (int): The sample length of melspectrogram maybe not
exactly as wished due to different fps, fix the length for batch
collation by truncating or padding. Default: 128.
"""
def __init__(self,
window_size=32,
step_size=16,
n_mels=80,
fixed_length=128):
if all(
isinstance(x, int)
for x in [window_size, step_size, n_mels, fixed_length]):
self.window_size = window_size
self.step_size = step_size
self.n_mels = n_mels
self.fixed_length = fixed_length
else:
raise TypeError('All arguments should be int.')
def __call__(self, results):
"""Perform MelSpectrogram transformation.
Args:
results (dict): The resulting dict to be modified and passed
to the next transform in pipeline.
"""
try:
import librosa
except ImportError:
raise ImportError('Install librosa first.')
signals = results['audios']
sample_rate = results['sample_rate']
n_fft = int(round(sample_rate * self.window_size / 1000))
hop_length = int(round(sample_rate * self.step_size / 1000))
melspectrograms = list()
for clip_idx in range(results['num_clips']):
clip_signal = signals[clip_idx]
mel = librosa.feature.melspectrogram(
y=clip_signal,
sr=sample_rate,
n_fft=n_fft,
hop_length=hop_length,
n_mels=self.n_mels)
if mel.shape[0] >= self.fixed_length:
mel = mel[:self.fixed_length, :]
else:
mel = np.pad(
mel, ((0, mel.shape[-1] - self.fixed_length), (0, 0)),
mode='edge')
melspectrograms.append(mel)
results['audios'] = np.array(melspectrograms)
return results
def __repr__(self):
repr_str = (f'{self.__class__.__name__}'
f'(window_size={self.window_size}), '
f'step_size={self.step_size}, '
f'n_mels={self.n_mels}, '
f'fixed_length={self.fixed_length})')
return repr_str
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