Source code for mmaction.datasets.transforms.processing
# Copyright (c) OpenMMLab. All rights reserved.
import random
import warnings
from numbers import Number
from typing import Sequence
import cv2
import mmcv
import mmengine
import numpy as np
from mmcv.transforms import BaseTransform
from mmcv.transforms.utils import cache_randomness
from torch.nn.modules.utils import _pair
from mmaction.registry import TRANSFORMS
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'
[docs]@TRANSFORMS.register_module()
class Fuse(BaseTransform):
"""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".
"""
[docs] def transform(self, results):
"""Fuse lazy operations.
Args:
results (dict): The resulting dict to be modified and passed
to the next transform in pipeline.
"""
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
[docs]@TRANSFORMS.register_module()
class RandomCrop(BaseTransform):
"""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):
"""Static method for cropping keypoint."""
return kps - crop_bbox[:2]
@staticmethod
def _crop_imgs(imgs, crop_bbox):
"""Static method for cropping images."""
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
[docs] def transform(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
[docs]@TRANSFORMS.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 mmengine.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 mmengine.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)}')
[docs] @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
[docs] def transform(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
[docs]@TRANSFORMS.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 mmengine.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
[docs] def transform(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
[docs]@TRANSFORMS.register_module()
class Resize(BaseTransform):
"""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,
accepted values are "nearest", "bilinear", "bicubic", "area",
"lanczos". 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):
"""Static method for resizing keypoint."""
return [
mmcv.imresize(
img, (new_w, new_h), interpolation=self.interpolation)
for img in imgs
]
@staticmethod
def _resize_kps(kps, scale_factor):
"""Static method for resizing keypoint."""
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
[docs] def transform(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
[docs]@TRANSFORMS.register_module()
class RandomRescale(BaseTransform):
"""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 mmengine.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
[docs] def transform(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
[docs]@TRANSFORMS.register_module()
class Flip(BaseTransform):
"""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):
"""Utility function for flipping images."""
_ = [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):
"""Utility function for flipping keypoint."""
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_
[docs] def transform(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
[docs]@TRANSFORMS.register_module()
class ColorJitter(BaseTransform):
"""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)
[docs] def transform(self, results):
"""Perform ColorJitter.
Args:
results (dict): The resulting dict to be modified and passed
to the next transform in pipeline.
"""
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
[docs]@TRANSFORMS.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 mmengine.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)}')
[docs] def transform(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
[docs]@TRANSFORMS.register_module()
class ThreeCrop(BaseTransform):
"""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 mmengine.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)}')
[docs] def transform(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
[docs]@TRANSFORMS.register_module()
class TenCrop(BaseTransform):
"""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 mmengine.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)}')
[docs] def transform(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
@TRANSFORMS.register_module()
class RandomErasing(BaseTransform):
"""Randomly selects a rectangle region in an image and erase pixels.
basically refer mmcls.
**Required Keys:**
- img
**Modified Keys:**
- img
Args:
erase_prob (float): Probability that image will be randomly erased.
Default: 0.5
min_area_ratio (float): Minimum erased area / input image area
Default: 0.02
max_area_ratio (float): Maximum erased area / input image area
Default: 1/3
aspect_range (sequence | float): Aspect ratio range of erased area.
if float, it will be converted to (aspect_ratio, 1/aspect_ratio)
Default: (3/10, 10/3)
mode (str): Fill method in erased area, can be:
- const (default): All pixels are assign with the same value.
- rand: each pixel is assigned with a random value in [0, 255]
fill_color (sequence | Number): Base color filled in erased area.
Defaults to (128, 128, 128).
fill_std (sequence | Number, optional): If set and ``mode`` is 'rand',
fill erased area with random color from normal distribution
(mean=fill_color, std=fill_std); If not set, fill erased area with
random color from uniform distribution (0~255). Defaults to None.
Note:
See `Random Erasing Data Augmentation
<https://arxiv.org/pdf/1708.04896.pdf>`_
This paper provided 4 modes: RE-R, RE-M, RE-0, RE-255, and use RE-M as
default. The config of these 4 modes are:
- RE-R: RandomErasing(mode='rand')
- RE-M: RandomErasing(mode='const', fill_color=(123.67, 116.3, 103.5))
- RE-0: RandomErasing(mode='const', fill_color=0)
- RE-255: RandomErasing(mode='const', fill_color=255)
"""
def __init__(self,
erase_prob=0.5,
min_area_ratio=0.02,
max_area_ratio=1 / 3,
aspect_range=(3 / 10, 10 / 3),
mode='const',
fill_color=(128, 128, 128),
fill_std=None):
assert isinstance(erase_prob, float) and 0. <= erase_prob <= 1.
assert isinstance(min_area_ratio, float) and 0. <= min_area_ratio <= 1.
assert isinstance(max_area_ratio, float) and 0. <= max_area_ratio <= 1.
assert min_area_ratio <= max_area_ratio, \
'min_area_ratio should be smaller than max_area_ratio'
if isinstance(aspect_range, float):
aspect_range = min(aspect_range, 1 / aspect_range)
aspect_range = (aspect_range, 1 / aspect_range)
assert isinstance(aspect_range, Sequence) and len(aspect_range) == 2 \
and all(isinstance(x, float) for x in aspect_range), \
'aspect_range should be a float or Sequence with two float.'
assert all(x > 0 for x in aspect_range), \
'aspect_range should be positive.'
assert aspect_range[0] <= aspect_range[1], \
'In aspect_range (min, max), min should be smaller than max.'
assert mode in ['const', 'rand'], \
'Please select `mode` from ["const", "rand"].'
if isinstance(fill_color, Number):
fill_color = [fill_color] * 3
assert isinstance(fill_color, Sequence) and len(fill_color) == 3 \
and all(isinstance(x, Number) for x in fill_color), \
'fill_color should be a float or Sequence with three int.'
if fill_std is not None:
if isinstance(fill_std, Number):
fill_std = [fill_std] * 3
assert isinstance(fill_std, Sequence) and len(fill_std) == 3 \
and all(isinstance(x, Number) for x in fill_std), \
'fill_std should be a float or Sequence with three int.'
self.erase_prob = erase_prob
self.min_area_ratio = min_area_ratio
self.max_area_ratio = max_area_ratio
self.aspect_range = aspect_range
self.mode = mode
self.fill_color = fill_color
self.fill_std = fill_std
def _img_fill_pixels(self, img, top, left, h, w):
"""Fill pixels to the patch of image."""
if self.mode == 'const':
patch = np.empty((h, w, 3), dtype=np.uint8)
patch[:, :] = np.array(self.fill_color, dtype=np.uint8)
elif self.fill_std is None:
# Uniform distribution
patch = np.random.uniform(0, 256, (h, w, 3)).astype(np.uint8)
else:
# Normal distribution
patch = np.random.normal(self.fill_color, self.fill_std, (h, w, 3))
patch = np.clip(patch.astype(np.int32), 0, 255).astype(np.uint8)
img[top:top + h, left:left + w] = patch
return img
def _fill_pixels(self, imgs, top, left, h, w):
"""Fill pixels to the patch of each image in frame clip."""
return [self._img_fill_pixels(img, top, left, h, w) for img in imgs]
@cache_randomness
def random_disable(self):
"""Randomly disable the transform."""
return np.random.rand() > self.erase_prob
@cache_randomness
def random_patch(self, img_h, img_w):
"""Randomly generate patch the erase."""
# convert the aspect ratio to log space to equally handle width and
# height.
log_aspect_range = np.log(
np.array(self.aspect_range, dtype=np.float32))
aspect_ratio = np.exp(np.random.uniform(*log_aspect_range))
area = img_h * img_w
area *= np.random.uniform(self.min_area_ratio, self.max_area_ratio)
h = min(int(round(np.sqrt(area * aspect_ratio))), img_h)
w = min(int(round(np.sqrt(area / aspect_ratio))), img_w)
top = np.random.randint(0, img_h - h) if img_h > h else 0
left = np.random.randint(0, img_w - w) if img_w > w else 0
return top, left, h, w
def transform(self, results):
"""
Args:
results (dict): Results dict from pipeline
Returns:
dict: Results after the transformation.
"""
if self.random_disable():
return results
imgs = results['imgs']
img_h, img_w = imgs[0].shape[:2]
imgs = self._fill_pixels(imgs, *self.random_patch(img_h, img_w))
results['imgs'] = imgs
return results
def __repr__(self):
repr_str = self.__class__.__name__
repr_str += f'(erase_prob={self.erase_prob}, '
repr_str += f'min_area_ratio={self.min_area_ratio}, '
repr_str += f'max_area_ratio={self.max_area_ratio}, '
repr_str += f'aspect_range={self.aspect_range}, '
repr_str += f'mode={self.mode}, '
repr_str += f'fill_color={self.fill_color}, '
repr_str += f'fill_std={self.fill_std})'
return repr_str