# Copyright (c) OpenMMLab. All rights reserved.
import copy
import warnings
from typing import Dict, Sequence, Tuple, Union
import cv2
import mmcv
import numpy as np
from mmcv.transforms.base import BaseTransform
from mmcv.transforms.utils import cache_randomness
from mmengine.utils import is_tuple_of
from numpy import random
from scipy.ndimage import gaussian_filter
from mmseg.datasets.dataset_wrappers import MultiImageMixDataset
from mmseg.registry import TRANSFORMS
@TRANSFORMS.register_module()
class ResizeToMultiple(BaseTransform):
"""Resize images & seg to multiple of divisor.
Required Keys:
- img
- gt_seg_map
Modified Keys:
- img
- img_shape
- pad_shape
Args:
size_divisor (int): images and gt seg maps need to resize to multiple
of size_divisor. Default: 32.
interpolation (str, optional): The interpolation mode of image resize.
Default: None
"""
def __init__(self, size_divisor=32, interpolation=None):
self.size_divisor = size_divisor
self.interpolation = interpolation
def transform(self, results: dict) -> dict:
"""Call function to resize images, semantic segmentation map to
multiple of size divisor.
Args:
results (dict): Result dict from loading pipeline.
Returns:
dict: Resized results, 'img_shape', 'pad_shape' keys are updated.
"""
# Align image to multiple of size divisor.
img = results['img']
img = mmcv.imresize_to_multiple(
img,
self.size_divisor,
scale_factor=1,
interpolation=self.interpolation
if self.interpolation else 'bilinear')
results['img'] = img
results['img_shape'] = img.shape[:2]
results['pad_shape'] = img.shape[:2]
# Align segmentation map to multiple of size divisor.
for key in results.get('seg_fields', []):
gt_seg = results[key]
gt_seg = mmcv.imresize_to_multiple(
gt_seg,
self.size_divisor,
scale_factor=1,
interpolation='nearest')
results[key] = gt_seg
return results
def __repr__(self):
repr_str = self.__class__.__name__
repr_str += (f'(size_divisor={self.size_divisor}, '
f'interpolation={self.interpolation})')
return repr_str
@TRANSFORMS.register_module()
class Rerange(BaseTransform):
"""Rerange the image pixel value.
Required Keys:
- img
Modified Keys:
- img
Args:
min_value (float or int): Minimum value of the reranged image.
Default: 0.
max_value (float or int): Maximum value of the reranged image.
Default: 255.
"""
def __init__(self, min_value=0, max_value=255):
assert isinstance(min_value, float) or isinstance(min_value, int)
assert isinstance(max_value, float) or isinstance(max_value, int)
assert min_value < max_value
self.min_value = min_value
self.max_value = max_value
def transform(self, results: dict) -> dict:
"""Call function to rerange images.
Args:
results (dict): Result dict from loading pipeline.
Returns:
dict: Reranged results.
"""
img = results['img']
img_min_value = np.min(img)
img_max_value = np.max(img)
assert img_min_value < img_max_value
# rerange to [0, 1]
img = (img - img_min_value) / (img_max_value - img_min_value)
# rerange to [min_value, max_value]
img = img * (self.max_value - self.min_value) + self.min_value
results['img'] = img
return results
def __repr__(self):
repr_str = self.__class__.__name__
repr_str += f'(min_value={self.min_value}, max_value={self.max_value})'
return repr_str
@TRANSFORMS.register_module()
class CLAHE(BaseTransform):
"""Use CLAHE method to process the image.
See `ZUIDERVELD,K. Contrast Limited Adaptive Histogram Equalization[J].
Graphics Gems, 1994:474-485.` for more information.
Required Keys:
- img
Modified Keys:
- img
Args:
clip_limit (float): Threshold for contrast limiting. Default: 40.0.
tile_grid_size (tuple[int]): Size of grid for histogram equalization.
Input image will be divided into equally sized rectangular tiles.
It defines the number of tiles in row and column. Default: (8, 8).
"""
def __init__(self, clip_limit=40.0, tile_grid_size=(8, 8)):
assert isinstance(clip_limit, (float, int))
self.clip_limit = clip_limit
assert is_tuple_of(tile_grid_size, int)
assert len(tile_grid_size) == 2
self.tile_grid_size = tile_grid_size
def transform(self, results: dict) -> dict:
"""Call function to Use CLAHE method process images.
Args:
results (dict): Result dict from loading pipeline.
Returns:
dict: Processed results.
"""
for i in range(results['img'].shape[2]):
results['img'][:, :, i] = mmcv.clahe(
np.array(results['img'][:, :, i], dtype=np.uint8),
self.clip_limit, self.tile_grid_size)
return results
def __repr__(self):
repr_str = self.__class__.__name__
repr_str += f'(clip_limit={self.clip_limit}, '\
f'tile_grid_size={self.tile_grid_size})'
return repr_str
@TRANSFORMS.register_module()
class RandomCrop(BaseTransform):
"""Random crop the image & seg.
Required Keys:
- img
- gt_seg_map
Modified Keys:
- img
- img_shape
- gt_seg_map
Args:
crop_size (Union[int, Tuple[int, int]]): Expected size after cropping
with the format of (h, w). If set to an integer, then cropping
width and height are equal to this integer.
cat_max_ratio (float): The maximum ratio that single category could
occupy.
ignore_index (int): The label index to be ignored. Default: 255
"""
def __init__(self,
crop_size: Union[int, Tuple[int, int]],
cat_max_ratio: float = 1.,
ignore_index: int = 255):
super().__init__()
assert isinstance(crop_size, int) or (
isinstance(crop_size, tuple) and len(crop_size) == 2
), 'The expected crop_size is an integer, or a tuple containing two '
'intergers'
if isinstance(crop_size, int):
crop_size = (crop_size, crop_size)
assert crop_size[0] > 0 and crop_size[1] > 0
self.crop_size = crop_size
self.cat_max_ratio = cat_max_ratio
self.ignore_index = ignore_index
@cache_randomness
def crop_bbox(self, results: dict) -> tuple:
"""get a crop bounding box.
Args:
results (dict): Result dict from loading pipeline.
Returns:
tuple: Coordinates of the cropped image.
"""
def generate_crop_bbox(img: np.ndarray) -> tuple:
"""Randomly get a crop bounding box.
Args:
img (np.ndarray): Original input image.
Returns:
tuple: Coordinates of the cropped image.
"""
margin_h = max(img.shape[0] - self.crop_size[0], 0)
margin_w = max(img.shape[1] - self.crop_size[1], 0)
offset_h = np.random.randint(0, margin_h + 1)
offset_w = np.random.randint(0, margin_w + 1)
crop_y1, crop_y2 = offset_h, offset_h + self.crop_size[0]
crop_x1, crop_x2 = offset_w, offset_w + self.crop_size[1]
return crop_y1, crop_y2, crop_x1, crop_x2
img = results['img']
crop_bbox = generate_crop_bbox(img)
if self.cat_max_ratio < 1.:
# Repeat 10 times
for _ in range(10):
seg_temp = self.crop(results['gt_seg_map'], crop_bbox)
labels, cnt = np.unique(seg_temp, return_counts=True)
cnt = cnt[labels != self.ignore_index]
if len(cnt) > 1 and np.max(cnt) / np.sum(
cnt) < self.cat_max_ratio:
break
crop_bbox = generate_crop_bbox(img)
return crop_bbox
def crop(self, img: np.ndarray, crop_bbox: tuple) -> np.ndarray:
"""Crop from ``img``
Args:
img (np.ndarray): Original input image.
crop_bbox (tuple): Coordinates of the cropped image.
Returns:
np.ndarray: The cropped image.
"""
crop_y1, crop_y2, crop_x1, crop_x2 = crop_bbox
img = img[crop_y1:crop_y2, crop_x1:crop_x2, ...]
return img
def transform(self, results: dict) -> dict:
"""Transform function to randomly crop images, semantic segmentation
maps.
Args:
results (dict): Result dict from loading pipeline.
Returns:
dict: Randomly cropped results, 'img_shape' key in result dict is
updated according to crop size.
"""
img = results['img']
crop_bbox = self.crop_bbox(results)
# crop the image
img = self.crop(img, crop_bbox)
# crop semantic seg
for key in results.get('seg_fields', []):
results[key] = self.crop(results[key], crop_bbox)
img_shape = img.shape
results['img'] = img
results['img_shape'] = img_shape
return results
def __repr__(self):
return self.__class__.__name__ + f'(crop_size={self.crop_size})'
@TRANSFORMS.register_module()
class RandomRotate(BaseTransform):
"""Rotate the image & seg.
Required Keys:
- img
- gt_seg_map
Modified Keys:
- img
- gt_seg_map
Args:
prob (float): The rotation probability.
degree (float, tuple[float]): Range of degrees to select from. If
degree is a number instead of tuple like (min, max),
the range of degree will be (``-degree``, ``+degree``)
pad_val (float, optional): Padding value of image. Default: 0.
seg_pad_val (float, optional): Padding value of segmentation map.
Default: 255.
center (tuple[float], optional): Center point (w, h) of the rotation in
the source image. If not specified, the center of the image will be
used. Default: None.
auto_bound (bool): Whether to adjust the image size to cover the whole
rotated image. Default: False
"""
def __init__(self,
prob,
degree,
pad_val=0,
seg_pad_val=255,
center=None,
auto_bound=False):
self.prob = prob
assert prob >= 0 and prob <= 1
if isinstance(degree, (float, int)):
assert degree > 0, f'degree {degree} should be positive'
self.degree = (-degree, degree)
else:
self.degree = degree
assert len(self.degree) == 2, f'degree {self.degree} should be a ' \
f'tuple of (min, max)'
self.pal_val = pad_val
self.seg_pad_val = seg_pad_val
self.center = center
self.auto_bound = auto_bound
@cache_randomness
def generate_degree(self):
return np.random.rand() < self.prob, np.random.uniform(
min(*self.degree), max(*self.degree))
def transform(self, results: dict) -> dict:
"""Call function to rotate image, semantic segmentation maps.
Args:
results (dict): Result dict from loading pipeline.
Returns:
dict: Rotated results.
"""
rotate, degree = self.generate_degree()
if rotate:
# rotate image
results['img'] = mmcv.imrotate(
results['img'],
angle=degree,
border_value=self.pal_val,
center=self.center,
auto_bound=self.auto_bound)
# rotate segs
for key in results.get('seg_fields', []):
results[key] = mmcv.imrotate(
results[key],
angle=degree,
border_value=self.seg_pad_val,
center=self.center,
auto_bound=self.auto_bound,
interpolation='nearest')
return results
def __repr__(self):
repr_str = self.__class__.__name__
repr_str += f'(prob={self.prob}, ' \
f'degree={self.degree}, ' \
f'pad_val={self.pal_val}, ' \
f'seg_pad_val={self.seg_pad_val}, ' \
f'center={self.center}, ' \
f'auto_bound={self.auto_bound})'
return repr_str
@TRANSFORMS.register_module()
class RGB2Gray(BaseTransform):
"""Convert RGB image to grayscale image.
Required Keys:
- img
Modified Keys:
- img
- img_shape
This transform calculate the weighted mean of input image channels with
``weights`` and then expand the channels to ``out_channels``. When
``out_channels`` is None, the number of output channels is the same as
input channels.
Args:
out_channels (int): Expected number of output channels after
transforming. Default: None.
weights (tuple[float]): The weights to calculate the weighted mean.
Default: (0.299, 0.587, 0.114).
"""
def __init__(self, out_channels=None, weights=(0.299, 0.587, 0.114)):
assert out_channels is None or out_channels > 0
self.out_channels = out_channels
assert isinstance(weights, tuple)
for item in weights:
assert isinstance(item, (float, int))
self.weights = weights
def transform(self, results: dict) -> dict:
"""Call function to convert RGB image to grayscale image.
Args:
results (dict): Result dict from loading pipeline.
Returns:
dict: Result dict with grayscale image.
"""
img = results['img']
assert len(img.shape) == 3
assert img.shape[2] == len(self.weights)
weights = np.array(self.weights).reshape((1, 1, -1))
img = (img * weights).sum(2, keepdims=True)
if self.out_channels is None:
img = img.repeat(weights.shape[2], axis=2)
else:
img = img.repeat(self.out_channels, axis=2)
results['img'] = img
results['img_shape'] = img.shape
return results
def __repr__(self):
repr_str = self.__class__.__name__
repr_str += f'(out_channels={self.out_channels}, ' \
f'weights={self.weights})'
return repr_str
@TRANSFORMS.register_module()
class AdjustGamma(BaseTransform):
"""Using gamma correction to process the image.
Required Keys:
- img
Modified Keys:
- img
Args:
gamma (float or int): Gamma value used in gamma correction.
Default: 1.0.
"""
def __init__(self, gamma=1.0):
assert isinstance(gamma, float) or isinstance(gamma, int)
assert gamma > 0
self.gamma = gamma
inv_gamma = 1.0 / gamma
self.table = np.array([(i / 255.0)**inv_gamma * 255
for i in np.arange(256)]).astype('uint8')
def transform(self, results: dict) -> dict:
"""Call function to process the image with gamma correction.
Args:
results (dict): Result dict from loading pipeline.
Returns:
dict: Processed results.
"""
results['img'] = mmcv.lut_transform(
np.array(results['img'], dtype=np.uint8), self.table)
return results
def __repr__(self):
return self.__class__.__name__ + f'(gamma={self.gamma})'
@TRANSFORMS.register_module()
class SegRescale(BaseTransform):
"""Rescale semantic segmentation maps.
Required Keys:
- gt_seg_map
Modified Keys:
- gt_seg_map
Args:
scale_factor (float): The scale factor of the final output.
"""
def __init__(self, scale_factor=1):
self.scale_factor = scale_factor
def transform(self, results: dict) -> dict:
"""Call function to scale the semantic segmentation map.
Args:
results (dict): Result dict from loading pipeline.
Returns:
dict: Result dict with semantic segmentation map scaled.
"""
for key in results.get('seg_fields', []):
if self.scale_factor != 1:
results[key] = mmcv.imrescale(
results[key], self.scale_factor, interpolation='nearest')
return results
def __repr__(self):
return self.__class__.__name__ + f'(scale_factor={self.scale_factor})'
@TRANSFORMS.register_module()
class PhotoMetricDistortion(BaseTransform):
"""Apply photometric distortion to image sequentially, every transformation
is applied with a probability of 0.5. The position of random contrast is in
second or second to last.
1. random brightness
2. random contrast (mode 0)
3. convert color from BGR to HSV
4. random saturation
5. random hue
6. convert color from HSV to BGR
7. random contrast (mode 1)
Required Keys:
- img
Modified Keys:
- img
Args:
brightness_delta (int): delta of brightness.
contrast_range (tuple): range of contrast.
saturation_range (tuple): range of saturation.
hue_delta (int): delta of hue.
"""
def __init__(self,
brightness_delta: int = 32,
contrast_range: Sequence[float] = (0.5, 1.5),
saturation_range: Sequence[float] = (0.5, 1.5),
hue_delta: int = 18):
self.brightness_delta = brightness_delta
self.contrast_lower, self.contrast_upper = contrast_range
self.saturation_lower, self.saturation_upper = saturation_range
self.hue_delta = hue_delta
def convert(self,
img: np.ndarray,
alpha: int = 1,
beta: int = 0) -> np.ndarray:
"""Multiple with alpha and add beat with clip.
Args:
img (np.ndarray): The input image.
alpha (int): Image weights, change the contrast/saturation
of the image. Default: 1
beta (int): Image bias, change the brightness of the
image. Default: 0
Returns:
np.ndarray: The transformed image.
"""
img = img.astype(np.float32) * alpha + beta
img = np.clip(img, 0, 255)
return img.astype(np.uint8)
def brightness(self, img: np.ndarray) -> np.ndarray:
"""Brightness distortion.
Args:
img (np.ndarray): The input image.
Returns:
np.ndarray: Image after brightness change.
"""
if random.randint(2):
return self.convert(
img,
beta=random.uniform(-self.brightness_delta,
self.brightness_delta))
return img
def contrast(self, img: np.ndarray) -> np.ndarray:
"""Contrast distortion.
Args:
img (np.ndarray): The input image.
Returns:
np.ndarray: Image after contrast change.
"""
if random.randint(2):
return self.convert(
img,
alpha=random.uniform(self.contrast_lower, self.contrast_upper))
return img
def saturation(self, img: np.ndarray) -> np.ndarray:
"""Saturation distortion.
Args:
img (np.ndarray): The input image.
Returns:
np.ndarray: Image after saturation change.
"""
if random.randint(2):
img = mmcv.bgr2hsv(img)
img[:, :, 1] = self.convert(
img[:, :, 1],
alpha=random.uniform(self.saturation_lower,
self.saturation_upper))
img = mmcv.hsv2bgr(img)
return img
def hue(self, img: np.ndarray) -> np.ndarray:
"""Hue distortion.
Args:
img (np.ndarray): The input image.
Returns:
np.ndarray: Image after hue change.
"""
if random.randint(2):
img = mmcv.bgr2hsv(img)
img[:, :,
0] = (img[:, :, 0].astype(int) +
random.randint(-self.hue_delta, self.hue_delta)) % 180
img = mmcv.hsv2bgr(img)
return img
def transform(self, results: dict) -> dict:
"""Transform function to perform photometric distortion on images.
Args:
results (dict): Result dict from loading pipeline.
Returns:
dict: Result dict with images distorted.
"""
img = results['img']
# random brightness
img = self.brightness(img)
# mode == 0 --> do random contrast first
# mode == 1 --> do random contrast last
mode = random.randint(2)
if mode == 1:
img = self.contrast(img)
# random saturation
img = self.saturation(img)
# random hue
img = self.hue(img)
# random contrast
if mode == 0:
img = self.contrast(img)
results['img'] = img
return results
def __repr__(self):
repr_str = self.__class__.__name__
repr_str += (f'(brightness_delta={self.brightness_delta}, '
f'contrast_range=({self.contrast_lower}, '
f'{self.contrast_upper}), '
f'saturation_range=({self.saturation_lower}, '
f'{self.saturation_upper}), '
f'hue_delta={self.hue_delta})')
return repr_str
@TRANSFORMS.register_module()
class RandomCutOut(BaseTransform):
"""CutOut operation.
Randomly drop some regions of image used in
`Cutout <https://arxiv.org/abs/1708.04552>`_.
Required Keys:
- img
- gt_seg_map
Modified Keys:
- img
- gt_seg_map
Args:
prob (float): cutout probability.
n_holes (int | tuple[int, int]): Number of regions to be dropped.
If it is given as a list, number of holes will be randomly
selected from the closed interval [`n_holes[0]`, `n_holes[1]`].
cutout_shape (tuple[int, int] | list[tuple[int, int]]): The candidate
shape of dropped regions. It can be `tuple[int, int]` to use a
fixed cutout shape, or `list[tuple[int, int]]` to randomly choose
shape from the list.
cutout_ratio (tuple[float, float] | list[tuple[float, float]]): The
candidate ratio of dropped regions. It can be `tuple[float, float]`
to use a fixed ratio or `list[tuple[float, float]]` to randomly
choose ratio from the list. Please note that `cutout_shape`
and `cutout_ratio` cannot be both given at the same time.
fill_in (tuple[float, float, float] | tuple[int, int, int]): The value
of pixel to fill in the dropped regions. Default: (0, 0, 0).
seg_fill_in (int): The labels of pixel to fill in the dropped regions.
If seg_fill_in is None, skip. Default: None.
"""
def __init__(self,
prob,
n_holes,
cutout_shape=None,
cutout_ratio=None,
fill_in=(0, 0, 0),
seg_fill_in=None):
assert 0 <= prob and prob <= 1
assert (cutout_shape is None) ^ (cutout_ratio is None), \
'Either cutout_shape or cutout_ratio should be specified.'
assert (isinstance(cutout_shape, (list, tuple))
or isinstance(cutout_ratio, (list, tuple)))
if isinstance(n_holes, tuple):
assert len(n_holes) == 2 and 0 <= n_holes[0] < n_holes[1]
else:
n_holes = (n_holes, n_holes)
if seg_fill_in is not None:
assert (isinstance(seg_fill_in, int) and 0 <= seg_fill_in
and seg_fill_in <= 255)
self.prob = prob
self.n_holes = n_holes
self.fill_in = fill_in
self.seg_fill_in = seg_fill_in
self.with_ratio = cutout_ratio is not None
self.candidates = cutout_ratio if self.with_ratio else cutout_shape
if not isinstance(self.candidates, list):
self.candidates = [self.candidates]
@cache_randomness
def do_cutout(self):
return np.random.rand() < self.prob
@cache_randomness
def generate_patches(self, results):
cutout = self.do_cutout()
h, w, _ = results['img'].shape
if cutout:
n_holes = np.random.randint(self.n_holes[0], self.n_holes[1] + 1)
else:
n_holes = 0
x1_lst = []
y1_lst = []
index_lst = []
for _ in range(n_holes):
x1_lst.append(np.random.randint(0, w))
y1_lst.append(np.random.randint(0, h))
index_lst.append(np.random.randint(0, len(self.candidates)))
return cutout, n_holes, x1_lst, y1_lst, index_lst
def transform(self, results: dict) -> dict:
"""Call function to drop some regions of image."""
cutout, n_holes, x1_lst, y1_lst, index_lst = self.generate_patches(
results)
if cutout:
h, w, c = results['img'].shape
for i in range(n_holes):
x1 = x1_lst[i]
y1 = y1_lst[i]
index = index_lst[i]
if not self.with_ratio:
cutout_w, cutout_h = self.candidates[index]
else:
cutout_w = int(self.candidates[index][0] * w)
cutout_h = int(self.candidates[index][1] * h)
x2 = np.clip(x1 + cutout_w, 0, w)
y2 = np.clip(y1 + cutout_h, 0, h)
results['img'][y1:y2, x1:x2, :] = self.fill_in
if self.seg_fill_in is not None:
for key in results.get('seg_fields', []):
results[key][y1:y2, x1:x2] = self.seg_fill_in
return results
def __repr__(self):
repr_str = self.__class__.__name__
repr_str += f'(prob={self.prob}, '
repr_str += f'n_holes={self.n_holes}, '
repr_str += (f'cutout_ratio={self.candidates}, ' if self.with_ratio
else f'cutout_shape={self.candidates}, ')
repr_str += f'fill_in={self.fill_in}, '
repr_str += f'seg_fill_in={self.seg_fill_in})'
return repr_str
@TRANSFORMS.register_module()
class RandomMosaic(BaseTransform):
"""Mosaic augmentation. Given 4 images, mosaic transform combines them into
one output image. The output image is composed of the parts from each sub-
image.
.. code:: text
mosaic transform
center_x
+------------------------------+
| pad | pad |
| +-----------+ |
| | | |
| | image1 |--------+ |
| | | | |
| | | image2 | |
center_y |----+-------------+-----------|
| | cropped | |
|pad | image3 | image4 |
| | | |
+----|-------------+-----------+
| |
+-------------+
The mosaic transform steps are as follows:
1. Choose the mosaic center as the intersections of 4 images
2. Get the left top image according to the index, and randomly
sample another 3 images from the custom dataset.
3. Sub image will be cropped if image is larger than mosaic patch
Required Keys:
- img
- gt_seg_map
- mix_results
Modified Keys:
- img
- img_shape
- ori_shape
- gt_seg_map
Args:
prob (float): mosaic probability.
img_scale (Sequence[int]): Image size after mosaic pipeline of
a single image. The size of the output image is four times
that of a single image. The output image comprises 4 single images.
Default: (640, 640).
center_ratio_range (Sequence[float]): Center ratio range of mosaic
output. Default: (0.5, 1.5).
pad_val (int): Pad value. Default: 0.
seg_pad_val (int): Pad value of segmentation map. Default: 255.
"""
def __init__(self,
prob,
img_scale=(640, 640),
center_ratio_range=(0.5, 1.5),
pad_val=0,
seg_pad_val=255):
assert 0 <= prob and prob <= 1
assert isinstance(img_scale, tuple)
self.prob = prob
self.img_scale = img_scale
self.center_ratio_range = center_ratio_range
self.pad_val = pad_val
self.seg_pad_val = seg_pad_val
@cache_randomness
def do_mosaic(self):
return np.random.rand() < self.prob
def transform(self, results: dict) -> dict:
"""Call function to make a mosaic of image.
Args:
results (dict): Result dict.
Returns:
dict: Result dict with mosaic transformed.
"""
mosaic = self.do_mosaic()
if mosaic:
results = self._mosaic_transform_img(results)
results = self._mosaic_transform_seg(results)
return results
def get_indices(self, dataset: MultiImageMixDataset) -> list:
"""Call function to collect indexes.
Args:
dataset (:obj:`MultiImageMixDataset`): The dataset.
Returns:
list: indexes.
"""
indexes = [random.randint(0, len(dataset)) for _ in range(3)]
return indexes
@cache_randomness
def generate_mosaic_center(self):
# mosaic center x, y
center_x = int(
random.uniform(*self.center_ratio_range) * self.img_scale[1])
center_y = int(
random.uniform(*self.center_ratio_range) * self.img_scale[0])
return center_x, center_y
def _mosaic_transform_img(self, results: dict) -> dict:
"""Mosaic transform function.
Args:
results (dict): Result dict.
Returns:
dict: Updated result dict.
"""
assert 'mix_results' in results
if len(results['img'].shape) == 3:
mosaic_img = np.full(
(int(self.img_scale[0] * 2), int(self.img_scale[1] * 2), 3),
self.pad_val,
dtype=results['img'].dtype)
else:
mosaic_img = np.full(
(int(self.img_scale[0] * 2), int(self.img_scale[1] * 2)),
self.pad_val,
dtype=results['img'].dtype)
# mosaic center x, y
self.center_x, self.center_y = self.generate_mosaic_center()
center_position = (self.center_x, self.center_y)
loc_strs = ('top_left', 'top_right', 'bottom_left', 'bottom_right')
for i, loc in enumerate(loc_strs):
if loc == 'top_left':
result_patch = copy.deepcopy(results)
else:
result_patch = copy.deepcopy(results['mix_results'][i - 1])
img_i = result_patch['img']
h_i, w_i = img_i.shape[:2]
# keep_ratio resize
scale_ratio_i = min(self.img_scale[0] / h_i,
self.img_scale[1] / w_i)
img_i = mmcv.imresize(
img_i, (int(w_i * scale_ratio_i), int(h_i * scale_ratio_i)))
# compute the combine parameters
paste_coord, crop_coord = self._mosaic_combine(
loc, center_position, img_i.shape[:2][::-1])
x1_p, y1_p, x2_p, y2_p = paste_coord
x1_c, y1_c, x2_c, y2_c = crop_coord
# crop and paste image
mosaic_img[y1_p:y2_p, x1_p:x2_p] = img_i[y1_c:y2_c, x1_c:x2_c]
results['img'] = mosaic_img
results['img_shape'] = mosaic_img.shape
results['ori_shape'] = mosaic_img.shape
return results
def _mosaic_transform_seg(self, results: dict) -> dict:
"""Mosaic transform function for label annotations.
Args:
results (dict): Result dict.
Returns:
dict: Updated result dict.
"""
assert 'mix_results' in results
for key in results.get('seg_fields', []):
mosaic_seg = np.full(
(int(self.img_scale[0] * 2), int(self.img_scale[1] * 2)),
self.seg_pad_val,
dtype=results[key].dtype)
# mosaic center x, y
center_position = (self.center_x, self.center_y)
loc_strs = ('top_left', 'top_right', 'bottom_left', 'bottom_right')
for i, loc in enumerate(loc_strs):
if loc == 'top_left':
result_patch = copy.deepcopy(results)
else:
result_patch = copy.deepcopy(results['mix_results'][i - 1])
gt_seg_i = result_patch[key]
h_i, w_i = gt_seg_i.shape[:2]
# keep_ratio resize
scale_ratio_i = min(self.img_scale[0] / h_i,
self.img_scale[1] / w_i)
gt_seg_i = mmcv.imresize(
gt_seg_i,
(int(w_i * scale_ratio_i), int(h_i * scale_ratio_i)),
interpolation='nearest')
# compute the combine parameters
paste_coord, crop_coord = self._mosaic_combine(
loc, center_position, gt_seg_i.shape[:2][::-1])
x1_p, y1_p, x2_p, y2_p = paste_coord
x1_c, y1_c, x2_c, y2_c = crop_coord
# crop and paste image
mosaic_seg[y1_p:y2_p, x1_p:x2_p] = gt_seg_i[y1_c:y2_c,
x1_c:x2_c]
results[key] = mosaic_seg
return results
def _mosaic_combine(self, loc: str, center_position_xy: Sequence[float],
img_shape_wh: Sequence[int]) -> tuple:
"""Calculate global coordinate of mosaic image and local coordinate of
cropped sub-image.
Args:
loc (str): Index for the sub-image, loc in ('top_left',
'top_right', 'bottom_left', 'bottom_right').
center_position_xy (Sequence[float]): Mixing center for 4 images,
(x, y).
img_shape_wh (Sequence[int]): Width and height of sub-image
Returns:
tuple[tuple[float]]: Corresponding coordinate of pasting and
cropping
- paste_coord (tuple): paste corner coordinate in mosaic image.
- crop_coord (tuple): crop corner coordinate in mosaic image.
"""
assert loc in ('top_left', 'top_right', 'bottom_left', 'bottom_right')
if loc == 'top_left':
# index0 to top left part of image
x1, y1, x2, y2 = max(center_position_xy[0] - img_shape_wh[0], 0), \
max(center_position_xy[1] - img_shape_wh[1], 0), \
center_position_xy[0], \
center_position_xy[1]
crop_coord = img_shape_wh[0] - (x2 - x1), img_shape_wh[1] - (
y2 - y1), img_shape_wh[0], img_shape_wh[1]
elif loc == 'top_right':
# index1 to top right part of image
x1, y1, x2, y2 = center_position_xy[0], \
max(center_position_xy[1] - img_shape_wh[1], 0), \
min(center_position_xy[0] + img_shape_wh[0],
self.img_scale[1] * 2), \
center_position_xy[1]
crop_coord = 0, img_shape_wh[1] - (y2 - y1), min(
img_shape_wh[0], x2 - x1), img_shape_wh[1]
elif loc == 'bottom_left':
# index2 to bottom left part of image
x1, y1, x2, y2 = max(center_position_xy[0] - img_shape_wh[0], 0), \
center_position_xy[1], \
center_position_xy[0], \
min(self.img_scale[0] * 2, center_position_xy[1] +
img_shape_wh[1])
crop_coord = img_shape_wh[0] - (x2 - x1), 0, img_shape_wh[0], min(
y2 - y1, img_shape_wh[1])
else:
# index3 to bottom right part of image
x1, y1, x2, y2 = center_position_xy[0], \
center_position_xy[1], \
min(center_position_xy[0] + img_shape_wh[0],
self.img_scale[1] * 2), \
min(self.img_scale[0] * 2, center_position_xy[1] +
img_shape_wh[1])
crop_coord = 0, 0, min(img_shape_wh[0],
x2 - x1), min(y2 - y1, img_shape_wh[1])
paste_coord = x1, y1, x2, y2
return paste_coord, crop_coord
def __repr__(self):
repr_str = self.__class__.__name__
repr_str += f'(prob={self.prob}, '
repr_str += f'img_scale={self.img_scale}, '
repr_str += f'center_ratio_range={self.center_ratio_range}, '
repr_str += f'pad_val={self.pad_val}, '
repr_str += f'seg_pad_val={self.pad_val})'
return repr_str
@TRANSFORMS.register_module()
class GenerateEdge(BaseTransform):
"""Generate Edge for CE2P approach.
Edge will be used to calculate loss of
`CE2P <https://arxiv.org/abs/1809.05996>`_.
Modified from https://github.com/liutinglt/CE2P/blob/master/dataset/target_generation.py # noqa:E501
Required Keys:
- img_shape
- gt_seg_map
Added Keys:
- gt_edge (np.ndarray, uint8): The edge annotation generated from the
seg map by extracting border between different semantics.
Args:
edge_width (int): The width of edge. Default to 3.
ignore_index (int): Index that will be ignored. Default to 255.
"""
def __init__(self, edge_width: int = 3, ignore_index: int = 255) -> None:
super().__init__()
self.edge_width = edge_width
self.ignore_index = ignore_index
def transform(self, results: Dict) -> Dict:
"""Call function to generate edge from segmentation map.
Args:
results (dict): Result dict.
Returns:
dict: Result dict with edge mask.
"""
h, w = results['img_shape']
edge = np.zeros((h, w), dtype=np.uint8)
seg_map = results['gt_seg_map']
# down
edge_down = edge[1:h, :]
edge_down[(seg_map[1:h, :] != seg_map[:h - 1, :])
& (seg_map[1:h, :] != self.ignore_index) &
(seg_map[:h - 1, :] != self.ignore_index)] = 1
# left
edge_left = edge[:, :w - 1]
edge_left[(seg_map[:, :w - 1] != seg_map[:, 1:w])
& (seg_map[:, :w - 1] != self.ignore_index) &
(seg_map[:, 1:w] != self.ignore_index)] = 1
# up_left
edge_upleft = edge[:h - 1, :w - 1]
edge_upleft[(seg_map[:h - 1, :w - 1] != seg_map[1:h, 1:w])
& (seg_map[:h - 1, :w - 1] != self.ignore_index) &
(seg_map[1:h, 1:w] != self.ignore_index)] = 1
# up_right
edge_upright = edge[:h - 1, 1:w]
edge_upright[(seg_map[:h - 1, 1:w] != seg_map[1:h, :w - 1])
& (seg_map[:h - 1, 1:w] != self.ignore_index) &
(seg_map[1:h, :w - 1] != self.ignore_index)] = 1
kernel = cv2.getStructuringElement(cv2.MORPH_RECT,
(self.edge_width, self.edge_width))
edge = cv2.dilate(edge, kernel)
results['gt_edge'] = edge
results['edge_width'] = self.edge_width
return results
def __repr__(self):
repr_str = self.__class__.__name__
repr_str += f'edge_width={self.edge_width}, '
repr_str += f'ignore_index={self.ignore_index})'
return repr_str
@TRANSFORMS.register_module()
class ResizeShortestEdge(BaseTransform):
"""Resize the image and mask while keeping the aspect ratio unchanged.
Modified from https://github.com/facebookresearch/detectron2/blob/main/detectron2/data/transforms/augmentation_impl.py#L130 # noqa:E501
Copyright (c) Facebook, Inc. and its affiliates.
Licensed under the Apache-2.0 License
This transform attempts to scale the shorter edge to the given
`scale`, as long as the longer edge does not exceed `max_size`.
If `max_size` is reached, then downscale so that the longer
edge does not exceed `max_size`.
Required Keys:
- img
- gt_seg_map (optional)
Modified Keys:
- img
- img_shape
- gt_seg_map (optional))
Added Keys:
- scale
- scale_factor
- keep_ratio
Args:
scale (Union[int, Tuple[int, int]]): The target short edge length.
If it's tuple, will select the min value as the short edge length.
max_size (int): The maximum allowed longest edge length.
"""
def __init__(self, scale: Union[int, Tuple[int, int]],
max_size: int) -> None:
super().__init__()
self.scale = scale
self.max_size = max_size
# Create a empty Resize object
self.resize = TRANSFORMS.build({
'type': 'Resize',
'scale': 0,
'keep_ratio': True
})
def _get_output_shape(self, img, short_edge_length) -> Tuple[int, int]:
"""Compute the target image shape with the given `short_edge_length`.
Args:
img (np.ndarray): The input image.
short_edge_length (Union[int, Tuple[int, int]]): The target short
edge length. If it's tuple, will select the min value as the
short edge length.
"""
h, w = img.shape[:2]
if isinstance(short_edge_length, int):
size = short_edge_length * 1.0
elif isinstance(short_edge_length, tuple):
size = min(short_edge_length) * 1.0
scale = size / min(h, w)
if h < w:
new_h, new_w = size, scale * w
else:
new_h, new_w = scale * h, size
if max(new_h, new_w) > self.max_size:
scale = self.max_size * 1.0 / max(new_h, new_w)
new_h *= scale
new_w *= scale
new_h = int(new_h + 0.5)
new_w = int(new_w + 0.5)
return (new_w, new_h)
def transform(self, results: Dict) -> Dict:
self.resize.scale = self._get_output_shape(results['img'], self.scale)
return self.resize(results)
@TRANSFORMS.register_module()
class BioMedical3DRandomCrop(BaseTransform):
"""Crop the input patch for medical image & segmentation mask.
Required Keys:
- img (np.ndarray): Biomedical image with shape (N, Z, Y, X),
N is the number of modalities, and data type is float32.
- gt_seg_map (np.ndarray, optional): Biomedical semantic segmentation mask
with shape (Z, Y, X).
Modified Keys:
- img
- img_shape
- gt_seg_map (optional)
Args:
crop_shape (Union[int, Tuple[int, int, int]]): Expected size after
cropping with the format of (z, y, x). If set to an integer,
then cropping width and height are equal to this integer.
keep_foreground (bool): If keep_foreground is True, it will sample a
voxel of foreground classes randomly, and will take it as the
center of the crop bounding-box. Default to True.
"""
def __init__(self,
crop_shape: Union[int, Tuple[int, int, int]],
keep_foreground: bool = True):
super().__init__()
assert isinstance(crop_shape, int) or (
isinstance(crop_shape, tuple) and len(crop_shape) == 3
), 'The expected crop_shape is an integer, or a tuple containing '
'three integers'
if isinstance(crop_shape, int):
crop_shape = (crop_shape, crop_shape, crop_shape)
assert crop_shape[0] > 0 and crop_shape[1] > 0 and crop_shape[2] > 0
self.crop_shape = crop_shape
self.keep_foreground = keep_foreground
def random_sample_location(self, seg_map: np.ndarray) -> dict:
"""sample foreground voxel when keep_foreground is True.
Args:
seg_map (np.ndarray): gt seg map.
Returns:
dict: Coordinates of selected foreground voxel.
"""
num_samples = 10000
# at least 1% of the class voxels need to be selected,
# otherwise it may be too sparse
min_percent_coverage = 0.01
class_locs = {}
foreground_classes = []
all_classes = np.unique(seg_map)
for c in all_classes:
if c == 0:
# to avoid the segmentation mask full of background 0
# and the class_locs is just void dictionary {} when it return
# there add a void list for background 0.
class_locs[c] = []
else:
all_locs = np.argwhere(seg_map == c)
target_num_samples = min(num_samples, len(all_locs))
target_num_samples = max(
target_num_samples,
int(np.ceil(len(all_locs) * min_percent_coverage)))
selected = all_locs[np.random.choice(
len(all_locs), target_num_samples, replace=False)]
class_locs[c] = selected
foreground_classes.append(c)
selected_voxel = None
if len(foreground_classes) > 0:
selected_class = np.random.choice(foreground_classes)
voxels_of_that_class = class_locs[selected_class]
selected_voxel = voxels_of_that_class[np.random.choice(
len(voxels_of_that_class))]
return selected_voxel
def random_generate_crop_bbox(self, margin_z: int, margin_y: int,
margin_x: int) -> tuple:
"""Randomly get a crop bounding box.
Args:
seg_map (np.ndarray): Ground truth segmentation map.
Returns:
tuple: Coordinates of the cropped image.
"""
offset_z = np.random.randint(0, margin_z + 1)
offset_y = np.random.randint(0, margin_y + 1)
offset_x = np.random.randint(0, margin_x + 1)
crop_z1, crop_z2 = offset_z, offset_z + self.crop_shape[0]
crop_y1, crop_y2 = offset_y, offset_y + self.crop_shape[1]
crop_x1, crop_x2 = offset_x, offset_x + self.crop_shape[2]
return crop_z1, crop_z2, crop_y1, crop_y2, crop_x1, crop_x2
def generate_margin(self, results: dict) -> tuple:
"""Generate margin of crop bounding-box.
If keep_foreground is True, it will sample a voxel of foreground
classes randomly, and will take it as the center of the bounding-box,
and return the margin between of the bounding-box and image.
If keep_foreground is False, it will return the difference from crop
shape and image shape.
Args:
results (dict): Result dict from loading pipeline.
Returns:
tuple: The margin for 3 dimensions of crop bounding-box and image.
"""
seg_map = results['gt_seg_map']
if self.keep_foreground:
selected_voxel = self.random_sample_location(seg_map)
if selected_voxel is None:
# this only happens if some image does not contain
# foreground voxels at all
warnings.warn(f'case does not contain any foreground classes'
f': {results["img_path"]}')
margin_z = max(seg_map.shape[0] - self.crop_shape[0], 0)
margin_y = max(seg_map.shape[1] - self.crop_shape[1], 0)
margin_x = max(seg_map.shape[2] - self.crop_shape[2], 0)
else:
margin_z = max(0, selected_voxel[0] - self.crop_shape[0] // 2)
margin_y = max(0, selected_voxel[1] - self.crop_shape[1] // 2)
margin_x = max(0, selected_voxel[2] - self.crop_shape[2] // 2)
margin_z = max(
0, min(seg_map.shape[0] - self.crop_shape[0], margin_z))
margin_y = max(
0, min(seg_map.shape[1] - self.crop_shape[1], margin_y))
margin_x = max(
0, min(seg_map.shape[2] - self.crop_shape[2], margin_x))
else:
margin_z = max(seg_map.shape[0] - self.crop_shape[0], 0)
margin_y = max(seg_map.shape[1] - self.crop_shape[1], 0)
margin_x = max(seg_map.shape[2] - self.crop_shape[2], 0)
return margin_z, margin_y, margin_x
def crop(self, img: np.ndarray, crop_bbox: tuple) -> np.ndarray:
"""Crop from ``img``
Args:
img (np.ndarray): Original input image.
crop_bbox (tuple): Coordinates of the cropped image.
Returns:
np.ndarray: The cropped image.
"""
crop_z1, crop_z2, crop_y1, crop_y2, crop_x1, crop_x2 = crop_bbox
if len(img.shape) == 3:
# crop seg map
img = img[crop_z1:crop_z2, crop_y1:crop_y2, crop_x1:crop_x2]
else:
# crop image
assert len(img.shape) == 4
img = img[:, crop_z1:crop_z2, crop_y1:crop_y2, crop_x1:crop_x2]
return img
def transform(self, results: dict) -> dict:
"""Transform function to randomly crop images, semantic segmentation
maps.
Args:
results (dict): Result dict from loading pipeline.
Returns:
dict: Randomly cropped results, 'img_shape' key in result dict is
updated according to crop size.
"""
margin = self.generate_margin(results)
crop_bbox = self.random_generate_crop_bbox(*margin)
# crop the image
img = results['img']
results['img'] = self.crop(img, crop_bbox)
results['img_shape'] = results['img'].shape[1:]
# crop semantic seg
seg_map = results['gt_seg_map']
results['gt_seg_map'] = self.crop(seg_map, crop_bbox)
return results
def __repr__(self):
return self.__class__.__name__ + f'(crop_shape={self.crop_shape})'
@TRANSFORMS.register_module()
class BioMedicalGaussianNoise(BaseTransform):
"""Add random Gaussian noise to image.
Modified from https://github.com/MIC-DKFZ/batchgenerators/blob/7651ece69faf55263dd582a9f5cbd149ed9c3ad0/batchgenerators/transforms/noise_transforms.py#L53 # noqa:E501
Copyright (c) German Cancer Research Center (DKFZ)
Licensed under the Apache License, Version 2.0
Required Keys:
- img (np.ndarray): Biomedical image with shape (N, Z, Y, X),
N is the number of modalities, and data type is float32.
Modified Keys:
- img
Args:
prob (float): Probability to add Gaussian noise for
each sample. Default to 0.1.
mean (float): Mean or “centre” of the distribution. Default to 0.0.
std (float): Standard deviation of distribution. Default to 0.1.
"""
def __init__(self,
prob: float = 0.1,
mean: float = 0.0,
std: float = 0.1) -> None:
super().__init__()
assert 0.0 <= prob <= 1.0 and std >= 0.0
self.prob = prob
self.mean = mean
self.std = std
def transform(self, results: Dict) -> Dict:
"""Call function to add random Gaussian noise to image.
Args:
results (dict): Result dict.
Returns:
dict: Result dict with random Gaussian noise.
"""
if np.random.rand() < self.prob:
rand_std = np.random.uniform(0, self.std)
noise = np.random.normal(
self.mean, rand_std, size=results['img'].shape)
# noise is float64 array, convert to the results['img'].dtype
noise = noise.astype(results['img'].dtype)
results['img'] = results['img'] + noise
return results
def __repr__(self):
repr_str = self.__class__.__name__
repr_str += f'(prob={self.prob}, '
repr_str += f'mean={self.mean}, '
repr_str += f'std={self.std})'
return repr_str
@TRANSFORMS.register_module()
class BioMedicalGaussianBlur(BaseTransform):
"""Add Gaussian blur with random sigma to image.
Modified from https://github.com/MIC-DKFZ/batchgenerators/blob/7651ece69faf55263dd582a9f5cbd149ed9c3ad0/batchgenerators/transforms/noise_transforms.py#L81 # noqa:E501
Copyright (c) German Cancer Research Center (DKFZ)
Licensed under the Apache License, Version 2.0
Required Keys:
- img (np.ndarray): Biomedical image with shape (N, Z, Y, X),
N is the number of modalities, and data type is float32.
Modified Keys:
- img
Args:
sigma_range (Tuple[float, float]|float): range to randomly
select sigma value. Default to (0.5, 1.0).
prob (float): Probability to apply Gaussian blur
for each sample. Default to 0.2.
prob_per_channel (float): Probability to apply Gaussian blur
for each channel (axis N of the image). Default to 0.5.
different_sigma_per_channel (bool): whether to use different
sigma for each channel (axis N of the image). Default to True.
different_sigma_per_axis (bool): whether to use different
sigma for axis Z, X and Y of the image. Default to True.
"""
def __init__(self,
sigma_range: Tuple[float, float] = (0.5, 1.0),
prob: float = 0.2,
prob_per_channel: float = 0.5,
different_sigma_per_channel: bool = True,
different_sigma_per_axis: bool = True) -> None:
super().__init__()
assert 0.0 <= prob <= 1.0
assert 0.0 <= prob_per_channel <= 1.0
assert isinstance(sigma_range, Sequence) and len(sigma_range) == 2
self.sigma_range = sigma_range
self.prob = prob
self.prob_per_channel = prob_per_channel
self.different_sigma_per_channel = different_sigma_per_channel
self.different_sigma_per_axis = different_sigma_per_axis
def _get_valid_sigma(self, value_range) -> Tuple[float, ...]:
"""Ensure the `value_range` to be either a single value or a sequence
of two values. If the `value_range` is a sequence, generate a random
value with `[value_range[0], value_range[1]]` based on uniform
sampling.
Modified from https://github.com/MIC-DKFZ/batchgenerators/blob/7651ece69faf55263dd582a9f5cbd149ed9c3ad0/batchgenerators/augmentations/utils.py#L625 # noqa:E501
Args:
value_range (tuple|list|float|int): the input value range
"""
if (isinstance(value_range, (list, tuple))):
if (value_range[0] == value_range[1]):
value = value_range[0]
else:
orig_type = type(value_range[0])
value = np.random.uniform(value_range[0], value_range[1])
value = orig_type(value)
return value
def _gaussian_blur(self, data_sample: np.ndarray) -> np.ndarray:
"""Random generate sigma and apply Gaussian Blur to the data
Args:
data_sample (np.ndarray): data sample with multiple modalities,
the data shape is (N, Z, Y, X)
"""
sigma = None
for c in range(data_sample.shape[0]):
if np.random.rand() < self.prob_per_channel:
# if no `sigma` is generated, generate one
# if `self.different_sigma_per_channel` is True,
# re-generate random sigma for each channel
if (sigma is None or self.different_sigma_per_channel):
if (not self.different_sigma_per_axis):
sigma = self._get_valid_sigma(self.sigma_range)
else:
sigma = [
self._get_valid_sigma(self.sigma_range)
for _ in data_sample.shape[1:]
]
# apply gaussian filter with `sigma`
data_sample[c] = gaussian_filter(
data_sample[c], sigma, order=0)
return data_sample
def transform(self, results: Dict) -> Dict:
"""Call function to add random Gaussian blur to image.
Args:
results (dict): Result dict.
Returns:
dict: Result dict with random Gaussian noise.
"""
if np.random.rand() < self.prob:
results['img'] = self._gaussian_blur(results['img'])
return results
def __repr__(self):
repr_str = self.__class__.__name__
repr_str += f'(prob={self.prob}, '
repr_str += f'prob_per_channel={self.prob_per_channel}, '
repr_str += f'sigma_range={self.sigma_range}, '
repr_str += 'different_sigma_per_channel='\
f'{self.different_sigma_per_channel}, '
repr_str += 'different_sigma_per_axis='\
f'{self.different_sigma_per_axis})'
return repr_str
@TRANSFORMS.register_module()
class BioMedicalRandomGamma(BaseTransform):
"""Using random gamma correction to process the biomedical image.
Modified from
https://github.com/MIC-DKFZ/batchgenerators/blob/master/batchgenerators/transforms/color_transforms.py#L132 # noqa:E501
With licence: Apache 2.0
Required Keys:
- img (np.ndarray): Biomedical image with shape (N, Z, Y, X),
N is the number of modalities, and data type is float32.
Modified Keys:
- img
Args:
prob (float): The probability to perform this transform. Default: 0.5.
gamma_range (Tuple[float]): Range of gamma values. Default: (0.5, 2).
invert_image (bool): Whether invert the image before applying gamma
augmentation. Default: False.
per_channel (bool): Whether perform the transform each channel
individually. Default: False
retain_stats (bool): Gamma transformation will alter the mean and std
of the data in the patch. If retain_stats=True, the data will be
transformed to match the mean and standard deviation before gamma
augmentation. Default: False.
"""
def __init__(self,
prob: float = 0.5,
gamma_range: Tuple[float] = (0.5, 2),
invert_image: bool = False,
per_channel: bool = False,
retain_stats: bool = False):
assert 0 <= prob and prob <= 1
assert isinstance(gamma_range, tuple) and len(gamma_range) == 2
assert isinstance(invert_image, bool)
assert isinstance(per_channel, bool)
assert isinstance(retain_stats, bool)
self.prob = prob
self.gamma_range = gamma_range
self.invert_image = invert_image
self.per_channel = per_channel
self.retain_stats = retain_stats
@cache_randomness
def _do_gamma(self):
"""Whether do adjust gamma for image."""
return np.random.rand() < self.prob
def _adjust_gamma(self, img: np.array):
"""Gamma adjustment for image.
Args:
img (np.array): Input image before gamma adjust.
Returns:
np.arrays: Image after gamma adjust.
"""
if self.invert_image:
img = -img
def _do_adjust(img):
if retain_stats_here:
img_mean = img.mean()
img_std = img.std()
if np.random.random() < 0.5 and self.gamma_range[0] < 1:
gamma = np.random.uniform(self.gamma_range[0], 1)
else:
gamma = np.random.uniform(
max(self.gamma_range[0], 1), self.gamma_range[1])
img_min = img.min()
img_range = img.max() - img_min # range
img = np.power(((img - img_min) / float(img_range + 1e-7)),
gamma) * img_range + img_min
if retain_stats_here:
img = img - img.mean()
img = img / (img.std() + 1e-8) * img_std
img = img + img_mean
return img
if not self.per_channel:
retain_stats_here = self.retain_stats
img = _do_adjust(img)
else:
for c in range(img.shape[0]):
img[c] = _do_adjust(img[c])
if self.invert_image:
img = -img
return img
def transform(self, results: dict) -> dict:
"""Call function to perform random gamma correction
Args:
results (dict): Result dict from loading pipeline.
Returns:
dict: Result dict with random gamma correction performed.
"""
do_gamma = self._do_gamma()
if do_gamma:
results['img'] = self._adjust_gamma(results['img'])
else:
pass
return results
def __repr__(self):
repr_str = self.__class__.__name__
repr_str += f'(prob={self.prob}, '
repr_str += f'gamma_range={self.gamma_range},'
repr_str += f'invert_image={self.invert_image},'
repr_str += f'per_channel={self.per_channel},'
repr_str += f'retain_stats={self.retain_stats}'
return repr_str
@TRANSFORMS.register_module()
class BioMedical3DPad(BaseTransform):
"""Pad the biomedical 3d image & biomedical 3d semantic segmentation maps.
Required Keys:
- img (np.ndarry): Biomedical image with shape (N, Z, Y, X) by default,
N is the number of modalities.
- gt_seg_map (np.ndarray, optional): Biomedical seg map with shape
(Z, Y, X) by default.
Modified Keys:
- img (np.ndarry): Biomedical image with shape (N, Z, Y, X) by default,
N is the number of modalities.
- gt_seg_map (np.ndarray, optional): Biomedical seg map with shape
(Z, Y, X) by default.
Added Keys:
- pad_shape (Tuple[int, int, int]): The padded shape.
Args:
pad_shape (Tuple[int, int, int]): Fixed padding size.
Expected padding shape (Z, Y, X).
pad_val (float): Padding value for biomedical image.
The padding mode is set to "constant". The value
to be filled in padding area. Default: 0.
seg_pad_val (int): Padding value for biomedical 3d semantic
segmentation maps. The padding mode is set to "constant".
The value to be filled in padding area. Default: 0.
"""
def __init__(self,
pad_shape: Tuple[int, int, int],
pad_val: float = 0.,
seg_pad_val: int = 0) -> None:
# check pad_shape
assert pad_shape is not None
if not isinstance(pad_shape, tuple):
assert len(pad_shape) == 3
self.pad_shape = pad_shape
self.pad_val = pad_val
self.seg_pad_val = seg_pad_val
def _pad_img(self, results: dict) -> None:
"""Pad images according to ``self.pad_shape``
Args:
results (dict): Result dict from loading pipeline.
Returns:
dict: The dict contains the padded image and shape
information.
"""
padded_img = self._to_pad(
results['img'], pad_shape=self.pad_shape, pad_val=self.pad_val)
results['img'] = padded_img
results['pad_shape'] = padded_img.shape[1:]
def _pad_seg(self, results: dict) -> None:
"""Pad semantic segmentation map according to ``self.pad_shape`` if
``gt_seg_map`` is not None in results dict.
Args:
results (dict): Result dict from loading pipeline.
Returns:
dict: Update the padded gt seg map in dict.
"""
if results.get('gt_seg_map', None) is not None:
pad_gt_seg = self._to_pad(
results['gt_seg_map'][None, ...],
pad_shape=results['pad_shape'],
pad_val=self.seg_pad_val)
results['gt_seg_map'] = pad_gt_seg[1:]
@staticmethod
def _to_pad(img: np.ndarray,
pad_shape: Tuple[int, int, int],
pad_val: Union[int, float] = 0) -> np.ndarray:
"""Pad the given 3d image to a certain shape with specified padding
value.
Args:
img (ndarray): Biomedical image with shape (N, Z, Y, X)
to be padded. N is the number of modalities.
pad_shape (Tuple[int,int,int]): Expected padding shape (Z, Y, X).
pad_val (float, int): Values to be filled in padding areas
and the padding_mode is set to 'constant'. Default: 0.
Returns:
ndarray: The padded image.
"""
# compute pad width
d = max(pad_shape[0] - img.shape[1], 0)
pad_d = (d // 2, d - d // 2)
h = max(pad_shape[1] - img.shape[2], 0)
pad_h = (h // 2, h - h // 2)
w = max(pad_shape[2] - img.shape[2], 0)
pad_w = (w // 2, w - w // 2)
pad_list = [(0, 0), pad_d, pad_h, pad_w]
img = np.pad(img, pad_list, mode='constant', constant_values=pad_val)
return img
def transform(self, results: dict) -> dict:
"""Call function to pad images, semantic segmentation maps.
Args:
results (dict): Result dict from loading pipeline.
Returns:
dict: Updated result dict.
"""
self._pad_img(results)
self._pad_seg(results)
return results
def __repr__(self):
repr_str = self.__class__.__name__
repr_str += f'pad_shape={self.pad_shape}, '
repr_str += f'pad_val={self.pad_val}), '
repr_str += f'seg_pad_val={self.seg_pad_val})'
return repr_str