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#
# This source code is licensed under the Apache License, Version 2.0
# found in the LICENSE file in the root directory of this source tree.
import logging
import cv2
import numpy as np
import random
import torch
import torchvision
from torchvision import transforms
from skimage import color as skimage_color
from skimage.color import rgb2hed, hed2rgb
from einops import rearrange
from PIL import Image
from .transforms import (
GaussianBlur,
make_normalize_transform,
)
logger = logging.getLogger("dinov2")
class RandStainNA(torch.nn.Module):
"""
RandStainNA: Random Stain Normalization and Augmentation.
Bridges stain normalization and augmentation by constraining variable stain styles
in a practicable range using virtual template generation.
Based on: "RandStainNA: Learning Stain-Agnostic Features from Histology Slides
by Bridging Stain Augmentation and Normalization" (MICCAI 2022)
Reference: https://github.com/yiqings/RandStainNA
"""
# Default statistics for LAB color space from CRC dataset
# These can be overridden by providing a yaml_file
DEFAULT_LAB_STATS = {
'L': {'avg': {'mean': 158.033, 'std': 48.792}, 'std': {'mean': 36.899, 'std': 14.383}},
'A': {'avg': {'mean': 151.187, 'std': 10.958}, 'std': {'mean': 8.134, 'std': 2.822}},
'B': {'avg': {'mean': 116.812, 'std': 6.643}, 'std': {'mean': 6.129, 'std': 2.013}},
}
# Default statistics for HED color space
DEFAULT_HED_STATS = {
'H': {'avg': {'mean': 0.05, 'std': 0.02}, 'std': {'mean': 0.03, 'std': 0.01}},
'E': {'avg': {'mean': 0.02, 'std': 0.01}, 'std': {'mean': 0.02, 'std': 0.008}},
'D': {'avg': {'mean': 0.0, 'std': 0.005}, 'std': {'mean': 0.01, 'std': 0.005}},
}
def __init__(
self,
color_space='LAB',
std_hyper=-0.3,
distribution='normal',
probability=1.0,
):
super().__init__()
assert distribution in ['normal', 'laplace', 'uniform'], \
f"Unsupported distribution: {distribution}"
assert color_space in ['LAB', 'HSV', 'HED'], \
f"Unsupported color space: {color_space}"
self.color_space = color_space
self.std_hyper = std_hyper
self.distribution = distribution
self.probability = probability
if color_space == 'LAB':
stats = self.DEFAULT_LAB_STATS
self.channels = ['L', 'A', 'B']
elif color_space == 'HED':
stats = self.DEFAULT_HED_STATS
self.channels = ['H', 'E', 'D']
else:
stats = self.DEFAULT_LAB_STATS
self.channels = ['H', 'S', 'V']
self.channel_avgs_mean = [stats[c]['avg']['mean'] for c in self.channels]
self.channel_avgs_std = [stats[c]['avg']['std'] for c in self.channels]
self.channel_stds_mean = [stats[c]['std']['mean'] for c in self.channels]
self.channel_stds_std = [stats[c]['std']['std'] for c in self.channels]
def _getavgstd(self, image):
"""Get mean and std for each channel."""
avgs = []
stds = []
for idx in range(image.shape[2]):
avgs.append(np.mean(image[:, :, idx]))
stds.append(np.std(image[:, :, idx]))
return np.array(avgs), np.array(stds)
def _normalize(self, img, img_avgs, img_stds, tar_avgs, tar_stds):
"""Normalize image to target statistics."""
img_stds = np.clip(img_stds, 0.0001, 255)
img = (img - img_avgs) * (tar_stds / img_stds) + tar_avgs
if self.color_space in ['LAB', 'HSV']:
img = np.clip(img, 0, 255).astype(np.uint8)
return img
def _generate_virtual_template(self):
"""Generate virtual template statistics based on distribution."""
tar_avgs = []
tar_stds = []
if self.distribution == 'uniform':
for idx in range(3):
tar_avg = np.random.uniform(
low=self.channel_avgs_mean[idx] - 3 * self.channel_avgs_std[idx],
high=self.channel_avgs_mean[idx] + 3 * self.channel_avgs_std[idx],
)
tar_std = np.random.uniform(
low=self.channel_stds_mean[idx] - 3 * self.channel_stds_std[idx],
high=self.channel_stds_mean[idx] + 3 * self.channel_stds_std[idx],
)
tar_avgs.append(tar_avg)
tar_stds.append(tar_std)
else:
if self.distribution == 'normal':
np_distribution = np.random.normal
else:
np_distribution = np.random.laplace
for idx in range(3):
tar_avg = np_distribution(
loc=self.channel_avgs_mean[idx],
scale=self.channel_avgs_std[idx] * (1 + self.std_hyper),
)
tar_std = np_distribution(
loc=self.channel_stds_mean[idx],
scale=self.channel_stds_std[idx] * (1 + self.std_hyper),
)
tar_avgs.append(tar_avg)
tar_stds.append(tar_std)
return np.array(tar_avgs), np.array(tar_stds)
def augment(self, img):
"""Apply stain augmentation."""
if isinstance(img, Image.Image):
image = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)
was_pil = True
else:
image = img
was_pil = False
# Color space conversion
if self.color_space == 'LAB':
image = cv2.cvtColor(image, cv2.COLOR_BGR2LAB)
elif self.color_space == 'HSV':
image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
elif self.color_space == 'HED':
image = skimage_color.rgb2hed(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
# Generate virtual template and normalize
tar_avgs, tar_stds = self._generate_virtual_template()
img_avgs, img_stds = self._getavgstd(image)
image = self._normalize(
img=image,
img_avgs=img_avgs,
img_stds=img_stds,
tar_avgs=tar_avgs,
tar_stds=tar_stds,
)
# Convert back to BGR/RGB
if self.color_space == 'LAB':
image = cv2.cvtColor(image, cv2.COLOR_LAB2BGR)
elif self.color_space == 'HSV':
image = cv2.cvtColor(image, cv2.COLOR_HSV2BGR)
elif self.color_space == 'HED':
nimg = skimage_color.hed2rgb(image)
imin = nimg.min()
imax = nimg.max()
rsimg = (255 * (nimg - imin) / (imax - imin + 1e-8)).astype('uint8')
image = cv2.cvtColor(rsimg, cv2.COLOR_RGB2BGR)
# Convert back to RGB for PIL
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
if was_pil:
return Image.fromarray(image)
return image
def forward(self, img):
if random.random() > self.probability:
return img
return self.augment(img)
class hed_mod(torch.nn.Module):
"""
HED color space augmentation for H&E stained histopathology images.
Randomly perturbs Hematoxylin, Eosin, and DAB channels.
"""
def __init__(self, probability=0.5, perturbation_range=0.05):
super().__init__()
self.probability = probability
self.mini = -perturbation_range
self.maxi = perturbation_range
def forward(self, img, label=None):
if random.random() > self.probability:
return img
if img is not None:
img = torchvision.transforms.functional.pil_to_tensor(img)
img = rearrange(img, 'c h w -> h w c')
hed_image = rgb2hed(img)
hed_image[..., 0] += random.uniform(self.mini, self.maxi) # H
hed_image[..., 1] += random.uniform(self.mini, self.maxi) # E
hed_image[..., 2] += random.uniform(self.mini, self.maxi) # D
hed_image = np.clip(hed_image, 0, 1)
img = hed2rgb(hed_image)
img = rearrange(img, 'h w c -> c h w')
img = torch.from_numpy(img)
img = torchvision.transforms.functional.to_pil_image(img)
if label is not None:
label = rearrange(label, 'c h w -> h w c')
hed_image = rgb2hed(label)
hed_image[..., 0] += random.uniform(self.mini, self.maxi)
hed_image[..., 1] += random.uniform(self.mini, self.maxi)
hed_image[..., 2] += random.uniform(self.mini, self.maxi)
label = rearrange(label, 'h w c -> c h w')
label = torch.from_numpy(label)
return img, label
return img
class RandomRotation90(torch.nn.Module):
"""
Random 90-degree rotation augmentation for histopathology images.
Pathology images are rotation-invariant, so we can apply 0, 90, 180, or 270 degree rotations.
"""
def __init__(self):
super().__init__()
self.angles = [0, 90, 180, 270]
def forward(self, img):
angle = random.choice(self.angles)
if angle == 0:
return img
return transforms.functional.rotate(img, angle)
class DataAugmentationDINO(object):
"""
Data augmentation pipeline for DINOv2 training on histopathology images.
Includes pathology-specific augmentations:
- RandStainNA: Stain normalization/augmentation in LAB color space
- HED augmentation: Color space perturbation
- 90-degree rotations: Rotation invariance
- Vertical and horizontal flips
- Gaussian blur
- Color jitter (no grayscale for H&E images)
"""
def __init__(
self,
global_crops_scale,
local_crops_scale,
local_crops_number,
global_crops_size=224,
local_crops_size=96,
):
self.global_crops_scale = global_crops_scale
self.local_crops_scale = local_crops_scale
self.local_crops_number = local_crops_number
self.global_crops_size = global_crops_size
self.local_crops_size = local_crops_size
logger.info("###################################")
logger.info("Using data augmentation parameters:")
logger.info(f"global_crops_scale: {global_crops_scale}")
logger.info(f"local_crops_scale: {local_crops_scale}")
logger.info(f"local_crops_number: {local_crops_number}")
logger.info(f"global_crops_size: {global_crops_size}")
logger.info(f"local_crops_size: {local_crops_size}")
logger.info("###################################")
# Geometric augmentations with rotation and both flips
self.geometric_augmentation_global = transforms.Compose(
[
# RandomRotation90(),
transforms.RandomResizedCrop(
global_crops_size, scale=global_crops_scale, interpolation=transforms.InterpolationMode.BICUBIC
),
transforms.RandomHorizontalFlip(p=0.5),
# transforms.RandomVerticalFlip(p=0.5),
]
)
self.geometric_augmentation_local = transforms.Compose(
[
# RandomRotation90(),
transforms.RandomResizedCrop(
local_crops_size, scale=local_crops_scale, interpolation=transforms.InterpolationMode.BICUBIC
),
transforms.RandomHorizontalFlip(p=0.5),
# transforms.RandomVerticalFlip(p=0.5),
]
)
# Normalization (ImageNet stats used by default)
self.normalize = transforms.Compose(
[
transforms.ToTensor(),
make_normalize_transform(),
]
)
# Pathology-specific stain augmentations
randstainna = RandStainNA(
color_space='LAB',
std_hyper=-0.3,
distribution='normal',
probability=0.5,
)
hed_aug = hed_mod(probability=0.5, perturbation_range=0.05)
self.global_transfo1 = transforms.Compose([
# randstainna,
hed_aug,
transforms.RandomApply([transforms.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.1, hue=0.05)], p=0.8),
transforms.RandomGrayscale(p=0.2),
GaussianBlur(p=1.0),
self.normalize
])
self.global_transfo2 = transforms.Compose([
# randstainna,
hed_aug,
transforms.RandomApply([transforms.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.1, hue=0.05)], p=0.8),
transforms.RandomGrayscale(p=0.2),
GaussianBlur(p=0.1),
self.normalize
])
self.local_transfo = transforms.Compose([
# randstainna,
hed_aug,
transforms.RandomApply([transforms.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.1, hue=0.05)], p=0.8),
transforms.RandomGrayscale(p=0.2),
GaussianBlur(p=0.5),
self.normalize
])
def __call__(self, image):
output = {}
# Global crops
im1_base = self.geometric_augmentation_global(image)
global_crop_1 = self.global_transfo1(im1_base)
im2_base = self.geometric_augmentation_global(image)
global_crop_2 = self.global_transfo2(im2_base)
output["global_crops"] = [global_crop_1, global_crop_2]
output["global_crops_teacher"] = [global_crop_1, global_crop_2]
# Local crops
local_crops = [
self.local_transfo(self.geometric_augmentation_local(image)) for _ in range(self.local_crops_number)
]
output["local_crops"] = local_crops
output["offsets"] = ()
return output
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