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"""
Copied from https://github.com/talshaharabany/AutoSAM
"""
import os
from PIL import Image
import torch.utils.data as data
import torchvision.transforms as transforms
import numpy as np
import random
import torch
from dataloaders.PolypTransforms import get_polyp_transform
import cv2
KVASIR = "Kvasir"
CLINIC_DB = "CVC-ClinicDB"
COLON_DB = "CVC-ColonDB"
ETIS_DB = "ETIS-LaribPolypDB"
CVC300 = "CVC-300"
DATASETS = (KVASIR, CLINIC_DB, COLON_DB, ETIS_DB)
EXCLUDE_DS = (CVC300, )
def create_suppport_set_for_polyps(n_support=10):
"""
create a text file contating n_support_images for each dataset
"""
root_dir = "/disk4/Lev/Projects/Self-supervised-Fewshot-Medical-Image-Segmentation/data/PolypDataset/TrainDataset"
supp_images = []
supp_masks = []
image_dir = os.path.join(root_dir, "images")
mask_dir = os.path.join(root_dir, "masks")
# randonly sample n_support images and masks
image_paths = sorted([os.path.join(image_dir, f) for f in os.listdir(
image_dir) if f.endswith('.jpg') or f.endswith('.png')])
mask_paths = sorted([os.path.join(mask_dir, f) for f in os.listdir(
mask_dir) if f.endswith('.png')])
while len(supp_images) < n_support:
index = random.randint(0, len(image_paths) - 1)
# check that the index is not already in the support set
if image_paths[index] in supp_images:
continue
supp_images.append(image_paths[index])
supp_masks.append(mask_paths[index])
with open(os.path.join(root_dir, "support.txt"), 'w') as file:
for image_path, mask_path in zip(supp_images, supp_masks):
file.write(f"{image_path} {mask_path}\n")
def create_train_val_test_split_for_polyps():
root_dir = "/disk4/Lev/Projects/Self-supervised-Fewshot-Medical-Image-Segmentation/data/PolypDataset/"
# for each subdir in root_dir, create a split file
num_train_images_per_dataset = {
"CVC-ClinicDB": 548, "Kvasir": 900, "CVC-300": 0, "CVC-ColonDB": 0}
num_test_images_per_dataset = {
"CVC-ClinicDB": 64, "Kvasir": 100, "CVC-300": 60, "CVC-ColonDB": 380}
for subdir in os.listdir(root_dir):
subdir_path = os.path.join(root_dir, subdir)
if os.path.isdir(subdir_path):
split_file = os.path.join(subdir_path, "split.txt")
image_dir = os.path.join(subdir_path, "images")
create_train_val_test_split(
image_dir, split_file, train_number=num_train_images_per_dataset[subdir], test_number=num_test_images_per_dataset[subdir])
def create_train_val_test_split(root, split_file, train_number=100, test_number=20):
"""
Create a train, val, test split file for a dataset
root: root directory of dataset
split_file: name of split file to create
train_ratio: ratio of train set
val_ratio: ratio of val set
test_ratio: ratio of test set
"""
# Get all files in root directory
files = os.listdir(root)
# Filter out non-image files, remove suffix
files = [f.split('.')[0]
for f in files if f.endswith('.jpg') or f.endswith('.png')]
# Shuffle files
random.shuffle(files)
# Calculate number of files for each split
num_files = len(files)
num_train = train_number
num_test = test_number
num_val = num_files - num_train - num_test
print(f"num_train: {num_train}, num_val: {num_val}, num_test: {num_test}")
# Create splits
train = files[:num_train]
val = files[num_train:num_train + num_val]
test = files[num_train + num_val:]
# Write splits to file
with open(split_file, 'w') as file:
file.write("train\n")
for f in train:
file.write(f + "\n")
file.write("val\n")
for f in val:
file.write(f + "\n")
file.write("test\n")
for f in test:
file.write(f + "\n")
class PolypDataset(data.Dataset):
"""
dataloader for polyp segmentation tasks
"""
def __init__(self, root, image_root=None, gt_root=None, trainsize=352, augmentations=None, train=True, sam_trans=None, datasets=DATASETS, image_size=(1024, 1024), ds_mean=None, ds_std=None):
self.trainsize = trainsize
self.augmentations = augmentations
self.datasets = datasets
if isinstance(image_size, int):
image_size = (image_size, image_size)
self.image_size = image_size
if image_root is not None and gt_root is not None:
self.images = [
os.path.join(image_root, f) for f in os.listdir(image_root) if f.endswith('.jpg') or f.endswith('.png')]
self.gts = [
os.path.join(gt_root, f) for f in os.listdir(gt_root) if f.endswith('.png')]
# also look in subdirectories
for subdir in os.listdir(image_root):
# if not dir, continue
if not os.path.isdir(os.path.join(image_root, subdir)):
continue
subdir_image_root = os.path.join(image_root, subdir)
subdir_gt_root = os.path.join(gt_root, subdir)
self.images.extend([os.path.join(subdir_image_root, f) for f in os.listdir(
subdir_image_root) if f.endswith('.jpg') or f.endswith('.png')])
self.gts.extend([os.path.join(subdir_gt_root, f) for f in os.listdir(
subdir_gt_root) if f.endswith('.png')])
else:
self.images, self.gts = self.get_image_gt_pairs(
root, split="train" if train else "test", datasets=self.datasets)
self.images = sorted(self.images)
self.gts = sorted(self.gts)
if not 'VPS' in root:
self.filter_files_and_get_ds_mean_and_std()
if ds_mean is not None and ds_std is not None:
self.mean, self.std = ds_mean, ds_std
self.size = len(self.images)
self.train = train
self.sam_trans = sam_trans
if self.sam_trans is not None:
# sam trans takes care of norm
self.mean, self.std = 0 , 1
def get_image_gt_pairs(self, dir_root: str, split="train", datasets: tuple = DATASETS):
"""
for each folder in dir root, get all image-gt pairs. Assumes each subdir has a split.txt file
dir_root: root directory of all subdirectories, each subdirectory contains images and masks folders
split: train, val, or test
"""
image_paths = []
gt_paths = []
for folder in os.listdir(dir_root):
if folder not in datasets:
continue
split_file = os.path.join(dir_root, folder, "split.txt")
if os.path.isfile(split_file):
image_root = os.path.join(dir_root, folder, "images")
gt_root = os.path.join(dir_root, folder, "masks")
image_paths_tmp, gt_paths_tmp = self.get_image_gt_pairs_from_text_file(
image_root, gt_root, split_file, split=split)
image_paths.extend(image_paths_tmp)
gt_paths.extend(gt_paths_tmp)
else:
print(
f"No split.txt file found in {os.path.join(dir_root, folder)}")
return image_paths, gt_paths
def get_image_gt_pairs_from_text_file(self, image_root: str, gt_root: str, text_file: str, split: str = "train"):
"""
image_root: root directory of images
gt_root: root directory of ground truth
text_file: text file containing train, val, test split with the following format:
train:
image1
image2
...
val:
image1
image2
...
test:
image1
image2
...
split: train, val, or test
"""
# Initialize a dictionary to hold file names for each split
splits = {"train": [], "val": [], "test": []}
current_split = None
# Read the text file and categorize file names under each split
with open(text_file, 'r') as file:
for line in file:
line = line.strip()
if line in splits:
current_split = line
elif line and current_split:
splits[current_split].append(line)
# Get the file names for the requested split
file_names = splits[split]
# Create image-ground truth pairs
image_paths = []
gt_paths = []
for name in file_names:
image_path = os.path.join(image_root, name + '.png')
gt_path = os.path.join(gt_root, name + '.png')
image_paths.append(image_path)
gt_paths.append(gt_path)
return image_paths, gt_paths
def get_support_from_dirs(self, support_image_dir, support_mask_dir, n_support=1):
support_images = []
support_labels = []
# get all images and masks
support_image_paths = sorted([os.path.join(support_image_dir, f) for f in os.listdir(
support_image_dir) if f.endswith('.jpg') or f.endswith('.png')])
support_mask_paths = sorted([os.path.join(support_mask_dir, f) for f in os.listdir(
support_mask_dir) if f.endswith('.png')])
# sample n_support images and masks
for i in range(n_support):
index = random.randint(0, len(support_image_paths) - 1)
support_img = self.cv2_loader(
support_image_paths[index], is_mask=False)
support_mask = self.cv2_loader(
support_mask_paths[index], is_mask=True)
support_images.append(support_img)
support_labels.append(support_mask)
if self.augmentations:
support_images = [self.augmentations(
img, mask)[0] for img, mask in zip(support_images, support_labels)]
support_labels = [self.augmentations(
img, mask)[1] for img, mask in zip(support_images, support_labels)]
support_images = [(support_image - self.mean) / self.std if support_image.max() == 255 and support_image.min() == 0 else support_image for support_image in support_images]
if self.sam_trans is not None:
support_images = [self.sam_trans.preprocess(
img).squeeze(0) for img in support_images]
support_labels = [self.sam_trans.preprocess(
mask) for mask in support_labels]
else:
image_size = self.image_size
support_images = [torch.nn.functional.interpolate(img.unsqueeze(
0), size=image_size, mode='bilinear', align_corners=False).squeeze(0) for img in support_images]
support_labels = [torch.nn.functional.interpolate(mask.unsqueeze(0).unsqueeze(
0), size=image_size, mode='nearest').squeeze(0).squeeze(0) for mask in support_labels]
return torch.stack(support_images), torch.stack(support_labels)
def get_support_from_text_file(self, text_file, n_support=1):
"""
each row in the file has 2 paths divided by space, the first is the image path and the second is the mask path
"""
support_images = []
support_labels = []
with open(text_file, 'r') as file:
for line in file:
image_path, mask_path = line.strip().split()
support_images.append(image_path)
support_labels.append(mask_path)
# indices = random.choices(range(len(support_images)), k=n_support)
if n_support > len(support_images):
raise ValueError(f"n_support ({n_support}) is larger than the number of images in the text file ({len(support_images)})")
n_support_images = support_images[:n_support]
n_support_labels = support_labels[:n_support]
return n_support_images, n_support_labels
def get_support(self, n_support=1, support_image_dir=None, support_mask_dir=None, text_file=None):
"""
Get support set from specified directories, text file or from the dataset itself
"""
if support_image_dir is not None and support_mask_dir:
return self.get_support_from_dirs(support_image_dir, support_mask_dir, n_support=n_support)
elif text_file is not None:
support_image_paths, support_gt_paths = self.get_support_from_text_file(text_file, n_support=n_support)
else:
# randomly sample n_support images and masks from the dataset
indices = random.choices(range(self.size), k=n_support)
# indices = list(range(n_support))
print(f"support indices:{indices}")
support_image_paths = [self.images[index] for index in indices]
support_gt_paths = [self.gts[index] for index in indices]
support_images = []
support_gts = []
for image_path, gt_path in zip(support_image_paths, support_gt_paths):
support_img = self.cv2_loader(image_path, is_mask=False)
support_mask = self.cv2_loader(gt_path, is_mask=True)
out = self.process_image_gt(support_img, support_mask)
support_images.append(out['image'].unsqueeze(0))
support_gts.append(out['label'].unsqueeze(0))
if len(support_images) >= n_support:
break
return support_images, support_gts, out['case']
# return torch.stack(support_images), torch.stack(support_gts), out['case']
def process_image_gt(self, image, gt, dataset=""):
"""
image and gt are expected to be output from self.cv2_loader
"""
original_size = tuple(image.shape[-2:])
if self.augmentations:
image, mask = self.augmentations(image, gt)
if self.sam_trans:
image, mask = self.sam_trans.apply_image_torch(
image.unsqueeze(0)), self.sam_trans.apply_image_torch(mask)
elif image.max() <= 255 and image.min() >= 0:
image = (image - self.mean) / self.std
mask[mask > 0.5] = 1
mask[mask <= 0.5] = 0
# image_size = tuple(img.shape[-2:])
image_size = self.image_size
if self.sam_trans is None:
image = torch.nn.functional.interpolate(image.unsqueeze(
0), size=image_size, mode='bilinear', align_corners=False).squeeze(0)
mask = torch.nn.functional.interpolate(mask.unsqueeze(0).unsqueeze(
0), size=image_size, mode='nearest').squeeze(0).squeeze(0)
# img = (img - img.min()) / (img.max() - img.min()) # TODO uncomment this if results get worse
return {'image': self.sam_trans.preprocess(image).squeeze(0) if self.sam_trans else image,
'label': self.sam_trans.preprocess(mask) if self.sam_trans else mask,
'original_size': torch.Tensor(original_size),
'image_size': torch.Tensor(image_size),
'case': dataset} # case to be compatible with polyp video dataset
def get_dataset_name_from_path(self, path):
for dataset in self.datasets:
if dataset in path:
return dataset
return ""
def __getitem__(self, index):
image = self.cv2_loader(self.images[index], is_mask=False)
gt = self.cv2_loader(self.gts[index], is_mask=True)
dataset = self.get_dataset_name_from_path(self.images[index])
return self.process_image_gt(image, gt, dataset)
def filter_files_and_get_ds_mean_and_std(self):
assert len(self.images) == len(self.gts)
images = []
gts = []
ds_mean = 0
ds_std = 0
for img_path, gt_path in zip(self.images, self.gts):
if any([ex_ds in img_path for ex_ds in EXCLUDE_DS]):
continue
img = Image.open(img_path)
gt = Image.open(gt_path)
if img.size == gt.size:
images.append(img_path)
gts.append(gt_path)
ds_mean += np.array(img).mean()
ds_std += np.array(img).std()
self.images = images
self.gts = gts
self.mean = ds_mean / len(self.images)
self.std = ds_std / len(self.images)
def rgb_loader(self, path):
with open(path, 'rb') as f:
img = Image.open(f)
return img.convert('RGB')
def binary_loader(self, path):
# with open(path, 'rb') as f:
# img = Image.open(f)
# return img.convert('1')
img = cv2.imread(path, 0)
return img
def cv2_loader(self, path, is_mask):
if is_mask:
img = cv2.imread(path, 0)
img[img > 0] = 1
else:
img = cv2.cvtColor(cv2.imread(
path, cv2.IMREAD_COLOR), cv2.COLOR_BGR2RGB)
return img
def resize(self, img, gt):
assert img.size == gt.size
w, h = img.size
if h < self.trainsize or w < self.trainsize:
h = max(h, self.trainsize)
w = max(w, self.trainsize)
return img.resize((w, h), Image.BILINEAR), gt.resize((w, h), Image.NEAREST)
else:
return img, gt
def __len__(self):
# return 32
return self.size
class SuperpixPolypDataset(PolypDataset):
def __init__(self, root, image_root=None, gt_root=None, trainsize=352, augmentations=None, train=True, sam_trans=None, datasets=DATASETS, image_size=(1024, 1024), ds_mean=None, ds_std=None):
self.trainsize = trainsize
self.augmentations = augmentations
self.datasets = datasets
self.image_size = image_size
# print(self.augmentations)
if image_root is not None and gt_root is not None:
self.images = [
os.path.join(image_root, f) for f in os.listdir(image_root) if f.endswith('.jpg') or f.endswith('.png')]
self.gts = [
os.path.join(gt_root, f) for f in os.listdir(gt_root) if f.endswith('.png') and 'superpix' in f]
# also look in subdirectories
for subdir in os.listdir(image_root):
# if not dir, continue
if not os.path.isdir(os.path.join(image_root, subdir)):
continue
subdir_image_root = os.path.join(image_root, subdir)
subdir_gt_root = os.path.join(gt_root, subdir)
self.images.extend([os.path.join(subdir_image_root, f) for f in os.listdir(
subdir_image_root) if f.endswith('.jpg') or f.endswith('.png')])
self.gts.extend([os.path.join(subdir_gt_root, f) for f in os.listdir(
subdir_gt_root) if f.endswith('.png')])
else:
self.images, self.gts = self.get_image_gt_pairs(
root, split="train" if train else "test", datasets=self.datasets)
self.images = sorted(self.images)
self.gts = sorted(self.gts)
if not 'VPS' in root:
self.filter_files_and_get_ds_mean_and_std()
if ds_mean is not None and ds_std is not None:
self.mean, self.std = ds_mean, ds_std
self.size = len(self.images)
self.train = train
self.sam_trans = sam_trans
if self.sam_trans is not None:
# sam trans takes care of norm
self.mean, self.std = 0 , 1
def __getitem__(self, index):
image = self.cv2_loader(self.images[index], is_mask=False)
gt = self.cv2_loader(self.gts[index], is_mask=False)
gt = gt[:, :, 0]
fgpath = os.path.basename(self.gts[index]).split('.png')[0].split('superpix-MIDDLE_')
fgpath = os.path.join(os.path.dirname(self.gts[index]), 'fgmask_' + fgpath[1] + '.png')
fg = self.cv2_loader(fgpath, is_mask=True)
dataset = self.get_dataset_name_from_path(self.images[index])
# randomly choose a superpixels from the gt
gt[1-fg] = 0
sp_id = random.choice(np.unique(gt)[1:])
sp = (gt == sp_id).astype(np.uint8)
out = self.process_image_gt(image, gt, dataset)
support_image, support_sp, dataset = out["image"], out["label"], out["case"]
out = self.process_image_gt(image, sp, dataset)
query_image, query_sp, dataset = out["image"], out["label"], out["case"]
# TODO tile the masks to have 3 channels?
support_bg_mask = 1 - support_sp
support_masks = {"fg_mask": support_sp, "bg_mask": support_bg_mask}
batch = {"support_images" : [[support_image]],
"support_mask" : [[support_masks]],
"query_images" : [query_image],
"query_labels" : [query_sp],
"scan_id" : [dataset]
}
return batch
def get_superpix_polyp_dataset(image_size:tuple=(1024,1024), sam_trans=None):
transform_train, transform_test = get_polyp_transform()
image_root = './data/PolypDataset/TrainDataset/images/'
gt_root = './data/PolypDataset/TrainDataset/superpixels/'
ds_train = SuperpixPolypDataset(root=image_root, image_root=image_root, gt_root=gt_root,
augmentations=transform_train,
sam_trans=sam_trans,
image_size=image_size)
return ds_train
def get_polyp_dataset(image_size, sam_trans=None):
transform_train, transform_test = get_polyp_transform()
image_root = './data/PolypDataset/TrainDataset/images/'
gt_root = './data/PolypDataset/TrainDataset/masks/'
ds_train = PolypDataset(root=image_root, image_root=image_root, gt_root=gt_root,
augmentations=transform_test, sam_trans=sam_trans, train=True, image_size=image_size)
image_root = './data/PolypDataset/TestDataset/test/images/'
gt_root = './data/PolypDataset/TestDataset/test/masks/'
ds_test = PolypDataset(root=image_root, image_root=image_root, gt_root=gt_root, train=False,
augmentations=transform_test, sam_trans=sam_trans, image_size=image_size)
return ds_train, ds_test
def get_tests_polyp_dataset(sam_trans):
transform_train, transform_test = get_polyp_transform()
image_root = './data/polyp/TestDataset/Kvasir/images/'
gt_root = './data/polyp/TestDataset/Kvasir/masks/'
ds_Kvasir = PolypDataset(
image_root, gt_root, augmentations=transform_test, train=False, sam_trans=sam_trans)
image_root = './data/polyp/TestDataset/CVC-ClinicDB/images/'
gt_root = './data/polyp/TestDataset/CVC-ClinicDB/masks/'
ds_ClinicDB = PolypDataset(
image_root, gt_root, augmentations=transform_test, train=False, sam_trans=sam_trans)
image_root = './data/polyp/TestDataset/CVC-ColonDB/images/'
gt_root = './data/polyp/TestDataset/CVC-ColonDB/masks/'
ds_ColonDB = PolypDataset(
image_root, gt_root, augmentations=transform_test, train=False, sam_trans=sam_trans)
image_root = './data/polyp/TestDataset/ETIS-LaribPolypDB/images/'
gt_root = './data/polyp/TestDataset/ETIS-LaribPolypDB/masks/'
ds_ETIS = PolypDataset(
image_root, gt_root, augmentations=transform_test, train=False, sam_trans=sam_trans)
return ds_Kvasir, ds_ClinicDB, ds_ColonDB, ds_ETIS
if __name__ == '__main__':
# create_train_val_test_split_for_polyps()
create_suppport_set_for_polyps()
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