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"""
Dataset functions for applying Normalized Cut.
Code adapted from SelfMask: https://github.com/NoelShin/selfmask
"""
import os
from typing import Optional, Tuple, Union
from pycocotools.coco import COCO
import numpy as np
import torch
import torchvision
from PIL import Image
from torch.utils.data import Dataset
from torchvision import transforms as T
from datasets.utils import unnormalize
from datasets.geometric_transforms import resize
from datasets.VOC import get_voc_detection_gt, create_gt_masks_if_voc, create_VOC_loader
from datasets.augmentations import geometric_augmentations, photometric_augmentations
from datasets.uod_datasets import UODDataset
NORMALIZE = T.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
def set_dataset_dir(dataset_name, root_dir):
if dataset_name == "ECSSD":
dataset_dir = os.path.join(root_dir, "ECSSD")
img_dir = os.path.join(dataset_dir, "images")
gt_dir = os.path.join(dataset_dir, "ground_truth_mask")
elif dataset_name == "DUTS-TEST":
dataset_dir = os.path.join(root_dir, "DUTS")
img_dir = os.path.join(dataset_dir, "DUTS-TE-Image")
gt_dir = os.path.join(dataset_dir, "DUTS-TE-Mask")
elif dataset_name == "DUTS-TR":
dataset_dir = os.path.join(root_dir, "DUTS")
img_dir = os.path.join(dataset_dir, "DUTS-TR-Image")
gt_dir = os.path.join(dataset_dir, "DUTS-TR-Mask")
elif dataset_name == "DUT-OMRON":
dataset_dir = os.path.join(root_dir, "DUT-OMRON")
img_dir = os.path.join(dataset_dir, "DUT-OMRON-image")
gt_dir = os.path.join(dataset_dir, "pixelwiseGT-new-PNG")
elif dataset_name == "VOC07":
dataset_dir = os.path.join(root_dir, "VOC2007")
img_dir = dataset_dir
gt_dir = dataset_dir
elif dataset_name == "VOC12":
dataset_dir = os.path.join('/datasets_local/osimeoni', "VOC2012")
img_dir = dataset_dir
gt_dir = dataset_dir
elif dataset_name == "COCO17":
dataset_dir = os.path.join(root_dir, "COCO")
img_dir = dataset_dir
gt_dir = dataset_dir
elif dataset_name == "ImageNet":
dataset_dir = os.path.join(root_dir, "ImageNet")
img_dir = dataset_dir
gt_dir = dataset_dir
else:
raise ValueError(f"Unknown dataset {dataset_name}")
return img_dir, gt_dir
def build_dataset(
root_dir: str,
dataset_name: str,
dataset_set: Optional[str] = None,
for_eval: bool = False,
config=None,
evaluation_type="saliency", # uod,
):
"""
Build dataset
"""
if evaluation_type == "saliency":
img_dir, gt_dir = set_dataset_dir(dataset_name, root_dir)
dataset = FoundDataset(
name=dataset_name,
img_dir=img_dir,
gt_dir=gt_dir,
dataset_set=dataset_set,
config=config,
for_eval=for_eval,
evaluation_type=evaluation_type,
)
elif evaluation_type == "uod":
assert dataset_name in ["VOC07", "VOC12", "COCO20k"]
dataset_set = "trainval" if dataset_name in ["VOC07", "VOC12"] else "train"
no_hards = False
dataset = UODDataset(
dataset_name,
dataset_set,
root_dir=root_dir,
remove_hards=no_hards,
)
return dataset
class FoundDataset(Dataset):
def __init__(
self,
name: str,
img_dir: str,
gt_dir: str,
dataset_set: Optional[str] = None,
config=None,
for_eval:bool = False,
evaluation_type:str = "saliency",
) -> None:
"""
Args:
root_dir (string): Directory with all the images.
transform (callable, optional): Optional transform to be applied
on a sample.
"""
self.for_eval = for_eval
self.use_aug = not for_eval
self.evaluation_type = evaluation_type
assert evaluation_type in ["saliency"]
self.name = name
self.dataset_set = dataset_set
self.img_dir = img_dir
self.gt_dir = gt_dir
# if VOC dataset
self.loader = None
self.cocoGt = None
self.config = config
if "VOC" in self.name:
self.loader = create_VOC_loader(self.img_dir, dataset_set, evaluation_type)
# if ImageNet dataset
elif "ImageNet" in self.name:
self.loader = torchvision.datasets.ImageNet(
self.img_dir,
split=dataset_set,
transform=None,
target_transform=None,
)
elif "COCO" in self.name:
year = int("20"+self.name[-2:])
annFile=f'/datasets_local/COCO/annotations/instances_{dataset_set}{str(year)}.json'
self.cocoGt=COCO(annFile)
self.img_ids = list(sorted(self.cocoGt.getImgIds()))
self.img_dir = f'/datasets_local/COCO/images/{dataset_set}{str(year)}/'
# Transformations
if self.for_eval:
full_img_transform, no_norm_full_img_transform = self.get_init_transformation(
isVOC="VOC" in name
)
self.full_img_transform = full_img_transform
self.no_norm_full_img_transform = no_norm_full_img_transform
# Images
self.list_images = None
if not "VOC" in self.name and not "COCO" in self.name:
self.list_images = [
os.path.join(img_dir, i) for i in sorted(os.listdir(img_dir))
]
self.ignore_index = -1
self.mean = NORMALIZE.mean
self.std = NORMALIZE.std
self.to_tensor_and_normalize = T.Compose([T.ToTensor(), NORMALIZE])
self.normalize = NORMALIZE
if config is not None and self.use_aug:
self._set_aug(config)
def get_init_transformation(self, isVOC: bool = False):
if isVOC:
t = T.Compose([T.PILToTensor(), T.ConvertImageDtype(torch.float), NORMALIZE])
t_nonorm = T.Compose([T.PILToTensor(), T.ConvertImageDtype(torch.float)])
return t, t_nonorm
else:
t = T.Compose([T.ToTensor(), NORMALIZE])
t_nonorm = T.Compose([T.ToTensor()])
return t, t_nonorm
def _set_aug(self, config):
"""
Set augmentation based on config.
"""
photometric_aug = config.training["photometric_aug"]
self.cropping_strategy = config.training["cropping_strategy"]
if self.cropping_strategy == "center_crop":
self.use_aug = False # default strategy, not considered to be a data aug
self.scale_range = config.training["scale_range"]
self.crop_size = config.training["crop_size"]
self.center_crop_transforms = T.Compose(
[
T.CenterCrop((self.crop_size, self.crop_size)),
T.ToTensor(),
]
)
self.center_crop_only_transforms = T.Compose(
[T.CenterCrop((self.crop_size, self.crop_size)), T.PILToTensor()]
)
self.proba_photometric_aug = config.training["proba_photometric_aug"]
self.random_color_jitter = False
self.random_grayscale = False
self.random_gaussian_blur = False
if photometric_aug == "color_jitter":
self.random_color_jitter = True
elif photometric_aug == "grayscale":
self.random_grayscale = True
elif photometric_aug == "gaussian_blur":
self.random_gaussian_blur = True
def _preprocess_data_aug(
self,
image: Image.Image,
mask: Image.Image,
ignore_index: Optional[int] = None,
) -> Tuple[torch.Tensor, torch.Tensor]:
"""Prepare data in a proper form for either training (data augmentation) or validation."""
# resize to base size
image = resize(
image,
size=self.crop_size,
edge="shorter",
interpolation="bilinear",
)
mask = resize(
mask,
size=self.crop_size,
edge="shorter",
interpolation="bilinear",
)
if not isinstance(mask, torch.Tensor):
mask: torch.Tensor = torch.tensor(np.array(mask))
random_scale_range = None
random_crop_size = None
random_hflip_p = None
if self.cropping_strategy == "random_scale":
random_scale_range = self.scale_range
elif self.cropping_strategy == "random_crop":
random_crop_size = self.crop_size
elif self.cropping_strategy == "random_hflip":
random_hflip_p = 0.5
elif self.cropping_strategy == "random_crop_and_hflip":
random_hflip_p = 0.5
random_crop_size = self.crop_size
if random_crop_size or random_hflip_p or random_scale_range:
image, mask = geometric_augmentations(
image=image,
mask=mask,
random_scale_range=random_scale_range,
random_crop_size=random_crop_size,
ignore_index=ignore_index,
random_hflip_p=random_hflip_p,
)
if random_scale_range:
# resize to (self.crop_size, self.crop_size)
image = resize(
image,
size=self.crop_size,
interpolation="bilinear",
)
mask = resize(
mask,
size=(self.crop_size, self.crop_size),
interpolation="bilinear",
)
image = photometric_augmentations(
image,
random_color_jitter=self.random_color_jitter,
random_grayscale=self.random_grayscale,
random_gaussian_blur=self.random_gaussian_blur,
proba_photometric_aug=self.proba_photometric_aug,
)
# to tensor + normalize image
image = self.to_tensor_and_normalize(image)
return image, mask
def __len__(self) -> int:
if "VOC" in self.name:
return len(self.loader)
elif "ImageNet" in self.name:
return len(self.loader)
elif "COCO" in self.name:
return len(self.img_ids)
return len(self.list_images)
def _apply_center_crop(
self, image: Image.Image, mask: Union[Image.Image, np.ndarray, torch.Tensor]
) -> Tuple[torch.Tensor, torch.Tensor]:
img_t = self.center_crop_transforms(image)
# need to normalize image
img_t = self.normalize(img_t)
mask_gt = self.center_crop_transforms(mask).squeeze()
return img_t, mask_gt
def __getitem__(self, idx, get_mask_gt=True):
if "VOC" in self.name:
img, gt_labels = self.loader[idx]
if self.evaluation_type == "uod":
gt_labels, _ = get_voc_detection_gt(
gt_labels, remove_hards=False
)
elif self.evaluation_type == "saliency":
mask_gt = create_gt_masks_if_voc(gt_labels)
img_path = self.loader.images[idx]
elif "ImageNet" in self.name:
img, _ = self.loader[idx]
img_path = self.loader.imgs[idx][0]
# empty mask since no gt mask, only class label
zeros = np.zeros(np.array(img).shape[:2])
mask_gt = Image.fromarray(zeros)
elif "COCO" in self.name:
img_id = self.img_ids[idx]
path = self.cocoGt.loadImgs(img_id)[0]["file_name"]
img = Image.open(os.path.join(self.img_dir, path)).convert("RGB")
_ = self.cocoGt.loadAnns(self.cocoGt.getAnnIds(id))
img_path = self.img_ids[idx] # What matters most is the id for eval
# empty mask since no gt mask, only class label
zeros = np.zeros(np.array(img).shape[:2])
mask_gt = Image.fromarray(zeros)
# For all others
else:
img_path = self.list_images[idx]
with open(img_path, "rb") as f:
img = Image.open(f)
img = img.convert("RGB")
im_name = img_path.split("/")[-1]
mask_gt = Image.open(
os.path.join(self.gt_dir, im_name.replace(".jpg", ".png"))
).convert("L")
if self.for_eval:
img_t = self.full_img_transform(img)
img_init = self.no_norm_full_img_transform(img)
if self.evaluation_type == "saliency":
mask_gt = torch.tensor(np.array(mask_gt)).squeeze()
mask_gt = np.array(mask_gt)
mask_gt = mask_gt == 255
mask_gt = torch.tensor(mask_gt)
else:
if self.use_aug:
img_t, mask_gt = self._preprocess_data_aug(
image=img, mask=mask_gt, ignore_index=self.ignore_index
)
mask_gt = np.array(mask_gt)
mask_gt = mask_gt == 255
mask_gt = torch.tensor(mask_gt)
else:
# no data aug
img_t, mask_gt = self._apply_center_crop(image=img, mask=mask_gt)
gt_labels = self.center_crop_only_transforms(gt_labels).squeeze()
mask_gt = np.asarray(mask_gt, np.int64)
mask_gt = mask_gt == 1
mask_gt = torch.tensor(mask_gt)
img_init = unnormalize(img_t)
if not get_mask_gt:
mask_gt = None
if self.evaluation_type == "uod":
gt_labels = torch.tensor(gt_labels)
mask_gt = gt_labels
return img_t, img_init, mask_gt, img_path
def fullimg_mode(self):
self.val_full_image = True
def training_mode(self):
self.val_full_image = False |