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import json
import glob
import random
from torch.utils.data import Dataset
from PIL import Image
from torchvision.transforms import transforms
import torch
import numpy as np
try:
from torchvision.transforms import InterpolationMode
BICUBIC = InterpolationMode.BICUBIC
except ImportError:
BICUBIC = Image.BICUBIC
# modify for transformation for vit
# modfify wider crop-person images
###### Base data loader ######
class DataSet(Dataset):
def __init__(
self,
ann_files,
augs,
img_size,
dataset,
):
self.dataset = dataset
self.ann_files = ann_files
self.augment = self.augs_function(augs, img_size)
self.transform = transforms.Compose(
[transforms.ToTensor(), transforms.Normalize(mean=[0, 0, 0], std=[1, 1, 1])]
# In this paper, we normalize the image data to [0, 1]
# You can also use the so called 'ImageNet' Normalization method
)
self.anns = []
self.load_anns()
print(self.augment)
# in wider dataset we use vit models
# so transformation has been changed
if self.dataset == "wider":
self.transform = transforms.Compose(
[
transforms.ToTensor(),
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
]
)
def augs_function(self, augs, img_size):
t = []
if "randomflip" in augs:
t.append(transforms.RandomHorizontalFlip())
if "ColorJitter" in augs:
t.append(
transforms.ColorJitter(
brightness=0.5, contrast=0.5, saturation=0.5, hue=0
)
)
if "resizedcrop" in augs:
t.append(transforms.RandomResizedCrop(img_size, scale=(0.7, 1.0)))
if "RandAugment" in augs:
t.append(RandAugment())
t.append(transforms.Resize((img_size, img_size)))
return transforms.Compose(t)
def load_anns(self):
self.anns = []
for ann_file in self.ann_files:
json_data = json.load(open(ann_file, "r"))
self.anns += json_data
def __len__(self):
return len(self.anns)
def __getitem__(self, idx):
idx = idx % len(self)
ann = self.anns[idx]
img = Image.open(ann["img_path"]).convert("RGB")
if self.dataset == "wider":
x, y, w, h = ann["bbox"]
img_area = img.crop([x, y, x + w, y + h])
img_area = self.augment(img_area)
img_area = self.transform(img_area)
message = {
"img_path": ann["img_path"],
"target": torch.Tensor(ann["target"]),
"img": img_area,
}
else: # voc and coco
img = self.augment(img)
img = self.transform(img)
message = {
"img_path": ann["img_path"],
"target": torch.Tensor(ann["target"]),
"img": img,
}
return message
# finally, if we use dataloader to get the data, we will get
# {
# "img_path": list, # length = batch_size
# "target": Tensor, # shape: batch_size * num_classes
# "img": Tensor, # shape: batch_size * 3 * 224 * 224
# }
def preprocess_scribble(img, img_size):
transform = transforms.Compose(
[
transforms.Resize(img_size, BICUBIC),
transforms.CenterCrop(img_size),
#_convert_image_to_rgb,
transforms.ToTensor(),
]
)
return transform(img)
class DataSetMaskSup(Dataset):
"""
Data loader with scribbles.
"""
def __init__(
self,
ann_files,
augs,
img_size,
dataset,
):
self.dataset = dataset
self.ann_files = ann_files
self.img_size = img_size
self.augment = self.augs_function(augs, img_size)
self.transform = transforms.Compose(
[transforms.ToTensor(), transforms.Normalize(mean=[0, 0, 0], std=[1, 1, 1])]
# In this paper, we normalize the image data to [0, 1]
# You can also use the so called 'ImageNet' Normalization method
)
self.anns = []
self.load_anns()
print(self.augment)
# scribbles
self._scribbles_folder = "./datasets/SCRIBBLES"
# Type of masks to use, this is hardcoded since we find that high masks
# work better in MSL. See paper for details.
# for low masks
# self._scribbles = sorted(glob.glob(self._scribbles_folder + "/*.png"))[
# :1000
# ]
# for high masks
self._scribbles = sorted(glob.glob(self._scribbles_folder + "/*.png"))[::-1][
:1000
]
# in wider dataset we use vit models
# so transformation has been changed
if self.dataset == "wider":
self.transform = transforms.Compose(
[
transforms.ToTensor(),
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
]
)
def augs_function(self, augs, img_size):
t = []
if "randomflip" in augs:
t.append(transforms.RandomHorizontalFlip())
if "ColorJitter" in augs:
t.append(
transforms.ColorJitter(
brightness=0.5, contrast=0.5, saturation=0.5, hue=0
)
)
if "resizedcrop" in augs:
t.append(transforms.RandomResizedCrop(img_size, scale=(0.7, 1.0)))
if "RandAugment" in augs:
t.append(RandAugment())
t.append(transforms.Resize((img_size, img_size)))
return transforms.Compose(t)
def load_anns(self):
self.anns = []
for ann_file in self.ann_files:
json_data = json.load(open(ann_file, "r"))
self.anns += json_data
def __len__(self):
return len(self.anns)
def __getitem__(self, idx):
idx = idx % len(self)
ann = self.anns[idx]
img = Image.open(ann["img_path"]).convert("RGB")
# get scribble
scribble_path = self._scribbles[
random.randint(0, 950)
]
scribble = Image.open(scribble_path).convert('P')
scribble = preprocess_scribble(scribble, self.img_size)
scribble_t = (scribble > 0).float() # threshold to [0,1]
inv_scribble = (torch.max(scribble_t) - scribble_t) # inverted scribble
if self.dataset == "wider":
x, y, w, h = ann["bbox"]
img_area = img.crop([x, y, x + w, y + h])
img_area = self.augment(img_area)
img_area = self.transform(img_area)
# masked image
masked_image = img_area * inv_scribble
message = {
"img_path": ann["img_path"],
"target": torch.Tensor(ann["target"]),
"img": img_area,
"masked_img": masked_image,
#"scribble": inv_scribble,
}
else: # voc and coco
img = self.augment(img)
img = self.transform(img)
# masked image
masked_image = img * inv_scribble
message = {
"img_path": ann["img_path"],
"target": torch.Tensor(ann["target"]),
"img": img,
"masked_img": masked_image,
#"scribble": inv_scribble,
}
return message
# finally, if we use dataloader to get the data, we will get
# {
# "img_path": list, # length = batch_size
# "target": Tensor, # shape: batch_size * num_classes
# "img": Tensor, # shape: batch_size * 3 * 224 * 224
# "masked_img": Tensor, # shape: batch_size * 3 * 224 * 224
# }
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