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import os
from glob import glob
import cv2
import h5py
import numpy as np
import torch
import torch.utils.data as data
from PIL import Image, ImageFilter
from torchvision.datasets import ImageNet
class ImageNet_blur(ImageNet):
def __getitem__(self, index):
"""
Args:
index (int): Index
Returns:
tuple: (sample, target) where target is class_index of the target class.
"""
path, target = self.samples[index]
sample = self.loader(path)
gauss_blur = ImageFilter.GaussianBlur(11)
median_blur = ImageFilter.MedianFilter(11)
blurred_img1 = sample.filter(gauss_blur)
blurred_img2 = sample.filter(median_blur)
blurred_img = Image.blend(blurred_img1, blurred_img2, 0.5)
if self.transform is not None:
sample = self.transform(sample)
blurred_img = self.transform(blurred_img)
if self.target_transform is not None:
target = self.target_transform(target)
return (sample, blurred_img), target
class Imagenet_Segmentation(data.Dataset):
CLASSES = 2
def __init__(self, path, transform=None, target_transform=None):
self.path = path
self.transform = transform
self.target_transform = target_transform
# self.h5py = h5py.File(path, 'r+')
self.h5py = None
tmp = h5py.File(path, "r")
self.data_length = len(tmp["/value/img"])
tmp.close()
del tmp
def __getitem__(self, index):
if self.h5py is None:
self.h5py = h5py.File(self.path, "r")
img = np.array(self.h5py[self.h5py["/value/img"][index, 0]]).transpose(
(2, 1, 0)
)
target = np.array(
self.h5py[self.h5py[self.h5py["/value/gt"][index, 0]][0, 0]]
).transpose((1, 0))
img = Image.fromarray(img).convert("RGB")
target = Image.fromarray(target)
if self.transform is not None:
img = self.transform(img)
if self.target_transform is not None:
target = np.array(self.target_transform(target)).astype("int32")
target = torch.from_numpy(target).long()
return img, target
def __len__(self):
# return len(self.h5py['/value/img'])
return self.data_length
class Imagenet_Segmentation_Blur(data.Dataset):
CLASSES = 2
def __init__(self, path, transform=None, target_transform=None):
self.path = path
self.transform = transform
self.target_transform = target_transform
# self.h5py = h5py.File(path, 'r+')
self.h5py = None
tmp = h5py.File(path, "r")
self.data_length = len(tmp["/value/img"])
tmp.close()
del tmp
def __getitem__(self, index):
if self.h5py is None:
self.h5py = h5py.File(self.path, "r")
img = np.array(self.h5py[self.h5py["/value/img"][index, 0]]).transpose(
(2, 1, 0)
)
target = np.array(
self.h5py[self.h5py[self.h5py["/value/gt"][index, 0]][0, 0]]
).transpose((1, 0))
img = Image.fromarray(img).convert("RGB")
target = Image.fromarray(target)
gauss_blur = ImageFilter.GaussianBlur(11)
median_blur = ImageFilter.MedianFilter(11)
blurred_img1 = img.filter(gauss_blur)
blurred_img2 = img.filter(median_blur)
blurred_img = Image.blend(blurred_img1, blurred_img2, 0.5)
# blurred_img1 = cv2.GaussianBlur(img, (11, 11), 5)
# blurred_img2 = np.float32(cv2.medianBlur(img, 11))
# blurred_img = (blurred_img1 + blurred_img2) / 2
if self.transform is not None:
img = self.transform(img)
blurred_img = self.transform(blurred_img)
if self.target_transform is not None:
target = np.array(self.target_transform(target)).astype("int32")
target = torch.from_numpy(target).long()
return (img, blurred_img), target
def __len__(self):
# return len(self.h5py['/value/img'])
return self.data_length
class Imagenet_Segmentation_eval_dir(data.Dataset):
CLASSES = 2
def __init__(self, path, eval_path, transform=None, target_transform=None):
self.transform = transform
self.target_transform = target_transform
self.h5py = h5py.File(path, "r+")
# 500 each file
self.results = glob(os.path.join(eval_path, "*.npy"))
def __getitem__(self, index):
img = np.array(self.h5py[self.h5py["/value/img"][index, 0]]).transpose(
(2, 1, 0)
)
target = np.array(
self.h5py[self.h5py[self.h5py["/value/gt"][index, 0]][0, 0]]
).transpose((1, 0))
res = np.load(self.results[index])
img = Image.fromarray(img).convert("RGB")
target = Image.fromarray(target)
if self.transform is not None:
img = self.transform(img)
if self.target_transform is not None:
target = np.array(self.target_transform(target)).astype("int32")
target = torch.from_numpy(target).long()
return img, target
def __len__(self):
return len(self.h5py["/value/img"])
if __name__ == "__main__":
import scipy.io as sio
import torchvision.transforms as transforms
from imageio import imsave
from tqdm import tqdm
# meta = sio.loadmat('/home/shirgur/ext/Data/Datasets/temp/ILSVRC2012_devkit_t12/data/meta.mat', squeeze_me=True)['synsets']
# Data
normalize = transforms.Normalize(
mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
)
test_img_trans = transforms.Compose(
[
transforms.Resize((224, 224)),
transforms.ToTensor(),
normalize,
]
)
test_lbl_trans = transforms.Compose(
[
transforms.Resize((224, 224), Image.NEAREST),
]
)
ds = Imagenet_Segmentation(
"/home/shirgur/ext/Data/Datasets/imagenet-seg/other/gtsegs_ijcv.mat",
transform=test_img_trans,
target_transform=test_lbl_trans,
)
for i, (img, tgt) in enumerate(tqdm(ds)):
tgt = (tgt.numpy() * 255).astype(np.uint8)
imsave("/home/shirgur/ext/Code/C2S/run/imagenet/gt/{}.png".format(i), tgt)
print("here")
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