text stringlengths 1 93.6k |
|---|
sa = httpd.socket.getsockname()
|
print "Serving HTTP Proxy on", sa[0], "port", sa[1], "..."
|
httpd.serve_forever()
|
if __name__ == '__main__':
|
test()
|
# <FILESEP>
|
import glob
|
import random
|
import os
|
import numpy as np
|
from PIL import Image
|
from torch.utils.data import Dataset
|
import torchvision.transforms as transforms
|
from torch.utils.data import DataLoader
|
import torch
|
def load_img_path(dataset_path, is_train=True, model=None):
|
A_path = []
|
B_path = []
|
L_path = []
|
is_train_val = True
|
is_train = is_train_val
|
f1 = open(os.path.join(dataset_path, "image.txt"), 'r')
|
f2 = open(os.path.join(dataset_path, "image2.txt"), 'r')
|
if model == "SC":
|
for line in f1.readlines():
|
A_path.append(line.strip().split(" ")[0])
|
f1.close()
|
for line in f2.readlines():
|
B_path.append(line.strip().split(" ")[0])
|
f2.close()
|
else:
|
for line in f1.readlines():
|
A_path.append(line.strip())
|
f1.close()
|
for line in f2.readlines():
|
B_path.append(line.strip())
|
f2.close()
|
if is_train:
|
if model == "SC":
|
f1 = open(os.path.join(dataset_path, "image.txt"), 'r')
|
for line in f1.readlines():
|
L_path.append(int(line.strip().split(" ")[-1])) # format image_path label--> xx/xx.png 0 or xx/xx.png 1
|
f1.close()
|
else:
|
f3 = open(os.path.join(dataset_path, "label.txt"), 'r')
|
for line in f3.readlines():
|
L_path.append(line.strip())
|
f3.close()
|
return A_path, B_path, L_path
|
class ImageDataset(Dataset):
|
def __init__(self, root, transforms_=None, transforms_L=None, is_train=True, model=None):
|
self.transform = transforms.Compose(transforms_)
|
self.transforml = transforms.Compose(transforms_L)
|
self.transform_l = self.transform
|
self.model = model
|
# self.files = sorted(glob.glob(root + '/*.*'))
|
self.list_A, self.list_B, self.list_L = load_img_path(dataset_path=root, is_train=is_train, model=model)
|
# print("---------------------------------")
|
# print(len(self.list_A))
|
# print(len(self.list_B))
|
# print(len(self.list_L))
|
# print("---------------------------------")
|
def __getitem__(self, index):
|
# name = int(self.files[index].split('/')[-1].split('.')[0])
|
# img = Image.open(self.files[index]).convert('RGB')
|
name = os.path.split(self.list_A[index])[-1]
|
img1 = Image.open(self.list_A[index]).convert('RGB')
|
# print(index)
|
img2 = Image.open(self.list_B[index]).convert('RGB')
|
# Image.fromarray(np.uint8()).save(os.path.join("./AugMix", uname[0].split(".")[0]+"_d1.jpg"))
|
# img2.save(os.path.join("./AugMix", "%d_d1.jpg" % index))
|
if self.model == "SC":
|
# self.transform_l = transforms.ToTensor()
|
# self.transform_l = transforms.Compose([transforms.ToTensor()])
|
labl = self.list_L[index]
|
return self.transform(img1), self.transform(img2),np.array(img1), np.array(img2), labl, name
|
else:
|
labl = Image.open(self.list_L[index]).convert('P')
|
return self.transform(img1), self.transform(img2), np.array(img1), np.array(img2), self.transforml(labl), name
|
def __len__(self):
|
return len(self.list_L) # len(self.files1)
|
class ImageDataset_test(Dataset):
|
def __init__(self, root, transforms_=None, transforms_L=None, is_train=False, model=None):
|
self.transform = transforms.Compose(transforms_)
|
self.transforml = transforms.Compose(transforms_L)
|
self.model = model
|
self.list_A, self.list_B, self.list_L = load_img_path(dataset_path=root, is_train=is_train, model=model)
|
def __getitem__(self, index):
|
name = os.path.split(self.list_A[index])[-1]
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.