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jblitzar
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github-python

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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]