Spaces:
Running
on
Zero
Running
on
Zero
File size: 3,001 Bytes
77771e4 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 |
import os.path
from data.base_dataset import BaseDataset, get_params, get_transform
from data.image_folder import make_dataset
from PIL import Image, ImageEnhance
import random
import numpy as np
import torch
import torch.nn.functional as F
import cv2
class SingleCoDataset(BaseDataset):
@staticmethod
def modify_commandline_options(parser, is_train):
return parser
def __init__(self, opt):
self.opt = opt
self.root = opt.dataroot
self.dir_A = os.path.join(opt.dataroot, opt.phase, opt.folder, 'imgs')
self.A_paths = make_dataset(self.dir_A)
self.A_paths = sorted(self.A_paths)
self.A_size = len(self.A_paths)
# self.transform = get_transform(opt)
def __getitem__(self, index):
A_path = self.A_paths[index]
A_img = Image.open(A_path).convert('RGB')
# enhancer = ImageEnhance.Brightness(A_img)
# A_img = enhancer.enhance(1.5)
if os.path.exists(A_path.replace('imgs','line')[:-4]+'.jpg'):
# L_img = Image.open(A_path.replace('imgs','line')[:-4]+'.png')
L_img = cv2.imread(A_path.replace('imgs','line')[:-4]+'.jpg')
kernel = np.ones((3,3), np.uint8)
L_img = cv2.erode(L_img, kernel, iterations=1)
L_img = Image.fromarray(L_img)
else:
L_img = A_img
if A_img.size!=L_img.size:
# L_img = L_img.resize(A_img.size, Image.ANTIALIAS)
A_img = A_img.resize(L_img.size, Image.ANTIALIAS)
if A_img.size[1]>2500:
A_img = A_img.resize((A_img.size[0]//2, A_img.size[1]//2), Image.ANTIALIAS)
ow, oh = A_img.size
transform_params = get_params(self.opt, A_img.size)
A_transform = get_transform(self.opt, transform_params, grayscale=False)
L_transform = get_transform(self.opt, transform_params, grayscale=True)
A = A_transform(A_img)
L = L_transform(L_img)
# base = 2**9
# h = int((oh+base-1) // base * base)
# w = int((ow+base-1) // base * base)
# A = F.pad(A.unsqueeze(0), (0,w-ow, 0,h-oh), 'replicate').squeeze(0)
# L = F.pad(L.unsqueeze(0), (0,w-ow, 0,h-oh), 'replicate').squeeze(0)
tmp = A[0, ...] * 0.299 + A[1, ...] * 0.587 + A[2, ...] * 0.114
Ai = tmp.unsqueeze(0)
return {'A': A, 'Ai': Ai, 'L': L,
'B': torch.zeros(1), 'Bs': torch.zeros(1), 'Bi': torch.zeros(1), 'Bl': torch.zeros(1),
'A_paths': A_path, 'h': oh, 'w': ow}
def __len__(self):
return self.A_size
def name(self):
return 'SingleCoDataset'
def M_transform(feat, opt, params=None):
outfeat = feat.copy()
oh,ow = feat.shape[1:]
x1, y1 = params['crop_pos']
tw = th = opt.crop_size
if (ow > tw or oh > th):
outfeat = outfeat[:,y1:y1+th,x1:x1+tw]
if params['flip']:
outfeat = np.flip(outfeat, 2)#outfeat[:,:,::-1]
return torch.from_numpy(outfeat.copy()).float()*2-1.0 |