Spaces:
Sleeping
Sleeping
File size: 5,314 Bytes
0f691e2 |
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 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 |
# -- coding: utf-8 --
import os.path
import random
import torchvision.transforms as transforms
import torch
from data.base_dataset import BaseDataset
from data.image_folder import make_dataset
from PIL import Image, ImageFilter
import numpy as np
import cv2
import math
from util import util
from scipy.io import loadmat
from PIL import Image
import PIL
class AlignedDataset(BaseDataset):
@staticmethod
def modify_commandline_options(parser, is_train):
return parser
def initialize(self, opt):
self.opt = opt
self.root = opt.dataroot
self.partpath = opt.partroot
self.dir_AB = os.path.join(opt.dataroot, opt.phase)
self.AB_paths = sorted(make_dataset(self.dir_AB))
self.is_real = opt.is_real
# assert(opt.resize_or_crop == 'resize_and_crop')
assert(opt.resize_or_crop == 'degradation')
def AddNoise(self,img): # noise
if random.random() > 0.9: #
return img
self.sigma = np.random.randint(1, 11)
img_tensor = torch.from_numpy(np.array(img)).float()
noise = torch.randn(img_tensor.size()).mul_(self.sigma/1.0)
noiseimg = torch.clamp(noise+img_tensor,0,255)
return Image.fromarray(np.uint8(noiseimg.numpy()))
def AddBlur(self,img): # gaussian blur or motion blur
if random.random() > 0.9: #
return img
img = np.array(img)
if random.random() > 0.35: ##gaussian blur
blursize = random.randint(1,17) * 2 + 1 ##3,5,7,9,11,13,15
blursigma = random.randint(3, 20)
img = cv2.GaussianBlur(img, (blursize,blursize), blursigma/10)
else: #motion blur
M = random.randint(1,32)
KName = './data/MotionBlurKernel/m_%02d.mat' % M
k = loadmat(KName)['kernel']
k = k.astype(np.float32)
k /= np.sum(k)
img = cv2.filter2D(img,-1,k)
return Image.fromarray(img)
def AddDownSample(self,img): # downsampling
if random.random() > 0.95: #
return img
sampler = random.randint(20, 100)*1.0
img = img.resize((int(self.opt.fineSize/sampler*10.0), int(self.opt.fineSize/sampler*10.0)), Image.BICUBIC)
return img
def AddJPEG(self,img): # JPEG compression
if random.random() > 0.6: #
return img
imQ = random.randint(40, 80)
img = np.array(img)
encode_param = [int(cv2.IMWRITE_JPEG_QUALITY),imQ] # (0,100),higher is better,default is 95
_, encA = cv2.imencode('.jpg',img,encode_param)
img = cv2.imdecode(encA,1)
return Image.fromarray(img)
def AddUpSample(self,img):
return img.resize((self.opt.fineSize, self.opt.fineSize), Image.BICUBIC)
def __getitem__(self, index): #
AB_path = self.AB_paths[index]
Imgs = Image.open(AB_path).convert('RGB')
# #
A = Imgs.resize((self.opt.fineSize, self.opt.fineSize))
A = transforms.ColorJitter(0.3, 0.3, 0.3, 0)(A)
C = A
A = self.AddUpSample(self.AddJPEG(self.AddNoise(self.AddDownSample(self.AddBlur(A)))))
tmps = AB_path.split('/')
ImgName = tmps[-1]
Part_locations = self.get_part_location(self.partpath, ImgName, 2)
A = transforms.ToTensor()(A) #
C = transforms.ToTensor()(C)
##
A = transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))(A) #
C = transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))(C) #
return {'A':A, 'C':C, 'A_paths': AB_path,'Part_locations': Part_locations}
def get_part_location(self, landmarkpath, imgname, downscale=1):
Landmarks = []
with open(os.path.join(landmarkpath,imgname+'.txt'),'r') as f:
for line in f:
tmp = [np.float(i) for i in line.split(' ') if i != '\n']
Landmarks.append(tmp)
Landmarks = np.array(Landmarks)/downscale # 512 * 512
Map_LE = list(np.hstack((range(17,22), range(36,42))))
Map_RE = list(np.hstack((range(22,27), range(42,48))))
Map_NO = list(range(29,36))
Map_MO = list(range(48,68))
#left eye
Mean_LE = np.mean(Landmarks[Map_LE],0)
L_LE = np.max((np.max(np.max(Landmarks[Map_LE],0) - np.min(Landmarks[Map_LE],0))/2,16))
Location_LE = np.hstack((Mean_LE - L_LE + 1, Mean_LE + L_LE)).astype(int)
#right eye
Mean_RE = np.mean(Landmarks[Map_RE],0)
L_RE = np.max((np.max(np.max(Landmarks[Map_RE],0) - np.min(Landmarks[Map_RE],0))/2,16))
Location_RE = np.hstack((Mean_RE - L_RE + 1, Mean_RE + L_RE)).astype(int)
#nose
Mean_NO = np.mean(Landmarks[Map_NO],0)
L_NO = np.max((np.max(np.max(Landmarks[Map_NO],0) - np.min(Landmarks[Map_NO],0))/2,16))
Location_NO = np.hstack((Mean_NO - L_NO + 1, Mean_NO + L_NO)).astype(int)
#mouth
Mean_MO = np.mean(Landmarks[Map_MO],0)
L_MO = np.max((np.max(np.max(Landmarks[Map_MO],0) - np.min(Landmarks[Map_MO],0))/2,16))
Location_MO = np.hstack((Mean_MO - L_MO + 1, Mean_MO + L_MO)).astype(int)
return Location_LE, Location_RE, Location_NO, Location_MO
def __len__(self): #
return len(self.AB_paths)
def name(self):
return 'AlignedDataset'
|