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Dataset.py
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import cv2,os
import numpy as np
from torch.utils.data import Dataset
import torch
# 1. Single Input
class SingleDataset(Dataset):
def __init__(self, img_list, class_to_int, transforms = None):
super().__init__()
self.img_list = img_list # 이미지 경로 리스트
self.class_to_int = class_to_int # class_to_int = {'normal':0,'flatfeet':1}
self.transforms = transforms # 이미지 전처리를 위한 torchvision.transform
def getImg(self,x):
x = cv2.imread(x, cv2.IMREAD_COLOR)
x = cv2.cvtColor(x, cv2.COLOR_BGR2RGB).astype(np.float32)
x /= 255.0
return x
def __getitem__(self, index):
img_path = self.img_list[index]
#Reading image
image = self.getImg(img_path)
#Retriving class label
label = img_path.split("/")[-2]
label = self.class_to_int[label]
#Applying transforms on image
if self.transforms:
image = self.transforms(image)
return image,label
def __len__(self):
return len(self.img_list)
# 2. Dual INPUT
class DualDataset(Dataset):
def __init__(self, imgL_list, imgR_list, class_to_int, transforms = None):
super().__init__()
self.imgL_list = imgL_list # 이미지 경로 리스트
self.imgR_list = imgR_list
self.class_to_int = class_to_int # class_to_int = {'normal':0,'flatfeet':1}
self.transforms = transforms # 이미지 전처리를 위한 torchvision.transform
def getImg(self, x):
x = cv2.imread(x, cv2.IMREAD_COLOR)
x = cv2.cvtColor(x, cv2.COLOR_BGR2RGB).astype(np.float32)
x /= 255.0
return x
def __getitem__(self, index):
img = None
imgL_path = self.imgL_list[index]
imgR_path = self.imgR_list[index]
#Reading image
imageL = self.getImg(imgL_path)
imageR = self.getImg(imgR_path)
#Retriving class label
label = imgL_path.split("/")[-3]
label = self.class_to_int[label]
#Applying transforms on image
if self.transforms:
imageL = self.transforms(imageL)
imageR = self.transforms(imageR)
return imageL, imageR, label
def __len__(self):
return len(self.imgL_list)
# 3. Triple input
class TripleDataset(Dataset):
def __init__(self, imgL_list, imgR_list, imgF_list, class_to_int, transforms = None):
super().__init__()
self.imgL_list = imgL_list # 이미지 경로 리스트
self.imgR_list = imgR_list
self.imgF_list = imgF_list
self.class_to_int = class_to_int # class_to_int = {'normal':0,'flatfeet':1}
self.transforms = transforms # 이미지 전처리를 위한 torchvision.transform
def getImg(self, x):
x = cv2.imread(x, cv2.IMREAD_COLOR)
x = cv2.cvtColor(x, cv2.COLOR_BGR2RGB).astype(np.float32)
x /= 255.0
return x
def __getitem__(self, index):
img = None
imgL_path = self.imgL_list[index]
imgR_path = self.imgR_list[index]
imgF_path = self.imgF_list[index]
#Reading image
imageL = self.getImg(imgL_path)
imageR = self.getImg(imgR_path)
imageF = self.getImg(imgF_path)
#Retriving class label
label = imgL_path.split("/")[-3]
label = self.class_to_int[label]
#Applying transforms on image
if self.transforms:
imageL = self.transforms(imageL)
imageR = self.transforms(imageR)
imageF = self.transforms(imageF)
return imageL, imageR, imageF, label
def __len__(self):
return len(self.imgL_list)
# + channel 6개
class FeetDatasetC6(Dataset):
def __init__(self, imgL_list, imgR_list, class_to_int, transforms = None):
super().__init__()
self.imgL_list = imgL_list # 이미지 경로 리스트
self.imgR_list = imgR_list
self.class_to_int = class_to_int # class_to_int = {'normal':0,'flatfeet':1}
self.transforms = transforms # 이미지 전처리를 위한 torchvision.transform
def getImg(self,x,is_flip):
x = cv2.imread(x, cv2.IMREAD_COLOR)
if is_flip:
x = cv2.flip(x,1)
x = cv2.cvtColor(x, cv2.COLOR_BGR2RGB).astype(np.float32)
x /= 255.0
return x
def __getitem__(self, index):
img = None
imgL_path = self.imgL_list[index]
imgR_path = self.imgR_list[index]
#Reading image
imageL = self.getImg(imgL_path, False)
imageR = self.getImg(imgR_path, True)
#Retriving class label
label = imgL_path.split("/")[-3]
label = self.class_to_int[label]
#Applying transforms on image
if self.transforms:
imageL = self.transforms(imageL)
imageR = self.transforms(imageR)
#print(imageL.size())
img = torch.cat([imageL,imageR],0)
return img,label
def __len__(self):
return len(self.imgL_list)
def getDataset(datasetN, _path, _transform):
## 1. path 저장
labels = {'normal':0, 'flatfeet':1}
if datasetN == 'SingleDataset':
imgCs =[]
for _class in ['normal/','flatfeet/']:
for img in os.listdir(_path + _class):
imgCs.append(_path + _class+img)
return SingleDataset(imgCs, labels, _transform)
imgLs, imgRs, imgFs = [],[],[]
for _class in ['normal','flatfeet']:
for img in os.listdir(_path + _class+'/L'):
imgLs.append(_path + _class+'/L' + "/" + img)
imgRs.append(_path + _class+'/R' + "/" + img)
imgFs.append(_path + _class+'/F' + "/" + img)
## 2. dataset에 맞춰 return
if datasetN=='FeetDatasetC6':
return FeetDatasetC6(imgLs, imgRs, labels, _transform)
elif datasetN=='DualDataset':
return DualDataset(imgLs, imgRs, labels, _transform)
elif datasetN=='TripleDataset':
return TripleDataset(imgLs,imgRs,imgFs, labels, _transform)