Files
Stomach_Cancer_Pytorch/Load_process/Loading_Tools.py

90 lines
2.7 KiB
Python

import os
import glob
class Load_Data_Prepare:
def __init__(self) -> None:
self.__Label_List = []
self.__Data_List = []
self.__Contect_Dictionary = {}
self.__Final_Dict_data = {}
self.__PreSave_Data_Root = [] # 所有要讀取資料所在的位置
self.__Data_Content = []
pass
def Set_Data_Content(self, Content, Length):
tmp = []
for i in range(Length):
tmp.append(Content)
self.__Data_Content = tmp
def Set_Label_List(self, Label_List): # 為讀取檔案準備label list
self.__Label_List = Label_List
pass
def Set_Data_List(self, Data_List):
self.__Data_List = Data_List
pass
def Set_Data_Dictionary(self, Label : list, Content : list, Total_Label_Size : int):
'''將資料合併成1個Dict'''
for i in range(Total_Label_Size):
temp = {Label[i] : Content[i]}
self.__Contect_Dictionary.update(temp)
pass
def Set_Final_Dict_Data(self, Name : list, Label_Root : list, Label_LabelEncoding : list, Label_Len : int):
'''
Name : 讀取出來的Data Root的名字
Label_Root: 所有影像資料的路徑
Label_LabelEncoding: LabelEncoding後的資料
Label_Len: Label的大小
'''
for i in range(Label_Len):
temp = {Name[i] + "_Data_Root" : Label_Root[Name[i]]}
self.__Final_Dict_data.update(temp)
for i in range(Label_Len):
temp = {Name[i] + "_Data_LabelEncoding" : Label_LabelEncoding[i]}
self.__Final_Dict_data.update(temp)
def Set_PreSave_Data_Root(self, PreSave_Roots : list):
for Root in PreSave_Roots:
self.__PreSave_Data_Root.append(Root)
def Get_Label_List(self):
'''
將private的資料讀取出來
現在要放入需要的Label 需要先Set Label
'''
return self.__Label_List
def Get_Data_List(self):
return self.__Data_List
def Get_Data_Dict(self):
return self.__Contect_Dictionary
def Get_Final_Data_Dict(self):
return self.__Final_Dict_data
def Get_PreSave_Data_Root(self):
return self.__PreSave_Data_Root
def Get_Data_Content(self):
return self.__Data_Content
class Load_Data_Tools():
def __init__(self) -> None:
pass
def get_data_root(self, root, data_dict, classify_label, judge = True) -> dict :
'''取得資料路徑'''
for label in classify_label:
if judge:
path = os.path.join(root, label, "*")
else:
path = os.path.join(root, "*")
path = glob.glob(path)
data_dict[label] = path
return data_dict