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import os
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import cv2
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import numpy as np
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import tkinter as tk
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from tkinter import filedialog
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from read_data import *
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from PIL import Image
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class ModelObj: # 网络对象
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def __init__(self, ObjID, ObjType, ObjLable, ParaString, ObjX, ObjY):
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self.ObjID = ObjID # 图元号
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self.ObjType = ObjType # 图元类别
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self.ObjLable = ObjLable # 对象标签
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self.ParaString = ParaString # 参数字符串
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self.ObjX = ObjX # 对象位置x坐标
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self.ObjY = ObjY # 对象位置y坐标
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def image_to_array(path, height, width):
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img = Image.open(path).convert("L") # 转换为灰度模式
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img = img.resize((height, width)) # 将图片缩放为height*width的固定大小
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data = np.array(img) # 将图片转换为数组格式
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data = data / 255.0 # 将数组中的数值归一化，除以255
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return data
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class Data_Class(ModelObj): # 数据集网络对象
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def __init__(self, ObjID, ObjType, ObjLable, ParaString, ObjX, ObjY):
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super().__init__(ObjID, ObjType, ObjLable, ParaString, ObjX, ObjY)
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self.LoadData = self.load_data # 基本操作函数
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self.SetDataPara = self.SetLoadData # 参数设置函数
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def load_data(self, DataPara): # 定义加载数据集load_data()
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global SubFolders
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listimages = [] # 存储图片的列表
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list_path = []
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# read_folders方法读取文件夹中子文件夹中数据集
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SubFolders, train = read_folders(DataPara["train_imgPath"])
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list_path.append(train)
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_, path_list = read_folders(DataPara["test_imgPath"])
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list_path.append(path_list)
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for data_path in list_path:
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images = [] # 存储图片的列表
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for path in data_path:
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img = image_to_array(path, DataPara["img_width"],
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DataPara["img_height"]) # 读取图片数据
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img = img.T # 转置，图像的行和列将互换位置。
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images.append(img) # 将图片数组添加到训练集图片列表中
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listimages.append(np.array(images)) # 返回转换后的数组
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return listimages[0], listimages[1]
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def SetLoadData(self):# 定义加载数据集的参数SetLoadData()
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# 设置数据集路径信息
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# 训练集文件夹的位置
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train_imgPath = input("请输入训练集文件夹的位置：") # 'data_classification/train/'
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# 测试集文件夹的位置
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test_imgPath = input("请输入测试集文件夹的位置：") # 'data_classification/test/'
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img_width = int(input("请输入图片宽度：")) # 48
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img_height = int(input("请输入图片高度：")) # 48
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# 设置每批次读入图片的数量
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batch_size = int(input("请输入每批次读入图片的数量：")) # 批次大小 32
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# 返回DataPara参数，这里用一个字典来存储
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DataPara = {"train_imgPath": train_imgPath,
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"test_imgPath": test_imgPath,
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"img_width": img_width,
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"img_height": img_height,
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"batch_size": batch_size}
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return DataPara
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if __name__ == '__main__':
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DataSet = Data_Class("DataSet1", 1, "数据集1", [], 120, 330)
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# setload_data()函数，获取加载数据集的参数
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DataPara = DataSet.SetDataPara()
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train_images, test_images = DataSet.LoadData(DataPara)
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