第一种（CIFAR）

制作过程参考：参考（点←）里面提供了data、但还有三个参数。只能自己慢慢加了。

readCifar.py

这里主要是拿制作的跟原样本格式比对，最终显示

import pickle
import numpy as np
import chardetdef unpickle(file):import picklewith open(file, 'rb') as fo:dict = pickle.load(fo, encoding='latin-1')return dictcc1 = unpickle("E:/pythonwork/makemycifar/img2array.bin")
print(cc1)
cc2 = unpickle("E:/pythonwork/makemycifar/data_batch_0")
print(cc2)

显示结果，第一个{}是最终完善后的，看来可行，第二个是原格式：

可以看到，数据集由字典dic构成，格式为
data（数据，用矩阵表示）、labels（类标签）、filenames（图片名称）、batch_label(整个模块标签)

cc1为我改写的样本，cc2为cifar数据集的格式。经过改写，下方原样本格式是基本一样，似乎可行。

dump.py

这里是获得四个标签

from PIL import Image
import numpy as np
import pickle, glob, os
# 加载标签
def seplabel(fname):filestr = fname.split(".")[0]label = int(filestr.split("_")[0])return labelarr = [[]]
n = 1for infile in glob.glob("E:/pythonwork/makemycifar/zuichutup/*.png"):file, ext = os.path.splitext(infile)Img = Image.open(infile)print(Img.mode, file)  # 图片尺寸和文件名（用于调试过程中定位错误）if Img.mode != 'RGB':Img = Img.convert('RGB')width = Img.size[0]height = Img.size[1]# print('{} imagesize is:{} X {}'.format(n, width, height))n += 1  # zzzif width > height:tmp = int((width - height) / 2)Img = Img.crop([width - height - tmp, 0, width - tmp, height])if height > width:tmp = int((height - width) / 2)Img = Img.crop([0, height - width - tmp, width, height - tmp])size = 32, 32Img.thumbnail(size, Image.ANTIALIAS)r, g, b = Img.split()r_array = np.array(r).reshape([1024])g_array = np.array(g).reshape([1024])b_array = np.array(b).reshape([1024])merge_array = np.concatenate((r_array, g_array, b_array))if arr == [[]]:arr = [merge_array]continue  # 拼接arr = np.concatenate((arr, [merge_array]), axis=0)  # 打乱顺序# 定义标签和文件名
labels = []
filenames = []imglist = glob.glob(r"E:/pythonwork/makemycifar/zuichutup/*.png")  # 将图片存入列表
print(imglist[0])num = len(imglist)  # 获取路径下图片的长度 11
for i in range(0, num):imglist2 = os.path.basename(imglist[i])  # 获取路径下图片名称labels.append(seplabel(imglist2))print("aaa")filenames.append(imglist2)print(imglist2)
# 加入字典
dic = {'data': arr, 'labels': labels, 'filenames': filenames, 'batch_label': 'testing batch 1 of 1'}f = open('E:/pythonwork/makemycifar/img2array.bin', 'wb')
pickle.dump(dic, f)

一步步测试，把数据一点点抠出来，这是拿四个里的文件名filenames，其他雷同，然后装入list，最后放入dic字典中。

第二种（CIFAR）

也是cifar格式的制作过程参考：参考（点←）问题主要是路径。


这里放的原图，运行后自动处理成小图了

这里放原格式，最后就是会直接修改他的原格式

这里放原图

这里放原格式。
然后是代码：

# -*- coding: utf-8 -*-
import numpy as np
from numpy import *
from PIL import Image
# import operator
from os import listdir
# import sys
import pickle
import time# import random
data = {}
list1 = []
list2 = []
list3 = []
list4 = []def seplabel(fname):filestr = fname.split(".")[0]label = int(filestr.split("_")[0])return labeldef img_tra():for k in range(0, num):currentpath = folder_init + "/" + imglist[k]im = Image.open(currentpath)# width=im.size[0]# height=im.size[1]x_s = 32y_s = 32out = im.resize((x_s, y_s), Image.ANTIALIAS)print('sss')print(str(imglist[k]))print('sss')out.save(folder_changed + "/" + str(imglist[k]))def addWord(theIndex, word, adder):theIndex.setdefault(word, []).append(adder)def mkcf():global dataglobal list1global list2global list3global list4for k in range(0, num):currentpath = folder_changed + "/" + imglist[k]im = Image.open(currentpath)with open(binpath, 'a') as f:for i in range(0, 32):for j in range(0, 32):cl = im.getpixel((i, j))# print(imglist[k])# print(type(cl[0]))# with open(binpath, 'a') as f:# print(str(cl[0]))list1.append(cl[0])# print(list1)for i in range(0, 32):for j in range(0, 32):cl = im.getpixel((i, j))# with open(binpath, 'a') as f:# mid=str(cl[1])# f.write(mid)list1.append(cl[1])for i in range(0, 32):for j in range(0, 32):cl = im.getpixel((i, j))# with open(binpath, 'a') as f:# mid=str(cl[2])# f.write(mid)list1.append(cl[2])list2.append(list1)list1 = []#        arr2=np.array(list2)# print(arr4)f.close()print("image" + str(k + 1) + "saved.")lfilenames.append(imglist[k].encode('utf-8'))arr22 = np.array(list2, dtype=np.uint8)# print(list2)print(lj1.shape)#    while True:#        time.sleep(5)#    print(arr22.shape)arr2 = np.concatenate((lj1, arr22))data['batch_label'.encode('utf-8')] = 'training batch 5 of 5'.encode('utf-8')# addWord(cifar10,"batch_label".encode('utf-8'),'training batch 5 of 5'.encode('utf-8'))data.setdefault('labels'.encode('utf-8'), llabel)data.setdefault('data'.encode('utf-8'), arr2)# addWord(cifar10,'labels'.encode('utf-8'),label)# addWord(cifar10,'data'.encode('utf-8'),arr2)data.setdefault('filenames'.encode('utf-8'), lfilenames)# addWord(cifar10,'filenames'.encode('utf-8'),list3)output = open(binpath, 'wb')pickle.dump(data, output)output.close()def unpickle(file):import picklewith open(file, 'rb') as fo:dict = pickle.load(fo, encoding='iso-8859-1')return dict# cc = unpickle("F:/tiaotong/lijing/data_batch_5")
cc = unpickle("E:/pythonwork/finaldata/yuanshi/data_batch_5")
# print(cc)
# print(type(cc))
total = 10000
lj1 = cc['data']def delpar():global lj1del cc['labels'][i - n]del cc['filenames'][i - n]lj1 = np.delete(lj1, i - n, 0)lj = []
n = 0
for i in range(0, total):if cc['labels'][i - n] == 9:delpar()n = n + 1
llabel = cc['labels']
lfilenames = cc['filenames']# folder = "F:/tiaotong/55"
# folder_ad = "F:/tiaotong/data_batch_5"
folder_init = "E:/pythonwork/finaldata/init"
folder_changed = "E:/pythonwork/finaldata/data_batch_5"
print("****")
print(folder_changed)
imglist = listdir(folder_changed)
print("****")
print(imglist)
print("****")
num = len(imglist)
print("****")
print(num)
print("****")
img_tra()  #这个方法目录下的是把你的图片处理了再保存，所以直接把图片放到他的目录下就可以了
# label=[]
for i in range(0, num):lj = seplabel(imglist[i])llabel.append(lj)# print(label)# binpath = "F:/tiaotong/get/data_batch_5"
binpath = "E:/pythonwork/finaldata/get/data_batch_5"mkcf()

看更新时间，看来成功了。代码大致意思知道，具体没看，就改了路径和一些bug跑了下。

就是不知道为什么多个’b’

第三种（txt格式）

#!/usr/bin/env python
# coding: utf-8import os, random, shutil
from PIL import Image
import numpy as np
from torchvision import datasets, transforms
import torchvision
import torch# 创建Map，这个Map的Key是图片的路径，而他的Value就是Label
dataMap = {}
base_path = os.getcwd()
# phonepicture目录下有若干个，表示类别
categories = os.listdir(r'E:/pythonwork/phonepicture')print(categories)
rawdata_path = os.path.join(base_path, r"E:/pythonwork/phonepicture")
print(rawdata_path)
for c in categories:# a_image_folder里面存的是当前文件夹的路径，如果再加上图片名就是该图片的绝对路径了，而这正是我们想要的。a_image_folder = os.path.join(rawdata_path, c)# 某一类别的图片名字(不是绝对路径)都在image_files里面了，而c就是他的类别名image_files = os.listdir(a_image_folder)for image in image_files:dataMap[a_image_folder + "\\" + image] = c# 将其存储为Txt文件
with open(r"E:/pythonwork/phonetype.txt", 'w') as f:for k, v in dataMap.items():f.write(k + ' ' + v + '\n')# 读取Txt文件
with open(r"E:/pythonwork/phonetype.txt", 'r') as f:theList = f.readlines()
print(theList[1].split()[0])
print(theList[1].split()[1])class MyDataset(torch.utils.data.Dataset):  # 创建自己的类：MyDataset,这个类是继承的torch.utils.data.Datasetdef __init__(self, root, transform=None, target_transform=None):super(MyDataset, self).__init__()# 按照传入的路径和txt文本参数，打开这个文本，并读取内容fh = open(root, 'r')# 创建Listself.dataList = fh.readlines()self.transform = transformself.target_transform = target_transformdef __getitem__(self, index):# 这个Index应该就是你给的List的len，他都有了嘛，所以他可以随机取或者怎么的# 但是这里的getitem真的就是只取了一个!item,这里就是 一行 （绝对路径加Label）path_label = self.dataList[index]path, label = path_label.split()[0], path_label.split()[1]#         print(path_label)#         print(path)#         print(label)img = Image.open(path)# img.show()img = transforms.ToTensor()(img)return img, labeldef __len__(self):  # 这个函数也必须要写，它返回的是数据集的长度，也就是多少张图片，要和loader的长度作区分return len(self.dataList)myData = MyDataset(r"E:/pythonwork/phonetype.txt")
# 注意Dataset和Dataloader的区别
train_dataloader = torch.utils.data.DataLoader(myData, batch_size=1, shuffle=True)
image, label = iter(train_dataloader).next()
torchvision.transforms.ToPILImage()(image.squeeze(0)).show()
print(label)