机器视觉识别缺陷、划痕、缺角缺陷（3.28，准确率90-96）

__author__ = 'Administrator'
#导入模块
import tensorflow as tf
import numpy as np
import os
import glob
from sklearn.utils import shuffle
import cv2
import pandas as pd
import matplotlib.pyplot as plt
#读取数据集
def load_train(train_path,img_size,classes):
    images=[]
    labels=[]
    img_names=[]
    cls=[]
    print("读取训练图片...")
    for fields in classes:
        index=classes.index(fields)
        print("Now going to read {} files (Index:{})".format(fields,index))
        path=os.path.join(train_path,fields,'*g')    #os.path.join()函数：连接两个或更多的路径名组件
                                                     #1.如果各组件名首字母不包含’/’，则函数会自动加上
                                                     #2.如果有一个组件是一个绝对路径，则在它之前的所有组件均会被舍弃
                                                     #3.如果最后一个组件为空，则生成的路径以一个’/’分隔符结尾
        files=glob.glob(path)
        for fl in files:
            image=cv2.imread(fl)
            image=cv2.resize(image,(img_size,img_size),0,0,cv2.INTER_LINEAR)
            image=image.astype(np.float32)
            image=np.multiply(image,1.0/255.0)
            images.append(image)
            label=np.zeros(len(classes))
            label[index]=1.0
            labels.append(label)
            flbase=os.path.basename(fl)
            img_names.append(flbase)
            cls.append(fields)
    images=np.array(images)
    labels=np.array(labels)
    img_names=np.array(img_names)
    cls=np.array(cls)
    return images,labels,img_names,cls
class DataSet(object):
    def __init__(self,images,labels,img_names,cls):
        self._num_examples = images.shape[0]
        self._images=images
        self._labels=labels
        self._img_names=img_names
        self._cls=cls
        self._epochs_done=0
        self._index_in_epoch=0
    def images(self):
        return self._images
    def labels(self):
        return self._labels
    def img_names(self):
        return self._img_names
    def cls(self):
        return self._cls
    d