生成pdf有的内容显示不出来_为什么ug程序生成导轨不显示

import os 
import tensorflow as tf 
from PIL import Image  

#图片路径
cwd = 'F:\\flowersdata\\trainimages\\'
#文件路径
filepath = 'F:\\flowersdata\\tfrecord\\'
#存放图片个数
bestnum = 1000
#第几个图片
num = 0
#第几个TFRecord文件
recordfilenum = 0
#类别
classes=['daisy',
         'dandelion',
         'roses',
         'sunflowers',
         'tulips']
#tfrecords格式文件名
ftrecordfilename = ("traindata.tfrecords-%.3d" % recordfilenum)
writer= tf.python_io.TFRecordWriter(filepath+ftrecordfilename)
#类别和路径
for index,name in enumerate(classes):
    print(index)
    print(name)
    class_path=cwd+name+'\\'
    for img_name in os.listdir(class_path): 
        num=num+1
        if num>bestnum:
          num = 1
          recordfilenum = recordfilenum + 1
          #tfrecords格式文件名
          ftrecordfilename = ("traindata.tfrecords-%.3d" % recordfilenum)
          writer= tf.python_io.TFRecordWriter(filepath+ftrecordfilename)
        #print('路径',class_path)
        #print('第几个图片：',num)
        #print('文件的个数',recordfilenum)
        #print('图片名：',img_name)
        
        img_path = class_path+img_name #每一个图片的地址
        img=Image.open(img_path,'r')
        size = img.size
        print(size[1],size[0])
        print(size)
        #print(img.mode)
        img_raw=img.tobytes()#将图片转化为二进制格式
        example = tf.train.Example(
             features=tf.train.Features(feature={
            'label': tf.train.Feature(int64_list=tf.train.Int64List(value=[index])),
            'img_raw': tf.train.Feature(bytes_list=tf.train.BytesList(value=[img_raw])),
            'img_width':tf.train.Feature(int64_list=tf.train.Int64List(value=[size[0]])),
            'img_height':tf.train.Feature(int64_list=tf.train.Int64List(value=[size[1]]))
        })) 
        writer.write(example.SerializeToString())  #序列化为字符串
writer.close()

bestnum = 1000

example = tf.train.Example(
             features=tf.train.Features(feature={
            'label': tf.train.Feature(int64_list=tf.train.Int64List(value=[index])),
            'img_raw': tf.train.Feature(bytes_list=tf.train.BytesList(value=[img_raw])),
            'img_width':tf.train.Feature(int64_list=tf.train.Int64List(value=[size[0]])),
            'img_height':tf.train.Feature(int64_list=tf.train.Int64List(value=[size[1]]))
        })) 

import tensorflow as tf 
from PIL import Image  
import matplotlib.pyplot as plt

#写入图片路径
swd = 'F:\\flowersdata\\show\\'
#TFRecord文件路径
data_path = 'F:\\flowersdata\\tfrecord\\traindata.tfrecords-003'
# 获取文件名列表
data_files = tf.gfile.Glob(data_path)
print(data_files)
# 文件名列表生成器

filename_queue = tf.train.string_input_producer(data_files,shuffle=True) 
reader = tf.TFRecordReader()
_, serialized_example = reader.read(filename_queue)   #返回文件名和文件
features = tf.parse_single_example(serialized_example,
                                   features={
                                       'label': tf.FixedLenFeature([], tf.int64),
                                       'img_raw' : tf.FixedLenFeature([], tf.string),
                                       'img_width': tf.FixedLenFeature([], tf.int64),
                                       'img_height': tf.FixedLenFeature([], tf.int64),
                                   })  #取出包含image和label的feature对象
#tf.decode_raw可以将字符串解析成图像对应的像素数组
image = tf.decode_raw(features['img_raw'], tf.uint8)
height = tf.cast(features['img_height'],tf.int32)
width = tf.cast(features['img_width'],tf.int32)
label = tf.cast(features['label'], tf.int32)
channel = 3
image = tf.reshape(image, [height,width,channel])


with tf.Session() as sess: #开始一个会话
    init_op = tf.initialize_all_variables()
    sess.run(init_op)
    #启动多线程
    coord=tf.train.Coordinator()
    threads= tf.train.start_queue_runners(coord=coord)
    for i in range(15):
        #image_down = np.asarray(image_down.eval(), dtype='uint8')
        plt.imshow(image.eval())
        plt.show()
        single,l = sess.run([image,label])#在会话中取出image和label
        img=Image.fromarray(single, 'RGB')#这里Image是之前提到的
        img.save(swd+str(i)+'_''Label_'+str(l)+'.jpg')#存下图片
        #print(single,l)
    coord.request_stop()
    coord.join(threads)

image = tf.reshape(image, [height,width,channel])