加速，改成多线程转VOC矩形框和生成tfrecord

　　改生成VOC2007矩形框：

　　# -*- coding: utf-8 -*-

　　"""

　　Created on Thu Sep 19 14:51:00 2019

　　@author: Andrea

　　"""

　　import os

　　import numpy as np

　　import codecs

　　import json

　　from glob import glob

　　import cv2

　　import shutil

　　from sklearn.model_selection import train_test_split

　　import threading

　　#1.标签路径

　　labelme_path = "I:\\biaozhutuxiang\\fangdichan1106-banannanan" #原始labelme标注数据路径

　　saved_path = "I:\\biaozhutuxiang\\VOC2007-fangdichan1106-banannanan\\" #保存路径

　　#2.创建要求文件夹

　　if not os.path.exists(saved_path + "Annotations"):

　　os.makedirs(saved_path + "Annotations")

　　if not os.path.exists(saved_path + "JPEGImages/"):

　　os.makedirs(saved_path + "JPEGImages/")

　　if not os.path.exists(saved_path + "ImageSets/Main/"):

　　os.makedirs(saved_path + "ImageSets/Main/")

　　"""重新定义带返回值的线程类----民国档案------"""

　　class LoadThread(threading.Thread):

　　#class LoadThread_rep:

　　def __init__(self, json_file_):

　　super(LoadThread, self).__init__()

　　self.json_file_ = json_file_

　　def run(self):

　　if('.json' not in self.json_file_):

　　return self.json_file_

　　else:

　　json_file_ = self.json_file_.split('.json')[0]

　　print(json_file_)

　　json_filename = os.path.join(labelme_path , json_file_ + ".json")

　　print(json_filename)

　　json_file = json.load(open(json_filename,"r",encoding="utf-8"))

　　print(os.path.join(labelme_path , json_file_ +".jpg"))

　　height, width, channels = cv2.imread(os.path.join(labelme_path , json_file_ +".jpg")).shape

　　with codecs.open(saved_path + "Annotations/"+json_file_ + ".xml","w","utf-8") as xml:

　　xml.write('\n')

　　xml.write('\t' + 'UAV_data' + '\n')

　　xml.write('\t' + json_file_ + ".jpg" + '\n')

　　xml.write('\t\n')

　　xml.write('\t\tThe UAV autolanding\n')

　　xml.write('\t\tUAV AutoLanding\n')

　　xml.write('\t\tflickr\n')

　　xml.write('\t\tNULL\n')

　　xml.write('\t\n')

　　xml.write('\t\n')

　　xml.write('\t\tNULL\n')

　　xml.write('\t\tYuanyiqin\n')

　　xml.write('\t\n')

　　xml.write('\t\n')

　　xml.write('\t\t'+ str(width) + '\n')

　　xml.write('\t\t'+ str(height) + '\n')

　　xml.write('\t\t' + str(channels) + '\n')

　　xml.write('\t\n')

　　xml.write('\t\t0\n')

　　for multi in json_file["shapes"]:

　　points = np.array(multi["points"])

　　xmin = min(points[:,0])

　　xmax = max(points[:,0])

　　ymin = min(points[:,1])

　　ymax = max(points[:,1])

　　label = multi["label"]

　　if xmax <= xmin:

　　pass

　　elif ymax <= ymin:

　　pass

　　else:

　　xml.write('\t\n')

　　print(json_filename,xmin,ymin,xmax,ymax,label)

　　xml.write('')

　　self.json_file_

　　def get_result(self):

　　return self.json_file_

　　##3.获取待处理文件

　　#files = glob(labelme_path + "*.json")

　　#print(files)

　　#files = [i.split("/")[-1].split(".json")[0] for i in files]

　　#4.读取标注信息并写入 xml

　　threadnum = 64

　　if __name__ == '__main__':

　　# for json_file_ in os.listdir(labelme_path):

　　img_list = os.listdir(labelme_path)

　　img_length = len(img_list)

　　# threadnum = 4

　　for i in range(0,int(img_length/threadnum)+1):

　　# for i in range(int(img_length/threadnum)+1):

　　print('i,int(img_length/threadnum):',i,int(img_length/threadnum))

　　li = []

　　for j in range(i*threadnum,min(i*threadnum+threadnum,img_length)):

　　# for j in range(i*threadnum,min(i*threadnum+threadnum,img_length)):

　　json_file_ = img_list[j]

　　print('json_file_:',json_file_)

　　thread = LoadThread(json_file_)

　　li.append(thread)

　　thread.start()

　　for thread in li:

　　thread.join() # 一定要join，不然主线程比子线程跑的快，会拿不到结果

　　json_file_ = thread.get_result()

　　print('Down json_file_:',json_file_)

　　#5.复制图片到 VOC2007/JPEGImages/下

　　image_files = glob(labelme_path + "*.jpg")

　　print("copy image files to VOC007/JPEGImages/")

　　for image in image_files:

　　shutil.copy(image,saved_path +"JPEGImages/")

　　#6.split files for txt

　　txtsavepath = saved_path + "ImageSets/Main/"

　　ftrainval = open(txtsavepath+'/trainval.txt', 'w')

　　ftest = open(txtsavepath+'/test.txt', 'w')

　　ftrain = open(txtsavepath+'/train.txt', 'w')

　　fval = open(txtsavepath+'/val.txt', 'w')

　　total_files = glob("./VOC2007/Annotations/*.xml")

　　total_files = [i.split("/")[-1].split(".xml")[0] for i in total_files]

　　#test_filepath = ""

　　for file in total_files:

　　ftrainval.write(file + "\n")

　　#test

　　#for file in os.listdir(test_filepath):

　　# ftest.write(file.split(".jpg")[0] + "\n")

　　#split

　　train_files,val_files = train_test_split(total_files,test_size=0.15,random_state=42)

　　#train

　　for file in train_files:

　　ftrain.write(file + "\n")

　　#val

　　for file in val_files:

　　fval.write(file + "\n")

　　ftrainval.close()

　　ftrain.close()

　　fval.close()

　　#ftest.close()

　　改成多线程生成tfrecord:

　　# -*- coding: utf-8 -*-

　　from __future__ import division, print_function, absolute_import

　　import sys

　　sys.path.append('../../')

　　import xml.etree.cElementTree as ET

　　import numpy as np

　　import tensorflow as tf

　　import math

　　import glob

　　import cv2

　　from libs.label_name_dict.label_dict import *

　　from help_utils.tools import *

　　import threading

　　import random

　　tf.app.flags.DEFINE_string('VOC_dir', '/home/yuanyq/Detect_DL/FPN_Tensorflow/data/io/VOC2007/', 'Voc dir')

　　tf.app.flags.DEFINE_string('xml_dir', 'Annotations', 'xml dir')

　　tf.app.flags.DEFINE_string('image_dir', 'JPEGImages', 'image dir')

　　tf.app.flags.DEFINE_string('save_name', 'train', 'save name')

　　tf.app.flags.DEFINE_string('save_dir', '../tfrecord/', 'save name')

　　tf.app.flags.DEFINE_string('img_format', '.jpg', 'format of image')

　　tf.app.flags.DEFINE_string('dataset', 'pascal', 'dataset')

　　FLAGS = tf.app.flags.FLAGS

　　threadnum = 128

　　global count

　　count = 0

　　class LoadThread(threading.Thread):

　　def __init__(self,xml,image_path,xml_path,writer):

　　super(LoadThread,self).__init__()

　　self.xml = xml

　　self.image_path = image_path

　　self.xml_path = xml_path

　　self.writer = writer

　　def run(self):

　　# to avoid path error in different development platform

　　xml = self.xml.replace('\\', '/')

　　img_name = xml.split('/')[-1].split('.')[0] + FLAGS.img_format

　　img_path = self.image_path + '/' + img_name

　　print('xml:',xml)

　　if not os.path.exists(img_path):

　　print('{} is not exist!'.format(img_path))

　　#return self.xml

　　img_height, img_width, gtbox_label = read_xml_gtbox_and_label(xml)

　　# img = np.array(Image.open(img_path))

　　img = cv2.imread(img_path)[:, :, ::-1]

　　feature = tf.train.Features(feature={

　　# do not need encode() in linux

　　'img_name': _bytes_feature(img_name.encode()),

　　# 'img_name': _bytes_feature(img_name),

　　'img_height': _int64_feature(img_height),

　　'img_width': _int64_feature(img_width),

　　'img': _bytes_feature(img.tostring()),

　　'gtboxes_and_label': _bytes_feature(gtbox_label.tostring()),

　　'num_objects': _int64_feature(gtbox_label.shape[0])

　　})郑州妇科医院 http://www.120zzzy.com/

　　example = tf.train.Example(features=feature)

　　self.writer.write(example.SerializeToString())

　　#view_bar('Conversion progress', count + 1, len(glob.glob(self.xml_path + '/*.xml')))

　　return self.xml

　　def get_result(self):

　　print(self.xml)

　　return self.xml

　　def _int64_feature(value):

　　return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))

　　def _bytes_feature(value):

　　return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))

　　def read_xml_gtbox_and_label(xml_path):

　　"""

　　:param xml_path: the path of voc xml

　　:return: a list contains gtboxes and labels, shape is [num_of_gtboxes, 5],

　　and has [xmin, ymin, xmax, ymax, label] in a per row

　　"""

　　tree = ET.parse(xml_path)

　　root = tree.getroot()

　　img_width = None

　　img_height = None

　　box_list = []

　　for child_of_root in root:

　　# if child_of_root.tag == 'filename':

　　# assert child_of_root.text == xml_path.split('/')[-1].split('.')[0] \

　　# + FLAGS.img_format, 'xml_name and img_name cannot match'

　　if child_of_root.tag == 'size':

　　for child_item in child_of_root:

　　if child_item.tag == 'width':

　　img_width = int(child_item.text)

　　if child_item.tag == 'height':

　　img_height = int(child_item.text)

　　if child_of_root.tag == 'object':

　　label = None

　　for child_item in child_of_root:

　　# print('child_item.tag:',child_item.tag)

　　# print('child_item.text:',child_item.text)

　　# print('NAME_LABEL_MAP:',NAME_LABEL_MAP)

　　if child_item.tag == 'name':

　　if(child_item.text == '0002X'):

　　child_item.text = '0002'

　　if(child_item.text == 'X0002'):

　　child_item.text = '0002'

　　if(child_item.text =='000Z1'):

　　child_item.text = '0001'

　　if(child_item.text =='A0001'):

　　child_item.text = '0001'

　　if(child_item.text =='c0002'):

　　child_item.text = '0002'

　　if(child_item.text !='0001' and child_item.text !='0002' and child_item.text !='0003'):

　　label = 1

　　else:

　　label = NAME_LABEL_MAP[child_item.text]

　　if child_item.tag == 'bndbox':

　　tmp_box = []

　　for node in child_item:

　　tmp_box.append(math.ceil(float(node.text)))

　　assert label is not None, 'label is none, error'

　　tmp_box.append(label)

　　box_list.append(tmp_box)

　　gtbox_label = np.array(box_list, dtype=np.int32)

　　return img_height, img_width, gtbox_label

　　def convert_pascal_to_tfrecord():

　　xml_path = FLAGS.VOC_dir + FLAGS.xml_dir

　　image_path = FLAGS.VOC_dir + FLAGS.image_dir

　　save_path = FLAGS.save_dir + FLAGS.dataset + '_' + FLAGS.save_name + '.tfrecord'

　　mkdir(FLAGS.save_dir)

　　# print('xml_path:',xml_path)

　　# print('save_path:',save_path)

　　# print('image_path:',image_path)

　　# writer_options = tf.python_io.TFRecordOptions(tf.python_io.TFRecordCompressionType.ZLIB)

　　# writer = tf.python_io.TFRecordWriter(path=save_path, options=writer_options)

　　writer = tf.python_io.TFRecordWriter(path=save_path)

　　img_list = os.listdir(xml_path)

　　random.shuffle(img_list)

　　img_length = len(img_list)

　　for i in range(0,int(img_length/threadnum)+1):

　　li = []

　　for j in range(i*threadnum,min(i*threadnum+threadnum,img_length)):

　　xml = os.path.join(xml_path,img_list[j])

　　thread = LoadThread(xml,image_path,xml_path,writer)

　　thread.daemon = True

　　li.append(thread)

　　thread.start()

　　for thread in li:

　　thread.join() # 一定要join，不然主线程比子线程跑的快，会拿不到结果

　　xml = thread.get_result()

　　print('img_name done:',xml)

　　# to avoid path error in different development platform

　　print('\nConversion is complete!')

　　if __name__ == '__main__':

　　# xml_path = '../data/dataset/VOCdevkit/VOC2007/Annotations/000005.xml'

　　# read_xml_gtbox_and_label(xml_path)

　　convert_pascal_to_tfrecord()