Python threading Thread多线程的使用方法

小鱼儿 2022-03-09 14:54 305阅读 0赞

# Python threading Thread多线程的使用方法 #

参考资料：《Python 多线程》[http://www.runoob.com/python/python-multithreading.html][http_www.runoob.com_python_python-multithreading.html]

1.使用Threading模块创建线程

2.Thread线程同步

3.使用多线程读取图像并返回数据

--------------------

## 1.使用Threading模块创建线程 ##

使用Threading模块创建线程，直接从threading.Thread继承，然后重写\_\_init\_\_方法和run方法：

#!/usr/bin/python
    # -*- coding: UTF-8 -*-
     
    import threading
    import time
     
    exitFlag = 0
     
    class myThread (threading.Thread):   #继承父类threading.Thread
        def __init__(self, threadID, name, counter):
            threading.Thread.__init__(self)
            self.threadID = threadID
            self.name = name
            self.counter = counter
        def run(self):                   #把要执行的代码写到run函数里面 线程在创建后会直接运行run函数 
            print "Starting " + self.name
            print_time(self.name, self.counter, 5)
            print "Exiting " + self.name
     
    def print_time(threadName, delay, counter):
        while counter:
            if exitFlag:
                (threading.Thread).exit()
            time.sleep(delay)
            print "%s: %s" % (threadName, time.ctime(time.time()))
            counter -= 1
     
    # 创建新线程
    thread1 = myThread(1, "Thread-1", 1)
    thread2 = myThread(2, "Thread-2", 2)
     
    # 开启线程
    thread1.start()
    thread2.start()
     
    print "Exiting Main Thread"

以上程序执行结果如下；

Starting Thread-1
    Starting Thread-2
    Exiting Main Thread
    Thread-1: Thu Mar 21 09:10:03 2013
    Thread-1: Thu Mar 21 09:10:04 2013
    Thread-2: Thu Mar 21 09:10:04 2013
    Thread-1: Thu Mar 21 09:10:05 2013
    Thread-1: Thu Mar 21 09:10:06 2013
    Thread-2: Thu Mar 21 09:10:06 2013
    Thread-1: Thu Mar 21 09:10:07 2013
    Exiting Thread-1
    Thread-2: Thu Mar 21 09:10:08 2013
    Thread-2: Thu Mar 21 09:10:10 2013
    Thread-2: Thu Mar 21 09:10:12 2013
    Exiting Thread-2

## 2.Thread线程同步 ##

如果多个线程共同对某个数据修改，则可能出现不可预料的结果，为了保证数据的正确性，需要对多个线程进行同步。使用Thread对象的Lock和Rlock可以实现简单的线程同步，这两个对象都有acquire方法和release方法，对于那些需要每次只允许一个线程操作的数据，可以将其操作放到acquire和release方法之间。

多线程的优势在于可以同时运行多个任务（至少感觉起来是这样）。但是当线程需要共享数据时，可能存在数据不同步的问题。

考虑这样一种情况：一个列表里所有元素都是0，线程"set"从后向前把所有元素改成1，而线程"print"负责从前往后读取列表并打印。

那么，可能线程"set"开始改的时候，线程"print"便来打印列表了，输出就成了一半0一半1，这就是数据的不同步。为了避免这种情况，引入了锁的概念。

锁有两种状态——锁定和未锁定。每当一个线程比如"set"要访问共享数据时，必须先获得锁定；如果已经有别的线程比如"print"获得锁定了，那么就让线程"set"暂停，也就是同步阻塞；等到线程"print"访问完毕，释放锁以后，再让线程"set"继续。

经过这样的处理，打印列表时要么全部输出0，要么全部输出1，不会再出现一半0一半1的尴尬场面。

# -*-coding: utf-8 -*-
    """
        @Project: cluster
        @File   : thread_processing.py
        @Author : panjq
        @E-mail : pan_jinquan@163.com
        @Date   : 2019-03-13 13:43:49
    """
    # !/usr/bin/python
    # -*- coding: UTF-8 -*-
    
    # !/usr/bin/python
    # -*- coding: UTF-8 -*-
    
    import threading
    import time
    
    
    class myThread(threading.Thread):
        def __init__(self, threadID, name, counter):
            threading.Thread.__init__(self)
            self.threadID = threadID
            self.name = name
            self.counter = counter
    
        def run(self):
            print("Starting " + self.name)
            # 获得锁，成功获得锁定后返回True
            # 可选的timeout参数不填时将一直阻塞直到获得锁定
            # 否则超时后将返回False
            threadLock.acquire()
            self.print_time(self.name, self.counter, 3)
            # 释放锁
            threadLock.release()
    
    
        def print_time(self,threadName, delay, counter):
            while counter:
                time.sleep(delay)
                print("%s: %s" % (threadName, time.ctime(time.time())))
                counter -= 1
    
    
    threadLock = threading.Lock()
    threads = []
    
    # 创建新线程
    thread1 = myThread(1, "Thread-1", 1)
    thread2 = myThread(2, "Thread-2", 2)
    
    # 开启新线程
    thread1.start()
    thread2.start()
    
    # 添加线程到线程列表
    threads.append(thread1)
    threads.append(thread2)
    
    # 等待所有线程完成
    for t in threads:
        t.join()
    print("Exiting Main Thread")

## 3.使用多线程读取图像并返回数据 ##

下面是使用多线程的方法，实现读取图片的方法，注意这里增加了一个函数get\_result，用于返回每个线程处理后的数据

其中*file\_processing*和*image\_processing*是本人包装好的文件处理方法和图像处理方法，具体实现的代码，这里不贴出来了，源码可查看：

> file\_processing：《[Python常用的模块的使用技巧][Python]》[https://panjinquan.blog.csdn.net/article/details/80805807\#file\_processing.py][https_panjinquan.blog.csdn.net_article_details_80805807_file_processing.py]
> 
> image\_processing：《[Python常用的模块的使用技巧][Python]》[https://panjinquan.blog.csdn.net/article/details/80805807\#image\_processing.py][https_panjinquan.blog.csdn.net_article_details_80805807_image_processing.py]

# -*-coding: utf-8 -*-
    """
        @Project: cluster
        @File   : thread_operate.py
        @Author : panjq
        @E-mail : pan_jinquan@163.com
        @Date   : 2019-03-13 13:43:49
    """
    
    from utils import file_processing,image_processing
    import threading
    import time
    
    threadLock = threading.Lock()#创建线程锁
    
    class FeatureThread(threading.Thread):
        def __init__(self, thread_id, func, args=()):
            '''
            :param thread_id:
            :param func:
            :param args:
            '''
            threading.Thread.__init__(self)
            self.thread_id = thread_id
            self.func = func
            self.args = args
    
        def run(self):
            print("Starting thread_id:{} ".format(self.thread_id))
            # 获得锁，成功获得锁定后返回True， 可选的timeout参数不填时将一直阻塞直到获得锁定， 否则超时后将返回False
            # threadLock.acquire() #线程加锁
            self.result = self.func(*self.args)
            # threadLock.release()# 释放锁
        def get_result(self):
            try:
                return self.result
            except Exception:
                return None
    
    def test_fun(images_list):
        time.sleep(2)
        print(images_list)
        images=[]
        for filename in images_list:
            image = image_processing.read_image(filename, resize_height=224, resize_width=224, normalization=False)
            images.append(image)
        return images
    
    def split_data_list(data_list, split_nums):
        '''
        :param data_list: 数据列表
        :param split_nums: 将列表分成多少块，注意split_nums块必须小于data_list的长度
        :return: 返回data_list分块后的索引
        '''
        data_size=len(data_list)
        if split_nums>data_size:
            print("illegal arguments,split_nums must be less than len(data_size)")
            exit(0)
        batch_index=[]
        for i in range(split_nums):
            start = int(data_size / split_nums * i)
            end = int(data_size / split_nums * (i + 1))
            if (i == split_nums - 1) :
                end = data_size
            batch_index.append((start,end))
        return batch_index
    
    def thread_test(images_list, nums_thread=4):
        thread_collection = []#创建线程容器
        # 创建新线程
        batch_index=split_data_list(images_list, split_nums=nums_thread)
        print("batch_index:{}".format(batch_index))
        for i in range(nums_thread):
            start,end=batch_index[i]
            batch_image_list=images_list[start:end]
            thread = FeatureThread(thread_id=i, func=test_fun, args=(batch_image_list,))
            thread.start()    # 开启新线程
            thread_collection.append(thread)# 添加线程到线程列表
    
        # 等待所有线程完成
        for thread in thread_collection:
            thread.join()
            batch_image=thread.get_result()
            image_processing.show_image(title="image",image=batch_image[0])
    
        print("Exiting Main Thread")
    
    
    if __name__=='__main__':
        image_dir="../dataset/test_images"
        images_list = file_processing.get_images_list(image_dir, postfix=['*.png', '*.JPG'])
        print(images_list)
        thread_test(images_list, nums_thread=4)

[http_www.runoob.com_python_python-multithreading.html]: http://www.runoob.com/python/python-multithreading.html
[Python]: https://panjinquan.blog.csdn.net/article/details/80805807
[https_panjinquan.blog.csdn.net_article_details_80805807_file_processing.py]: https://panjinquan.blog.csdn.net/article/details/80805807#file_processing.py
[https_panjinquan.blog.csdn.net_article_details_80805807_image_processing.py]: https://panjinquan.blog.csdn.net/article/details/80805807#image_processing.py