需求

本次的需求是边读图像，边处理图像（各种变组合），处理完后还要把处理好的图像保存到指定的文件夹。而且图像也挺多的，如果按顺序一个一个处理，那肯定要不少时间。所以就想到了多线程并发编程。

实现

先导入本次需要用到的包

import os
import threading
from queue import Queue
import cv2

一些辅助函数

如下函数是得到指定后缀的文件

IMG_EXTENSIONS = ('.jpg', '.jpeg', '.png', '.ppm', '.bmp', '.pgm', '.tif', '.tiff', '.webp')def get_all_files(base, extensions):"""get all files in extensions from base folder, it's a generator"""for root, _, files in sorted(os.walk(base, followlinks=True)):for file in sorted(files):if file.endswith(extensions):yield os.path.join(root, file)def get_all_images(base, image_extensions):"""get all images"""return get_all_files(base, image_extensions)

如下的函数一个是读图像，一个是把RGB转成BGR

def default_loader_cv2(path):return cv2.cvtColor(cv2.imread(path), cv2.COLOR_BGR2RGB)def rgb_2_bgr(img):return cv2.cvtColor(img, cv2.COLOR_RGB2BGR)

下面是主要的几个处理函数

def load_image(target_dir, source_file):"""load image here"""target_file = get_save_path(target_dir, source_file)img = default_loader_cv2(source_file)return (target_file, img)def transform(stain_normalizer, img):"""Description:- transform image method, basic resize here, you could do other transform here"""return cv2.resize(img, (256, 256))def save(save_path, img):"""save image method"""cv2.imwrite(save_path, rgb_2_bgr(img))

在上面几个函数构建对应的处理函数

def do_load_image(load_queue: Queue, trainsform_queue: Queue, target_dir:str):while True:file = load_queue.get()if file is None: breaktarget_file = os.path.join(target_dir, source_file)if not os.path.exists(target_file):      # skip all the transformed imagesimg = default_loader_cv2(file)trainsform_queue.put((target_file, img))else:passdef do_transforms(trainsform_queue: Queue, save_queue: Queue, stain_normalizer):while True:data = trainsform_queue.get()if data is None: breaktarget_file, img = dataimg_norm = transform(stain_normalizer, img)save_queue.put((target_file, img_norm))def do_save(save_queue:Queue):while True:data = save_queue.get()if data is None:  breaktarget_file, img_norm = datasave(target_file, img_norm)

main函数

在这里，是整个程度的启动，特别注意线程的启动与结束顺序，不要搞错了，不然程序会进行死循环。
一般生产者消费者，大家看到的都是只有两个函数（一个生产者，一个消费者），这里实行的是3个函数，load是transform的生产者，transform是save的生产者，这里利用队列实行了3个队列，实行了数据间的传递。可以利用这种思想实行更多层级的生产者与消费者模式。

def main(source_dir, target_dir):# 4104 image, took 224.6297sfiles = get_all_images(source_dir, IMG_EXTENSIONS)  # generator could only be iterated 1 time# transform will be the slowest, so load queue would be too much data if you donot maximizeload_queue = Queue(maxsize=5000) trainsform_queue = Queue()save_queue = Queue()for file in files:load_queue.put(file)# start load_threadsload_threads = []for _ in range(2):t = threading.Thread(target=do_load_image,args=(load_queue, trainsform_queue, target_dir))t.start()load_threads.append(t)# start transform_threadstransform_threads = []for _ in range(6):t = threading.Thread(target=do_transforms,args=(trainsform_queue, save_queue, stain_normalizer))t.start()transform_threads.append(t)# start save_threadssave_threads = []for _ in range(4):t = threading.Thread(target=do_save,args=(save_queue,))t.start()save_threads.append(t)# put sentinel load_threads to break the loop# DONOT put thread.join() under this loopfor _ in load_threads:load_queue.put(None)for thread in load_threads:thread.join()# put sentinel transform_threads to break the loop# DONOT put thread.join() under this loopfor thread in transform_threads:trainsform_queue.put(None)for thread in transform_threads:thread.join()# put sentinel transform_threads to break the loop# DONOT put thread.join() under this loopfor thread in save_threads:save_queue.put(None)for thread in save_threads:thread.join()

按顺序执行

def single_thread(source_dir, target_dir):# 4104 image, took 486.4547sfiles = get_all_images(source_dir, IMG_EXTENSIONS)for file in files:target_file, img = load_image(target_dir, file)img_transform = transform(stain_normalizer, img)save(target_file, img_transform)

结果

从代码来看，单线程的顺序执行比多线程少不小的代码，而且结果也相对简单，基本上不会出什么问题。然后单线程的所要花费的时间却是多线程的2倍还要多。图像一共是4104张512x512的3通道png图像。单线程花费时间是486.4547s，而多线程花费时间是224.6297s。是虽然多线程的代码多了点，但是从性能上来说，还是比单线程顺序执行快不少，还是蛮值得的