python多线程与队列
各位好，之前写了多线程，但是在实际的生产中，往往情况比较复杂，要处理一批任务（比如要处理列表中所有元素），这时候不可能创建很多的线程，线程过多反而不好，还会造成资源开销太大，这时候想到了队列。
目前创新互联已为成百上千的企业提供了网站建设、域名、虚拟主机、网站托管、企业网站设计、名山网站维护等服务，公司将坚持客户导向、应用为本的策略，正道将秉承"和谐、参与、激情"的文化，与客户和合作伙伴齐心协力一起成长，共同发展。
Queue队列
Queue用于建立和操作队列，常和threading类一起用来建立一个简单的线程队列。
- Queue.Queue(maxsize) FIFO（先进先出队列）
- Queue.LifoQueue(maxsize) LIFO（先进后出队列）
- Queue.PriorityQueue(maxsize) 为优先级越高的越先出来,对于一个队列中的所有元素组成的entries，优先队列优先返回的一个元素是sorted(list(entries))[0]。至于对于一般的数据，优先队列取什么东西作为优先度要素进行判断，官方文档给出的建议是一个tuple如(priority, data)，取priority作为优先度。
如果设置的maxsize小于1，则表示队列的长度无限长
FIFO是常用的队列，常用的方法有：
- Queue.qsize() 返回队列大小
- Queue.empty() 判断队列是否为空
Queue.full() 判断队列是否满了
Queue.get([block[,timeout]]) 从队列头删除并返回一个item，block默认为True，表示当队列为空却去get的时候会阻塞线程，等待直到有有item出现为止来get出这个item。如果是False的话表明当队列为空你却去get的时候，会引发异常。
在block为True的情况下可以再设置timeout参数。表示当队列为空，get阻塞timeout指定的秒数之后还没有get到的话就引发Full异常。Queue.put(...[,block[,timeout]]) 向队尾插入一个item，同样若block=True的话队列满时就阻塞等待有空位出来再put，block=False时引发异常。
同get的timeout，put的timeout是在block为True的时候进行超时设置的参数。
Queue.task_done() 从场景上来说，处理完一个get出来的item之后，调用task_done将向队列发出一个信号，表示本任务已经完成。- Queue.join() 监视所有item并阻塞主线程，直到所有item都调用了task_done之后主线程才继续向下执行。这么做的好处在于，假如一个线程开始处理最后一个任务，它从任务队列中拿走最后一个任务，此时任务队列就空了但最后那个线程还没处理完。当调用了join之后，主线程就不会因为队列空了而擅自结束，而是等待最后那个线程处理完成了。
队列-单线程
import threading
import queue
import time
class worker(threading.Thread):
def __init__(self, queue):
threading.Thread.__init__(self)
self.queue = queue
self.thread_stop = False
def run(self):
while not self.thread_stop:
print("thread%d %s: waiting for tast" % (self.ident, self.name))
try:
task = q.get(block=True, timeout=2) # 接收消息
except queue.Empty:
print("Nothing to do! I will go home!")
self.thread_stop = True
break
print("tasking: %s ,task No:%d" % (task[0], task[1]))
print("I am working")
time.sleep(3)
print("work finished!")
q.task_done() # 完成一个任务
res = q.qsize() # 判断消息队列大小(队列中还有几个任务)
if res > 0:
print("fuck! Still %d tasks to do" % (res))
def stop(self):
self.thread_stop = True
if __name__ == "__main__":
q = queue.Queue(3) # 创建队列（大小为3）
worker = worker(q) # 将队列加入类中
worker.start() # 启动类
q.put(["produce cup!", 1], block=True, timeout=None) # 向队列中添加元素，产生任务消息
q.put(["produce desk!", 2], block=True, timeout=None)
q.put(["produce apple!", 3], block=True, timeout=None)
q.put(["produce banana!", 4], block=True, timeout=None)
q.put(["produce bag!", 5], block=True, timeout=None)
print("***************leader:wait for finish!")
q.join() # 等待所有任务完成
print("***************leader:all task finished!")
输出：
thread9212 Thread-1: waiting for tast
tasking: produce cup! ,task No:1
I am working
work finished!
fuck! Still 3 tasks to do
thread9212 Thread-1: waiting for tast
tasking: produce desk! ,task No:2
I am working
***************leader:wait for finish!
work finished!
fuck! Still 3 tasks to do
thread9212 Thread-1: waiting for tast
tasking: produce apple! ,task No:3
I am working
work finished!
fuck! Still 2 tasks to do
thread9212 Thread-1: waiting for tast
tasking: produce banana! ,task No:4
I am working
work finished!
fuck! Still 1 tasks to do
thread9212 Thread-1: waiting for tast
tasking: produce bag! ,task No:5
I am working
work finished!
thread9212 Thread-1: waiting for tast
***************leader:all task finished!
Nothing to do!i will go home!队列-多线程
import threading
import time
from queue import Queue
img_lists = ['lipei_00006.mp3','lipei_00007.mp3','lipei_00012.mp3','lipei_00014.mp3',
'lipei_00021.mp3','lipei_00027.mp3','lipei_00028.mp3','lipei_00035.mp3',
'lipei_00039.mp3','lipei_00044.mp3','lipei_00047.mp3','lipei_00049.mp3',
'lipei_00057.mp3','lipei_00058.mp3','lipei_00059.mp3','lipei_00061.mp3',
'lipei_00066.mp3','lipei_00068.mp3','lipei_00070.mp3','lipei_00081.mp3',
'lipei_00087.mp3','lipei_00104.mp3','lipei_00106.mp3','lipei_00117.mp3',
'lipei_00123.mp3','lipei_00129.mp3',]
q = Queue(10)
class Music_Cols(threading.Thread):
def __init__(self, name):
super().__init__(name=name)
def run(self):
global img_lists
global q
while True:
try:
music = img_lists.pop(0)
q.put(music)
except IndexError:
break
class Music_Play(threading.Thread):
def __init__(self, name):
super().__init__(name=name)
def run(self):
global q
while True:
if q.not_empty:
music = q.get()
print('{}正在播放{}'.format(threading.current_thread(), music))
time.sleep(5)
q.task_done()
print('{}播放结束'.format(music))
else:
break
if __name__ == '__main__':
mc_thread = Music_Cols('music_cols')
mc_thread.setDaemon(True) # 设置为守护进程，主线程退出时，子进程也kill掉
mc_thread.start() # 启动进程
for i in range(5): # 设置线程个数（批量任务时，线程数不必太大，注意内存及CPU负载）
mp_thread = Music_Play('music_play')
mp_thread.setDaemon(True)
mp_thread.start()
q.join() # 线程阻塞（等待所有子线程处理完成，再退出）
输出：
正在播放lipei_00006.mp3
正在播放lipei_00007.mp3
正在播放lipei_00012.mp3
正在播放lipei_00014.mp3
正在播放lipei_00021.mp3
lipei_00014.mp3播放结束
... ...
正在播放lipei_00117.mp3
lipei_00066.mp3播放结束
正在播放lipei_00123.mp3
lipei_00104.mp3播放结束
正在播放lipei_00129.mp3
lipei_00123.mp3播放结束
lipei_00117.mp3播放结束
lipei_00087.mp3播放结束
lipei_00106.mp3播放结束
lipei_00129.mp3播放结束 或者(效果与上述一样)
import threading
import time
from queue import Queue
img_lists = ['lipei_00006.mp3','lipei_00007.mp3','lipei_00012.mp3','lipei_00014.mp3',
'lipei_00021.mp3','lipei_00027.mp3','lipei_00028.mp3','lipei_00035.mp3',
'lipei_00039.mp3','lipei_00044.mp3','lipei_00047.mp3','lipei_00049.mp3',
'lipei_00057.mp3','lipei_00058.mp3','lipei_00059.mp3','lipei_00061.mp3',
'lipei_00066.mp3','lipei_00068.mp3','lipei_00070.mp3','lipei_00081.mp3',
'lipei_00087.mp3','lipei_00104.mp3','lipei_00106.mp3','lipei_00117.mp3',
'lipei_00123.mp3','lipei_00129.mp3',]
q = Queue(10)
class Music_Cols(threading.Thread):
def __init__(self, name):
super().__init__(name=name)
def run(self):
while True:
try:
music = img_lists.pop(0)
q.put(music)
except IndexError:
break
class Music_Play(threading.Thread):
def __init__(self, name):
super().__init__(name=name)
def run(self):
while True:
if q.not_empty:
music = q.get()
print('{}正在播放{}'.format(threading.current_thread(), music))
time.sleep(5)
q.task_done()
print('{}播放结束'.format(music))
else:
break
if __name__ == '__main__':
mc_thread = Music_Cols('music_cols')
mc_thread.setDaemon(True) # 设置为守护进程，主线程退出时，子进程也kill掉
mc_thread.start() # 启动进程
for i in range(5): # 设置线程个数（批量任务时，线程数不必太大，注意内存及CPU负载）
mp_thread = Music_Play('music_play')
mp_thread.setDaemon(True)
mp_thread.start()
q.join() # 线程阻塞（等待所有子线程处理完成，再退出）队列-多线程—图像增强实例
"""
开启多线程：图像增强
"""
import os
import random
import queue
import numpy as np
import cv2
import time
import threading
def Affine_transformation(img_array):
rows, cols = img_array.shape[:2]
pointsA = np.float32([[30, 80], [180, 60], [80, 230]]) # 左偏
pointsB = np.float32([[60, 50], [220, 70], [20, 180]]) # 右偏
pointsC = np.float32([[70, 60], [180, 50], [50, 200]]) # 前偏
pointsD = np.float32([[40, 50], [210, 60], [70, 180]]) # 后偏
points1 = np.float32([[50, 50], [200, 50], [50, 200]])
points2 = random.choice((pointsA, pointsB, pointsC, pointsD))
matrix = cv2.getAffineTransform(points1, points2)
Affine_transfor_img = cv2.warpAffine(img_array, matrix, (cols, rows))
return Affine_transfor_img
def random_rotate_img(img):
rows, cols= img.shape[:2]
angle = random.choice([25, 90, -25, -90, 180])
Matrix = cv2.getRotationMatrix2D((cols / 2, rows / 2), angle, 1)
res = cv2.warpAffine(img, Matrix, (cols, rows), borderMode=cv2.BORDER_CONSTANT)
return res
def random_hsv_transform(img, hue_vari, sat_vari, val_vari):
"""
:param img:
:param hue_vari: 色调变化比例范围(0,360)
:param sat_vari: 饱和度变化比例范围(0,1)
:param val_vari: 明度变化比例范围(0,1)
:return:
"""
hue_delta = np.random.randint(-hue_vari, hue_vari)
sat_mult = 1 + np.random.uniform(-sat_vari, sat_vari)
val_mult = 1 + np.random.uniform(-val_vari, val_vari)
img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV).astype(np.float)
img_hsv[:, :, 0] = (img_hsv[:, :, 0] + hue_delta) % 180
img_hsv[:, :, 1] *= sat_mult
img_hsv[:, :, 2] *= val_mult
img_hsv[img_hsv > 255] = 255
return cv2.cvtColor(np.round(img_hsv).astype(np.uint8), cv2.COLOR_HSV2BGR)
def random_gamma_transform(img, gamma_vari):
"""
:param img:
:param gamma_vari:
:return:
"""
log_gamma_vari = np.log(gamma_vari)
alpha = np.random.uniform(-log_gamma_vari, log_gamma_vari)
gamma = np.exp(alpha)
gamma_table = [np.power(x / 255.0, gamma) * 255.0 for x in range(256)]
gamma_table = np.round(np.array(gamma_table)).astype(np.uint8)
return cv2.LUT(img, gamma_table)
def random_flip_img(img):
"""
0 = X axis, 1 = Y axis, -1 = both
:param img:
:return:
"""
flip_val = [0,1,-1]
random_flip_val = random.choice(flip_val)
res = cv2.flip(img, random_flip_val)
return res
def clamp(pv): #防止像素溢出
if pv > 255:
return 255
if pv < 0:
return 0
else:
return pv
def gaussian_noise(image): # 加高斯噪声
"""
:param image:
:return:
"""
h, w, c = image.shape
for row in range(h):
for col in range(w):
s = np.random.normal(0, 20, 3)
b = image[row, col, 0] # blue
g = image[row, col, 1] # green
r = image[row, col, 2] # red
image[row, col, 0] = clamp(b + s[0])
image[row, col, 1] = clamp(g + s[1])
image[row, col, 2] = clamp(r + s[2])
return image
def get_img(input_dir):
img_path_list = []
for (root_path,dirname,filenames) in os.walk(input_dir):
for filename in filenames:
Suffix_name = ['.png', '.jpg', '.tif', '.jpeg']
if filename.endswith(tuple(Suffix_name)):
img_path = root_path+"/"+filename
img_path_list.append(img_path)
return img_path_list
class IMG_QUEUE(threading.Thread):
def __init__(self, name):
super().__init__(name=name)
def run(self):
while True:
try:
img_path = img_path_list.pop(0)
q.put(img_path)
except IndexError:
break
class IMG_AUG(threading.Thread):
def __init__(self, name):
super().__init__(name=name)
self.q = q
def run(self):
while True:
if q.not_empty:
img_path = q.get()
try:
print("doing...")
img_array = cv2.imread(img_path)
Affine_transfor_img = Affine_transformation(img_array)
cv2.imwrite(output_dir + "/" + img_path[len(input_dir):-4] + '_Affine_transfor.png', Affine_transfor_img)
res_rotate = random_rotate_img(img_array)
cv2.imwrite(output_dir + "/" + img_path[len(input_dir):-4] + '_rotate_img.png',res_rotate)
GAMMA_IMG = random_gamma_transform(img_array, 0.3)
cv2.imwrite(output_dir + "/" + img_path[len(input_dir):-4] + '_GAMMA_IMG.png',GAMMA_IMG)
res_flip = random_flip_img(img_array)
cv2.imwrite(output_dir + "/" + img_path[len(input_dir):-4] + '_flip_img.png',res_flip)
G_Noiseimg = gaussian_noise(img_array)
cv2.imwrite(output_dir + "/" + img_path[len(input_dir):-4] + '_G_Noise_img.png',G_Noiseimg)
HSV_IMG = random_hsv_transform(img_array, 2, 0.3, 0.6)
cv2.imwrite(output_dir + "/" + img_path[len(input_dir):-4] + '_HSV_IMG.png',HSV_IMG)
except:
print("图像格式错误！")
pass
q.task_done()
else:
break
if __name__ == '__main__':
input_dir = './cccc'
output_dir = './eeee'
start_time = time.time() # 开始计时
img_path_list = get_img(input_dir) # 获取图像数据
q = queue.Queue(10) # 设置队列元素个数
my_thread = IMG_QUEUE('IMG_QUEUE') # 实例化
my_thread.setDaemon(True) # 设置为守护进程，主线程退出时，子进程也kill掉
my_thread.start() # 启动进程
for i in range(5): # 设置线程个数（批量任务时，线程数不必太大，注意内存及CPU负载）
mp_thread = IMG_AUG('IMG_AUG')
mp_thread.setDaemon(True)
mp_thread.start()
q.join() # 线程阻塞（等待所有子线程处理完成，再退出）
end_time = time.time()
print("Total Spend time：", str((end_time - start_time) / 60)[0:6] + "分钟")多线程-创建图像缩略图（等比缩放图像）
import os
from PIL import Image
import threading
import time
import queue
def get_img(input_dir):
img_path_list = []
for (root_path,dirname,filenames) in os.walk(input_dir):
for filename in filenames:
Suffix_name = ['.png', '.jpg', '.tif', '.jpeg']
if filename.endswith(tuple(Suffix_name)):
img_path = root_path+"/"+filename
img_path_list.append(img_path)
return img_path_list
class IMG_QUEUE(threading.Thread):
def __init__(self, name):
super().__init__(name=name)
def run(self):
while True:
try:
img_path = img_path_list.pop(0)
q.put(img_path)
except IndexError:
break
class IMG_RESIZE(threading.Thread):
def __init__(self, name):
super().__init__(name=name)
def run(self):
while True:
if q.not_empty:
img_path = q.get()
try:
im = Image.open(img_path)
im.thumbnail((size, size))
print(im.format, im.size, im.mode)
im.save(img_path, 'JPEG')
except:
print("图像格式错误！")
pass
q.task_done()
else:
break
if __name__=='__main__':
input_dir = 'D:\\20190112_20190114_all' #需要创建缩略图，图片的目录
start_time = time.time() # 开始计时
img_path_list = get_img(input_dir) # 获取图像数据
size = 800
q = queue.Queue(100) # 设置队列元素个数
my_thread = IMG_QUEUE('IMG_QUEUE') # 实例化
my_thread.setDaemon(True) # 设置为守护进程，主线程退出时，子进程也kill掉
my_thread.start() # 启动进程
for i in range(5): # 设置线程个数（批量任务时，线程数不必太大，注意内存及CPU负载）
mp_thread = IMG_RESIZE(str(i))
mp_thread.setDaemon(True)
mp_thread.start()
q.join() # 线程阻塞（等待所有子线程处理完成，再退出）
end_time = time.time() # 计时结束
print("Total Spend time：", str((end_time - start_time) / 60)[0:6] + "分钟")名称栏目：python多线程与队列
标题来源：http://6mz.cn/article/ipdpii.html