我有一个python脚本：
我决定制作这个脚本的新版本以加快速度。我的新脚本确实：
我对我创建的代码存在一些问题。
我不相信解决这个问题需要ROS知识。我正确使用多处理库吗？
import glob
import os
import argparse
from datetime import datetime
import numpy as np
import cv2
import sys
import copy
import rosbag
import rospy
from sensor_msgs.msg import CompressedImage
from cv_bridge import CvBridge
from multiprocessing import Process, Manager, Queue, cpu_count, queues
# Image constants
IMG_W = 1280
IMG_H = 1024
NUM_CHANNEL = 3
bridge = CvBridge()
parser = argparse.ArgumentParser(
description='Creates a bag containing the image data')
parser.add_argument(
'input_dirs',
nargs='+',
help='Input and Output directory for bag files.')
args = parser.parse_args()
queue = Queue()
manager = Manager()
converted_imgs = manager.list()
bag_path=''
out_path=''
if len(args.input_dirs) == 2:
bag_path = args.input_dirs[0]
out_path = args.input_dirs[1]
os.chdir(bag_path)
else:
print('Please enter the input and output bag path.')
exit()
bag_names = []
# Add all bags in bag_path to bag_names
for file in sorted(os.listdir(bag_path)):
if file.endswith(".bag"):
bag_names.append(file)
camera_image_topics = {
'/cam_a/image_raw': '/cam_a/compressed',
'/cam_b/image_raw': '/cam_b/compressed',
'/cam_c/image_raw': '/cam_c/compressed'
}
# Display progress function
# Prints out the current progress on the same line
def display_progress(progress):
sys.stdout.write("\r" + 'PROGRESS ' + format(progress, '.2f') + '%')
sys.stdout.flush()
# Convert each Image to CompressedImage
def create_msg(img_total):
global queue
global converted_imgs
while not queue.empty():
topic, raw_data, t = queue.get()
# Display progress
progress = ((img_total - queue.qsize()) * 100.00) / img_total
display_progress(progress)
# Create CompressedImage msg and compress the image
cmprs_msg = CompressedImage()
cmprs_msg.format = "png"
cmprs_msg.data = np.array(cv2.imencode('.png', raw_data, [int(cv2.IMWRITE_PNG_COMPRESSION), 3])[1]).tostring()
cmprs_msg.header.stamp = t
# Add message to be written to the outbag
converted_imgs.append((topic, cmprs_msg))
return
for i in range(len(bag_names)):
print('Reading bag ' + bag_names[i])
with rosbag.Bag(out_path + 'compressed_' + bag_names[i], 'w') as outbag:
count = 0.0 # Init as float
msg_total = rosbag.Bag(bag_names[i]).get_message_count()
print('Copying non image ros messages into outbag,')
print('also adding image messages to the queue')
# print('Copying msgs and compressing images into outbag.')
for topic, msg, t in rosbag.Bag(bag_names[i]).read_messages():
if topic not in camera_image_topics:
outbag.write(topic, msg, t)
else:
# Convert image data to numpy matrix and add to queue
cv2_matrix = bridge.imgmsg_to_cv2(msg, msg.encoding)
raw_data = np.asarray(cv2_matrix).reshape((IMG_H, IMG_W, NUM_CHANNEL))
queue.put((camera_image_topics[topic], raw_data, t))
# Increase count of finished messages
count += 1
# Display progress
progress = (count/msg_total) * 100
display_progress(progress)
# Finished
print(' [DONE]')
img_total = queue.qsize()
print('Compressing images within the queue')
processes = []
for cpu_num in xrange(cpu_count()):
p = Process(target=create_msg, args=(img_total,))
processes.append(p)
for p in processes:
p.start()
# Finished
for p in processes:
p.join()
print(' [DONE]')
print('Adding images to the outbag')
for topic, msg in converted_imgs:
outbag.write(topic, msg, msg.header.stamp)
更新1：按要求提供MVP。它似乎使用了所有核心......
from multiprocessing import Process, Manager, Queue, cpu_count, queues
queue = Queue()
manager = Manager()
converted_imgs = manager.list()
# Convert each Image to CompressedImage
def create_msg(img_total):
global queue
global converted_imgs
while not queue.empty():
x = queue.get()
# Add message to be written to the outbag
converted_imgs.append(x ** 2)
return
print('Add to queue')
for x in range(0, 100000):
queue.put(10)
img_total = queue.qsize()
print('Calculate square of each queue member')
processes = []
for cpu_num in xrange(cpu_count()):
p = Process(target=create_msg, args=(img_total,))
processes.append(p)
for p in processes:
p.start()
# Finished
for p in processes:
p.join()
print(' [DONE]')
print('Adding images to the outbag')
for x in converted_imgs:
print(x)
更新2：我认为我写的代码是正确的。正如罗斯在评论中所说，它最有可能在队列中序列化。我添加评论以确认他写的内容。看起来只有两个进程正在使用，然后它下降到一个。我认为对象太大了，不能让程序有多个进程。
Copying non image ros messages into outbag,
also adding image messages to the queue
PROGRESS 100.00% [DONE]
Compressing images within the queue
Process 0
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[DONE]
答案 0 :(得分：1)
我对全局，队列和进程的交互方式并不是特别熟悉，但看起来这可能是你的瓶颈。尝试让您的进程通过管道将值返回给主线程！
# Convert each Image to CompressedImage
def create_msg(img_total, pipe):
# You won't need these:
# global queue
# global converted_imgs
while not YOUR_COUNTER():
x = queue.get()
# Add message to be written to the outbag
pipe.send(x ** 2)
return
# create a list to keep all processes
processes = []
# create a list to keep connections
parent_connections = []
for j in range(cpu_count()):
# create a pipe for communication
parent_conn, child_conn = Pipe()
parent_connections.append(parent_conn)
process_args = (YOUR_ARGS, child_conn)
# create the process, pass instance and connection
process = Process(target=create_msg, args=(process_args,))
processes.append(process)
# start all processes
for process in processes:
process.start()
# make sure that all processes have finished
for process in processes:
process.join()
for parent_connection in parent_connections:
while parent_connection.poll():
recv = parent_connection.recv()
# Redefine this list somewhere else to use
converted_imgs.append(recv)