我有一个 python 脚本，它将：
- 遍历给定目录中的 rosbags
- 每个非图像消息直接复制到新包中，每个图像消息将被压缩并添加到新包中
我决定制作这个脚本的新版本以加快速度。我的新脚本可以：
- 遍历给定目录中的 rosbags
- 每条非图片消息直接复制到新包中
- 每个图像消息都添加到队列中
- 处理完包中的所有消息后，我创建从队列中读取、压缩图像并将其添加到完成列表的进程。
- 循环完成列表以将压缩图像添加到新包中
我创建的代码有一些问题。
- 只有一个 CPU 似乎处于 100% 使用率。
- 当它移动到下一个 rosbag 时，内存使用率仍然很高。好像有些东西没有被删除。
我不相信需要 ROS 知识来解决这个问题。我是否正确使用了多处理库？
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，它很可能在队列中序列化。我添加了评论以确认他写的内容。看起来只使用了两个进程，然后降为一个。我认为对象太大，无法使程序具有多个进程。
Copying non image ros messages into outbag,
also adding image messages to the queue
PROGRESS 100.00% [DONE]
Compressing images within the queue
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[DONE]