Ros image message rate is low.

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I'm trying to get VREP vision sensor output processed with opencv via ROS api. I did manage to set up scene and get scripts running, but the problem is that I get somewhat like 4-5 fps even without actual processing (currently I just push images directly to output).

This issue does not seem to depend on image resolution, since both 1024512 and 128128 captures result in exactly the same fps.

This also is not a matter of blocking calls, although I'm posting single-theaded code, I do have quite complex multithreaded processing pipeline which performs rather well with actual cameras (~50 fps).

Lua scripts on VREP's side do not seem to be a problem also, since I've tried to play with video retranslation examples provided by vrep, and they seem to achieve rather satisfying fps.

So it seems like image streaming is a bottleneck.

Here's my sample script:

# coding=utf-8

import rclpy
import rclpy.node as node
import cv2
import numpy as np
import sensor_msgs.msg as msg

import third_party.ros.ros as ros

class TestDisplayNode(node.Node):
    def __init__(self):
        super().__init__('IProc_TestDisplayNode')
        self.__window_name = "img"
        self.sub = self.create_subscription(msg.Image, 'Vision_sensor', self.msg_callback)

    def msg_callback(self, m : msg.Image):
        np_img = np.reshape(m.data, (m.height, m.width, 3)).astype(np.uint8)
        self.display(np_img)

    def display(self, img : np.ndarray):
        cv2.imshow(self.__window_name, cv2.cvtColor(img, cv2.COLOR_RGB2BGR))
        cv2.waitKey(1)

def main():
    ros_core = ros.RclpyWrapper()

    node = TestDisplayNode()

    rclpy.spin(node)

    node.destroy_node()
    rclpy.shutdown()

if __name__ == "__main__":
    main()

I also have to mention that I run it with ros bridge, since I need processing done with python3, which is supported by ROS2 only, and VREP seems to work only with ROS1 (although I'm just starting to work with these systems, so I'm not confident in that case).

UPDATE:

It seems like I have bee mistaken a bit about ros bandwidth capabilities. Frame rate actually changes with frame resolution.

But what actually puzzles me is how it is actually supposed to work? The best result I could get is 6464 video with decent frame rate. Even two 6464 sensors result in lower frame rate. How machine vision dependent projects are supposed to be done in with ROS?

I was actually hoping to use simulation with ROS since it could provide a lot of background and simplify testing and evaluation. Although I have not try to use gazebo yet.

Any advice is welcome.

Copying some comments, since I need to remove my update, which was originally created as an answer post.

@William : I haven't tried sending images with rclpy -> rclpy , but our image demos in C++ perform a lot better than that on most machines. Maybe as a base line you could try one of our image demos to see if the issue exists there too: https://github.com/ros2/ros2/wiki/Qua...

@William : Good performance with images (even large ones) is definitely possible with ROS 1 ... (more)