imagezmq

==================================== imagezmq: Transporting OpenCV images

imagezmq is a set of Python classes that transport OpenCV images from one computer to another using PyZMQ messaging. For example, here is a screen on a Mac computer showing simultaneous video streams from 8 Raspberry Pi cameras:

.. image:: https://raw.githubusercontent.com/jeffbass/imagezmq/master/docs/images/screenshottest.png

Using imagezmq, this is possible with 11 lines of Python on each Raspberry Pi and with 8 lines of Python on the Mac.

First, run this receiver program on the Mac (or other display computer):

.. code-block:: python

# run this program on the Mac to display image streams from multiple RPis
import cv2
import imagezmq
image_hub = imagezmq.ImageHub()
while True:  # show streamed images until Ctrl-C
    rpi_name, image = image_hub.recv_image()
    cv2.imshow(rpi_name, image) # 1 window for each RPi
    cv2.waitKey(1)
    image_hub.send_reply(b'OK')

Then, on each Raspberry Pi, run this sender program:

.. code-block:: python

# run this program on each RPi to send a labelled image stream
# you can run it on multiple RPi's; 8 RPi's running in above example
import socket
import time
from picamera2 import Picamera2
import imagezmq

sender = imagezmq.ImageSender(connect_to='tcp://jeff-macbook:5555')

rpi_name = socket.gethostname() # send RPi hostname with each image
picam = Picamera2()
picam.start()
time.sleep(2)  # allow camera sensor to warm up
while True:  # send images as stream until Ctrl-C
    image = picam.capture_array()
    sender.send_image(rpi_name, image)

Wow! A video surveillance system with 8 (or more!) Raspberry Pi cameras in 19 lines of Python.

Why use imagezmq?

imagezmq is an easy to use image transport mechanism for a distributed image processing network. For example, a network of a dozen Raspberry Pis with cameras can send images to a more powerful central computer. The Raspberry Pis perform image capture and simple image processing like flipping, blurring and motion detection. Then the images are passed via imagezmq to the central computer for more complex image processing like image tagging, text extraction, feature recognition, etc.

Each imageZMQ message is a (text_message, image) tuple. The text portion of the tuple identifies the source and other info about the image. In the example above, the text_message portion identifies which RPi is sending the the image so that the receiver can put each unique RPi image stream into a specific window. More details about the imageZMQ tuples in the above example are here <https://github.com/jeffbass/imagezmq>_.

Features

• Sends OpenCV images from one computer to another using ZMQ.
• Can send jpeg compressed OpenCV images, to lighten network loads.
• Uses the powerful ZMQ messaging library through PyZMQ bindings.
• Allows a choice of 2 different ZMQ messaging patterns (REQ/REP or PUB/SUB).
• Enables the image hub to receive and process images from multiple image senders simultaneously.

Why ZMQ? Why not some other messaging protocol?

There are a number of high quality and well maintained messaging protocols for passing messages between computers. I looked at MQTT, RabbitMQ, AMQP and ROS as alternatives. I chose ZMQ and its Python PyZMQ bindings for several reasons:

• ZMQ does not require a message broker. It is a peer to peer protocol that does not need to pass an image first to a message broker and then to the imagehub. This means fewer running processes and less “double handling” of images. OpenCV images are large compared to simple text messages, so the absence of a message broker is important.
• ZMQ is very fast for passing OpenCV images. It enables high throughput between image senders and image hubs.
• ZMQ and its PyZMQ bindings are easy to install.

imagezmq has been transporting images from a dozen Raspberry Pi computers scattered around my farm to 2 linux image hub servers for over 2 years. The RPi's capture and send dozens to thousands of frames frames a day. imagezmq has worked very reliably and is very fast. You can learn more about my "science experiment urban permaculture farm" project at Yin Yang Ranch project overview <https://github.com/jeffbass/yin-yang-ranch>_.

Messaging Patterns: REQ/REP versus PUB/SUB

ZMQ allows many different messaging patterns. Two are implemented in imagezmq:

• REQ/REP: Each RPi sends an image and waits for a REPLY from the central image hub. The RPi sends a new image only when the REPLY is received. In the REQ/REP messaging pattern, each image sender must await a REPLY before continuing. It is a "blocking" pattern for the sender.
• PUB/SUB: Each RPi sends an image, but does not expect a REPLY from the central image hub. It can continue sending images without awaiting any acknowledgement from the image hub. The image hub provides no REPLY. It is a "non-blocking" pattern for the sender.

There are advantages and disadvantages for each pattern. REQ/REP is the default. See the documentation (link below) for more details.

Dependencies and Installation

imagezmq has been tested with:

• Python 3.5, 3.6, 3.7, 3.8, 3.9, 3.10 and 3.11
• PyZMQ 16.0, 17.1, 19.0 and 26.0
• Numpy 1.13, 1.16, 1.18 and 1.24
• OpenCV 3.3, 4.0, 4.1 and 4.6
• Raspberry Pi OS Bookworm and Bullseye using PiCamera2
• Raspbian OS Buster, Stretch and Raspbian Jessie using legacy PiCamera

Install OpenCV, including Numpy, into a Python Virtual Environment. Then be sure to install imagezmq into the same virtual environment. For example, on a Raspberry Pi running Raspberry Pi OS Bookworm, my virtual environment is named py311cv4.

Install imageZMQ using pip:

.. code-block:: bash

workon py311cv4  # use your virtual environment name
pip install imagezmq

imagezmq has a directory of tests organized into sender and receiver pairs. You will get all the source code for imagezmq including all the test programs by cloning the GitHub repository:

.. code-block:: bash

git clone https://github.com/jeffbass/imagezmq.git

Source Code and Full Documentation

imagezmq is open source. The source code, tests and documentation are at Imagezmq on GitHub <https://github.com/jeffbass/imagezmq>_. The documentation, including links to application examples, starts from the table of contents in the README.