17 posts tagged with "tutorials"

The Robot Operating System (ROS) stands as a versatile framework for developing sophisticated robotic applications with various sensors, including cameras. These cameras are relatively inexpensive and widely used as they can provide a wealth of information about the robot's environment.

Processing camera output with computer vision requires efficient solutions to handle massive amounts of data in real time. ROS 2 is designed with this in mind, but it is a communication middleware and does not provide a built-in solution for storing and managing large volumes of image data.

Addressing this challenge, this blog post will guide you through setting up ROS 2 with ReductStore—a time-series database for unstructured data optimized for edge computing, ensuring your robotic applications can process and store camera outputs effectively.

The MQTT protocol is a simple way to connect different sources of data to applications, which makes it very popular for IoT (Internet of Things) applications. Some MQTT brokers can save messages for a while, even when the MQTT client is offline. However, sometimes, you need to keep this data for a longer time. In those cases, it's a good idea to use a time series database.

There are many time series databases available, but if you need to store things like images, sensor data, or Protobuf messages, you might want to use ReductStore. This database is designed to store a lot of blob data and works well with IoT and edge computing.

ReductStore has client SDKs (Software Development Kits) for many programming languages. This means you can easily use it in your existing system. In this example, we'll be using the Python SDK from ReductStore.

Now, let's make a simple MQTT application to see how this all works.

When it comes to computer vision, data storage is a critical component. You need to be able to store images for model training, as well as the results of the processing for model validation. There are a few ways to go about this, each with its own advantages and disadvantages. In this post, we’ll take a look at three different ways to store data in computer vision applications: a file system, an S3-like object storage and ReductStore. We’ll also discuss some of the pros and cons of each option.

A Simple Computer Vision Application​

For demonstration purposes, we’ll use a simple computer vision application which is connected to a CV camera and runs on an edge device:

The camera driver captures images from the CV camera every second and forwards them to the model, which then detects objects and displays the results in the user interface.

Your images and results need to be stored for training and validation purposes. The customer may also wish to view images featuring anomalous objects. These requirements present the challenge of maintaining a history of blob or unstructured data.

The MQTT protocol is widely used in IoT applications because of its simplicity and ability to connect different data sources to applications using a publish/subscribe model. While many MQTT brokers support persistent sessions and can store message history as long as an MQTT client is not available, there may be cases where data needs to be stored for a longer period. In such cases, it is recommended to use a time series database. There are many options available, but if you need to store unstructured data such as images, sensor data, or Protobuf messages, consider using ReductStore. It is a time series database specifically designed for storing large amounts of blob data and optimized for IoT and edge computing.

ReductStore provides client SDKs for many programming languages to integrate it into your infrastructure. In this example, we will use the client SDK for JavaScript.

Let’s make a simple MQTT application to see how it works.

This article provides an introduction to ReductStore and explains how to use the Reduct C++ SDK to subscribe to data from the database.

Prerequisites​

To subscribe to new records, we should use a continuous query, which has been supported by ReductStore since version v1.4. We can use the following Docker command to run it:

docker pull reduct/store:latest
docker run -p 8383:8383 reduct/store:latest 

Now, we need to install the Reduct Client SDK for C++. Please refer to these instructions.

The ReductStore Project hosts the free "cats" dataset, which contains about 10K photos of cats in JPEG format with eye coordinates, a month, and ears as labels. In this example, we can learn how to download the dataset from the ReductStore instance and draw the features using the OpenCV library.

Installing Dependencies​

First, we need to install the ReductStore Client SDK for Python to download the photos and labels. We also need the OpenCV Python library for drawing features and Pillow to display images in the Jupyter environment:

pip install reduct-py opencv-python Pillow

Getting Data​

To retrieve data we need the URL of the ReductStore instance, the bucket name, where we store our datasets and an API token with read access, so that we can connect to the database by using the Client class:

from reduct import Client, Bucket

HOST = "https://play.reduct.store"
API_TOKEN = "dataset-read-eab13e4f5f2df1e64363806443eea7ba83406ce701d49378d2f54cfbf02850f5"
BUCKET = "datasets"

client = Client(HOST, api_token=API_TOKEN)

bucket: Bucket = await client.get_bucket(BUCKET)

Using snap, users can now easily install and manage ReductStore on various Linux distributions.

Snap is a universal package manager developed by Canonical, the company behind Ubuntu Linux. It allows developers to package their applications and dependencies into a single package that can be installed on any Linux distribution that supports snap, without worrying about different packaging formats and dependency conflicts.

We are excited to announce the release of ReductStore Client SDK for C++ v1.3.0! This release includes support for the ReductStore API v1.3.0 with labels and content type.

Hey everyone,

we are pleased to announce the release of version 1.3.0 of the ReductStore SDK for JavaScript. This version supports the new features of ReductStore v1.3, including labels and content type.

Now you can write a record with MIME type and labels to ReductStore:

const client = new Client("https://play.reduct.store");

const bucket = await client.getOrCreateBucket("bucket");
    
const record = await bucket.beginWrite("entry-1", {
    contentType:"text/plain", 
    labels: {type:"example"}
})

await record.write("Some text");

We are happy to announce the release of version 1.3.0 of the ReductStore SDK for Python! This release introduces several new features to help users better organize and filter their data.

One of the most notable new features is the ability to attach labels to data when writing and querying. Labels are key-value pairs that can be used to classify and categorize data. For example, you might use labels to store metadata about a record, such as its md5 sum or class. To start using labels, you need the version of the ReductStore database higher than 1.3.0.