Introduction

Docker components are the fundamental building blocks of Docker, a platform for building, running, and managing containerized applications. These components work together to provide a consistent and efficient way to develop and deploy applications across different environments.

The components in Docker are categorized into two groups:

• Basic
• Advanced

Basic Docker Components:

1. Docker Client: The Docker client is a command-line tool that allows users to interact with the Docker daemon and manage Docker objects, such as images, containers, and volumes. It provides a user-friendly interface for executing Docker commands and managing Docker environments.
2. Docker Daemon: The Docker daemon, also known as the Docker engine, is the backend service that manages Docker containers. It listens for instructions from the Docker client and executes them, creating, running, and managing containers according to the user’s requests.
3. Docker Image: A Docker image is a read-only template that contains the instructions for creating a Docker container. It includes the operating system, application code, dependencies, and configuration files necessary to run the application.
4. Docker Container: A Docker container is a running instance of a Docker image. It is an isolated and self-contained environment that packages the application and its dependencies, allowing it to run consistently across different environments.
5. Docker Registry: A Docker registry is a centralized location for storing and sharing Docker images. It allows users to pull and push images to make them available for use in different environments.
6. Docker Networking: Docker networking facilitates container communication and comprises five primary network drivers described below.

• None: Disabling the networking system prevents container connectivity with others.
• Bridge: This default driver links multiple containers to the same Docker host.
• Host: For scenarios where container-host isolation isn’t needed, the host network driver removes this isolation.
• Overlay: The overlay network driver enables communication among various swarm services across different hosts.
• macvlan: By assigning a MAC address and directing traffic through it, the macvlan driver makes a container appear as a physical device.

Advanced Docker Components:

• Docker Compose: Docker Compose is a tool for defining and running multi-container Docker applications. It allows users to define the services that make up an application, their dependencies, and how they should be connected together.
• Docker Swarm: Docker Swarm is a clustering tool for managing multiple Docker hosts as a single swarm. It allows users to deploy and manage applications across multiple hosts, providing scalability and fault tolerance.
• Docker Buildx: Docker Buildx is a tool for building Docker images. It provides advanced features for building images, such as support for multi-stage builds, caching, and parallel builds.
• Docker Content Trust: Docker Content Trust is a security feature that allows users to verify the integrity and authenticity of Docker images. It uses cryptographic signatures to ensure that images have not been tampered with.
• Docker Desktop: Docker Desktop is a bundled Docker environment for macOS, Windows, and Linux. It includes the Docker client, daemon, and other tools, making it easy to get started with Docker on local machines.

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BOM stands for Browser Object Model. The Browser Object Model (BOM) in JavaScript refers to a set of objects that are available in the browser’s window object, It is used to interact with the browser.

Some of the key objects in the BOM include:-

window object:

In JavaScript, Window object is the top-level object that represents the browser window. It provides access to various properties and methods, such as the document object, frame object, location object, history object, navigator object and Screen object.

Document object:

In JavaScript, Document object represents the web page that is currently loaded in the browser window. It provides access to various properties and methods, such as the title, URL, and various DOM (Document Object Model) elements.

History object:

In JavaScript, History object represents the browser history, which includes the URLs of previously visited web pages. It provides methods to navigate back and forward in the history, as well as to manipulate the history by adding or replacing entries.

navigator object:

The Navigator object in JavaScript represents the browser or user agent that is running the current script. It provides information about the browser, such as the name, version, and platform, as well as other details such as whether cookies are enabled and whether the browser supports specific features.

Screen object:

This object represents the physical display screen of the user’s computer. It provides information about the screen size, color depth, and resolution, as well as methods to control the display, such as resizing or scrolling the window.

Location object:

In JavaScript, Location object represents the current URL of the web page loaded in the browser window. It provides methods to manipulate the URL, such as changing the hostname, pathname, or search parameters.

Note:- BOM provides web developers with a powerful set of tools for manipulating the browser window and its contents, which can be used to create rich and interactive web applications.

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A huge amount of the data collected today is made up of images and videos. That is why effective image processing for translating and obtaining information is crucial for businesses. 

Data scientists usually preprocess the images before feeding it to machine learning models to achieve desired results. Consequently, it is paramount to understand the capabilities of various image processing libraries to streamline their workflows.

In this article, we are listing down the top image processing libraries in Python:

1. Scikit-image

Scikit-image uses NumPy arrays as image objects by transforming the original pictures. These ndarrys can either be integers (signed or unsigned) or floats. And as NumPy is built in C programming, it is very fast, making it an effective library for image processing. Among different methods, data scientists often utilise greyscale technique where each pixel is a shade of grey.

2. OpenCV

First released in 2000, OpenCV has become a popular library due to its ease of use and readability. The library is focused on image processing, face detection, object detection, and more. It is written in C++ but also comes with Python wrapper and can work in tandem with NumPy, SciPy, and Matplotlib. Backed by more than one thousand contributors on GitHub, the computer vision library keeps enhancing for an effortless image processing.

3. Mahotas

Mahotas allows developers to use its advanced features such as haralick, local binary patterns, and more. It can compute 2D and 3D images through mahotas.features.haralick module and perform advanced image processing by extracting information from pictures. Mahotas has over 100 functionalities for computer vision capabilities that can enable you to carry out processes like watershed, morphological processing, convolution, and more.

Here’s the link to the documentation and GitHub.

3. SimplelTK

Unlike other libraries that consider images as arrays, SimpleITK treats images as a set of points on a physical region in space. The region occupied by images is defined as origin, spacing, size, and direction cosine matrix. This modus operandi enables it to effectively process images. It supports a wide range of dimensions that includes 2D, 3D, and 4D.

4. SciPy

SciPy is primarily used for mathematics and scientific computations, but you can also implement algorithms for image manipulation by importing scippy.ndimage module. You can carry out binary morphology, object measurements, linear and non-linear filtering. Besides, one can draw contour lines, adjust interpolation, filter, effects, denoising, and other similar extraction and segmentation on images.SEE ALSO

5. Pillow

The library is an advanced version of PIL, which is supported by Tidelift. It includes various processes in image processing such as point operations, filtering, manipulating, and more. Pillow also supports a wide range of image formats, thus makes its must-have library for handling images.

6. Matplotlib

Matplotlib is mostly used for 2D visualisations, but it can also be leveraged for image processing. Although it does not support all the file formats, Matplotlib is effective in altering images for extracting information out of it.

Outlook

Image and video processing techniques are rapidly being adopted across the globe due to its many use cases. More recently, Indian Railways is using facial recognition for identifying criminals. Besides, it has also become an integral part of data science and artificial intelligence workflow for gathering information out of images or videos. 

While we have compiled a few most widely used libraries, there are numerous others in the technology marketplace that can be used for specific requirements. Therefore, you should identify your needs and based on that you can determine the best-fit image processing library.

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A UCLA research team has devised a technique that extends the capabilities of fluorescence microscopy, which allows scientists to precisely label parts of living cells and tissue with dyes that glow under special lighting. The researchers use artificial intelligence to turn two-dimensional images into stacks of virtual three-dimensional slices showing activity inside organisms.

In a study published in Nature Methods, the scientists also reported that their framework, called “Deep-Z,” was able to fix errors or aberrations in images, such as when a sample is tilted or curved. Further, they demonstrated that the system could take 2-D images from one type of microscope and virtually create 3-D images of the sample as if they were obtained by another, more advanced microscope.

“This is a very powerful new method that is enabled by deep learning to perform 3-D imaging of live specimens, with the least exposure to light, which can be toxic to samples,” said senior author Aydogan Ozcan, UCLA chancellor’s professor of electrical and computer engineering and associate director of the California NanoSystems Institute at UCLA.

In addition to sparing specimens from potentially damaging doses of light, this system could offer biologists and life science researchers a new tool for 3-D imaging that is simpler, faster and much less expensive than current methods. The opportunity to correct for aberrations may allow scientists studying live organisms to collect data from images that otherwise would be unusable. Investigators could also gain virtual access to expensive and complicated equipment.

This research builds on an earlier technique Ozcan and his colleagues developed that allowed them to render 2-D fluorescence microscope images in super-resolution. Both techniques advance microscopy by relying upon deep learning—using data to “train” a neural network, a computer system inspired by the human brain.

Deep-Z was taught using experimental images from a scanning fluorescence microscope, which takes pictures focused at multiple depths to achieve 3-D imaging of samples. In thousands of training runs, the neural network learned how to take a 2-D image and infer accurate 3-D slices at different depths within a sample. Then, the framework was tested blindly—fed with images that were not part of its training, with the virtual images compared to the actual 3-D slices obtained from a scanning microscope, providing an excellent match.

Ozcan and his colleagues applied Deep-Z to images of C. elegans, a roundworm that is a common model in neuroscience because of its simple and well-understood nervous system. Converting a 2-D movie of a worm to 3-D, frame by frame, the researchers were able to track the activity of individual neurons within the worm body. And starting with one or two 2-D images of C. elegans taken at different depths, Deep-Z produced virtual 3-D images that allowed the team to identify individual neurons within the worm, matching a scanning microscope’s 3-D output, except with much less light exposure to the living organism.

The researchers also found that Deep-Z could produce 3-D images from 2-D surfaces where samples were tilted or curved—even though the neural network was trained only with 3-D slices that were perfectly parallel to the surface of the sample.

“This feature was actually very surprising,” said Yichen Wu, a UCLA graduate student who is co-first author of the publication. “With it, you can see through curvature or other complex topology that is very challenging to image.”

In other experiments, Deep-Z was trained with images from two types of fluorescence microscopes: wide-field, which exposes the entire sample to a light source; and confocal, which uses a laser to scan a sample part by part. Ozcan and his team showed that their framework could then use 2-D wide-field microscope images of samples to produce 3-D images nearly identical to ones taken with a confocal microscope.

This conversion is valuable because the confocal microscope creates images that are sharper, with more contrast, compared to the wide field. On the other hand, the wide-field microscope captures images at less expense and with fewer technical requirements.

“This is a platform that is generally applicable to various pairs of microscopes, not just the wide-field-to-confocal conversion,” said co-first author Yair Rivenson, UCLA assistant adjunct professor of electrical and computer engineering. “Every microscope has its own advantages and disadvantages. With this framework, you can get the best of both worlds by using AI to connect different types of microscopes digitally.”

Other authors are graduate students Hongda Wang and Yilin Luo, postdoctoral fellow Eyal Ben-David and Laurent Bentolila, scientific director of the California NanoSystems Institute’s Advanced Light Microscopy and Spectroscopy Laboratory, all of UCLA; and Christian Pritz of Hebrew University of Jerusalem in Israel.

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WebPurify has launched a limited beta release of Optical Character Recognition Profanity Filter Service. It combines WebPurify’s Optical Character Recognition technology with its profanity filter solution into a single API.

The software can detect profane text in memes and other images in blogs, forums, social media apps, children’s sites, in-game chats, interactive agency campaigns and more. According to WebPurify, the proliferation of memes and images have made it more difficult to filter out profanity, as many optical character recognition programs can only extract text from documents.

According to WebPurify, its text extraction technology is trained on user-generated content, making it an effective tool for scanning content such as images and memes for offensive language. Clients can also customize a “block and allow” list to address any additional concerns.

“We initially created this as an assistant to our live image moderation teams,” said Jonathan Freger, founder and CTO of WebPurify. “It was such a successful addition to our moderations platform that we decided to make it publicly available.”

While there are many systems that identify profanity to filter it out, Deep Analysis founder Alan Pelz-Sharpe said doing it at the OCR level is not common.

“There are many other vendors that analyze and extract text from images and memes, though they don’t necessarily utilize OCR,” he said. “Today, newer vendors tend to also leverage machine learning, computer vision and artificial intelligence.”

Pelz-Sharpe added that products like WebPurify or Microsoft Azure Content Moderator are good first steps in stopping profanity before it is published or distributed.

“The bigger problem is in capturing the context of a message and the words used within that context,” he said. “That’s a bigger challenge to resolve.”

Additionally, Pelz-Sharpe said identifying text in images is not an exact science but continues to improve.

“In truth, though, analyzing text in images remains tricky and inconsistent,” he said. “The increased use of deep learning and advanced AI techniques is improving the accuracy rate.”

According to Freger, the OCR Profanity Filter Service will be generally available in early January.

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In June, Google told stock photography website Shutterstock that its researchers had found a weakness that could destroy the site’s entire business.

Google’s researchers had built an AI-powered tool that could easily remove the watermarks Shutterstock uses to protect all of its images across the site. If a company less “not evil” had done so, it could theoretically clone and steal Shutterstock’s entire database of images.

Google’s exploit, which it later posted on its research blog, analyzed hundreds of pictures with consistent watermarks, like Shutterstock’s. Once the algorithm learned to look at a photo and decide which pixel was a watermark and which wasn’t, it could just remove all the watermark pixels. Many watermarks are semi-transparent, so the algorithm already knew what should be in the watermark’s place as well.

Anyone with the right capability could remove “the watermark and build a parallel marketplace, basically copyright infringement,” Sultan Mahmood, a Shutterstock director of engineering, told Quartz. “Anybody with malicious intent could use this as a proof of concept.”

People can manually remove watermarks today, using image editing tools like Photoshop, but Google’s approach is automatic. It could clean the watermarks off hundreds of images in the time it would take a human to clean one. Mahmood said Shutterstock is mainly worried about the possibility of someone being able to quickly copy millions of photos.

Mahmood said Shutterstock could lessen its risk by making its watermarks random. If the pattern changes across every image, an algorithm would have a much tougher time removing it completely.

Following Google’s warning, Shutterstock assigned five engineers to the problem, who figured out how to generate randomized watermarks, decided on a design, and began applying the changes to its catalog of more than 150 million images. Though the fix is surprisingly simple, Mahmood said the company isn’t satisfied it’s a long-term solution, and it’s working on even more secure ways of watermarking images.

But smaller outfits still have the watermark problem. Independent photographers often use software like Photoshop to type their names on images as a watermark. Other stock services might not have the engineering capabilities to move so quickly. If malicious AI developers take advantage of Google’s research, that could start a cat-and-mouse game for even Shutterstock.

These AI-driven vulnerabilities are popping up more frequently among researchers, though they have yet to result in a major attack. Researchers studying the AI systems for self-driving cars ask whether malicious AI developers could print a texture on a stop sign to trick autonomous vehicles into missing it entirely, or even activate voice assistants like Siri and Google Assistant with a sound unintelligible to humans. For every attack, a temporary solution is found, but researchers are rarely confident about how long it will last.

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