Artificial intelligence (AI) has revolutionized the way we interact with images, and the current AI image solutions are a testament to this. AI image solutions are applications of AI that can identify, classify, and manipulate images with remarkable accuracy and speed. With the rise of deep learning and computer vision, AI image solutions have become increasingly sophisticated, and their applications have expanded to fields such as healthcare, finance, entertainment, and more. Some of the most prominent AI image solutions and their applications.

Image Recognition

Image recognition is the process of identifying and classifying objects, people, or other elements in an image. AI-powered image recognition solutions use deep learning algorithms to recognize images with high accuracy. Image recognition is widely used in various fields, including healthcare, security, and retail. For example, in healthcare, AI image solutions are used to analyze medical images such as X-rays and MRIs to detect diseases and conditions such as cancer, brain injuries, and more. In security, AI-powered image recognition solutions are used to identify faces, license plates, and other elements in surveillance videos. In retail, AI image solutions are used to identify and classify products and improve inventory management.

Object Detection

Object detection is a subset of image recognition that involves detecting the location of specific objects in an image. AI-powered object detection solutions can identify and locate objects in an image with high accuracy. Object detection is used in various fields, including self-driving cars, security, and e-commerce. For example, in self-driving cars, AI image solutions are used to detect pedestrians, traffic lights, and other objects on the road. In security, AI-powered object detection solutions are used to identify suspicious behavior and detect objects such as weapons and explosives. In e-commerce, AI image solutions are used to detect and locate products in images, improving search and recommendation algorithms.

1. TensorFlow: TensorFlow is an open-source platform for building and training machine learning models, including image recognition models. It provides a wide range of tools and resources for image processing and can be used for tasks such as image classification, object detection, and segmentation.
2. Keras: Keras is a high-level neural networks API written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It is a user-friendly and efficient way to build and train deep learning models, including image recognition models.
3. PyTorch: PyTorch is another popular open-source machine learning framework that can be used for image recognition. It provides a flexible and easy-to-use platform for building and training deep learning models and supports various image processing tasks.
4. OpenCV: OpenCV is a widely used computer vision library that provides tools and algorithms for image processing, including image recognition. It can be used for tasks such as object detection, face recognition, and motion detection.
5. YOLO: YOLO (You Only Look Once) is an open-source object detection system that can recognize objects in images and videos with high accuracy and speed. It is based on deep learning and can be trained on custom datasets.
6. ImageAI: ImageAI is an open-source library that provides pre-trained models for image recognition tasks such as object detection, classification, and segmentation. It supports several deep learning frameworks, including TensorFlow, Keras, and PyTorch.
7. Detectron2: Detectron2 is an open-source object detection framework built on top of PyTorch. It provides a flexible and efficient platform for building and training object detection models and supports various architectures such as Faster R-CNN, Mask R-CNN, and RetinaNet.
8. MXNet GluonCV: MXNet GluonCV is an open-source computer vision library built on top of MXNet. It provides a wide range of tools and resources for building and training object detection models and supports various architectures such as Faster R-CNN, Mask R-CNN, and SSD.
9. SimpleDet: SimpleDet is an open-source object detection framework based on PyTorch. It provides a simple and efficient platform for building and training object detection models and supports various architectures such as Faster R-CNN and RetinaNet.

Image Segmentation

Image segmentation is the process of dividing an image into multiple segments or regions based on specific criteria. AI-powered image segmentation solutions use deep learning algorithms to identify different objects and elements in an image and segment them accordingly. Image segmentation is widely used in various fields, including healthcare, entertainment, and transportation. For example, in healthcare, AI image solutions are used to segment medical images such as CT scans and MRIs to aid in diagnosis and treatment. In entertainment, AI-powered image segmentation solutions are used to create special effects and manipulate images in movies and games. In transportation, AI image solutions are used to segment images of roads and traffic to aid in autonomous driving.

1. Mask R-CNN: Mask R-CNN is an open-source framework based on deep learning that can perform instance segmentation, which involves identifying and segmenting each object in an image. It is built on top of the Faster R-CNN architecture and supports various deep learning frameworks such as TensorFlow and PyTorch.
2. U-Net: U-Net is a popular open-source deep learning architecture for image segmentation. It is based on a fully convolutional neural network and has been widely used for tasks such as medical image segmentation.
3. DeepLab: DeepLab is an open-source image segmentation framework based on deep learning. It supports various architectures such as DeepLab v3 and DeepLab v3+, which have achieved state-of-the-art performance on several benchmarks.
4. SegNet: SegNet is an open-source deep learning architecture for image segmentation. It is based on an encoder-decoder network and has been widely used for tasks such as road segmentation and object detection.
5. PyTorch-UNet: PyTorch-UNet is an open-source implementation of the U-Net architecture in PyTorch. It provides a simple and efficient platform for building and training image segmentation models.
6. SimpleITK: SimpleITK is an open-source library for image analysis that provides tools and algorithms for image segmentation, registration, and filtering. It supports various programming languages such as Python, C++, and Java.

Image Generation

Image generation is the process of creating new images using AI algorithms. AI-powered image generation solutions can generate highly realistic images based on specific inputs, such as text descriptions or reference images. Image generation is used in various fields, including art, fashion, and advertising. For example, in art, AI image solutions are used to generate unique and creative designs and artworks. In fashion, AI-powered image generation solutions are used to create new designs and prototypes. In advertising, AI image solutions are used to generate highly realistic product images and visualizations.

1. GAN (Generative Adversarial Networks): GANs are a type of deep learning model that can generate realistic images by training two neural networks, a generator and a discriminator, in a adversarial setting. Several open-source implementations of GANs are available, including TensorFlow-GAN, PyTorch-GAN, and Keras-GAN.
2. StyleGAN (Style-Based Generative Adversarial Networks): StyleGAN is a type of GAN that can generate high-quality images with fine-grained control over the image style and content. It has been used to generate realistic human faces, animals, and other objects. An open-source implementation of StyleGAN is available on GitHub.
3. DeepDream: DeepDream is a visualization technique that uses convolutional neural networks to generate abstract and surreal images. It works by optimizing the input image to maximize the activation of specific neurons in the network. An open-source implementation of DeepDream is available in the TensorFlow library.
4. PixelRNN (Pixel Recurrent Neural Networks): PixelRNN is a type of neural network that can generate images pixel by pixel. It works by predicting the probability distribution of each pixel value based on the context of previous pixels. An open-source implementation of PixelRNN is available in the PyTorch library.
5. VAE (Variational Autoencoder): VAEs are a type of deep learning model that can generate images by learning a low-dimensional representation of the image space. They work by encoding an input image into a lower-dimensional latent space and then decoding it back into an image. Several open-source implementations of VAEs are available, including TensorFlow-VAE and PyTorch-VAE.

AI image solutions are transforming the way we interact with images and are being used in various fields to improve efficiency, accuracy, and creativity. From image recognition and object detection to image segmentation and generation, AI image solutions are increasingly sophisticated and capable. As AI technology continues to evolve, we can expect to see more applications of AI image solutions in the future, and they will undoubtedly play an essential role in many industries.

The Future

Some of the tools are already used in new ways, to help reconstruct or understand archaeology dig sites. They help analyse genes and will help to discover new remedies or cures for illnesses in the future.

Even though many complain about the exponential growth of AI, it brings so many positive angels into the mix, allowing us to fix and elevate our lives. Sadly change often comes at a cost, but it lies in our hands to direct and secure AI technology to help and not destroy lives.

First a bit of context :)

What is Gettext?

Translation within WordPress is based of Gettext. Gettext is a software internationalization and localization (i18n) framework used in many programming languages to facilitate the translation of software applications into different languages. It provides a set of tools and libraries for managing multilingual strings and translating them into various languages.

The primary goal of Gettext is to separate the text displayed in an application from the code that generates it. It allows developers to mark strings in their code that need to be translated and provides mechanisms for extracting those strings into a separate file known as a „message catalog“ or „translation template.“

The translation template file contains the original strings and serves as a basis for translators to provide translations for different languages. Translators use specialized tools, such as poedit or Lokalize, to work with the message catalog files. These tools help them associate translated strings with their corresponding original strings, making the translation process more manageable.

At runtime, Gettext libraries are used to retrieve the appropriate translated strings based on the user’s language preferences and the available translations in the message catalog. It allows applications to display the user interface, messages, and other text elements in the language preferred by the user.

Gettext is widely used in various programming languages, including C, C++, Python, Ruby, PHP, and many others. It has become a de facto standard for internationalization and localization in the software development community due to its flexibility and extensive support in different programming environments.

What are WordPress language files?

WordPress uses dedicated language files to store translations of your strings of text in other languages. Two formats are used:

1. Portable Object (PO) files: They serve as the message catalog, containing the original strings that need to be translated and placeholders for the translations. Each entry in a PO file consists of several fields:
   • msgid: This field represents the original string that needs to be translated.
   • msgstr: This field holds the translated string corresponding to the msgid field.
   • Comments: PO files may include comments that provide context or instructions for translators. These comments start with a „#“ symbol.
2. Machine Object (MO) files: MO files are generated from PO files using a compilation process. The compilation process converts the human-readable PO file, which contains the original strings and their translations, into a more compact and efficient binary format suitable for runtime use.
   
   The purpose of MO files is to provide a faster and more efficient way of accessing translations during the execution of a software application. Instead of parsing and processing the text-based PO files at runtime, the Gettext libraries directly read the pre-compiled MO files.

Caching Language Files!

There has been a discussion for years, if it makes sense to cache mo-files, to speedup WordPress when multiple languages are in use. Discussion of the WordPress Core team.

There have been a couple of plugins trying to fix this and prevent reloading of language files on every pageload.

• https://de.wordpress.org/plugins/fast-translate/
• https://de.wordpress.org/plugins/mo-cache/
• https://de.wordpress.org/plugins/speed-up-translation/
• https://github.com/inpsyde/translation-cache
• https://www.bjornjohansen.com/wordpress-translations-loading-time

Most of these use transients in your database and object caches if active.

Does it really make any sense?

Well it all depends on the amount of language files and your infrastructure. This load/parse operation is quite CPU-intensive and does spend quite a significant amount of time.

I have decided, not to bother with it in the past :) But I am always looking for ways to speed up multi-lingual websites, as that is my daily bread & butter ;) Checkout Index WP MySQL For Speed & Index WP Users For Speed, which optimizes the MySQL Indexes for optimal speed. That change, really does make a bit difference!

Native Gettext for WordPress

One thing that can help to speed up things, is to use the native gettext extension and not the WordPress integration of it. This will indeed help speedup translation processing and help big multilingual websites.

Native Gettext for WordPress by Colin Leroy-Mira, provides just that.

Disable Gettext, when only using English!

Create a must use plugin and add this:

This will prevent any .mo file from loading.

Use the gettext filter to change any translatable string.

Please consider the performance impact. Read about it at the main documentation.

Enjoy ….

WHAT IS IPANORAMA 360?

iPanorama 360 for WordPress is a specialized plugin that enables users to create and display interactive 360-degree virtual tours or panoramic images on WordPress websites. It extends the functionality of the WordPress content management system by providing a user-friendly interface and a range of features specifically tailored for creating and showcasing 360-degree content.

With iPanorama 360 for WordPress, you can upload panoramic images or sets of photographs and convert them into interactive virtual tours or panoramic sliders. The plugin offers customization options to adjust the appearance and behavior of the panoramas, such as controlling the speed of rotation, choosing navigation controls, adding hotspots, and incorporating multimedia elements like images, videos, or audio.

The plugin integrates seamlessly with WordPress, allowing you to embed the created virtual tours or panoramic images directly into your website pages or posts. You can also customize the tour settings, such as enabling or disabling auto-rotation, adjusting the initial zoom level, or defining the starting viewpoint.

iPanorama 360 for WordPress typically provides an intuitive visual editor that enables users to create and edit their virtual tours or panoramas using a drag-and-drop interface. This makes it easier to add hotspots, link to other panoramas or external content, and customize the appearance and functionality of the tour.

IPANORAMA 360 FEATURES

• 3 Scene Types – cube, sphere and cylinder
• HotSpots – allows you to mark different areas that you want to point out
• Virtual Tour – travel from one to another scene by clicking on special hotspots
• Tooltip System – the plugin has a build-in powerful tooltip system
• Compass – you can be sure to look forward
• Preview Image – you can set an image that will show as a preview
• Fullscreen – you can toggle from the normal state to fullscreen and back
• Mouse Wheel – zoom with mouse wheel scrolling
• Keyboard – arrows can be used for navigation through the scene
• Touch – touch was used for navigation tasks
• Autoplay – the scene autorotate around a vertical axis
• Awesome Looks – 2 predefined themes
• View customization – create your own theme

You can testdrive iPanorama 360 using the Lite version or get the Pro version. The only limitation for the Lite version, is the ability to only create one panorama.

ADDING AN ARIAL / FLOOR MAP TO IPANORAMA 360!

I have tested many different solutions for 360 panoramas in the past and many have been lacking in one or more areas. I always seem to be coming back to iPanorama 360 for my projects, due to its solid editor and support.

One major problem for iPanorama 360 has been the ability to attach an Arial-map / Floor-map to the panorama. Others tried to fill that void, but mostly failed on Mobile.

There are solutions out there that could be reused (Image Map Pro / ImageLinks ) and integrated with iPanorama 360.

I decided to build an image hotspot solution myself, that can also tie into iPanorama. I am building out all the things I need and more :)

CURRENT & PLANNED FEATURES

1. Hotspot drag and drop positioning.
2. Image Hotspot or SVG based hotspots (Size, Fill , Border, Color).
3. Hotspot in and out animations.
4. Tooltip system, supporting full Media and HTML. Still expanding on that. I am not sure yet, what will finally be in the first version.
5. Popover option.
6. iPanorama 360 – Add arial / floor map support. Guided setup, with full automatic integration in the future. Only 1 week into development, so this might change.
7. iPanorama 360 – Radar view, which hooks into the current camera view and shows you what you are seeing on the map.
8. External link support.
9. Custom ID and classes per hotspot, to allow additional hooks or visual changes.
10. Hotspot JS Events (Click, Over, Out and Active)
11. REST Api
12. Shortcode support. Fully detached from iPanorama, which allows to add it to a popover, slideout or any other design construct you envision.
13. TWIG Template support, for the shortcode, to easily tweak the layout.
14. Elementor support.
15. Gutenberg support.

The first version is basically done and I will be using it for a client shortly. If you are interested, please feel free to get in touch. Will setup a demo page soon or link to the client using it ;)

PREVIEW

Cheers

Developer Tools

IT Tools is an amazing docker image that gives you access to a ton of developer tools for different categories. All these are nicely wrapped into an intuitive web-interface.

The online tools can perform different functions. These include generating tokens, hashing text, generating UUIDs, encrypting and decrypting text, generating BIP39 passphrases, converting dates and times, converting numbers between different bases and converting Roman numerals to numbers and vice versa. Other tools listed include base64 encoders/decoders, color converters, HTML editors, user-agent parsers, crontab generators, Docker run to Docker compose converters, and various calculators for items such as IPv4 subnets, ETA and temperatures.

I am using it locally and added some of my own. These tools are a nice addition for developers to streamline their workflow and are tools you often search online anyway.

Available Categories

• Crypto
  Token generator, Hash text, Bcrypt, UUID generator …
• Converter
  Date-time, Base64, Yaml to JSON ….
• Web
  Escape html entities, Url Parser, Basic Auth generator …
• Images
  QR Code Generator, SVG placeholder …
• Development
  Git Cheatcheet, Radom Port generator, Crontab generator …
• Network
  IPv4 subnet calculator, MAC address lookup …
• Math
  Math evaluator, ETA calculator
• Measurement
  Chronometer, Temperature converter …
• Text
  Lorem Ipsum, Text stats
• Data
  Phone parser …
• Add your own
  There is a tool that allows you to deploy / integrate your own

Setup

Docker is a containerization platform that allows you to create and manage containers for your applications. Not going into detail, but here the docker run command to get you started.

Preview
Github
Feature request

What is Bookstack

BookStack is an open-source, web-based platform for organizing, storing, and sharing knowledge and documentation. It was developed to provide a user-friendly and intuitive interface for creating and managing knowledge bases, wikis, and other types of structured content.

BookStack allows users to create books, chapters, and pages, and to organize them hierarchically. Users can also add images, files, and links to their content, and collaborate with others by assigning roles and permissions. BookStack also supports full-text search, version control, and commenting, making it easy to find and update information.

BookStack is written in PHP and uses the Laravel framework, and it is available for free under the MIT License.

What are you using it for?

This is my internal homelab documentation system, I use it for private and client related content. Bookstack is fast, clean and well documented. It has really elevated the way I store and access important reminders / how-tos and documentation.

The system provides API access and has many ways to tweak it. Bookstack documentation.

Why display CSV files?

CSV stands for „Comma-Separated Values“. It is a simple file format used to store tabular data, such as spreadsheets or databases, in plain text. In a CSV file, each line represents a row of data, and each column is separated by a comma (or sometimes a semicolon or tab).

There are always CSV exports or other data stored in CSV format, that i need quick access to sometimes.

Bookstack allows to add files to pages and insert links to them, but it does not embed or parse those files in any way. There are similar hacks to mine, this example allows to embed PDFs and here the main Hacks page.

Lets get started!

We need something to parse the CSV file and something to display the information in a nice visual & flexible grid.

1. I am choosing Papa Parse for the CSV parsing, wrote about it a long time ago and still using it actively for many projects.
   
   Papaparse can handle CSV files with different delimiters, encodings, and line endings, and can also handle complex data formats, such as quoted values, escaped characters, and multi-line cells. The library is widely used in web applications, data analysis tools, and other software that deal with CSV data.
2. Grid.js is my library of choice to display the parsed information in a nice grid.
   
   Grid.js is a modern and open-source JavaScript library for building responsive and customizable data tables in web applications.
   
   It provides a simple and intuitive API for creating, styling, and populating tables with data from various sources, including JSON, CSV, and Excel files.
   
   Grid.js supports features such as sorting, filtering, pagination, column resizing, and cell formatting, as well as advanced features such as tree grids, custom renderers, and editable cells. The library also includes built-in plugins for exporting data to different formats, such as CSV, Excel, and PDF.

Include the file in Bookstack

Included and linked CSV files look something like that in source.

Tweak the header

The settings allow you to add custom code to your instance. Another option would be to tweak template files, but its easier to do these light tweaks using the header customizer.

This can be found under: https://bookstack.instance/settings/customization

Add required libs

Either link them externally or better use locally stored versions of these.

Papa Parse
Grid.JS

Find CSV links in the page and append to link

1. This searches the page_content for elements that have „.csv“ in their textContent.
2. Gets textContent and the href of the link.
3. The link is passed to Papa Parse for processing.
4. On completion the data result is split into the header row and the rest.
5. That data is passed to Grid.JS and added to a container element after the link.
   Grid.JS provides a ton of configuration options, I added the most common as a starting point. This includes different ways to style the grid.

Github: Will be adding more hacks, as I clean some of them up :)

A bonus

You can use the title attribute to pass some config data, in JSON format. Currently only to strip columns, but will extend that a bit more for flexible usage ;)

The result

Should look something like that …

Enjoy coding …