pixlab VS TileDB

In this blog post, I will show you a live demo and how you can implement this in your Nextjs/React app using PixLab's powerful computer vision APIs.

PixLab (https://pixlab.io) & FACEIO (https://faceio.net) | Full-or-part-time | Remote | Computer Vision / Full stack Engineers |

PixLab, a leading provider of Machine Vision, Face Recognition & Media Processing APIs is looking for:

* Embedded C & Computer Vision engineer(s) to work on the SOD (https://sod.pixlab.io), embedded computer vision library.

* Senior Python engineer with proficiency in PyTorch to work on FACEIO (https://faceio.net), our facial authentication web framework for web sites & apps.

* C++ developer with ML expertise to work on the port of Tiny-Dream (https://pixlab.io/tiny-dream), our embedded Stable Diffusion C++ library from ncnn to ggml.

* React/Vue JS Web developer(s) with expertise in fabric.js to work on a brand new, web based photo editing software backed by generative AI.

Reach out to Vincent via contact AT pixlab.io with your resume if interested.

We are pleased to introduce FACEIO, a product developed from scratch here at PixLab in the past few years. We look forward to hear your thoughts about this...

Hi HN,

We are the core developers behind FACEIO (https://faceio.net), a product developed from scratch here at PixLab (https://pixlab.io) in the past few years.

FACEIO is a cross-browser, Cloud & On-Premise deployable, facial authentication framework, with a client-side JavaScript library (fio.js) that integrates seamlessly with any website or web application desiring to offer secure facial recognition experience to their users.

Put it simply, FACEIO is the easiest way to add passwordless authentication to web based applications. Simply implement fio.js on your website, and you will be able to instantly authenticate your existing users, and enroll new ones via Face Recognition using their computer Webcam or smartphone frontal camera on their favorite browser.

FACEIO works with regular Webcams or smartphones frontal camera on all modern browsers, does not require biometric sensors to be available on the client side, and works seemingly with all websites and web applications regardless of the underlying front-end JavaScript framework or server-side language or technology.

Implementing FACEIO is straightforward. Before so, you need to create a new application first on the FACEIO Console (https://console.faceio.net), and link this resource to your website or web application. The checklist below highlights the steps to follow for a smooth integration of fio.js on your site:

1. Create a new FACEIO application first: Follow the Application Wizard on the FACEIO Console to create your first application and link it to your website or web application.

2. Select a Facial Recognition Engine: Review Security & Privacy settings, Cloud or On-Premise deployment and customize the Widget look & feel, all done via the Application Wizard (https://console.faceio.net).

3. Add the fio.js library to your Website: Implement fio.js (https://faceio.net/getting-started), our facial recognition library on your website before rolling facial authentication to your audience...

4. Enroll & Authenticate your first used via the enroll() & authenticate() methods respectively, the only two exported methods from the fio.js library.

The details:

Each enrolled user on your website represented by its feature vector (biometrics hashes, mapped by the selected facial recognition engine), alongside with his Unique Facial ID (https://faceio.net/facialid), as well as, any metadata you have already linked to a particular user, is stored in a sand-boxed binary index called Application in the FACEIO jargon. Think of FACEIO Application as an isolated container of your users' data. Only your application with its encryption key can gain access to this index (features vectors & metadata). You can retrieve your encryption key via the Application Manager on the FACEIO Console.

You can create a new application via the FACEIO Console in a matter of minutes. This is easily done thanks to the Application Wizard. The wizard should automate the creation process for you. Usually, this involve inputting an application name, selecting a facial Recognition engine, reviewing security options, customizing the Widget layout, and so forth.

We have baked privacy and security directly into our infrastructure. We collect and store the minimum amount of personal information needed to authenticate users, and we back that up with intelligence-backed security monitoring. The underlying Facial Recognition Engines that FACEIO rely on such as PixLab Insight or AWS Rekognition only stores hash signatures of your facial features, a stream of meaningless floating point numbers anonymously, after your full explicit consent, and/or until you submits a removal request.

FACEIO itself (the service) including this Website, the fio.js facial authentication library, the Embedded Widget, the Rest API, the Console) does not store or handle biometrics nor even know anything about them. It is the responsibility of the selected facial recognition engine by the application owner (eg website or web application you use) to choose a cloud storage region or opt for on-premises deployment for storing biometrics hash.

Finally, The following tutorials, and guides should help you get started with FACEIO:

1.Getting Started Tutorial: Learn the fundamentals. Your first steps with FACEIO - https://faceio.net/getting-started.

2.Integration Guide: Learn how to implement fio.js, our facial recognition library on your website before rolling facial authentication to your audience - https://faceio.net/integration-guide

3.Developer Center: Code samples, documentation, support channels, and all the resources you need to implement FACEIO on your website - https://faceio.net/dev-guides

4. Trust Center: Learn how we handle your data securely and in compliance with privacy and legal requirements. - https://faceio.net/trust-center | https://faceio.net/apps-best-practice

import 'package:flutter/material.dart'; import 'package:dio/dio.dart'; void main() { runApp( const MaterialApp( home: FaceBlurPage(), ), ); } class FaceBlurPage extends StatefulWidget { const FaceBlurPage({super.key}); @override State createState() => _FaceBlurPageState(); } class _FaceBlurPageState extends State { String pixlabkey = "74389de25cb37a10adf615e8a79c8da4"; String imagelink = "https://pixlab.io/images/m3.jpg"; String? blurImagelink; // Instentiating Dio var dio = Dio(); // Detect faces using pixlab API Future detectFaces(String image) async { return dio.get( "https://api.pixlab.io/facedetect", queryParameters: { "img": image, "key": pixlabkey, }, ); } // Blurring faces using facial coordinates Future blurface(String image, List coordinates) async { return await dio.post( "https://api.pixlab.io/mogrify", data: { "img": image, "key": pixlabkey, "cord": coordinates, }, options: Options(contentType: "application/json"), ); } @override Widget build(BuildContext context) { return Scaffold( appBar: AppBar( title: const Text("Face Blur Example"), ), body: Column( children: [ Image.network(imagelink), blurImagelink != null ? Image.network(blurImagelink!) : const Text("No image provided"), ], ), floatingActionButton: FloatingActionButton( onPressed: () async { Response faces = await detectFaces(imagelink); Response blurfaceImageResponse = await blurface(imagelink, faces.data["faces"]); setState(() { blurImagelink = blurfaceImageResponse.data["ssl_link"]; }); }, child: const Icon(Icons.auto_mode_rounded), ), ); } }

Hi HN,

My team and I, developed PixLab as a independent product (kind of subsidiary now) back in 2017 for our parent company (Symisc Systems). Since then, the platform has grown to over 130 Computer Vision API endpoints[1], and hundreds of API consumers. We currently serve over 29 million API requests each month. We’ve bootstrapped PixLab entirely ourselves. Our goal is to build a product that developers enjoy using.

PixLab is a unified API platform that integrates vision, storage, prediction, annotation, and media processing. We offer cloud Rest APIs (https://pixlab.io/api), on-premises deployment (https://pixlab.io/on-premises), and embedded C/C++ SDK (https://sod.pixlab.io). Our API offering includes Face Detection|Blur|Recognition, Content Moderation, NSFW Classification, Passports/ID Scan, Gender/Age detection, Image Tagging, and many others[1]. We’d love you to try PixLab and let us know what you think. You can find out some production ready code samples at the samples page[2] and the Github repository[3]

[1]: https://pixlab.io/cmdls

[2]: https://pixlab.io/examples

[3]: https://github.com/symisc/pixlab

TileDB, Inc. | Full-Time | REMOTE | USA, Greece/EU | [https://tiledb.com](https://tiledb.com/)

TileDB has recently announced a $34 million Series B fund-raise and is actively hiring for engineers across a range of roles (SRE, backend/distributed systems, database internals, and more). You will have the opportunity to work on innovative technology that creates impact for challenging problems in genomics, geospatial, machine learning, distributed systems, and many other areas.

TileDB Cloud is the modern database, allowing developers and scientists to capture, analyze, and share any data with any tool. We build on a broad foundation of open source, maintaining the TileDB storage engine, libraries for genomics (single-cell and population), geospatial (raster, point clouds, and more), a TileDB visualization engine extending Babylon.js, and much more ([github.com/TileDB-Inc/TileDB](http://github.com/TileDB-Inc/TileDB))

With TileDB, all data — tables, genomics, images, videos, location, time-series — is captured as multi-dimensional arrays. To supercharge this data, TileDB Cloud implements a serverless infrastructure delivering query execution, access control, data and code sharing, and distributed computing at global scale — eliminating cluster management, minimizing TCO, and promoting scientific collaboration and reproducibility.

Website: [https://tiledb.com](https://tiledb.com/) | GitHub: https://github.com/TileDB-Inc/TileDB | Blog: https://tiledb.com/blog

We are actively hiring for several roles including:

- Site Reliability Engineer (k8s, Terraform, automation, Prometheus, CloudWatch, GitOps; Golang, Python)

- single cell genomics: in collaboration with the Chan-Zuckerberg Initiative, we recently released TileDB-SOMA for single cell data, with APIs for both Python and R built around a common storage specification: https://tiledb.com/blog/tiledb-101-single-cell

With TileDB, all data — tables, genomics, images, videos, location, time-series — across multiple domains is captured as multi-dimensional arrays. TileDB Cloud implements a totally serverless infrastructure and delivers access control, easier data and code sharing and distributed computing at global scale, eliminating cluster management, minimizing TCO and promoting scientific collaboration and reproducibility.

Website: https://tiledb.com

GitHub: https://github.com/TileDB-Inc/TileDB

Stavros from TileDB here (Founder and CEO). I thought of requesting some feedback from the community on this blog. It was only natural for a multi-dimensional array database like TileDB to offer vector (i.e., 1D array) search capabilities. But the team managed to do it very well and the results surprised us. We are just getting started in this domain and a lot of new algorithms and features are coming up, but the sooner we get feedback the better.

TileDB-Vector-Search Github repo: https://github.com/TileDB-Inc/TileDB-Vector-Search

TileDB-Embedded (core array engine) Github repo: https://github.com/TileDB-Inc/TileDB

TileDB 101: Vector Search (blog to get kickstarted): https://tiledb.com/blog/tiledb-101-vector-search/

TileDB, Inc. | Full-Time | REMOTE | USA | Greece | https://tiledb.com

TileDB is the database for complex data, allowing data scientists, researchers, and analysts to access, analyze, and share any data with any tool at global scale. We have just launched a vector search library leveraging TileDB and TileDB Cloud for powerful local search and seamless scaling to multi-modal organizational datasets and batched computation: https://tiledb.com/blog/why-tiledb-as-a-vector-database

With TileDB, all data — tables, genomics, images, videos, location, time-series — across multiple domains is captured as multi-dimensional arrays. Our vector search library and other offerings are designed to empower these datasets with extreme interoperability via numerous APIs and tool integrations across the data science ecosystem, eliminating the hassles and inefficiencies of data conversion. TileDB Cloud implements a totally serverless infrastructure and delivers access control, easier data and code sharing and distributed computing at global scale, eliminating cluster management, minimizing TCO and promoting scientific collaboration and reproducibility.

TileDB, Inc. | Full-Time | REMOTE | USA | Greece | https://tiledb.com

TileDB transforms the lives of analytics professionals and data scientists with a universal database, allowing them to access, analyze, and share any data with any tool at global scale. TileDB unifies the way we think about data, delivering superior performance and foundational data management capabilities. All data — tables, genomics, images, videos, location, time-series — across multiple domains is captured as multi-dimensional arrays. TileDB offers extreme interoperability via numerous APIs and tool integrations across the data science ecosystem, eliminating the hassles and inefficiencies of data conversion. TileDB Cloud implements a totally serverless infrastructure and delivers access control, easier data and code sharing and distributed computing at global scale, eliminating cluster management, minimizing TCO and promoting scientific collaboration and reproducibility.

TileDB, Inc. was spun out of MIT and Intel Labs in May 2017 and is backed by Two Bear Capital, Nexus Venture Partners, Uncorrelated Ventures, Intel Capital and Big Pi.

Recent HN article: https://news.ycombinator.com/item?id=23896131

Website: https://tiledb.com

GitHub: https://github.com/TileDB-Inc/TileDB

Docs: https://docs.tiledb.com

Blog: https://tiledb.com/blog

Our headquarters are located in Cambridge, MA and we have a subsidiary in Athens, Greece. We offer the ability to work remotely. If you are located outside of the USA and Greece we have options to accommodate this, don't hesitate to apply!

We have several open positions aimed at increasing TileDB’s feature set, growth and adoption. You will have the opportunity to work on innovative technology that creates impact on challenging and exciting problems in Genomics, Geospatial, Time Series, and more. Immediate features on the roadmap for TileDB Cloud include, advanced distributed computations, advanced computation pushdown, improved multi-cloud deployments and more.

We are actively seeking:

- Senior Golang Engineer

- Senior Python Engineer

- Site Reliability Engineer

- React Frontend Engineer

Apply today at https://tiledb.workable.com !

Interesting. Have you come across TileDB before?

https://tiledb.com/

Hi folks, Stavros from TileDB here. Here are my two cents on tabular data. TileDB (Embedded) is a very serious competitor to Parquet, the only other sane choice IMO when it comes to storing large volumes of tabular data (especially when combined with Arrow). Admittedly, we haven’t been advertising TileDB’s tabular capabilities, but that’s only because we were busy with much more challenging applications, such as genomics (population and single-cell), LiDAR, imaging and other very convoluted (from a data format perspective) domains.

Similar to Parquet:

* TileDB is columnar and comes with a lot of compressors, checksum and encryption filters.

* TileDB is built in C++ with multi-threading and vectorization in mind

* TileDB integrates with Arrow, using zero-copy techniques

* TileDB has numerous optimized APIs (C, C++, C#, Python, R, Java, Go)

* TileDB pushes compute down to storage, similar to what Arrow does

Better than Parquet:

* TileDB is multi-dimensional, allowing rapid multi-column conditions

* TileDB builds versioning and time-traveling into the format (no need for Delta Lake, Iceberg, etc)

* TileDB allows for lock-free parallel writes / parallel reads with ACID properties (no need for Delta Lake, Iceberg, etc)

* TileDB can handle more than tables, for example n-dimensional dense arrays (e.g., for imaging, video, etc)

Useful links:

* Github repo (https://github.com/TileDB-Inc/TileDB)

* TileDB Embedded overview (https://tiledb.com/products/tiledb-embedded/)

* Docs (https://docs.tiledb.com/)

* Webinar on why arrays as a universal data model (https://tiledb.com/blog/why-arrays-as-a-universal-data-model)

Happy to hear everyone’s thoughts.

It could be worth checking out TileDB: https://github.com/TileDB-Inc/TileDB The entire system, down to the data format itself, is optimized around storing multi-dimensional arrays. It also supports timestamps and real numbers as dimensions, which could be handy given your example data. [Full disclosure: I currently work for TileDB.]