sod VS pixlab

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.

Shameless plug: take a look to our embedded computer vision library SOD: https://sod.pixlab.io.

Khalid Elborai elaborates on their experiences integrating the SOD library into a Raku module.

PixLab is a Machine Learning SaaS platform that offers Computer Vision and Media Processing APIs either via a straightforward HTTP RESTful API or offline SDK via the SOD Embedded CV library.

In this tutorial, we are building a Next.js application - PicFil, that filters image uploads with PixLab's NSFW API. Users can upload an image or enter a link to the image they want to check.

When asked to select a Facial Recognition Engine for the application, choose PixLab Insight. The engine will be used for mapping each enrolled user’s face in real-time into a mathematical feature vector, known as a biometric hash, which is in turn stored in a sand-boxed binary index. This is used to protect the data of users when they are authenticated with FACEIO.

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...

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