Face Emotion now predict the Age and Gender of the target face

Good news for PixLab customers!

The /facemotion endpoint now besides outputting the rectangle coordinates for each detected human face, you'll be able to accurately extract their gender, age and emotion pattern via their facial shapes in just a matter of few milliseconds thanks to our newly deployed machine learning models hosted on OVH and AWS instances simultaneously for worldwide availability.

face emotion, gender and age

Below a Python sample to show you how easy is to predict the Age and Gender of any human face.

import requests
import json

# Detect all human faces present in a given image and try to guess their age, gender and emotion state via their facial shapes

# Target image: Feel free to change to whatever image holding as many human faces as you want
img = 'http://www.scienceforums.com/uploads/1282315190/gallery_1625_35_9165.jpg'

req = requests.get('http://api.pixlab.io/facemotion',params={
    'img': img,
reply = req.json()
if reply['status'] != 200:
    print (reply['error'])

total = len(reply['faces']) # Total detected faces
print(str(total)+" faces were detected")
# Extract each face now 
for face in reply['faces']:
    cord = face['rectangle']
    print ('Face coordinate: width: ' + str(cord['width']) + ' height: ' + str(cord['height']) + ' x: ' + str(cord['left']) +' y: ' + str(cord['top']))
    # Guess emotion
    for emotion in face['emotion']:
        if emotion['score'] > 0.5:
            print ("Emotion - "+emotion['state']+': '+str(emotion['score']))
    # Grab the age and gender
    print ("Age ~: " + str(face['age']))
    print ("Gender: " + str(face['gender']))

You can visit the PixLab Github repository for additional code samples in various programming languages including PHP and Java.

SOD Embedded 1.1.7 Released

Symisc Systems is pleased to release the first major version of the SOD library! SOD is an embedded, modern cross-platform computer vision and machine learning software library that expose a set of APIs for deep-learning, advanced media analysis & processing including real-time, multi-class object detection and model training on embedded systems with limited computational resource and IoT devices.

SOD was built to provide a common infrastructure for computer vision applications and to accelerate the use of machine perception in open source as well commercial products.

Notable SOD features

  • Built for real world and real-time applications.
  • State-of-the-art, CPU optimized deep-neural networks including the brand new, exclusive RealNets architecture.
  • Patent-free, advanced computer vision algorithms.
  • Support major image format.
  • Simple, clean and easy to use API.
  • Brings deep learning on limited computational resource, embedded systems and IoT devices.
  • Easy interpolatable with OpenCV or any other proprietary API.
  • Pre-trained models available for most architectures.
  • CPU capable, RealNets model training.
  • Production ready, cross-platform, high quality source code.
  • SOD is dependency free, written in C, compile and run unmodified on virtually any platform & architecture with a decent C compiler.
  • Amalgamated - All SOD source files are combined into a single C file (sod.c) for easy deployment.
  • Open-source, actively developed & maintained product.
  • Developer friendly support channels.

Programming Interfaces

The documentation works both as an API reference and a programming tutorial. It describes the internal structure of the library and guides one in creating applications with a few lines of code. Note that SOD is straightforward to learn, even for new programmer.

SOD in 5 minutes or less

A quick introduction to programming with the SOD Embedded C/C++ API with real-world code samples implemented in C.

C/C++ API Reference Guide

This document describes each API function in details. This is the reference document you should rely on.

SOD Github Repository

The official Github repository.

C/C++ Code Samples

Real world code samples on how to embed, load models and start experimenting with SOD.

Real-Time ASCII Art Rendering Library Released

The PixLab engineering team is pleased to announce the immediate availability of the Real-Time ASCII Art C/C++ Rendering Library.

ASCII Art is a single file C/C++ library that let you transform an input image or video frame into printable ASCII characters at real-time using a single decision tree. Real-time performance is achieved by using pixel intensity comparison inside internal nodes of the tree.

  1. For a general overview on how the algorithm works, please visit the demonstration page at https://art.pixlab.io.
  2. The Github Repository at https://github.com/symisc/ascii_art.
  3. The ASCII Art API at https://pixlab.io/art.


PixLab Officially Launched

Dear folks,

We are pleased to announce the immediate availability of our machine learning SaaS platform to the public.

PixLab is set of unified Restful APIs for all your media analysis & processing tasks. It is shipped with over 130 commands (API endpoints) including:

  1. Face detection, recognition, emotion, generation, lookup, landmarks, etc.
  2. Content Moderation & Extraction: nsfw, sfw, urlcapture, header, ocr, tagimg.
  3. Pixel Generation/Image processing.
  4. The ability to train your own object detector.

With this in hand, you can achieve amazing transformation to your input images & videos including:

  1. Mimic Snapchat filters
  2. Content filtering
  3. Blurring/Cropping human faces.
  4. MEME Creation.

and finally here is some useful links to start playing with:

  1. The PixLab API in 5 minutes or less: https://pixlab.io/#/start
  2. API Reference Guide: https://pixlab.io/#/api
  3. List of Images Analysis & Processing Commands: https://pixlab.io/#/cmdls
  4. The PixLab Sample Set: https://pixlab.io/#/examples

We are a small bootstrapped startup mostly composed of engineers distributed around the globe. It took us 10 months of tedious work to ship the first stable version of PixLab so we really hope that you enjoy it and we look forward to hear back from you guys!