PixLab Blog

The PixLab development team is pleased to announce the immediate availability of the 1.1.8 release of our Embedded Computer Vision & Machine Learning library SOD.

SOD is an embedded, modern, cross-platform, computer vision and machine learning C/C++ 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. At PixLab, we believe SOD is:

• Suitable for deep learning on limited computational resource, embedded systems and IoT devices.
• Easy to integrate with existing code bases. Interpolatable with OpenCV and/or any other proprietary API.

SOD is shipped with a real-time face detection & tracking model (download link) that has been ported to Unity, Unreal Engine and WebAssembly.

Finally, you can find out more information about the SOD project via the following links:

• Project Homepage.
• Github Repository.
• Downloads Page.
• Code Samples.
• C/C++ Documentation.

This article share the technique used by PixLab to port the real-time face detection runtime written in pure C of the SOD Computer Vision library to WebAssembly to achieve real-time face detection on the browser.

The final result including the WASM binary, face model and the exported Javascript interfaces are available to download here and ready to be integrated into existing projects in-need for real-time face detection in the browser.

The full article is available to consult here.

The PixLab Computer Vision team is pleased to announce that a milestone have been reached for the Not Safe For Work API endpoint. Over the course of the last 12 months, the /nsfw API endpoint have already analyzed millions of our user's media files with high accuracy.

For those not familiar with this endpoint. /nsfw let you detect not suitable for work (i.e. nudity & adult) content in a given image or video frame. NSFW is of particular interest, if mixed with some media processing API endpoints like /blur, /encrypt or /mogrify to censor images on the fly according to their nsfw score.

A typical blurred image with a high NSFW score should look like the following:

To obtain such image result, two endpoints were actually used:

• /NSFW is the analysis endpoint that must be called first. It does perform nudity & adult content detection and return a score value between 0..1. The more this value approaches 1, the more your picture/frame is highly nsfw.
• /blur is called later only if the nsfw score value returned earlier is greater than certain threshold. In our case, it is set to 0.5.

The Python code below was used to generate the blurred picture programmatically without any human intervention. This can help you automate things such as verifying user's uploads:

import requests
import json

# Target Image: Change to any link (Possibly adult) you want or switch to POST if you want to upload your image directly, refer to the sample set for more info.
img = 'https://i.redd.it/oetdn9wc13by.jpg' 
# Your PixLab key
key = 'Pixlab_Key'

# Censor an image according to its NSFW score
req = requests.get('https://api.pixlab.io/nsfw',params={'img':img,'key':key})
reply = req.json()
if reply['status'] != 200:
    print (reply['error'])
elif reply['score'] < 0.5 :
    print ("No adult content were detected on this picture")
else:
    # Highly NSFW picture
    print ("Censoring NSFW picture...")
    # Call blur with the highest possible radius and sigma
    req = requests.get('https://api.pixlab.io/blur',params={'img':img,'key':key,'rad':50,'sig':30})
    reply = req.json()
    if reply['status'] != 200:
        print (reply['error'])
    else:
        print ("Censored image: "+ reply['link'])

Finally, the official endpoint documentation is available to consult at https://pixlab.io/cmd?id=nsfw and a set of working samples in various programming language are available at the PixLab samples pages.

The SOD development team just published a new computer vision article on how to detect vehicles registration plates without heavy Machine Learning techniques, just standard image processing routines already implemented in the SOD library. The article is available to consult here.

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.

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,
    'key':'PixLab_API_Key',
})
reply = req.json()
if reply['status'] != 200:
    print (reply['error'])
    exit();

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.

The PixLab team is pleased to announce major enhancement to the /tagimg endpoint.

The image labeling endpoint let you programmatically generates a description of an image in human readable language with complete sentences. The description is based on the visual content as reported by our state-of-the-art image labeling algorithm. More than one description can be generated for each image. Descriptions are ordered by their confidence score. All descriptions are in English.

the /tagimg endpoint documentation is available to consult here and below a working Python code sample:

import requests
import json

# Tag an image based on detected visual content which mean running a CNN on top of it.

# Target Image
img = 'https://s-media-cache-ak0.pinimg.com/originals/35/d0/f6/35d0f6ee0e40306c41cfd714c625f78e.jpg' 
# Your PixLab key
key = 'My_PixLab_Key'

req = requests.get('https://api.pixlab.io/tagimg',params={'img':img,'key':key})
reply = req.json()
if reply['status'] != 200:
    print (reply['error'])
else:
    total = len(reply['tags']) # Total tags
    print ("Total tags: "+str(total))
    for tag in reply['tags']:
        print("Tag: "+tag['name']+" - Confidence: "+str(tag['confidence']))

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

The PixLab team is pleased to introduce the PDF to Image API endpoint which let you convert any PDF file to a high resolution JPEG/PNG image format.

the /pdftoimg endpoint documentation is available to consult here and below a working Python code sample:

import requests
import json

# Convert a PDF document to JPEG/PNG image via /pdftoimg endpoint.

req = requests.get('https://api.pixlab.io/pdftoimg',params={
  'src':'https://www.getharvest.com/downloads/Invoice_Template.pdf',
  'export': 'jpeg',
  'key':'My_PixLab_Key'
})
reply = req.json()
if reply['status'] != 200:
    print (reply['error'])
else:
    print ("Link to the image output (Converted PDF page): "+ reply['link'])

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