Production ready, code samples available in various programming languages to start using PixLab like a pro!
The code samples set are of practical usage, production ready snippets available in various programming languages intended to familiarize the reader with the PixLab API. For an introduction course see Getting Started with PixLab, The PixLab Rest API and The Endpoints Reference Guide. You’re welcome to copy/paste and run these examples to see the API in action.
These examples and dozen of others are available on the PixLab Github Sample Repository.
Blur/Censor images uploads according to their NSFW score
Given an input image or video frame, perform nudity & adult content detection at first and if the NSFW score is high enough, apply a blur filter on the input picture. A typical blurred image should look like the following after processing:
To perform such operation, two endpoints are actually needed. One for the analysis phase, the other for processing:
The Code
This code and dozen of others are available on the PixLab Github Repository.
Face Recognition - Verify identify
In this example, we'll verify whether two faces belong to the same person or not. Let's verify these two:
The Code
This code and dozen of others are available on the PixLab Github Repository.
Convert a PDF document to a high resolution JPEG/PNG image
In this sample, we'll convert a PDF document to a high resolution JPEG or PNG image using a single API endpoint:
PDF invoice converted to a high resolution PNG.
The Code
This code and dozen of others are available on the PixLab Github Repository.
Image Tagging & Visual Content Extraction
Let's generate a description of an image in human readable language with complete sentences. This imply running a Convolutional neural network on top of the target image and extract content of interest.
Input Image
The Code
This code and dozen of others are available on the PixLab Github Repository.
Generate GIF form a Set of Static Images
Let's generate a GIF file from a set of static images:
Output GIF
To perform such operation, only one endpoint is actually needed:
MAKEGIF is the only processing endpoint needed here and it expect a set of static images which can be of different format plus a bunch of other options and the output is a GIF file where each frame correspond to one entry from the set of the given images. Find out more about makegif here.
PixLab ships with others GIF processing endpoints such as cropgif for cropping, gifcomposite to composite stuff on, resizegif for resizing and so forth.
The Code
This code and dozen of others are available on the PixLab Github Repository.
Generate Funny MEME
In this sample, we'll programmatically generate a funny MEME by drawing some funny text on top of the famous Cool Cat. Given an input image, output the following:
Input Picture
Output Picture
The Code
This code and dozen of others are available on the PixLab Github Repository.
Image Encryption & Decryption
In this sample, plain pixels are converted to enciphered pixels using ENCRYPT. The image is not readable until it has been deciphered via DECRYPT. A typical enciphered image should look like the following:
Input Picture
Output Picture (Enciphered Pixels)
The Code
This code and dozen of others are available on the PixLab Github Repository.
Random Pixel Generation
In this sample, we shall dynamically generate a new image of desired height and width, and fill it with random pixels using a mix of standard image processing and soon Machine Learning algorithms. Below, a typical output image generated via this endpoint.
Randomly Generated Image
The Code
This code and dozen of others are available on the PixLab Github Repository.
Scan US Driver's License
In this sample, we'll learn how to scan a given US Driver's License issued by any state despite the huge difference in shape & form which varies from one US state to another. This is done using a single PixLab API endpoint which also extracts (crop) any detected face within the target document with the mandatory fields that is the driver's name, address & license number, and many others. A typical input US Driver's License specimen, and the extracted output fields should look like the following after processing:
Extracted License Fields
To learn more about the DOCSCAN API endpoint capabilities, including the list of supported documents & code samples, please refer to the following blog posts:
The Code
This code and dozen of others are available on the PixLab Github Repository.
Scan a government issued Passport
In this sample, we'll scan a government issued, travel document such as Passport, Visa, Driver license issued by any US state, or simply an ID card from various countries using a single API endpoint. Besides its highly accurate text scanning capability, this endpoint should also extract (crop) any detected face within the target document. A typical input passport specimen and the extracted output fields should look like the following after processing:
Input Passport Specimen
Parsed MRZ Fields
Extracted License Fields
To learn more about the DOCSCAN API endpoint capabilities, including the list of supported documents & code samples, please refer to the following blog posts:
The Code
This code and dozen of others are available on the PixLab Github Repository.
Mimic Snapachat Filters
In this sample, we shall mimic the famous Snapchat filters on a set of human faces. Actually, producing those filters is quite easy and require only three API endpoints: facelandmarks, smartresize & merge plus a bunch of other optional endpoints for cool background effects if desired.
The Code
This code and dozen of others are available on the PixLab Github Repository.
OCR - Extract text content from a given image
Given an input image or video frame with human readable characters. Detect input language & extract text content from there. OCR stand for Optical Character Recognition and PixLab uses state-of-the-art machine learning algorithms, so expect very good results given a good quality image.
Typical Input Picture
The Code
This code and dozen of others are available on the PixLab Github Repository.
Detect all human faces present in a given image and extract each one of them
In this sample, we'll perform human face detection using the FACEDETECT endpoint, and finally extract each detected face via CROP.
Input Picture
After Crop
To perform such operation, two endpoints are actually needed: One for the analysis phase, the other for processing:
The Code
This code and dozen of others are available on the PixLab Github Repository.
Detect & Blur human faces
In this sample, we shall apply a blur filter on all the detected human faces in a given image or video frame. A typical output picture after processing should look like the following:
Input Picture
Output (all faces blurred) Picture
To perform such operation, two endpoints are actually needed:
The Code
This code and dozen of others are available on the PixLab Github Repository.
Face Lookup - Facial Shape Based Search
In this example, we'll perform a search operation for a person based on its facial shape. That is, given a human face, try to find it in a crowd. This involves iterating all over the detected faces in the crowd and performing face to face comparison.
The Code
This code and dozen of others are available on the PixLab Github Repository.
Upload a Local Media File
Most Analysis & Processing endpoints let you upload a media file to be analyzed or processed directly by sending a multipart/form-data POST request. Nevertheless, very few endpoints such as OCR, MOGRIFY, FACECOMPARE, etc. require that the target file be remotely available before processing. In such case, STORE is of particular help.
The STORE endpoint let you upload a local file whether video or image to the remote pixlab.xyz storage server or your own S3 bucket depending on the configuration from your dashboard. After that, you can use the output link for other purposes such as processing via MOGRIFY, DRAWRECTANGLES, etc. or simply serving content. Find out more information about the store endpoint here.
The Code
This code and dozen of others are available on the PixLab Github Repository.
Age, Gender and Emotion Patten Extraction
In this sample, we'll guess the age, gender and emotion pattern by just looking at the facial shape of any present human face in a given picture or video frame using state-of-the-art machine vision models.
facemotion typical output
The Code
This code and dozen of others are available on the PixLab Github Repository.
Composite a static image on top of a GIF
The Code
This code and dozen of others are available on the PixLab Github Repository.
Dynamically create a new image & draw some text on top
In this example, we'll dynamically create a 300x300 pixels, PNG image with a yellow background and draw some text on top. This is very useful, if you want for example to challenge bots on your platform by implementing a minimalistic yet powerful Captcha-like challenge. A typical generated image should look like the following after processing:
The Code
This code and dozen of others are available on the PixLab Github Repository.
Smart Image Resize
In case you need to check the width & height or format (eg. PNG or JPEG) of an uploaded image for example and dynamically resize/convert that image to the required dimension or format. Two endpoints are needed: One for the analysis phase, the other for processing...
The Code
This code and dozen of others are available on the PixLab Github Repository.
Cool Filters Effect
With over 130 API Endpoints, PixLab ships with dozens of filters without a lot of tedious programming effort on your side. All you need to do is call the target filter endpoint with some parameters if any and the desired effect is obtained immediately like the following:
The Code
This code and dozen of others are available on the PixLab Github Repository.
Composite two or more images on top of another
The Code
This code and dozen of others are available on the PixLab Github Repository.
Dynamically created using 
