Tag: api

Passports, Travel Docments & ID Cards Scan API Endpoint Available

The PixLab OCR team is pleased to introduce the /docscan API endpoint which let you in a single call scan a government issued document such as passports or various ID cards from certain countries.

Besides its accurate text scanning capabilities, the /docscan API endpoint shall Extract (crop) any detected face and transform the extracted text content such as Passport Machine Readable Zone (MRZ) into valuable information. Below, a typical output result of the /docscan endpoint for a passport input image:

Input Passport Specimen

Input Image URL

Parsed Fields

MRZ Fields

Face extraction is automatically performed using the /facedetect API endpoint. For a general purpose Optical Character Recognition engine, you should rely on the /OCR endpoint instead. If you are dealing with PDF documents, you can convert them at first to raw images via the /pdftoimg endpoint. This endpoint is available starting from the Prod Plan and up.

Below, a typical Python code snippet for scanning passports:

import requests
import json

# Given a government issued passport document, extract the user face and parse all MRZ fields.
#
# PixLab recommend that you connect your AWS S3 bucket via your dashboard at https://pixlab.io/dashboard
# so that any cropped face or MRZ crop is stored automatically on your S3 bucket rather than the PixLab one.
# This feature should give you full control over your analyzed media files.
#
# https://pixlab.io/#/cmd?id=docscan for additional information.

req = requests.get('https://api.pixlab.io/docscan',params={
    'img':'https://i.stack.imgur.com/oJY2K.png', # Passport sample
    'type':'passport', # Type of document we are a going to scan
    'key':'Pixlab_key'
})
reply = req.json()
if reply['status'] != 200:
    print (reply['error'])
else:
    print ("User Cropped Face: " + reply['face_url'])
    print ("MRZ Cropped Image: " + reply['mrz_img_url'])
    print ("Raw MRZ Text: " + reply['mrz_raw_text'])
    print ("MRZ Fields: ")
    # Display all parsed MRZ fields
    print ("\tIssuing Country: " + reply['fields']['issuingCountry'])
    print ("\tFull Name: "       + reply['fields']['fullName'])
    print ("\tDocument Number: " + reply['fields']['documentNumber'])
    print ("\tCheck Digit: "   + reply['fields']['checkDigit'])
    print ("\tNationality: "   + reply['fields']['nationality'])
    print ("\tDate Of Birth: " + reply['fields']['dateOfBirth'])
    print ("\tSex: "           + reply['fields']['sex'])
    print ("\tDate Of Expiry: "    + reply['fields']['dateOfExpiry'])
    print ("\tPersonal Number: "   + reply['fields']['personalNumber'])
    print ("\tFinal Check Digit: " + reply['fields']['finalcheckDigit'])

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

OCR performance improved

As requested by our users, our /OCR endpoint gets more support for various languages including Arabic, Modern Hebrew, Russian & simplified Chinese.

bounding box coordinates are now enabled by default. For each request, besides the full text output, you get a bbox array where each entry of this array hold the target word and its bounding box (i.e. rectangle) coordinates. Each entry in this array is identified by an instance of the following JSON object:


{
    word: Extracted word,
    x: X coordinate of the top left corner,
    y: Y coordinate of the top left corner,
    w: Width of the rectangle that englobe this word,
    h: Height of the rectangle that englobe this word
}

The documentation is updated and available to consult at https://pixlab.io/cmd?id=ocr and a Python sample is provided on Github at https://github.com/symisc/pixlab/blob/master/python/ocr.py.

With that in hand, you can further tune your analysis phase for example by extracting each word via /crop and perform another pass if desired.

List of face detection & recognition endpoints

As requested by our users, the following are the list of endpoints that is provided by PixLab for all your facial detection, recognition, generation & landmarks extraction tasks. These includes:

Here is two working Python code to illustrate this:

1.Detect all human faces present in a given image or video frame via facedetect and extract each one of them via crop:

import requests
import json
     
    # Target image: Feel free to change to whatever image holding as many human faces you want
    img = 'http://cf.broadsheet.ie/wp-content/uploads/2015/03/jeremy-clarkson_3090507b.jpg'
     
    req = requests.get('https://api.pixlab.io/facedetect',params={
        'img': img,
        'key':'My_Pix_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 via crop now 
    for face in reply['faces']:
        req = requests.get('https://api.pixlab.io/crop',params={
            'img':img,
            'key':'My_Pix_Key',
            'width': face['width'],
            'height': face['height'],
            'x': face['left'],
            'y': face['top']
        })
        reply = req.json()
        if reply['status'] != 200:
            print (reply['error'])
        else:
            print ("Face #"+str(face['face_id'])+" location: "+ reply['link'])
  1. Detect all human faces in a given image via facedetect and censure all of them via mogrify:

import requests
import json

img = 'http://anewscafe.com/wp-content/uploads/2012/05/Brave-Faces-Group-shot.jpg' 

# Detect all human faces in a given image via facedetect and censure all of them via mogrify.
req = requests.get('https://api.pixlab.io/facedetect',params={
    'img': img,
    'key':'Pix_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")
if total < 1:
    # No faces were detected, exit immediately
    exit()
# Pass the detected faces coordinates untouched to mogrify 
coordinates = reply['faces']
# Call mogrify & proceed to the censure
req = requests.post('https://api.pixlab.io/mogrify',headers={'Content-Type':'application/json'},data=json.dumps({
    'img': img,
    'key':'Pix_Key',
    'cord': coordinates #The field of interest
}))
reply = req.json()
if reply['status'] != 200:
    print (reply['error'])
else:
    print ("Censured pic location: "+ reply['link'])

You can take a look at the PixLab Github repository for additional samples on working with faces or refer to the PixLab Endpoints list for the official documentation.

Generate MEMEs Programmtically

MEMEs are de facto internet standard nowadays. At least, dozen if not hundred of daily top posts on Imgur or Reddit are probably MEMEs. That is, a pop culture image with sarcastic text (always) displayed on Top, Bottom or Center of that image. A lot of web tools out there let you create memes graphically but a few ones actually propose an API for generating memes from your favorite programming language.

In this blog post, we'll try to generate a few MEMEs programmatically using Python, PHP or whatever language that support HTTP requests with the help of the PixLab API but before that, lets dive a little bit into the tools needed to build a MEME generator.

Crafting a MEME API

Building a RESTful API capable of generating memes at request is not that difficult. The most important part is to find a good image processing library that support the annotate operation (i.e. Text drawing). The most capable & open source libraries are the ImageMagick suite and its popular fork GraphicsMagick. Both provides advanced annotate & draw capability such as selecting the target font, its size, text position, the stroke width & height and beyond. Both should be a good fit and up to the task. Here is some good tutorials to follow if you wanna build your own RESTful API:

In our case, we'll stick with the PixLab API due to the fact that is shipped with robust API endpoints such as Image compositing, facial landmarks extraction, dynamic image creation that proves of great help when working with complex stuff such as cloning Snapchat filters or playing with GIFs. So, without further ado, let's start programming some memes..

First MEME

Given an input image of the famous Michael Jordan crying face:

JDR face

Draw some funny text on top & bottom of that image to obtain something like this:

JDR Draw

Using the following code:

import requests
import json
# Draw some funny text on top & button of the famous Michael Jordan crying face.
# https://pixlab.io/cmd?id=drawtext is the target command
req = requests.get('https://api.pixlab.io/drawtext',params={
    'img': 'https://pixlab.io/images/jdr.jpg',
    'top': 'someone bumps the table',
    'bottom':'right before you win',
    'cap':True, # Capitalize text,
    'strokecolor': 'black',
    'key':'Pix_Key',
})
reply = req.json()
if reply['status'] != 200:
    print (reply['error'])
else:
    print ("Meme: "+ reply['link'])

make_meme.py/php snippet available on the PixLab Github Repository.

If this is the first time you've seen the PixLab API in action, your are invited to take a look at the excellent introduction to the API in 5 minutes or less. Only one command (API endpoint) is actually needed in order to generate such a meme:

drawtext is the command used for text annotation. It expect the text to be displayed on Top, Center or Bottom of the target image and support a bunch of other options such as selecting the text font, its size & colors, whether to capitalize the text or not, stroke width & opacity and so on. You can find out all the options the drawtext command takes here.

There is a more flexible command named drawtextat that let you draw text on any desired region of the input image by specifying the target coordinates (X,Y) of where the text should be displayed. Here is an usage example.

Dynamic MEME

This example is similar to the previous one except that the image we'll draw something on top is generated dynamically. That is, we will request from the PixLab API server to create a new image for us with a specified height, width, background color and output format and finally we'll draw our text at the center of the generated image to obtain something like this:

dynamic image

Using this code:

import requests
import json

# Dynamically create a 300x300 PNG image with a yellow background and draw some text on the center of it later.
# Refer to https://pixlab.io/cmd?id=newimage && https://pixlab.io/cmd?id=drawtext for additional information.

req = requests.get('https://api.pixlab.io/newimage',params={
    'key':'My_Pix_Key',
    "width":300,
    "height":300,
    "color":"yellow"
})
reply = req.json()
if reply['status'] != 200:
    print (reply['error'])
    exit();
# Link to the new image
img = reply['link'];

# Draw some text now on the new image
req = requests.get('https://api.pixlab.io/drawtext',params={
    'img':img, #The newly created image
    'key':'My_Pix_Key',
    "cap":True, #Uppercase
    "color":"black", #Text color
    "font":"wolf",
    "center":"bonjour"
})
reply = req.json()
if reply['status'] != 200:
    print (reply['error'])
else:
    print ("Pic location: "+ reply['link'])

dynamic_meme.py/php snippet available on the PixLab Github Repository.

Here, we request a new image using the newimage API endpoint which export to PNG by default but you can change the output format at request. We set the image height, width and the background color respectively to 300x300 with a yellow background color.

Note that if one of the height or width parameter is missing (but not both), then the available length is applied to the missing side and if you want a transparent image, set the color parameter to none.

We finally draw our text at the center of the newly created image using the wolf font, black color and 35 px font size. Of course, one could draw lines, a rectangle for example to surround faces, merge with other images and so forth...

Image Composite

Let's generate the following by compositing two smiley on top of Jordan's face:

jdr smiley

Using this code:

import requests
import json

# Composite two smiley on top of the famous Michael jordan crying face.
# A more sophisticated approach would be to extract the face landmarks using facelandmarks and composite something on the different regions.
# https://pixlab.io/cmd?id=merge for more info.

req = requests.post('https://api.pixlab.io/merge',
    headers={'Content-Type':'application/json'},
    data=json.dumps({
        'src':'https://pbs.twimg.com/media/CcEfpp0W4AEQVPf.jpg',
        'key':'My_Pix_Key',
        'cord':[
        {
           'img': 'http://www.wowpng.com/wp-content/uploads/2016/10/lol-troll-face-png-image-october-2016-370x297.png',
           'x': 30,
           'y': 320
        },
        {
           'img': 'http://orig08.deviantart.net/67d1/f/2010/216/6/7/lol_face_by_bloodyhalfdemon.png',
           'x': 630,
           'y': 95
        }]
    })
)
reply = req.json()
if reply['status'] != 200:
    print (reply['error'])
else:
    print ("Pic Link: "+ reply['link'])

face_composite.py/php snippet available on the PixLab Github Repository.

This operation is named compositing and is done via a single PixLab command named merge. this command expects a list of coordinates (X, Y) and a list of images to be composited (i.e. The two smiley in our case) on top of the target region. The coordinates here were set randomly and passed verbatim to merge but a more sophisticated approach would gather the facial coordinates and composite something on top of them such as a flower crown to mimic the Snapchat filters for example. Refer to the PixLab documentation for additional information on the merge command.

Mimic Snapchat Filters

This last example, although relatively unrelated to our subject here is about to show how to mimic the famous Snapchat filters programmatically. So, given an input image: plain woman face and this eye mask: eye_mask

located at. pixlab.xyz/images/eye_mask.png

plus this mustache: mustache located at. pixlab.xyz/images/mustache.png

output something like this: snapchat filter Well, in order to achieve that effect except for the MEME we draw on the bottom of that image, lots of computer vision algorithms are involved here such as face detection, facial landmarks extraction, pose estimation and so on. You are invited to take a look at our previous blog post on how such filter is produced, what techniques are involved and so on: Mimic Snapchat Filters Programmatically.

Conclusion

Generating MEMEs is quite easy providing a good image manipulation library. We saw that ImageMagick and GraphicsMagick with their PHP/Node.js binding can be used to create your own MEME Restful API. Our simple yet elegant solution is to rely on the PixLab API. Not only generating MEMEs is straightforward but also, you'll be able to perform advanced analysis & processing operations on your input media such as face analysis, nsfw content detection and so forth. Your are invited to take a look at the Github sample page for dozen of the others interesting samples in action such as censoring images based on their nsfw score, blurring human faces, making gifs, etc. All of them are documented on the PixLab API endpoints reference doc and the 5 minutes intro the the API. Finally, if you have any suggestion or critics, please leave a comment below.