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Hello

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Create authentication token

Step 1: Get user to create token for

django.contrib.auth.models.User.objects.all() return error AttributeError: Manager isn't available; 'auth.User' has been swapped for 'users.User'

from django.contrib.auth import get_user_model
In [5]: get_user_model()
Out[5]: socgram.users.models.User
In [8]: get_user_model().object.all()

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card_no:
  x: 392
  y: 118
  w: 227
  h: 48
  binarize: false

date_of_expiry:
  x: 413
  y: 195

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1. Install cmake
2. Add to cmake to PATH

PATH=$PATH:/Applications/CMake.app/Contents/bin

3. Clone OpenCV repository

cd ~/<my_working _directory>
git clone https://github.com/opencv/opencv.git

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[
  {
    "Classification": "zeppelin-env",
    "Properties": {
      
    },
    "Configurations": [
      {
        "Classification": "export",
        "Properties": {

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Error message when tesseract is not install properly. Please install the correct OCR library required - http://code.google.com/p/tesseract-ocr/ http://code.google.com/p/python-tesseract/ Need to do 2 things to fix this:

Install tesseract

brew install tesseract

Install python-tesseract Wrapper

1. Download binary from https://code.google.com/archive/p/python-tesseract/downloads
2. Use brew --cache

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Mat src=imread("card.jpg");
 Mat thr;
 cvtColor(src,thr,CV_BGR2GRAY);
 threshold( thr, thr, 70, 255,CV_THRESH_BINARY );

 vector< vector <Point> > contours; // Vector for storing contour
 vector< Vec4i > hierarchy;
 int largest_contour_index=0;
 int largest_area=0;

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Keys for obtaining US Driver's license data

Standard for US Driver's Licenses defines 9 different barcode standards (AAMVA versions) with over 80 different fields encoded inside a barcode. Some fields exist on all barcode standards, other exist only on some. To standardize the API, we have structured the fields in the following sections:

1. Determining AAMVA version
2. Keys existing on all barcode versions
   • Mandatory values
     • Personal data
     • License data
   • Optional values

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from pyspark.sql.functions import split
df = sc.parallelize([[1, 'Foo:10'], [2, 'Bar:11'], [3,'Car:12']]).toDF(['Event', 'eventtype'])
df = df.withColumn('Thing', split(df.eventtype, ':')[0])
df = df.withColumn('Ranking', split(df.eventtype, ':')[1])
df.collect()

# [Row(Event=1, eventtype=u'Foo:10', Thing=u'Foo', Ranking=u'10'),
#  Row(Event=2, eventtype=u'Bar:11', Thing=u'Bar', Ranking=u'11'),
#  Row(Event=3, eventtype=u'Car:12', Thing=u'Car', Ranking=u'12')]

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// Use Gists to store code you would like to remember later on
console.log(window); // log the "window" object to the console