# gem install multipart-post
require 'net/http/post/multipart'

url = URI.parse('https://api.platerecognizer.com/v1/plate-reader/')
path = '/path/to/car.jpg'
File.open(path) do |jpg|
  req = Net::HTTP::Post::Multipart.new url.path,
    "upload" => UploadIO.new(jpg, "image/jpeg", path)
  req['Authorization'] = 'Token my-token******'
  res = Net::HTTP.start(url.host, url.port, use_ssl: true) do |http|
    http.request(req)
  end
end

<?php
// CREATE FILE READY TO UPLOAD WITH CURL
$file = realpath('example.jpg');
if (function_exists('curl_file_create')) { // php 5.5+
  $cFile = curl_file_create($file);
} else {
  $cFile = '@' . realpath($file);
}

//ADD PARAMETER IN REQUEST LIKE regions
$data = array(
    'upload' => $cFile,
    'regions' => 'us-ca' // Optional
);

// Prepare new cURL resource
$ch = curl_init('https://api.platerecognizer.com/v1/plate-reader/');
curl_setopt($ch, CURLOPT_RETURNTRANSFER, true);
curl_setopt($ch, CURLINFO_HEADER_OUT, true);
curl_setopt($ch, CURLOPT_POST, true);
curl_setopt($ch, CURLOPT_POSTFIELDS, $data);
curl_setopt($ch, CURLOPT_HTTP_VERSION, CURL_HTTP_VERSION_2TLS);

// Set HTTP Header for POST request
curl_setopt($ch, CURLOPT_HTTPHEADER, array(
    "Authorization: Token my-token******"  //API KEY
    )
);

// Submit the POST request and close cURL session handle
$result = curl_exec($ch);
print_r($result);exit;
curl_close($ch);
?>

# pip install requests
import requests
from pprint import pprint
regions = ['mx', 'us-ca'] # Change to your country
with open('/path/to/car.jpg', 'rb') as fp:
    response = requests.post(
        'https://api.platerecognizer.com/v1/plate-reader/',
        data=dict(regions=regions),  # Optional
        files=dict(upload=fp),
        headers={'Authorization': 'Token my-token******'})
pprint(response.json())

# Calling with a custom engine configuration
import json
with open('/path/to/car.jpg', 'rb') as fp:
    response = requests.post(
        'https://api.platerecognizer.com/v1/plate-reader/',
        data=dict(regions=['us-ca'], config=json.dumps(dict(region="strict"))),  # Optional
        files=dict(upload=fp),
        headers={'Authorization': 'Token my-token******'})

# On Linux
# Calling the API with 2 regions (Mexico and California).
curl -o /tmp/car.jpg https://app.platerecognizer.com/static/demo.jpg # Get an image
curl -F "upload=@/tmp/car.jpg" \
  -F regions=mx \
  -F regions=us-ca \
  -H "Authorization: Token my-token******" \
  https://api.platerecognizer.com/v1/plate-reader/

# Calling the API with a custom engine configuration and region California.
curl -F "upload=@/path/to/car.jpg" \
  -F regions=us-ca \
  -F config="{\"region\":\"strict\"}" \
  -H "Authorization: Token my-token******" \
  https://api.platerecognizer.com/v1/plate-reader/

# Calling the API with an image URL.
curl -X POST -F upload_url="https://app.platerecognizer.com/static/demo.jpg" -H "Authorization: Token my-token*****" https://api.platerecognizer.com/v1/plate-reader

# On Windows
# Calling the API with 2 regions (Mexico and California).
curl -o car.jpg https://app.platerecognizer.com/static/demo.jpg
curl -F "upload=@car.jpg" ^
  -F regions=mx ^
  -F regions=us-ca ^
  -H "Authorization: Token my-token******" ^
  https://api.platerecognizer.com/v1/plate-reader/

# Calling the API with a custom engine configuration and region California.
curl -F "upload=@c:\path\to\car.jpg" ^
  -F regions=us-ca ^
  -F config="{\"region\":\"strict\"}" ^
  -H "Authorization: Token my-token******" ^
  https://api.platerecognizer.com/v1/plate-reader/

// Using Node-RED?
// Check https://github.com/parkpow/node-red-contrib-plate-recognizer

const fetch = require("node-fetch");
const FormData = require("form-data");
const fs = require("fs");

let image_path = "/path/to/car.jpg";
let body = new FormData();
body.append("upload", fs.createReadStream(image_path));
// Or body.append('upload', base64Image);
body.append("regions", "us-ca"); // Change to your country
fetch("https://api.platerecognizer.com/v1/plate-reader/", {
  method: "POST",
  headers: {
    Authorization: "Token my-token******",
  },
  body: body,
})
  .then((res) => res.json())
  .then((json) => console.log(json))
  .catch((err) => {
    console.log(err);
  });

// Complete Example:

// https://github.com/marcbelmont/deep-license-plate-recognition/tree/master/csharp/PlateRecognizer

// Function calling the API:

// https://github.com/marcbelmont/deep-license-plate-recognition/blob/master/csharp/PlateRecognizer/PlateReader.cs#L23

// Complete Example:

// https://github.com/marcbelmont/deep-license-plate-recognition/tree/master/java/PlateRecognizer

// Function calling the API:

// https://github.com/marcbelmont/deep-license-plate-recognition/blob/master/java/PlateRecognizer/src/main/java/com/mycompany/numberplate/recognize.java#L17    

// Complete Android App Example:

// https://github.com/parkpow/alpr-anpr-android/

// Complete Example (Windows or Linux):

// https://github.com/marcbelmont/deep-license-plate-recognition/tree/master/java/PlateRecognizer

// Function calling the API:

// https://github.com/marcbelmont/deep-license-plate-recognition/blob/master/cpp/linux/numberPlate.cpp#L25
// https://github.com/marcbelmont/deep-license-plate-recognition/blob/master/cpp/windows/ConsoleApplication2/ConsoleApplication2.cpp#L29

{
  "processing_time": 288.758,
  "results": [
    {
      "box": {
        "xmin": 143,
        "ymin": 481,
        "xmax": 282,
        "ymax": 575
      },
      "plate": "nhk552",
      "region": {
        "code": "gb",
        "score": 0.747
      },
      "vehicle": {
        "score": 0.798,
        "type": "Sedan",
        "box": {
          "xmin": 67,
          "ymin": 113,
          "xmax": 908,
          "ymax": 653
        }
      },
      "score": 0.904,
      "candidates": [
        {
          "score": 0.904,
          "plate": "nhk552"
        }
      ],
      "dscore": 0.99,
      // Make Model, Orientation and Color are only available if you set mmc=true
      "model_make": [
        {
          "make": "Riley",
          "model": "RMF",
          "score": 0.306
        }
      ],
      "color": [
        {
          "color": "black",
          "score": 0.937
        }
      ],
      "orientation": [
        {
          "orientation": "Front",
          "score": 0.937
        }
      ]
    }
  ],
  "filename": "1617_7M83K_car.jpg",
  "version": 1,
  "camera_id": null,
  "timestamp": "2020-10-12T16:17:27.574008Z"
}

# On Linux
# Custom threshold and fast mode:
curl -F "upload=@/path/to/car.jpg" \
  -F config="{\"mode\":\"fast\", \"threshold_d\":0.2, \"threshold_o\":0.6}" \
  -H "Authorization: Token my-token******" \
  https://api.platerecognizer.com/v1/plate-reader/

# Strict region matching:
curl -F "upload=@/path/to/car.jpg" \
  -F config="{\"region\":\"strict\"}" \
  -F region=us-ca \
  -H "Authorization: Token my-token******" \
  https://api.platerecognizer.com/v1/plate-reader/

# Prediction must include a vehicle:
curl -F "upload=@/path/to/car.jpg" \
  -F config="{\"detection_rule\":\"strict\"}" \
  -H "Authorization: Token my-token******" \
  https://api.platerecognizer.com/v1/plate-reader/

# On Windows
# Custom threshold and fast mode:
curl -F "upload=@c:\path\to\car.jpg" ^
  -F config="{\"mode\":\"fast\", \"threshold_d\":0.2, \"threshold_o\":0.6}" ^
  -H "Authorization: Token my-token******" ^
  https://api.platerecognizer.com/v1/plate-reader/

# Strict region matching:
curl -F "upload=@c:\path\to\car.jpg" ^
  -F config="{\"region\":\"strict\"}" ^
  -F region=us-ca ^
  -H "Authorization: Token my-token******" ^
  https://api.platerecognizer.com/v1/plate-reader/

# Prediction must include a vehicle:
curl -F "upload=@c:\path\to\car.jpg" ^
  -F config="{\"detection_rule\":\"strict\"}" ^
  -H "Authorization: Token my-token******" ^
  https://api.platerecognizer.com/v1/plate-reader/

import json
with open('/path/to/car.jpg', 'rb') as fp:
    response = requests.post(
        'https://api.platerecognizer.com/v1/plate-reader/',
        data=dict(config=json.dumps(dict(region="strict"))),
        files=dict(upload=fp),
        headers={'Authorization': 'Token my-token******'})

{
  "filename": "car.jpg",
  "timestamp": "2021-11-23 17:56:47.896406",
  "camera_id": "my-camera",
  "processing_time": 58.184,
  "results": [
    {
      "plate": {
        "type": "Plate",
        "score": 0.925,
        "box": {
          "xmin": 146,
          "ymin": 481,
          "xmax": 276,
          "ymax": 576
        },
        "props": {
          "plate": [
            {
              "value": "nhk552",
              "score": 0.838
            }
          ],
          "region": [
            {
              "value": "gb",
              "score": 0.931
            }
          ]
        }
      },
      "vehicle": {
        "type": "Sedan",
        "score": 0.834,
        "box": {
          "xmin": 68,
          "ymin": 103,
          "xmax": 914,
          "ymax": 643
        },
        "props": {
          "make_model": [
            {
              "make": "Riley",
              "model": "RMF",
              "score": 0.339
            }
          ],
          "orientation": [
            {
              "value": "Front",
              "score": 0.939
            }
          ],
          "color": [
            {
              "value": "black",
              "score": 0.846
            }
          ]
        }
      }
    }
  ]
}

# On Linux
# Calling the API with just the image
curl -o car.jpg https://app.platerecognizer.com/static/demo.jpg # Get an image
curl -F "upload=@car.jpg" \
  http://localhost:8080/v1/plate-reader/

# Calling with API with optional parameters config and mmc
# The region is set to Mexico and California
curl -F "upload=@/path/to/car.jpg" \
  -F regions=mx \
  -F regions=us-ca \
  -F mmc=true \
  -F config="{\"mode\":\"fast\"}" \
  http://localhost:8080/v1/plate-reader/

# On Windows
# Calling the API with just the image
curl -o car.jpg https://app.platerecognizer.com/static/demo.jpg
curl -F "upload=@car.jpg" ^
  http://localhost:8080/v1/plate-reader/

# Calling with API with optional parameters config and mmc
# The region is set to Mexico and California
curl -F "upload=@c:\path\to\car.jpg" ^
  -F regions=mx ^
  -F regions=us-ca ^
  -F mmc=true ^
  -F config="{\"mode\":\"fast\"}" ^
  http://localhost:8080/v1/plate-reader/

{
  "usage": {
    "max_calls": 1000,
    "calls": 44
  },
  "results": [
    // Same as cloud API example above.
  ],
  "camera_id": "null",
  "timestamp": "2020-01-16 17:00:00",
  "filename": "car.jpg"
}

{
  "version": "1.3.8",
  "license_key": "XXX",
  "total_calls": 2500,
  "usage": {"calls": 10},
  "webhooks": []
}

{
  "hook": {
    "target": "http://localhost:8081/",
    "id": 2,
    "event": "image.done"
  },
  "data": {
    "processing_time": 0.186,
    "timestamp": "2019-08-31T14:22:06.983Z",
    "results": [
      {
        "box": {
          "xmin": 563,
          "ymin": 530,
          "ymax": 579,
          "xmax": 702
        },
        "plate": "765410",
        "score": 0.9131534678711563,
        "dscore": 0.4084282568529709
      },
      {
        "box": {
          "xmin": 563,
          "ymin": 530,
          "ymax": 579,
          "xmax": 702
        },
        "plate": "830265",
        "score": 0.7700640306685624,
        "dscore": 0.8493143507578083
      }
    ],
    "filename": "14_22_test.jpeg",
    "version": 1,
    "camera_id": null
  }
}

curl -H "Authorization: Token my-token******" \
  https://api.platerecognizer.com/v1/statistics/

{
  "usage": {
    "month": 1,
    "calls": 128,
    "year": 2019,
    "resets_on": "2019-01-14T00:00:00Z"
  },
  "total_calls": 2500
}