Plate Recognizer Snapshot API

https://docs.platerecognizer.com/
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Plate Recognizer Snapshot API is a license plate recognition service that reads and decodes license...
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### GET /info/

Source: https://docs.platerecognizer.com

Retrieves SDK version, license key, and webhook information.

```APIDOC
## GET /info/

### Description
Retrieves SDK version, license key, and webhook information.

### Method
GET

### Endpoint
http://localhost:8080/info/

### Parameters
#### Path Parameters
None

#### Query Parameters
None

### Request Example
```json
{
  "example": "GET http://localhost:8080/info/"
}
```

### Response
#### Success Response (200)
- **version** (string) - The SDK version.
- **license_key** (string) - The license key.
- **total_calls** (integer) - Total number of API calls made.
- **usage** (object) - Information about API call usage.
  - **calls** (integer) - Number of calls made.
- **webhooks** (array) - A list of configured webhooks.

#### Response Example
```json
{
  "version": "1.3.8",
  "license_key": "XXX",
  "total_calls": 2500,
  "usage": { "calls": 10 },
  "webhooks": []
}
```
```

--------------------------------

### Recognition API Requests

Source: https://docs.platerecognizer.com

Examples for calling the recognition API with images and optional parameters on Linux and Windows.

```bash
#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/  
  
#Sending the image in base64  
curl -F "upload=$(base64 'image.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/
```

```bash
#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/
```

--------------------------------

### Upload File with Ruby

Source: https://docs.platerecognizer.com

Upload an image file using Ruby's Net::HTTP::Post::Multipart library. Ensure the 'multipart-post' gem is installed.

```ruby
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 YOUR_API_TOKEN'
  res = Net::HTTP.start(url.host, url.port, use_ssl: true) do |http|
    http.request(req)
  end
end
```

--------------------------------

### Download Test Image with Curl

Source: https://docs.platerecognizer.com

Use this command to download a free test image for API testing. Ensure you have curl installed.

```shell
curl -o /tmp/car.jpg https://app.platerecognizer.com/static/demo.jpg
```

--------------------------------

### GET /v1/statistics/

Source: https://docs.platerecognizer.com

Retrieves monthly usage statistics for the current month. This is available for Snapshot Cloud only.

```APIDOC
## GET /v1/statistics/

### Description
Retrieves monthly usage statistics for the current month. This is available for Snapshot Cloud only.

### Method
GET

### Endpoint
https://api.platerecognizer.com/v1/statistics/

### Parameters
#### Path Parameters
None

#### Query Parameters
None

#### Request Body
None

### Request Example
```bash
curl -H "Authorization: Token YOUR_API_TOKEN" \
  https://api.platerecognizer.com/v1/statistics/
```

### Response
#### Success Response (200)
- **usage** (object) - Usage statistics for the current period.
  - **month** (integer) - The current month.
  - **calls** (integer) - Number of API calls made during the current period.
  - **year** (integer) - The current year.
  - **resets_on** (string) - The date when the counter will reset (ISO 8601 format).
- **total_calls** (integer) - Maximum number of API calls allowed during the period.

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

--------------------------------

### Statistics JSON Response Format

Source: https://docs.platerecognizer.com

Example of the JSON structure returned by the statistics endpoint.

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

--------------------------------

### PHP: Upload Image with cURL

Source: https://docs.platerecognizer.com

Upload an image file using PHP cURL. This example shows how to prepare the file for upload and set the 'regions' parameter. It includes compatibility for PHP versions older than 5.5.

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

```

--------------------------------

### Python: Upload OpenCV Image

Source: https://docs.platerecognizer.com

Upload an image directly from OpenCV memory without saving it to disk. Ensure you have the 'requests' library installed.

```python
import cv2
success, image_jpg = cv2.imencode('.jpg', image)
files={'upload':image_jpg.tostring()}
response = requests.post(
            'https://api.platerecognizer.com/v1/plate-reader/',
            headers={'Authorization': 'Token YOUR_API_TOKEN'})

```

--------------------------------

### Configure Plate Reader via Shell (Windows)

Source: https://docs.platerecognizer.com

Use curl with caret line continuations to send images and configuration parameters on Windows systems.

```shell
#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 YOUR_API_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 YOUR_API_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 YOUR_API_TOKEN" ^  
  https://api.platerecognizer.com/v1/plate-reader/  
```

--------------------------------

### SDK Information Request

Source: https://docs.platerecognizer.com

Retrieve SDK version, license key, and webhook information.

```http
GET http://localhost:8080/info/
```

--------------------------------

### Custom Configuration and Region with Curl (Windows)

Source: https://docs.platerecognizer.com

Upload an image with custom configuration and a specific region using curl on Windows. Note the use of '^' for line continuation.

```shell
curl -F "upload=@c:\path\to\car.jpg" ^
    -F regions='us-ca' ^
    -F config="{\"region\":\"strict\"}" ^
    -H "Authorization: Token YOUR_API_TOKEN" ^
    https://api.platerecognizer.com/v1/plate-reader/
```

--------------------------------

### Configure Plate Reader via Shell (Linux)

Source: https://docs.platerecognizer.com

Use curl to send images with specific configuration parameters like mode, thresholds, and detection rules on Linux systems.

```shell
#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 YOUR_API_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 YOUR_API_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 YOUR_API_TOKEN" \
  https://api.platerecognizer.com/v1/plate-reader/  
```

--------------------------------

### Configure Plate Reader via Python

Source: https://docs.platerecognizer.com

Use the requests library to post an image and JSON-encoded configuration to the plate reader endpoint.

```python
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 YOUR_API_TOKEN'})  
```

--------------------------------

### Engine Configuration Parameters

Source: https://docs.platerecognizer.com

Details on how to customize the engine behavior using the config parameter.

```APIDOC
## Engine Configuration

### Description
A lookup request can accept the parameter `config`. It is a JSON value used to change the engine configuration for detection, filtering, and accuracy settings.

### Parameters
#### Request Body
- **config** (JSON) - Optional - Configuration object containing settings like detection_rule, detection_mode, region, thresholds, mode, text_formats, plates_per_vehicle, and zoom_in_vehicles.
```

--------------------------------

### Custom Engine Configuration with Python Requests

Source: https://docs.platerecognizer.com

Upload an image with custom engine configuration using Python's requests library. The 'config' parameter should be a JSON string.

```python
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 YOUR_API_TOKEN'})

```

--------------------------------

### Specify Regions with Curl (Windows)

Source: https://docs.platerecognizer.com

Upload a local image and specify regions using curl on Windows. Note the use of '^' for line continuation.

```shell
curl -F "upload=@car.jpg" ^
    -F regions=`mx` ^
    -F regions='us-ca' ^
    -H "Authorization: Token YOUR_API_TOKEN" ^
    https://api.platerecognizer.com/v1/plate-reader/
```

--------------------------------

### Custom Configuration and Regions with Curl

Source: https://docs.platerecognizer.com

Upload an image with custom engine configuration and specify regions using curl. The config parameter accepts a JSON string.

```shell
curl -F "upload=@/path/to/car.jpg" \
    -F regions=mx,us-ca \
    -F config="{\"region\":\"strict\"}" \
    -H "Authorization: Token YOUR_API_TOKEN" \
    https://api.platerecognizer.com/v1/plate-reader/
```

--------------------------------

### Process Local Image with Curl (Windows)

Source: https://docs.platerecognizer.com

Upload a local image file using curl on Windows. Note the use of '^' for line continuation.

```shell
curl -X POST -F "upload=@car.jpg" ^
    -H "Authorization: Token YOUR_API_TOKEN" ^
    https://api.platerecognizer.com/v1/plate-reader
```

--------------------------------

### Retrieve API Usage Statistics

Source: https://docs.platerecognizer.com

Fetch the current month's recognition call count for Snapshot Cloud accounts.

```shell
curl -H "Authorization: Token YOUR_API_TOKEN" \
  https://api.platerecognizer.com/v1/statistics/  
```

--------------------------------

### SDK Information JSON Response

Source: https://docs.platerecognizer.com

The JSON response containing SDK version and usage statistics.

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

--------------------------------

### POST http://localhost:8080/v1/plate-reader/ (On-Premise)

Source: https://docs.platerecognizer.com

The on-premise recognition API endpoint. It has a similar interface to the cloud API but is hosted locally.

```APIDOC
## POST http://localhost:8080/v1/plate-reader/

### Description
The on-premise recognition API endpoint. It has a similar interface to the cloud API but is hosted locally. The CORS policy allows requests from all origins, and there are no calls per second limits.

### Method
POST

### Endpoint
http://localhost:8080/v1/plate-reader/

### Parameters
#### Path Parameters
None

#### Query Parameters
None

#### Request Body
- **upload** (file) - Required - The image file to process.
- **config** (string) - Optional - A JSON string containing configuration options.

### Request Example
```bash
# Example using curl (replace with your actual image path)
curl -F "upload=@/path/to/your/car.jpg" \
  -F config="{\"mode\": \"fast\"}" \
  http://localhost:8080/v1/plate-reader/
```

### Response
#### Success Response (200)
- **results** (array) - A list of detected license plates (structure similar to cloud API).

#### Response Example
```json
{
  "results": [
    {
      "plate": "XYZ789",
      "confidence": 0.92,
      "region": "us-ny",
      "region_confidence": 0.88,
      "coordinates": {
        "x_min": 50,
        "y_min": 100,
        "x_max": 250,
        "y_max": 150
      }
    }
  ]
}
```
```

--------------------------------

### Specify Regions with Curl

Source: https://docs.platerecognizer.com

Upload an image and specify multiple regions for plate matching using curl. Regions can be comma-separated or provided as multiple arguments.

```shell
curl -F "upload=@/tmp/car.jpg" \
    -F regions=`mx` \
    -F regions=us-ca \
    -H "Authorization: Token YOUR_API_TOKEN" \
    https://api.platerecognizer.com/v1/plate-reader/
```

--------------------------------

### Process Image with Curl

Source: https://docs.platerecognizer.com

Upload an image file using curl to the Plate Recognizer API. Replace YOUR_API_TOKEN with your actual token.

```shell
curl -X POST -F "upload=@/tmp/car.jpg" \
    -H "Authorization: Token YOUR_API_TOKEN" \
    https://api.platerecognizer.com/v1/plate-reader
```

--------------------------------

### Upload Image with Python Requests

Source: https://docs.platerecognizer.com

Upload an image file using Python's requests library. The 'upload' parameter takes a file-like object.

```python
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 YOUR_API_TOKEN'})

pprint(response.json())
```

--------------------------------

### Node.js: Upload Image with node-fetch

Source: https://docs.platerecognizer.com

Upload an image file using Node.js with 'node-fetch' and 'form-data'. You can also upload a base64 encoded image. Specify the 'regions' parameter for country-specific recognition.

```javascript
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 YOUR_API_TOKEN",
  },
  body: body,
})
  .then((res) => res.json())
  .then((json) => console.log(json))
  .catch((err) => {
    console.log(err);
  });

```

--------------------------------

### POST /v1/plate-reader/

Source: https://docs.platerecognizer.com

Reads license plates from a provided image.

```APIDOC
## POST /v1/plate-reader/

### Description
Reads all license plates from an image. By default, only vehicles with a license plate are returned.

### Method
POST

### Endpoint
https://api.platerecognizer.com/v1/plate-reader/

### Parameters
#### Request Body
- **Authorization** (header) - Required - Token YOUR_API_TOKEN
- **detection_mode** (string) - Optional - Set to "vehicle" to include vehicles without a license plate.
```

--------------------------------

### Process Image from URL with Curl

Source: https://docs.platerecognizer.com

Upload an image directly from a URL using curl. The upload_url parameter is an alternative to uploading a file.

```shell
curl -X POST -F upload_url="https://app.platerecognizer.com/static/demo.jpg" \
    -H "Authorization: Token YOUR_API_TOKEN" \
    https://api.platerecognizer.com/v1/plate-reader
```

--------------------------------

### POST /v1/plate-reader/

Source: https://docs.platerecognizer.com

Reads license plates from an uploaded image. Supports various methods for image submission and optional parameters for configuration.

```APIDOC
## POST /v1/plate-reader/

### Description
Reads license plates from an uploaded image. Supports various methods for image submission and optional parameters for configuration.

### Method
POST

### Endpoint
http://localhost:8080/v1/plate-reader/

### Parameters
#### Path Parameters
None

#### Query Parameters
None

#### Request Body
- **upload** (file) - Required - The image file to process.
- **regions** (string) - Optional - Specifies the regions for plate recognition (e.g., `mx`, `us-ca`). Can be provided multiple times.
- **mmc** (boolean) - Optional - Enables or disables MMC (Multi-Motorcycle Classification).
- **config** (JSON string) - Optional - Configuration settings for the recognition process (e.g., `{"mode":"fast"}`).

### Request Example
```json
{
  "example": "curl -F \"upload=@car.jpg\" -F regions=mx -F regions=us-ca -F mmc=true -F config=\"{\\\"mode\\\":\\\"fast\\\"}\" http://localhost:8080/v1/plate-reader/"
}
```

### Response
#### Success Response (200)
- **usage** (object) - Information about API call usage.
  - **max_calls** (integer) - Maximum allowed calls.
  - **calls** (integer) - Number of calls made.
- **results** (array) - An array of detected license plates.
- **camera_id** (string) - The ID of the camera, if applicable.
- **timestamp** (string) - The timestamp of the recognition.
- **filename** (string) - The name of the processed file.

#### Response Example
```json
{
  "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"
}
```
```

--------------------------------

### POST /v1/plate-reader/

Source: https://docs.platerecognizer.com

Reads license plates from uploaded images. Supports various configuration options for fine-tuning the recognition process.

```APIDOC
## POST /v1/plate-reader/

### Description
Reads license plates from uploaded images. Supports various configuration options for fine-tuning the recognition process.

### Method
POST

### Endpoint
https://api.platerecognizer.com/v1/plate-reader/

### Parameters
#### Path Parameters
None

#### Query Parameters
None

#### Request Body
- **upload** (file) - Required - The image file to process.
- **config** (string) - Optional - A JSON string containing configuration options. Examples:
  - `{"mode":"fast", "threshold_d":0.2, "threshold_o":0.6}`
  - `{"region":"strict"}`
- **region** (string) - Optional - Specifies a region for strict matching (e.g., `us-ca`).

### Request Example
```json
{
  "upload": "/path/to/car.jpg",
  "config": "{\"mode\": \"fast\", \"threshold_d\": 0.2, \"threshold_o\": 0.6}",
  "region": "us-ca"
}
```

### Response
#### Success Response (200)
- **results** (array) - A list of detected license plates.
  - **plate** (string) - The recognized license plate.
  - **confidence** (number) - The confidence score of the recognition.
  - **region** (string) - The detected region of the license plate.
  - **region_confidence** (number) - The confidence score for the detected region.
  - **coordinates** (object) - Bounding box coordinates of the license plate.
    - **x_min** (integer)
    - **y_min** (integer)
    - **x_max** (integer)
    - **y_max** (integer)

#### Response Example
```json
{
  "results": [
    {
      "plate": "ABC1234",
      "confidence": 0.95,
      "region": "us-ca",
      "region_confidence": 0.90,
      "coordinates": {
        "x_min": 100,
        "y_min": 150,
        "x_max": 300,
        "y_max": 200
      }
    }
  ]
}
```
```

--------------------------------

### JSON Response for detection_mode = plate

Source: https://docs.platerecognizer.com

This structure is returned when detection_mode is set to plate, providing license plate details and optional vehicle metadata.

```json
{
  "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
        }
      ],
      // Year requires mmc=true and is available from version 1.56.0 onwards
      "year": {
        "year_range": [2014, 2020],
        "score": 0.913
      },
      // Direction is only available is you set mmc=true and direction=true
      "direction": 134,
      "direction_score": 0.76
    }
  ],
  "filename": "1617_7M83K_car.jpg",
  "version": 1,
  "camera_id": null,
  "timestamp": "2020-10-12T16:17:27.574008Z"
}
```

--------------------------------

### Prediction Result Fields

Source: https://docs.platerecognizer.com

Explanation of the fields returned in the API response.

```APIDOC
## Prediction Results

### Description
Breakdown of the fields returned in the results object of the API response.

### Response Fields
- **results/region/score** (float) - Confidence level for license plate region.
- **results/candidates** (array) - List of predictions for the license plate value.
- **results/model_make/make** (string) - Prediction of vehicle make.
- **results/model_make/model** (string) - Prediction of vehicle model.
- **results/model_make/score** (float) - Confidence level for vehicle make and model.
- **results/color/color** (string) - Vehicle color (black, blue, brown, green, red, silver, white, yellow, unknown).
- **results/orientation/orientation** (string) - Vehicle orientation (Front, Rear, Unknown).
- **results/year/year_range** (string) - Vehicle year range (Requires mmc=true).
- **results/direction** (float) - Vehicle direction of travel in degrees.
```

--------------------------------

### Recognition API JSON Response

Source: https://docs.platerecognizer.com

The expected JSON structure returned by the recognition API.

```json
{
  "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"
}
```

--------------------------------

### JSON Response for detection_mode = vehicle

Source: https://docs.platerecognizer.com

This structure is returned when detection_mode is set to vehicle, allowing for the detection of vehicles without license plates and providing nested property objects.

```json
{
  "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
        },
        // Make Model, Orientation and Color are only available if you set mmc=true
        "props": {
          "make_model": [
            {
              "make": "Riley",
              "model": "RMF",
              "score": 0.339
            }
          ],
          "orientation": [
            {
              "value": "Front",
              "score": 0.939
            }
          ],
          "color": [
            {
              "value": "black",
              "score": 0.846
            }
          ],
          // Year requires mmc=true and is available from version 1.56.0 onwards
          "year": {
            "year_range": [2014, 2020],
            "score": 0.913
          }
        }
      },
      // Direction is only available is you set mmc=true and direction=true
      "direction": null,
      "direction_score": 0.0
    }
  ]
}
```

--------------------------------

### POST /v1/plate-reader

Source: https://docs.platerecognizer.com

Processes an image to extract license plate information. The image can be provided as a file upload or a URL.

```APIDOC
## POST /v1/plate-reader

### Description
Processes an image to extract license plate information. Supports file uploads (multipart/form-data) or image URLs.

### Method
POST

### Endpoint
https://api.platerecognizer.com/v1/plate-reader

### Parameters
#### Request Body
- **upload** (file/base64) - Required (unless upload_url is provided) - The file to be uploaded as bytes or base64 encoded image.
- **upload_url** (string) - Optional - The URL of the file to be uploaded.
- **regions** (string/array) - Optional - Match the license plate pattern of specific state(s) or country(ies).
- **camera_id** (string) - Optional - Unique camera identifier.
- **timestamp** (string) - Optional - ISO 8601 UTC timestamp.
- **mmc** (boolean) - Optional - Predict vehicle make, model, orientation, color, and year.
- **direction** (boolean) - Optional - Predict vehicle's direction of travel (requires mmc=true).
- **config** (string) - Optional - Additional engine configuration.

### Request Example
curl -X POST -F "upload=@/tmp/car.jpg" -H "Authorization: Token YOUR_API_TOKEN" https://api.platerecognizer.com/v1/plate-reader

### Response
#### Success Response (200)
- **results** (array) - List of detected license plates and associated vehicle data.
```

--------------------------------

### Plate and Vehicle Detection API

Source: https://docs.platerecognizer.com

This API endpoint is used to detect license plates and vehicles within an image. The response structure varies based on the `detection_mode` parameter.

```APIDOC
## POST /websites/platerecognizer

### Description
Detects license plates and/or vehicles in an image. The output format and content depend on the `detection_mode` query parameter.

### Method
POST

### Endpoint
/websites/platerecognizer

### Query Parameters
- **detection_mode** (string) - Optional - Specifies the detection mode. Supported values: 'plate' (default, detects only plates), 'vehicle' (detects vehicles and optionally plates).
- **mmc** (boolean) - Optional - If true, includes make, model, and color information for vehicles. Requires version 1.56.0 or later for year information.
- **direction** (boolean) - Optional - If true and `mmc` is true, includes direction information for vehicles.

### Request Body
- **image** (file) - Required - The image file to process.

### Response
#### Success Response (200)
- **processing_time** (float) - Time taken for processing in milliseconds.
- **results** (array) - A list of detection results.
  - **box** (object) - Bounding box coordinates (xmin, ymin, xmax, ymax) for the detected object.
  - **plate** (object) - Information about the detected license plate (present when `detection_mode` is 'plate' or when a plate is found in 'vehicle' mode).
    - **type** (string) - Type of the detected object (e.g., 'Plate').
    - **score** (float) - Confidence score for plate detection.
    - **region** (object) - Region information for the plate.
      - **code** (string) - ISO 3166-1 alpha-2 country code for the plate's region.
      - **score** (float) - Confidence score for the region detection.
    - **props** (object) - Additional properties of the plate.
      - **plate** (array) - List of candidate plate texts with their scores.
      - **region** (array) - List of candidate regions with their scores.
  - **vehicle** (object) - Information about the detected vehicle (present when `detection_mode` is 'vehicle' or when a vehicle is found).
    - **type** (string) - Type of the detected vehicle (e.g., 'Sedan', 'SUV').
    - **score** (float) - Confidence score for vehicle type prediction.
    - **box** (object) - Bounding box coordinates for the vehicle.
    - **props** (object) - Additional properties of the vehicle.
      - **make_model** (array) - List of detected make and model with their scores.
      - **orientation** (array) - List of detected orientations with their scores.
      - **color** (array) - List of detected colors with their scores.
      - **year** (object) - Detected year range and score (available if `mmc` is true and version >= 1.56.0).
        - **year_range** (array) - Array of two integers representing the year range.
        - **score** (float) - Confidence score for the year detection.
  - **dscore** (float) - Confidence level for plate detection.
  - **score** (float) - Confidence level for reading the license plate text.
  - **candidates** (array) - List of candidate license plate texts with their scores.
  - **model_make** (array) - List of detected make and model with their scores (deprecated, use `vehicle.props.make_model`).
  - **color** (array) - List of detected colors with their scores (deprecated, use `vehicle.props.color`).
  - **orientation** (array) - List of detected orientations with their scores (deprecated, use `vehicle.props.orientation`).
  - **year** (object) - Detected year range and score (deprecated, use `vehicle.props.year`).
  - **direction** (float) - Detected direction in degrees (available if `mmc` is true and `direction` is true).
  - **direction_score** (float) - Confidence score for direction detection.
- **filename** (string) - The name of the processed image file.
- **version** (integer) - API version used for processing.
- **camera_id** (string or null) - Identifier for the camera, if provided.
- **timestamp** (string) - Timestamp of when the image was processed.

#### Response Example (detection_mode = plate)
```json
{
  "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,
      "model_make": [
        {
          "make": "Riley",
          "model": "RMF",
          "score": 0.306
        }
      ],
      "color": [
        {
          "color": "black",
          "score": 0.937
        }
      ],
      "orientation": [
        {
          "orientation": "Front",
          "score": 0.937
        }
      ],
      "year": {
        "year_range": [2014, 2020],
        "score": 0.913
      },
      "direction": 134,
      "direction_score": 0.76
    }
  ],
  "filename": "1617_7M83K_car.jpg",
  "version": 1,
  "camera_id": null,
  "timestamp": "2020-10-12T16:17:27.574008Z"
}
```

#### Response Example (detection_mode = vehicle)
```json
{
  "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
            }
          ],
          "year": {
            "year_range": [2014, 2020],
            "score": 0.913
          }
        }
      },
      "direction": null,
      "direction_score": 0.0
    }
  ]
}
```

### Error Handling
- **400 Bad Request**: Invalid input, such as a non-image file or missing required parameters.
- **500 Internal Server Error**: An error occurred during processing.
```
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