Context

Transformation context essentially refers to the output of image transformations in a JSON format. The response output gives you detailed information about the image, the types of transformations applied and the result of each transformation, etc.

After applying some transformations, follow the below steps to get the context in PixelBin Console.

Click on the Context button on the Playground page.
Context Icon
Click on the Fetch button.
Fetch Icon
API JSON will be fetched for the specific image transformations.
JSON Fetched

Context Response

The response will be in JSON format with four root properties, steps, metadata, headers and params.

The below table lists the properties which you will get in the context response.

Property	Description
`steps`	A list that contains all the transformations applied to an image in a sequential manner.
`steps[].name`	Name of an individual transformation. If tranformation is `t.blur()` then name will be `blur`. If tranformation is `remove.bg()` then name will be `bg`. If tranformation is `af.remove()` then name will be `remove`.
`steps[].identifier`	Unique identifier of an individual transformation. If tranformation is `t.blur()` then identifier will be `t`. If tranformation is `remove.bg()` then identifier will be `remove`. If tranformation is `af.remove()` then identifier will be `af`.
`steps[].operation`	Name of the transformation's operation. For basic transformations, it is `Basic` and for other transformations it is the name of the transformation. If tranformation is `t.blur()` then operation will be `Basic`. If tranformation is `remove.bg()` then operation will be `RemoveBG`. If tranformation is `af.remove()` then operation will be `Artifact`.
`steps[].data`	The image data after this specific transformation is applied. Only AWS Rekognition Moderation, AWS Rekognition DetectLabels, Google Vision DetectLabels, Extract Text, Crop Image, Detect Watermarks, Detect Number Plate, and Detect NSFW transformations provide this data.
`steps[].params`	The parameters used for this transformation.
`steps[].metadata`	The image metadata after this specific transformation is applied. The properties are similar to root `metadata` properties as show below.
`metadata`	The metadata of the original image.
`metadata.width`	Width of the original image in bytes.
`metadata.height`	Height of the original image in bytes.
`metadata.channels`	Number of bands. `3` for sRGB and `4` for CMYK.
`metadata.extension`	File extension.
`metadata.format`	Actual file format, derived from file data.
`metadata.contentType`	Actual type of the returned file.
`metadata.size`	Size of the original image in bytes.
`metadata.assetType`	Type of the asset/file. Possible values are `image`, `audio`, `video` or `raw`.
`metadata.isImageAsset`	Whether the file is an image.
`metadata.isAudioAsset`	Whether the file is an audio.
`metadata.isVideoAsset`	Whether the file is an video.
`metadata.isRawAsset`	Whether the file is raw data.
`metadata.isTransformationSupported`	Whether transformations can be applied on the file.
`headers`	Response headers of the API call.
`params`	Query params used in the request API call. Values can be `preview`, `download` and `f_auto`.

info

All the transformations will return the context data, but not all transformations will return a modified image. Such transformations only return output as JSON data, which can only be fetched from the Context API.

Those transformations are AWS Rekognition Moderation, AWS Rekognition DetectLabels, Google Vision DetectLabels, Extract Text, Detect Watermarks, Detect Number Plate, and Detect NSFW.

In addition, the Crop Image transformation will provide output as a transformed image as well as the data in JSON format.

Examples

1. Creating placeholder thumbnail

Let us see an example of transformation context response using a simple chained transformation with resize, blur and convert. The resultant transformation pattern will be t.resize(w:200,h:200)~t.blur(s:2)~t.toFormat(f:webp).

The original image is transformed as shown.

The below JSON data shows the output of the Context API.

{
    "steps": [
        {
            "data": {},
            "name": "resize",
            "params": {
                "b": "000000",
                "f": "cover",
                "h": "200",
                "k": "lanczos3",
                "p": "center",
                "w": "200",
                "dpr": 1
            },
            "metadata": {
                "size": 3283,
                "depth": "uchar",
                "space": "srgb",
                "width": 200,
                "format": "jpeg",
                "height": 200,
                "density": 72,
                "channels": 3,
                "hasAlpha": false,
                "hasProfile": false,
                "isProgressive": false,
                "chromaSubsampling": "4:2:0"
            },
            "operation": "Basic",
            "identifier": "t"
        },
        {
            "data": {},
            "name": "blur",
            "params": {
                "s": "2",
                "dpr": "1"
            },
            "metadata": {
                "size": 2241,
                "depth": "uchar",
                "space": "srgb",
                "width": 200,
                "format": "jpeg",
                "height": 200,
                "density": 72,
                "channels": 3,
                "hasAlpha": false,
                "hasProfile": false,
                "isProgressive": false,
                "chromaSubsampling": "4:2:0"
            },
            "operation": "Basic",
            "identifier": "t"
        },
        {
            "data": {},
            "name": "toFormat",
            "params": {
                "f": "webp"
            },
            "metadata": {
                "size": 918,
                "depth": "uchar",
                "space": "srgb",
                "width": 200,
                "format": "webp",
                "height": 200,
                "channels": 3,
                "hasAlpha": false,
                "hasProfile": false,
                "isProgressive": false
            },
            "operation": "Basic",
            "identifier": "t"
        }
    ],
    "params": {},
    "headers": {
        "host": "api.pixelbin.io",
        "x-real-ip": {REQUEST_DEVICE_IP_ADDRESS},
        "origin": {REQUEST_ORIGIN_DOMAIN_ADDRESS},
        "accept": "application/json, text/plain, */*",
        "accept-encoding": "gzip, deflate, br",
        "accept-language": "en-US,en;q=0.9"
    },
    "metadata": {
        "size": 296302,
        "width": 3240,
        "format": "jpeg",
        "height": 2592,
        "channels": 3,
        "assetType": "image",
        "extension": "jpeg",
        "isRawAsset": false,
        "contentType": "image/jpeg",
        "isAudioAsset": false,
        "isImageAsset": true,
        "isVideoAsset": false,
        "isTransformationSupported": true
    }
}

From the above JSON data after looking at the metadata properties, we can infer that 3 transformations were applied to the original image. The metadata section of the original image is colored green while that of each transformation step is brown colored.

You can track the changes in the size and dimensions of the image after each step is applied, in comparison with the original values below.

Step	Size (bytes)	Width (px)	Height (px)
Original image	296392	3240	2592
After `t.resize(w:200,h:200)`	3283	200	200
After `t.blur(s:2)`	2241	200	200
After `t.toFormat(f:webp)`	918	200	200

2. Detect labels using AWS Rekognition

Let us look at another example of context API using the AWS Rekognition DetectLabels add-on. Here we want to get all the labels associated with an image. The resultant transformation pattern will be awsRek.detectLabels().

note

The transformation CDN URL will return the original image in response as no visible image modification is done.

The below JSON data shows the output of the Context API.

{
    "steps": [
        {
            "data": {
                "Cup": {
                    "Name": "Cup",
                    "Aliases": [
                        {
                            "Name": "Mug"
                        }
                    ],
                    "Parents": [],
                    "Instances": [
                        {
                            "Confidence": 99.8639907836914,
                            "BoundingBox": {
                                "Top": 1949.9951877593994,
                                "Left": 2494.801290035248,
                                "Width": 589.9919235706329,
                                "Height": 420.92675971984863
                            }
                        }
                    ],
                    "Categories": [
                        {
                            "Name": "Kitchen and Dining"
                        }
                    ],
                    "Confidence": 99.8639907836914
                },
                "Plant": {
                    "Name": "Plant",
                    "Aliases": [],
                    "Parents": [],
                    "Instances": [
                        {
                            "Confidence": 97.5615234375,
                            "BoundingBox": {
                                "Top": 1125.0892553329468,
                                "Left": 42.312018536031246,
                                "Width": 1386.9211435317993,
                                "Height": 1293.3147954940796
                            }
                        }
                    ],
                    "Categories": [
                        {
                            "Name": "Plants and Flowers"
                        }
                    ],
                    "Confidence": 99.92650604248047
                },
                "Ikebana": {
                    "Name": "Ikebana",
                    "Aliases": [],
                    "Parents": [
                        {
                            "Name": "Flower"
                        },
                        {
                            "Name": "Flower Arrangement"
                        },
                        {
                            "Name": "Plant"
                        }
                    ],
                    "Instances": [],
                    "Categories": [
                        {
                            "Name": "Home and Indoors"
                        }
                    ],
                    "Confidence": 91.78543853759766
                },
                "Windowsill": {
                    "Name": "Windowsill",
                    "Aliases": [],
                    "Parents": [
                        {
                            "Name": "Window"
                        }
                    ],
                    "Instances": [],
                    "Categories": [
                        {
                            "Name": "Home and Indoors"
                        }
                    ],
                    "Confidence": 92.34557342529297
                },
                "Flower Arrangement": {
                    "Name": "Flower Arrangement",
                    "Aliases": [],
                    "Parents": [
                        {
                            "Name": "Flower"
                        },
                        {
                            "Name": "Plant"
                        }
                    ],
                    "Instances": [],
                    "Categories": [
                        {
                            "Name": "Plants and Flowers"
                        }
                    ],
                    "Confidence": 99.92650604248047
                }
            },
            "name": "detectLabels",
            "params": {
                "l": 5,
                "c": 55
            },
            "metadata": {
                "size": 296302,
                "depth": "uchar",
                "space": "srgb",
                "width": 3240,
                "format": "jpeg",
                "height": 2592,
                "density": 72,
                "channels": 3,
                "hasAlpha": false,
                "hasProfile": false,
                "isProgressive": false,
                "chromaSubsampling": "4:2:0"
            },
            "operation": "AWS Rekognition Plugin",
            "identifier": "awsRek"
        }
    ],
    "params": {},
    "headers": {
        "host": "api.pixelbin.io",
        "x-real-ip": {REQUEST_DEVICE_IP_ADDRESS},
        "origin": {REQUEST_ORIGIN_DOMAIN_ADDRESS},
        "accept": "application/json, text/plain, */*",
        "accept-encoding": "gzip, deflate, br",
        "accept-language": "en-US,en;q=0.9"
    },
    "metadata": {
        "size": 296302,
        "width": 3240,
        "format": "jpeg",
        "height": 2592,
        "channels": 3,
        "assetType": "image",
        "extension": "jpeg",
        "isRawAsset": false,
        "contentType": "image/jpeg",
        "isAudioAsset": false,
        "isImageAsset": true,
        "isVideoAsset": false,
        "isTransformationSupported": true
    }
}

The AWS Rekognition DetectLabels add-on has returned all possible labels in the data property in the steps list. The data section, as well as the root and step metadata section, are highlighted in the preceding JSON. In this example, it has returned the Cup, Plant, Ikebana, Windowsill, and Flower Arrangement labels.

There is no change in the metadata properties of the original image metadata and the add-on metadata. You can compare the change in the size and dimensions of the image after the add-on is applied with the original image below.

Step	Size (bytes)	Width (px)	Height (px)
Original image	296302	3240	2592
After `awsRek.detectLabels()`	296302	3240	2592

Using `context` data in transformations

Context although useful in their own right, gets more powerful when used in combination with image transformations. You can use the context information of previous transformations by using the $ special character.

For instance, let use look at the below transformation.

Detect number plate and hide
Original

https://cdn.pixelbin.io/v2/dummy-cloudname/FIDrmb/numPlate.detect()~t.merge(m:wrap,polys:$steps.0.data.polygons,bg:f2994a)/images/transformation/race_car.jpeg

<PixelBinImage
    url="https://cdn.pixelbin.io/v2/dummy-cloudname/FIDrmb/numPlate.detect()~t.merge(m:wrap,polys:$steps.0.data.polygons,bg:f2994a)/images/transformation/race_car.jpeg"
/>

import Pixelbin from "@pixelbin/core";

const obj = {
    cloudName: "dummy-cloudname",
    zone: "FIDrmb",
    version: "v2",
    transformations: [{"name":"detect","plugin":"numPlate"},{"name":"merge","plugin":"t","values":[{"key":"m","value":"wrap"},{"key":"polys","value":"$steps.0.data.polygons"},{"key":"bg","value":"f2994a"}]}],
    filePath: "images/transformation/race_car.jpeg",
    baseUrl: "https://cdn.pixelbin.io",
};

const url = Pixelbin.default.utils.objToUrl(obj);

// url
// https://cdn.pixelbin.io/v2/dummy-cloudname/FIDrmb/numPlate.detect()~t.merge(m:wrap,polys:$steps.0.data.polygons,bg:f2994a)/images/transformation/race_car.jpeg

const Pixelbin = require("@pixelbin/admin");

const obj = {
    cloudName: "dummy-cloudname",
    zone: "FIDrmb",
    version: "v2",
    transformations: [{"name":"detect","plugin":"numPlate"},{"name":"merge","plugin":"t","values":[{"key":"m","value":"wrap"},{"key":"polys","value":"$steps.0.data.polygons"},{"key":"bg","value":"f2994a"}]}],
    filePath: "images/transformation/race_car.jpeg",
    baseUrl: "https://cdn.pixelbin.io",
};

const url = Pixelbin.url.objToUrl(obj);

// url
// https://cdn.pixelbin.io/v2/dummy-cloudname/FIDrmb/numPlate.detect()~t.merge(m:wrap,polys:$steps.0.data.polygons,bg:f2994a)/images/transformation/race_car.jpeg

from pixelbin.utils.url import obj_to_url

obj = {
    "cloudName": "dummy-cloudname",
    "zone": "FIDrmb",
    "version": "v2",
    "transformations": [{"name":"detect","plugin":"numPlate"},{"name":"merge","plugin":"t","values":[{"key":"m","value":"wrap"},{"key":"polys","value":"$steps.0.data.polygons"},{"key":"bg","value":"f2994a"}]}],
    "filePath": "images/transformation/race_car.jpeg",
    "baseUrl": "https://cdn.pixelbin.io",
}

url = obj_to_url(obj)

# url
# https://cdn.pixelbin.io/v2/dummy-cloudname/FIDrmb/numPlate.detect()~t.merge(m:wrap,polys:$steps.0.data.polygons,bg:f2994a)/images/transformation/race_car.jpeg

package main

import (
 "fmt"
 "github.com/pixelbin-dev/pixelbin-go/v2/sdk/utils/url"
)

func main() {
    obj := map[string]interface{}{
        "cloudName": "dummy-cloudname",
        "zoneSlug": "FIDrmb",
        "version": "v2",
        "transformations": []map[string]interface{}{
            {"plugin": "numPlate", "name": "detect"},,
{"plugin": "t", "name": "merge", "values": []map[string]interface{}{ {"key": "m", "value": "wrap"},{"key": "polys", "value": "$steps.0.data.polygons"},{"key": "bg", "value": "f2994a"},}},
        },
        "filePath": "images/transformation/race_car.jpeg",
        "baseUrl": "https://cdn.pixelbin.io",
        "options": map[string]interface{}{},
    }

    urlstring, err := url.ObjToUrl(obj)

    if err != nil {
        fmt.Println(err)
    }
}

// url
// https://cdn.pixelbin.io/v2/dummy-cloudname/FIDrmb/numPlate.detect()~t.merge(m:wrap,polys:$steps.0.data.polygons,bg:f2994a)/images/transformation/race_car.jpeg

import com.pixelbin.url.UrlObj
import com.pixelbin.Utils
import com.pixelbin.transformation.TransformationObj

fun main(){
    val obj = UrlObj(
        cloudName = "dummy-cloudname",
        zone = "FIDrmb",
        version = "v2",
        transformation = arrayListOf(
            TransformationObj( plugin = "numPlate", name = "detect", ),
TransformationObj( plugin = "t", name = "merge", values = hashMapOf( "m" to "wrap","polys" to "$steps.0.data.polygons","bg" to "f2994a"), )
        ),
        filePath = "images/transformation/race_car.jpeg",
        baseUrl = "https://cdn.pixelbin.io"
    )

    val url = Utils.objToUrl(obj)
}

// url
// https://cdn.pixelbin.io/v2/dummy-cloudname/FIDrmb/numPlate.detect()~t.merge(m:wrap,polys:$steps.0.data.polygons,bg:f2994a)/images/transformation/race_car.jpeg

using System;
using System.Collections.Generic;
using System.IO;
using Pixelbin.Utils;

namespace ExampleNamespace
{
    class ExampleClass
    {
        static void Main(string[] args)
        {
            UrlObj obj = new UrlObj(
                version: "v2",
                cloudName: "dummy-cloudname",
                filePath: "images/transformation/race_car.jpeg",
                zone: "FIDrmb",
                baseUrl: "https://cdn.pixelbin.io",
                transformations: new List<UrlTransformation>() {
                    new UrlTransformation(plugin: "numPlate", name: "detect", ),
new UrlTransformation(plugin: "t", name: "merge", values: new List<Dictionary<string, string>>() { new Dictionary<string, string>() { { "key", "m" }, { "value", "wrap" } }, new Dictionary<string, string>() { { "key", "polys" }, { "value", "$steps.0.data.polygons" } }, new Dictionary<string, string>() { { "key", "bg" }, { "value", "f2994a" } }} )
                }
            );

            string url = Url.ObjToUrl(obj);
        }
    }
}

// url
// https://cdn.pixelbin.io/v2/dummy-cloudname/FIDrmb/numPlate.detect()~t.merge(m:wrap,polys:$steps.0.data.polygons,bg:f2994a)/images/transformation/race_car.jpeg

<?php
require 'vendor/autoload.php';
use Pixelbin\Utils\Url;

$obj = [
    "cloudName" => "dummy-cloudname",
    "zone" => "FIDrmb",
    "version" => "v2",
    "options" => [],
    "transformations" => [
        ["plugin" => "numPlate", "name" => "detect", ],
["plugin" => "t", "name" => "merge", "values" => [ [ "key" => "m", "value" => "wrap" ], [ "key" => "polys", "value" => "$steps.0.data.polygons" ], [ "key" => "bg", "value" => "f2994a" ], ], ],
    ],
    "filePath" => "images/transformation/race_car.jpeg",
    "baseUrl" => "https://cdn.pixelbin.io",
];

$url = Url::obj_to_url($obj);

// url
// https://cdn.pixelbin.io/v2/dummy-cloudname/FIDrmb/numPlate.detect()~t.merge(m:wrap,polys:$steps.0.data.polygons,bg:f2994a)/images/transformation/race_car.jpeg
?>

https://cdn.pixelbin.io/v2/dummy-cloudname/FIDrmb/original/images/transformation/race_car.jpeg

<PixelBinImage
    url="https://cdn.pixelbin.io/v2/dummy-cloudname/FIDrmb/original/images/transformation/race_car.jpeg"
/>

import Pixelbin from "@pixelbin/core";

const obj = {
    cloudName: "dummy-cloudname",
    zone: "FIDrmb",
    version: "v2",
    transformations: [],
    filePath: "images/transformation/race_car.jpeg",
    baseUrl: "https://cdn.pixelbin.io",
};

const url = Pixelbin.default.utils.objToUrl(obj);

// url
// https://cdn.pixelbin.io/v2/dummy-cloudname/FIDrmb/original/images/transformation/race_car.jpeg

const Pixelbin = require("@pixelbin/admin");

const obj = {
    cloudName: "dummy-cloudname",
    zone: "FIDrmb",
    version: "v2",
    transformations: [],
    filePath: "images/transformation/race_car.jpeg",
    baseUrl: "https://cdn.pixelbin.io",
};

const url = Pixelbin.url.objToUrl(obj);

// url
// https://cdn.pixelbin.io/v2/dummy-cloudname/FIDrmb/original/images/transformation/race_car.jpeg

from pixelbin.utils.url import obj_to_url

obj = {
    "cloudName": "dummy-cloudname",
    "zone": "FIDrmb",
    "version": "v2",
    "transformations": [],
    "filePath": "images/transformation/race_car.jpeg",
    "baseUrl": "https://cdn.pixelbin.io",
}

url = obj_to_url(obj)

# url
# https://cdn.pixelbin.io/v2/dummy-cloudname/FIDrmb/original/images/transformation/race_car.jpeg

package main

import (
 "fmt"
 "github.com/pixelbin-dev/pixelbin-go/v2/sdk/utils/url"
)

func main() {
    obj := map[string]interface{}{
        "cloudName": "dummy-cloudname",
        "zoneSlug": "FIDrmb",
        "version": "v2",
        "transformations": []map[string]interface{}{
            
        },
        "filePath": "images/transformation/race_car.jpeg",
        "baseUrl": "https://cdn.pixelbin.io",
        "options": map[string]interface{}{},
    }

    urlstring, err := url.ObjToUrl(obj)

    if err != nil {
        fmt.Println(err)
    }
}

// url
// https://cdn.pixelbin.io/v2/dummy-cloudname/FIDrmb/original/images/transformation/race_car.jpeg

import com.pixelbin.url.UrlObj
import com.pixelbin.Utils
import com.pixelbin.transformation.TransformationObj

fun main(){
    val obj = UrlObj(
        cloudName = "dummy-cloudname",
        zone = "FIDrmb",
        version = "v2",
        transformation = arrayListOf(
            
        ),
        filePath = "images/transformation/race_car.jpeg",
        baseUrl = "https://cdn.pixelbin.io"
    )

    val url = Utils.objToUrl(obj)
}

// url
// https://cdn.pixelbin.io/v2/dummy-cloudname/FIDrmb/original/images/transformation/race_car.jpeg

using System;
using System.Collections.Generic;
using System.IO;
using Pixelbin.Utils;

namespace ExampleNamespace
{
    class ExampleClass
    {
        static void Main(string[] args)
        {
            UrlObj obj = new UrlObj(
                version: "v2",
                cloudName: "dummy-cloudname",
                filePath: "images/transformation/race_car.jpeg",
                zone: "FIDrmb",
                baseUrl: "https://cdn.pixelbin.io",
                transformations: new List<UrlTransformation>() {
                    
                }
            );

            string url = Url.ObjToUrl(obj);
        }
    }
}

// url
// https://cdn.pixelbin.io/v2/dummy-cloudname/FIDrmb/original/images/transformation/race_car.jpeg

<?php
require 'vendor/autoload.php';
use Pixelbin\Utils\Url;

$obj = [
    "cloudName" => "dummy-cloudname",
    "zone" => "FIDrmb",
    "version" => "v2",
    "options" => [],
    "transformations" => [
        
    ],
    "filePath" => "images/transformation/race_car.jpeg",
    "baseUrl" => "https://cdn.pixelbin.io",
];

$url = Url::obj_to_url($obj);

// url
// https://cdn.pixelbin.io/v2/dummy-cloudname/FIDrmb/original/images/transformation/race_car.jpeg
?>

As you can see, the number plate of the car is hidden using a colored block. The transformation pattern used for the above output image is:

numPlate.detect()~t.merge(m:wrap,polys:$steps.0.data.polygons,bg:f2994a)

The transformation pattern can be broken down into two major steps, first the Detect Number Plate step and then the merge step.

The output of the Detect Number Plate step is a JSON object that lists down 4 sets of x and y coordinates for all available number plates in the given image. This JSON object is then fed into the merge step using the polys parameter and all the number plates in the transformed image are wrapped with the color f2994a which is passed using the bg parameter.

The context data for the above transformation is:

{
    "steps": [
        {
            "name": "detect",
            "operation": "NumberPlateDetection",
            "identifier": "numPlate",
            "data": {
                "polygons": [
                    [
                        [451, 290],
                        [517, 288],
                        [518, 309],
                        [451, 312]
                    ]
                ]
            },
            "params": {},
            "metadata": {
                "format": "jpeg",
                "size": 64330,
                "width": 640,
                "height": 427,
                "space": "srgb",
                "channels": 3,
                "depth": "uchar",
                "density": 72,
                "chromaSubsampling": "4:2:0",
                "isProgressive": false,
                "hasProfile": false,
                "hasAlpha": false
            }
        },
        {
            "name": "merge",
            "operation": "Basic",
            "identifier": "t",
            "data": {},
            "params": {
                "b": "over",
                "g": "center",
                "h": 0,
                "i": "",
                "l": 0,
                "m": "wrap",
                "r": false,
                "t": 0,
                "w": 0,
                "bg": "f2994a",
                "tr": "",
                "polys": "$steps.0.data.polygons"
            }
            "metadata": {
                "format": "jpeg",
                "size": 174180,
                "width": 640,
                "height": 427,
                "space": "srgb",
                "channels": 3,
                "depth": "uchar",
                "density": 72,
                "chromaSubsampling": "4:2:0",
                "isProgressive": false,
                "hasProfile": false,
                "hasAlpha": false
            }
        }
    ],
    "metadata": {
        "width": 640,
        "height": 427,
        "channels": 3,
        "extension": "jpeg",
        "format": "jpeg",
        "contentType": "image/jpeg",
        "size": 64330,
        "assetType": "image",
        "isImageAsset": true,
        "isAudioAsset": false,
        "isVideoAsset": false,
        "isRawAsset": false,
        "isTransformationSupported": true
    },
    "headers": {
        "host": "api.pixelbin.io",
        "x-real-ip": {REQUEST_DEVICE_IP_ADDRESS},
        "origin": {REQUEST_ORIGIN_DOMAIN_ADDRESS},
        "accept": "application/json, text/plain, */*",
        "accept-encoding": "gzip, deflate, br",
        "accept-language": "en-US,en;q=0.9"
    },
    "params": {}
}

We have two objects in the steps property from the above JSON. The data value from the Detect Number Plate step is highlighted. This data is then fed into the merge polys parameter using the $steps.0.data.polygons value.

To use the context object in the transformation pattern in this way, you need to first access the context using the $ character and then traverse the context JSON using the dot . notation.

For example, let us break down this value: $steps.0.data.polygons.

First, access the steps using $steps to get the below JSON,

[
    {
        "name": "detect",
        "operation": "NumberPlateDetection",
        "identifier": "numPlate",
        "data": {
            "polygons": [
                [
                    [451, 290],
                    [517, 288],
                    [518, 309],
                    [451, 312]
                ]
            ]
        },
        "params": {},
        "metadata": {
            "format": "jpeg",
            "size": 64330,
            ...
        }
    },
    {
        "name": "merge",
        "operation": "Basic",
        ...
    }
]

Then, get any item in the steps array using the index notation. In this case, we need the first item, so we will use $steps.0. The result is,

{
    "name": "detect",
    "operation": "NumberPlateDetection",
    "identifier": "numPlate",
    "data": {
        "polygons": [
            [
                [451, 290],
                [517, 288],
                [518, 309],
                [451, 312]
            ]
        ]
    },
    ...
}

Then, get the data value in the NumberPlateDetection operation using $steps.0.data.

{
    "polygons": [
        [
            [451, 290],
            [517, 288],
            [518, 309],
            [451, 312]
        ]
    ]
}

Finally, get the actual value which will be used in the merge step using $steps.0.data.polygons.
[
    [
        [451, 290],
        [517, 288],
        [518, 309],
        [451, 312]
    ]
]

Simiarly, you can get other values in the context data using $params, $headers, $metadata, etc.

Context Response

Examples

1. Creating placeholder thumbnail

2. Detect labels using AWS Rekognition

Using context data in transformations

Using `context` data in transformations