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image-similarity API

Introduction

This image-similarity API enables you to compare two images and determine how visually similar they’re to each other using Amazon Mechanical Turk (MTurk). This is useful in the field of computer vision, for example when building a visual search that returns similar looking products.

The API takes as input the URLs of the two images to be analyzed and returns a score between 0 and 1 indicating how similar the images are to each other with 1 being the most similar. When you call this API, it uses your MTurk Requester account and AWS account to automatically create HITs for Workers on MTurk to complete, and automatically processes answers from Workers and returns the result.

Example request:

{
  "input": {
    "image1": {"url":"https://requester.mturk.com/assets/lucy.jpg"},
    "image2": {"url":"https://requester.mturk.com/assets/gold-finding-puppy.jpg"}
   }
}

Image Preview

"image1" "image2"
Image1 Image2

Note you can host images on Amazon S3 if you do not have your own server.

Example Result:

{
  "result": {
    "similarityScore": 0.5
  }
}

Here’s how the images might be presented to Workers on MTurk.

Worker Preview of HIT

The API implements an adjudication strategy based on Worker agreement and returns results only when it reaches confidence.

All of the single purpose APIs follow the same REST pattern as described in our REST API reference. They require a function name indicating which API you want to call, and a unique Task name used to represent your input and the corresponding result.

image-similarity-test is a version of the image-similarity API meant for testing your integration. It has the same interface as the production version, but Tasks always complete quickly with mock results. HITs are not created on MTurk, so you don’t need to pay Workers for rewards.

Note: This will return a random mock result when you call get_task with image-similarity-test.

The rest of this article provides instructions on using the image-similarity API using the MTurk Python Client. Download Sample Python code for image-similarity-test.

Create a Task

This Python sample calls the image-similarity API using Boto3 and the MTurk Python client.

Tasks are created by calling put_task. For more details for how to use put_task, check out Instructions for creating your first Task for a MTurk single purpose API.

Note Before you create your Task, make sure you try to view the images in your browser to ensure they display correctly for the Workers.

The body of a put_task request looks like this:

{
  "input": {
    "image1": {"url": "<image you want analyzed>"},                 
    "image2": {"url": "<image you want analyzed>"} 
}
Input structure
Name Description Type Required
"image1" first image you want analyzed JSON Object Yes
"image2" second image you want analyzed JSON Object Yes
"url" link to image hosted online (<= 400 characters) String Yes

Note you can host images on Amazon S3 if you do not have your own server.

Example request

image1 = 'https://s3-us-west-2.amazonaws.com/mturk-sample-media/images-to-compare/image-similarity-a1.jpg'
image2 = 'https://s3-us-west-2.amazonaws.com/mturk-sample-media/images-to-compare/image-similarity-g1.png'


put_result = crowd_client.put_task('image-similarity', 'my-task-name',                         
                              {'image1': {'url': image1}, 'image2': {'url': image2}})

Example response

{
  "taskName": "my-task-name",
   "input": {
    "image1": {"url":"https://requester.mturk.com/assets/lucy.jpg"},
    "image2": {"url":"https://requester.mturk.com/assets/gold-finding-puppy.jpg"}
   },
  "problemDetails": null,
  "state": "processing",
  "result": null
}

Get a Result

This Python sample calls the image-similarity API using Boto3 and the MTurk Python client.

After creating a Task, users can call the get_task to poll its current state.

Remember it can take some time for a Worker to complete a Task, so you might have to wait before getting a result.

For more details for how to use get_task, check out Instructions for creating your first Task for a MTurk single purpose API.

Example request

client.get_task('image-similarity', 'my-task-name')

Response structure:

Name Description Type
"taskName" user-provided Task name String
"input" input provided by user when Task was created JSON Object
"problemDetails" if the "state" is "failed" - details about a failed Task JSON Object or null
"state" current Task state - one of "processing", "completed" or "failed" String
"result" if the "state" is "completed" - the results of the completed Task JSON Object or null

Result structure:

Name Value Type
"similarityScore" a score between 0 and 1 indicating similarity of images with 1 being the most similar Integer

Example response

Example response for a successful Task:

{
  "taskName": "my-task-name",
   "input": {
    "image1": {"url":"https://requester.mturk.com/assets/lucy.jpg"},
    "image2": {"url":"https://requester.mturk.com/assets/gold-finding-puppy.jpg"}
   },
  "problemDetails": null,
  "state": "completed",
  "result": {
    "similarityScore": 0.5
  }
}

Example response for a failed Task:

{
  "taskName": "my-task-name",
   "input": {
    "image1": {"url":"https://requester.mturk.com/assets/lucy.jpg"},
    "image2": {"url":"https://requester.mturk.com/assets/gold-finding-puppy.jpg"}
   },
  "problemDetails": {
    "code": "Expired",
    "message": "Not enough Workers provided answers for your task within the allotted time."
  },
  "state": "failed",
  "result": null
}

Delete Task when Task is complete

This Python sample calls the image-similarity API using Boto3 and the MTurk Python client.

A user can optionally delete a finished Task (one whose state is either “completed” or “failed”). After you delete a Task, you can reuse the Task name for a future Task.

Note: Deleting a Task that is still being processed (i.e., whose state is “processing”) is not allowed.

For more details for how to use delete_task, check out Instructions for creating your first Task for a MTurk single purpose API.

Example Input

client.delete_task('image-similarity', 'my-task-name')

Example Output

A successful request for the delete_task operation returns with no errors and an empty body.

Get started with image-similarity

  1. Set up your Amazon Mechanical Turk (MTurk) Requester account and AWS account
  2. Set up permissions to call an MTurk single purpose API
  3. Instructions for creating your first Task for a MTurk single purpose API

MTurk has released several APIs for common use cases, click here to see a list of all the available APIs.

If you have any questions or feedback, such as methods you wish our client supported, please contact our product team, or submit a pull request on GitHub adding additional functionality to our client.