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Searchable Image Repository REST API

Table of Contents

Core Features

  • handles single and bulk image uploads with minimal response time
  • url based image manipulation to resize images on the fly
  • serving images through cdn
  • two types of image searching is available.
    • i. Full Text Search (user enters some keywords or a phrase or a country location)
    • ii. Image Search (user enters an image, optionally can also enter full text search parameters that aid narrow down the search space)
  • access protection, users can only delete images uploaded by him/her

Implementation Details

  • built using Django Rest Framework
  • uses RabbitMQ as message broker and Celery as asynchronous task queue to carry out long tasks
  • uses PostgreSQL Full Text Search to perform full text search of images based on image meta
  • extracts ORB descriptors from images and uses open cv based matcher for image search
  • images are stored in AWS S3 storage
  • images are served through AWS Cloudfront CDN
  • URL based Image Manipulation is available through an AWS Lambda function
  • served through nginx on an AWS EC2 instance.

API Documentation

This API documentation explains some important endpoints in detail. Find the full Swagger UI documentation available here: /api/doc

Getting an Authentication Token

POST api/signup

Hitting the endpoint with the following request body would register a user with this email. This endpoint can be used only once with the same username and email. Check POST api/api-token-auth to retrieve a registered user's auth token.

Request Body:

{
  "username": "string",
  "email": "[email protected]",
  "password": "string"
}

Example HTTP 200 Response:

{
  "detail": "success",
  "content": {
    "email": "[email protected]",
    "username": "string",
    "password": "string",
    "token": "be151b8865d43783fa80cd147ff0df472395d340"
  }
}

POST api/api-token-auth

Request Body:

{
  "username": "string",
  "email": "[email protected]",
  "password": "string"
}

Example HTTP 200 Response:

{
  "token": "string"
}

This token can be used as the bearer token in the http authorization header to authenticate some non-public endpoints.

Upload Image(s)

POST api/images

ContentType of this request must be multipart/form-data . This is general for all endpoints accepting an image file binary.

Header:

auth: Bearer <token>

Request Body:

{
  "img": <image_file_binary>,
  "country": "2-digit-country-code", # use the GET /api/country endpoint for a list of options
  "description": "image caption",
  "categories": [
    <category_ids> # use the GET /api/images/categories endpoint for a list of options
  ]
}

Example HTTP 200 Response:

{
  "detail": "You request is being processed. You can check the status of your request using the /task-result/{task_id} endpoint",
  "content": {
    "<task_id>": "https://d24fj9qq8rdx7g.cloudfront.net/iidb/bojack-horseman-png" # image url when the upload will be completed
  }
}

In the process of uploading to a storage bucket, image features are also generated from the image which is used for image searching. Clients do not need to wait for all this, the API returns a task_id assigned to this image upload task. Clients implementing this can easily query the task_id GET /api/task-result/{task_id}** to check if the upload is complete.

POST api/images/bulk

ContentType of this request must be multipart/form-data . This is general for all endpoints accepting an image file binary.

Header:

auth: Bearer <token>

Request Body:

{
  "images": [
    <image_file_binary>
  ],
  "meta": [
    {
      "country": "2-digit-country-code", # use the GET /api/country endpoint for a list of options,
      "description": "image caption",
      "categories": [
        <category_ids> # use the GET /api/images/categories endpoint for a list of options
      ]
    }
  ]
}

The number of items in the meta array can be either exactly 1 or exactly the number of items in images array. In the first case, the single meta properties are applied to all the images in the bulk. In the latter case, meta properties are assigned to the corresponding indexed image in the images array. e.g: meta[i] is assigned to images[i].

Example HTTP 200 Response:

{
  "detail": "You request is being processed. You can check the status of your request using the GET /task-result/{task_id} endpoint",
  "content": [
    {
      "<task_id>": "https://d24fj9qq8rdx7g.cloudfront.net/iidb/bojack-horseman-png" # image url when the upload will be completed
    }
  ]
}

The response is similar to the single upload api. However, in this case returns a task_id for each image.

Search

POST api/images/full-text-search

Request Body:

{
  "phrase": "string",
  "keywords": [
    "string"
  ],
  "country_name_or_code": "string"
}

Need to provide at least one of the three fields phrase, keywords, country_name_or_code.

Example HTTP 200 Response:

{
  "detail": "success",
  "content": [
    {
      "id": 1,
      "country": {
        "code": "AU",
        "name": "Australia"
      },
      "categories": [
        {
          "id": 1,
          "name": "wallpapers"
        }
      ],
      "owner": {
        "username": "test"
      },
      "img": "https://d24fj9qq8rdx7g.cloudfront.net/iidb/47183395_178349jpg",
      "description": "pop os wallpapers",
      "date_added": "2021-05-07T11:21:54.520145Z",
      "date_modified": "2021-05-07T11:21:54.520159Z"
    }
  ]
}

POST api/images/image-search

ContentType of this request must be multipart/form-data . This is general for all endpoints accepting an image file binary.

Request Body:

{
  "img": <image_file_binary>,
  "phrase": "string",
  "keywords": [
    "string"
  ],
  "country_name_or_code": "string"
}

Example HTTP 200 Response:

{
  "detail": "You request is being processed. You can check the status of your request using the /task-result/{task_id} endpoint",
  "content": "<task_id>"
}

Image Searching can take a while, since it needs to compare the queried image's ORB Descriptors. Therefore, this API returns a task_id assigned to this image search task. Clients implementing this can easily query the task_id GET /api/task-result/{task_id}** to check if the searching is complete and also, fetch the search results.

Fetching Async Task Results

GET /api/task-result/{task_id}

task_id (a path parameter) value can be found in the image search / upload api responses.

Example HTTP 200 Response:

{
  "detail": "success",
  "content": {
    "status": "SUCCESS",
    "result": [
      {
        "img": "https://d24fj9qq8rdx7g.cloudfront.net/iidb/41250564_2_0mdb3d4jpeg",
        "match": 1
      },
      {
        "img": "https://d24fj9qq8rdx7g.cloudfront.net/iidb/19634438_5webp",
        "match": 0.54
      },
      {
        "img": "https://d24fj9qq8rdx7g.cloudfront.net/iidb/57189077_5_jyxcpm3webp",
        "match": 0.28
      }
    ]
  }
}

The field result contains the result objects (single image, array of search result images etc)

Error Handling

All responses follow a generic format both for error and non-error responses. The detail field returns a detailed error message of the http error triggered. The content field can be ignored for error responses.

{
  "detail": "string",
  "content": "string"
}

Common http errors handled are,

401 UNAUTHORIZED

{
  "detail": "Token invalid/unavailable in header ",
  "content": null
}

422 UNPROCESSABLE ENTITY

{
  "detail": "Fields missing in request \"username\", \"password\"",
  "content": null
}

405 METHOD NOT ALLOWED

{
  "detail": "Method \"POST\" not allowed.",
  "content": null
}

URL based Image Manipulation

In order to resize an image url of the form: https://d24fj9qq8rdx7g.cloudfront.net/{s3-key}

1. You need to create a JSON body of the form:

{
  "bucket": "s3-bucket-name",
  "key": <s3-key>,
  "edits": {
    "resize": {
      "width": 120,
      "height": 120,
      "fit": ("cover" or "contain" or "fill" or "inside" or "outside"),
      "background": {
        "r": 255,
        "g": 255,
        "b": 0,
        "alpha": 1
      }
    },
    "grayscale": true,
    "flip": true,
    "flop": true,
    "negate": true,
    "flatten": true,
    "normalise": true,
    "smartCrop": {
      "padding": 12
    }
  }
}

All keys under the edits key are optional. It all depends on the edits you want to perform to the image. There are a lot of powerful modifications (like resize, grayscale, negate, add a background, crop) you can do to the image just by manipulating the url.

2. Now, convert the JSON body into a BASE 64 string.

3. Finally, append the base 64 string to the cloudfront base url like so: https://d24fj9qq8rdx7g.cloudfront.net/{base-64-of-json-body}

Local Installation Guidelines

The project is fully dockerized, so it is very easy to start up the required containers from the docker-compose.yml file in the project root. All you need is docker and docker-compose installed in the system.

docker-compose up --build

However, This project requires some cloud resources like (AWS S3 file storage) in order to function properly. So, in order to run the project locally you need to have a .debug.env file in the project root which should look like the following:

DEBUG=1
DJANGO_APP_SECRET_KEY=anything
AWS_ACCESS_KEY_ID=****
AWS_SECRET_ACCESS_KEY=****
AWS_S3_REGION_NAME=****
AWS_STORAGE_BUCKET_NAME=****
AWS_CLOUDFRONT_DOMAIN=****
ALLOWED_HOSTS=127.0.0.1,localhost
RABBITMQ_DEFAULT_USER=anything
RABBITMQ_DEFAULT_PASS=anything
POSTGRES_USER=anything
POSTGRES_PASSWORD=anything
POSTGRES_DB=anything