Core Features
Implementation Details
API Documentation
- Getting an Authentication Token
- Upload Image(s)
- Single Image Upload
- Bulk Image Upload
- Search
- Full Text Search
- Image Search
- Fetching Async Task Results
- Error Handling
URL based Image Manipulation
Local Installation Guidelines
- 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
- built using
Django Rest Framework
- uses
RabbitMQ
as message broker andCelery
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.
This API documentation explains some important endpoints in detail. Find the full Swagger UI documentation available here: /api/doc
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": "user@example.com",
"password": "string"
}
Example HTTP 200 Response:
{
"detail": "success",
"content": {
"email": "user@example.com",
"username": "string",
"password": "string",
"token": "be151b8865d43783fa80cd147ff0df472395d340"
}
}
Request Body:
{
"username": "string",
"email": "user@example.com",
"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.
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.
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.
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"
}
]
}
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.
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)
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
}
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}
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