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https://github.com/dsys/match

:crystal_ball: Scalable reverse image search built on Kubernetes and Elasticsearch
https://github.com/dsys/match

elasticsearch image-signatures kubernetes-cluster

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:crystal_ball: Scalable reverse image search built on Kubernetes and Elasticsearch

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README 

        
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Scalable reverse image search
built on Kubernetes and Elasticsearch



GitHub stars Docker Pulls Kubernetes shield 



**Match** makes it easy to search for images that look similar to each other. Using a state-of-the-art perceptual hash, it is invariant to scaling and 90 degree rotations. Its HTTP API is quick to integrate and flexible for a number of reverse image search applications. Kubernetes and Elasticsearch allow Match to scale to billions of images with ease while giving you full control over where your data is stored. Match uses the awesome [ascribe/image-match](https://github.com/ascribe/image-match) under the hood for most of the image search legwork.



1. [Getting Started](#getting-started)

2. [API](#api)

3. [Development](#development)

4. [License and Acknowledgements](#license-and-acknowledgements)



## Getting Started



If you already have ElasticSearch running:

```

$ docker run -e ELASTICSEARCH_URL=https://daisy.us-west-1.es.amazonaws.com -it dsys/match

```



If you want to run ElasticSearch locally as well, have [`docker-compose`](https://docs.docker.com/compose/) installed on your system, clone this repository and type:

```

$ make dev

```



Match is packaged as a Docker container ([dsys/match](https://hub.docker.com/r/dsys/match/) on Docker Hub), making it highly portable and scalable to billions of images. You can configure a few options using environment variables:



* **WORKER_COUNT** *(default: `4`)*



  The number of gunicorn workers to spin up.



* **ELASTICSEARCH_URL** *(default: `elasticsearch:9200`)*



  A URL pointing to the Elasticsearch database where image signatures are to be stored. If you don't want to host your own Elasticsearch cluster, consider using [AWS Elasticsearch Service](https://aws.amazon.com/elasticsearch-service/). That's what we use.



* **ELASTICSEARCH_INDEX** *(default: images)*



  The index in the Elasticsearch database where image signatures are to be stored.



* **ELASTICSEARCH_DOC_TYPE** *(default: images)*



  The doc type used for storing image signatures.



### Using in your own Kubernetes cluster



You can configure the service, replication controller, and secret like so:



```yaml

# match-service.yml

apiVersion: v1

kind: Service

metadata:

  name: match

spec:

  ports:

  - name: http

    port: 80

    protocol: TCP

  selector:

    app: match

```



```yaml

# match-rc.yml

apiVersion: v1

kind: ReplicationController

metadata:

  name: match

spec:

  replicas: 1

  selector:

    app: match

  template:

    metadata:

      labels:

        app: match

    spec:

      containers:

      - name: match

        image: dsys/match:latest

        ports:

        - containerPort: 80

        env:

        - name: WORKER_COUNT

          value: "4"

        - name: ELASTICSEARCH_URL

          valueFrom:

            secretKeyRef:

              name: match

              key: elasticsearch.url

        - name: ELASTICSEARCH_INDEX

          valueFrom:

            secretKeyRef:

              name: match

              key: elasticsearch.index

        - name: ELASTICSEARCH_DOC_TYPE

          valueFrom:

            secretKeyRef:

              name: match

              key: elasticsearch.doc-type

```



```yaml

# match-secret.yml

apiVersion: v1

kind: Secret

metadata:

  name: match

data:

  # https://daisy.us-west-1.es.amazonaws.com (change me)

  elasticsearch.url: aHR0cHM6Ly9kYWlzeS51cy13ZXN0LTEuZXMuYW1hem9uYXdzLmNvbQ==



  # images

  elasticsearch.index: aW1hZ2Vz



  # images

  elasticsearch.doc-type: aW1hZ2Vz

```



## API



Match has a simple HTTP API. All request parameters are specified via `application/x-www-form-urlencoded` or `multipart/form-data`.



* [POST `/add`](#post-add)

* [DELETE `/delete`](#delete-delete)

* [POST `/search`](#post-search)

* [POST `/compare`](#post-compare)

* [GET `/count`](#get-count)

* [GET `/list`](#get-list)

* [GET `/ping`](#get-ping)



---



### POST `/add`



Adds an image signature to the database.



#### Parameters



* **url** or **image** *(required)*



  The image to add to the database. It may be provided as a URL via `url` or as a `multipart/form-data` file upload via `image`.



* **filepath** *(required)*



  The path to save the image to in the database. If another image already exists at the given path, it will be overwritten.



* **metadata** *(default: None)*



  An arbitrary JSON object featuring meta data to attach to the image.



#### Example Response



```json

{

  "status": "ok",

  "error": [],

  "method": "add",

  "result": []

}

```



---



### DELETE `/delete`



Deletes an image signature from the database.



#### Parameters



* **filepath** *(required)*



  The path of the image signature in the database.



#### Example Response



```json

{

  "status": "ok",

  "error": [],

  "method": "delete",

  "result": []

}

```



---



### POST `/search`



Searches for a similar image in the database. Scores range from 0 to 100, with 100 being a perfect match.



#### Parameters



* **url** or **image** *(required)*



  The image to add to the database. It may be provided as a URL via `url` or as a `multipart/form-data` file upload via `image`.



* **all_orientations** *(default: true)*



  Whether or not to search for similar 90 degree rotations of the image.



#### Example Response



```json

{

  "status": "ok",

  "error": [],

  "method": "search",

  "result": [

    {

      "score": 99.0,

      "filepath": "http://static.wixstatic.com/media/0149b5_345c8f862e914a80bcfcc98fcd432e97.jpg_srz_614_709_85_22_0.50_1.20_0.00_jpg_srz"

    }

  ]

}

```



---



### POST `/compare`



Compares two images, returning a score for their similarity. Scores range from 0 to 100, with 100 being a perfect match.



#### Parameters



* **url1** or **image1**, **url2** or **image2** *(required)*



  The images to compare. They may be provided as a URL via `url1`/`url2` or as a `multipart/form-data` file upload via `image1`/`image2`.



#### Example Response



```json

{

  "status": "ok",

  "error": [],

  "method": "compare",

  "result": [

    {

      "score": 99.0

    }

  ]

}

```



---



### GET `/count`



Count the number of image signatures in the database.



#### Example Response



```json

{

  "status": "ok",

  "error": [],

  "method": "list",

  "result": [420]

}

```



---



### GET `/list`



Lists the file paths for the image signatures in the database.



#### Parameters



* **offset** *(default: 0)*



  The location in the database to begin listing image paths.



* **limit** *(default: 20)*



  The number of image paths to retrieve.



#### Example Response



```json

{

  "status": "ok",

  "error": [],

  "method": "list",

  "result": [

    "http://img.youtube.com/vi/iqPqylKy-bY/0.jpg",

    "https://i.ytimg.com/vi/zbjIwBggt2k/hqdefault.jpg",

    "https://s-media-cache-ak0.pinimg.com/736x/3d/67/6d/3d676d3f7f3031c9fd91c10b17d56afe.jpg"

  ]

}

```



---



### GET `/ping`



Check for the health of the server.



#### Example Response



```json

{

  "status": "ok",

  "error": [],

  "method": "ping",

  "result": []

}

```



## Development



    $ export ELASTICSEARCH_URL=https://daisy.us-west-1.es.amazonaws.com

    $ make build

    $ make run

    $ make push



## License and Acknowledgements



Match is based on [ascribe/image-match](https://github.com/ascribe/image-match), which is in turn based on the paper [_An image signature for any kind of image_, Goldberg et al](http://www.cs.cmu.edu/~hcwong/Pdfs/icip02.ps). There is an existing [reference implementation](https://www.pureftpd.org/project/libpuzzle) which may be more suited to your needs.



Match itself is released under the [BSD 3-Clause license](https://github.com/dsys/match/blob/master/LICENSE). `ascribe/image-match` is released under the Apache 2.0 license.