https://github.com/mgubaidullin/dj-kamel

https://github.com/mgubaidullin/dj-kamel

Last synced: 3 months ago
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• Host: GitHub
• URL: https://github.com/mgubaidullin/dj-kamel
• Owner: mgubaidullin
• Created: 2021-03-12T01:30:41.000Z (over 3 years ago)
• Default Branch: master
• Last Pushed: 2021-03-15T15:40:09.000Z (over 3 years ago)
• Last Synced: 2024-06-20T14:07:16.565Z (5 months ago)
• Language: Groovy
• Size: 1.19 MB
• Stars: 1
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

• awesome-camel-k - Serverless Image Classification inference with DJL and Camel-K

README

        
## Serverless Image classification inference with DJL and Camel-K

Requires OpenShift or CRC, Camel-K CLI and trained model 

### Architecture

![architecture](architecture.png)

### What is Camel-K

Read [Introducing Camel K](https://www.nicolaferraro.me/2018/10/15/introducing-camel-k/) to learn more

### How to run

```

kamel run -d camel-djl -d camel-jackson -d camel-vertx-http \

-d mvn:ai.djl:api:0.9.0 \

-d mvn:ai.djl.mxnet:mxnet-engine:0.9.0 \

--env=ENGINE_CACHE_DIR=/tmp \

dj-kamel.groovy --dev

```

### How to infer

Requires OpenShift or CRC, Camel-K CLI

```

curl -i -X GET "http://APP_URL/image?protocol=http&url=IMAGE_URL"

```

Example

```

curl -i -X GET "http://dj-kamel-dj-kamel.apps-crc.testing/image?protocol=http&url=github.com/mgubaidullin/dj-kamel/raw/master/negative.jpg"

```

### Integration code

dj-kamel.groovy

``` groovy

import ai.djl.Model

import ai.djl.basicmodelzoo.cv.classification.ResNetV1

import ai.djl.modality.cv.transform.Resize

import ai.djl.modality.cv.transform.ToTensor

import ai.djl.modality.cv.translator.ImageClassificationTranslator

import ai.djl.ndarray.types.Shape

import ai.djl.training.util.DownloadUtils

import org.apache.camel.Exchange

import java.nio.file.Paths

camel {

    DownloadUtils.download('https://github.com/mgubaidullin/dj-kamel/raw/master/defects-0001.params', '/tmp/model-0001.params')

    def resNet = ResNetV1.builder().setImageShape(new Shape(3, 23, 23)).setNumLayers(20).setOutSize(2).build()

    def model = Model.newInstance('model')

    model.setBlock(resNet)

    model.load(Paths.get('/tmp'), 'model')

    def translator = ImageClassificationTranslator.builder()

            .addTransform(new Resize(23, 23))

            .addTransform(new ToTensor())

            .optApplySoftmax(true)

            .optSynset(List.of('0', '1'))

            .build()

    registry.bind('Model', model)

    registry.bind('Translator', translator)

}

rest('/image')

        .get()

        .param().name('protocol').endParam()

        .param().name('url').endParam()

        .route()

        .setHeader(Exchange.HTTP_METHOD, constant('GET'))

        .setHeader(Exchange.HTTP_URI, constant(''))

        .setHeader('imageUrl', simple('vertx-http:${header.protocol}://${header.url}'))

        .toD('${header.imageUrl}?bridgeEndpoint=true&throwExceptionOnFailure=false')

        .to('djl:cv/image_classification??model=Model&translator=Translator')

        .marshal().json(true)

```        

### Data

Dataset used for model training is ["Concrete Crack Images for Classification"](https://data.mendeley.com/datasets/5y9wdsg2zt/2)

by Çağlar Fırat Özgenel is licensed under CC BY 4.0