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September 1, 2024 10:15
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image to text try / published by https://github.com/dacr/code-examples-manager #b35b9b44-ec04-46f5-b904-43605e2b2107/e0d7d84a713d4e8b97de749920b57b77a731fa9
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| // summary : image to text try | |
| // keywords : djl, machine-learning, image-to-text, ai, @testable | |
| // publish : gist | |
| // authors : David Crosson | |
| // license : Apache NON-AI License Version 2.0 (https://raw.githubusercontent.com/non-ai-licenses/non-ai-licenses/main/NON-AI-APACHE2) | |
| // id : b35b9b44-ec04-46f5-b904-43605e2b2107 | |
| // created-on : 2024-07-20T08:42:43+02:00 | |
| // managed-by : https://github.com/dacr/code-examples-manager | |
| // run-with : scala-cli $file | |
| // --------------------- | |
| //> using scala "3.4.2" | |
| //> using dep "org.slf4j:slf4j-api:2.0.13" | |
| //> using dep "org.slf4j:slf4j-simple:2.0.13" | |
| //> using dep "net.java.dev.jna:jna:5.14.0" | |
| //> using dep "ai.djl:api:0.29.0" | |
| //> using dep "ai.djl:basicdataset:0.29.0" | |
| //> using dep "ai.djl:model-zoo:0.29.0" | |
| //> using dep "ai.djl.huggingface:tokenizers:0.29.0" | |
| //> using dep "ai.djl.mxnet:mxnet-engine:0.29.0" | |
| //> using dep "ai.djl.mxnet:mxnet-model-zoo:0.29.0" | |
| //> using dep "ai.djl.pytorch:pytorch-engine:0.29.0" | |
| //> using dep "ai.djl.pytorch:pytorch-model-zoo:0.29.0" | |
| //> using dep "ai.djl.tensorflow:tensorflow-engine:0.29.0" | |
| //> using dep "ai.djl.tensorflow:tensorflow-model-zoo:0.29.0" | |
| //> using dep "ai.djl.onnxruntime:onnxruntime-engine:0.29.0" | |
| // --------------------- | |
| //System.setProperty("org.slf4j.simpleLogger.defaultLogLevel", "debug") | |
| import ai.djl.Application | |
| import ai.djl.engine.Engine | |
| import ai.djl.modality.Classifications | |
| import ai.djl.modality.Classifications.Classification | |
| import ai.djl.modality.cv.Image | |
| import ai.djl.modality.cv.ImageFactory | |
| import ai.djl.repository.zoo.Criteria | |
| import ai.djl.repository.zoo.ModelZoo | |
| import ai.djl.repository.zoo.ZooModel | |
| import ai.djl.training.util.ProgressBar | |
| import java.nio.file.Files | |
| import java.nio.file.Path | |
| import java.nio.file.Paths | |
| import scala.jdk.CollectionConverters.* | |
| // ---------------------------------------------------------------------------------------------- | |
| val criteria = | |
| Criteria.builder | |
| .optApplication(Application.CV.IMAGE_CLASSIFICATION) | |
| .setTypes(classOf[Image], classOf[Classifications]) | |
| // ------------------------------------ | |
| //.optFilter("flavor","v1") | |
| //.optFilter("dataset","cifar10") | |
| // ------------------------------------ | |
| //.optFilter("flavor","v3_large") | |
| //.optFilter("dataset","imagenet") | |
| // ------------------------------------ | |
| .optFilter("flavor","v1d") | |
| .optFilter("dataset","imagenet") | |
| .optProgress(new ProgressBar) | |
| .build | |
| val model = ModelZoo.loadModel(criteria) | |
| val predictor = model.newPredictor() | |
| val inputImageURL = "https://mapland.fr/data/ai/images-samples/example-001.jpg" | |
| val img = ImageFactory.getInstance().fromUrl(inputImageURL) | |
| val found: Classifications = predictor.predict(img) | |
| found.items() | |
| .asScala | |
| .toList | |
| .asInstanceOf[List[Classification]] | |
| .filter(_.getProbability > 0.5d) | |
| .foreach{cl => | |
| println(cl.getClassName+" "+cl.getProbability) | |
| } |
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