Examples with ModelName com.google.cloud.automl.v1beta1.ModelName used on opensource projects

Example 26 with ModelName

use of com.google.cloud.automl.v1beta1.ModelName in project java-automl by googleapis.

the class VisionObjectDetectionPredict method predict.

static void predict(String projectId, String modelId, String filePath) throws IOException {
    // the "close" method on the client to safely clean up any remaining background resources.
    try (PredictionServiceClient client = PredictionServiceClient.create()) {
        // Get the full path of the model.
        ModelName name = ModelName.of(projectId, "us-central1", modelId);
        ByteString content = ByteString.copyFrom(Files.readAllBytes(Paths.get(filePath)));
        Image image = Image.newBuilder().setImageBytes(content).build();
        ExamplePayload payload = ExamplePayload.newBuilder().setImage(image).build();
        PredictRequest predictRequest = PredictRequest.newBuilder().setName(name.toString()).setPayload(payload).putParams("score_threshold", // [0.0-1.0] Only produce results higher than this value
        "0.5").build();
        PredictResponse response = client.predict(predictRequest);
        for (AnnotationPayload annotationPayload : response.getPayloadList()) {
            System.out.format("Predicted class name: %s\n", annotationPayload.getDisplayName());
            System.out.format("Predicted class score: %.2f\n", annotationPayload.getImageObjectDetection().getScore());
            BoundingPoly boundingPoly = annotationPayload.getImageObjectDetection().getBoundingBox();
            System.out.println("Normalized Vertices:");
            for (NormalizedVertex vertex : boundingPoly.getNormalizedVerticesList()) {
                System.out.format("\tX: %.2f, Y: %.2f\n", vertex.getX(), vertex.getY());
            }
        }
    }
}

Example 27 with ModelName

use of com.google.cloud.automl.v1beta1.ModelName in project java-automl by googleapis.

the class ClassificationUndeployModel method classificationUndeployModel.

// Deploy a model
static void classificationUndeployModel(String projectId, String modelId) throws IOException, ExecutionException, InterruptedException {
    // the "close" method on the client to safely clean up any remaining background resources.
    try (AutoMlClient client = AutoMlClient.create()) {
        // Get the full path of the model.
        ModelName modelFullId = ModelName.of(projectId, "us-central1", modelId);
        // Build deploy model request.
        UndeployModelRequest undeployModelRequest = UndeployModelRequest.newBuilder().setName(modelFullId.toString()).build();
        // Deploy a model with the deploy model request.
        OperationFuture<Empty, OperationMetadata> future = client.undeployModelAsync(undeployModelRequest);
        future.get();
        // Display the deployment details of model.
        System.out.println("Model undeploy finished");
    }
}

Example 28 with ModelName

use of com.google.cloud.automl.v1beta1.ModelName in project java-automl by googleapis.

the class ClassificationDeployModel method classificationDeployModel.

// Deploy a model
static void classificationDeployModel(String projectId, String modelId) throws IOException, ExecutionException, InterruptedException {
    // the "close" method on the client to safely clean up any remaining background resources.
    try (AutoMlClient client = AutoMlClient.create()) {
        // Get the full path of the model.
        ModelName modelFullId = ModelName.of(projectId, "us-central1", modelId);
        // Build deploy model request.
        DeployModelRequest deployModelRequest = DeployModelRequest.newBuilder().setName(modelFullId.toString()).build();
        // Deploy a model with the deploy model request.
        OperationFuture<Empty, OperationMetadata> future = client.deployModelAsync(deployModelRequest);
        future.get();
        // Display the deployment details of model.
        System.out.println("Model deployment finished");
    }
}

Example 29 with ModelName

use of com.google.cloud.automl.v1beta1.ModelName in project java-automl by googleapis.

the class ClassificationDeployModelNodeCount method classificationDeployModelNodeCount.

// Deploy a model with a specified node count
static void classificationDeployModelNodeCount(String projectId, String modelId) throws IOException, ExecutionException, InterruptedException {
    // the "close" method on the client to safely clean up any remaining background resources.
    try (AutoMlClient client = AutoMlClient.create()) {
        // Get the full path of the model.
        ModelName modelFullId = ModelName.of(projectId, "us-central1", modelId);
        // Set how many nodes the model is deployed on
        ImageClassificationModelDeploymentMetadata deploymentMetadata = ImageClassificationModelDeploymentMetadata.newBuilder().setNodeCount(2).build();
        DeployModelRequest request = DeployModelRequest.newBuilder().setName(modelFullId.toString()).setImageClassificationModelDeploymentMetadata(deploymentMetadata).build();
        // Deploy the model
        OperationFuture<Empty, OperationMetadata> future = client.deployModelAsync(request);
        future.get();
        System.out.println("Model deployment on 2 nodes finished");
    }
}

Example 30 with ModelName

use of com.google.cloud.automl.v1beta1.ModelName in project java-automl by googleapis.

the class PredictionApi method predict.

// [START automl_vision_predict]
/**
 * Demonstrates using the AutoML client to predict an image.
 *
 * @param projectId the Id of the project.
 * @param computeRegion the Region name.
 * @param modelId the Id of the model which will be used for text classification.
 * @param filePath the Local text file path of the content to be classified.
 * @param scoreThreshold the Confidence score. Only classifications with confidence score above
 *     scoreThreshold are displayed.
 */
static void predict(String projectId, String computeRegion, String modelId, String filePath, String scoreThreshold) {
    // Instantiate client for prediction service.
    try (PredictionServiceClient predictionClient = PredictionServiceClient.create()) {
        // Get the full path of the model.
        ModelName name = ModelName.of(projectId, computeRegion, modelId);
        // Read the image and assign to payload.
        ByteString content = ByteString.copyFrom(Files.readAllBytes(Paths.get(filePath)));
        Image image = Image.newBuilder().setImageBytes(content).build();
        ExamplePayload examplePayload = ExamplePayload.newBuilder().setImage(image).build();
        // Additional parameters that can be provided for prediction e.g. Score Threshold
        Map<String, String> params = new HashMap<>();
        if (scoreThreshold != null) {
            params.put("score_threshold", scoreThreshold);
        }
        // Perform the AutoML Prediction request
        PredictResponse response = predictionClient.predict(name, examplePayload, params);
        System.out.println("Prediction results:");
        for (AnnotationPayload annotationPayload : response.getPayloadList()) {
            System.out.println("Predicted class name :" + annotationPayload.getDisplayName());
            System.out.println("Predicted class score :" + annotationPayload.getClassification().getScore());
        }
    } catch (IOException e) {
        e.printStackTrace();
    }
}