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Rebecca Taylor
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AutomlVisionBatchPredict.java for staging
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‎vision/automl/staging/AutomlVisionBatchPredict.java

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/*
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* Copyright 2019 Google LLC
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* https://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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// DO NOT EDIT! This is a generated sample ("LongRunningRequestAsync", "automl_vision_batch_predict")
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// sample-metadata:
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// title: AutoML Batch Predict (AutoML Vision)
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// description: AutoML Batch Predict using AutoML Vision
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// usage: gradle run -PmainClass=com.google.cloud.examples.automl.v1beta1.AutomlVisionBatchPredict [--args='[--input_uri "gs://[BUCKET-NAME]/path/to/file-with-image-urls.csv"] [--output_uri "gs://[BUCKET-NAME]/directory-for-output-files/"] [--project "[Google Cloud Project ID]"] [--model_id "[Model ID]"]']
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package com.google.cloud.examples.automl.v1beta1;
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import com.google.api.gax.longrunning.OperationFuture;
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import com.google.cloud.automl.v1beta1.BatchPredictInputConfig;
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import com.google.cloud.automl.v1beta1.BatchPredictOutputConfig;
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import com.google.cloud.automl.v1beta1.BatchPredictRequest;
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import com.google.cloud.automl.v1beta1.BatchPredictResult;
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import com.google.cloud.automl.v1beta1.GcsDestination;
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import com.google.cloud.automl.v1beta1.GcsSource;
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import com.google.cloud.automl.v1beta1.ModelName;
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import com.google.cloud.automl.v1beta1.OperationMetadata;
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import com.google.cloud.automl.v1beta1.PredictionServiceClient;
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import java.util.Arrays;
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import java.util.HashMap;
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import java.util.List;
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import java.util.Map;
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import org.apache.commons.cli.CommandLine;
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import org.apache.commons.cli.DefaultParser;
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import org.apache.commons.cli.Option;
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import org.apache.commons.cli.Options;
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public class AutomlVisionBatchPredict {
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// [START automl_vision_batch_predict]
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/*
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* Please include the following imports to run this sample.
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*
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* import com.google.api.gax.longrunning.OperationFuture;
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* import com.google.cloud.automl.v1beta1.BatchPredictInputConfig;
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* import com.google.cloud.automl.v1beta1.BatchPredictOutputConfig;
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* import com.google.cloud.automl.v1beta1.BatchPredictRequest;
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* import com.google.cloud.automl.v1beta1.BatchPredictResult;
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* import com.google.cloud.automl.v1beta1.GcsDestination;
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* import com.google.cloud.automl.v1beta1.GcsSource;
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* import com.google.cloud.automl.v1beta1.ModelName;
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* import com.google.cloud.automl.v1beta1.OperationMetadata;
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* import com.google.cloud.automl.v1beta1.PredictionServiceClient;
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* import java.util.Arrays;
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* import java.util.HashMap;
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* import java.util.List;
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* import java.util.Map;
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*/
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/**
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* AutoML Batch Predict using AutoML Vision
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*
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* @param inputUri Google Cloud Storage URI to CSV file in your bucket that contains the paths to
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* the images to annotate, e.g. gs://[BUCKET-NAME]/path/to/images.csv Each line specifies a
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* separate path to an image in Google Cloud Storage.
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* @param outputUri Identifies where to store the output of your prediction request in your Google
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* Cloud Storage bucket. You must have write permissions to the Google Cloud Storage bucket.
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* @param project Required. Your Google Cloud Project ID.
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* @param modelId Model ID, e.g. VOT1234567890123456789
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*/
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public static void sampleBatchPredict(
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String inputUri, String outputUri, String project, String modelId) {
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try (PredictionServiceClient predictionServiceClient = PredictionServiceClient.create()) {
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// inputUri = "gs://[BUCKET-NAME]/path/to/file-with-image-urls.csv";
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// outputUri = "gs://[BUCKET-NAME]/directory-for-output-files/";
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// project = "[Google Cloud Project ID]";
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// modelId = "[Model ID]";
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ModelName name = ModelName.of(project, "us-central1", modelId);
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List<String> inputUris = Arrays.asList(inputUri);
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GcsSource gcsSource = GcsSource.newBuilder().addAllInputUris(inputUris).build();
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BatchPredictInputConfig inputConfig =
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BatchPredictInputConfig.newBuilder().setGcsSource(gcsSource).build();
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GcsDestination gcsDestination =
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GcsDestination.newBuilder().setOutputUriPrefix(outputUri).build();
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BatchPredictOutputConfig outputConfig =
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BatchPredictOutputConfig.newBuilder().setGcsDestination(gcsDestination).build();
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// A value from 0.0 to 1.0. When the model detects objects on video frames,
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// it will only produce bounding boxes that have at least this confidence score.
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// The default is 0.5.
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String paramsItem = "0.0";
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Map<String, String> params = new HashMap<>();
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params.put("score_threshold", paramsItem);
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BatchPredictRequest request =
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BatchPredictRequest.newBuilder()
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.setName(name.toString())
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.setInputConfig(inputConfig)
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.setOutputConfig(outputConfig)
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.putAllParams(params)
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.build();
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OperationFuture<BatchPredictResult, OperationMetadata> future =
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predictionServiceClient.batchPredictAsync(request);
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System.out.println("Waiting for operation to complete...");
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BatchPredictResult response = future.get();
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System.out.println("Batch Prediction results saved to specified Cloud Storage bucket.");
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} catch (Exception exception) {
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System.err.println("Failed to create the client due to: " + exception);
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}
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}
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// [END automl_vision_batch_predict]
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public static void main(String[] args) throws Exception {
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Options options = new Options();
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options.addOption(Option.builder("").required(false).hasArg(true).longOpt("input_uri").build());
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options.addOption(
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Option.builder("").required(false).hasArg(true).longOpt("output_uri").build());
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options.addOption(Option.builder("").required(false).hasArg(true).longOpt("project").build());
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options.addOption(Option.builder("").required(false).hasArg(true).longOpt("model_id").build());
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CommandLine cl = (new DefaultParser()).parse(options, args);
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String inputUri =
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cl.getOptionValue("input_uri", "gs://[BUCKET-NAME]/path/to/file-with-image-urls.csv");
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String outputUri =
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cl.getOptionValue("output_uri", "gs://[BUCKET-NAME]/directory-for-output-files/");
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String project = cl.getOptionValue("project", "[Google Cloud Project ID]");
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String modelId = cl.getOptionValue("model_id", "[Model ID]");
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sampleBatchPredict(inputUri, outputUri, project, modelId);
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}
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}

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