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VisionClassificationPredict.java
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/
VisionClassificationPredict.java
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/*
* Copyright 2019 Google LLC
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package com.example.automl;
// [START automl_vision_classification_predict]
import com.google.cloud.automl.v1.AnnotationPayload;
import com.google.cloud.automl.v1.ExamplePayload;
import com.google.cloud.automl.v1.Image;
import com.google.cloud.automl.v1.ModelName;
import com.google.cloud.automl.v1.PredictRequest;
import com.google.cloud.automl.v1.PredictResponse;
import com.google.cloud.automl.v1.PredictionServiceClient;
import com.google.protobuf.ByteString;
import java.io.IOException;
import java.nio.file.Files;
import java.nio.file.Paths;
class VisionClassificationPredict {
public static void main(String[] args) throws IOException {
// TODO(developer): Replace these variables before running the sample.
String projectId = "YOUR_PROJECT_ID";
String modelId = "YOUR_MODEL_ID";
String filePath = "path_to_local_file.jpg";
predict(projectId, modelId, filePath);
}
static void predict(String projectId, String modelId, String filePath) throws IOException {
// Initialize client that will be used to send requests. This client only needs to be created
// once, and can be reused for multiple requests. After completing all of your requests, call
// 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", "0.8") // [0.0-1.0] Only produce results higher than this value
.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.getClassification().getScore());
}
}
}
}
// [END automl_vision_classification_predict]