androidでtensorflow

概要

androidでtensorflowやってみた。
api17でやってみた。

写真

成果物

環境

windows 7
ADT v22.6
android 4.2.2 api17

ビルド手順

githubから、以下を入手

graph_label_strings.txt
mnist_model_graph.pb
libtensorflow_inference.so
tensorflow java

プロジェクトに配置

graph_label_strings.txt
mnist_model_graph.pb
assetsに置く
libtensorflow_inference.so
libsに置く
tensorflow java
srcに置く

サンプルコード

package com.ohisamallc.ohiapp142;

import android.content.res.AssetManager;
import android.util.Log;
import org.tensorflow.Operation;
import org.tensorflow.contrib.android.TensorFlowInferenceInterface;
import java.io.BufferedReader;
import java.io.IOException;
import java.io.InputStreamReader;
import java.util.ArrayList;
import java.util.Comparator;
import java.util.List;
import java.util.PriorityQueue;
import java.util.Vector;

public class TensorFlowImageClassifier implements Classifier {
    private static final String TAG = "TensorFlowImageClassifier";
    private static final int MAX_RESULTS = 3;
    private static final float THRESHOLD = 0.1f;
    private String inputName;
    private String outputName;
    private int inputSize;
    private Vector<String> labels = new Vector<String>();
    private float[] outputs;
    private String[] outputNames;
    private boolean logStats = false;
    private TensorFlowInferenceInterface inferenceInterface;
    private TensorFlowImageClassifier() {
    }
    public static Classifier create(AssetManager assetManager, String modelFilename, String labelFilename, int inputSize, String inputName, String outputName)
            throws IOException {
        TensorFlowImageClassifier c = new TensorFlowImageClassifier();
        c.inputName = inputName;
        c.outputName = outputName;
        String actualFilename = labelFilename.split("file:///android_asset/")[1];
        Log.i(TAG, "Reading labels from: " + actualFilename);
        BufferedReader br = null;
        br = new BufferedReader(new InputStreamReader(assetManager.open(actualFilename)));
        String line;
        while ((line = br.readLine()) != null)
        {
            c.labels.add(line);
        }
        br.close();
        c.inferenceInterface = new TensorFlowInferenceInterface(assetManager, modelFilename);
        final Operation operation = c.inferenceInterface.graphOperation(outputName);
        final int numClasses = (int) operation.output(0).shape().size(1);
        Log.i(TAG, "Read " + c.labels.size() + " labels, output layer size is " + numClasses);
        c.inputSize = inputSize;
        c.outputNames = new String[] {
            outputName
        };
        c.outputs = new float[numClasses];
        return c;
    }
    @Override
    public List<Recognition> recognizeImage(final float[] pixels) {
        inferenceInterface.feed(inputName, pixels, 784);
        inferenceInterface.run(outputNames, logStats);
        inferenceInterface.fetch(outputName, outputs);
        PriorityQueue<Recognition> pq = new PriorityQueue<Recognition>(3, new Comparator<Recognition>() {
            @Override
            public int compare(Recognition lhs, Recognition rhs) {
                return Float.compare(rhs.getConfidence(), lhs.getConfidence());
            }
        });
        for (int i = 0; i < outputs.length; ++i)
        {
            if (outputs[i] > THRESHOLD)
            {
                pq.add(new Recognition("" + i, labels.size() > i ? labels.get(i) : "unknown", outputs[i], null));
            }
        }
        final ArrayList<Recognition> recognitions = new ArrayList<Recognition>();
        int recognitionsSize = Math.min(pq.size(), MAX_RESULTS);
        for (int i = 0; i < recognitionsSize; ++i)
        {
            recognitions.add(pq.poll());
        }
        return recognitions;
    }
    @Override
    public void enableStatLogging(boolean debug) {
        //inferenceInterface.enableStatLogging(debug);
    }
    @Override
    public String getStatString() {
        return inferenceInterface.getStatString();
    }
    @Override
    public void close() {
        inferenceInterface.close();
    }
}