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Commit be137c8

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Add Classifier for TensorFlow Object Detection
1 parent 4e51973 commit be137c8

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/*
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* Copyright (C) 2017 MINDORKS NEXTGEN PRIVATE LIMITED
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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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* http://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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package com.mindorks.tensorflowexample;
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import android.graphics.Bitmap;
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import android.graphics.RectF;
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import java.util.List;
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/**
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* Created by amitshekhar on 06/03/17.
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*/
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/**
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* Generic interface for interacting with different recognition engines.
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*/
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public interface Classifier {
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/**
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* An immutable result returned by a Classifier describing what was recognized.
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*/
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public class Recognition {
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/**
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* A unique identifier for what has been recognized. Specific to the class, not the instance of
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* the object.
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*/
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private final String id;
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/**
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* Display name for the recognition.
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*/
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private final String title;
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/**
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* A sortable score for how good the recognition is relative to others. Higher should be better.
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*/
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private final Float confidence;
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/**
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* Optional location within the source image for the location of the recognized object.
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*/
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private RectF location;
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public Recognition(
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final String id, final String title, final Float confidence, final RectF location) {
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this.id = id;
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this.title = title;
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this.confidence = confidence;
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this.location = location;
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}
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public String getId() {
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return id;
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}
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public String getTitle() {
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return title;
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}
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public Float getConfidence() {
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return confidence;
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}
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public RectF getLocation() {
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return new RectF(location);
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}
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public void setLocation(RectF location) {
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this.location = location;
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}
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@Override
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public String toString() {
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String resultString = "";
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if (id != null) {
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resultString += "[" + id + "] ";
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}
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if (title != null) {
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resultString += title + " ";
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}
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if (confidence != null) {
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resultString += String.format("(%.1f%%) ", confidence * 100.0f);
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}
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if (location != null) {
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resultString += location + " ";
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}
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return resultString.trim();
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}
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}
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List<Recognition> recognizeImage(Bitmap bitmap);
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void enableStatLogging(final boolean debug);
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String getStatString();
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void close();
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}
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/*
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* Copyright (C) 2017 MINDORKS NEXTGEN PRIVATE LIMITED
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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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* http://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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package com.mindorks.tensorflowexample;
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import android.content.res.AssetManager;
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import android.graphics.Bitmap;
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import android.os.Trace;
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import android.util.Log;
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import org.tensorflow.contrib.android.TensorFlowInferenceInterface;
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import java.io.BufferedReader;
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import java.io.IOException;
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import java.io.InputStreamReader;
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import java.util.ArrayList;
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import java.util.Comparator;
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import java.util.List;
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import java.util.PriorityQueue;
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import java.util.Vector;
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/**
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* Created by amitshekhar on 06/03/17.
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*/
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/**
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* A classifier specialized to label images using TensorFlow.
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*/
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public class TensorFlowImageClassifier implements Classifier {
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private static final String TAG = "TensorFlowImageClassifier";
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// Only return this many results with at least this confidence.
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private static final int MAX_RESULTS = 3;
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private static final float THRESHOLD = 0.1f;
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// Config values.
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private String inputName;
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private String outputName;
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private int inputSize;
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private int imageMean;
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private float imageStd;
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// Pre-allocated buffers.
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private Vector<String> labels = new Vector<String>();
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private int[] intValues;
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private float[] floatValues;
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private float[] outputs;
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private String[] outputNames;
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private TensorFlowInferenceInterface inferenceInterface;
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private TensorFlowImageClassifier() {
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}
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/**
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* Initializes a native TensorFlow session for classifying images.
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*
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* @param assetManager The asset manager to be used to load assets.
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* @param modelFilename The filepath of the model GraphDef protocol buffer.
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* @param labelFilename The filepath of label file for classes.
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* @param inputSize The input size. A square image of inputSize x inputSize is assumed.
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* @param imageMean The assumed mean of the image values.
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* @param imageStd The assumed std of the image values.
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* @param inputName The label of the image input node.
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* @param outputName The label of the output node.
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* @throws IOException
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*/
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public static Classifier create(
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AssetManager assetManager,
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String modelFilename,
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String labelFilename,
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int inputSize,
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int imageMean,
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float imageStd,
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String inputName,
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String outputName)
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throws IOException {
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TensorFlowImageClassifier c = new TensorFlowImageClassifier();
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c.inputName = inputName;
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c.outputName = outputName;
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// Read the label names into memory.
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// TODO(andrewharp): make this handle non-assets.
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String actualFilename = labelFilename.split("file:///android_asset/")[1];
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Log.i(TAG, "Reading labels from: " + actualFilename);
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BufferedReader br = null;
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br = new BufferedReader(new InputStreamReader(assetManager.open(actualFilename)));
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String line;
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while ((line = br.readLine()) != null) {
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c.labels.add(line);
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}
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br.close();
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c.inferenceInterface = new TensorFlowInferenceInterface();
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if (c.inferenceInterface.initializeTensorFlow(assetManager, modelFilename) != 0) {
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throw new RuntimeException("TF initialization failed");
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}
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// The shape of the output is [N, NUM_CLASSES], where N is the batch size.
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int numClasses =
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(int) c.inferenceInterface.graph().operation(outputName).output(0).shape().size(1);
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Log.i(TAG, "Read " + c.labels.size() + " labels, output layer size is " + numClasses);
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// Ideally, inputSize could have been retrieved from the shape of the input operation. Alas,
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// the placeholder node for input in the graphdef typically used does not specify a shape, so it
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// must be passed in as a parameter.
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c.inputSize = inputSize;
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c.imageMean = imageMean;
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c.imageStd = imageStd;
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// Pre-allocate buffers.
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c.outputNames = new String[]{outputName};
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c.intValues = new int[inputSize * inputSize];
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c.floatValues = new float[inputSize * inputSize * 3];
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c.outputs = new float[numClasses];
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return c;
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}
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@Override
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public List<Recognition> recognizeImage(final Bitmap bitmap) {
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// Log this method so that it can be analyzed with systrace.
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Trace.beginSection("recognizeImage");
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Trace.beginSection("preprocessBitmap");
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// Preprocess the image data from 0-255 int to normalized float based
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// on the provided parameters.
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bitmap.getPixels(intValues, 0, bitmap.getWidth(), 0, 0, bitmap.getWidth(), bitmap.getHeight());
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for (int i = 0; i < intValues.length; ++i) {
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final int val = intValues[i];
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floatValues[i * 3 + 0] = (((val >> 16) & 0xFF) - imageMean) / imageStd;
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floatValues[i * 3 + 1] = (((val >> 8) & 0xFF) - imageMean) / imageStd;
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floatValues[i * 3 + 2] = ((val & 0xFF) - imageMean) / imageStd;
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}
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Trace.endSection();
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// Copy the input data into TensorFlow.
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Trace.beginSection("fillNodeFloat");
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inferenceInterface.fillNodeFloat(
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inputName, new int[]{1, inputSize, inputSize, 3}, floatValues);
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Trace.endSection();
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// Run the inference call.
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Trace.beginSection("runInference");
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inferenceInterface.runInference(outputNames);
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Trace.endSection();
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// Copy the output Tensor back into the output array.
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Trace.beginSection("readNodeFloat");
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inferenceInterface.readNodeFloat(outputName, outputs);
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Trace.endSection();
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// Find the best classifications.
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PriorityQueue<Recognition> pq =
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new PriorityQueue<Recognition>(
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3,
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new Comparator<Recognition>() {
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@Override
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public int compare(Recognition lhs, Recognition rhs) {
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// Intentionally reversed to put high confidence at the head of the queue.
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return Float.compare(rhs.getConfidence(), lhs.getConfidence());
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}
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});
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for (int i = 0; i < outputs.length; ++i) {
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if (outputs[i] > THRESHOLD) {
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pq.add(
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new Recognition(
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"" + i, labels.size() > i ? labels.get(i) : "unknown", outputs[i], null));
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}
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}
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final ArrayList<Recognition> recognitions = new ArrayList<Recognition>();
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int recognitionsSize = Math.min(pq.size(), MAX_RESULTS);
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for (int i = 0; i < recognitionsSize; ++i) {
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recognitions.add(pq.poll());
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}
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Trace.endSection(); // "recognizeImage"
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return recognitions;
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}
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@Override
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public void enableStatLogging(boolean debug) {
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inferenceInterface.enableStatLogging(debug);
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}
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@Override
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public String getStatString() {
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return inferenceInterface.getStatString();
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}
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@Override
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public void close() {
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inferenceInterface.close();
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}
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}

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