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December 8, 2018 17:50
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/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. | |
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.android.tflitecamerademo; | |
import android.app.Activity; | |
import java.io.IOException; | |
/** | |
* This classifier works with the Inception-v3 slim model. | |
* It applies floating point inference rather than using a quantized model. | |
*/ | |
public class ImageClassifierFloatInception extends ImageClassifier { | |
/** | |
* The inception net requires additional normalization of the used input. | |
*/ | |
private static final int IMAGE_MEAN = 128; | |
private static final float IMAGE_STD = 128.0f; | |
/** | |
* An array to hold inference results, to be feed into Tensorflow Lite as outputs. | |
* This isn't part of the super class, because we need a primitive array here. | |
*/ | |
private float[][] labelProbArray = null; | |
/** | |
* Initializes an {@code ImageClassifier}. | |
* | |
* @param activity | |
*/ | |
ImageClassifierFloatInception(Activity activity) throws IOException { | |
super(activity); | |
labelProbArray = new float[1][getNumLabels()]; | |
} | |
@Override | |
protected String getModelPath() { | |
// you can download this file from | |
// https://storage.googleapis.com/download.tensorflow.org/models/tflite/inception_v3_slim_2016_android_2017_11_10.zip | |
return "model.tflite"; | |
} | |
@Override | |
protected String getLabelPath() { | |
return "labels.txt"; | |
} | |
@Override | |
protected int getImageSizeX() { | |
return 227; | |
} | |
@Override | |
protected int getImageSizeY() { | |
return 227; | |
} | |
@Override | |
protected int getNumBytesPerChannel() { | |
// a 32bit float value requires 4 bytes | |
return 4; | |
} | |
@Override | |
protected void addPixelValue(int pixelValue) { | |
imgData.putFloat((((pixelValue >> 16) & 0xFF) - IMAGE_MEAN) / IMAGE_STD); | |
imgData.putFloat((((pixelValue >> 8) & 0xFF) - IMAGE_MEAN) / IMAGE_STD); | |
imgData.putFloat(((pixelValue & 0xFF) - IMAGE_MEAN) / IMAGE_STD); | |
} | |
@Override | |
protected float getProbability(int labelIndex) { | |
return labelProbArray[0][labelIndex]; | |
} | |
@Override | |
protected void setProbability(int labelIndex, Number value) { | |
labelProbArray[0][labelIndex] = value.floatValue(); | |
} | |
@Override | |
protected float getNormalizedProbability(int labelIndex) { | |
// TODO the following value isn't in [0,1] yet, but may be greater. Why? | |
return getProbability(labelIndex); | |
} | |
@Override | |
protected void runInference() { | |
tflite.run(imgData, labelProbArray); | |
} | |
} |
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