//Tensorflow 给的demo只支持 mobilenet_quant_v1_224.tflite 模型，如果换成其他模型如 mobilenet_v1_1.0_224.tflite 则会出现invalid interpreter handle之类的异常。这大概是调试最久的bug，从2018年12月份一直到2019年3月6日才算结束。之所以结束，是因为我把原来下载的模型删掉，重新下载了，结果竟然好了。真是，坑。其实，tensorflow 也提供了对应的支持float型的代码，位置在
https://github.com/tensorflow/examples/tree/master/lite/examples/image_classification/android/app/src/main/java/org/tensorflow/lite/examples/classification/tflite，但是本博文是为了解决tensorflow-poets中的demo的。思想大同小异。啥也不说了，也不知道怎么说才能明白，就把代码贴上去。这是别人的代码，也不知道怎么引用才是合法，这里只能感谢。请仔细查看，修改部分。
/* 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.ailabby.hellotflite;
import android.app.Activity;
import android.content.res.AssetFileDescriptor;
import android.graphics.Bitmap;
import android.os.SystemClock;
import android.util.Log;
import org.tensorflow.lite.Interpreter;
import java.io.BufferedReader;
import java.io.FileInputStream;
import java.io.IOException;
import java.io.InputStreamReader;
import java.nio.ByteBuffer;
import java.nio.ByteOrder;
import java.nio.MappedByteBuffer;
import java.nio.channels.FileChannel;
import java.util.AbstractMap;
import java.util.ArrayList;
import java.util.Comparator;
import java.util.List;
import java.util.Map;
import java.util.PriorityQueue;
/** Classifies images with Tensorflow Lite. */
public class ImageClassifier {
/** Tag for the {@link Log}. */
private static final String TAG = "TfLiteCameraDemo";
/** Name of the model file stored in Assets. */
// private static final String MODEL_PATH = "mobilenet_quant_v1_224.tflite";
// ******************此处修改
private static final String MODEL_PATH = "mobilenet_v1_1.0_224.tflite";
/** Name of the label file stored in Assets. */
private static final String LABEL_PATH = "labels.txt";
/** Number of results to show in the UI. */
private static final int RESULTS_TO_SHOW = 3;
/** Dimensions of inputs. */
private static final int DIM_BATCH_SIZE = 1;
private static final int DIM_PIXEL_SIZE = 3;
static final int DIM_IMG_SIZE_X = 224;
static final int DIM_IMG_SIZE_Y = 224;
//************* 此处修改
private static final int IMAGE_MEAN = 128;
private static final float IMAGE_STD = 128.0f;
/* Preallocated buffers for storing image data in. */
private int[] intValues = new int[DIM_IMG_SIZE_X * DIM_IMG_SIZE_Y];
/** An instance of the driver class to run model inference with Tensorflow Lite. */
private Interpreter tflite;
/** Labels corresponding to the output of the vision model. */
private List<String> labelList;
/** A ByteBuffer to hold image data, to be feed into Tensorflow Lite as inputs. */
private ByteBuffer imgData = null;
/** An array to hold inference results, to be feed into Tensorflow Lite as outputs. */
//**********************此处修改
private float[][] labelProbArray = null;
/** multi-stage low pass filter * */
//**********************此处修改
private float[][] filterLabelProbArray = null;
private static final int FILTER_STAGES = 3;
private static final float FILTER_FACTOR = 0.4f;
private PriorityQueue<Map.Entry<String, Float>> sortedLabels =
new PriorityQueue<>(
RESULTS_TO_SHOW,
new Comparator<Map.Entry<String, Float>>() {
@Override
public int compare(Map.Entry<String, Float> o1, Map.Entry<String, Float> o2) {
return (o1.getValue()).compareTo(o2.getValue());
}
});
/** Initializes an {@code ImageClassifier}. */
ImageClassifier(Activity activity) throws IOException {
tflite = new Interpreter(loadModelFile(activity));
labelList = loadLabelList(activity);
//*******************此处修改
imgData =
ByteBuffer.allocateDirect(
4*DIM_BATCH_SIZE * DIM_IMG_SIZE_X * DIM_IMG_SIZE_Y * DIM_PIXEL_SIZE);
imgData.order(ByteOrder.nativeOrder());
//**********************此处修改
labelProbArray = new float[1][labelList.size()];
filterLabelProbArray = new float[FILTER_STAGES][labelList.size()];
Log.d(TAG, "Created a Tensorflow Lite Image Classifier.");
}
/** Classifies a frame from the preview stream. */
String classifyFrame(Bitmap bitmap) {
if (tflite == null) {
Log.e(TAG, "Image classifier has not been initialized; Skipped.");
return "Uninitialized Classifier.";
}
convertBitmapToByteBuffer(bitmap);
// Here's where the magic happens!!!
long startTime = SystemClock.uptimeMillis();
tflite.run(imgData, labelProbArray);
long endTime = SystemClock.uptimeMillis();
Log.d(TAG, "Timecost to run model inference: " + Long.toString(endTime - startTime));
// Smooth the results across frames.
applyFilter();
// Print the results.
String textToShow = printTopKLabels();
textToShow = Long.toString(endTime - startTime) + "ms" + textToShow;
return textToShow;
}
void applyFilter() {
int numLabels = labelList.size();
// Low pass filter `labelProbArray` into the first stage of the filter.
for (int j = 0; j < numLabels; ++j) {
filterLabelProbArray[0][j] +=
FILTER_FACTOR * (labelProbArray[0][j] - filterLabelProbArray[0][j]);
}
// Low pass filter each stage into the next.
for (int i = 1; i < FILTER_STAGES; ++i) {
for (int j = 0; j < numLabels; ++j) {
filterLabelProbArray[i][j] +=
FILTER_FACTOR * (filterLabelProbArray[i - 1][j] - filterLabelProbArray[i][j]);
}
}
// Copy the last stage filter output back to `labelProbArray`.
for (int j = 0; j < numLabels; ++j) {
labelProbArray[0][j] = filterLabelProbArray[FILTER_STAGES - 1][j];
}
}
/** Closes tflite to release resources. */
public void close() {
tflite.close();
tflite = null;
}
/** Reads label list from Assets. */
private List<String> loadLabelList(Activity activity) throws IOException {
List<String> labelList = new ArrayList<String>();
BufferedReader reader =
new BufferedReader(new InputStreamReader(activity.getAssets().open(LABEL_PATH)));
String line;
while ((line = reader.readLine()) != null) {
labelList.add(line);
}
reader.close();
return labelList;
}
/** Memory-map the model file in Assets. */
private MappedByteBuffer loadModelFile(Activity activity) throws IOException {
Log.d(TAG, activity.getAssets().toString());
AssetFileDescriptor fileDescriptor = activity.getAssets().openFd(MODEL_PATH);
FileInputStream inputStream = new FileInputStream(fileDescriptor.getFileDescriptor());
FileChannel fileChannel = inputStream.getChannel();
long startOffset = fileDescriptor.getStartOffset();
long declaredLength = fileDescriptor.getDeclaredLength();
return fileChannel.map(FileChannel.MapMode.READ_ONLY, startOffset, declaredLength);
}
/** Writes Image data into a {@code ByteBuffer}. */
private void convertBitmapToByteBuffer(Bitmap bitmap) {
if (imgData == null) {
return;
}
imgData.rewind();
bitmap.getPixels(intValues, 0, bitmap.getWidth(), 0, 0, bitmap.getWidth(), bitmap.getHeight());
// Convert the image to floating point.
int pixel = 0;
long startTime = SystemClock.uptimeMillis();
for (int i = 0; i < DIM_IMG_SIZE_X; ++i) {
for (int j = 0; j < DIM_IMG_SIZE_Y; ++j) {
final int val = intValues[pixel++];
// imgData.putFloat(((val >> 16) & 0xFF));
// imgData.putFloat(((val >> 8) & 0xFF));
// imgData.putFloat((val & 0xFF));
//**********************此处修改
imgData.putFloat((((val >> 16) & 0xFF)-IMAGE_MEAN)/IMAGE_STD);
imgData.putFloat((((val >> 8) & 0xFF)-IMAGE_MEAN)/IMAGE_STD);
imgData.putFloat((((val) & 0xFF)-IMAGE_MEAN)/IMAGE_STD);
}
}
long endTime = SystemClock.uptimeMillis();
Log.d(TAG, "Timecost to put values into ByteBuffer: " + Long.toString(endTime - startTime));
}
/** Prints top-K labels, to be shown in UI as the results. */
private String printTopKLabels() {
for (int i = 0; i < labelList.size(); ++i) {
sortedLabels.add(
new AbstractMap.SimpleEntry<>(labelList.get(i), labelProbArray[0][i]));
if (sortedLabels.size() > RESULTS_TO_SHOW) {
sortedLabels.poll();
}
}
String textToShow = "";
final int size = sortedLabels.size();
for (int i = 0; i < size; ++i) {
Map.Entry<String, Float> label = sortedLabels.poll();
textToShow = String.format("\n%s: %4.2f", label.getKey(), label.getValue()) + textToShow;
}
return textToShow;
}
}