【发布时间】：2017-08-22 13:24:48
【问题描述】：
我正在使用 android studio 和 tensorflow，android 版本进行图像识别。 它不是连续跟踪和识别，只是对一张图像的识别。 我已经有图形 pb 和标签 txt 文件，并设置了所需的设置。 但是有一个大问题。 关于图像，尺寸错误，我反复遇到同样的错误。 这是错误日志和我的源代码。
java.lang.IllegalArgumentException: input must be 4-dimensional[1,1,299,299,3]
[[Node: ResizeBilinear = ResizeBilinear[T=DT_FLOAT, align_corners=false, _device="/job:localhost/replica:0/task:0/cpu:0"](ExpandDims, ResizeBilinear/size)]]
at org.tensorflow.Session.run(Native Method)
at org.tensorflow.Session.access$100(Session.java:48)
at org.tensorflow.Session$Runner.runHelper(Session.java:295)
at org.tensorflow.Session$Runner.run(Session.java:245)
at org.tensorflow.contrib.android.TensorFlowInferenceInterface.run(TensorFlowInferenceInterface.java:144)
at com.example.yuuuuu.tensorTest.TensorFlowImageClassifier.recognizeImage(TensorFlowImageClassifier.java:119)
at com.example.yuuuuu.tensorTest.MainActivity.runTensor(MainActivity.java:69)
at com.example.yuuuuu.tensorTest.MainActivity$1.onClick(MainActivity.java:42)
at android.view.View.performClick(View.java:6205)
at android.widget.TextView.performClick(TextView.java:11103)
at android.view.View$PerformClick.run(View.java:23653)
at android.os.Handler.handleCallback(Handler.java:751)
at android.os.Handler.dispatchMessage(Handler.java:95)
at android.os.Looper.loop(Looper.java:154)
at android.app.ActivityThread.main(ActivityThread.java:6682)
at java.lang.reflect.Method.invoke(Native Method)
at com.android.internal.os.ZygoteInit$MethodAndArgsCaller.run(ZygoteInit.java:1520)
at com.android.internal.os.ZygoteInit.main(ZygoteInit.java:1410)
我不知道问题出在哪里，第一行，[1,1,299,299,3]。我认为两个 299 是 ImageSize，一个 1 是 ImageStd，但我不知道另一个 1 和 3 是什么...... 我在 tensorflow github 中输入了与官方代码相同的代码，只是更改了一些部分。 这是 MainActivity。
public class MainActivity extends AppCompatActivity {
private static final String MODEL_FILE = "file:///android_asset/optimized_graph.pb";
private static final String LABEL_FILE = "file:///android_asset/output_labels.txt";
private static final String INPUT_NAME = "Cast";
private static final String OUTPUT_NAME = "final_result";
private static final int INPUT_SIZE = 299;
private static final int IMAGE_MEAN = 117;
private static final float IMAGE_STD = 1;
private Classifier classifier;
private TextView textView;
private ImageView img;
private Button button;
@Override
protected void onCreate(Bundle savedInstanceState) {
super.onCreate(savedInstanceState);
setContentView(R.layout.activity_main);
textView = (TextView)findViewById(R.id.textView);
button = (Button)findViewById(R.id.btn);
img = (ImageView)findViewById(R.id.img);
button.setOnClickListener(new View.OnClickListener(){
public void onClick(View v){
runTensor();
}
});
initTensor();
}
public void initTensor(){
classifier = TensorFlowImageClassifier.create(
getAssets(),
MODEL_FILE,
LABEL_FILE,
INPUT_SIZE,
IMAGE_MEAN,
IMAGE_STD,
INPUT_NAME,
OUTPUT_NAME
);
}
public void runTensor(){
Bitmap bitmap = BitmapFactory.decodeResource(getResources(), R.drawable.test);
bitmap = Bitmap.createScaledBitmap(bitmap, INPUT_SIZE, INPUT_SIZE, false);
img = (ImageView)findViewById(R.id.img);
img.setImageBitmap(bitmap);
final List<Classifier.Recognition> results = classifier.recognizeImage(bitmap);
textView.setText(results.toString());
}
protected void onDestroy(){
super.onDestroy();
classifier.close();
}
}
这是分类器，和官方代码一样。
public interface Classifier {
public class Recognition{
private final String id;
private final String title;
private final Float confidence;
private RectF location;
public Recognition(
final String id, final String title, final Float confidence, final RectF location){
this.id = id;
this.title = title;
this.confidence = confidence;
this.location = location;
}
public String getId(){return id;}
public String getTitle(){return title;}
public Float getConfidence(){return confidence;}
public RectF getLocation(){return location;}
public void setLocation(RectF location){this.location = location;}
public String toString(){
String resultString = "";
if (id != null) {
resultString += "[" + id + "] ";
}
if (title != null) {
resultString += title + " ";
}
if (confidence != null) {
resultString += String.format("(%.1f%%) ", confidence * 100.0f);
}
if (location != null) {
resultString += location + " ";
}
return resultString.trim();
}
}
List<Recognition> recognizeImage(Bitmap bitmap);
void enableStatLogging(final boolean debug);
String getStatString();
void close();
}
最后是TensorFlowImageClassifier，官方也一样。
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 int imageMean;
private float imageStd;
private Vector<String> labels = new Vector<String>();
private int[] intValues;
private float[] floatValues;
private float[] outputs;
private String[] outputNames;
private boolean logStats = false;
private TensorFlowInferenceInterface inferenceInterface;
private TensorFlowImageClassifier() {}
/*
assetManager : assets 로드하는데 사용
modelFilename : pb 파일
labelFilename : txt 파일
inputSize : 정사각형 길이, inputSize * inputSize
imageMean : image values 평균값
imageStd : image values 표준값?
inputName : image input 노드 레이블
outputName : output 노드 레이블
*/
public static Classifier create(
AssetManager assetManager, String modelFilename, String labelFilename, int inputSize, int imageMean, float imageStd, String inputName, String outputName){
TensorFlowImageClassifier c = new TensorFlowImageClassifier();
c.inputName = inputName;
c.outputName = outputName;
String actualFilename = labelFilename.split("file:///android_asset/")[1];
Log.d(TAG, "reading labels from : " + actualFilename);
BufferedReader br = null;
try {
br = new BufferedReader(new InputStreamReader(assetManager.open(actualFilename)));
String line;
while((line = br.readLine()) != null){
c.labels.add(line);
}
br.close();
} catch (IOException e) {
throw new RuntimeException("failed reading labels" , e);
}
c.inferenceInterface = new TensorFlowInferenceInterface(assetManager, modelFilename);
final Operation operation = c.inferenceInterface.graphOperation(outputName);
final int numClasses = (int)operation.output(0).shape().size(1);
Log.d(TAG, "reading " + c.labels.size() + " labels, size of output layers : " + numClasses);
c.inputSize = inputSize;
c.imageMean = imageMean;
c.imageStd = imageStd;
c.outputNames = new String[]{outputName};
c.intValues = new int[inputSize * inputSize];
c.floatValues = new float[inputSize * inputSize * 3];
c.outputs = new float[numClasses];
return c;
}
@RequiresApi(api = Build.VERSION_CODES.JELLY_BEAN_MR2)
public List<Recognition> recognizeImage(final Bitmap bitmap){
beginSection("recognizeImage");
beginSection("preprocessBitmap");
bitmap.getPixels(intValues, 0, bitmap.getWidth(), 0, 0, bitmap.getWidth(), bitmap.getHeight());
for(int i = 0; i < intValues.length; i++){
final int val = intValues[i];
floatValues[i*3+0] = (((val >> 16) & 0xFF) - imageMean) / imageStd;
floatValues[i*3+1] = (((val >> 8) & 0xFF) - imageMean) / imageStd;
floatValues[i*3+2] = ((val & 0xFF) - imageMean) / imageStd;
}
endSection();
beginSection("feed");
inferenceInterface.feed(inputName, floatValues, 1, inputSize, inputSize, 3);
endSection();
beginSection("run");
inferenceInterface.run(outputNames, logStats);
endSection();
beginSection("fetch");
inferenceInterface.fetch(outputName, outputs);
endSection();
PriorityQueue<Recognition> pq = new PriorityQueue<Recognition>(
3,
new Comparator<Recognition>(){
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 recognitionSize = Math.min(pq.size(), MAX_RESULTS);
for(int i = 0; i < recognitionSize; ++i){
recognitions.add(pq.poll());
}
endSection();
return recognitions;
}
public void enableStatLogging(boolean logStats){this.logStats = logStats;}
public String getStatString(){return inferenceInterface.getStatString();}
public void close(){inferenceInterface.close();}
}
如果您知道如何修复这些代码，请告诉我如何解决。
【问题讨论】：
标签： android tensorflow