如何在tflite中使用posenet模型的输出 [英] How to use outputs of posenet model in tflite
问题描述
我正在使用这里.它获取输入1 * 353 * 257 * 3输入图像并返回4个变暗数组1 * 23 * 17 * 17、1 * 23 * 17 * 34、1 * 23 * 17 * 64和1 * 23 * 17 * 1.该模型的输出步幅为16.如何获得输入图像上所有17个姿势点的坐标?我尝试从out1阵列的热图中打印置信度得分，但每个像素的值接近0.00.代码如下:
I am using the tflite model for posenet from here. It takes input 1*353*257*3 input image and returns 4 arrays of dimens 1*23*17*17, 1*23*17*34, 1*23*17*64 and 1*23*17*1. The model has an output stride of 16. How can I get the coordinates of all 17 pose points on my input image? I have tried printing the confidence scores from the heatmap of out1 array but I get near to 0.00 values for each pixel. Code is given below:
public class MainActivity extends AppCompatActivity {
private static final int CAMERA_REQUEST = 1888;
private ImageView imageView;
private static final int MY_CAMERA_PERMISSION_CODE = 100;
Interpreter tflite = null;
private String TAG = "rohit";
//private Canvas canvas;
Map<Integer, Object> outputMap = new HashMap<>();
float[][][][] out1 = new float[1][23][17][17];
float[][][][] out2 = new float[1][23][17][34];
float[][][][] out3 = new float[1][23][17][64];
float[][][][] out4 = new float[1][23][17][1];
@Override
protected void onCreate(Bundle savedInstanceState) {
super.onCreate(savedInstanceState);
setContentView(R.layout.activity_main);
String modelFile="multi_person_mobilenet_v1_075_float.tflite";
try {
tflite=new Interpreter(loadModelFile(MainActivity.this,modelFile));
} catch (IOException e) {
e.printStackTrace();
}
final Tensor no = tflite.getInputTensor(0);
Log.d(TAG, "onCreate: Input shape"+ Arrays.toString(no.shape()));
int c = tflite.getOutputTensorCount();
Log.d(TAG, "onCreate: Output Count" +c );
for (int i = 0; i <4 ; i++) {
final Tensor output = tflite.getOutputTensor(i);
Log.d(TAG, "onCreate: Output shape" + Arrays.toString(output.shape()));
}
this.imageView = this.findViewById(R.id.imageView1);
Button photoButton = this.findViewById(R.id.button1);
photoButton.setOnClickListener(new View.OnClickListener() {
@Override
public void onClick(View v) {
if (checkSelfPermission(Manifest.permission.CAMERA)
!= PackageManager.PERMISSION_GRANTED) {
requestPermissions(new String[]{Manifest.permission.CAMERA},
MY_CAMERA_PERMISSION_CODE);
} else {
Intent cameraIntent = new Intent(android.provider.MediaStore.ACTION_IMAGE_CAPTURE);
startActivityForResult(cameraIntent, CAMERA_REQUEST);
}
}
});
}
public void onRequestPermissionsResult(int requestCode, @NonNull String[] permissions, @NonNull int[] grantResults) {
super.onRequestPermissionsResult(requestCode, permissions, grantResults);
if (requestCode == MY_CAMERA_PERMISSION_CODE) {
if (grantResults[0] == PackageManager.PERMISSION_GRANTED) {
Toast.makeText(this, "camera permission granted", Toast.LENGTH_LONG).show();
Intent cameraIntent = new
Intent(android.provider.MediaStore.ACTION_IMAGE_CAPTURE);
startActivityForResult(cameraIntent, CAMERA_REQUEST);
} else {
Toast.makeText(this, "camera permission denied", Toast.LENGTH_LONG).show();
}
}
}
protected void onActivityResult ( int requestCode, int resultCode, Intent data){
if (requestCode == CAMERA_REQUEST && resultCode == Activity.RESULT_OK) {
Bitmap photo = (Bitmap) data.getExtras().get("data");
Log.d(TAG,"bhai:"+photo.getWidth()+":"+photo.getHeight());
//imageView.setImageBitmap(photo);
photo = Bitmap.createScaledBitmap(photo, 353, 257, false);
photo = photo.copy(Bitmap.Config.ARGB_8888,true);
Log.d(TAG, "onActivityResult: Bitmap resized");
int width =photo.getWidth();
int height = photo.getHeight();
float[][][][] result = new float[1][width][height][3];
int[] pixels = new int[width*height];
photo.getPixels(pixels, 0, width, 0, 0, width, height);
int pixelsIndex = 0;
for (int i = 0; i < width; i++)
{
for (int j = 0; j < height; j++)
{
// result[i][j] = pixels[pixelsIndex];
int p = pixels[pixelsIndex];
result[0][i][j][0] = (p >> 16) & 0xff;
result[0][i][j][1] = (p >> 8) & 0xff;
result[0][i][j][2] = p & 0xff;
pixelsIndex++;
}
}
Object [] inputs = {result};
//inputs[0] = inp;
outputMap.put(0, out1);
outputMap.put(1, out2);
outputMap.put(2, out3);
outputMap.put(3, out4);
tflite.runForMultipleInputsOutputs(inputs,outputMap);
out1 = (float[][][][]) outputMap.get(0);
out2 = (float[][][][]) outputMap.get(1);
out3 = (float[][][][]) outputMap.get(2);
out4 = (float[][][][]) outputMap.get(3);
Canvas canvas = new Canvas(photo);
Paint p = new Paint();
p.setColor(Color.RED);
float[][][] scores = new float[out1[0].length][out1[0][0].length][17];
int[][] heatmap_pos = new int[17][2];
for(int i=0;i<17;i++)
{
float max = -1;
for(int j=0;j<out1[0].length;j++)
{
for(int k=0;k<out1[0][0].length;k++)
{
// Log.d("mylog", "onActivityResult: "+out1[0][j][k][i]);
scores[j][k][i] = sigmoid(out1[0][j][k][i]);
if(max<scores[j][k][i])
{
max = scores[j][k][i];
heatmap_pos[i][0] = j;
heatmap_pos[i][1] = k;
}
}
}
// Log.d(TAG, "onActivityResult: "+max+" "+heatmap_pos[i][0]+" "+heatmap_pos[i][1]);
}
for(int i=0;i<17;i++)
{
float max = -1;
for(int j=0;j<out1[0].length;j++)
{
for(int k=0;k<out1[0][0].length;k++)
{
Log.d("mylog", "onActivityResult: "+out1[0][j][k][i]);
scores[j][k][i] = sigmoid(out1[0][j][k][i]);
if(max<scores[j][k][i])
{
max = scores[j][k][i];
heatmap_pos[i][0] = j;
heatmap_pos[i][1] = k;
}
}
}
// Log.d(TAG, "onActivityResult: "+max+" "+heatmap_pos[i][0]+" "+heatmap_pos[i][1]);
}
for(int i=0;i<17;i++)
{
Log.d("heatlog", "onActivityResult: "+heatmap_pos[i][0]+" "+heatmap_pos[i][1]);
}
float[][] offset_vector = new float[17][2];
float[][] keypoint_pos = new float[17][2];
for(int i=0;i<17;i++)
{
offset_vector[i][0] = out2[0][heatmap_pos[i][0]][heatmap_pos[i][1]][i];
offset_vector[i][1] = out2[0][heatmap_pos[i][0]][heatmap_pos[i][1]][i+17];
Log.d("myoff",offset_vector[i][0]+":"+offset_vector[i][1]);
keypoint_pos[i][0] = heatmap_pos[i][0]*16+offset_vector[i][0];
keypoint_pos[i][1] = heatmap_pos[i][1]*16+offset_vector[i][1];
Log.d(TAG, "onActivityResult: "+keypoint_pos[i][0]+" "+keypoint_pos[i][1]);
canvas.drawCircle(keypoint_pos[i][0]+353/2,keypoint_pos[i][1]-257/2,5,p);
}
imageView.setImageBitmap(photo);
}
}
private MappedByteBuffer loadModelFile(Activity activity, String MODEL_FILE) throws IOException {
AssetFileDescriptor fileDescriptor = activity.getAssets().openFd(MODEL_FILE);
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);
}
public float sigmoid(float value) {
float p = (float)(1.0 / (1 + Math.exp(-value)));
return p;
}
}
推荐答案
我认为此tflite模型文件存在问题. 因此，我尝试使用模型中的权重创建posenet tflite模型. 该模型中的所有权重都可以从tfjs-models下载: https://github.com/tensorflow/tfjs-models/tree/master/姿势网
I think there are something wrong with this tflite model file. So I tried to create the posenet tflite model using the weights in the model. All the weights in the model can be downloaded from tfjs-models: https://github.com/tensorflow/tfjs-models/tree/master/posenet
然后，您可以生成模型并按照以下回购进行所有的前处理和后处理: https://github.com/zg9uagfv/tf_posenet
Then you can generate the model and do all the pre and post process as the follow repo: https://github.com/zg9uagfv/tf_posenet
生成posenet模型后，可以导出到.pb文件或.tflite文件. 我已经成功尝试了该过程，并且posenet模型可以在具有GPU的Android应用中成功运行.
After the posenet model generated, you can export to .pb file or .tflite file. I have tried the process successfully, and the posenet model can be run in my Android App with GPU successfully.
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