我正试图在Android上运行头发分割，但它不起作用。它返回空结果作为输出，而人类分割工作得很好。
这是代码。
public class MainActivity extends AppCompatActivity {
MLImageSegmentationAnalyzer analyzer;
Bitmap originialBitmap;
Bitmap foregroundBitmap;
ImageView imageView;
@Override
protected void onCreate(Bundle savedInstanceState) {
super.onCreate(savedInstanceState);
setContentView(R.layout.activity_main);
imageView = (ImageView) findViewById(R.id.imageView);
Bitmap bitmap = BitmapFactory.decodeResource(getResources(), R.drawable.person);
bitmap = Bitmap.createScaledBitmap(bitmap, 1000, 1500, true);
createImageTransactor(bitmap);
}
private void createImageTransactor(Bitmap bitmap) {
MLImageSegmentationSetting setting = new MLImageSegmentationSetting.Factory()
.setExact(true)
.setAnalyzerType(MLImageSegmentationSetting.HAIR_SEG)
.create();
this.analyzer = MLAnalyzerFactory.getInstance().getImageSegmentationAnalyzer(setting);
MLFrame mlFrame = new MLFrame.Creator().setBitmap(bitmap).create();
Task<MLImageSegmentation> task = this.analyzer.asyncAnalyseFrame(mlFrame);
task.addOnSuccessListener(new OnSuccessListener<MLImageSegmentation>() {
@Override
public void onSuccess(MLImageSegmentation mlImageSegmentationResults) {
// Transacting logic for segment success.
if (mlImageSegmentationResults != null) {
Toast.makeText(MainActivity.this, "Success", Toast.LENGTH_SHORT).show();
foregroundBitmap = mlImageSegmentationResults.getGrayscale();
imageView.setImageBitmap(foregroundBitmap);
} else {
Toast.makeText(MainActivity.this, "Failure", Toast.LENGTH_SHORT).show();
}
}
}).addOnFailureListener(new OnFailureListener() {
@Override
public void onFailure(Exception e) {
Toast.makeText(MainActivity.this, "Failure", Toast.LENGTH_SHORT).show();
// Transacting logic for segment failure.
return;
}
});
}
}
这是gradle文件。
implementation 'com.huawei.hms:ml-computer-vision-cloud:2.0.5.300'
implementation 'com.huawei.hms:ml-computer-vision-segmentation:2.2.0.300'
implementation 'com.huawei.hms:ml-computer-vision-image-segmentation-body-model:2.2.0.300'
implementation 'com.huawei.hms:ml-computer-vision-image-segmentation-multiclass-model:2.2.0.300'
implementation 'com.huawei.hms:ml-computer-vision-image-segmentation-hair-model:2.2.0.300'
但是当代码运行时，它输出空图像，并且分割结果中的掩码包含数组中的整个0。
请告诉我有什么问题。
您可以参考示例代码中的HairSegmentationStillAnalyseActivity类:
public class HairSegmentationStillAnalyseActivity extends AppCompatActivity implements View.OnClickListener {
private static final String TAG = HairSegmentationStillAnalyseActivity.class.getSimpleName();
private MLImageSegmentationAnalyzer analyzer;
private ImageView mImageView;
private Bitmap bitmap;
@Override
protected void onCreate(Bundle savedInstanceState) {
super.onCreate(savedInstanceState);
this.setContentView(R.layout.activity_image_segmentation_analyse);
this.findViewById(R.id.segment_detect).setOnClickListener(this);
this.mImageView = this.findViewById(R.id.image_result);
}
@Override
public void onClick(View v) {
switch (v.getId()){
case R.id.segment_detect:
this.analyzer();
break;
default:
break;
}
}
private void analyzer() {
/**
* Configure image segmentation analyzer with custom parameter MLImageSegmentationSetting.
*
* setExact(): Set the segmentation fine mode, true is the fine segmentation mode,
* and false is the speed priority segmentation mode.
* setAnalyzerType(): Set the segmentation mode. When segmenting a static image, support setting
* MLImageSegmentationSetting.BODY_SEG (only segment human body and background)
* and MLImageSegmentationSetting.IMAGE_SEG (segment 10 categories of scenes, including human bodies)
* setScene(): Set the type of the returned results. This configuration takes effect only in
* MLImageSegmentationSetting.BODY_SEG mode. In MLImageSegmentationSetting.IMAGE_SEG mode,
* only pixel-level tagging information is returned.
* Supports setting MLImageSegmentationScene.ALL (returns all segmentation results,
* including: pixel-level tag information, portrait images with transparent backgrounds
* and portraits are white, gray background with black background),
* MLImageSegmentationScene.MASK_ONLY (returns only pixel-level tag information),
* MLImageSegmentationScene .FOREGROUND_ONLY (returns only portrait images with transparent background),
* MLImageSegmentationScene.GRAYSCALE_ONLY (returns only grayscale images with white portrait and black background).
*/
MLImageSegmentationSetting setting = new MLImageSegmentationSetting.Factory()
.setExact(false)
.setAnalyzerType(MLImageSegmentationSetting.HAIR_SEG)
.create();
this.analyzer = MLAnalyzerFactory.getInstance().getImageSegmentationAnalyzer(setting);
// Create an MLFrame by using android.graphics.Bitmap. Recommended image size: large than 224*224.
this.bitmap = BitmapFactory.decodeResource(this.getResources(), R.drawable.imgseg_foreground);
MLFrame mlFrame = new MLFrame.Creator().setBitmap(this.bitmap).create();
Task<MLImageSegmentation> task = this.analyzer.asyncAnalyseFrame(mlFrame);
task.addOnSuccessListener(new OnSuccessListener<MLImageSegmentation>() {
@Override
public void onSuccess(MLImageSegmentation imageSegmentationResult) {
// Processing logic for recognition success.
if (imageSegmentationResult != null) {
HairSegmentationStillAnalyseActivity.this.displaySuccess(imageSegmentationResult);
} else {
HairSegmentationStillAnalyseActivity.this.displayFailure("imageSegmentationResult is null.");
}
}
}).addOnFailureListener(new OnFailureListener() {
@Override
public void onFailure(Exception e) {
// Processing logic for recognition failure.
HairSegmentationStillAnalyseActivity.this.displayFailure(e.getMessage());
}
});
}
private void displaySuccess(MLImageSegmentation imageSegmentationResult) {
if (this.bitmap == null) {
this.displayFailure("bitmap is null.");
return;
}
// Draw the portrait with a transparent background.
if (imageSegmentationResult.getMasks() == null) {
return;
}
Bitmap processedBitmap = null;
int[] pixels = byteArrToIntArr(imageSegmentationResult.getMasks());
processedBitmap = Bitmap.createBitmap(pixels, 0, bitmap.getWidth(), bitmap.getWidth(), bitmap.getHeight(), Bitmap.Config.ARGB_8888);
if (processedBitmap != null) {
this.mImageView.setImageBitmap(processedBitmap);
} else {
this.displayFailure("bitmapFore is null.");
}
}
private void displayFailure(String str) {
Log.e(TAG, str);
}
@Override
protected void onDestroy() {
super.onDestroy();
if (this.analyzer != null) {
try {
this.analyzer.stop();
} catch (IOException e) {
Log.e(TAG, "Stop failed: " + e.getMessage());
}
}
}
/**
* masking
* @param masks
* @return
*/
private int[] byteArrToIntArr(byte[] masks) {
int[] results = new int[masks.length];
for (int i = 0; i < masks.length; i++) {
if (masks[i] == 1) {
results[i] = Color.WHITE;
} else if (masks[i] == 2) {
results[i] = Color.BLUE;
} else if (masks[i] == 3) {
results[i] = Color.DKGRAY;
} else if (masks[i] == 4) {
results[i] = Color.YELLOW;
} else if (masks[i] == 5) {
results[i] = Color.LTGRAY;
} else if (masks[i] == 6) {
results[i] = Color.CYAN;
} else if (masks[i] == 7) {
results[i] = Color.RED;
} else if (masks[i] == 8) {
results[i] = Color.GRAY;
} else if (masks[i] == 9) {
results[i] = Color.MAGENTA;
} else if (masks[i] == 10) {
results[i] = Color.GREEN;
} else {
results[i] = Color.BLACK;
}
}
return results;
}
}