private void CreateSegmentAnalyzer()
{
// Create a segmentation analyzer. You can create an analyzer using the provided customized face detection parameter: MLImageSegmentationSetting
MLImageSegmentationSetting setting = new MLImageSegmentationSetting.Factory()
.SetExact(false)
.SetScene(MLImageSegmentationScene.ForegroundOnly)
.SetAnalyzerType(MLImageSegmentationSetting.BodySeg)
.Create();
this.analyzer = MLAnalyzerFactory.Instance.GetImageSegmentationAnalyzer(setting);
this.analyzer.SetTransactor(new MLTransactor(this));
}
private async void Analyze()
{
/**
* 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.BodySeg (only segment human body and background)
* and MLImageSegmentationSetting.ImageSeg (segment 10 categories of scenes, including human bodies)
* SetScene(): Set the type of the returned results. This configuration takes effect only in
* MLImageSegmentationSetting.BodySeg mode. In MLImageSegmentationSetting.ImageSeg 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.MaskOnly (returns only pixel-level tag information),
* MLImageSegmentationScene.ForegroundOnly (returns only portrait images with transparent background),
* MLImageSegmentationScene.GrayscaleOnly (returns only grayscale images with white portrait and black background).
*/
MLImageSegmentationSetting setting = new MLImageSegmentationSetting.Factory()
.SetExact(false)
.SetAnalyzerType(MLImageSegmentationSetting.BodySeg)
.SetScene(MLImageSegmentationScene.All)
.Create();
this.analyzer = MLAnalyzerFactory.Instance.GetImageSegmentationAnalyzer(setting);
// Create an MLFrame by using android.graphics.Bitmap. Recommended image size: large than 224*224.
MLFrame mlFrame = new MLFrame.Creator().SetBitmap(this.mBitmapTarget).Create();
Task <MLImageSegmentation> task = this.analyzer.AnalyseFrameAsync(mlFrame);
try
{
await task;
if (task.IsCompleted && task.Result != null)
{
// Analyze success
var imageSegmentationResult = task.Result;
this.DisplaySuccess(imageSegmentationResult);
}
else
{
// Analyze failure
this.DisplayFailure();
}
}
catch (Exception e)
{
// Operation failure
this.DisplayFailure();
}
}
