Update java image segmenter to always output confidence masks and optionally output category mask.
PiperOrigin-RevId: 521804641
This commit is contained in:
MediaPipe Team
Copybara-Service
parent
7c2930102d
commit
33cad24a5a
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@ -79,15 +79,10 @@ public final class ImageSegmenter extends BaseVisionTaskApi {
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private static final List<String> INPUT_STREAMS =
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Collections.unmodifiableList(
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Arrays.asList("IMAGE:" + IMAGE_IN_STREAM_NAME, "NORM_RECT:" + NORM_RECT_IN_STREAM_NAME));
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private static final List<String> OUTPUT_STREAMS =
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Collections.unmodifiableList(
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Arrays.asList(
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"GROUPED_SEGMENTATION:segmented_mask_out",
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"IMAGE:image_out",
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"SEGMENTATION:0:segmentation"));
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private static final int GROUPED_SEGMENTATION_OUT_STREAM_INDEX = 0;
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private static final int CONFIDENCE_MASKS_OUT_STREAM_INDEX = 0;
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private static final int IMAGE_OUT_STREAM_INDEX = 1;
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private static final int SEGMENTATION_OUT_STREAM_INDEX = 2;
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private static final int CONFIDENCE_MASK_OUT_STREAM_INDEX = 2;
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private static final int CATEGORY_MASK_OUT_STREAM_INDEX = 3;
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private static final String TASK_GRAPH_NAME =
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"mediapipe.tasks.vision.image_segmenter.ImageSegmenterGraph";
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private static final String TENSORS_TO_SEGMENTATION_CALCULATOR_NAME =
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@ -104,6 +99,13 @@ public final class ImageSegmenter extends BaseVisionTaskApi {
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*/
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public static ImageSegmenter createFromOptions(
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Context context, ImageSegmenterOptions segmenterOptions) {
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List<String> outputStreams = new ArrayList<>();
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outputStreams.add("CONFIDENCE_MASKS:confidence_masks");
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outputStreams.add("IMAGE:image_out");
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outputStreams.add("CONFIDENCE_MASK:0:confidence_mask");
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if (segmenterOptions.outputCategoryMask()) {
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outputStreams.add("CATEGORY_MASK:category_mask");
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}
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// TODO: Consolidate OutputHandler and TaskRunner.
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OutputHandler<ImageSegmenterResult, MPImage> handler = new OutputHandler<>();
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handler.setOutputPacketConverter(
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@ -111,50 +113,62 @@ public final class ImageSegmenter extends BaseVisionTaskApi {
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@Override
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public ImageSegmenterResult convertToTaskResult(List<Packet> packets)
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throws MediaPipeException {
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if (packets.get(GROUPED_SEGMENTATION_OUT_STREAM_INDEX).isEmpty()) {
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if (packets.get(CONFIDENCE_MASKS_OUT_STREAM_INDEX).isEmpty()) {
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return ImageSegmenterResult.create(
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new ArrayList<>(),
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packets.get(GROUPED_SEGMENTATION_OUT_STREAM_INDEX).getTimestamp());
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Optional.empty(),
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packets.get(CONFIDENCE_MASKS_OUT_STREAM_INDEX).getTimestamp());
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}
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List<MPImage> segmentedMasks = new ArrayList<>();
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int width = PacketGetter.getImageWidth(packets.get(SEGMENTATION_OUT_STREAM_INDEX));
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int height = PacketGetter.getImageHeight(packets.get(SEGMENTATION_OUT_STREAM_INDEX));
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int imageFormat =
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segmenterOptions.outputType() == ImageSegmenterOptions.OutputType.CONFIDENCE_MASK
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? MPImage.IMAGE_FORMAT_VEC32F1
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: MPImage.IMAGE_FORMAT_ALPHA;
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int imageListSize =
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PacketGetter.getImageListSize(packets.get(GROUPED_SEGMENTATION_OUT_STREAM_INDEX));
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ByteBuffer[] buffersArray = new ByteBuffer[imageListSize];
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List<MPImage> confidenceMasks = new ArrayList<>();
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int width = PacketGetter.getImageWidth(packets.get(CONFIDENCE_MASK_OUT_STREAM_INDEX));
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int height = PacketGetter.getImageHeight(packets.get(CONFIDENCE_MASK_OUT_STREAM_INDEX));
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int confidenceMasksListSize =
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PacketGetter.getImageListSize(packets.get(CONFIDENCE_MASKS_OUT_STREAM_INDEX));
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ByteBuffer[] buffersArray = new ByteBuffer[confidenceMasksListSize];
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// If resultListener is not provided, the resulted MPImage is deep copied from mediapipe
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// graph. If provided, the result MPImage is wrapping the mediapipe packet memory.
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if (!segmenterOptions.resultListener().isPresent()) {
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for (int i = 0; i < imageListSize; i++) {
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buffersArray[i] =
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ByteBuffer.allocateDirect(
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width * height * (imageFormat == MPImage.IMAGE_FORMAT_VEC32F1 ? 4 : 1));
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boolean copyImage = !segmenterOptions.resultListener().isPresent();
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if (copyImage) {
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for (int i = 0; i < confidenceMasksListSize; i++) {
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buffersArray[i] = ByteBuffer.allocateDirect(width * height * 4);
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}
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}
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if (!PacketGetter.getImageList(
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packets.get(GROUPED_SEGMENTATION_OUT_STREAM_INDEX),
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buffersArray,
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!segmenterOptions.resultListener().isPresent())) {
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packets.get(CONFIDENCE_MASKS_OUT_STREAM_INDEX), buffersArray, copyImage)) {
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throw new MediaPipeException(
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MediaPipeException.StatusCode.INTERNAL.ordinal(),
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"There is an error getting segmented masks. It usually results from incorrect"
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+ " options of unsupported OutputType of given model.");
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"There is an error getting segmented masks.");
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}
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for (ByteBuffer buffer : buffersArray) {
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ByteBufferImageBuilder builder =
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new ByteBufferImageBuilder(buffer, width, height, imageFormat);
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segmentedMasks.add(builder.build());
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new ByteBufferImageBuilder(buffer, width, height, MPImage.IMAGE_FORMAT_VEC32F1);
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confidenceMasks.add(builder.build());
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}
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Optional<MPImage> categoryMask = Optional.empty();
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if (segmenterOptions.outputCategoryMask()) {
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ByteBuffer buffer;
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if (copyImage) {
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buffer = ByteBuffer.allocateDirect(width * height);
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if (!PacketGetter.getImageData(
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packets.get(CATEGORY_MASK_OUT_STREAM_INDEX), buffer)) {
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throw new MediaPipeException(
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MediaPipeException.StatusCode.INTERNAL.ordinal(),
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"There is an error getting category mask.");
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}
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} else {
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buffer =
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PacketGetter.getImageDataDirectly(packets.get(CATEGORY_MASK_OUT_STREAM_INDEX));
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}
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ByteBufferImageBuilder builder =
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new ByteBufferImageBuilder(buffer, width, height, MPImage.IMAGE_FORMAT_ALPHA);
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categoryMask = Optional.of(builder.build());
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}
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return ImageSegmenterResult.create(
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segmentedMasks,
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confidenceMasks,
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categoryMask,
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BaseVisionTaskApi.generateResultTimestampMs(
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segmenterOptions.runningMode(),
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packets.get(GROUPED_SEGMENTATION_OUT_STREAM_INDEX)));
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packets.get(CONFIDENCE_MASKS_OUT_STREAM_INDEX)));
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}
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@Override
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@ -174,7 +188,7 @@ public final class ImageSegmenter extends BaseVisionTaskApi {
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.setTaskRunningModeName(segmenterOptions.runningMode().name())
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.setTaskGraphName(TASK_GRAPH_NAME)
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.setInputStreams(INPUT_STREAMS)
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.setOutputStreams(OUTPUT_STREAMS)
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.setOutputStreams(outputStreams)
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.setTaskOptions(segmenterOptions)
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.setEnableFlowLimiting(segmenterOptions.runningMode() == RunningMode.LIVE_STREAM)
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.build(),
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@ -553,8 +567,8 @@ public final class ImageSegmenter extends BaseVisionTaskApi {
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*/
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public abstract Builder setDisplayNamesLocale(String value);
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/** The output type from image segmenter. */
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public abstract Builder setOutputType(OutputType value);
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/** Whether to output category mask. */
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public abstract Builder setOutputCategoryMask(boolean value);
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/**
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* Sets an optional {@link ResultListener} to receive the segmentation results when the graph
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@ -594,27 +608,17 @@ public final class ImageSegmenter extends BaseVisionTaskApi {
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abstract String displayNamesLocale();
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abstract OutputType outputType();
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abstract boolean outputCategoryMask();
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abstract Optional<ResultListener<ImageSegmenterResult, MPImage>> resultListener();
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abstract Optional<ErrorListener> errorListener();
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/** The output type of segmentation results. */
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public enum OutputType {
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// Gives a single output mask where each pixel represents the class which
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// the pixel in the original image was predicted to belong to.
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CATEGORY_MASK,
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// Gives a list of output masks where, for each mask, each pixel represents
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// the prediction confidence, usually in the [0, 1] range.
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CONFIDENCE_MASK
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}
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public static Builder builder() {
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return new AutoValue_ImageSegmenter_ImageSegmenterOptions.Builder()
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.setRunningMode(RunningMode.IMAGE)
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.setDisplayNamesLocale("en")
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.setOutputType(OutputType.CATEGORY_MASK);
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.setOutputCategoryMask(false);
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}
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/**
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@ -633,14 +637,6 @@ public final class ImageSegmenter extends BaseVisionTaskApi {
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SegmenterOptionsProto.SegmenterOptions.Builder segmenterOptionsBuilder =
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SegmenterOptionsProto.SegmenterOptions.newBuilder();
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if (outputType() == OutputType.CONFIDENCE_MASK) {
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segmenterOptionsBuilder.setOutputType(
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SegmenterOptionsProto.SegmenterOptions.OutputType.CONFIDENCE_MASK);
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} else if (outputType() == OutputType.CATEGORY_MASK) {
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segmenterOptionsBuilder.setOutputType(
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SegmenterOptionsProto.SegmenterOptions.OutputType.CATEGORY_MASK);
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}
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taskOptionsBuilder.setSegmenterOptions(segmenterOptionsBuilder);
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return CalculatorOptions.newBuilder()
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.setExtension(
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@ -19,6 +19,7 @@ import com.google.mediapipe.framework.image.MPImage;
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import com.google.mediapipe.tasks.core.TaskResult;
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import java.util.Collections;
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import java.util.List;
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import java.util.Optional;
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/** Represents the segmentation results generated by {@link ImageSegmenter}. */
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@AutoValue
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@ -27,18 +28,24 @@ public abstract class ImageSegmenterResult implements TaskResult {
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/**
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* Creates an {@link ImageSegmenterResult} instance from a list of segmentation MPImage.
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*
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* @param segmentations a {@link List} of MPImage representing the segmented masks. If OutputType
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* is CATEGORY_MASK, the masks will be in IMAGE_FORMAT_ALPHA format. If OutputType is
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* CONFIDENCE_MASK, the masks will be in IMAGE_FORMAT_VEC32F1 format.
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* @param confidenceMasks a {@link List} of MPImage in IMAGE_FORMAT_VEC32F1 format representing
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* the confidence masks, where, for each mask, each pixel represents the prediction
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* confidence, usually in the [0, 1] range.
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* @param categoryMask an {@link Optional} MPImage in IMAGE_FORMAT_ALPHA format representing a
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* category mask, where each pixel represents the class which the pixel in the original image
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* was predicted to belong to.
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* @param timestampMs a timestamp for this result.
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*/
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// TODO: consolidate output formats across platforms.
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public static ImageSegmenterResult create(List<MPImage> segmentations, long timestampMs) {
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public static ImageSegmenterResult create(
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List<MPImage> confidenceMasks, Optional<MPImage> categoryMask, long timestampMs) {
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return new AutoValue_ImageSegmenterResult(
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Collections.unmodifiableList(segmentations), timestampMs);
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Collections.unmodifiableList(confidenceMasks), categoryMask, timestampMs);
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}
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public abstract List<MPImage> segmentations();
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public abstract List<MPImage> confidenceMasks();
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public abstract Optional<MPImage> categoryMask();
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@Override
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public abstract long timestampMs();
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@ -133,6 +133,7 @@ public final class InteractiveSegmenter extends BaseVisionTaskApi {
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if (packets.get(GROUPED_SEGMENTATION_OUT_STREAM_INDEX).isEmpty()) {
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return ImageSegmenterResult.create(
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new ArrayList<>(),
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Optional.empty(),
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packets.get(GROUPED_SEGMENTATION_OUT_STREAM_INDEX).getTimestamp());
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}
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List<MPImage> segmentedMasks = new ArrayList<>();
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@ -172,6 +173,7 @@ public final class InteractiveSegmenter extends BaseVisionTaskApi {
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return ImageSegmenterResult.create(
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segmentedMasks,
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Optional.empty(),
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BaseVisionTaskApi.generateResultTimestampMs(
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RunningMode.IMAGE, packets.get(GROUPED_SEGMENTATION_OUT_STREAM_INDEX)));
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}
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@ -61,14 +61,13 @@ public class ImageSegmenterTest {
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ImageSegmenterOptions options =
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ImageSegmenterOptions.builder()
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.setBaseOptions(BaseOptions.builder().setModelAssetPath(DEEPLAB_MODEL_FILE).build())
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.setOutputType(ImageSegmenterOptions.OutputType.CATEGORY_MASK)
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.setOutputCategoryMask(true)
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.build();
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ImageSegmenter imageSegmenter =
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ImageSegmenter.createFromOptions(ApplicationProvider.getApplicationContext(), options);
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ImageSegmenterResult actualResult = imageSegmenter.segment(getImageFromAsset(inputImageName));
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List<MPImage> segmentations = actualResult.segmentations();
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assertThat(segmentations.size()).isEqualTo(1);
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MPImage actualMaskBuffer = actualResult.segmentations().get(0);
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assertThat(actualResult.categoryMask().isPresent()).isTrue();
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MPImage actualMaskBuffer = actualResult.categoryMask().get();
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MPImage expectedMaskBuffer = getImageFromAsset(goldenImageName);
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verifyCategoryMask(
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actualMaskBuffer, expectedMaskBuffer, GOLDEN_MASK_SIMILARITY, MAGNIFICATION_FACTOR);
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@ -81,15 +80,14 @@ public class ImageSegmenterTest {
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ImageSegmenterOptions options =
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ImageSegmenterOptions.builder()
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.setBaseOptions(BaseOptions.builder().setModelAssetPath(DEEPLAB_MODEL_FILE).build())
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.setOutputType(ImageSegmenterOptions.OutputType.CONFIDENCE_MASK)
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.build();
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ImageSegmenter imageSegmenter =
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ImageSegmenter.createFromOptions(ApplicationProvider.getApplicationContext(), options);
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ImageSegmenterResult actualResult = imageSegmenter.segment(getImageFromAsset(inputImageName));
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List<MPImage> segmentations = actualResult.segmentations();
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List<MPImage> segmentations = actualResult.confidenceMasks();
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assertThat(segmentations.size()).isEqualTo(21);
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// Cat category index 8.
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MPImage actualMaskBuffer = actualResult.segmentations().get(8);
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MPImage actualMaskBuffer = segmentations.get(8);
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MPImage expectedMaskBuffer = getImageFromAsset(goldenImageName);
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verifyConfidenceMask(actualMaskBuffer, expectedMaskBuffer, GOLDEN_MASK_SIMILARITY);
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}
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@ -102,40 +100,36 @@ public class ImageSegmenterTest {
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ImageSegmenterOptions.builder()
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.setBaseOptions(
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BaseOptions.builder().setModelAssetPath(SELFIE_128x128_MODEL_FILE).build())
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.setOutputType(ImageSegmenterOptions.OutputType.CONFIDENCE_MASK)
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.build();
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ImageSegmenter imageSegmenter =
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ImageSegmenter.createFromOptions(ApplicationProvider.getApplicationContext(), options);
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ImageSegmenterResult actualResult = imageSegmenter.segment(getImageFromAsset(inputImageName));
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List<MPImage> segmentations = actualResult.segmentations();
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List<MPImage> segmentations = actualResult.confidenceMasks();
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assertThat(segmentations.size()).isEqualTo(2);
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// Selfie category index 1.
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MPImage actualMaskBuffer = actualResult.segmentations().get(1);
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MPImage actualMaskBuffer = segmentations.get(1);
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MPImage expectedMaskBuffer = getImageFromAsset(goldenImageName);
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verifyConfidenceMask(actualMaskBuffer, expectedMaskBuffer, GOLDEN_MASK_SIMILARITY);
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}
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// TODO: enable this unit test once activation option is supported in metadata.
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// @Test
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// public void segment_successWith144x256Segmentation() throws Exception {
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// final String inputImageName = "mozart_square.jpg";
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// final String goldenImageName = "selfie_segm_144_256_3_expected_mask.jpg";
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// ImageSegmenterOptions options =
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// ImageSegmenterOptions.builder()
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// .setBaseOptions(
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// BaseOptions.builder().setModelAssetPath(SELFIE_144x256_MODEL_FILE).build())
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// .setOutputType(ImageSegmenterOptions.OutputType.CONFIDENCE_MASK)
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// .build();
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// ImageSegmenter imageSegmenter =
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// ImageSegmenter.createFromOptions(ApplicationProvider.getApplicationContext(), options);
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// ImageSegmenterResult actualResult =
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// imageSegmenter.segment(getImageFromAsset(inputImageName));
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// List<MPImage> segmentations = actualResult.segmentations();
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// assertThat(segmentations.size()).isEqualTo(1);
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// MPImage actualMaskBuffer = actualResult.segmentations().get(0);
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// MPImage expectedMaskBuffer = getImageFromAsset(goldenImageName);
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// verifyConfidenceMask(actualMaskBuffer, expectedMaskBuffer, GOLDEN_MASK_SIMILARITY);
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// }
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@Test
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public void segment_successWith144x256Segmentation() throws Exception {
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final String inputImageName = "mozart_square.jpg";
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final String goldenImageName = "selfie_segm_144_256_3_expected_mask.jpg";
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ImageSegmenterOptions options =
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ImageSegmenterOptions.builder()
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.setBaseOptions(
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BaseOptions.builder().setModelAssetPath(SELFIE_144x256_MODEL_FILE).build())
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.build();
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ImageSegmenter imageSegmenter =
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ImageSegmenter.createFromOptions(ApplicationProvider.getApplicationContext(), options);
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ImageSegmenterResult actualResult = imageSegmenter.segment(getImageFromAsset(inputImageName));
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List<MPImage> segmentations = actualResult.confidenceMasks();
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assertThat(segmentations.size()).isEqualTo(1);
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MPImage actualMaskBuffer = segmentations.get(0);
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MPImage expectedMaskBuffer = getImageFromAsset(goldenImageName);
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verifyConfidenceMask(actualMaskBuffer, expectedMaskBuffer, GOLDEN_MASK_SIMILARITY);
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}
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@Test
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public void getLabels_success() throws Exception {
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@ -165,7 +159,6 @@ public class ImageSegmenterTest {
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ImageSegmenterOptions options =
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ImageSegmenterOptions.builder()
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.setBaseOptions(BaseOptions.builder().setModelAssetPath(DEEPLAB_MODEL_FILE).build())
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.setOutputType(ImageSegmenterOptions.OutputType.CONFIDENCE_MASK)
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.build();
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ImageSegmenter imageSegmenter =
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ImageSegmenter.createFromOptions(ApplicationProvider.getApplicationContext(), options);
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@ -287,16 +280,15 @@ public class ImageSegmenterTest {
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ImageSegmenterOptions options =
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ImageSegmenterOptions.builder()
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.setBaseOptions(BaseOptions.builder().setModelAssetPath(DEEPLAB_MODEL_FILE).build())
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.setOutputType(ImageSegmenterOptions.OutputType.CONFIDENCE_MASK)
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.setRunningMode(RunningMode.IMAGE)
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.build();
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ImageSegmenter imageSegmenter =
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ImageSegmenter.createFromOptions(ApplicationProvider.getApplicationContext(), options);
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ImageSegmenterResult actualResult = imageSegmenter.segment(getImageFromAsset(inputImageName));
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List<MPImage> segmentations = actualResult.segmentations();
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List<MPImage> segmentations = actualResult.confidenceMasks();
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assertThat(segmentations.size()).isEqualTo(21);
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// Cat category index 8.
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MPImage actualMaskBuffer = actualResult.segmentations().get(8);
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MPImage actualMaskBuffer = segmentations.get(8);
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MPImage expectedMaskBuffer = getImageFromAsset(goldenImageName);
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verifyConfidenceMask(actualMaskBuffer, expectedMaskBuffer, GOLDEN_MASK_SIMILARITY);
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}
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@ -309,12 +301,11 @@ public class ImageSegmenterTest {
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ImageSegmenterOptions options =
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ImageSegmenterOptions.builder()
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.setBaseOptions(BaseOptions.builder().setModelAssetPath(DEEPLAB_MODEL_FILE).build())
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.setOutputType(ImageSegmenterOptions.OutputType.CONFIDENCE_MASK)
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.setRunningMode(RunningMode.IMAGE)
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.setResultListener(
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(segmenterResult, inputImage) -> {
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verifyConfidenceMask(
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segmenterResult.segmentations().get(8),
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segmenterResult.confidenceMasks().get(8),
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expectedResult,
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GOLDEN_MASK_SIMILARITY);
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})
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@ -331,7 +322,6 @@ public class ImageSegmenterTest {
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ImageSegmenterOptions options =
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ImageSegmenterOptions.builder()
|
||||
.setBaseOptions(BaseOptions.builder().setModelAssetPath(DEEPLAB_MODEL_FILE).build())
|
||||
.setOutputType(ImageSegmenterOptions.OutputType.CONFIDENCE_MASK)
|
||||
.setRunningMode(RunningMode.VIDEO)
|
||||
.build();
|
||||
ImageSegmenter imageSegmenter =
|
||||
|
|
@ -341,10 +331,10 @@ public class ImageSegmenterTest {
|
|||
ImageSegmenterResult actualResult =
|
||||
imageSegmenter.segmentForVideo(
|
||||
getImageFromAsset(inputImageName), /* timestampsMs= */ i);
|
||||
List<MPImage> segmentations = actualResult.segmentations();
|
||||
List<MPImage> segmentations = actualResult.confidenceMasks();
|
||||
assertThat(segmentations.size()).isEqualTo(21);
|
||||
// Cat category index 8.
|
||||
MPImage actualMaskBuffer = actualResult.segmentations().get(8);
|
||||
MPImage actualMaskBuffer = segmentations.get(8);
|
||||
verifyConfidenceMask(actualMaskBuffer, expectedMaskBuffer, GOLDEN_MASK_SIMILARITY);
|
||||
}
|
||||
}
|
||||
|
|
@ -357,12 +347,11 @@ public class ImageSegmenterTest {
|
|||
ImageSegmenterOptions options =
|
||||
ImageSegmenterOptions.builder()
|
||||
.setBaseOptions(BaseOptions.builder().setModelAssetPath(DEEPLAB_MODEL_FILE).build())
|
||||
.setOutputType(ImageSegmenterOptions.OutputType.CONFIDENCE_MASK)
|
||||
.setRunningMode(RunningMode.VIDEO)
|
||||
.setResultListener(
|
||||
(segmenterResult, inputImage) -> {
|
||||
verifyConfidenceMask(
|
||||
segmenterResult.segmentations().get(8),
|
||||
segmenterResult.confidenceMasks().get(8),
|
||||
expectedResult,
|
||||
GOLDEN_MASK_SIMILARITY);
|
||||
})
|
||||
|
|
@ -384,12 +373,11 @@ public class ImageSegmenterTest {
|
|||
ImageSegmenterOptions options =
|
||||
ImageSegmenterOptions.builder()
|
||||
.setBaseOptions(BaseOptions.builder().setModelAssetPath(DEEPLAB_MODEL_FILE).build())
|
||||
.setOutputType(ImageSegmenterOptions.OutputType.CONFIDENCE_MASK)
|
||||
.setRunningMode(RunningMode.LIVE_STREAM)
|
||||
.setResultListener(
|
||||
(segmenterResult, inputImage) -> {
|
||||
verifyConfidenceMask(
|
||||
segmenterResult.segmentations().get(8),
|
||||
segmenterResult.confidenceMasks().get(8),
|
||||
expectedResult,
|
||||
GOLDEN_MASK_SIMILARITY);
|
||||
})
|
||||
|
|
@ -411,12 +399,11 @@ public class ImageSegmenterTest {
|
|||
ImageSegmenterOptions options =
|
||||
ImageSegmenterOptions.builder()
|
||||
.setBaseOptions(BaseOptions.builder().setModelAssetPath(DEEPLAB_MODEL_FILE).build())
|
||||
.setOutputType(ImageSegmenterOptions.OutputType.CONFIDENCE_MASK)
|
||||
.setRunningMode(RunningMode.LIVE_STREAM)
|
||||
.setResultListener(
|
||||
(segmenterResult, inputImage) -> {
|
||||
verifyConfidenceMask(
|
||||
segmenterResult.segmentations().get(8),
|
||||
segmenterResult.confidenceMasks().get(8),
|
||||
expectedResult,
|
||||
GOLDEN_MASK_SIMILARITY);
|
||||
})
|
||||
|
|
|
|||
|
|
@ -60,7 +60,10 @@ public class InteractiveSegmenterTest {
|
|||
ApplicationProvider.getApplicationContext(), options);
|
||||
MPImage image = getImageFromAsset(inputImageName);
|
||||
ImageSegmenterResult actualResult = imageSegmenter.segment(image, roi);
|
||||
List<MPImage> segmentations = actualResult.segmentations();
|
||||
// TODO update to correct category mask output.
|
||||
// After InteractiveSegmenter updated according to (b/276519300), update this to use
|
||||
// categoryMask field instead of confidenceMasks.
|
||||
List<MPImage> segmentations = actualResult.confidenceMasks();
|
||||
assertThat(segmentations.size()).isEqualTo(1);
|
||||
}
|
||||
|
||||
|
|
@ -79,7 +82,7 @@ public class InteractiveSegmenterTest {
|
|||
ApplicationProvider.getApplicationContext(), options);
|
||||
ImageSegmenterResult actualResult =
|
||||
imageSegmenter.segment(getImageFromAsset(inputImageName), roi);
|
||||
List<MPImage> segmentations = actualResult.segmentations();
|
||||
List<MPImage> segmentations = actualResult.confidenceMasks();
|
||||
assertThat(segmentations.size()).isEqualTo(2);
|
||||
}
|
||||
}
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user