Project import generated by Copybara.

GitOrigin-RevId: e207bb2a1b26cd799055d7735ed35ad2f0e56b83

mediapipe/java/com/google/mediapipe/components/ExternalTextureConverter.java

@@ -103,6 +103,18 @@ public class ExternalTextureConverter implements TextureFrameProducer {
     }
   }
 
+  /**
+   * Re-renders the current frame. Notifies all consumers as if it were a new frame. This should not
+   * typically be used but can be useful for cases where the consumer has lost ownership of the most
+   * recent frame and needs to get it again. This does nothing if no frame has yet been received.
+   */
+  public void rerenderCurrentFrame() {
+    SurfaceTexture surfaceTexture = getSurfaceTexture();
+    if (thread != null && surfaceTexture != null && thread.getHasReceivedFirstFrame()) {
+      thread.onFrameAvailable(surfaceTexture);
+    }
+  }
+
   /**
    * Sets the new buffer pool size. This is safe to set at any time.
    *
@@ -278,6 +290,7 @@ public class ExternalTextureConverter implements TextureFrameProducer {
     private volatile SurfaceTexture internalSurfaceTexture = null;
     private int[] textures = null;
     private final List<TextureFrameConsumer> consumers;
+    private volatile boolean hasReceivedFirstFrame = false;
 
     private final Queue<PoolTextureFrame> framesAvailable = new ArrayDeque<>();
     private int framesInUse = 0;
@@ -335,6 +348,7 @@ public class ExternalTextureConverter implements TextureFrameProducer {
     }
 
     public void setSurfaceTexture(SurfaceTexture texture, int width, int height) {
+      hasReceivedFirstFrame = false;
       if (surfaceTexture != null) {
         surfaceTexture.setOnFrameAvailableListener(null);
       }
@@ -381,6 +395,10 @@ public class ExternalTextureConverter implements TextureFrameProducer {
       return surfaceTexture != null ? surfaceTexture : internalSurfaceTexture;
     }
 
+    public boolean getHasReceivedFirstFrame() {
+      return hasReceivedFirstFrame;
+    }
+
     @Override
     public void onFrameAvailable(SurfaceTexture surfaceTexture) {
       handler.post(() -> renderNext(surfaceTexture));
@@ -427,6 +445,7 @@ public class ExternalTextureConverter implements TextureFrameProducer {
         // pending on the handler. When that happens, we should simply disregard the call.
         return;
       }
+      hasReceivedFirstFrame = true;
       try {
         synchronized (consumers) {
           boolean frameUpdated = false;

mediapipe/tasks/cc/components/calculators/tensor/tensors_to_segmentation_calculator.cc

@@ -159,12 +159,12 @@ absl::Status TensorsToSegmentationCalculator::Process(
     std::tie(output_width, output_height) = kOutputSizeIn(cc).Get();
   }
   Shape output_shape = {
-      .height = output_height,
+      /* height= */ output_height,
-      .width = output_width,
+      /* width= */ output_width,
-      .channels = options_.segmenter_options().output_type() ==
+      /* channels= */ options_.segmenter_options().output_type() ==
-                          SegmenterOptions::CATEGORY_MASK
+              SegmenterOptions::CATEGORY_MASK
-                      ? 1
+          ? 1
-                      : input_shape.channels};
+          : input_shape.channels};
 
   std::vector<Image> segmented_masks = GetSegmentationResult(
       input_shape, output_shape, input_tensor.GetCpuReadView().buffer<float>());

mediapipe/tasks/cc/components/classification_postprocessing.cc

@@ -148,8 +148,9 @@ absl::StatusOr<ClassificationHeadsProperties> GetClassificationHeadsProperties(
                         num_output_tensors, output_tensors_metadata->size()),
         MediaPipeTasksStatus::kMetadataInconsistencyError);
   }
-  return ClassificationHeadsProperties{.num_heads = num_output_tensors,
+  return ClassificationHeadsProperties{
-                                       .quantized = num_quantized_tensors > 0};
+      /* num_heads= */ num_output_tensors,
+      /* quantized= */ num_quantized_tensors > 0};
 }
 
 // Builds the label map from the tensor metadata, if available.

mediapipe/tasks/cc/components/image_preprocessing.cc

@@ -226,12 +226,14 @@ class ImagePreprocessingSubgraph : public Subgraph {
 
     // Connect outputs.
     return {
-        .tensors = image_to_tensor[Output<std::vector<Tensor>>(kTensorsTag)],
+        /* tensors= */ image_to_tensor[Output<std::vector<Tensor>>(
-        .matrix = image_to_tensor[Output<std::array<float, 16>>(kMatrixTag)],
+            kTensorsTag)],
-        .letterbox_padding =
+        /* matrix= */
-            image_to_tensor[Output<std::array<float, 4>>(kLetterboxPaddingTag)],
+        image_to_tensor[Output<std::array<float, 16>>(kMatrixTag)],
-        .image_size = image_size[Output<std::pair<int, int>>(kSizeTag)],
+        /* letterbox_padding= */
-        .image = pass_through[Output<Image>("")],
+        image_to_tensor[Output<std::array<float, 4>>(kLetterboxPaddingTag)],
+        /* image_size= */ image_size[Output<std::pair<int, int>>(kSizeTag)],
+        /* image= */ pass_through[Output<Image>("")],
     };
   }
 };

mediapipe/tasks/cc/vision/hand_landmark/hand_landmark_detector_graph.cc

@@ -388,13 +388,13 @@ class HandLandmarkDetectorGraph : public core::ModelTaskGraph {
         hand_rect_transformation[Output<NormalizedRect>("")];
 
     return {{
-        .hand_landmarks = projected_landmarks,
+        /* hand_landmarks= */ projected_landmarks,
-        .world_hand_landmarks = projected_world_landmarks,
+        /* world_hand_landmarks= */ projected_world_landmarks,
-        .hand_rect_next_frame = hand_rect_next_frame,
+        /* hand_rect_next_frame= */ hand_rect_next_frame,
-        .hand_presence = hand_presence,
+        /* hand_presence= */ hand_presence,
-        .hand_presence_score = hand_presence_score,
+        /* hand_presence_score= */ hand_presence_score,
-        .handedness = handedness,
+        /* handedness= */ handedness,
-        .image_size = image_size,
+        /* image_size= */ image_size,
     }};
   }
 };

mediapipe/tasks/cc/vision/object_detector/object_detector_graph.cc

@@ -531,9 +531,9 @@ class ObjectDetectorGraph : public core::ModelTaskGraph {
     // Outputs the labeled detections and the processed image as the subgraph
     // output streams.
     return {{
-        .detections =
+        /* detections= */
-            detection_label_id_to_text[Output<std::vector<Detection>>("")],
+        detection_label_id_to_text[Output<std::vector<Detection>>("")],
-        .image = preprocessing[Output<Image>(kImageTag)],
+        /* image= */ preprocessing[Output<Image>(kImageTag)],
     }};
   }
 };