Project import generated by Copybara.

GitOrigin-RevId: 33adfdf31f3a5cbf9edc07ee1ea583e95080bdc5

mediapipe/graphs/face_detection/BUILD

@@ -11,6 +11,10 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+load(
+    "//mediapipe/framework/tool:mediapipe_graph.bzl",
+    "mediapipe_binary_graph",
+)
 
 licenses(["notice"])
 
@@ -24,8 +28,8 @@ cc_library(
         "//mediapipe/calculators/util:detections_to_render_data_calculator",
         "//mediapipe/gpu:gpu_buffer_to_image_frame_calculator",
         "//mediapipe/gpu:image_frame_to_gpu_buffer_calculator",
-        "//mediapipe/modules/face_detection:face_detection_front_cpu",
-        "//mediapipe/modules/face_detection:face_detection_front_gpu",
+        "//mediapipe/modules/face_detection:face_detection_short_range_cpu",
+        "//mediapipe/modules/face_detection:face_detection_short_range_gpu",
     ],
 )
 
@@ -35,7 +39,7 @@ cc_library(
         "//mediapipe/calculators/core:flow_limiter_calculator",
         "//mediapipe/calculators/util:annotation_overlay_calculator",
         "//mediapipe/calculators/util:detections_to_render_data_calculator",
-        "//mediapipe/modules/face_detection:face_detection_front_cpu",
+        "//mediapipe/modules/face_detection:face_detection_short_range_cpu",
     ],
 )
 
@@ -45,15 +49,10 @@ cc_library(
         "//mediapipe/calculators/core:flow_limiter_calculator",
         "//mediapipe/calculators/util:annotation_overlay_calculator",
         "//mediapipe/calculators/util:detections_to_render_data_calculator",
-        "//mediapipe/modules/face_detection:face_detection_front_gpu",
+        "//mediapipe/modules/face_detection:face_detection_short_range_gpu",
     ],
 )
 
-load(
-    "//mediapipe/framework/tool:mediapipe_graph.bzl",
-    "mediapipe_binary_graph",
-)
-
 mediapipe_binary_graph(
     name = "face_detection_mobile_cpu_binary_graph",
     graph = "face_detection_mobile_cpu.pbtxt",
@@ -67,3 +66,30 @@ mediapipe_binary_graph(
     output_name = "face_detection_mobile_gpu.binarypb",
     deps = [":mobile_calculators"],
 )
+
+cc_library(
+    name = "face_detection_full_range_mobile_gpu_deps",
+    deps = [
+        "//mediapipe/calculators/core:flow_limiter_calculator",
+        "//mediapipe/calculators/util:annotation_overlay_calculator",
+        "//mediapipe/calculators/util:detections_to_render_data_calculator",
+        "//mediapipe/modules/face_detection:face_detection_full_range_gpu",
+    ],
+)
+
+mediapipe_binary_graph(
+    name = "face_detection_full_range_mobile_gpu_binary_graph",
+    graph = "face_detection_full_range_mobile_gpu.pbtxt",
+    output_name = "face_detection_full_range_mobile_gpu.binarypb",
+    deps = [":face_detection_full_range_mobile_gpu_deps"],
+)
+
+cc_library(
+    name = "face_detection_full_range_desktop_live_deps",
+    deps = [
+        "//mediapipe/calculators/core:flow_limiter_calculator",
+        "//mediapipe/calculators/util:annotation_overlay_calculator",
+        "//mediapipe/calculators/util:detections_to_render_data_calculator",
+        "//mediapipe/modules/face_detection:face_detection_full_range_cpu",
+    ],
+)

mediapipe/graphs/face_detection/face_detection_back_desktop_live.pbtxt

@@ -1,169 +0,0 @@
-# MediaPipe graph that performs face detection with TensorFlow Lite on CPU.
-# Used in the examples in
-# mediapipe/examples/desktop/face_detection:face_detection_cpu.
-
-# Images on GPU coming into and out of the graph.
-input_stream: "input_video"
-output_stream: "output_video"
-
-# Throttles the images flowing downstream for flow control. It passes through
-# the very first incoming image unaltered, and waits for
-# TfLiteTensorsToDetectionsCalculator downstream in the graph to finish
-# generating the corresponding detections before it passes through another
-# image. All images that come in while waiting are dropped, limiting the number
-# of in-flight images between this calculator and
-# TfLiteTensorsToDetectionsCalculator to 1. This prevents the nodes in between
-# from queuing up incoming images and data excessively, which leads to increased
-# latency and memory usage, unwanted in real-time mobile applications. It also
-# eliminates unnecessarily computation, e.g., a transformed image produced by
-# ImageTransformationCalculator may get dropped downstream if the subsequent
-# TfLiteConverterCalculator or TfLiteInferenceCalculator is still busy
-# processing previous inputs.
-node {
-  calculator: "FlowLimiterCalculator"
-  input_stream: "input_video"
-  input_stream: "FINISHED:detections"
-  input_stream_info: {
-    tag_index: "FINISHED"
-    back_edge: true
-  }
-  output_stream: "throttled_input_video"
-}
-
-# Transforms the input image on CPU to a 128x128 image. To scale the input
-# image, the scale_mode option is set to FIT to preserve the aspect ratio,
-# resulting in potential letterboxing in the transformed image.
-node: {
-  calculator: "ImageTransformationCalculator"
-  input_stream: "IMAGE:throttled_input_video"
-  output_stream: "IMAGE:transformed_input_video_cpu"
-  output_stream: "LETTERBOX_PADDING:letterbox_padding"
-  node_options: {
-    [type.googleapis.com/mediapipe.ImageTransformationCalculatorOptions] {
-      output_width: 192
-      output_height: 192
-      scale_mode: FIT
-    }
-  }
-}
-
-# Converts the transformed input image on CPU into an image tensor stored as a
-# TfLiteTensor.
-node {
-  calculator: "TfLiteConverterCalculator"
-  input_stream: "IMAGE:transformed_input_video_cpu"
-  output_stream: "TENSORS:image_tensor"
-}
-
-# Runs a TensorFlow Lite model on CPU that takes an image tensor and outputs a
-# vector of tensors representing, for instance, detection boxes/keypoints and
-# scores.
-node {
-  calculator: "TfLiteInferenceCalculator"
-  input_stream: "TENSORS:image_tensor"
-  output_stream: "TENSORS:detection_tensors"
-  node_options: {
-    [type.googleapis.com/mediapipe.TfLiteInferenceCalculatorOptions] {
-      model_path: "mediapipe/modules/face_detection/face_detection_back.tflite"
-    }
-  }
-}
-
-# Generates a single side packet containing a vector of SSD anchors based on
-# the specification in the options.
-node {
-  calculator: "SsdAnchorsCalculator"
-  output_side_packet: "anchors"
-  node_options: {
-    [type.googleapis.com/mediapipe.SsdAnchorsCalculatorOptions] {
-      num_layers: 1
-      min_scale: 0.1484375
-      max_scale: 0.75
-      input_size_height: 192
-      input_size_width: 192
-      anchor_offset_x: 0.5
-      anchor_offset_y: 0.5
-      strides: 4
-      aspect_ratios: 1.0
-      fixed_anchor_size: true
-      interpolated_scale_aspect_ratio: 0.0
-    }
-  }
-}
-
-# Decodes the detection tensors generated by the TensorFlow Lite model, based on
-# the SSD anchors and the specification in the options, into a vector of
-# detections. Each detection describes a detected object.
-node {
-  calculator: "TfLiteTensorsToDetectionsCalculator"
-  input_stream: "TENSORS:detection_tensors"
-  input_side_packet: "ANCHORS:anchors"
-  output_stream: "DETECTIONS:detections"
-  node_options: {
-    [type.googleapis.com/mediapipe.TfLiteTensorsToDetectionsCalculatorOptions] {
-      num_classes: 1
-      num_boxes: 2304
-      num_coords: 16
-      box_coord_offset: 0
-      keypoint_coord_offset: 4
-      num_keypoints: 6
-      num_values_per_keypoint: 2
-      sigmoid_score: true
-      score_clipping_thresh: 100.0
-      reverse_output_order: true
-      x_scale: 192.0
-      y_scale: 192.0
-      h_scale: 192.0
-      w_scale: 192.0
-      min_score_thresh: 0.6
-    }
-  }
-}
-
-# Performs non-max suppression to remove excessive detections.
-node {
-  calculator: "NonMaxSuppressionCalculator"
-  input_stream: "detections"
-  output_stream: "filtered_detections"
-  node_options: {
-    [type.googleapis.com/mediapipe.NonMaxSuppressionCalculatorOptions] {
-      min_suppression_threshold: 0.3
-      overlap_type: INTERSECTION_OVER_UNION
-      algorithm: WEIGHTED
-      return_empty_detections: true
-    }
-  }
-}
-
-# Adjusts detection locations (already normalized to [0.f, 1.f]) on the
-# letterboxed image (after image transformation with the FIT scale mode) to the
-# corresponding locations on the same image with the letterbox removed (the
-# input image to the graph before image transformation).
-node {
-  calculator: "DetectionLetterboxRemovalCalculator"
-  input_stream: "DETECTIONS:filtered_detections"
-  input_stream: "LETTERBOX_PADDING:letterbox_padding"
-  output_stream: "DETECTIONS:output_detections"
-}
-
-# Converts the detections to drawing primitives for annotation overlay.
-node {
-  calculator: "DetectionsToRenderDataCalculator"
-  input_stream: "DETECTIONS:output_detections"
-  output_stream: "RENDER_DATA:render_data"
-  node_options: {
-    [type.googleapis.com/mediapipe.DetectionsToRenderDataCalculatorOptions] {
-      thickness: 4.0
-      color { r: 255 g: 0 b: 0 }
-    }
-  }
-}
-
-# Draws annotations and overlays them on top of the input images.
-node {
-  calculator: "AnnotationOverlayCalculator"
-  input_stream: "IMAGE:throttled_input_video"
-  input_stream: "render_data"
-  output_stream: "IMAGE:output_video"
-}
-

mediapipe/graphs/face_detection/face_detection_back_mobile_gpu.pbtxt

@@ -1,169 +0,0 @@
-# MediaPipe graph that performs face detection with TensorFlow Lite on GPU.
-# Used in the examples in
-# mediapipie/examples/android/src/java/com/mediapipe/apps/facedetectiongpu and
-# mediapipie/examples/ios/facedetectiongpu.
-
-# Images on GPU coming into and out of the graph.
-input_stream: "input_video"
-output_stream: "output_video"
-
-# Throttles the images flowing downstream for flow control. It passes through
-# the very first incoming image unaltered, and waits for
-# TfLiteTensorsToDetectionsCalculator downstream in the graph to finish
-# generating the corresponding detections before it passes through another
-# image. All images that come in while waiting are dropped, limiting the number
-# of in-flight images between this calculator and
-# TfLiteTensorsToDetectionsCalculator to 1. This prevents the nodes in between
-# from queuing up incoming images and data excessively, which leads to increased
-# latency and memory usage, unwanted in real-time mobile applications. It also
-# eliminates unnecessarily computation, e.g., a transformed image produced by
-# ImageTransformationCalculator may get dropped downstream if the subsequent
-# TfLiteConverterCalculator or TfLiteInferenceCalculator is still busy
-# processing previous inputs.
-node {
-  calculator: "FlowLimiterCalculator"
-  input_stream: "input_video"
-  input_stream: "FINISHED:detections"
-  input_stream_info: {
-    tag_index: "FINISHED"
-    back_edge: true
-  }
-  output_stream: "throttled_input_video"
-}
-
-# Transforms the input image on GPU to a 128x128 image. To scale the input
-# image, the scale_mode option is set to FIT to preserve the aspect ratio,
-# resulting in potential letterboxing in the transformed image.
-node: {
-  calculator: "ImageTransformationCalculator"
-  input_stream: "IMAGE_GPU:throttled_input_video"
-  output_stream: "IMAGE_GPU:transformed_input_video"
-  output_stream: "LETTERBOX_PADDING:letterbox_padding"
-  node_options: {
-    [type.googleapis.com/mediapipe.ImageTransformationCalculatorOptions] {
-      output_width: 192
-      output_height: 192
-      scale_mode: FIT
-    }
-  }
-}
-
-# Converts the transformed input image on GPU into an image tensor stored as a
-# TfLiteTensor.
-node {
-  calculator: "TfLiteConverterCalculator"
-  input_stream: "IMAGE_GPU:transformed_input_video"
-  output_stream: "TENSORS_GPU:image_tensor"
-}
-
-# Runs a TensorFlow Lite model on GPU that takes an image tensor and outputs a
-# vector of tensors representing, for instance, detection boxes/keypoints and
-# scores.
-node {
-  calculator: "TfLiteInferenceCalculator"
-  input_stream: "TENSORS_GPU:image_tensor"
-  output_stream: "TENSORS_GPU:detection_tensors"
-  node_options: {
-    [type.googleapis.com/mediapipe.TfLiteInferenceCalculatorOptions] {
-      model_path: "mediapipe/modules/face_detection/face_detection_back.tflite"
-    }
-  }
-}
-
-# Generates a single side packet containing a vector of SSD anchors based on
-# the specification in the options.
-node {
-  calculator: "SsdAnchorsCalculator"
-  output_side_packet: "anchors"
-  node_options: {
-    [type.googleapis.com/mediapipe.SsdAnchorsCalculatorOptions] {
-      num_layers: 1
-      min_scale: 0.1484375
-      max_scale: 0.75
-      input_size_height: 192
-      input_size_width: 192
-      anchor_offset_x: 0.5
-      anchor_offset_y: 0.5
-      strides: 4
-      aspect_ratios: 1.0
-      fixed_anchor_size: true
-      interpolated_scale_aspect_ratio: 0.0
-    }
-  }
-}
-
-# Decodes the detection tensors generated by the TensorFlow Lite model, based on
-# the SSD anchors and the specification in the options, into a vector of
-# detections. Each detection describes a detected object.
-node {
-  calculator: "TfLiteTensorsToDetectionsCalculator"
-  input_stream: "TENSORS_GPU:detection_tensors"
-  input_side_packet: "ANCHORS:anchors"
-  output_stream: "DETECTIONS:detections"
-  node_options: {
-    [type.googleapis.com/mediapipe.TfLiteTensorsToDetectionsCalculatorOptions] {
-      num_classes: 1
-      num_boxes: 2304
-      num_coords: 16
-      box_coord_offset: 0
-      keypoint_coord_offset: 4
-      num_keypoints: 6
-      num_values_per_keypoint: 2
-      sigmoid_score: true
-      score_clipping_thresh: 100.0
-      reverse_output_order: true
-      x_scale: 192.0
-      y_scale: 192.0
-      h_scale: 192.0
-      w_scale: 192.0
-      min_score_thresh: 0.6
-    }
-  }
-}
-
-# Performs non-max suppression to remove excessive detections.
-node {
-  calculator: "NonMaxSuppressionCalculator"
-  input_stream: "detections"
-  output_stream: "filtered_detections"
-  node_options: {
-    [type.googleapis.com/mediapipe.NonMaxSuppressionCalculatorOptions] {
-      min_suppression_threshold: 0.3
-      overlap_type: INTERSECTION_OVER_UNION
-      algorithm: WEIGHTED
-      return_empty_detections: true
-    }
-  }
-}
-
-# Adjusts detection locations (already normalized to [0.f, 1.f]) on the
-# letterboxed image (after image transformation with the FIT scale mode) to the
-# corresponding locations on the same image with the letterbox removed (the
-# input image to the graph before image transformation).
-node {
-  calculator: "DetectionLetterboxRemovalCalculator"
-  input_stream: "DETECTIONS:filtered_detections"
-  input_stream: "LETTERBOX_PADDING:letterbox_padding"
-  output_stream: "DETECTIONS:output_detections"
-}
-
-# Converts the detections to drawing primitives for annotation overlay.
-node {
-  calculator: "DetectionsToRenderDataCalculator"
-  input_stream: "DETECTIONS:output_detections"
-  output_stream: "RENDER_DATA:render_data"
-  node_options: {
-    [type.googleapis.com/mediapipe.DetectionsToRenderDataCalculatorOptions] {
-      thickness: 4.0
-      color { r: 255 g: 0 b: 0 }
-    }
-  }
-}
-
-# Draws annotations and overlays them on top of the input images.
-node {
-  calculator: "AnnotationOverlayCalculator"
-  input_stream: "IMAGE_GPU:throttled_input_video"
-  input_stream: "render_data"
-  output_stream: "IMAGE_GPU:output_video"
-}

mediapipe/graphs/face_detection/face_detection_desktop_live.pbtxt

@@ -31,7 +31,7 @@ node {
 
 # Subgraph that detects faces.
 node {
-  calculator: "FaceDetectionFrontCpu"
+  calculator: "FaceDetectionShortRangeCpu"
   input_stream: "IMAGE:throttled_input_video"
   output_stream: "DETECTIONS:face_detections"
 }

mediapipe/graphs/face_detection/face_detection_full_range_desktop_live.pbtxt

@@ -0,0 +1,60 @@
+# MediaPipe graph that performs face detection with TensorFlow Lite on CPU.
+# Used in the examples in
+# mediapipe/examples/desktop/face_detection:face_detection_cpu.
+
+# Images on GPU coming into and out of the graph.
+input_stream: "input_video"
+output_stream: "output_video"
+
+# Throttles the images flowing downstream for flow control. It passes through
+# the very first incoming image unaltered, and waits for
+# TfLiteTensorsToDetectionsCalculator downstream in the graph to finish
+# generating the corresponding detections before it passes through another
+# image. All images that come in while waiting are dropped, limiting the number
+# of in-flight images between this calculator and
+# TfLiteTensorsToDetectionsCalculator to 1. This prevents the nodes in between
+# from queuing up incoming images and data excessively, which leads to increased
+# latency and memory usage, unwanted in real-time mobile applications. It also
+# eliminates unnecessarily computation, e.g., a transformed image produced by
+# ImageTransformationCalculator may get dropped downstream if the subsequent
+# TfLiteConverterCalculator or TfLiteInferenceCalculator is still busy
+# processing previous inputs.
+node {
+  calculator: "FlowLimiterCalculator"
+  input_stream: "input_video"
+  input_stream: "FINISHED:detections"
+  input_stream_info: {
+    tag_index: "FINISHED"
+    back_edge: true
+  }
+  output_stream: "throttled_input_video"
+}
+
+# Detects faces.
+node {
+  calculator: "FaceDetectionFullRangeCpu"
+  input_stream: "IMAGE:throttled_input_video"
+  output_stream: "DETECTIONS:detections"
+}
+
+# Converts the detections to drawing primitives for annotation overlay.
+node {
+  calculator: "DetectionsToRenderDataCalculator"
+  input_stream: "DETECTIONS:detections"
+  output_stream: "RENDER_DATA:render_data"
+  node_options: {
+    [type.googleapis.com/mediapipe.DetectionsToRenderDataCalculatorOptions] {
+      thickness: 4.0
+      color { r: 255 g: 0 b: 0 }
+    }
+  }
+}
+
+# Draws annotations and overlays them on top of the input images.
+node {
+  calculator: "AnnotationOverlayCalculator"
+  input_stream: "IMAGE:throttled_input_video"
+  input_stream: "render_data"
+  output_stream: "IMAGE:output_video"
+}
+

mediapipe/graphs/face_detection/face_detection_full_range_mobile_gpu.pbtxt

@@ -0,0 +1,60 @@
+# MediaPipe graph that performs face detection with TensorFlow Lite on GPU.
+# Used in the examples in
+# mediapipie/examples/android/src/java/com/mediapipe/apps/facedetectiongpu and
+# mediapipie/examples/ios/facedetectiongpu.
+
+# Images on GPU coming into and out of the graph.
+input_stream: "input_video"
+output_stream: "output_video"
+
+# Throttles the images flowing downstream for flow control. It passes through
+# the very first incoming image unaltered, and waits for
+# TfLiteTensorsToDetectionsCalculator downstream in the graph to finish
+# generating the corresponding detections before it passes through another
+# image. All images that come in while waiting are dropped, limiting the number
+# of in-flight images between this calculator and
+# TfLiteTensorsToDetectionsCalculator to 1. This prevents the nodes in between
+# from queuing up incoming images and data excessively, which leads to increased
+# latency and memory usage, unwanted in real-time mobile applications. It also
+# eliminates unnecessarily computation, e.g., a transformed image produced by
+# ImageTransformationCalculator may get dropped downstream if the subsequent
+# TfLiteConverterCalculator or TfLiteInferenceCalculator is still busy
+# processing previous inputs.
+node {
+  calculator: "FlowLimiterCalculator"
+  input_stream: "input_video"
+  input_stream: "FINISHED:output_video"
+  input_stream_info: {
+    tag_index: "FINISHED"
+    back_edge: true
+  }
+  output_stream: "throttled_input_video"
+}
+
+# Detects faces.
+node {
+  calculator: "FaceDetectionFullRangeGpu"
+  input_stream: "IMAGE:throttled_input_video"
+  output_stream: "DETECTIONS:detections"
+}
+
+# Converts the detections to drawing primitives for annotation overlay.
+node {
+  calculator: "DetectionsToRenderDataCalculator"
+  input_stream: "DETECTIONS:detections"
+  output_stream: "RENDER_DATA:render_data"
+  node_options: {
+    [type.googleapis.com/mediapipe.DetectionsToRenderDataCalculatorOptions] {
+      thickness: 4.0
+      color { r: 255 g: 0 b: 0 }
+    }
+  }
+}
+
+# Draws annotations and overlays them on top of the input images.
+node {
+  calculator: "AnnotationOverlayCalculator"
+  input_stream: "IMAGE_GPU:throttled_input_video"
+  input_stream: "render_data"
+  output_stream: "IMAGE_GPU:output_video"
+}

mediapipe/graphs/face_detection/face_detection_mobile_cpu.pbtxt

@@ -41,7 +41,7 @@ node: {
 
 # Subgraph that detects faces.
 node {
-  calculator: "FaceDetectionFrontCpu"
+  calculator: "FaceDetectionShortRangeCpu"
   input_stream: "IMAGE:input_video_cpu"
   output_stream: "DETECTIONS:face_detections"
 }

mediapipe/graphs/face_detection/face_detection_mobile_gpu.pbtxt

@@ -31,7 +31,7 @@ node {
 
 # Subgraph that detects faces.
 node {
-  calculator: "FaceDetectionFrontGpu"
+  calculator: "FaceDetectionShortRangeGpu"
   input_stream: "IMAGE:throttled_input_video"
   output_stream: "DETECTIONS:face_detections"
 }

mediapipe/graphs/face_effect/subgraphs/BUILD

@@ -39,7 +39,7 @@ mediapipe_simple_subgraph(
         "//mediapipe/calculators/core:concatenate_detection_vector_calculator",
         "//mediapipe/calculators/core:split_vector_calculator",
         "//mediapipe/calculators/image:image_properties_calculator",
-        "//mediapipe/modules/face_detection:face_detection_front_gpu",
+        "//mediapipe/modules/face_detection:face_detection_short_range_gpu",
         "//mediapipe/modules/face_geometry:face_geometry_from_detection",
     ],
 )

mediapipe/graphs/face_effect/subgraphs/single_face_geometry_from_detection_gpu.pbtxt

@@ -24,7 +24,7 @@ output_stream: "MULTI_FACE_GEOMETRY:multi_face_geometry"
 # Subgraph that detects faces and corresponding landmarks using the face
 # detection pipeline.
 node {
-  calculator: "FaceDetectionFrontGpu"
+  calculator: "FaceDetectionShortRangeGpu"
   input_stream: "IMAGE:input_image"
   output_stream: "DETECTIONS:multi_face_detection"
 }

mediapipe/graphs/face_mesh/subgraphs/BUILD

@@ -24,7 +24,7 @@ package(default_visibility = ["//visibility:public"])
 cc_library(
     name = "renderer_calculators",
     deps = [
-        "//mediapipe/calculators/core:split_normalized_landmark_list_calculator",
+        "//mediapipe/calculators/core:split_landmarks_calculator",
         "//mediapipe/calculators/util:annotation_overlay_calculator",
         "//mediapipe/calculators/util:detections_to_render_data_calculator",
         "//mediapipe/calculators/util:landmarks_to_render_data_calculator",

mediapipe/graphs/holistic_tracking/BUILD

@@ -30,7 +30,7 @@ mediapipe_simple_subgraph(
         "//mediapipe/calculators/core:concatenate_normalized_landmark_list_calculator",
         "//mediapipe/calculators/core:concatenate_vector_calculator",
         "//mediapipe/calculators/core:merge_calculator",
-        "//mediapipe/calculators/core:split_normalized_landmark_list_calculator",
+        "//mediapipe/calculators/core:split_landmarks_calculator",
         "//mediapipe/calculators/core:split_vector_calculator",
         "//mediapipe/calculators/util:detections_to_render_data_calculator",
         "//mediapipe/calculators/util:landmarks_to_render_data_calculator",

mediapipe/graphs/iris_tracking/subgraphs/BUILD

@@ -26,7 +26,7 @@ cc_library(
     deps = [
         "//mediapipe/calculators/core:concatenate_normalized_landmark_list_calculator",
         "//mediapipe/calculators/core:concatenate_vector_calculator",
-        "//mediapipe/calculators/core:split_normalized_landmark_list_calculator",
+        "//mediapipe/calculators/core:split_landmarks_calculator",
         "//mediapipe/calculators/util:annotation_overlay_calculator",
         "//mediapipe/calculators/util:detection_label_id_to_text_calculator",
         "//mediapipe/calculators/util:detections_to_render_data_calculator",

mediapipe/graphs/pose_tracking/subgraphs/BUILD

@@ -26,7 +26,7 @@ mediapipe_simple_subgraph(
     graph = "pose_renderer_gpu.pbtxt",
     register_as = "PoseRendererGpu",
     deps = [
-        "//mediapipe/calculators/core:split_normalized_landmark_list_calculator",
+        "//mediapipe/calculators/core:split_landmarks_calculator",
         "//mediapipe/calculators/util:annotation_overlay_calculator",
         "//mediapipe/calculators/util:detections_to_render_data_calculator",
         "//mediapipe/calculators/util:landmarks_to_render_data_calculator",
@@ -40,7 +40,7 @@ mediapipe_simple_subgraph(
     graph = "pose_renderer_cpu.pbtxt",
     register_as = "PoseRendererCpu",
     deps = [
-        "//mediapipe/calculators/core:split_normalized_landmark_list_calculator",
+        "//mediapipe/calculators/core:split_landmarks_calculator",
         "//mediapipe/calculators/util:annotation_overlay_calculator",
         "//mediapipe/calculators/util:detections_to_render_data_calculator",
         "//mediapipe/calculators/util:landmarks_to_render_data_calculator",