Project import generated by Copybara.

GitOrigin-RevId: 9295f8ea2339edb71073695ed4fb3fded2f48c60
2020-08-13 01:32:08 -04:00
parent 6b0ab0e012
commit d7c287c4e9
248 changed files with 10356 additions and 5328 deletions
@@ -9,4 +9,5 @@ Each module (represented as a subfolder) provides subgraphs and corresponding re
 | [`face_detection`](face_detection/README.md) | Subgraphs to detect faces. |
 | [`face_landmark`](face_landmark/README.md) | Subgraphs to detect and track face landmarks. |
 | [`iris_landmark`](iris_landmark/README.md) | Subgraphs to detect iris landmarks. |
-
+| [`pose_detection`](pose_detection/README.md) | Subgraphs to detect poses. |
+| [`pose_landmark`](pose_landmark/README.md) | Subgraphs to detect and track pose landmarks. |
@@ -0,0 +1,58 @@
+# Copyright 2019 The MediaPipe Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# 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_simple_subgraph",
+)
+
+licenses(["notice"])  # Apache 2.0
+
+package(default_visibility = ["//visibility:public"])
+
+mediapipe_simple_subgraph(
+    name = "pose_detection_cpu",
+    graph = "pose_detection_cpu.pbtxt",
+    register_as = "PoseDetectionCpu",
+    deps = [
+        "//mediapipe/calculators/image:image_transformation_calculator",
+        "//mediapipe/calculators/tflite:ssd_anchors_calculator",
+        "//mediapipe/calculators/tflite:tflite_converter_calculator",
+        "//mediapipe/calculators/tflite:tflite_inference_calculator",
+        "//mediapipe/calculators/tflite:tflite_tensors_to_detections_calculator",
+        "//mediapipe/calculators/util:detection_letterbox_removal_calculator",
+        "//mediapipe/calculators/util:non_max_suppression_calculator",
+    ],
+)
+
+mediapipe_simple_subgraph(
+    name = "pose_detection_gpu",
+    graph = "pose_detection_gpu.pbtxt",
+    register_as = "PoseDetectionGpu",
+    deps = [
+        "//mediapipe/calculators/image:image_transformation_calculator",
+        "//mediapipe/calculators/tflite:ssd_anchors_calculator",
+        "//mediapipe/calculators/tflite:tflite_converter_calculator",
+        "//mediapipe/calculators/tflite:tflite_inference_calculator",
+        "//mediapipe/calculators/tflite:tflite_tensors_to_detections_calculator",
+        "//mediapipe/calculators/util:detection_letterbox_removal_calculator",
+        "//mediapipe/calculators/util:non_max_suppression_calculator",
+    ],
+)
+
+exports_files(
+    srcs = [
+        "pose_detection.tflite",
+    ],
+)
@@ -0,0 +1,7 @@
+# pose_detection
+
+Subgraphs|Details
+:--- | :---
+[`PoseDetectionCpu`](https://github.com/google/mediapipe/tree/master/mediapipe/modules/pose_detection/pose_detection_cpu.pbtxt)| Detects poses. (CPU input, and inference is executed on CPU.)
+[`PoseDetectionGpu`](https://github.com/google/mediapipe/tree/master/mediapipe/modules/pose_detection/pose_detection_gpu.pbtxt)| Detects poses. (GPU input, and inference is executed on GPU.)
+
@@ -0,0 +1,155 @@
+# MediaPipe graph to detect poses. (CPU input, and inference is executed on
+# CPU.)
+#
+# It is required that "pose_detection.tflite" is available at
+# "mediapipe/modules/pose_detection/pose_detection.tflite"
+# path during execution.
+#
+# EXAMPLE:
+#   node {
+#     calculator: "PoseDetectionCpu"
+#     input_stream: "IMAGE:image"
+#     output_stream: "DETECTIONS:pose_detections"
+#   }
+
+type: "PoseDetectionCpu"
+
+# CPU image. (ImageFrame)
+input_stream: "IMAGE:image"
+
+# Detected poses. (std::vector<Detection>)
+# Bounding box in each pose detection is currently set to the bounding box of
+# the detected face. However, 4 additional key points are available in each
+# detection, which are used to further calculate a (rotated) bounding box that
+# encloses the body region of interest. Among the 4 key points, the first two
+# are for identifying the full-body region, and the second two for upper body
+# only:
+#
+# Key point 0 - mid hip center
+# Key point 1 - point that encodes size & rotation (for full body)
+# Key point 2 - mid shoulder center
+# Key point 3 - point that encodes size & rotation (for upper body)
+#
+# NOTE: there will not be an output packet in the DETECTIONS stream for this
+# particular timestamp if none of poses detected. However, the MediaPipe
+# framework will internally inform the downstream calculators of the absence of
+# this packet so that they don't wait for it unnecessarily.
+output_stream: "DETECTIONS:detections"
+
+# 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:image"
+  output_stream: "IMAGE:transformed_image"
+  output_stream: "LETTERBOX_PADDING:letterbox_padding"
+  options: {
+    [mediapipe.ImageTransformationCalculatorOptions.ext] {
+      output_width: 128
+      output_height: 128
+      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_image"
+  output_stream: "TENSORS:input_tensors"
+}
+
+# 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:input_tensors"
+  output_stream: "TENSORS:detection_tensors"
+  options: {
+    [mediapipe.TfLiteInferenceCalculatorOptions.ext] {
+      model_path: "mediapipe/modules/pose_detection/pose_detection.tflite"
+      delegate { xnnpack {} }
+    }
+  }
+}
+
+# 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"
+  options: {
+    [mediapipe.SsdAnchorsCalculatorOptions.ext] {
+      num_layers: 4
+      min_scale: 0.1484375
+      max_scale: 0.75
+      input_size_height: 128
+      input_size_width: 128
+      anchor_offset_x: 0.5
+      anchor_offset_y: 0.5
+      strides: 8
+      strides: 16
+      strides: 16
+      strides: 16
+      aspect_ratios: 1.0
+      fixed_anchor_size: true
+    }
+  }
+}
+
+# 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:unfiltered_detections"
+  options: {
+    [mediapipe.TfLiteTensorsToDetectionsCalculatorOptions.ext] {
+      num_classes: 1
+      num_boxes: 896
+      num_coords: 12
+      box_coord_offset: 0
+      keypoint_coord_offset: 4
+      num_keypoints: 4
+      num_values_per_keypoint: 2
+      sigmoid_score: true
+      score_clipping_thresh: 100.0
+      reverse_output_order: true
+      x_scale: 128.0
+      y_scale: 128.0
+      h_scale: 128.0
+      w_scale: 128.0
+      min_score_thresh: 0.5
+    }
+  }
+}
+
+# Performs non-max suppression to remove excessive detections.
+node {
+  calculator: "NonMaxSuppressionCalculator"
+  input_stream: "unfiltered_detections"
+  output_stream: "filtered_detections"
+  options: {
+    [mediapipe.NonMaxSuppressionCalculatorOptions.ext] {
+      min_suppression_threshold: 0.3
+      overlap_type: INTERSECTION_OVER_UNION
+      algorithm: WEIGHTED
+    }
+  }
+}
+
+# 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:detections"
+}
@@ -0,0 +1,155 @@
+# MediaPipe graph to detect poses. (GPU input, and inference is executed on
+# GPU.)
+#
+# It is required that "pose_detection.tflite" is available at
+# "mediapipe/modules/pose_detection/pose_detection.tflite"
+# path during execution.
+#
+# EXAMPLE:
+#   node {
+#     calculator: "PoseDetectionGpu"
+#     input_stream: "IMAGE:image"
+#     output_stream: "DETECTIONS:pose_detections"
+#   }
+
+type: "PoseDetectionGpu"
+
+# GPU image. (GpuBuffer)
+input_stream: "IMAGE:image"
+
+# Detected poses. (std::vector<Detection>)
+# Bounding box in each pose detection is currently set to the bounding box of
+# the detected face. However, 4 additional key points are available in each
+# detection, which are used to further calculate a (rotated) bounding box that
+# encloses the body region of interest. Among the 4 key points, the first two
+# are for identifying the full-body region, and the second two for upper body
+# only:
+#
+# Key point 0 - mid hip center
+# Key point 1 - point that encodes size & rotation (for full body)
+# Key point 2 - mid shoulder center
+# Key point 3 - point that encodes size & rotation (for upper body)
+#
+# NOTE: there will not be an output packet in the DETECTIONS stream for this
+# particular timestamp if none of poses detected. However, the MediaPipe
+# framework will internally inform the downstream calculators of the absence of
+# this packet so that they don't wait for it unnecessarily.
+output_stream: "DETECTIONS:detections"
+
+# 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:image"
+  output_stream: "IMAGE_GPU:transformed_image"
+  output_stream: "LETTERBOX_PADDING:letterbox_padding"
+  options: {
+    [mediapipe.ImageTransformationCalculatorOptions.ext] {
+      output_width: 128
+      output_height: 128
+      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_image"
+  output_stream: "TENSORS_GPU:input_tensors"
+}
+
+# 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:input_tensors"
+  # TODO: we can use TENSORS_GPU here and in the downstream calculator
+  output_stream: "TENSORS:detection_tensors"
+  options: {
+    [mediapipe.TfLiteInferenceCalculatorOptions.ext] {
+      model_path: "mediapipe/modules/pose_detection/pose_detection.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"
+  options: {
+    [mediapipe.SsdAnchorsCalculatorOptions.ext] {
+      num_layers: 4
+      min_scale: 0.1484375
+      max_scale: 0.75
+      input_size_height: 128
+      input_size_width: 128
+      anchor_offset_x: 0.5
+      anchor_offset_y: 0.5
+      strides: 8
+      strides: 16
+      strides: 16
+      strides: 16
+      aspect_ratios: 1.0
+      fixed_anchor_size: true
+    }
+  }
+}
+
+# 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:unfiltered_detections"
+  options: {
+    [mediapipe.TfLiteTensorsToDetectionsCalculatorOptions.ext] {
+      num_classes: 1
+      num_boxes: 896
+      num_coords: 12
+      box_coord_offset: 0
+      keypoint_coord_offset: 4
+      num_keypoints: 4
+      num_values_per_keypoint: 2
+      sigmoid_score: true
+      score_clipping_thresh: 100.0
+      reverse_output_order: true
+      x_scale: 128.0
+      y_scale: 128.0
+      h_scale: 128.0
+      w_scale: 128.0
+      min_score_thresh: 0.5
+    }
+  }
+}
+
+# Performs non-max suppression to remove excessive detections.
+node {
+  calculator: "NonMaxSuppressionCalculator"
+  input_stream: "unfiltered_detections"
+  output_stream: "filtered_detections"
+  options: {
+    [mediapipe.NonMaxSuppressionCalculatorOptions.ext] {
+      min_suppression_threshold: 0.3
+      overlap_type: INTERSECTION_OVER_UNION
+      algorithm: WEIGHTED
+    }
+  }
+}
+
+# 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:detections"
+}
@@ -0,0 +1,124 @@
+# Copyright 2020 The MediaPipe Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# 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_simple_subgraph",
+)
+
+licenses(["notice"])  # Apache 2.0
+
+package(default_visibility = ["//visibility:public"])
+
+mediapipe_simple_subgraph(
+    name = "pose_landmark_upper_body_by_roi_gpu",
+    graph = "pose_landmark_upper_body_by_roi_gpu.pbtxt",
+    register_as = "PoseLandmarkUpperBodyByRoiGpu",
+    deps = [
+        "//mediapipe/calculators/core:gate_calculator",
+        "//mediapipe/calculators/core:split_vector_calculator",
+        "//mediapipe/calculators/image:image_cropping_calculator",
+        "//mediapipe/calculators/image:image_transformation_calculator",
+        "//mediapipe/calculators/tflite:tflite_converter_calculator",
+        "//mediapipe/calculators/tflite:tflite_inference_calculator",
+        "//mediapipe/calculators/tflite:tflite_tensors_to_floats_calculator",
+        "//mediapipe/calculators/tflite:tflite_tensors_to_landmarks_calculator",
+        "//mediapipe/calculators/util:landmark_letterbox_removal_calculator",
+        "//mediapipe/calculators/util:landmark_projection_calculator",
+        "//mediapipe/calculators/util:thresholding_calculator",
+    ],
+)
+
+mediapipe_simple_subgraph(
+    name = "pose_landmark_upper_body_by_roi_cpu",
+    graph = "pose_landmark_upper_body_by_roi_cpu.pbtxt",
+    register_as = "PoseLandmarkUpperBodyByRoiCpu",
+    deps = [
+        "//mediapipe/calculators/core:gate_calculator",
+        "//mediapipe/calculators/core:split_vector_calculator",
+        "//mediapipe/calculators/image:image_cropping_calculator",
+        "//mediapipe/calculators/image:image_transformation_calculator",
+        "//mediapipe/calculators/tflite:tflite_converter_calculator",
+        "//mediapipe/calculators/tflite:tflite_inference_calculator",
+        "//mediapipe/calculators/tflite:tflite_tensors_to_floats_calculator",
+        "//mediapipe/calculators/tflite:tflite_tensors_to_landmarks_calculator",
+        "//mediapipe/calculators/util:landmark_letterbox_removal_calculator",
+        "//mediapipe/calculators/util:landmark_projection_calculator",
+        "//mediapipe/calculators/util:thresholding_calculator",
+    ],
+)
+
+mediapipe_simple_subgraph(
+    name = "pose_landmark_upper_body_gpu",
+    graph = "pose_landmark_upper_body_gpu.pbtxt",
+    register_as = "PoseLandmarkUpperBodyGpu",
+    deps = [
+        ":pose_detection_to_roi",
+        ":pose_landmark_upper_body_by_roi_gpu",
+        ":pose_landmark_upper_body_landmarks_to_roi",
+        "//mediapipe/calculators/core:gate_calculator",
+        "//mediapipe/calculators/core:merge_calculator",
+        "//mediapipe/calculators/core:packet_presence_calculator",
+        "//mediapipe/calculators/core:previous_loopback_calculator",
+        "//mediapipe/calculators/core:split_vector_calculator",
+        "//mediapipe/calculators/image:image_properties_calculator",
+        "//mediapipe/modules/pose_detection:pose_detection_gpu",
+    ],
+)
+
+mediapipe_simple_subgraph(
+    name = "pose_landmark_upper_body_cpu",
+    graph = "pose_landmark_upper_body_cpu.pbtxt",
+    register_as = "PoseLandmarkUpperBodyCpu",
+    deps = [
+        ":pose_detection_to_roi",
+        ":pose_landmark_upper_body_by_roi_cpu",
+        ":pose_landmark_upper_body_landmarks_to_roi",
+        "//mediapipe/calculators/core:gate_calculator",
+        "//mediapipe/calculators/core:merge_calculator",
+        "//mediapipe/calculators/core:packet_presence_calculator",
+        "//mediapipe/calculators/core:previous_loopback_calculator",
+        "//mediapipe/calculators/core:split_vector_calculator",
+        "//mediapipe/calculators/image:image_properties_calculator",
+        "//mediapipe/modules/pose_detection:pose_detection_cpu",
+    ],
+)
+
+exports_files(
+    srcs = [
+        "pose_landmark_upper_body.tflite",
+    ],
+)
+
+mediapipe_simple_subgraph(
+    name = "pose_detection_to_roi",
+    graph = "pose_detection_to_roi.pbtxt",
+    register_as = "PoseDetectionToRoi",
+    deps = [
+        "//mediapipe/calculators/util:alignment_points_to_rects_calculator",
+        "//mediapipe/calculators/util:rect_transformation_calculator",
+    ],
+)
+
+mediapipe_simple_subgraph(
+    name = "pose_landmark_upper_body_landmarks_to_roi",
+    graph = "pose_landmark_upper_body_landmarks_to_roi.pbtxt",
+    register_as = "PoseLandmarkUpperBodyLandmarksToRoi",
+    deps = [
+        "//mediapipe/calculators/core:split_vector_calculator",
+        "//mediapipe/calculators/util:alignment_points_to_rects_calculator",
+        "//mediapipe/calculators/util:landmarks_to_detection_calculator",
+        "//mediapipe/calculators/util:rect_transformation_calculator",
+    ],
+)
@@ -0,0 +1,9 @@
+# pose_landmark
+
+Subgraphs|Details
+:--- | :---
+[`PoseLandmarkUpperBodyByRoiCpu`](https://github.com/google/mediapipe/tree/master/mediapipe/modules/pose_landmark/pose_landmark_upper_body_by_roi_cpu.pbtxt)| Detects landmarks of a single pose. See landmarks (key points) [scheme](https://github.com/google/mediapipe/tree/master/mediapipe/modules/pose_landmark/pose_landmark_upper_body_topology.svg). (CPU input, and inference is executed on CPU.)
+[`PoseLandmarkUpperBodyByRoiGpu`](https://github.com/google/mediapipe/tree/master/mediapipe/modules/pose_landmark/pose_landmark_upper_body_gpu.pbtxt)| Detects landmarks of a single pose. See landmarks (key points) [scheme](https://github.com/google/mediapipe/tree/master/mediapipe/modules/pose_landmark/pose_landmark_upper_body_topology.svg). (GPU input, and inference is executed on GPU)
+[`PoseLandmarkUpperBodyCpu`](https://github.com/google/mediapipe/tree/master/mediapipe/modules/pose_landmark/pose_landmark_upper_body_cpu.pbtxt)| Detects and tracks landmarks of a single pose. See landmarks (key points) [scheme](https://github.com/google/mediapipe/tree/master/mediapipe/modules/pose_landmark/pose_landmark_upper_body_topology.svg). (CPU input, and inference is executed on CPU)
+[`PoseLandmarkUpperBodyGpu`](https://github.com/google/mediapipe/tree/master/mediapipe/modules/pose_landmark/pose_landmark_upper_body_gpu.pbtxt)| Detects and tracks landmarks of a single pose. See landmarks (key points) [scheme](https://github.com/google/mediapipe/tree/master/mediapipe/modules/pose_landmark/pose_landmark_upper_body_topology.svg). (GPU input, and inference is executed on GPU.)
+
@@ -0,0 +1,46 @@
+# MediaPipe graph to calculate pose region of interest (ROI) from a detection
+# provided by "PoseDetectionCpu" or "PoseDetectionGpu"
+#
+# NOTE: this graph is subject to change and should not be used directly.
+
+type: "PoseDetectionToRoi"
+
+# Pose detection. (Detection)
+input_stream: "DETECTION:detection"
+# Frame size (width and height). (std::pair<int, int>)
+input_stream: "IMAGE_SIZE:image_size"
+# ROI according to the first detection of input detections. (NormalizedRect)
+output_stream: "ROI:roi"
+
+# Converts pose detection into a rectangle based on center and scale alignment
+# points. Pose detection contains four key points: first two for full-body pose
+# and two more for upper-body pose.
+node {
+  calculator: "AlignmentPointsRectsCalculator"
+  input_stream: "DETECTION:detection"
+  input_stream: "IMAGE_SIZE:image_size"
+  output_stream: "NORM_RECT:raw_roi"
+  options: {
+    [mediapipe.DetectionsToRectsCalculatorOptions.ext] {
+      rotation_vector_start_keypoint_index: 2
+      rotation_vector_end_keypoint_index: 3
+      rotation_vector_target_angle_degrees: 90
+      output_zero_rect_for_empty_detections: true
+    }
+  }
+}
+
+# Expands pose rect with marging used during training.
+node {
+  calculator: "RectTransformationCalculator"
+  input_stream: "NORM_RECT:raw_roi"
+  input_stream: "IMAGE_SIZE:image_size"
+  output_stream: "roi"
+  options: {
+    [mediapipe.RectTransformationCalculatorOptions.ext] {
+      scale_x: 1.5
+      scale_y: 1.5
+      square_long: true
+    }
+  }
+}
@@ -0,0 +1,192 @@
+# MediaPipe graph to detect/predict upper-body pose landmarks. (CPU input, and
+# inference is executed on CPU.)
+#
+# It is required that "pose_landmark_upper_body.tflite" is available at
+# "mediapipe/modules/pose_landmark/pose_landmark_upper_body.tflite"
+# path during execution.
+#
+# EXAMPLE:
+#   node {
+#     calculator: "PoseLandmarkUpperBodyByRoiCpu"
+#     input_stream: "IMAGE:image"
+#     input_stream: "ROI:roi"
+#     output_stream: "LANDMARKS:landmarks"
+#   }
+
+type: "PoseLandmarkUpperBodyByRoiCpu"
+
+# CPU image. (ImageFrame)
+input_stream: "IMAGE:image"
+# ROI (region of interest) within the given image where a pose is located.
+# (NormalizedRect)
+input_stream: "ROI:roi"
+
+# Pose landmarks within the given ROI. (NormalizedLandmarkList)
+# We have 25 (upper-body) landmarks
+# (see pose_landmark_upper_body_topology.svg), and there are other auxiliary key
+# points.
+# 0 - nose
+# 1 - right eye (inner)
+# 2 - right eye
+# 3 - right eye (outer)
+# 4 - left eye (inner)
+# 5 - left eye
+# 6 - left eye (outer)
+# 7 - right ear
+# 8 - left ear
+# 9 - mouth (right)
+# 10 - mouth (left)
+# 11 - right shoulder
+# 12 - left shoulder
+# 13 - right elbow
+# 14 - left elbow
+# 15 - right wrist
+# 16 - left wrist
+# 17 - right pinky
+# 18 - left pinky
+# 19 - right index
+# 20 - left index
+# 21 - right thumb
+# 22 - left thumb
+# 23 - right hip
+# 24 - left hip
+#
+# NOTE: if a pose is not present within the given ROI, for this particular
+# timestamp there will not be an output packet in the LANDMARKS stream. However,
+# the MediaPipe framework will internally inform the downstream calculators of
+# the absence of this packet so that they don't wait for it unnecessarily.
+output_stream: "LANDMARKS:landmarks"
+
+# Crops the rectangle that contains a pose from the input image.
+node {
+  calculator: "ImageCroppingCalculator"
+  input_stream: "IMAGE:image"
+  input_stream: "NORM_RECT:roi"
+  output_stream: "IMAGE:pose_region"
+  options: {
+    [mediapipe.ImageCroppingCalculatorOptions.ext] {
+      border_mode: BORDER_REPLICATE
+    }
+  }
+}
+
+# Transforms the input image on CPU to a 256x256 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:pose_region"
+  output_stream: "IMAGE:transformed_pose_region"
+  output_stream: "LETTERBOX_PADDING:letterbox_padding"
+  options: {
+    [mediapipe.ImageTransformationCalculatorOptions.ext] {
+      output_width: 256
+      output_height: 256
+      scale_mode: FIT
+    }
+  }
+}
+
+# Converts the transformed input image on CPU into a tensor.
+node {
+  calculator: "TfLiteConverterCalculator"
+  input_stream: "IMAGE:transformed_pose_region"
+  output_stream: "TENSORS:input_tensors"
+  options: {
+    [mediapipe.TfLiteConverterCalculatorOptions.ext] {
+      zero_center: false
+    }
+  }
+}
+
+# Runs a TensorFlow Lite model inference on CPU.
+node {
+  calculator: "TfLiteInferenceCalculator"
+  input_stream: "TENSORS:input_tensors"
+  output_stream: "TENSORS:output_tensors"
+  options: {
+    [mediapipe.TfLiteInferenceCalculatorOptions.ext] {
+      model_path: "mediapipe/modules/pose_landmark/pose_landmark_upper_body.tflite"
+      delegate { xnnpack {} }
+    }
+  }
+}
+
+# Splits a vector of TFLite tensors to multiple vectors according to the ranges
+# specified in option.
+node {
+  calculator: "SplitTfLiteTensorVectorCalculator"
+  input_stream: "output_tensors"
+  output_stream: "landmark_tensors"
+  output_stream: "pose_flag_tensor"
+  options: {
+    [mediapipe.SplitVectorCalculatorOptions.ext] {
+      ranges: { begin: 0 end: 1 }
+      ranges: { begin: 1 end: 2 }
+    }
+  }
+}
+
+# Converts the pose-flag tensor into a float that represents the confidence
+# score of pose presence.
+node {
+  calculator: "TfLiteTensorsToFloatsCalculator"
+  input_stream: "TENSORS:pose_flag_tensor"
+  output_stream: "FLOAT:pose_presence_score"
+}
+
+# Applies a threshold to the confidence score to determine whether a pose is
+# present.
+node {
+  calculator: "ThresholdingCalculator"
+  input_stream: "FLOAT:pose_presence_score"
+  output_stream: "FLAG:pose_presence"
+  options: {
+    [mediapipe.ThresholdingCalculatorOptions.ext] {
+      threshold: 0.5
+    }
+  }
+}
+
+# Drop landmarks tensors if pose is not present.
+node {
+  calculator: "GateCalculator"
+  input_stream: "landmark_tensors"
+  input_stream: "ALLOW:pose_presence"
+  output_stream: "ensured_landmark_tensors"
+}
+
+# Decodes the landmark tensors into a vector of lanmarks, where the landmark
+# coordinates are normalized by the size of the input image to the model.
+node {
+  calculator: "TfLiteTensorsToLandmarksCalculator"
+  input_stream: "TENSORS:ensured_landmark_tensors"
+  output_stream: "NORM_LANDMARKS:raw_landmarks"
+  options: {
+    [mediapipe.TfLiteTensorsToLandmarksCalculatorOptions.ext] {
+      num_landmarks: 31
+      input_image_width: 256
+      input_image_height: 256
+    }
+  }
+}
+
+# Adjusts landmarks (already normalized to [0.f, 1.f]) on the letterboxed pose
+# image (after image transformation with the FIT scale mode) to the
+# corresponding locations on the same image with the letterbox removed (pose
+# image before image transformation).
+node {
+  calculator: "LandmarkLetterboxRemovalCalculator"
+  input_stream: "LANDMARKS:raw_landmarks"
+  input_stream: "LETTERBOX_PADDING:letterbox_padding"
+  output_stream: "LANDMARKS:adjusted_landmarks"
+}
+
+# Projects the landmarks from the cropped pose image to the corresponding
+# locations on the full image before cropping (input to the graph).
+node {
+  calculator: "LandmarkProjectionCalculator"
+  input_stream: "NORM_LANDMARKS:adjusted_landmarks"
+  input_stream: "NORM_RECT:roi"
+  output_stream: "NORM_LANDMARKS:landmarks"
+}
@@ -0,0 +1,191 @@
+# MediaPipe graph to detect/predict upper-body pose landmarks. (GPU input, and
+# inference is executed on GPU.)
+#
+# It is required that "pose_landmark_upper_body.tflite" is available at
+# "mediapipe/modules/pose_landmark/pose_landmark_upper_body.tflite"
+# path during execution.
+#
+# EXAMPLE:
+#   node {
+#     calculator: "PoseLandmarkUpperBodyByRoiGpu"
+#     input_stream: "IMAGE:image"
+#     input_stream: "ROI:roi"
+#     output_stream: "LANDMARKS:landmarks"
+#   }
+
+type: "PoseLandmarkUpperBodyByRoiGpu"
+
+# GPU image. (GpuBuffer)
+input_stream: "IMAGE:image"
+# ROI (region of interest) within the given image where a pose is located.
+# (NormalizedRect)
+input_stream: "ROI:roi"
+
+# Pose landmarks within the given ROI. (NormalizedLandmarkList)
+# We have 25 (upper-body) landmarks
+# (see pose_landmark_upper_body_topology.svg), and there are other auxiliary key
+# points.
+# 0 - nose
+# 1 - right eye (inner)
+# 2 - right eye
+# 3 - right eye (outer)
+# 4 - left eye (inner)
+# 5 - left eye
+# 6 - left eye (outer)
+# 7 - right ear
+# 8 - left ear
+# 9 - mouth (right)
+# 10 - mouth (left)
+# 11 - right shoulder
+# 12 - left shoulder
+# 13 - right elbow
+# 14 - left elbow
+# 15 - right wrist
+# 16 - left wrist
+# 17 - right pinky
+# 18 - left pinky
+# 19 - right index
+# 20 - left index
+# 21 - right thumb
+# 22 - left thumb
+# 23 - right hip
+# 24 - left hip
+#
+# NOTE: if a pose is not present within the given ROI, for this particular
+# timestamp there will not be an output packet in the LANDMARKS stream. However,
+# the MediaPipe framework will internally inform the downstream calculators of
+# the absence of this packet so that they don't wait for it unnecessarily.
+output_stream: "LANDMARKS:landmarks"
+
+# Crops the rectangle that contains a pose from the input image.
+node {
+  calculator: "ImageCroppingCalculator"
+  input_stream: "IMAGE_GPU:image"
+  input_stream: "NORM_RECT:roi"
+  output_stream: "IMAGE_GPU:pose_region"
+  options: {
+    [mediapipe.ImageCroppingCalculatorOptions.ext] {
+      border_mode: BORDER_REPLICATE
+    }
+  }
+}
+
+# Transforms the input image on GPU to a 256x256 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:pose_region"
+  output_stream: "IMAGE_GPU:transformed_pose_region"
+  output_stream: "LETTERBOX_PADDING:letterbox_padding"
+  options: {
+    [mediapipe.ImageTransformationCalculatorOptions.ext] {
+      output_width: 256
+      output_height: 256
+      scale_mode: FIT
+    }
+  }
+}
+
+# Converts the transformed input image on GPU into a tensor.
+node {
+  calculator: "TfLiteConverterCalculator"
+  input_stream: "IMAGE_GPU:transformed_pose_region"
+  output_stream: "TENSORS_GPU:input_tensors"
+  options: {
+    [mediapipe.TfLiteConverterCalculatorOptions.ext] {
+      zero_center: false
+    }
+  }
+}
+
+# Runs a TensorFlow Lite model inference on GPU.
+node {
+  calculator: "TfLiteInferenceCalculator"
+  input_stream: "TENSORS_GPU:input_tensors"
+  output_stream: "TENSORS:output_tensors"
+  options: {
+    [mediapipe.TfLiteInferenceCalculatorOptions.ext] {
+      model_path: "mediapipe/modules/pose_landmark/pose_landmark_upper_body.tflite"
+    }
+  }
+}
+
+# Splits a vector of TFLite tensors to multiple vectors according to the ranges
+# specified in option.
+node {
+  calculator: "SplitTfLiteTensorVectorCalculator"
+  input_stream: "output_tensors"
+  output_stream: "landmark_tensors"
+  output_stream: "pose_flag_tensor"
+  options: {
+    [mediapipe.SplitVectorCalculatorOptions.ext] {
+      ranges: { begin: 0 end: 1 }
+      ranges: { begin: 1 end: 2 }
+    }
+  }
+}
+
+# Converts the pose-flag tensor into a float that represents the confidence
+# score of pose presence.
+node {
+  calculator: "TfLiteTensorsToFloatsCalculator"
+  input_stream: "TENSORS:pose_flag_tensor"
+  output_stream: "FLOAT:pose_presence_score"
+}
+
+# Applies a threshold to the confidence score to determine whether a pose is
+# present.
+node {
+  calculator: "ThresholdingCalculator"
+  input_stream: "FLOAT:pose_presence_score"
+  output_stream: "FLAG:pose_presence"
+  options: {
+    [mediapipe.ThresholdingCalculatorOptions.ext] {
+      threshold: 0.5
+    }
+  }
+}
+
+# Drop landmarks tensors if pose is not present.
+node {
+  calculator: "GateCalculator"
+  input_stream: "landmark_tensors"
+  input_stream: "ALLOW:pose_presence"
+  output_stream: "ensured_landmark_tensors"
+}
+
+# Decodes the landmark tensors into a vector of lanmarks, where the landmark
+# coordinates are normalized by the size of the input image to the model.
+node {
+  calculator: "TfLiteTensorsToLandmarksCalculator"
+  input_stream: "TENSORS:ensured_landmark_tensors"
+  output_stream: "NORM_LANDMARKS:raw_landmarks"
+  options: {
+    [mediapipe.TfLiteTensorsToLandmarksCalculatorOptions.ext] {
+      num_landmarks: 31
+      input_image_width: 256
+      input_image_height: 256
+    }
+  }
+}
+
+# Adjusts landmarks (already normalized to [0.f, 1.f]) on the letterboxed pose
+# image (after image transformation with the FIT scale mode) to the
+# corresponding locations on the same image with the letterbox removed (pose
+# image before image transformation).
+node {
+  calculator: "LandmarkLetterboxRemovalCalculator"
+  input_stream: "LANDMARKS:raw_landmarks"
+  input_stream: "LETTERBOX_PADDING:letterbox_padding"
+  output_stream: "LANDMARKS:adjusted_landmarks"
+}
+
+# Projects the landmarks from the cropped pose image to the corresponding
+# locations on the full image before cropping (input to the graph).
+node {
+  calculator: "LandmarkProjectionCalculator"
+  input_stream: "NORM_LANDMARKS:adjusted_landmarks"
+  input_stream: "NORM_RECT:roi"
+  output_stream: "NORM_LANDMARKS:landmarks"
+}
@@ -0,0 +1,170 @@
+# MediaPipe graph to detect/predict pose landmarks. (CPU input, and inference is
+# executed on CPU.) This graph tries to skip pose detection as much as possible
+# by using previously detected/predicted landmarks for new images.
+#
+# It is required that "pose_detection.tflite" is available at
+# "mediapipe/modules/pose_detection/pose_detection.tflite"
+# path during execution.
+#
+# It is required that "pose_landmark_upper_body.tflite" is available at
+# "mediapipe/modules/pose_landmark/pose_landmark_upper_body.tflite"
+# path during execution.
+#
+# EXAMPLE:
+#   node {
+#     calculator: "PoseLandmarkUpperBodyCpu"
+#     input_stream: "IMAGE:image"
+#     output_stream: "LANDMARKS:pose_landmarks"
+#   }
+
+type: "PoseLandmarkUpperBodyCpu"
+
+# CPU image. (ImageFrame)
+input_stream: "IMAGE:image"
+
+# Pose landmarks within the given ROI. (NormalizedLandmarkList)
+# We have 25 (upper-body) landmarks
+# (see pose_landmark_upper_body_topology.svg), and there are other auxiliary key
+# points.
+# 0 - nose
+# 1 - right eye (inner)
+# 2 - right eye
+# 3 - right eye (outer)
+# 4 - left eye (inner)
+# 5 - left eye
+# 6 - left eye (outer)
+# 7 - right ear
+# 8 - left ear
+# 9 - mouth (right)
+# 10 - mouth (left)
+# 11 - right shoulder
+# 12 - left shoulder
+# 13 - right elbow
+# 14 - left elbow
+# 15 - right wrist
+# 16 - left wrist
+# 17 - right pinky
+# 18 - left pinky
+# 19 - right index
+# 20 - left index
+# 21 - right thumb
+# 22 - left thumb
+# 23 - right hip
+# 24 - left hip
+#
+# NOTE: if a pose is not present within the given ROI, for this particular
+# timestamp there will not be an output packet in the LANDMARKS stream. However,
+# the MediaPipe framework will internally inform the downstream calculators of
+# the absence of this packet so that they don't wait for it unnecessarily.
+output_stream: "LANDMARKS:pose_landmarks"
+
+# Extra outputs (for debugging, for instance).
+# Detected poses. (Detection)
+output_stream: "DETECTION:pose_detection"
+# Regions of interest calculated based on landmarks. (NormalizedRect)
+output_stream: "ROI_FROM_LANDMARKS:pose_rect_from_landmarks"
+# Regions of interest calculated based on pose detections. (NormalizedRect)
+output_stream: "ROI_FROM_DETECTION:pose_rect_from_detection"
+
+# Caches pose rects calculated from landmarks, and upon the arrival of the next
+# input image, sends out the cached rects with timestamps replaced by that of
+# the input image, essentially generating a packet that carries the previous
+# pose rects. Note that upon the arrival of the very first input image, a
+# timestamp bound update occurs to jump start the feedback loop.
+node {
+  calculator: "PreviousLoopbackCalculator"
+  input_stream: "MAIN:image"
+  input_stream: "LOOP:pose_rect_from_landmarks"
+  input_stream_info: {
+    tag_index: "LOOP"
+    back_edge: true
+  }
+  output_stream: "PREV_LOOP:prev_pose_rect_from_landmarks"
+}
+
+# Checks if there's previous pose rect calculatoed from landmarks.
+node: {
+  calculator: "PacketPresenceCalculator"
+  input_stream: "PACKET:prev_pose_rect_from_landmarks"
+  output_stream: "PRESENCE:prev_pose_rect_from_landmarks_is_present"
+}
+
+# Calculates size of the image.
+node {
+  calculator: "ImagePropertiesCalculator"
+  input_stream: "IMAGE:image"
+  output_stream: "SIZE:image_size"
+}
+
+# Drops the incoming image if PoseLandmarkUpperBodyByRoiCpu was able to identify
+# pose presence in the previous image. Otherwise, passes the incoming image
+# through to trigger a new round of pose detection in PoseDetectionCpu.
+node {
+  calculator: "GateCalculator"
+  input_stream: "image"
+  input_stream: "image_size"
+  input_stream: "DISALLOW:prev_pose_rect_from_landmarks_is_present"
+  output_stream: "image_for_pose_detection"
+  output_stream: "image_size_for_pose_detection"
+  options: {
+    [mediapipe.GateCalculatorOptions.ext] {
+      empty_packets_as_allow: true
+    }
+  }
+}
+
+# Detects poses.
+node {
+  calculator: "PoseDetectionCpu"
+  input_stream: "IMAGE:image_for_pose_detection"
+  output_stream: "DETECTIONS:pose_detections"
+}
+
+# Gets the very first detection from "pose_detections" vector.
+node {
+  calculator: "SplitDetectionVectorCalculator"
+  input_stream: "pose_detections"
+  output_stream: "pose_detection"
+  options: {
+    [mediapipe.SplitVectorCalculatorOptions.ext] {
+      ranges: { begin: 0 end: 1 }
+      element_only: true
+    }
+  }
+}
+
+# Calculates region of interest based on pose detection, so that can be used
+# to detect landmarks.
+node {
+  calculator: "PoseDetectionToRoi"
+  input_stream: "DETECTION:pose_detection"
+  input_stream: "IMAGE_SIZE:image_size_for_pose_detection"
+  output_stream: "ROI:pose_rect_from_detection"
+}
+
+# Selects either pose rect (or ROI) calculated from detection or from previously
+# detected landmarks if available (in this case, calculation of pose rect from
+# detection is skipped).
+node {
+  calculator: "MergeCalculator"
+  input_stream: "pose_rect_from_detection"
+  input_stream: "prev_pose_rect_from_landmarks"
+  output_stream: "pose_rect"
+}
+
+# Detects pose landmarks within specified region of interest of the image.
+node {
+  calculator: "PoseLandmarkUpperBodyByRoiCpu"
+  input_stream: "IMAGE:image"
+  input_stream: "ROI:pose_rect"
+  output_stream: "LANDMARKS:pose_landmarks"
+}
+
+# Calculates region of interest based on pose landmarks, so that can be reused
+# for subsequent image.
+node {
+  calculator: "PoseLandmarkUpperBodyLandmarksToRoi"
+  input_stream: "LANDMARKS:pose_landmarks"
+  input_stream: "IMAGE_SIZE:image_size"
+  output_stream: "ROI:pose_rect_from_landmarks"
+}
@@ -0,0 +1,170 @@
+# MediaPipe graph to detect/predict pose landmarks. (GPU input, and inference is
+# executed on GPU.) This graph tries to skip pose detection as much as possible
+# by using previously detected/predicted landmarks for new images.
+#
+# It is required that "pose_detection.tflite" is available at
+# "mediapipe/modules/pose_detection/pose_detection.tflite"
+# path during execution.
+#
+# It is required that "pose_landmark_upper_body.tflite" is available at
+# "mediapipe/modules/pose_landmark/pose_landmark_upper_body.tflite"
+# path during execution.
+#
+# EXAMPLE:
+#   node {
+#     calculator: "PoseLandmarkUpperBodyGpu"
+#     input_stream: "IMAGE:image"
+#     output_stream: "LANDMARKS:pose_landmarks"
+#   }
+
+type: "PoseLandmarkUpperBodyGpu"
+
+# GPU image. (GpuBuffer)
+input_stream: "IMAGE:image"
+
+# Pose landmarks within the given ROI. (NormalizedLandmarkList)
+# We have 25 (upper-body) landmarks
+# (see pose_landmark_upper_body_topology.svg), and there are other auxiliary key
+# points.
+# 0 - nose
+# 1 - right eye (inner)
+# 2 - right eye
+# 3 - right eye (outer)
+# 4 - left eye (inner)
+# 5 - left eye
+# 6 - left eye (outer)
+# 7 - right ear
+# 8 - left ear
+# 9 - mouth (right)
+# 10 - mouth (left)
+# 11 - right shoulder
+# 12 - left shoulder
+# 13 - right elbow
+# 14 - left elbow
+# 15 - right wrist
+# 16 - left wrist
+# 17 - right pinky
+# 18 - left pinky
+# 19 - right index
+# 20 - left index
+# 21 - right thumb
+# 22 - left thumb
+# 23 - right hip
+# 24 - left hip
+#
+# NOTE: if a pose is not present within the given ROI, for this particular
+# timestamp there will not be an output packet in the LANDMARKS stream. However,
+# the MediaPipe framework will internally inform the downstream calculators of
+# the absence of this packet so that they don't wait for it unnecessarily.
+output_stream: "LANDMARKS:pose_landmarks"
+
+# Extra outputs (for debugging, for instance).
+# Detected poses. (Detection)
+output_stream: "DETECTION:pose_detection"
+# Regions of interest calculated based on landmarks. (NormalizedRect)
+output_stream: "ROI_FROM_LANDMARKS:pose_rect_from_landmarks"
+# Regions of interest calculated based on pose detections. (NormalizedRect)
+output_stream: "ROI_FROM_DETECTION:pose_rect_from_detection"
+
+# Caches pose rects calculated from landmarks, and upon the arrival of the next
+# input image, sends out the cached rects with timestamps replaced by that of
+# the input image, essentially generating a packet that carries the previous
+# pose rects. Note that upon the arrival of the very first input image, a
+# timestamp bound update occurs to jump start the feedback loop.
+node {
+  calculator: "PreviousLoopbackCalculator"
+  input_stream: "MAIN:image"
+  input_stream: "LOOP:pose_rect_from_landmarks"
+  input_stream_info: {
+    tag_index: "LOOP"
+    back_edge: true
+  }
+  output_stream: "PREV_LOOP:prev_pose_rect_from_landmarks"
+}
+
+# Checks if there's previous pose rect calculatoed from landmarks.
+node: {
+  calculator: "PacketPresenceCalculator"
+  input_stream: "PACKET:prev_pose_rect_from_landmarks"
+  output_stream: "PRESENCE:prev_pose_rect_from_landmarks_is_present"
+}
+
+# Calculates size of the image.
+node {
+  calculator: "ImagePropertiesCalculator"
+  input_stream: "IMAGE_GPU:image"
+  output_stream: "SIZE:image_size"
+}
+
+# Drops the incoming image if PoseLandmarkUpperBodyByRoiGpu was able to identify
+# pose presence in the previous image. Otherwise, passes the incoming image
+# through to trigger a new round of pose detection in PoseDetectionGpu.
+node {
+  calculator: "GateCalculator"
+  input_stream: "image"
+  input_stream: "image_size"
+  input_stream: "DISALLOW:prev_pose_rect_from_landmarks_is_present"
+  output_stream: "image_for_pose_detection"
+  output_stream: "image_size_for_pose_detection"
+  options: {
+    [mediapipe.GateCalculatorOptions.ext] {
+      empty_packets_as_allow: true
+    }
+  }
+}
+
+# Detects poses.
+node {
+  calculator: "PoseDetectionGpu"
+  input_stream: "IMAGE:image_for_pose_detection"
+  output_stream: "DETECTIONS:pose_detections"
+}
+
+# Gets the very first detection from "pose_detections" vector.
+node {
+  calculator: "SplitDetectionVectorCalculator"
+  input_stream: "pose_detections"
+  output_stream: "pose_detection"
+  options: {
+    [mediapipe.SplitVectorCalculatorOptions.ext] {
+      ranges: { begin: 0 end: 1 }
+      element_only: true
+    }
+  }
+}
+
+# Calculates region of interest based on pose detection, so that can be used
+# to detect landmarks.
+node {
+  calculator: "PoseDetectionToRoi"
+  input_stream: "DETECTION:pose_detection"
+  input_stream: "IMAGE_SIZE:image_size_for_pose_detection"
+  output_stream: "ROI:pose_rect_from_detection"
+}
+
+# Selects either pose rect (or ROI) calculated from detection or from previously
+# detected landmarks if available (in this case, calculation of pose rect from
+# detection is skipped).
+node {
+  calculator: "MergeCalculator"
+  input_stream: "pose_rect_from_detection"
+  input_stream: "prev_pose_rect_from_landmarks"
+  output_stream: "pose_rect"
+}
+
+# Detects pose landmarks within specified region of interest of the image.
+node {
+  calculator: "PoseLandmarkUpperBodyByRoiGpu"
+  input_stream: "IMAGE:image"
+  input_stream: "ROI:pose_rect"
+  output_stream: "LANDMARKS:pose_landmarks"
+}
+
+# Calculates region of interest based on pose landmarks, so that can be reused
+# for subsequent image.
+node {
+  calculator: "PoseLandmarkUpperBodyLandmarksToRoi"
+  input_stream: "LANDMARKS:pose_landmarks"
+  input_stream: "IMAGE_SIZE:image_size"
+  output_stream: "ROI:pose_rect_from_landmarks"
+}
@@ -0,0 +1,64 @@
+# MediaPipe graph to calculate pose region of interest (ROI) from landmarks
+# detected by "PoseLandmarkUpperBodyByRoiCpu" or
+# "PoseLandmarkUpperBodyByRoiGpu".
+#
+# NOTE: this graph is subject to change and should not be used directly.
+
+type: "PoseLandmarkUpperBodyLandmarksToRoi"
+
+# Normalized landmarks. (NormalizedLandmarkList)
+input_stream: "LANDMARKS:landmarks"
+# Frame size (width & height). (std::pair<int, int>)
+input_stream: "IMAGE_SIZE:image_size"
+# ROI according to landmarks. (NormalizedRect)
+output_stream: "ROI:roi"
+
+node {
+  calculator: "SplitNormalizedLandmarkListCalculator"
+  input_stream: "landmarks"
+  output_stream: "alignment_landmarks"
+  options: {
+    [mediapipe.SplitVectorCalculatorOptions.ext] {
+      ranges: { begin: 25 end: 27 }
+    }
+  }
+}
+
+# Converts landmarks to a detection that tightly encloses all landmarks.
+node {
+  calculator: "LandmarksToDetectionCalculator"
+  input_stream: "NORM_LANDMARKS:alignment_landmarks"
+  output_stream: "DETECTION:detection"
+}
+
+# Converts detection into a rectangle based on center and scale alignment
+# points.
+node {
+  calculator: "AlignmentPointsRectsCalculator"
+  input_stream: "DETECTION:detection"
+  input_stream: "IMAGE_SIZE:image_size"
+  output_stream: "NORM_RECT:raw_roi"
+  options: {
+    [mediapipe.DetectionsToRectsCalculatorOptions.ext] {
+      rotation_vector_start_keypoint_index: 0
+      rotation_vector_end_keypoint_index: 1
+      rotation_vector_target_angle_degrees: 90
+      output_zero_rect_for_empty_detections: true
+    }
+  }
+}
+
+# Expands pose rect with marging used during training.
+node {
+  calculator: "RectTransformationCalculator"
+  input_stream: "NORM_RECT:raw_roi"
+  input_stream: "IMAGE_SIZE:image_size"
+  output_stream: "roi"
+  options: {
+    [mediapipe.RectTransformationCalculatorOptions.ext] {
+      scale_x: 1.5
+      scale_y: 1.5
+      square_long: true
+    }
+  }
+}
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