Bump word-wrap from 1.2.3 to 1.2.4

Bumps [word-wrap](https://github.com/jonschlinkert/word-wrap) from 1.2.3 to 1.2.4.
- [Release notes](https://github.com/jonschlinkert/word-wrap/releases)
- [Commits](https://github.com/jonschlinkert/word-wrap/compare/1.2.3...1.2.4)

---
updated-dependencies:
- dependency-name: word-wrap
  dependency-type: indirect
...

Signed-off-by: dependabot[bot] <[email protected]>

WORKSPACE

@@ -157,22 +157,22 @@ http_archive(
 # 2020-08-21
 http_archive(
     name = "com_github_glog_glog",
-    strip_prefix = "glog-0a2e5931bd5ff22fd3bf8999eb8ce776f159cda6",
-    sha256 = "58c9b3b6aaa4dd8b836c0fd8f65d0f941441fb95e27212c5eeb9979cfd3592ab",
+    strip_prefix = "glog-0.6.0",
+    sha256 = "8a83bf982f37bb70825df71a9709fa90ea9f4447fb3c099e1d720a439d88bad6",
     urls = [
-        "https://github.com/google/glog/archive/0a2e5931bd5ff22fd3bf8999eb8ce776f159cda6.zip",
+        "https://github.com/google/glog/archive/v0.6.0.tar.gz",
     ],
 )
 http_archive(
     name = "com_github_glog_glog_no_gflags",
-    strip_prefix = "glog-0a2e5931bd5ff22fd3bf8999eb8ce776f159cda6",
-    sha256 = "58c9b3b6aaa4dd8b836c0fd8f65d0f941441fb95e27212c5eeb9979cfd3592ab",
+    strip_prefix = "glog-0.6.0",
+    sha256 = "8a83bf982f37bb70825df71a9709fa90ea9f4447fb3c099e1d720a439d88bad6",
     build_file = "@//third_party:glog_no_gflags.BUILD",
     urls = [
-        "https://github.com/google/glog/archive/0a2e5931bd5ff22fd3bf8999eb8ce776f159cda6.zip",
+        "https://github.com/google/glog/archive/v0.6.0.tar.gz",
     ],
     patches = [
-        "@//third_party:com_github_glog_glog_9779e5ea6ef59562b030248947f787d1256132ae.diff",
+        "@//third_party:com_github_glog_glog.diff",
     ],
     patch_args = [
         "-p1",

mediapipe/BUILD

@@ -68,30 +68,108 @@ config_setting(
     visibility = ["//visibility:public"],
 )
 
-# Note: this cannot just match "apple_platform_type": "macos" because that option
-# defaults to "macos" even when building on Linux!
-alias(
+# Generic MacOS.
+config_setting(
     name = "macos",
-    actual = select({
-        ":macos_i386": ":macos_i386",
-        ":macos_x86_64": ":macos_x86_64",
-        ":macos_arm64": ":macos_arm64",
-        "//conditions:default": ":macos_i386",  # Arbitrarily chosen from above.
-    }),
+    constraint_values = [
+        "@platforms//os:macos",
+    ],
     visibility = ["//visibility:public"],
 )
 
-# Note: this also matches on crosstool_top so that it does not produce ambiguous
-# selectors when used together with "android".
+# MacOS x86 64-bit.
+config_setting(
+    name = "macos_x86_64",
+    constraint_values = [
+        "@platforms//os:macos",
+        "@platforms//cpu:x86_64",
+    ],
+    visibility = ["//visibility:public"],
+)
+
+# MacOS ARM64.
+config_setting(
+    name = "macos_arm64",
+    constraint_values = [
+        "@platforms//os:macos",
+        "@platforms//cpu:arm64",
+    ],
+    visibility = ["//visibility:public"],
+)
+
+# Generic iOS.
 config_setting(
     name = "ios",
-    values = {
-        "crosstool_top": "@bazel_tools//tools/cpp:toolchain",
-        "apple_platform_type": "ios",
-    },
+    constraint_values = [
+        "@platforms//os:ios",
+    ],
     visibility = ["//visibility:public"],
 )
 
+# iOS device ARM32.
+config_setting(
+    name = "ios_armv7",
+    constraint_values = [
+        "@platforms//os:ios",
+        "@platforms//cpu:arm",
+    ],
+    visibility = ["//visibility:public"],
+)
+
+# iOS device ARM64.
+config_setting(
+    name = "ios_arm64",
+    constraint_values = [
+        "@platforms//os:ios",
+        "@platforms//cpu:arm64",
+    ],
+    visibility = ["//visibility:public"],
+)
+
+# iOS device ARM64E.
+config_setting(
+    name = "ios_arm64e",
+    constraint_values = [
+        "@platforms//os:ios",
+        "@platforms//cpu:arm64e",
+    ],
+    visibility = ["//visibility:public"],
+)
+
+# iOS simulator x86 32-bit.
+config_setting(
+    name = "ios_i386",
+    constraint_values = [
+        "@platforms//os:ios",
+        "@platforms//cpu:x86_32",
+        "@build_bazel_apple_support//constraints:simulator",
+    ],
+    visibility = ["//visibility:public"],
+)
+
+# iOS simulator x86 64-bit.
+config_setting(
+    name = "ios_x86_64",
+    constraint_values = [
+        "@platforms//os:ios",
+        "@platforms//cpu:x86_64",
+        "@build_bazel_apple_support//constraints:simulator",
+    ],
+    visibility = ["//visibility:public"],
+)
+
+# iOS simulator ARM64.
+config_setting(
+    name = "ios_sim_arm64",
+    constraint_values = [
+        "@platforms//os:ios",
+        "@platforms//cpu:arm64",
+        "@build_bazel_apple_support//constraints:simulator",
+    ],
+    visibility = ["//visibility:public"],
+)
+
+# Generic Apple.
 alias(
     name = "apple",
     actual = select({
@@ -102,49 +180,6 @@ alias(
     visibility = ["//visibility:public"],
 )
 
-config_setting(
-    name = "macos_i386",
-    values = {
-        "apple_platform_type": "macos",
-        "cpu": "darwin",
-    },
-    visibility = ["//visibility:public"],
-)
-
-config_setting(
-    name = "macos_x86_64",
-    values = {
-        "apple_platform_type": "macos",
-        "cpu": "darwin_x86_64",
-    },
-    visibility = ["//visibility:public"],
-)
-
-config_setting(
-    name = "macos_arm64",
-    values = {
-        "apple_platform_type": "macos",
-        "cpu": "darwin_arm64",
-    },
-    visibility = ["//visibility:public"],
-)
-
-[
-    config_setting(
-        name = arch,
-        values = {"cpu": arch},
-        visibility = ["//visibility:public"],
-    )
-    for arch in [
-        "ios_i386",
-        "ios_x86_64",
-        "ios_armv7",
-        "ios_arm64",
-        "ios_arm64e",
-        "ios_sim_arm64",
-    ]
-]
-
 config_setting(
     name = "windows",
     values = {"cpu": "x64_windows"},

mediapipe/calculators/core/BUILD

@@ -381,17 +381,6 @@ cc_library(
     alwayslink = 1,
 )
 
-cc_library(
-    name = "clip_detection_vector_size_calculator",
-    srcs = ["clip_detection_vector_size_calculator.cc"],
-    deps = [
-        ":clip_vector_size_calculator",
-        "//mediapipe/framework:calculator_framework",
-        "//mediapipe/framework/formats:detection_cc_proto",
-    ],
-    alwayslink = 1,
-)
-
 cc_test(
     name = "clip_vector_size_calculator_test",
     srcs = ["clip_vector_size_calculator_test.cc"],

mediapipe/calculators/core/clip_detection_vector_size_calculator.cc

@@ -1,26 +0,0 @@
-// 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.
-
-#include <vector>
-
-#include "mediapipe/calculators/core/clip_vector_size_calculator.h"
-#include "mediapipe/framework/formats/detection.pb.h"
-
-namespace mediapipe {
-
-typedef ClipVectorSizeCalculator<::mediapipe::Detection>
-    ClipDetectionVectorSizeCalculator;
-REGISTER_CALCULATOR(ClipDetectionVectorSizeCalculator);
-
-}  // namespace mediapipe

mediapipe/calculators/image/bilateral_filter_calculator.cc

@@ -112,7 +112,7 @@ class BilateralFilterCalculator : public CalculatorBase {
 REGISTER_CALCULATOR(BilateralFilterCalculator);
 
 absl::Status BilateralFilterCalculator::GetContract(CalculatorContract* cc) {
-  CHECK_GE(cc->Inputs().NumEntries(), 1);
+  RET_CHECK_GE(cc->Inputs().NumEntries(), 1);
 
   if (cc->Inputs().HasTag(kInputFrameTag) &&
       cc->Inputs().HasTag(kInputFrameTagGpu)) {

mediapipe/calculators/image/segmentation_smoothing_calculator.cc

@@ -110,7 +110,7 @@ REGISTER_CALCULATOR(SegmentationSmoothingCalculator);
 
 absl::Status SegmentationSmoothingCalculator::GetContract(
     CalculatorContract* cc) {
-  CHECK_GE(cc->Inputs().NumEntries(), 1);
+  RET_CHECK_GE(cc->Inputs().NumEntries(), 1);
 
   cc->Inputs().Tag(kCurrentMaskTag).Set<Image>();
   cc->Inputs().Tag(kPreviousMaskTag).Set<Image>();

mediapipe/calculators/image/set_alpha_calculator.cc

@@ -142,7 +142,7 @@ class SetAlphaCalculator : public CalculatorBase {
 REGISTER_CALCULATOR(SetAlphaCalculator);
 
 absl::Status SetAlphaCalculator::GetContract(CalculatorContract* cc) {
-  CHECK_GE(cc->Inputs().NumEntries(), 1);
+  RET_CHECK_GE(cc->Inputs().NumEntries(), 1);
 
   bool use_gpu = false;
 

mediapipe/calculators/tensor/inference_calculator_gl_advanced.cc

@@ -69,6 +69,7 @@ class InferenceCalculatorGlAdvancedImpl
             gpu_delegate_options);
     absl::Status ReadGpuCaches(tflite::gpu::TFLiteGPURunner* gpu_runner) const;
     absl::Status SaveGpuCaches(tflite::gpu::TFLiteGPURunner* gpu_runner) const;
+    bool UseSerializedModel() const { return use_serialized_model_; }
 
    private:
     bool use_kernel_caching_ = false;
@@ -150,8 +151,6 @@ InferenceCalculatorGlAdvancedImpl::GpuInferenceRunner::Process(
 }
 
 absl::Status InferenceCalculatorGlAdvancedImpl::GpuInferenceRunner::Close() {
-  MP_RETURN_IF_ERROR(
-      on_disk_cache_helper_.SaveGpuCaches(tflite_gpu_runner_.get()));
   return gpu_helper_.RunInGlContext([this]() -> absl::Status {
     tflite_gpu_runner_.reset();
     return absl::OkStatus();
@@ -226,9 +225,14 @@ InferenceCalculatorGlAdvancedImpl::GpuInferenceRunner::InitTFLiteGPURunner(
                          tflite_gpu_runner_->GetOutputShapes()[i].c};
   }
 
+  if (on_disk_cache_helper_.UseSerializedModel()) {
+    tflite_gpu_runner_->ForceOpenCLInitFromSerializedModel();
+  }
+
   MP_RETURN_IF_ERROR(
       on_disk_cache_helper_.ReadGpuCaches(tflite_gpu_runner_.get()));
-  return tflite_gpu_runner_->Build();
+  MP_RETURN_IF_ERROR(tflite_gpu_runner_->Build());
+  return on_disk_cache_helper_.SaveGpuCaches(tflite_gpu_runner_.get());
 }
 
 #if defined(MEDIAPIPE_ANDROID) || defined(MEDIAPIPE_CHROMIUMOS)

mediapipe/calculators/tensorflow/BUILD

@@ -927,7 +927,6 @@ cc_test(
         "//mediapipe/framework:timestamp",
         "//mediapipe/framework/formats:detection_cc_proto",
         "//mediapipe/framework/formats:image_frame",
-        "//mediapipe/framework/formats:image_frame_opencv",
         "//mediapipe/framework/formats:location",
         "//mediapipe/framework/formats:location_opencv",
         "//mediapipe/framework/port:gtest_main",

mediapipe/calculators/tensorflow/pack_media_sequence_calculator.cc

@@ -164,8 +164,8 @@ class PackMediaSequenceCalculator : public CalculatorBase {
       }
     }
 
-    CHECK(cc->Outputs().HasTag(kSequenceExampleTag) ||
-          cc->OutputSidePackets().HasTag(kSequenceExampleTag))
+    RET_CHECK(cc->Outputs().HasTag(kSequenceExampleTag) ||
+              cc->OutputSidePackets().HasTag(kSequenceExampleTag))
         << "Neither the output stream nor the output side packet is set to "
            "output the sequence example.";
     if (cc->Outputs().HasTag(kSequenceExampleTag)) {

mediapipe/calculators/tensorflow/pack_media_sequence_calculator_test.cc

@@ -23,7 +23,6 @@
 #include "mediapipe/framework/calculator_runner.h"
 #include "mediapipe/framework/formats/detection.pb.h"
 #include "mediapipe/framework/formats/image_frame.h"
-#include "mediapipe/framework/formats/image_frame_opencv.h"
 #include "mediapipe/framework/formats/location.h"
 #include "mediapipe/framework/formats/location_opencv.h"
 #include "mediapipe/framework/port/gmock.h"
@@ -96,7 +95,8 @@ TEST_F(PackMediaSequenceCalculatorTest, PacksTwoImages) {
   mpms::SetClipMediaId(test_video_id, input_sequence.get());
   cv::Mat image(2, 3, CV_8UC3, cv::Scalar(0, 0, 255));
   std::vector<uchar> bytes;
-  ASSERT_TRUE(cv::imencode(".jpg", image, bytes, {80}));
+  ASSERT_TRUE(
+      cv::imencode(".jpg", image, bytes, {cv::IMWRITE_HDR_COMPRESSION, 1}));
   OpenCvImageEncoderCalculatorResults encoded_image;
   encoded_image.set_encoded_image(bytes.data(), bytes.size());
   encoded_image.set_width(2);
@@ -139,7 +139,8 @@ TEST_F(PackMediaSequenceCalculatorTest, PacksTwoPrefixedImages) {
   mpms::SetClipMediaId(test_video_id, input_sequence.get());
   cv::Mat image(2, 3, CV_8UC3, cv::Scalar(0, 0, 255));
   std::vector<uchar> bytes;
-  ASSERT_TRUE(cv::imencode(".jpg", image, bytes, {80}));
+  ASSERT_TRUE(
+      cv::imencode(".jpg", image, bytes, {cv::IMWRITE_HDR_COMPRESSION, 1}));
   OpenCvImageEncoderCalculatorResults encoded_image;
   encoded_image.set_encoded_image(bytes.data(), bytes.size());
   encoded_image.set_width(2);
@@ -378,7 +379,8 @@ TEST_F(PackMediaSequenceCalculatorTest, PacksAdditionalContext) {
       Adopt(input_sequence.release());
   cv::Mat image(2, 3, CV_8UC3, cv::Scalar(0, 0, 255));
   std::vector<uchar> bytes;
-  ASSERT_TRUE(cv::imencode(".jpg", image, bytes, {80}));
+  ASSERT_TRUE(
+      cv::imencode(".jpg", image, bytes, {cv::IMWRITE_HDR_COMPRESSION, 1}));
   OpenCvImageEncoderCalculatorResults encoded_image;
   encoded_image.set_encoded_image(bytes.data(), bytes.size());
   auto image_ptr =
@@ -410,7 +412,8 @@ TEST_F(PackMediaSequenceCalculatorTest, PacksTwoForwardFlowEncodeds) {
 
   cv::Mat image(2, 3, CV_8UC3, cv::Scalar(0, 0, 255));
   std::vector<uchar> bytes;
-  ASSERT_TRUE(cv::imencode(".jpg", image, bytes, {80}));
+  ASSERT_TRUE(
+      cv::imencode(".jpg", image, bytes, {cv::IMWRITE_HDR_COMPRESSION, 1}));
   std::string test_flow_string(bytes.begin(), bytes.end());
   OpenCvImageEncoderCalculatorResults encoded_flow;
   encoded_flow.set_encoded_image(test_flow_string);
@@ -618,7 +621,8 @@ TEST_F(PackMediaSequenceCalculatorTest, PacksBBoxWithImages) {
   }
   cv::Mat image(height, width, CV_8UC3, cv::Scalar(0, 0, 255));
   std::vector<uchar> bytes;
-  ASSERT_TRUE(cv::imencode(".jpg", image, bytes, {80}));
+  ASSERT_TRUE(
+      cv::imencode(".jpg", image, bytes, {cv::IMWRITE_HDR_COMPRESSION, 1}));
   OpenCvImageEncoderCalculatorResults encoded_image;
   encoded_image.set_encoded_image(bytes.data(), bytes.size());
   encoded_image.set_width(width);
@@ -767,7 +771,8 @@ TEST_F(PackMediaSequenceCalculatorTest, MissingStreamOK) {
 
   cv::Mat image(2, 3, CV_8UC3, cv::Scalar(0, 0, 255));
   std::vector<uchar> bytes;
-  ASSERT_TRUE(cv::imencode(".jpg", image, bytes, {80}));
+  ASSERT_TRUE(
+      cv::imencode(".jpg", image, bytes, {cv::IMWRITE_HDR_COMPRESSION, 1}));
   std::string test_flow_string(bytes.begin(), bytes.end());
   OpenCvImageEncoderCalculatorResults encoded_flow;
   encoded_flow.set_encoded_image(test_flow_string);
@@ -813,7 +818,8 @@ TEST_F(PackMediaSequenceCalculatorTest, MissingStreamNotOK) {
   mpms::SetClipMediaId(test_video_id, input_sequence.get());
   cv::Mat image(2, 3, CV_8UC3, cv::Scalar(0, 0, 255));
   std::vector<uchar> bytes;
-  ASSERT_TRUE(cv::imencode(".jpg", image, bytes, {80}));
+  ASSERT_TRUE(
+      cv::imencode(".jpg", image, bytes, {cv::IMWRITE_HDR_COMPRESSION, 1}));
   std::string test_flow_string(bytes.begin(), bytes.end());
   OpenCvImageEncoderCalculatorResults encoded_flow;
   encoded_flow.set_encoded_image(test_flow_string);
@@ -970,7 +976,8 @@ TEST_F(PackMediaSequenceCalculatorTest, TestReconcilingAnnotations) {
   auto input_sequence = ::absl::make_unique<tf::SequenceExample>();
   cv::Mat image(2, 3, CV_8UC3, cv::Scalar(0, 0, 255));
   std::vector<uchar> bytes;
-  ASSERT_TRUE(cv::imencode(".jpg", image, bytes, {80}));
+  ASSERT_TRUE(
+      cv::imencode(".jpg", image, bytes, {cv::IMWRITE_HDR_COMPRESSION, 1}));
   OpenCvImageEncoderCalculatorResults encoded_image;
   encoded_image.set_encoded_image(bytes.data(), bytes.size());
   encoded_image.set_width(2);
@@ -1021,7 +1028,8 @@ TEST_F(PackMediaSequenceCalculatorTest, TestOverwritingAndReconciling) {
   auto input_sequence = ::absl::make_unique<tf::SequenceExample>();
   cv::Mat image(2, 3, CV_8UC3, cv::Scalar(0, 0, 255));
   std::vector<uchar> bytes;
-  ASSERT_TRUE(cv::imencode(".jpg", image, bytes, {80}));
+  ASSERT_TRUE(
+      cv::imencode(".jpg", image, bytes, {cv::IMWRITE_HDR_COMPRESSION, 1}));
   OpenCvImageEncoderCalculatorResults encoded_image;
   encoded_image.set_encoded_image(bytes.data(), bytes.size());
   int height = 2;

mediapipe/calculators/util/annotation_overlay_calculator.cc

@@ -172,7 +172,7 @@ class AnnotationOverlayCalculator : public CalculatorBase {
 REGISTER_CALCULATOR(AnnotationOverlayCalculator);
 
 absl::Status AnnotationOverlayCalculator::GetContract(CalculatorContract* cc) {
-  CHECK_GE(cc->Inputs().NumEntries(), 1);
+  RET_CHECK_GE(cc->Inputs().NumEntries(), 1);
 
   bool use_gpu = false;
 
@@ -189,13 +189,13 @@ absl::Status AnnotationOverlayCalculator::GetContract(CalculatorContract* cc) {
 #if !MEDIAPIPE_DISABLE_GPU
   if (cc->Inputs().HasTag(kGpuBufferTag)) {
     cc->Inputs().Tag(kGpuBufferTag).Set<mediapipe::GpuBuffer>();
-    CHECK(cc->Outputs().HasTag(kGpuBufferTag));
+    RET_CHECK(cc->Outputs().HasTag(kGpuBufferTag));
     use_gpu = true;
   }
 #endif  // !MEDIAPIPE_DISABLE_GPU
   if (cc->Inputs().HasTag(kImageFrameTag)) {
     cc->Inputs().Tag(kImageFrameTag).Set<ImageFrame>();
-    CHECK(cc->Outputs().HasTag(kImageFrameTag));
+    RET_CHECK(cc->Outputs().HasTag(kImageFrameTag));
   }
 
   // Data streams to render.

mediapipe/calculators/util/landmarks_to_render_data_calculator.cc

@@ -322,27 +322,30 @@ absl::Status LandmarksToRenderDataCalculator::Process(CalculatorContext* cc) {
           options_.presence_threshold(), options_.connection_color(), thickness,
           /*normalized=*/false, render_data.get());
     }
-    for (int i = 0; i < landmarks.landmark_size(); ++i) {
-      const Landmark& landmark = landmarks.landmark(i);
+    if (options_.render_landmarks()) {
+      for (int i = 0; i < landmarks.landmark_size(); ++i) {
+        const Landmark& landmark = landmarks.landmark(i);
 
-      if (!IsLandmarkVisibleAndPresent<Landmark>(
-              landmark, options_.utilize_visibility(),
-              options_.visibility_threshold(), options_.utilize_presence(),
-              options_.presence_threshold())) {
-        continue;
-      }
+        if (!IsLandmarkVisibleAndPresent<Landmark>(
+                landmark, options_.utilize_visibility(),
+                options_.visibility_threshold(), options_.utilize_presence(),
+                options_.presence_threshold())) {
+          continue;
+        }
 
-      auto* landmark_data_render = AddPointRenderData(
-          options_.landmark_color(), thickness, render_data.get());
-      if (visualize_depth) {
-        SetColorSizeValueFromZ(landmark.z(), z_min, z_max, landmark_data_render,
-                               options_.min_depth_circle_thickness(),
-                               options_.max_depth_circle_thickness());
+        auto* landmark_data_render = AddPointRenderData(
+            options_.landmark_color(), thickness, render_data.get());
+        if (visualize_depth) {
+          SetColorSizeValueFromZ(landmark.z(), z_min, z_max,
+                                 landmark_data_render,
+                                 options_.min_depth_circle_thickness(),
+                                 options_.max_depth_circle_thickness());
+        }
+        auto* landmark_data = landmark_data_render->mutable_point();
+        landmark_data->set_normalized(false);
+        landmark_data->set_x(landmark.x());
+        landmark_data->set_y(landmark.y());
       }
-      auto* landmark_data = landmark_data_render->mutable_point();
-      landmark_data->set_normalized(false);
-      landmark_data->set_x(landmark.x());
-      landmark_data->set_y(landmark.y());
     }
   }
 
@@ -368,27 +371,30 @@ absl::Status LandmarksToRenderDataCalculator::Process(CalculatorContext* cc) {
           options_.presence_threshold(), options_.connection_color(), thickness,
           /*normalized=*/true, render_data.get());
     }
-    for (int i = 0; i < landmarks.landmark_size(); ++i) {
-      const NormalizedLandmark& landmark = landmarks.landmark(i);
+    if (options_.render_landmarks()) {
+      for (int i = 0; i < landmarks.landmark_size(); ++i) {
+        const NormalizedLandmark& landmark = landmarks.landmark(i);
 
-      if (!IsLandmarkVisibleAndPresent<NormalizedLandmark>(
-              landmark, options_.utilize_visibility(),
-              options_.visibility_threshold(), options_.utilize_presence(),
-              options_.presence_threshold())) {
-        continue;
-      }
+        if (!IsLandmarkVisibleAndPresent<NormalizedLandmark>(
+                landmark, options_.utilize_visibility(),
+                options_.visibility_threshold(), options_.utilize_presence(),
+                options_.presence_threshold())) {
+          continue;
+        }
 
-      auto* landmark_data_render = AddPointRenderData(
-          options_.landmark_color(), thickness, render_data.get());
-      if (visualize_depth) {
-        SetColorSizeValueFromZ(landmark.z(), z_min, z_max, landmark_data_render,
-                               options_.min_depth_circle_thickness(),
-                               options_.max_depth_circle_thickness());
+        auto* landmark_data_render = AddPointRenderData(
+            options_.landmark_color(), thickness, render_data.get());
+        if (visualize_depth) {
+          SetColorSizeValueFromZ(landmark.z(), z_min, z_max,
+                                 landmark_data_render,
+                                 options_.min_depth_circle_thickness(),
+                                 options_.max_depth_circle_thickness());
+        }
+        auto* landmark_data = landmark_data_render->mutable_point();
+        landmark_data->set_normalized(true);
+        landmark_data->set_x(landmark.x());
+        landmark_data->set_y(landmark.y());
       }
-      auto* landmark_data = landmark_data_render->mutable_point();
-      landmark_data->set_normalized(true);
-      landmark_data->set_x(landmark.x());
-      landmark_data->set_y(landmark.y());
     }
   }
 

mediapipe/calculators/util/landmarks_to_render_data_calculator.proto

@@ -32,6 +32,10 @@ message LandmarksToRenderDataCalculatorOptions {
 
   // Color of the landmarks.
   optional Color landmark_color = 2;
+
+  // Whether to render landmarks as points.
+  optional bool render_landmarks = 14 [default = true];
+
   // Color of the connections.
   optional Color connection_color = 3;
 

mediapipe/calculators/video/BUILD

@@ -130,7 +130,6 @@ cc_library(
         "//mediapipe/framework/formats:video_stream_header",
         "//mediapipe/framework/port:opencv_imgproc",
         "//mediapipe/framework/port:opencv_video",
-        "//mediapipe/framework/port:ret_check",
         "//mediapipe/framework/port:status",
         "//mediapipe/framework/tool:status_util",
     ],
@@ -341,7 +340,6 @@ cc_test(
         "//mediapipe/framework/port:opencv_core",
         "//mediapipe/framework/port:parse_text_proto",
         "//mediapipe/framework/tool:test_util",
-        "@com_google_absl//absl/flags:flag",
     ],
 )
 
@@ -367,7 +365,6 @@ cc_test(
         "//mediapipe/framework/port:opencv_video",
         "//mediapipe/framework/port:parse_text_proto",
         "//mediapipe/framework/tool:test_util",
-        "@com_google_absl//absl/flags:flag",
     ],
 )
 
@@ -451,7 +448,6 @@ cc_test(
         "//mediapipe/framework/tool:test_util",
         "//mediapipe/util/tracking:box_tracker_cc_proto",
         "//mediapipe/util/tracking:tracking_cc_proto",
-        "@com_google_absl//absl/flags:flag",
     ],
 )
 

mediapipe/examples/android/solutions/gradle/wrapper/gradle-wrapper.properties

@@ -1,6 +1,6 @@
 distributionBase=GRADLE_USER_HOME
 distributionPath=wrapper/dists
-distributionUrl=https\://services.gradle.org/distributions/gradle-7.6.1-bin.zip
+distributionUrl=https\://services.gradle.org/distributions/gradle-7.6.2-bin.zip
 networkTimeout=10000
 zipStoreBase=GRADLE_USER_HOME
 zipStorePath=wrapper/dists

mediapipe/framework/BUILD

@@ -44,6 +44,9 @@ bzl_library(
         "encode_binary_proto.bzl",
     ],
     visibility = ["//visibility:public"],
+    deps = [
+        "@bazel_skylib//lib:paths",
+    ],
 )
 
 alias(

mediapipe/framework/api2/node.h

@@ -64,57 +64,13 @@ class CalculatorBaseFactoryFor<
 namespace api2 {
 namespace internal {
 
-// Defining a member of this type causes P to be ODR-used, which forces its
-// instantiation if it's a static member of a template.
-// Previously we depended on the pointer's value to determine whether the size
-// of a character array is 0 or 1, forcing it to be instantiated so the
-// compiler can determine the object's layout. But using it as a template
-// argument is more compact.
-template <auto* P>
-struct ForceStaticInstantiation {
-#ifdef _MSC_VER
-  // Just having it as the template argument does not count as a use for
-  // MSVC.
-  static constexpr bool Use() { return P != nullptr; }
-  char force_static[Use()];
-#endif  // _MSC_VER
-};
+MEDIAPIPE_STATIC_REGISTRATOR_TEMPLATE(
+    NodeRegistrator, mediapipe::CalculatorBaseRegistry, T::kCalculatorName,
+    absl::make_unique<mediapipe::internal::CalculatorBaseFactoryFor<T>>)
 
-// Helper template for forcing the definition of a static registration token.
-template <typename T>
-struct NodeRegistrationStatic {
-  static NoDestructor<mediapipe::RegistrationToken> registration;
-
-  static mediapipe::RegistrationToken Make() {
-    return mediapipe::CalculatorBaseRegistry::Register(
-        T::kCalculatorName,
-        absl::make_unique<mediapipe::internal::CalculatorBaseFactoryFor<T>>);
-  }
-
-  using RequireStatics = ForceStaticInstantiation<&registration>;
-};
-
-// Static members of template classes can be defined in the header.
-template <typename T>
-NoDestructor<mediapipe::RegistrationToken>
-    NodeRegistrationStatic<T>::registration(NodeRegistrationStatic<T>::Make());
-
-template <typename T>
-struct SubgraphRegistrationImpl {
-  static NoDestructor<mediapipe::RegistrationToken> registration;
-
-  static mediapipe::RegistrationToken Make() {
-    return mediapipe::SubgraphRegistry::Register(T::kCalculatorName,
-                                                 absl::make_unique<T>);
-  }
-
-  using RequireStatics = ForceStaticInstantiation<&registration>;
-};
-
-template <typename T>
-NoDestructor<mediapipe::RegistrationToken>
-    SubgraphRegistrationImpl<T>::registration(
-        SubgraphRegistrationImpl<T>::Make());
+MEDIAPIPE_STATIC_REGISTRATOR_TEMPLATE(SubgraphRegistrator,
+                                      mediapipe::SubgraphRegistry,
+                                      T::kCalculatorName, absl::make_unique<T>)
 
 }  // namespace internal
 
@@ -127,14 +83,7 @@ template <class Impl = void>
 class RegisteredNode;
 
 template <class Impl>
-class RegisteredNode : public Node {
- private:
-  // The member below triggers instantiation of the registration static.
-  // Note that the constructor of calculator subclasses is only invoked through
-  // the registration token, and so we cannot simply use the static in the
-  // constructor.
-  typename internal::NodeRegistrationStatic<Impl>::RequireStatics register_;
-};
+class RegisteredNode : public Node, private internal::NodeRegistrator<Impl> {};
 
 // No-op version for backwards compatibility.
 template <>
@@ -216,30 +165,27 @@ class NodeImpl : public RegisteredNode<Impl>, public Intf {
 // TODO: verify that the subgraph config fully implements the
 // declared interface.
 template <class Intf, class Impl>
-class SubgraphImpl : public Subgraph, public Intf {
- private:
-  typename internal::SubgraphRegistrationImpl<Impl>::RequireStatics register_;
-};
+class SubgraphImpl : public Subgraph,
+                     public Intf,
+                     private internal::SubgraphRegistrator<Impl> {};
 
 // This macro is used to register a calculator that does not use automatic
 // registration. Deprecated.
-#define MEDIAPIPE_NODE_IMPLEMENTATION(Impl)                                  \
-  static mediapipe::NoDestructor<mediapipe::RegistrationToken>               \
-  REGISTRY_STATIC_VAR(calculator_registration,                               \
-                      __LINE__)(mediapipe::CalculatorBaseRegistry::Register( \
-      Impl::kCalculatorName,                                                 \
-      absl::make_unique<mediapipe::internal::CalculatorBaseFactoryFor<Impl>>))
+#define MEDIAPIPE_NODE_IMPLEMENTATION(Impl)                       \
+  MEDIAPIPE_REGISTER_FACTORY_FUNCTION_QUALIFIED(                  \
+      mediapipe::CalculatorBaseRegistry, calculator_registration, \
+      Impl::kCalculatorName,                                      \
+      absl::make_unique<mediapipe::internal::CalculatorBaseFactoryFor<Impl>>)
 
 // This macro is used to register a non-split-contract calculator. Deprecated.
 #define MEDIAPIPE_REGISTER_NODE(name) REGISTER_CALCULATOR(name)
 
 // This macro is used to define a subgraph that does not use automatic
 // registration. Deprecated.
-#define MEDIAPIPE_SUBGRAPH_IMPLEMENTATION(Impl)                        \
-  static mediapipe::NoDestructor<mediapipe::RegistrationToken>         \
-  REGISTRY_STATIC_VAR(subgraph_registration,                           \
-                      __LINE__)(mediapipe::SubgraphRegistry::Register( \
-      Impl::kCalculatorName, absl::make_unique<Impl>))
+#define MEDIAPIPE_SUBGRAPH_IMPLEMENTATION(Impl)           \
+  MEDIAPIPE_REGISTER_FACTORY_FUNCTION_QUALIFIED(          \
+      mediapipe::SubgraphRegistry, subgraph_registration, \
+      Impl::kCalculatorName, absl::make_unique<Impl>)
 
 }  // namespace api2
 }  // namespace mediapipe

mediapipe/framework/deps/registration.h

@@ -144,6 +144,23 @@ template <typename T>
 struct WrapStatusOr<absl::StatusOr<T>> {
   using type = absl::StatusOr<T>;
 };
+
+// Defining a member of this type causes P to be ODR-used, which forces its
+// instantiation if it's a static member of a template.
+// Previously we depended on the pointer's value to determine whether the size
+// of a character array is 0 or 1, forcing it to be instantiated so the
+// compiler can determine the object's layout. But using it as a template
+// argument is more compact.
+template <auto* P>
+struct ForceStaticInstantiation {
+#ifdef _MSC_VER
+  // Just having it as the template argument does not count as a use for
+  // MSVC.
+  static constexpr bool Use() { return P != nullptr; }
+  char force_static[Use()];
+#endif  // _MSC_VER
+};
+
 }  // namespace registration_internal
 
 class NamespaceAllowlist {
@@ -396,11 +413,76 @@ class GlobalFactoryRegistry {
       new mediapipe::RegistrationToken(                              \
           RegistryType::Register(#name, __VA_ARGS__))
 
+#define MEDIAPIPE_REGISTER_FACTORY_FUNCTION_QUALIFIED(RegistryType, var_name, \
+                                                      name, ...)              \
+  static auto* REGISTRY_STATIC_VAR(var_name, __LINE__) =                      \
+      new mediapipe::RegistrationToken(                                       \
+          RegistryType::Register(name, __VA_ARGS__))
+
+// TODO: migrate to the above.
 #define REGISTER_FACTORY_FUNCTION_QUALIFIED(RegistryType, var_name, name, ...) \
   static auto* REGISTRY_STATIC_VAR(var_name, __LINE__) =                       \
       new mediapipe::RegistrationToken(                                        \
           RegistryType::Register(#name, __VA_ARGS__))
 
+// Defines a utility registrator class which can be used to automatically
+// register factory functions.
+//
+// Example:
+// === Defining a registry ================================================
+//
+//  class Component {};
+//
+//  using ComponentRegistry = GlobalFactoryRegistry<std::unique_ptr<Component>>;
+//
+// === Defining a registrator =============================================
+//
+//  MEDIAPIPE_STATIC_REGISTRATOR_TEMPLATE(ComponentRegistrator,
+//                                        ComponentRegistry, T::kName,
+//                                        absl::make_unique<T>);
+//
+// === Defining and registering a new component. ==========================
+//
+//  class MyComponent : public Component,
+//                      private ComponentRegistrator<MyComponent> {
+//   public:
+//    static constexpr char kName[] = "MyComponent";
+//    ...
+//  };
+//
+// NOTE:
+// - MyComponent is automatically registered in ComponentRegistry by
+//   "MyComponent" name.
+// - Every component is require to provide its name (T::kName here.)
+#define MEDIAPIPE_STATIC_REGISTRATOR_TEMPLATE(RegistratorName, RegistryType,  \
+                                              name, ...)                      \
+  template <typename T>                                                       \
+  struct Internal##RegistratorName {                                          \
+    static NoDestructor<mediapipe::RegistrationToken> registration;           \
+                                                                              \
+    static mediapipe::RegistrationToken Make() {                              \
+      return RegistryType::Register(name, __VA_ARGS__);                       \
+    }                                                                         \
+                                                                              \
+    using RequireStatics =                                                    \
+        registration_internal::ForceStaticInstantiation<&registration>;       \
+  };                                                                          \
+  /* Static members of template classes can be defined in the header. */      \
+  template <typename T>                                                       \
+  NoDestructor<mediapipe::RegistrationToken>                                  \
+      Internal##RegistratorName<T>::registration(                             \
+          Internal##RegistratorName<T>::Make());                              \
+                                                                              \
+  template <typename T>                                                       \
+  class RegistratorName {                                                     \
+   private:                                                                   \
+    /* The member below triggers instantiation of the registration static. */ \
+    /* Note that the constructor of calculator subclasses is only invoked  */ \
+    /* through the registration token, and so we cannot simply use the     */ \
+    /* static in theconstructor.                                           */ \
+    typename Internal##RegistratorName<T>::RequireStatics register_;          \
+  };
+
 }  // namespace mediapipe
 
 #endif  // MEDIAPIPE_DEPS_REGISTRATION_H_

mediapipe/framework/encode_binary_proto.bzl

@@ -37,29 +37,33 @@ Args:
   output: The desired name of the output file. Optional.
 """
 
+load("@bazel_skylib//lib:paths.bzl", "paths")
+
 PROTOC = "@com_google_protobuf//:protoc"
 
-def _canonicalize_proto_path_oss(all_protos, genfile_path):
-    """For the protos from external repository, canonicalize the proto path and the file name.
+def _canonicalize_proto_path_oss(f):
+    if not f.root.path:
+        return struct(
+            proto_path = ".",
+            file_name = f.short_path,
+        )
 
-    Returns:
-       Proto path list and proto source file list.
-    """
-    proto_paths = []
-    proto_file_names = []
-    for s in all_protos.to_list():
-        if s.path.startswith(genfile_path):
-            repo_name, _, file_name = s.path[len(genfile_path + "/external/"):].partition("/")
+    # `f.path` looks like "<genfiles>/external/<repo>/(_virtual_imports/<library>/)?<file_name>"
+    repo_name, _, file_name = f.path[len(paths.join(f.root.path, "external") + "/"):].partition("/")
+    if file_name.startswith("_virtual_imports/"):
+        # This is a virtual import; move "_virtual_imports/<library>" from `repo_name` to `file_name`.
+        repo_name = paths.join(repo_name, *file_name.split("/", 2)[:2])
+        file_name = file_name.split("/", 2)[-1]
+    return struct(
+        proto_path = paths.join(f.root.path, "external", repo_name),
+        file_name = file_name,
+    )
 
-            # handle virtual imports
-            if file_name.startswith("_virtual_imports"):
-                repo_name = repo_name + "/" + "/".join(file_name.split("/", 2)[:2])
-                file_name = file_name.split("/", 2)[-1]
-            proto_paths.append(genfile_path + "/external/" + repo_name)
-            proto_file_names.append(file_name)
-        else:
-            proto_file_names.append(s.path)
-    return ([" --proto_path=" + path for path in proto_paths], proto_file_names)
+def _map_root_path(f):
+    return _canonicalize_proto_path_oss(f).proto_path
+
+def _map_short_path(f):
+    return _canonicalize_proto_path_oss(f).file_name
 
 def _get_proto_provider(dep):
     """Get the provider for protocol buffers from a dependnecy.
@@ -90,24 +94,35 @@ def _encode_binary_proto_impl(ctx):
         sibling = textpb,
     )
 
-    path_list, file_list = _canonicalize_proto_path_oss(all_protos, ctx.genfiles_dir.path)
+    args = ctx.actions.args()
+    args.add(textpb)
+    args.add(binarypb)
+    args.add(ctx.executable._proto_compiler)
+    args.add(ctx.attr.message_type, format = "--encode=%s")
+    args.add("--proto_path=.")
+    args.add_all(
+        all_protos,
+        map_each = _map_root_path,
+        format_each = "--proto_path=%s",
+        uniquify = True,
+    )
+    args.add_all(
+        all_protos,
+        map_each = _map_short_path,
+        uniquify = True,
+    )
 
     # Note: the combination of absolute_paths and proto_path, as well as the exact
     # order of gendir before ., is needed for the proto compiler to resolve
     # import statements that reference proto files produced by a genrule.
     ctx.actions.run_shell(
-        tools = all_protos.to_list() + [textpb, ctx.executable._proto_compiler],
-        outputs = [binarypb],
-        command = " ".join(
-            [
-                ctx.executable._proto_compiler.path,
-                "--encode=" + ctx.attr.message_type,
-                "--proto_path=" + ctx.genfiles_dir.path,
-                "--proto_path=" + ctx.bin_dir.path,
-                "--proto_path=.",
-            ] + path_list + file_list +
-            ["<", textpb.path, ">", binarypb.path],
+        tools = depset(
+            direct = [textpb, ctx.executable._proto_compiler],
+            transitive = [all_protos],
         ),
+        outputs = [binarypb],
+        command = "${@:3} < $1 > $2",
+        arguments = [args],
         mnemonic = "EncodeProto",
     )
 

mediapipe/framework/port/BUILD

@@ -261,8 +261,8 @@ cc_library(
 )
 
 cc_library(
-    name = "opencv_highgui",
-    hdrs = ["opencv_highgui_inc.h"],
+    name = "opencv_photo",
+    hdrs = ["opencv_photo_inc.h"],
     deps = [
         ":opencv_core",
         "//third_party:opencv",
@@ -297,6 +297,15 @@ cc_library(
     ],
 )
 
+cc_library(
+    name = "opencv_highgui",
+    hdrs = ["opencv_highgui_inc.h"],
+    deps = [
+        ":opencv_core",
+        "//third_party:opencv",
+    ],
+)
+
 cc_library(
     name = "opencv_videoio",
     hdrs = ["opencv_videoio_inc.h"],

mediapipe/framework/port/opencv_highgui_inc.h

@@ -1,4 +1,4 @@
-// Copyright 2019 The MediaPipe Authors.
+// Copyright 2023 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.
@@ -12,8 +12,8 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-#ifndef MEDIAPIPE_PORT_OPENCV_HIGHGUI_INC_H_
-#define MEDIAPIPE_PORT_OPENCV_HIGHGUI_INC_H_
+#ifndef MEDIAPIPE_FRAMEWORK_PORT_OPENCV_HIGHGUI_INC_H_
+#define MEDIAPIPE_FRAMEWORK_PORT_OPENCV_HIGHGUI_INC_H_
 
 #include <opencv2/core/version.hpp>
 
@@ -25,4 +25,4 @@
 #include <opencv2/highgui.hpp>
 #endif
 
-#endif  // MEDIAPIPE_PORT_OPENCV_HIGHGUI_INC_H_
+#endif  // MEDIAPIPE_FRAMEWORK_PORT_OPENCV_HIGHGUI_INC_H_

mediapipe/framework/port/opencv_imgcodecs_inc.h

@@ -1,4 +1,4 @@
-// Copyright 2019 The MediaPipe Authors.
+// Copyright 2022 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.

mediapipe/framework/port/opencv_video_inc.h

@@ -1,4 +1,4 @@
-// Copyright 2019 The MediaPipe Authors.
+// Copyright 2022 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.

mediapipe/framework/stream_handler/mux_input_stream_handler.cc

@@ -48,6 +48,18 @@ class MuxInputStreamHandler : public InputStreamHandler {
       : InputStreamHandler(std::move(tag_map), cc_manager, options,
                            calculator_run_in_parallel) {}
 
+ private:
+  CollectionItemId GetControlStreamId() const {
+    return input_stream_managers_.EndId() - 1;
+  }
+  void RemoveOutdatedDataPackets(Timestamp timestamp) {
+    const CollectionItemId control_stream_id = GetControlStreamId();
+    for (CollectionItemId id = input_stream_managers_.BeginId();
+         id < control_stream_id; ++id) {
+      input_stream_managers_.Get(id)->ErasePacketsEarlierThan(timestamp);
+    }
+  }
+
  protected:
   // In MuxInputStreamHandler, a node is "ready" if:
   // - the control stream is done (need to call Close() in this case), or
@@ -58,9 +70,15 @@ class MuxInputStreamHandler : public InputStreamHandler {
     absl::MutexLock lock(&input_streams_mutex_);
 
     const auto& control_stream =
-        input_stream_managers_.Get(input_stream_managers_.EndId() - 1);
+        input_stream_managers_.Get(GetControlStreamId());
     bool empty;
     *min_stream_timestamp = control_stream->MinTimestampOrBound(&empty);
+
+    // Data streams may contain some outdated packets which failed to be popped
+    // out during "FillInputSet". (This handler doesn't sync input streams,
+    // hence "FillInputSet" can be triggerred before every input stream is
+    // filled with packets corresponding to the same timestamp.)
+    RemoveOutdatedDataPackets(*min_stream_timestamp);
     if (empty) {
       if (*min_stream_timestamp == Timestamp::Done()) {
         // Calculator is done if the control input stream is done.
@@ -78,11 +96,6 @@ class MuxInputStreamHandler : public InputStreamHandler {
     const auto& data_stream = input_stream_managers_.Get(
         input_stream_managers_.BeginId() + control_value);
 
-    // Data stream may contain some outdated packets which failed to be popped
-    // out during "FillInputSet". (This handler doesn't sync input streams,
-    // hence "FillInputSet" can be triggerred before every input stream is
-    // filled with packets corresponding to the same timestamp.)
-    data_stream->ErasePacketsEarlierThan(*min_stream_timestamp);
     Timestamp stream_timestamp = data_stream->MinTimestampOrBound(&empty);
     if (empty) {
       if (stream_timestamp <= *min_stream_timestamp) {
@@ -111,8 +124,7 @@ class MuxInputStreamHandler : public InputStreamHandler {
     CHECK(input_set);
     absl::MutexLock lock(&input_streams_mutex_);
 
-    const CollectionItemId control_stream_id =
-        input_stream_managers_.EndId() - 1;
+    const CollectionItemId control_stream_id = GetControlStreamId();
     auto& control_stream = input_stream_managers_.Get(control_stream_id);
     int num_packets_dropped = 0;
     bool stream_is_done = false;
@@ -140,15 +152,8 @@ class MuxInputStreamHandler : public InputStreamHandler {
     AddPacketToShard(&input_set->Get(data_stream_id), std::move(data_packet),
                      stream_is_done);
 
-    // Discard old packets on other streams.
-    // Note that control_stream_id is the last valid id.
-    auto next_timestamp = input_timestamp.NextAllowedInStream();
-    for (CollectionItemId id = input_stream_managers_.BeginId();
-         id < control_stream_id; ++id) {
-      if (id == data_stream_id) continue;
-      auto& other_stream = input_stream_managers_.Get(id);
-      other_stream->ErasePacketsEarlierThan(next_timestamp);
-    }
+    // Discard old packets on data streams.
+    RemoveOutdatedDataPackets(input_timestamp.NextAllowedInStream());
   }
 
  private:

mediapipe/framework/stream_handler/mux_input_stream_handler_test.cc

@@ -645,5 +645,41 @@ TEST(MuxInputStreamHandlerTest,
   MP_ASSERT_OK(graph.WaitUntilDone());
 }
 
+TEST(MuxInputStreamHandlerTest, RemovesUnusedDataStreamPackets) {
+  CalculatorGraphConfig config =
+      mediapipe::ParseTextProtoOrDie<CalculatorGraphConfig>(R"pb(
+        input_stream: "input0"
+        input_stream: "input1"
+        input_stream: "select"
+        node {
+          calculator: "MuxCalculator"
+          input_stream: "INPUT:0:input0"
+          input_stream: "INPUT:1:input1"
+          input_stream: "SELECT:select"
+          output_stream: "OUTPUT:output"
+          input_stream_handler { input_stream_handler: "MuxInputStreamHandler" }
+        }
+      )pb");
+  config.set_max_queue_size(1);
+  config.set_report_deadlock(true);
+
+  CalculatorGraph graph;
+  MP_ASSERT_OK(graph.Initialize(config));
+  MP_ASSERT_OK(graph.StartRun({}));
+  MP_ASSERT_OK(graph.AddPacketToInputStream(
+      "select", MakePacket<int>(0).At(Timestamp(2))));
+  MP_ASSERT_OK(graph.AddPacketToInputStream(
+      "input0", MakePacket<int>(1000).At(Timestamp(2))));
+  MP_ASSERT_OK(graph.WaitUntilIdle());
+
+  // Add two delayed packets to the deselected input. They should be discarded
+  // instead of triggering the deadlock detection (max_queue_size = 1).
+  MP_ASSERT_OK(graph.AddPacketToInputStream(
+      "input1", MakePacket<int>(900).At(Timestamp(1))));
+  MP_ASSERT_OK(graph.AddPacketToInputStream(
+      "input1", MakePacket<int>(900).At(Timestamp(2))));
+  MP_ASSERT_OK(graph.WaitUntilIdle());
+}
+
 }  // namespace
 }  // namespace mediapipe

mediapipe/gpu/gl_context_webgl.cc

@@ -109,9 +109,8 @@ absl::Status GlContext::CreateContext(
   }
   MP_RETURN_IF_ERROR(status);
 
-  LOG(INFO) << "Successfully created a WebGL context with major version "
-            << gl_major_version_ << " and handle " << context_;
-
+  VLOG(1) << "Successfully created a WebGL context with major version "
+          << gl_major_version_ << " and handle " << context_;
   return absl::OkStatus();
 }
 

mediapipe/gpu/gl_scaler_calculator.cc

@@ -104,6 +104,7 @@ class GlScalerCalculator : public CalculatorBase {
   bool vertical_flip_output_;
   bool horizontal_flip_output_;
   FrameScaleMode scale_mode_ = FrameScaleMode::kStretch;
+  bool use_nearest_neighbor_interpolation_ = false;
 };
 REGISTER_CALCULATOR(GlScalerCalculator);
 
@@ -186,7 +187,8 @@ absl::Status GlScalerCalculator::Open(CalculatorContext* cc) {
     scale_mode_ =
         FrameScaleModeFromProto(options.scale_mode(), FrameScaleMode::kStretch);
   }
-
+  use_nearest_neighbor_interpolation_ =
+      options.use_nearest_neighbor_interpolation();
   if (HasTagOrIndex(cc->InputSidePackets(), "OUTPUT_DIMENSIONS", 1)) {
     const auto& dimensions =
         TagOrIndex(cc->InputSidePackets(), "OUTPUT_DIMENSIONS", 1)
@@ -297,6 +299,11 @@ absl::Status GlScalerCalculator::Process(CalculatorContext* cc) {
       glBindTexture(src2.target(), src2.name());
     }
 
+    if (use_nearest_neighbor_interpolation_) {
+      glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_MAG_FILTER, GL_NEAREST);
+      glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_MIN_FILTER, GL_NEAREST);
+    }
+
     MP_RETURN_IF_ERROR(renderer->GlRender(
         src1.width(), src1.height(), dst.width(), dst.height(), scale_mode_,
         rotation_, horizontal_flip_output_, vertical_flip_output_,

mediapipe/gpu/gl_scaler_calculator.proto

@@ -19,7 +19,7 @@ package mediapipe;
 import "mediapipe/framework/calculator.proto";
 import "mediapipe/gpu/scale_mode.proto";
 
-// Next id: 8.
+// Next id: 9.
 message GlScalerCalculatorOptions {
   extend CalculatorOptions {
     optional GlScalerCalculatorOptions ext = 166373014;
@@ -39,4 +39,7 @@ message GlScalerCalculatorOptions {
   // Flip the output texture horizontally. This is applied after rotation.
   optional bool flip_horizontal = 5;
   optional ScaleMode.Mode scale_mode = 6;
+  // Whether to use nearest neighbor interpolation. Default to use linear
+  // interpolation.
+  optional bool use_nearest_neighbor_interpolation = 8 [default = false];
 }

mediapipe/gpu/gpu_buffer_format.cc

@@ -100,6 +100,10 @@ const GlTextureInfo& GlTextureInfoForGpuBufferFormat(GpuBufferFormat format,
                {GL_R8, GL_RED, GL_UNSIGNED_BYTE, 1},
 #endif  // TARGET_OS_OSX
            }},
+          {GpuBufferFormat::kOneComponent8Alpha,
+           {
+               {GL_ALPHA, GL_ALPHA, GL_UNSIGNED_BYTE, 1},
+           }},
           {GpuBufferFormat::kOneComponent8Red,
            {
                {GL_R8, GL_RED, GL_UNSIGNED_BYTE, 1},
@@ -221,6 +225,7 @@ ImageFormat::Format ImageFormatForGpuBufferFormat(GpuBufferFormat format) {
     case GpuBufferFormat::kRGBA32:
       // TODO: this likely maps to ImageFormat::SRGBA
     case GpuBufferFormat::kGrayHalf16:
+    case GpuBufferFormat::kOneComponent8Alpha:
     case GpuBufferFormat::kOneComponent8Red:
     case GpuBufferFormat::kTwoComponent8:
     case GpuBufferFormat::kTwoComponentHalf16:

mediapipe/gpu/gpu_buffer_format.h

@@ -43,6 +43,7 @@ enum class GpuBufferFormat : uint32_t {
   kGrayFloat32 = MEDIAPIPE_FOURCC('L', '0', '0', 'f'),
   kGrayHalf16 = MEDIAPIPE_FOURCC('L', '0', '0', 'h'),
   kOneComponent8 = MEDIAPIPE_FOURCC('L', '0', '0', '8'),
+  kOneComponent8Alpha = MEDIAPIPE_FOURCC('A', '0', '0', '8'),
   kOneComponent8Red = MEDIAPIPE_FOURCC('R', '0', '0', '8'),
   kTwoComponent8 = MEDIAPIPE_FOURCC('2', 'C', '0', '8'),
   kTwoComponentHalf16 = MEDIAPIPE_FOURCC('2', 'C', '0', 'h'),
@@ -101,6 +102,7 @@ inline OSType CVPixelFormatForGpuBufferFormat(GpuBufferFormat format) {
       return kCVPixelFormatType_OneComponent32Float;
     case GpuBufferFormat::kOneComponent8:
       return kCVPixelFormatType_OneComponent8;
+    case GpuBufferFormat::kOneComponent8Alpha:
     case GpuBufferFormat::kOneComponent8Red:
       return -1;
     case GpuBufferFormat::kTwoComponent8:

mediapipe/java/com/google/mediapipe/framework/AppTextureFrame.java

@@ -78,17 +78,21 @@ public class AppTextureFrame implements TextureFrame {
    * Use {@link waitUntilReleasedWithGpuSync} whenever possible.
    */
   public void waitUntilReleased() throws InterruptedException {
+    GlSyncToken tokenToRelease = null;
     synchronized (this) {
       while (inUse && releaseSyncToken == null) {
         wait();
       }
       if (releaseSyncToken != null) {
-        releaseSyncToken.waitOnCpu();
-        releaseSyncToken.release();
+        tokenToRelease = releaseSyncToken;
         inUse = false;
         releaseSyncToken = null;
       }
     }
+    if (tokenToRelease != null) {
+      tokenToRelease.waitOnCpu();
+      tokenToRelease.release();
+    }
   }
 
   /**
@@ -98,17 +102,21 @@ public class AppTextureFrame implements TextureFrame {
    * TextureFrame.
    */
   public void waitUntilReleasedWithGpuSync() throws InterruptedException {
+    GlSyncToken tokenToRelease = null;
     synchronized (this) {
       while (inUse && releaseSyncToken == null) {
         wait();
       }
       if (releaseSyncToken != null) {
-        releaseSyncToken.waitOnGpu();
-        releaseSyncToken.release();
+        tokenToRelease = releaseSyncToken;
         inUse = false;
         releaseSyncToken = null;
       }
     }
+    if (tokenToRelease != null) {
+      tokenToRelease.waitOnGpu();
+      tokenToRelease.release();
+    }
   }
 
   /**

mediapipe/java/com/google/mediapipe/framework/PacketGetter.java

@@ -239,7 +239,7 @@ public final class PacketGetter {
 
   /**
    * Assign the native image buffer array in given ByteBuffer array. It assumes given ByteBuffer
-   * array has the the same size of image list packet, and assumes the output buffer stores pixels
+   * array has the same size of image list packet, and assumes the output buffer stores pixels
    * contiguously. It returns false if this assumption does not hold.
    *
    * <p>If deepCopy is true, it assumes the given buffersArray has allocated the required size of

mediapipe/model_maker/python/core/data/BUILD

@@ -57,3 +57,14 @@ py_test(
     srcs = ["classification_dataset_test.py"],
     deps = [":classification_dataset"],
 )
+
+py_library(
+    name = "cache_files",
+    srcs = ["cache_files.py"],
+)
+
+py_test(
+    name = "cache_files_test",
+    srcs = ["cache_files_test.py"],
+    deps = [":cache_files"],
+)

mediapipe/model_maker/python/core/data/cache_files.py

@@ -0,0 +1,112 @@
+# Copyright 2023 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.
+"""Common TFRecord cache files library."""
+
+import dataclasses
+import os
+import tempfile
+from typing import Any, Mapping, Sequence
+
+import tensorflow as tf
+import yaml
+
+
+# Suffix of the meta data file name.
+METADATA_FILE_SUFFIX = '_metadata.yaml'
+
+
+@dataclasses.dataclass(frozen=True)
+class TFRecordCacheFiles:
+  """TFRecordCacheFiles dataclass to store and load cached TFRecord files.
+
+  Attributes:
+    cache_prefix_filename: The cache prefix filename. This is usually provided
+      as a hash of the original data source to avoid different data sources
+      resulting in the same cache file.
+    cache_dir: The cache directory to save TFRecord and metadata file. When
+      cache_dir is None, a temporary folder will be created and will not be
+      removed automatically after training which makes it can be used later.
+    num_shards: Number of shards for output tfrecord files.
+  """
+
+  cache_prefix_filename: str = 'cache_prefix'
+  cache_dir: str = dataclasses.field(default_factory=tempfile.mkdtemp)
+  num_shards: int = 1
+
+  def __post_init__(self):
+    if not self.cache_prefix_filename:
+      raise ValueError('cache_prefix_filename cannot be empty.')
+    if self.num_shards <= 0:
+      raise ValueError(
+          f'num_shards must be greater than 0, got {self.num_shards}'
+      )
+
+  @property
+  def cache_prefix(self) -> str:
+    """The cache prefix including the cache directory and the cache prefix filename."""
+    return os.path.join(self.cache_dir, self.cache_prefix_filename)
+
+  @property
+  def tfrecord_files(self) -> Sequence[str]:
+    """The TFRecord files."""
+    tfrecord_files = [
+        self.cache_prefix + '-%05d-of-%05d.tfrecord' % (i, self.num_shards)
+        for i in range(self.num_shards)
+    ]
+    return tfrecord_files
+
+  @property
+  def metadata_file(self) -> str:
+    """The metadata file."""
+    return self.cache_prefix + METADATA_FILE_SUFFIX
+
+  def get_writers(self) -> Sequence[tf.io.TFRecordWriter]:
+    """Gets an array of TFRecordWriter objects.
+
+    Note that these writers should each be closed using .close() when done.
+
+    Returns:
+      Array of TFRecordWriter objects
+    """
+    if not tf.io.gfile.exists(self.cache_dir):
+      tf.io.gfile.makedirs(self.cache_dir)
+    return [tf.io.TFRecordWriter(path) for path in self.tfrecord_files]
+
+  def save_metadata(self, metadata):
+    """Writes metadata to file.
+
+    Args:
+      metadata: A dictionary of metadata content to write. Exact format is
+        dependent on the specific dataset, but typically includes a 'size' and
+        'label_names' entry.
+    """
+    with tf.io.gfile.GFile(self.metadata_file, 'w') as f:
+      yaml.dump(metadata, f)
+
+  def load_metadata(self) -> Mapping[Any, Any]:
+    """Reads metadata from file.
+
+    Returns:
+      Dictionary object containing metadata
+    """
+    if not tf.io.gfile.exists(self.metadata_file):
+      return {}
+    with tf.io.gfile.GFile(self.metadata_file, 'r') as f:
+      metadata = yaml.load(f, Loader=yaml.FullLoader)
+    return metadata
+
+  def is_cached(self) -> bool:
+    """Checks whether this CacheFiles is already cached."""
+    all_cached_files = list(self.tfrecord_files) + [self.metadata_file]
+    return all(tf.io.gfile.exists(f) for f in all_cached_files)

mediapipe/model_maker/python/core/data/cache_files_test.py

@@ -0,0 +1,77 @@
+# Copyright 2023 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.
+
+import tensorflow as tf
+
+from mediapipe.model_maker.python.core.data import cache_files
+
+
+class CacheFilesTest(tf.test.TestCase):
+
+  def test_tfrecord_cache_files(self):
+    cf = cache_files.TFRecordCacheFiles(
+        cache_prefix_filename='tfrecord',
+        cache_dir='/tmp/cache_dir',
+        num_shards=2,
+    )
+    self.assertEqual(cf.cache_prefix, '/tmp/cache_dir/tfrecord')
+    self.assertEqual(
+        cf.metadata_file,
+        '/tmp/cache_dir/tfrecord' + cache_files.METADATA_FILE_SUFFIX,
+    )
+    expected_tfrecord_files = [
+        '/tmp/cache_dir/tfrecord-%05d-of-%05d.tfrecord' % (i, 2)
+        for i in range(2)
+    ]
+    self.assertEqual(cf.tfrecord_files, expected_tfrecord_files)
+
+    # Writing TFRecord Files
+    self.assertFalse(cf.is_cached())
+    for tfrecord_file in cf.tfrecord_files:
+      self.assertFalse(tf.io.gfile.exists(tfrecord_file))
+    writers = cf.get_writers()
+    for writer in writers:
+      writer.close()
+    for tfrecord_file in cf.tfrecord_files:
+      self.assertTrue(tf.io.gfile.exists(tfrecord_file))
+    self.assertFalse(cf.is_cached())
+
+    # Writing Metadata Files
+    original_metadata = {'size': 10, 'label_names': ['label1', 'label2']}
+    cf.save_metadata(original_metadata)
+    self.assertTrue(cf.is_cached())
+    metadata = cf.load_metadata()
+    self.assertEqual(metadata, original_metadata)
+
+  def test_recordio_cache_files_error(self):
+    with self.assertRaisesRegex(
+        ValueError, 'cache_prefix_filename cannot be empty'
+    ):
+      cache_files.TFRecordCacheFiles(
+          cache_prefix_filename='',
+          cache_dir='/tmp/cache_dir',
+          num_shards=2,
+      )
+    with self.assertRaisesRegex(
+        ValueError, 'num_shards must be greater than 0, got 0'
+    ):
+      cache_files.TFRecordCacheFiles(
+          cache_prefix_filename='tfrecord',
+          cache_dir='/tmp/cache_dir',
+          num_shards=0,
+      )
+
+
+if __name__ == '__main__':
+  tf.test.main()

mediapipe/model_maker/python/core/data/classification_dataset.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 """Common classification dataset library."""
 
-from typing import List, Tuple
+from typing import List, Optional, Tuple
 
 import tensorflow as tf
 
@@ -23,8 +23,12 @@ from mediapipe.model_maker.python.core.data import dataset as ds
 class ClassificationDataset(ds.Dataset):
   """Dataset Loader for classification models."""
 
-  def __init__(self, dataset: tf.data.Dataset, size: int,
-               label_names: List[str]):
+  def __init__(
+      self,
+      dataset: tf.data.Dataset,
+      label_names: List[str],
+      size: Optional[int] = None,
+  ):
     super().__init__(dataset, size)
     self._label_names = label_names
 

mediapipe/model_maker/python/core/data/classification_dataset_test.py

@@ -36,9 +36,14 @@ class ClassificationDatasetTest(tf.test.TestCase):
         value: A value variable stored by the mock dataset class for testing.
       """
 
-      def __init__(self, dataset: tf.data.Dataset, size: int,
-                   label_names: List[str], value: Any):
-        super().__init__(dataset=dataset, size=size, label_names=label_names)
+      def __init__(
+          self,
+          dataset: tf.data.Dataset,
+          label_names: List[str],
+          value: Any,
+          size: int,
+      ):
+        super().__init__(dataset=dataset, label_names=label_names, size=size)
         self.value = value
 
       def split(self, fraction: float) -> Tuple[_DatasetT, _DatasetT]:
@@ -52,7 +57,8 @@ class ClassificationDatasetTest(tf.test.TestCase):
     # Create data loader from sample data.
     ds = tf.data.Dataset.from_tensor_slices([[0, 1], [1, 1], [0, 0], [1, 0]])
     data = MagicClassificationDataset(
-        dataset=ds, size=len(ds), label_names=label_names, value=magic_value)
+        dataset=ds, label_names=label_names, value=magic_value, size=len(ds)
+    )
 
     # Train/Test data split.
     fraction = .25

mediapipe/model_maker/python/core/data/dataset.py

@@ -56,15 +56,14 @@ class Dataset(object):
   def size(self) -> Optional[int]:
     """Returns the size of the dataset.
 
-    Note that this function may return None becuase the exact size of the
-    dataset isn't a necessary parameter to create an instance of this class,
-    and tf.data.Dataset donesn't support a function to get the length directly
-    since it's lazy-loaded and may be infinite.
-    In most cases, however, when an instance of this class is created by helper
-    functions like 'from_folder', the size of the dataset will be preprocessed,
-    and this function can return an int representing the size of the dataset.
+    Same functionality as calling __len__. See the __len__ method definition for
+    more information.
+
+    Raises:
+      TypeError if self._size is not set and the cardinality of self._dataset
+        is INFINITE_CARDINALITY or UNKNOWN_CARDINALITY.
     """
-    return self._size
+    return self.__len__()
 
   def gen_tf_dataset(
       self,
@@ -116,8 +115,22 @@ class Dataset(object):
     # here.
     return dataset
 
-  def __len__(self):
-    """Returns the number of element of the dataset."""
+  def __len__(self) -> int:
+    """Returns the number of element of the dataset.
+
+    If size is not set, this method will fallback to using the __len__ method
+    of the tf.data.Dataset in self._dataset. Calling __len__ on a
+    tf.data.Dataset instance may throw a TypeError because the dataset may
+    be lazy-loaded with an unknown size or have infinite size.
+
+    In most cases, however, when an instance of this class is created by helper
+    functions like 'from_folder', the size of the dataset will be preprocessed,
+    and the _size instance variable will be already set.
+
+    Raises:
+      TypeError if self._size is not set and the cardinality of self._dataset
+        is INFINITE_CARDINALITY or UNKNOWN_CARDINALITY.
+    """
     if self._size is not None:
       return self._size
     else:
@@ -152,15 +165,25 @@ class Dataset(object):
 
     Returns:
       The splitted two sub datasets.
+
+    Raises:
+      ValueError: if the provided fraction is not between 0 and 1.
+      ValueError: if this dataset does not have a set size.
     """
-    assert (fraction > 0 and fraction < 1)
+    if not (fraction > 0 and fraction < 1):
+      raise ValueError(f'Fraction must be between 0 and 1. Got:{fraction}')
+    if not self._size:
+      raise ValueError(
+          'Dataset size unknown. Cannot split the dataset when '
+          'the size is unknown.'
+      )
 
     dataset = self._dataset
 
     train_size = int(self._size * fraction)
-    trainset = self.__class__(dataset.take(train_size), train_size, *args)
+    trainset = self.__class__(dataset.take(train_size), *args, size=train_size)
 
     test_size = self._size - train_size
-    testset = self.__class__(dataset.skip(train_size), test_size, *args)
+    testset = self.__class__(dataset.skip(train_size), *args, size=test_size)
 
     return trainset, testset

mediapipe/model_maker/python/text/core/bert_model_spec.py

@@ -46,13 +46,17 @@ class BertModelSpec:
   """
 
   downloaded_files: file_util.DownloadedFiles
-  hparams: hp.BaseHParams = hp.BaseHParams(
-      epochs=3,
-      batch_size=32,
-      learning_rate=3e-5,
-      distribution_strategy='mirrored')
-  model_options: bert_model_options.BertModelOptions = (
-      bert_model_options.BertModelOptions())
+  hparams: hp.BaseHParams = dataclasses.field(
+      default_factory=lambda: hp.BaseHParams(
+          epochs=3,
+          batch_size=32,
+          learning_rate=3e-5,
+          distribution_strategy='mirrored',
+      )
+  )
+  model_options: bert_model_options.BertModelOptions = dataclasses.field(
+      default_factory=bert_model_options.BertModelOptions
+  )
   do_lower_case: bool = True
   tflite_input_name: Dict[str, str] = dataclasses.field(
       default_factory=lambda: _DEFAULT_TFLITE_INPUT_NAME)

mediapipe/model_maker/python/text/text_classifier/dataset.py

@@ -85,4 +85,5 @@ class Dataset(classification_dataset.ClassificationDataset):
     text_label_ds = tf.data.Dataset.zip((text_ds, label_index_ds))
 
     return Dataset(
-        dataset=text_label_ds, size=len(texts), label_names=label_names)
+        dataset=text_label_ds, label_names=label_names, size=len(texts)
+    )

mediapipe/model_maker/python/text/text_classifier/dataset_test.py

@@ -53,7 +53,7 @@ class DatasetTest(tf.test.TestCase):
 
   def test_split(self):
     ds = tf.data.Dataset.from_tensor_slices(['good', 'bad', 'neutral', 'odd'])
-    data = dataset.Dataset(ds, 4, ['pos', 'neg'])
+    data = dataset.Dataset(ds, ['pos', 'neg'], 4)
     train_data, test_data = data.split(0.5)
     expected_train_data = [b'good', b'bad']
     expected_test_data = [b'neutral', b'odd']

mediapipe/model_maker/python/text/text_classifier/model_spec.py

@@ -47,11 +47,14 @@ class AverageWordEmbeddingClassifierSpec:
   """
 
   # `learning_rate` is unused for the average word embedding model
-  hparams: hp.AverageWordEmbeddingHParams = hp.AverageWordEmbeddingHParams(
-      epochs=10, batch_size=32, learning_rate=0
+  hparams: hp.AverageWordEmbeddingHParams = dataclasses.field(
+      default_factory=lambda: hp.AverageWordEmbeddingHParams(
+          epochs=10, batch_size=32, learning_rate=0
+      )
+  )
+  model_options: mo.AverageWordEmbeddingModelOptions = dataclasses.field(
+      default_factory=mo.AverageWordEmbeddingModelOptions
   )
-  model_options: mo.AverageWordEmbeddingModelOptions = (
-      mo.AverageWordEmbeddingModelOptions())
   name: str = 'AverageWordEmbedding'
 
 average_word_embedding_classifier_spec = functools.partial(
@@ -66,7 +69,7 @@ class BertClassifierSpec(bert_model_spec.BertModelSpec):
   inherited from the BertModelSpec.
   """
 
-  hparams: hp.BertHParams = hp.BertHParams()
+  hparams: hp.BertHParams = dataclasses.field(default_factory=hp.BertHParams)
 
 
 mobilebert_classifier_spec = functools.partial(

mediapipe/model_maker/python/vision/face_stylizer/dataset.py

@@ -115,5 +115,7 @@ class Dataset(classification_dataset.ClassificationDataset):
         ', '.join(label_names),
     )
     return Dataset(
-        dataset=image_label_ds, size=all_image_size, label_names=label_names
+        dataset=image_label_ds,
+        label_names=label_names,
+        size=all_image_size,
     )

mediapipe/model_maker/python/vision/gesture_recognizer/dataset.py

@@ -249,5 +249,6 @@ class Dataset(classification_dataset.ClassificationDataset):
             len(valid_hand_data), len(label_names), ','.join(label_names)))
     return Dataset(
         dataset=hand_embedding_label_ds,
+        label_names=label_names,
         size=len(valid_hand_data),
-        label_names=label_names)
+    )

mediapipe/model_maker/python/vision/image_classifier/dataset.py

@@ -15,28 +15,12 @@
 
 import os
 import random
-
-from typing import List, Optional
 import tensorflow as tf
-import tensorflow_datasets as tfds
 
 from mediapipe.model_maker.python.core.data import classification_dataset
 from mediapipe.model_maker.python.vision.core import image_utils
 
 
-def _create_data(
-    name: str, data: tf.data.Dataset, info: tfds.core.DatasetInfo,
-    label_names: List[str]
-) -> Optional[classification_dataset.ClassificationDataset]:
-  """Creates a Dataset object from tfds data."""
-  if name not in data:
-    return None
-  data = data[name]
-  data = data.map(lambda a: (a['image'], a['label']))
-  size = info.splits[name].num_examples
-  return Dataset(data, size, label_names)
-
-
 class Dataset(classification_dataset.ClassificationDataset):
   """Dataset library for image classifier."""
 
@@ -99,4 +83,5 @@ class Dataset(classification_dataset.ClassificationDataset):
         'Load image with size: %d, num_label: %d, labels: %s.', all_image_size,
         all_label_size, ', '.join(label_names))
     return Dataset(
-        dataset=image_label_ds, size=all_image_size, label_names=label_names)
+        dataset=image_label_ds, label_names=label_names, size=all_image_size
+    )

mediapipe/model_maker/python/vision/image_classifier/dataset_test.py

@@ -41,7 +41,7 @@ class DatasetTest(tf.test.TestCase):
 
   def test_split(self):
     ds = tf.data.Dataset.from_tensor_slices([[0, 1], [1, 1], [0, 0], [1, 0]])
-    data = dataset.Dataset(dataset=ds, size=4, label_names=['pos', 'neg'])
+    data = dataset.Dataset(dataset=ds, label_names=['pos', 'neg'], size=4)
     train_data, test_data = data.split(fraction=0.5)
 
     self.assertLen(train_data, 2)

mediapipe/model_maker/python/vision/image_classifier/image_classifier_test.py

@@ -52,8 +52,9 @@ class ImageClassifierTest(tf.test.TestCase, parameterized.TestCase):
     ds = tf.data.Dataset.from_generator(
         self._gen, (tf.uint8, tf.int64), (tf.TensorShape(
             [self.IMAGE_SIZE, self.IMAGE_SIZE, 3]), tf.TensorShape([])))
-    data = image_classifier.Dataset(ds, self.IMAGES_PER_CLASS * 3,
-                                    ['cyan', 'magenta', 'yellow'])
+    data = image_classifier.Dataset(
+        ds, ['cyan', 'magenta', 'yellow'], self.IMAGES_PER_CLASS * 3
+    )
     return data
 
   def setUp(self):

mediapipe/model_maker/python/vision/object_detector/BUILD

@@ -54,6 +54,7 @@ py_library(
     srcs = ["dataset.py"],
     deps = [
         ":dataset_util",
+        "//mediapipe/model_maker/python/core/data:cache_files",
         "//mediapipe/model_maker/python/core/data:classification_dataset",
     ],
 )
@@ -73,6 +74,7 @@ py_test(
 py_library(
     name = "dataset_util",
     srcs = ["dataset_util.py"],
+    deps = ["//mediapipe/model_maker/python/core/data:cache_files"],
 )
 
 py_test(

mediapipe/model_maker/python/vision/object_detector/dataset.py

@@ -16,8 +16,8 @@
 from typing import Optional
 
 import tensorflow as tf
-import yaml
 
+from mediapipe.model_maker.python.core.data import cache_files
 from mediapipe.model_maker.python.core.data import classification_dataset
 from mediapipe.model_maker.python.vision.object_detector import dataset_util
 from official.vision.dataloaders import tf_example_decoder
@@ -76,14 +76,16 @@ class Dataset(classification_dataset.ClassificationDataset):
       ValueError: If the label_name for id 0 is set to something other than
         the 'background' class.
     """
-    cache_files = dataset_util.get_cache_files_coco(data_dir, cache_dir)
-    if not dataset_util.is_cached(cache_files):
+    tfrecord_cache_files = dataset_util.get_cache_files_coco(
+        data_dir, cache_dir
+    )
+    if not tfrecord_cache_files.is_cached():
       label_map = dataset_util.get_label_map_coco(data_dir)
       cache_writer = dataset_util.COCOCacheFilesWriter(
           label_map=label_map, max_num_images=max_num_images
       )
-      cache_writer.write_files(cache_files, data_dir)
-    return cls.from_cache(cache_files.cache_prefix)
+      cache_writer.write_files(tfrecord_cache_files, data_dir)
+    return cls.from_cache(tfrecord_cache_files)
 
   @classmethod
   def from_pascal_voc_folder(
@@ -134,47 +136,48 @@ class Dataset(classification_dataset.ClassificationDataset):
     Raises:
       ValueError: if the input data directory is empty.
     """
-    cache_files = dataset_util.get_cache_files_pascal_voc(data_dir, cache_dir)
-    if not dataset_util.is_cached(cache_files):
+    tfrecord_cache_files = dataset_util.get_cache_files_pascal_voc(
+        data_dir, cache_dir
+    )
+    if not tfrecord_cache_files.is_cached():
       label_map = dataset_util.get_label_map_pascal_voc(data_dir)
       cache_writer = dataset_util.PascalVocCacheFilesWriter(
           label_map=label_map, max_num_images=max_num_images
       )
-      cache_writer.write_files(cache_files, data_dir)
+      cache_writer.write_files(tfrecord_cache_files, data_dir)
 
-    return cls.from_cache(cache_files.cache_prefix)
+    return cls.from_cache(tfrecord_cache_files)
 
   @classmethod
-  def from_cache(cls, cache_prefix: str) -> 'Dataset':
+  def from_cache(
+      cls, tfrecord_cache_files: cache_files.TFRecordCacheFiles
+  ) -> 'Dataset':
     """Loads the TFRecord data from cache.
 
     Args:
-      cache_prefix: The cache prefix including the cache directory and the cache
-        prefix filename, e.g: '/tmp/cache/train'.
+      tfrecord_cache_files: The TFRecordCacheFiles object containing the already
+        cached TFRecord and metadata files.
 
     Returns:
       ObjectDetectorDataset object.
+
+    Raises:
+      ValueError if tfrecord_cache_files are not already cached.
     """
-    # Get TFRecord Files
-    tfrecord_file_pattern = cache_prefix + '*.tfrecord'
-    matched_files = tf.io.gfile.glob(tfrecord_file_pattern)
-    if not matched_files:
-      raise ValueError('TFRecord files are empty.')
+    if not tfrecord_cache_files.is_cached():
+      raise ValueError(
+          'Cache files must be already cached to use the from_cache method.'
+      )
 
-    # Load meta_data.
-    meta_data_file = cache_prefix + dataset_util.META_DATA_FILE_SUFFIX
-    if not tf.io.gfile.exists(meta_data_file):
-      raise ValueError("Metadata file %s doesn't exist." % meta_data_file)
-    with tf.io.gfile.GFile(meta_data_file, 'r') as f:
-      meta_data = yaml.load(f, Loader=yaml.FullLoader)
+    metadata = tfrecord_cache_files.load_metadata()
 
-    dataset = tf.data.TFRecordDataset(matched_files)
+    dataset = tf.data.TFRecordDataset(tfrecord_cache_files.tfrecord_files)
     decoder = tf_example_decoder.TfExampleDecoder(regenerate_source_id=False)
     dataset = dataset.map(decoder.decode, num_parallel_calls=tf.data.AUTOTUNE)
 
-    label_map = meta_data['label_map']
+    label_map = metadata['label_map']
     label_names = [label_map[k] for k in sorted(label_map.keys())]
 
     return Dataset(
-        dataset=dataset, size=meta_data['size'], label_names=label_names
+        dataset=dataset, label_names=label_names, size=metadata['size']
     )

mediapipe/model_maker/python/vision/object_detector/dataset_util.py

@@ -15,25 +15,20 @@
 
 import abc
 import collections
-import dataclasses
 import hashlib
 import json
 import math
 import os
 import tempfile
-from typing import Any, Dict, List, Mapping, Optional, Sequence
+from typing import Any, Dict, List, Mapping, Optional
 import xml.etree.ElementTree as ET
 
 import tensorflow as tf
-import yaml
 
+from mediapipe.model_maker.python.core.data import cache_files
 from official.vision.data import tfrecord_lib
 
 
-# Suffix of the meta data file name.
-META_DATA_FILE_SUFFIX = '_meta_data.yaml'
-
-
 def _xml_get(node: ET.Element, name: str) -> ET.Element:
   """Gets a named child from an XML Element node.
 
@@ -71,18 +66,9 @@ def _get_dir_basename(data_dir: str) -> str:
   return os.path.basename(os.path.abspath(data_dir))
 
 
-@dataclasses.dataclass(frozen=True)
-class CacheFiles:
-  """Cache files for object detection."""
-
-  cache_prefix: str
-  tfrecord_files: Sequence[str]
-  meta_data_file: str
-
-
 def _get_cache_files(
     cache_dir: Optional[str], cache_prefix_filename: str, num_shards: int = 10
-) -> CacheFiles:
+) -> cache_files.TFRecordCacheFiles:
   """Creates an object of CacheFiles class.
 
   Args:
@@ -96,28 +82,16 @@ def _get_cache_files(
     An object of CacheFiles class.
   """
   cache_dir = _get_cache_dir_or_create(cache_dir)
-  # The cache prefix including the cache directory and the cache prefix
-  # filename, e.g: '/tmp/cache/train'.
-  cache_prefix = os.path.join(cache_dir, cache_prefix_filename)
-  tf.compat.v1.logging.info(
-      'Cache will be stored in %s with prefix filename %s. Cache_prefix is %s'
-      % (cache_dir, cache_prefix_filename, cache_prefix)
-  )
-
-  # Cached files including the TFRecord files and the meta data file.
-  tfrecord_files = [
-      cache_prefix + '-%05d-of-%05d.tfrecord' % (i, num_shards)
-      for i in range(num_shards)
-  ]
-  meta_data_file = cache_prefix + META_DATA_FILE_SUFFIX
-  return CacheFiles(
-      cache_prefix=cache_prefix,
-      tfrecord_files=tuple(tfrecord_files),
-      meta_data_file=meta_data_file,
+  return cache_files.TFRecordCacheFiles(
+      cache_prefix_filename=cache_prefix_filename,
+      cache_dir=cache_dir,
+      num_shards=num_shards,
   )
 
 
-def get_cache_files_coco(data_dir: str, cache_dir: str) -> CacheFiles:
+def get_cache_files_coco(
+    data_dir: str, cache_dir: str
+) -> cache_files.TFRecordCacheFiles:
   """Creates an object of CacheFiles class using a COCO formatted dataset.
 
   Args:
@@ -152,7 +126,9 @@ def get_cache_files_coco(data_dir: str, cache_dir: str) -> CacheFiles:
   return _get_cache_files(cache_dir, cache_prefix_filename, num_shards)
 
 
-def get_cache_files_pascal_voc(data_dir: str, cache_dir: str) -> CacheFiles:
+def get_cache_files_pascal_voc(
+    data_dir: str, cache_dir: str
+) -> cache_files.TFRecordCacheFiles:
   """Gets an object of CacheFiles using a PASCAL VOC formatted dataset.
 
   Args:
@@ -181,14 +157,6 @@ def get_cache_files_pascal_voc(data_dir: str, cache_dir: str) -> CacheFiles:
   return _get_cache_files(cache_dir, cache_prefix_filename, num_shards)
 
 
-def is_cached(cache_files: CacheFiles) -> bool:
-  """Checks whether cache files are already cached."""
-  all_cached_files = list(cache_files.tfrecord_files) + [
-      cache_files.meta_data_file
-  ]
-  return all(tf.io.gfile.exists(path) for path in all_cached_files)
-
-
 class CacheFilesWriter(abc.ABC):
   """CacheFilesWriter class to write the cached files."""
 
@@ -208,19 +176,22 @@ class CacheFilesWriter(abc.ABC):
     self.label_map = label_map
     self.max_num_images = max_num_images
 
-  def write_files(self, cache_files: CacheFiles, *args, **kwargs) -> None:
-    """Writes TFRecord and meta_data files.
+  def write_files(
+      self,
+      tfrecord_cache_files: cache_files.TFRecordCacheFiles,
+      *args,
+      **kwargs,
+  ) -> None:
+    """Writes TFRecord and metadata files.
 
     Args:
-      cache_files: CacheFiles object including a list of TFRecord files and the
-        meta data yaml file to save the meta_data including data size and
-        label_map.
+      tfrecord_cache_files: TFRecordCacheFiles object including a list of
+        TFRecord files and the meta data yaml file to save the metadata
+        including data size and label_map.
       *args: Non-keyword of parameters used in the `_get_example` method.
       **kwargs: Keyword parameters used in the `_get_example` method.
     """
-    writers = [
-        tf.io.TFRecordWriter(path) for path in cache_files.tfrecord_files
-    ]
+    writers = tfrecord_cache_files.get_writers()
 
     # Writes tf.Example into TFRecord files.
     size = 0
@@ -235,10 +206,9 @@ class CacheFilesWriter(abc.ABC):
     for writer in writers:
       writer.close()
 
-    # Writes meta_data into meta_data_file.
-    meta_data = {'size': size, 'label_map': self.label_map}
-    with tf.io.gfile.GFile(cache_files.meta_data_file, 'w') as f:
-      yaml.dump(meta_data, f)
+    # Writes metadata into metadata_file.
+    metadata = {'size': size, 'label_map': self.label_map}
+    tfrecord_cache_files.save_metadata(metadata)
 
   @abc.abstractmethod
   def _get_example(self, *args, **kwargs):

mediapipe/model_maker/python/vision/object_detector/dataset_util_test.py

@@ -19,7 +19,6 @@ import shutil
 from unittest import mock as unittest_mock
 
 import tensorflow as tf
-import yaml
 
 from mediapipe.model_maker.python.vision.core import test_utils
 from mediapipe.model_maker.python.vision.object_detector import dataset_util
@@ -30,13 +29,10 @@ class DatasetUtilTest(tf.test.TestCase):
 
   def _assert_cache_files_equal(self, cf1, cf2):
     self.assertEqual(cf1.cache_prefix, cf2.cache_prefix)
-    self.assertCountEqual(cf1.tfrecord_files, cf2.tfrecord_files)
-    self.assertEqual(cf1.meta_data_file, cf2.meta_data_file)
+    self.assertEqual(cf1.num_shards, cf2.num_shards)
 
   def _assert_cache_files_not_equal(self, cf1, cf2):
     self.assertNotEqual(cf1.cache_prefix, cf2.cache_prefix)
-    self.assertNotEqual(cf1.tfrecord_files, cf2.tfrecord_files)
-    self.assertNotEqual(cf1.meta_data_file, cf2.meta_data_file)
 
   def _get_cache_files_and_assert_neq_fn(self, cache_files_fn):
     def get_cache_files_and_assert_neq(cf, data_dir, cache_dir):
@@ -57,7 +53,7 @@ class DatasetUtilTest(tf.test.TestCase):
     self.assertEqual(
         cache_files.tfrecord_files[0], '/tmp/train-00000-of-00001.tfrecord'
     )
-    self.assertEqual(cache_files.meta_data_file, '/tmp/train_meta_data.yaml')
+    self.assertEqual(cache_files.metadata_file, '/tmp/train_metadata.yaml')
 
   def test_matching_get_cache_files_coco(self):
     cache_dir = self.create_tempdir()
@@ -118,7 +114,7 @@ class DatasetUtilTest(tf.test.TestCase):
     self.assertEqual(
         cache_files.tfrecord_files[0], '/tmp/train-00000-of-00001.tfrecord'
     )
-    self.assertEqual(cache_files.meta_data_file, '/tmp/train_meta_data.yaml')
+    self.assertEqual(cache_files.metadata_file, '/tmp/train_metadata.yaml')
 
   def test_matching_get_cache_files_pascal_voc(self):
     cache_dir = self.create_tempdir()
@@ -173,13 +169,13 @@ class DatasetUtilTest(tf.test.TestCase):
     cache_files = dataset_util.get_cache_files_coco(
         tasks_test_utils.get_test_data_path('coco_data'), cache_dir=tempdir
     )
-    self.assertFalse(dataset_util.is_cached(cache_files))
+    self.assertFalse(cache_files.is_cached())
     with open(cache_files.tfrecord_files[0], 'w') as f:
       f.write('test')
-    self.assertFalse(dataset_util.is_cached(cache_files))
-    with open(cache_files.meta_data_file, 'w') as f:
+    self.assertFalse(cache_files.is_cached())
+    with open(cache_files.metadata_file, 'w') as f:
       f.write('test')
-    self.assertTrue(dataset_util.is_cached(cache_files))
+    self.assertTrue(cache_files.is_cached())
 
   def test_get_label_map_coco(self):
     coco_dir = tasks_test_utils.get_test_data_path('coco_data')
@@ -203,13 +199,11 @@ class DatasetUtilTest(tf.test.TestCase):
     self.assertTrue(os.path.isfile(cache_files.tfrecord_files[0]))
     self.assertGreater(os.path.getsize(cache_files.tfrecord_files[0]), 0)
 
-    # Checks the meta_data file
-    self.assertTrue(os.path.isfile(cache_files.meta_data_file))
-    self.assertGreater(os.path.getsize(cache_files.meta_data_file), 0)
-    with tf.io.gfile.GFile(cache_files.meta_data_file, 'r') as f:
-      meta_data_dict = yaml.load(f, Loader=yaml.FullLoader)
-      # Size is 3 because some examples are skipped for having poor bboxes
-      self.assertEqual(meta_data_dict['size'], expected_size)
+    # Checks the metadata file
+    self.assertTrue(os.path.isfile(cache_files.metadata_file))
+    self.assertGreater(os.path.getsize(cache_files.metadata_file), 0)
+    metadata_dict = cache_files.load_metadata()
+    self.assertEqual(metadata_dict['size'], expected_size)
 
   def test_coco_cache_files_writer(self):
     tempdir = self.create_tempdir()

mediapipe/tasks/cc/vision/face_landmarker/BUILD

@@ -217,3 +217,8 @@ cc_library(
     ],
     alwayslink = 1,
 )
+
+cc_library(
+    name = "face_landmarks_connections",
+    hdrs = ["face_landmarks_connections.h"],
+)

mediapipe/tasks/cc/vision/face_landmarker/face_landmarks_connections.h

@@ -0,0 +1,651 @@
+/* Copyright 2023 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.
+==============================================================================*/
+
+#ifndef MEDIAPIPE_TASKS_CC_VISION_FACE_LANDMARKER_FACE_LANDMARKS_CONNECTIONS_H_
+#define MEDIAPIPE_TASKS_CC_VISION_FACE_LANDMARKER_FACE_LANDMARKS_CONNECTIONS_H_
+
+#include <array>
+
+namespace mediapipe {
+namespace tasks {
+namespace vision {
+namespace face_landmarker {
+
+struct FaceLandmarksConnections {
+  static constexpr std::array<std::array<int, 2>, 40> kFaceLandmarksLips{
+      {{61, 146},  {146, 91},  {91, 181},  {181, 84},  {84, 17},   {17, 314},
+       {314, 405}, {405, 321}, {321, 375}, {375, 291}, {61, 185},  {185, 40},
+       {40, 39},   {39, 37},   {37, 0},    {0, 267},   {267, 269}, {269, 270},
+       {270, 409}, {409, 291}, {78, 95},   {95, 88},   {88, 178},  {178, 87},
+       {87, 14},   {14, 317},  {317, 402}, {402, 318}, {318, 324}, {324, 308},
+       {78, 191},  {191, 80},  {80, 81},   {81, 82},   {82, 13},   {13, 312},
+       {312, 311}, {311, 310}, {310, 415}, {415, 308}}};
+
+  static constexpr std::array<std::array<int, 2>, 16> kFaceLandmarksLeftEye{
+      {{263, 249},
+       {249, 390},
+       {390, 373},
+       {373, 374},
+       {374, 380},
+       {380, 381},
+       {381, 382},
+       {382, 362},
+       {263, 466},
+       {466, 388},
+       {388, 387},
+       {387, 386},
+       {386, 385},
+       {385, 384},
+       {384, 398},
+       {398, 362}}};
+
+  static constexpr std::array<std::array<int, 2>, 8> kFaceLandmarksLeftEyeBrow{
+      {{276, 283},
+       {283, 282},
+       {282, 295},
+       {295, 285},
+       {300, 293},
+       {293, 334},
+       {334, 296},
+       {296, 336}}};
+
+  static constexpr std::array<std::array<int, 2>, 4> kFaceLandmarksLeftIris{
+      {{474, 475}, {475, 476}, {476, 477}, {477, 474}}};
+
+  static constexpr std::array<std::array<int, 2>, 16> kFaceLandmarksRightEye{
+      {{33, 7},
+       {7, 163},
+       {163, 144},
+       {144, 145},
+       {145, 153},
+       {153, 154},
+       {154, 155},
+       {155, 133},
+       {33, 246},
+       {246, 161},
+       {161, 160},
+       {160, 159},
+       {159, 158},
+       {158, 157},
+       {157, 173},
+       {173, 133}}};
+
+  static constexpr std::array<std::array<int, 2>, 8> kFaceLandmarksRightEyeBrow{
+      {{46, 53},
+       {53, 52},
+       {52, 65},
+       {65, 55},
+       {70, 63},
+       {63, 105},
+       {105, 66},
+       {66, 107}}};
+
+  static constexpr std::array<std::array<int, 2>, 4> kFaceLandmarksRightIris{
+      {{469, 470}, {470, 471}, {471, 472}, {472, 469}}};
+
+  static constexpr std::array<std::array<int, 2>, 36> kFaceLandmarksFaceOval{
+      {{10, 338},  {338, 297}, {297, 332}, {332, 284}, {284, 251}, {251, 389},
+       {389, 356}, {356, 454}, {454, 323}, {323, 361}, {361, 288}, {288, 397},
+       {397, 365}, {365, 379}, {379, 378}, {378, 400}, {400, 377}, {377, 152},
+       {152, 148}, {148, 176}, {176, 149}, {149, 150}, {150, 136}, {136, 172},
+       {172, 58},  {58, 132},  {132, 93},  {93, 234},  {234, 127}, {127, 162},
+       {162, 21},  {21, 54},   {54, 103},  {103, 67},  {67, 109},  {109, 10}}};
+
+  // Lips + Left Eye + Left Eye Brows + Right Eye + Right Eye Brows + Face Oval.
+  static constexpr std::array<std::array<int, 2>, 132> kFaceLandmarksConnectors{
+      {{61, 146},  {146, 91},  {91, 181},  {181, 84},  {84, 17},   {17, 314},
+       {314, 405}, {405, 321}, {321, 375}, {375, 291}, {61, 185},  {185, 40},
+       {40, 39},   {39, 37},   {37, 0},    {0, 267},   {267, 269}, {269, 270},
+       {270, 409}, {409, 291}, {78, 95},   {95, 88},   {88, 178},  {178, 87},
+       {87, 14},   {14, 317},  {317, 402}, {402, 318}, {318, 324}, {324, 308},
+       {78, 191},  {191, 80},  {80, 81},   {81, 82},   {82, 13},   {13, 312},
+       {312, 311}, {311, 310}, {310, 415}, {415, 30},  {263, 249}, {249, 390},
+       {390, 373}, {373, 374}, {374, 380}, {380, 381}, {381, 382}, {382, 362},
+       {263, 466}, {466, 388}, {388, 387}, {387, 386}, {386, 385}, {385, 384},
+       {384, 398}, {398, 362}, {276, 283}, {283, 282}, {282, 295}, {295, 285},
+       {300, 293}, {293, 334}, {334, 296}, {296, 336}, {33, 7},    {7, 163},
+       {163, 144}, {144, 145}, {145, 153}, {153, 154}, {154, 155}, {155, 133},
+       {33, 246},  {246, 161}, {161, 160}, {160, 159}, {159, 158}, {158, 157},
+       {157, 173}, {173, 13},  {46, 53},   {53, 52},   {52, 65},   {65, 55},
+       {70, 63},   {63, 105},  {105, 66},  {66, 107},  {10, 338},  {338, 297},
+       {297, 332}, {332, 284}, {284, 251}, {251, 389}, {389, 356}, {356, 454},
+       {454, 323}, {323, 361}, {361, 288}, {288, 397}, {397, 365}, {365, 379},
+       {379, 378}, {378, 400}, {400, 377}, {377, 152}, {152, 148}, {148, 176},
+       {176, 149}, {149, 150}, {150, 136}, {136, 172}, {172, 58},  {58, 132},
+       {132, 93},  {93, 234},  {234, 127}, {127, 162}, {162, 21},  {21, 54},
+       {54, 103},  {103, 67},  {67, 109},  {109, 10}}};
+
+  static constexpr std::array<std::array<int, 2>, 2556>
+      kFaceLandmarksTesselation{
+          {{127, 34},  {34, 139},  {139, 127}, {11, 0},    {0, 37},
+           {37, 11},   {232, 231}, {231, 120}, {120, 232}, {72, 37},
+           {37, 39},   {39, 72},   {128, 121}, {121, 47},  {47, 128},
+           {232, 121}, {121, 128}, {128, 232}, {104, 69},  {69, 67},
+           {67, 104},  {175, 171}, {171, 148}, {148, 175}, {118, 50},
+           {50, 101},  {101, 118}, {73, 39},   {39, 40},   {40, 73},
+           {9, 151},   {151, 108}, {108, 9},   {48, 115},  {115, 131},
+           {131, 48},  {194, 204}, {204, 211}, {211, 194}, {74, 40},
+           {40, 185},  {185, 74},  {80, 42},   {42, 183},  {183, 80},
+           {40, 92},   {92, 186},  {186, 40},  {230, 229}, {229, 118},
+           {118, 230}, {202, 212}, {212, 214}, {214, 202}, {83, 18},
+           {18, 17},   {17, 83},   {76, 61},   {61, 146},  {146, 76},
+           {160, 29},  {29, 30},   {30, 160},  {56, 157},  {157, 173},
+           {173, 56},  {106, 204}, {204, 194}, {194, 106}, {135, 214},
+           {214, 192}, {192, 135}, {203, 165}, {165, 98},  {98, 203},
+           {21, 71},   {71, 68},   {68, 21},   {51, 45},   {45, 4},
+           {4, 51},    {144, 24},  {24, 23},   {23, 144},  {77, 146},
+           {146, 91},  {91, 77},   {205, 50},  {50, 187},  {187, 205},
+           {201, 200}, {200, 18},  {18, 201},  {91, 106},  {106, 182},
+           {182, 91},  {90, 91},   {91, 181},  {181, 90},  {85, 84},
+           {84, 17},   {17, 85},   {206, 203}, {203, 36},  {36, 206},
+           {148, 171}, {171, 140}, {140, 148}, {92, 40},   {40, 39},
+           {39, 92},   {193, 189}, {189, 244}, {244, 193}, {159, 158},
+           {158, 28},  {28, 159},  {247, 246}, {246, 161}, {161, 247},
+           {236, 3},   {3, 196},   {196, 236}, {54, 68},   {68, 104},
+           {104, 54},  {193, 168}, {168, 8},   {8, 193},   {117, 228},
+           {228, 31},  {31, 117},  {189, 193}, {193, 55},  {55, 189},
+           {98, 97},   {97, 99},   {99, 98},   {126, 47},  {47, 100},
+           {100, 126}, {166, 79},  {79, 218},  {218, 166}, {155, 154},
+           {154, 26},  {26, 155},  {209, 49},  {49, 131},  {131, 209},
+           {135, 136}, {136, 150}, {150, 135}, {47, 126},  {126, 217},
+           {217, 47},  {223, 52},  {52, 53},   {53, 223},  {45, 51},
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+           {231, 230}, {230, 120}, {120, 231}, {113, 226}, {226, 247},
+           {247, 113}, {105, 63},  {63, 52},   {52, 105},  {241, 238},
+           {238, 242}, {242, 241}, {124, 46},  {46, 156},  {156, 124},
+           {95, 78},   {78, 96},   {96, 95},   {70, 46},   {46, 63},
+           {63, 70},   {116, 143}, {143, 227}, {227, 116}, {116, 123},
+           {123, 111}, {111, 116}, {1, 44},    {44, 19},   {19, 1},
+           {3, 236},   {236, 51},  {51, 3},    {207, 216}, {216, 205},
+           {205, 207}, {26, 154},  {154, 22},  {22, 26},   {165, 39},
+           {39, 167},  {167, 165}, {199, 200}, {200, 208}, {208, 199},
+           {101, 36},  {36, 100},  {100, 101}, {43, 57},   {57, 202},
+           {202, 43},  {242, 20},  {20, 99},   {99, 242},  {56, 28},
+           {28, 157},  {157, 56},  {124, 35},  {35, 113},  {113, 124},
+           {29, 160},  {160, 27},  {27, 29},   {211, 204}, {204, 210},
+           {210, 211}, {124, 113}, {113, 46},  {46, 124},  {106, 43},
+           {43, 204},  {204, 106}, {96, 62},   {62, 77},   {77, 96},
+           {227, 137}, {137, 116}, {116, 227}, {73, 41},   {41, 72},
+           {72, 73},   {36, 203},  {203, 142}, {142, 36},  {235, 64},
+           {64, 240},  {240, 235}, {48, 49},   {49, 64},   {64, 48},
+           {42, 41},   {41, 74},   {74, 42},   {214, 212}, {212, 207},
+           {207, 214}, {183, 42},  {42, 184},  {184, 183}, {210, 169},
+           {169, 211}, {211, 210}, {140, 170}, {170, 176}, {176, 140},
+           {104, 105}, {105, 69},  {69, 104},  {193, 122}, {122, 168},
+           {168, 193}, {50, 123},  {123, 187}, {187, 50},  {89, 96},
+           {96, 90},   {90, 89},   {66, 65},   {65, 107},  {107, 66},
+           {179, 89},  {89, 180},  {180, 179}, {119, 101}, {101, 120},
+           {120, 119}, {68, 63},   {63, 104},  {104, 68},  {234, 93},
+           {93, 227},  {227, 234}, {16, 15},   {15, 85},   {85, 16},
+           {209, 129}, {129, 49},  {49, 209},  {15, 14},   {14, 86},
+           {86, 15},   {107, 55},  {55, 9},    {9, 107},   {120, 100},
+           {100, 121}, {121, 120}, {153, 145}, {145, 22},  {22, 153},
+           {178, 88},  {88, 179},  {179, 178}, {197, 6},   {6, 196},
+           {196, 197}, {89, 88},   {88, 96},   {96, 89},   {135, 138},
+           {138, 136}, {136, 135}, {138, 215}, {215, 172}, {172, 138},
+           {218, 115}, {115, 219}, {219, 218}, {41, 42},   {42, 81},
+           {81, 41},   {5, 195},   {195, 51},  {51, 5},    {57, 43},
+           {43, 61},   {61, 57},   {208, 171}, {171, 199}, {199, 208},
+           {41, 81},   {81, 38},   {38, 41},   {224, 53},  {53, 225},
+           {225, 224}, {24, 144},  {144, 110}, {110, 24},  {105, 52},
+           {52, 66},   {66, 105},  {118, 229}, {229, 117}, {117, 118},
+           {227, 34},  {34, 234},  {234, 227}, {66, 107},  {107, 69},
+           {69, 66},   {10, 109},  {109, 151}, {151, 10},  {219, 48},
+           {48, 235},  {235, 219}, {183, 62},  {62, 191},  {191, 183},
+           {142, 129}, {129, 126}, {126, 142}, {116, 111}, {111, 143},
+           {143, 116}, {118, 117}, {117, 50},  {50, 118},  {223, 222},
+           {222, 52},  {52, 223},  {94, 19},   {19, 141},  {141, 94},
+           {222, 221}, {221, 65},  {65, 222},  {196, 3},   {3, 197},
+           {197, 196}, {45, 220},  {220, 44},  {44, 45},   {156, 70},
+           {70, 139},  {139, 156}, {188, 122}, {122, 245}, {245, 188},
+           {139, 71},  {71, 162},  {162, 139}, {149, 170}, {170, 150},
+           {150, 149}, {122, 188}, {188, 196}, {196, 122}, {206, 216},
+           {216, 92},  {92, 206},  {164, 2},   {2, 167},   {167, 164},
+           {242, 141}, {141, 241}, {241, 242}, {0, 164},   {164, 37},
+           {37, 0},    {11, 72},   {72, 12},   {12, 11},   {12, 38},
+           {38, 13},   {13, 12},   {70, 63},   {63, 71},   {71, 70},
+           {31, 226},  {226, 111}, {111, 31},  {36, 101},  {101, 205},
+           {205, 36},  {203, 206}, {206, 165}, {165, 203}, {126, 209},
+           {209, 217}, {217, 126}, {98, 165},  {165, 97},  {97, 98},
+           {237, 220}, {220, 218}, {218, 237}, {237, 239}, {239, 241},
+           {241, 237}, {210, 214}, {214, 169}, {169, 210}, {140, 171},
+           {171, 32},  {32, 140},  {241, 125}, {125, 237}, {237, 241},
+           {179, 86},  {86, 178},  {178, 179}, {180, 85},  {85, 179},
+           {179, 180}, {181, 84},  {84, 180},  {180, 181}, {182, 83},
+           {83, 181},  {181, 182}, {194, 201}, {201, 182}, {182, 194},
+           {177, 137}, {137, 132}, {132, 177}, {184, 76},  {76, 183},
+           {183, 184}, {185, 61},  {61, 184},  {184, 185}, {186, 57},
+           {57, 185},  {185, 186}, {216, 212}, {212, 186}, {186, 216},
+           {192, 214}, {214, 187}, {187, 192}, {139, 34},  {34, 156},
+           {156, 139}, {218, 79},  {79, 237},  {237, 218}, {147, 123},
+           {123, 177}, {177, 147}, {45, 44},   {44, 4},    {4, 45},
+           {208, 201}, {201, 32},  {32, 208},  {98, 64},   {64, 129},
+           {129, 98},  {192, 213}, {213, 138}, {138, 192}, {235, 59},
+           {59, 219},  {219, 235}, {141, 242}, {242, 97},  {97, 141},
+           {97, 2},    {2, 141},   {141, 97},  {240, 75},  {75, 235},
+           {235, 240}, {229, 24},  {24, 228},  {228, 229}, {31, 25},
+           {25, 226},  {226, 31},  {230, 23},  {23, 229},  {229, 230},
+           {231, 22},  {22, 230},  {230, 231}, {232, 26},  {26, 231},
+           {231, 232}, {233, 112}, {112, 232}, {232, 233}, {244, 189},
+           {189, 243}, {243, 244}, {189, 221}, {221, 190}, {190, 189},
+           {222, 28},  {28, 221},  {221, 222}, {223, 27},  {27, 222},
+           {222, 223}, {224, 29},  {29, 223},  {223, 224}, {225, 30},
+           {30, 224},  {224, 225}, {113, 247}, {247, 225}, {225, 113},
+           {99, 60},   {60, 240},  {240, 99},  {213, 147}, {147, 215},
+           {215, 213}, {60, 20},   {20, 166},  {166, 60},  {192, 187},
+           {187, 213}, {213, 192}, {243, 112}, {112, 244}, {244, 243},
+           {244, 233}, {233, 245}, {245, 244}, {245, 128}, {128, 188},
+           {188, 245}, {188, 114}, {114, 174}, {174, 188}, {134, 131},
+           {131, 220}, {220, 134}, {174, 217}, {217, 236}, {236, 174},
+           {236, 198}, {198, 134}, {134, 236}, {215, 177}, {177, 58},
+           {58, 215},  {156, 143}, {143, 124}, {124, 156}, {25, 110},
+           {110, 7},   {7, 25},    {31, 228},  {228, 25},  {25, 31},
+           {264, 356}, {356, 368}, {368, 264}, {0, 11},    {11, 267},
+           {267, 0},   {451, 452}, {452, 349}, {349, 451}, {267, 302},
+           {302, 269}, {269, 267}, {350, 357}, {357, 277}, {277, 350},
+           {350, 452}, {452, 357}, {357, 350}, {299, 333}, {333, 297},
+           {297, 299}, {396, 175}, {175, 377}, {377, 396}, {280, 347},
+           {347, 330}, {330, 280}, {269, 303}, {303, 270}, {270, 269},
+           {151, 9},   {9, 337},   {337, 151}, {344, 278}, {278, 360},
+           {360, 344}, {424, 418}, {418, 431}, {431, 424}, {270, 304},
+           {304, 409}, {409, 270}, {272, 310}, {310, 407}, {407, 272},
+           {322, 270}, {270, 410}, {410, 322}, {449, 450}, {450, 347},
+           {347, 449}, {432, 422}, {422, 434}, {434, 432}, {18, 313},
+           {313, 17},  {17, 18},   {291, 306}, {306, 375}, {375, 291},
+           {259, 387}, {387, 260}, {260, 259}, {424, 335}, {335, 418},
+           {418, 424}, {434, 364}, {364, 416}, {416, 434}, {391, 423},
+           {423, 327}, {327, 391}, {301, 251}, {251, 298}, {298, 301},
+           {275, 281}, {281, 4},   {4, 275},   {254, 373}, {373, 253},
+           {253, 254}, {375, 307}, {307, 321}, {321, 375}, {280, 425},
+           {425, 411}, {411, 280}, {200, 421}, {421, 18},  {18, 200},
+           {335, 321}, {321, 406}, {406, 335}, {321, 320}, {320, 405},
+           {405, 321}, {314, 315}, {315, 17},  {17, 314},  {423, 426},
+           {426, 266}, {266, 423}, {396, 377}, {377, 369}, {369, 396},
+           {270, 322}, {322, 269}, {269, 270}, {413, 417}, {417, 464},
+           {464, 413}, {385, 386}, {386, 258}, {258, 385}, {248, 456},
+           {456, 419}, {419, 248}, {298, 284}, {284, 333}, {333, 298},
+           {168, 417}, {417, 8},   {8, 168},   {448, 346}, {346, 261},
+           {261, 448}, {417, 413}, {413, 285}, {285, 417}, {326, 327},
+           {327, 328}, {328, 326}, {277, 355}, {355, 329}, {329, 277},
+           {309, 392}, {392, 438}, {438, 309}, {381, 382}, {382, 256},
+           {256, 381}, {279, 429}, {429, 360}, {360, 279}, {365, 364},
+           {364, 379}, {379, 365}, {355, 277}, {277, 437}, {437, 355},
+           {282, 443}, {443, 283}, {283, 282}, {281, 275}, {275, 363},
+           {363, 281}, {395, 431}, {431, 369}, {369, 395}, {299, 297},
+           {297, 337}, {337, 299}, {335, 273}, {273, 321}, {321, 335},
+           {348, 450}, {450, 349}, {349, 348}, {359, 446}, {446, 467},
+           {467, 359}, {283, 293}, {293, 282}, {282, 283}, {250, 458},
+           {458, 462}, {462, 250}, {300, 276}, {276, 383}, {383, 300},
+           {292, 308}, {308, 325}, {325, 292}, {283, 276}, {276, 293},
+           {293, 283}, {264, 372}, {372, 447}, {447, 264}, {346, 352},
+           {352, 340}, {340, 346}, {354, 274}, {274, 19},  {19, 354},
+           {363, 456}, {456, 281}, {281, 363}, {426, 436}, {436, 425},
+           {425, 426}, {380, 381}, {381, 252}, {252, 380}, {267, 269},
+           {269, 393}, {393, 267}, {421, 200}, {200, 428}, {428, 421},
+           {371, 266}, {266, 329}, {329, 371}, {432, 287}, {287, 422},
+           {422, 432}, {290, 250}, {250, 328}, {328, 290}, {385, 258},
+           {258, 384}, {384, 385}, {446, 265}, {265, 342}, {342, 446},
+           {386, 387}, {387, 257}, {257, 386}, {422, 424}, {424, 430},
+           {430, 422}, {445, 342}, {342, 276}, {276, 445}, {422, 273},
+           {273, 424}, {424, 422}, {306, 292}, {292, 307}, {307, 306},
+           {352, 366}, {366, 345}, {345, 352}, {268, 271}, {271, 302},
+           {302, 268}, {358, 423}, {423, 371}, {371, 358}, {327, 294},
+           {294, 460}, {460, 327}, {331, 279}, {279, 294}, {294, 331},
+           {303, 271}, {271, 304}, {304, 303}, {436, 432}, {432, 427},
+           {427, 436}, {304, 272}, {272, 408}, {408, 304}, {395, 394},
+           {394, 431}, {431, 395}, {378, 395}, {395, 400}, {400, 378},
+           {296, 334}, {334, 299}, {299, 296}, {6, 351},   {351, 168},
+           {168, 6},   {376, 352}, {352, 411}, {411, 376}, {307, 325},
+           {325, 320}, {320, 307}, {285, 295}, {295, 336}, {336, 285},
+           {320, 319}, {319, 404}, {404, 320}, {329, 330}, {330, 349},
+           {349, 329}, {334, 293}, {293, 333}, {333, 334}, {366, 323},
+           {323, 447}, {447, 366}, {316, 15},  {15, 315},  {315, 316},
+           {331, 358}, {358, 279}, {279, 331}, {317, 14},  {14, 316},
+           {316, 317}, {8, 285},   {285, 9},   {9, 8},     {277, 329},
+           {329, 350}, {350, 277}, {253, 374}, {374, 252}, {252, 253},
+           {319, 318}, {318, 403}, {403, 319}, {351, 6},   {6, 419},
+           {419, 351}, {324, 318}, {318, 325}, {325, 324}, {397, 367},
+           {367, 365}, {365, 397}, {288, 435}, {435, 397}, {397, 288},
+           {278, 344}, {344, 439}, {439, 278}, {310, 272}, {272, 311},
+           {311, 310}, {248, 195}, {195, 281}, {281, 248}, {375, 273},
+           {273, 291}, {291, 375}, {175, 396}, {396, 199}, {199, 175},
+           {312, 311}, {311, 268}, {268, 312}, {276, 283}, {283, 445},
+           {445, 276}, {390, 373}, {373, 339}, {339, 390}, {295, 282},
+           {282, 296}, {296, 295}, {448, 449}, {449, 346}, {346, 448},
+           {356, 264}, {264, 454}, {454, 356}, {337, 336}, {336, 299},
+           {299, 337}, {337, 338}, {338, 151}, {151, 337}, {294, 278},
+           {278, 455}, {455, 294}, {308, 292}, {292, 415}, {415, 308},
+           {429, 358}, {358, 355}, {355, 429}, {265, 340}, {340, 372},
+           {372, 265}, {352, 346}, {346, 280}, {280, 352}, {295, 442},
+           {442, 282}, {282, 295}, {354, 19},  {19, 370},  {370, 354},
+           {285, 441}, {441, 295}, {295, 285}, {195, 248}, {248, 197},
+           {197, 195}, {457, 440}, {440, 274}, {274, 457}, {301, 300},
+           {300, 368}, {368, 301}, {417, 351}, {351, 465}, {465, 417},
+           {251, 301}, {301, 389}, {389, 251}, {394, 395}, {395, 379},
+           {379, 394}, {399, 412}, {412, 419}, {419, 399}, {410, 436},
+           {436, 322}, {322, 410}, {326, 2},   {2, 393},   {393, 326},
+           {354, 370}, {370, 461}, {461, 354}, {393, 164}, {164, 267},
+           {267, 393}, {268, 302}, {302, 12},  {12, 268},  {312, 268},
+           {268, 13},  {13, 312},  {298, 293}, {293, 301}, {301, 298},
+           {265, 446}, {446, 340}, {340, 265}, {280, 330}, {330, 425},
+           {425, 280}, {322, 426}, {426, 391}, {391, 322}, {420, 429},
+           {429, 437}, {437, 420}, {393, 391}, {391, 326}, {326, 393},
+           {344, 440}, {440, 438}, {438, 344}, {458, 459}, {459, 461},
+           {461, 458}, {364, 434}, {434, 394}, {394, 364}, {428, 396},
+           {396, 262}, {262, 428}, {274, 354}, {354, 457}, {457, 274},
+           {317, 316}, {316, 402}, {402, 317}, {316, 315}, {315, 403},
+           {403, 316}, {315, 314}, {314, 404}, {404, 315}, {314, 313},
+           {313, 405}, {405, 314}, {313, 421}, {421, 406}, {406, 313},
+           {323, 366}, {366, 361}, {361, 323}, {292, 306}, {306, 407},
+           {407, 292}, {306, 291}, {291, 408}, {408, 306}, {291, 287},
+           {287, 409}, {409, 291}, {287, 432}, {432, 410}, {410, 287},
+           {427, 434}, {434, 411}, {411, 427}, {372, 264}, {264, 383},
+           {383, 372}, {459, 309}, {309, 457}, {457, 459}, {366, 352},
+           {352, 401}, {401, 366}, {1, 274},   {274, 4},   {4, 1},
+           {418, 421}, {421, 262}, {262, 418}, {331, 294}, {294, 358},
+           {358, 331}, {435, 433}, {433, 367}, {367, 435}, {392, 289},
+           {289, 439}, {439, 392}, {328, 462}, {462, 326}, {326, 328},
+           {94, 2},    {2, 370},   {370, 94},  {289, 305}, {305, 455},
+           {455, 289}, {339, 254}, {254, 448}, {448, 339}, {359, 255},
+           {255, 446}, {446, 359}, {254, 253}, {253, 449}, {449, 254},
+           {253, 252}, {252, 450}, {450, 253}, {252, 256}, {256, 451},
+           {451, 252}, {256, 341}, {341, 452}, {452, 256}, {414, 413},
+           {413, 463}, {463, 414}, {286, 441}, {441, 414}, {414, 286},
+           {286, 258}, {258, 441}, {441, 286}, {258, 257}, {257, 442},
+           {442, 258}, {257, 259}, {259, 443}, {443, 257}, {259, 260},
+           {260, 444}, {444, 259}, {260, 467}, {467, 445}, {445, 260},
+           {309, 459}, {459, 250}, {250, 309}, {305, 289}, {289, 290},
+           {290, 305}, {305, 290}, {290, 460}, {460, 305}, {401, 376},
+           {376, 435}, {435, 401}, {309, 250}, {250, 392}, {392, 309},
+           {376, 411}, {411, 433}, {433, 376}, {453, 341}, {341, 464},
+           {464, 453}, {357, 453}, {453, 465}, {465, 357}, {343, 357},
+           {357, 412}, {412, 343}, {437, 343}, {343, 399}, {399, 437},
+           {344, 360}, {360, 440}, {440, 344}, {420, 437}, {437, 456},
+           {456, 420}, {360, 420}, {420, 363}, {363, 360}, {361, 401},
+           {401, 288}, {288, 361}, {265, 372}, {372, 353}, {353, 265},
+           {390, 339}, {339, 249}, {249, 390}, {339, 448}, {448, 255},
+           {255, 339}}};
+};
+
+}  // namespace face_landmarker
+}  // namespace vision
+}  // namespace tasks
+}  // namespace mediapipe
+
+#endif  // MEDIAPIPE_TASKS_CC_VISION_FACE_LANDMARKER_FACE_LANDMARKS_CONNECTIONS_H_

mediapipe/tasks/cc/vision/face_stylizer/calculators/tensors_to_image_calculator.cc

@@ -111,6 +111,7 @@ class TensorsToImageCalculator : public Node {
  private:
   TensorsToImageCalculatorOptions options_;
   absl::Status CpuProcess(CalculatorContext* cc);
+  int tensor_position_;
 
 #if !MEDIAPIPE_DISABLE_GPU
 #if MEDIAPIPE_METAL_ENABLED
@@ -166,6 +167,7 @@ absl::Status TensorsToImageCalculator::Open(CalculatorContext* cc) {
         << "Must specify either `input_tensor_float_range` or "
            "`input_tensor_uint_range` in the calculator options";
   }
+  tensor_position_ = options_.tensor_position();
 
   return absl::OkStatus();
 }
@@ -202,17 +204,23 @@ absl::Status TensorsToImageCalculator::CpuProcess(CalculatorContext* cc) {
     return absl::OkStatus();
   }
   const auto& input_tensors = kInputTensors(cc).Get();
-  RET_CHECK_EQ(input_tensors.size(), 1)
-      << "Expect 1 input tensor, but have " << input_tensors.size();
+  RET_CHECK_GT(input_tensors.size(), tensor_position_)
+      << "Expect input tensor at position " << tensor_position_
+      << ", but have tensors of size " << input_tensors.size();
 
-  const auto& input_tensor = input_tensors[0];
+  const auto& input_tensor = input_tensors[tensor_position_];
   const int tensor_in_height = input_tensor.shape().dims[1];
   const int tensor_in_width = input_tensor.shape().dims[2];
   const int tensor_in_channels = input_tensor.shape().dims[3];
-  RET_CHECK_EQ(tensor_in_channels, 3);
+  RET_CHECK(tensor_in_channels == 3 || tensor_in_channels == 1);
 
-  auto output_frame = std::make_shared<ImageFrame>(
-      mediapipe::ImageFormat::SRGB, tensor_in_width, tensor_in_height);
+  auto format = mediapipe::ImageFormat::SRGB;
+  if (tensor_in_channels == 1) {
+    format = mediapipe::ImageFormat::GRAY8;
+  }
+
+  auto output_frame =
+      std::make_shared<ImageFrame>(format, tensor_in_width, tensor_in_height);
   cv::Mat output_matview = mediapipe::formats::MatView(output_frame.get());
 
   constexpr float kOutputImageRangeMin = 0.0f;
@@ -227,8 +235,9 @@ absl::Status TensorsToImageCalculator::CpuProcess(CalculatorContext* cc) {
                      GetValueRangeTransformation(
                          input_range.min(), input_range.max(),
                          kOutputImageRangeMin, kOutputImageRangeMax));
-    tensor_matview.convertTo(output_matview, CV_8UC3, transform.scale,
-                             transform.offset);
+    tensor_matview.convertTo(output_matview,
+                             CV_MAKETYPE(CV_8U, tensor_in_channels),
+                             transform.scale, transform.offset);
   } else if (input_tensor.element_type() == Tensor::ElementType::kUInt8) {
     cv::Mat tensor_matview(
         cv::Size(tensor_in_width, tensor_in_height),
@@ -239,8 +248,9 @@ absl::Status TensorsToImageCalculator::CpuProcess(CalculatorContext* cc) {
                      GetValueRangeTransformation(
                          input_range.min(), input_range.max(),
                          kOutputImageRangeMin, kOutputImageRangeMax));
-    tensor_matview.convertTo(output_matview, CV_8UC3, transform.scale,
-                             transform.offset);
+    tensor_matview.convertTo(output_matview,
+                             CV_MAKETYPE(CV_8U, tensor_in_channels),
+                             transform.scale, transform.offset);
   } else {
     return absl::InvalidArgumentError(
         absl::Substitute("Type of tensor must be kFloat32 or kUInt8, got: $0",
@@ -264,10 +274,14 @@ absl::Status TensorsToImageCalculator::MetalProcess(CalculatorContext* cc) {
     return absl::OkStatus();
   }
   const auto& input_tensors = kInputTensors(cc).Get();
-  RET_CHECK_EQ(input_tensors.size(), 1)
-      << "Expect 1 input tensor, but have " << input_tensors.size();
-  const int tensor_width = input_tensors[0].shape().dims[2];
-  const int tensor_height = input_tensors[0].shape().dims[1];
+  RET_CHECK_GT(input_tensors.size(), tensor_position_)
+      << "Expect input tensor at position " << tensor_position_
+      << ", but have tensors of size " << input_tensors.size();
+  const int tensor_width = input_tensors[tensor_position_].shape().dims[2];
+  const int tensor_height = input_tensors[tensor_position_].shape().dims[1];
+  const int tensor_channels = input_tensors[tensor_position_].shape().dims[3];
+  // TODO: Add 1 channel support.
+  RET_CHECK(tensor_channels == 3);
 
   // TODO: Fix unused variable
   [[maybe_unused]] id<MTLDevice> device = gpu_helper_.mtlDevice;
@@ -277,8 +291,8 @@ absl::Status TensorsToImageCalculator::MetalProcess(CalculatorContext* cc) {
       [command_buffer computeCommandEncoder];
   [compute_encoder setComputePipelineState:to_buffer_program_];
 
-  auto input_view =
-      mediapipe::MtlBufferView::GetReadView(input_tensors[0], command_buffer);
+  auto input_view = mediapipe::MtlBufferView::GetReadView(
+      input_tensors[tensor_position_], command_buffer);
   [compute_encoder setBuffer:input_view.buffer() offset:0 atIndex:0];
 
   mediapipe::GpuBuffer output =
@@ -355,7 +369,7 @@ absl::Status TensorsToImageCalculator::GlSetup(CalculatorContext* cc) {
       absl::StrCat(tflite::gpu::gl::GetShaderHeader(workgroup_size_), R"(
     precision highp float;
     layout(rgba8, binding = 0) writeonly uniform highp image2D output_texture;
-    uniform ivec2 out_size;
+    uniform ivec3 out_size;
   )");
 
   const std::string shader_body = R"(
@@ -366,10 +380,11 @@ absl::Status TensorsToImageCalculator::GlSetup(CalculatorContext* cc) {
     void main() {
       int out_width = out_size.x;
       int out_height = out_size.y;
+      int out_channels = out_size.z;
 
       ivec2 gid = ivec2(gl_GlobalInvocationID.xy);
       if (gid.x >= out_width || gid.y >= out_height) { return; }
-      int linear_index = 3 * (gid.y * out_width + gid.x);
+      int linear_index = out_channels * (gid.y * out_width + gid.x);
 
 #ifdef FLIP_Y_COORD
       int y_coord = out_height - gid.y - 1;
@@ -377,8 +392,14 @@ absl::Status TensorsToImageCalculator::GlSetup(CalculatorContext* cc) {
       int y_coord = gid.y;
 #endif  // defined(FLIP_Y_COORD)
 
+      vec4 out_value;
       ivec2 out_coordinate = ivec2(gid.x, y_coord);
-      vec4 out_value = vec4(input_data.elements[linear_index], input_data.elements[linear_index + 1], input_data.elements[linear_index + 2], 1.0);
+      if (out_channels == 3) {
+        out_value = vec4(input_data.elements[linear_index], input_data.elements[linear_index + 1], input_data.elements[linear_index + 2], 1.0);
+      } else {
+        float in_value = input_data.elements[linear_index];
+        out_value = vec4(in_value, in_value, in_value, 1.0);
+      }
       imageStore(output_texture, out_coordinate, out_value);
     })";
 
@@ -438,10 +459,15 @@ absl::Status TensorsToImageCalculator::GlProcess(CalculatorContext* cc) {
       return absl::OkStatus();
     }
     const auto& input_tensors = kInputTensors(cc).Get();
-    RET_CHECK_EQ(input_tensors.size(), 1)
-        << "Expect 1 input tensor, but have " << input_tensors.size();
-    const int tensor_width = input_tensors[0].shape().dims[2];
-    const int tensor_height = input_tensors[0].shape().dims[1];
+    RET_CHECK_GT(input_tensors.size(), tensor_position_)
+        << "Expect input tensor at position " << tensor_position_
+        << ", but have tensors of size " << input_tensors.size();
+
+    const auto& input_tensor = input_tensors[tensor_position_];
+    const int tensor_width = input_tensor.shape().dims[2];
+    const int tensor_height = input_tensor.shape().dims[1];
+    const int tensor_in_channels = input_tensor.shape().dims[3];
+    RET_CHECK(tensor_in_channels == 3 || tensor_in_channels == 1);
 
 #if MEDIAPIPE_OPENGL_ES_VERSION >= MEDIAPIPE_OPENGL_ES_31
 
@@ -454,7 +480,7 @@ absl::Status TensorsToImageCalculator::GlProcess(CalculatorContext* cc) {
     glBindImageTexture(output_index, out_texture->id(), 0, GL_FALSE, 0,
                        GL_WRITE_ONLY, GL_RGBA8);
 
-    auto read_view = input_tensors[0].GetOpenGlBufferReadView();
+    auto read_view = input_tensor.GetOpenGlBufferReadView();
     glBindBufferBase(GL_SHADER_STORAGE_BUFFER, 2, read_view.name());
 
     const tflite::gpu::uint3 workload = {tensor_width, tensor_height, 1};
@@ -462,8 +488,8 @@ absl::Status TensorsToImageCalculator::GlProcess(CalculatorContext* cc) {
         tflite::gpu::DivideRoundUp(workload, workgroup_size_);
 
     glUseProgram(gl_compute_program_->id());
-    glUniform2i(glGetUniformLocation(gl_compute_program_->id(), "out_size"),
-                tensor_width, tensor_height);
+    glUniform3i(glGetUniformLocation(gl_compute_program_->id(), "out_size"),
+                tensor_width, tensor_height, tensor_in_channels);
 
     MP_RETURN_IF_ERROR(gl_compute_program_->Dispatch(workgroups));
 
@@ -481,8 +507,8 @@ absl::Status TensorsToImageCalculator::GlProcess(CalculatorContext* cc) {
 
 #else
 
-    if (!input_tensors[0].ready_as_opengl_texture_2d()) {
-      (void)input_tensors[0].GetCpuReadView();
+    if (!input_tensor.ready_as_opengl_texture_2d()) {
+      (void)input_tensor.GetCpuReadView();
     }
 
     auto output_texture =
@@ -490,7 +516,7 @@ absl::Status TensorsToImageCalculator::GlProcess(CalculatorContext* cc) {
     gl_helper_.BindFramebuffer(output_texture);  // GL_TEXTURE0
     glActiveTexture(GL_TEXTURE1);
     glBindTexture(GL_TEXTURE_2D,
-                  input_tensors[0].GetOpenGlTexture2dReadView().name());
+                  input_tensor.GetOpenGlTexture2dReadView().name());
 
     MP_RETURN_IF_ERROR(gl_renderer_->GlRender(
         tensor_width, tensor_height, output_texture.width(),

mediapipe/tasks/cc/vision/face_stylizer/calculators/tensors_to_image_calculator.proto

@@ -48,4 +48,8 @@ message TensorsToImageCalculatorOptions {
     FloatRange input_tensor_float_range = 2;
     UIntRange input_tensor_uint_range = 3;
   }
+
+  // Determines which output tensor to slice when there are multiple output
+  // tensors available (e.g. network has multiple heads)
+  optional int32 tensor_position = 4 [default = 0];
 }

mediapipe/tasks/cc/vision/hand_landmarker/BUILD

@@ -153,6 +153,11 @@ cc_library(
     alwayslink = 1,
 )
 
+cc_library(
+    name = "hand_landmarks_connections",
+    hdrs = ["hand_landmarks_connections.h"],
+)
+
 # TODO: open source hand joints graph
 
 cc_library(

mediapipe/tasks/cc/vision/hand_landmarker/hand_landmarks_connections.h

@@ -0,0 +1,54 @@
+/* Copyright 2023 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.
+==============================================================================*/
+
+#ifndef MEDIAPIPE_TASKS_CC_VISION_HAND_LANDMARKER_HAND_LANDMARKS_CONNECTIONS_H_
+#define MEDIAPIPE_TASKS_CC_VISION_HAND_LANDMARKER_HAND_LANDMARKS_CONNECTIONS_H_
+
+#include <array>
+
+namespace mediapipe {
+namespace tasks {
+namespace vision {
+namespace hand_landmarker {
+
+static constexpr std::array<std::array<int, 2>, 6> kHandPalmConnections{
+    {{0, 1}, {0, 5}, {9, 13}, {13, 17}, {5, 9}, {0, 17}}};
+
+static constexpr std::array<std::array<int, 2>, 3> kHandThumbConnections{
+    {{1, 2}, {2, 3}, {3, 4}}};
+
+static constexpr std::array<std::array<int, 2>, 3> kHandIndexFingerConnections{
+    {{5, 6}, {6, 7}, {7, 8}}};
+
+static constexpr std::array<std::array<int, 2>, 3> kHandMiddleFingerConnections{
+    {{9, 10}, {10, 11}, {11, 12}}};
+
+static constexpr std::array<std::array<int, 2>, 3> kHandRingFingerConnections{
+    {{13, 14}, {14, 15}, {15, 16}}};
+
+static constexpr std::array<std::array<int, 2>, 3> kHandPinkyFingerConnections{
+    {{17, 18}, {18, 19}, {19, 20}}};
+
+static constexpr std::array<std::array<int, 2>, 21> kHandConnections{
+    {{0, 1},   {0, 5},   {9, 13},  {13, 17}, {5, 9},   {0, 17},  {1, 2},
+     {2, 3},   {3, 4},   {5, 6},   {6, 7},   {7, 8},   {9, 10},  {10, 11},
+     {11, 12}, {13, 14}, {14, 15}, {15, 16}, {17, 18}, {18, 19}, {19, 20}}};
+
+}  // namespace hand_landmarker
+}  // namespace vision
+}  // namespace tasks
+}  // namespace mediapipe
+
+#endif  // MEDIAPIPE_TASKS_CC_VISION_HAND_LANDMARKER_HAND_LANDMARKS_CONNECTIONS_H_

mediapipe/tasks/cc/vision/pose_landmarker/BUILD

@@ -155,3 +155,8 @@ cc_library(
         "//mediapipe/tasks/cc/components/containers:landmark",
     ],
 )
+
+cc_library(
+    name = "pose_landmarks_connections",
+    hdrs = ["pose_landmarks_connections.h"],
+)

mediapipe/tasks/cc/vision/pose_landmarker/pose_landmarks_connections.h

@@ -0,0 +1,39 @@
+/* Copyright 2023 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.
+==============================================================================*/
+
+#ifndef MEDIAPIPE_TASKS_CC_VISION_POSE_LANDMARKER_POSE_LANDMARKS_CONNECTIONS_H_
+#define MEDIAPIPE_TASKS_CC_VISION_POSE_LANDMARKER_POSE_LANDMARKS_CONNECTIONS_H_
+
+#include <array>
+
+namespace mediapipe {
+namespace tasks {
+namespace vision {
+namespace pose_landmarker {
+
+static constexpr std::array<std::array<int, 2>, 34> kPoseLandmarksConnections{{
+    {1, 2},   {0, 1},   {2, 3},   {3, 7},   {0, 4},   {4, 5},   {5, 6},
+    {6, 8},   {9, 10},  {11, 12}, {11, 13}, {13, 15}, {15, 17}, {15, 19},
+    {15, 21}, {17, 19}, {12, 14}, {14, 16}, {16, 18}, {16, 20}, {16, 22},
+    {18, 20}, {11, 23}, {12, 24}, {23, 24}, {23, 25}, {24, 26}, {25, 27},
+    {26, 28}, {27, 29}, {28, 30}, {29, 31}, {30, 32}, {27, 31},
+}};
+
+}  // namespace pose_landmarker
+}  // namespace vision
+}  // namespace tasks
+}  // namespace mediapipe
+
+#endif  // MEDIAPIPE_TASKS_CC_VISION_POSE_LANDMARKER_POSE_LANDMARKS_CONNECTIONS_H_

mediapipe/tasks/ios/test/vision/face_detector/MPPFaceDetectorTests.mm

@@ -25,7 +25,7 @@
 static NSDictionary *const kPortraitImage =
     @{@"name" : @"portrait", @"type" : @"jpg", @"orientation" : @(UIImageOrientationUp)};
 static NSDictionary *const kPortraitRotatedImage =
-    @{@"name" : @"portrait_rotated", @"type" : @"jpg", @"orientation" : @(UIImageOrientationRight)};
+    @{@"name" : @"portrait_rotated", @"type" : @"jpg", @"orientation" : @(UIImageOrientationLeft)};
 static NSDictionary *const kCatImage = @{@"name" : @"cat", @"type" : @"jpg"};
 static NSString *const kShortRangeBlazeFaceModel = @"face_detection_short_range";
 static NSArray<NSArray *> *const kPortraitExpectedKeypoints = @[

mediapipe/tasks/ios/test/vision/gesture_recognizer/MPPGestureRecognizerTests.m

@@ -343,7 +343,7 @@ static NSString *const kLiveStreamTestsDictExpectationKey = @"expectation";
   MPPGestureRecognizer *gestureRecognizer =
       [self createGestureRecognizerWithOptionsSucceeds:gestureRecognizerOptions];
   MPPImage *mppImage = [self imageWithFileInfo:kPointingUpRotatedImage
-                                   orientation:UIImageOrientationRight];
+                                   orientation:UIImageOrientationLeft];
 
   MPPGestureRecognizerResult *gestureRecognizerResult = [gestureRecognizer recognizeImage:mppImage
                                                                                     error:nil];

mediapipe/tasks/ios/test/vision/image_classifier/MPPImageClassifierTests.m

@@ -402,7 +402,7 @@ static NSString *const kLiveStreamTestsDictExpectationKey = @"expectation";
   ];
 
   MPPImage *image = [self imageWithFileInfo:kBurgerRotatedImage
-                                orientation:UIImageOrientationRight];
+                                orientation:UIImageOrientationLeft];
 
   [self assertResultsOfClassifyImage:image
                 usingImageClassifier:imageClassifier
@@ -425,7 +425,7 @@ static NSString *const kLiveStreamTestsDictExpectationKey = @"expectation";
                                 displayName:nil] ];
 
   MPPImage *image = [self imageWithFileInfo:kMultiObjectsRotatedImage
-                                orientation:UIImageOrientationRight];
+                                orientation:UIImageOrientationLeft];
 
   // roi around folding chair
   MPPImageClassifierResult *imageClassifierResult =

mediapipe/tasks/ios/test/vision/object_detector/MPPObjectDetectorTests.m

@@ -438,7 +438,7 @@ static NSString *const kLiveStreamTestsDictExpectationKey = @"expectation";
       [[MPPObjectDetectorResult alloc] initWithDetections:detections timestampInMilliseconds:0];
 
   MPPImage *image = [self imageWithFileInfo:kCatsAndDogsRotatedImage
-                                orientation:UIImageOrientationRight];
+                                orientation:UIImageOrientationLeft];
 
   [self assertResultsOfDetectInImage:image
                  usingObjectDetector:objectDetector

mediapipe/tasks/ios/vision/core/sources/MPPImage.h

@@ -62,10 +62,10 @@ NS_SWIFT_NAME(MPImage)
 
 /**
  * Initializes an `MPPImage` object with the given `UIImage`.
- * The orientation of the newly created `MPPImage` will be `UIImageOrientationUp`.
- * Hence, if this image is used as input for any MediaPipe vision tasks, inference will be
- * performed on the it without any rotation. To create an `MPPImage` with a different orientation,
- * please use `[MPPImage initWithImage:orientation:error:]`.
+ * The orientation of the newly created `MPPImage` will be equal to the `imageOrientation` of
+ * `UIImage` and when sent to the vision tasks for inference, rotation will be applied accordingly.
+ * To create an `MPPImage` with an orientation different from its `imageOrientation`, please use
+ * `[MPPImage initWithImage:orientation:error:]`.
  *
  * @param image The image to use as the source. Its `CGImage` property must not be `NULL`.
  * @param error An optional error parameter populated when there is an error in initializing the
@@ -77,14 +77,19 @@ NS_SWIFT_NAME(MPImage)
 - (nullable instancetype)initWithUIImage:(UIImage *)image error:(NSError **)error;
 
 /**
- * Initializes an `MPPImage` object with the given `UIImabe` and orientation.
+ * Initializes an `MPPImage` object with the given `UIImage` and orientation. The given orientation
+ * will be used to calculate the rotation to be applied to the `UIImage` before inference is
+ * performed on it by the vision tasks. The `imageOrientation` stored in the `UIImage` is ignored
+ * when `MPImage` objects created by this method are sent to the vision tasks for inference. Use
+ * `[MPPImage initWithImage:orientation:error:]` to initialize images with the `imageOrientation` of
+ * `UIImage`.
  *
  * If the newly created `MPPImage` is used as input for any MediaPipe vision tasks, inference
  * will be performed on a copy of the image rotated according to the orientation.
  *
  * @param image The image to use as the source. Its `CGImage` property must not be `NULL`.
  * @param orientation The display orientation of the image. This will be stored in the property
- *     `orientation`. `MPPImage`.
+ *     `orientation` `MPPImage` and will override the `imageOrientation` of the passed in `UIImage`.
  * @param error An optional error parameter populated when there is an error in initializing the
  *     `MPPImage`.
  *

mediapipe/tasks/ios/vision/core/sources/MPPVisionTaskRunner.mm

@@ -30,13 +30,13 @@ using ::mediapipe::tasks::core::PacketsCallback;
 }  // namespace
 
 /** Rotation degrees for a 90 degree rotation to the right. */
-static const NSInteger kMPPOrientationDegreesRight = -90;
+static const NSInteger kMPPOrientationDegreesRight = -270;
 
 /** Rotation degrees for a 180 degree rotation. */
 static const NSInteger kMPPOrientationDegreesDown = -180;
 
 /** Rotation degrees for a 90 degree rotation to the left. */
-static const NSInteger kMPPOrientationDegreesLeft = -270;
+static const NSInteger kMPPOrientationDegreesLeft = -90;
 
 static NSString *const kTaskPrefix = @"com.mediapipe.tasks.vision";
 

mediapipe/tasks/java/com/google/mediapipe/tasks/core/BUILD

@@ -32,6 +32,7 @@ android_library(
         "//mediapipe/tasks/cc/core/proto:base_options_java_proto_lite",
         "//mediapipe/tasks/cc/core/proto:external_file_java_proto_lite",
         "//mediapipe/tasks/java/com/google/mediapipe/tasks/core/jni:model_resources_cache_jni",
+        "//third_party:any_java_proto",
         "//third_party:autovalue",
         "@com_google_protobuf//:protobuf_javalite",
         "@maven//:com_google_guava_guava",

mediapipe/tasks/java/com/google/mediapipe/tasks/core/TaskInfo.java

@@ -20,6 +20,8 @@ import com.google.mediapipe.proto.CalculatorProto.CalculatorGraphConfig;
 import com.google.mediapipe.proto.CalculatorProto.CalculatorGraphConfig.Node;
 import com.google.mediapipe.proto.CalculatorProto.InputStreamInfo;
 import com.google.mediapipe.calculator.proto.FlowLimiterCalculatorProto.FlowLimiterCalculatorOptions;
+import com.google.mediapipe.framework.MediaPipeException;
+import com.google.protobuf.Any;
 import java.util.ArrayList;
 import java.util.List;
 
@@ -110,10 +112,21 @@ public abstract class TaskInfo<T extends TaskOptions> {
    */
   CalculatorGraphConfig generateGraphConfig() {
     CalculatorGraphConfig.Builder graphBuilder = CalculatorGraphConfig.newBuilder();
-    Node.Builder taskSubgraphBuilder =
-        Node.newBuilder()
-            .setCalculator(taskGraphName())
-            .setOptions(taskOptions().convertToCalculatorOptionsProto());
+    CalculatorOptions options = taskOptions().convertToCalculatorOptionsProto();
+    Any anyOptions = taskOptions().convertToAnyProto();
+    if (!(options == null ^ anyOptions == null)) {
+      throw new MediaPipeException(
+          MediaPipeException.StatusCode.INVALID_ARGUMENT.ordinal(),
+          "Only one of convertTo*Proto() method should be implemented for "
+              + taskOptions().getClass());
+    }
+    Node.Builder taskSubgraphBuilder = Node.newBuilder().setCalculator(taskGraphName());
+    if (options != null) {
+      taskSubgraphBuilder.setOptions(options);
+    }
+    if (anyOptions != null) {
+      taskSubgraphBuilder.addNodeOptions(anyOptions);
+    }
     for (String outputStream : outputStreams()) {
       taskSubgraphBuilder.addOutputStream(outputStream);
       graphBuilder.addOutputStream(outputStream);

mediapipe/tasks/java/com/google/mediapipe/tasks/core/TaskOptions.java

@@ -20,18 +20,26 @@ import com.google.mediapipe.proto.CalculatorOptionsProto.CalculatorOptions;
 import com.google.mediapipe.tasks.core.proto.AccelerationProto;
 import com.google.mediapipe.tasks.core.proto.BaseOptionsProto;
 import com.google.mediapipe.tasks.core.proto.ExternalFileProto;
+import com.google.protobuf.Any;
 import com.google.protobuf.ByteString;
 
 /**
  * MediaPipe Tasks options base class. Any MediaPipe task-specific options class should extend
- * {@link TaskOptions}.
+ * {@link TaskOptions} and implement exactly one of converTo*Proto() methods.
  */
 public abstract class TaskOptions {
   /**
    * Converts a MediaPipe Tasks task-specific options to a {@link CalculatorOptions} protobuf
    * message.
    */
-  public abstract CalculatorOptions convertToCalculatorOptionsProto();
+  public CalculatorOptions convertToCalculatorOptionsProto() {
+    return null;
+  }
+
+  /** Converts a MediaPipe Tasks task-specific options to an proto3 {@link Any} message. */
+  public Any convertToAnyProto() {
+    return null;
+  }
 
   /**
    * Converts a {@link BaseOptions} instance to a {@link BaseOptionsProto.BaseOptions} protobuf

mediapipe/tasks/web/components/containers/bounding_box.d.ts

@@ -24,4 +24,10 @@ export declare interface BoundingBox {
   width: number;
   /** The height of the bounding box, in pixels. */
   height: number;
+  /**
+   * Angle of rotation of the original non-rotated box around the top left
+   * corner of the original non-rotated box, in clockwise degrees from the
+   * horizontal.
+   */
+  angle: number;
 }

mediapipe/tasks/web/components/processors/detection_result.test.ts

@@ -58,7 +58,7 @@ describe('convertFromDetectionProto()', () => {
         categoryName: 'foo',
         displayName: 'bar',
       }],
-      boundingBox: {originX: 1, originY: 2, width: 3, height: 4},
+      boundingBox: {originX: 1, originY: 2, width: 3, height: 4, angle: 0},
       keypoints: [{
         x: 5,
         y: 6,
@@ -85,7 +85,7 @@ describe('convertFromDetectionProto()', () => {
         categoryName: '',
         displayName: '',
       }],
-      boundingBox: {originX: 0, originY: 0, width: 0, height: 0},
+      boundingBox: {originX: 0, originY: 0, width: 0, height: 0, angle: 0},
       keypoints: []
     });
   });

mediapipe/tasks/web/components/processors/detection_result.ts

@@ -42,7 +42,8 @@ export function convertFromDetectionProto(source: DetectionProto): Detection {
       originX: boundingBox.getXmin() ?? 0,
       originY: boundingBox.getYmin() ?? 0,
       width: boundingBox.getWidth() ?? 0,
-      height: boundingBox.getHeight() ?? 0
+      height: boundingBox.getHeight() ?? 0,
+      angle: 0.0,
     };
   }
 

mediapipe/tasks/web/core/task_runner.ts

@@ -25,9 +25,6 @@ import {SupportModelResourcesGraphService} from '../../../web/graph_runner/regis
 
 import {WasmFileset} from './wasm_fileset';
 
-// None of the MP Tasks ship bundle assets.
-const NO_ASSETS = undefined;
-
 // Internal stream names for temporarily keeping memory alive, then freeing it.
 const FREE_MEMORY_STREAM = 'free_memory';
 const UNUSED_STREAM_SUFFIX = '_unused_out';
@@ -61,7 +58,8 @@ export async function createTaskRunner<T extends TaskRunner>(
   };
 
   const instance = await createMediaPipeLib(
-      type, fileset.wasmLoaderPath, NO_ASSETS, canvas, fileLocator);
+      type, fileset.wasmLoaderPath, fileset.assetLoaderPath, canvas,
+      fileLocator);
   await instance.setOptions(options);
   return instance;
 }
@@ -96,65 +94,73 @@ export abstract class TaskRunner {
   abstract setOptions(options: TaskRunnerOptions): Promise<void>;
 
   /**
-   * Applies the current set of options, including any base options that have
-   * not been processed by the task implementation. The options are applied
-   * synchronously unless a `modelAssetPath` is provided. This ensures that
-   * for most use cases options are applied directly and immediately affect
+   * Applies the current set of options, including optionally any base options
+   * that have not been processed by the task implementation. The options are
+   * applied synchronously unless a `modelAssetPath` is provided. This ensures
+   * that for most use cases options are applied directly and immediately affect
    * the next inference.
+   *
+   * @param options The options for the task.
+   * @param loadTfliteModel Whether to load the model specified in
+   *     `options.baseOptions`.
    */
-  protected applyOptions(options: TaskRunnerOptions): Promise<void> {
-    const baseOptions: BaseOptions = options.baseOptions || {};
+  protected applyOptions(options: TaskRunnerOptions, loadTfliteModel = true):
+      Promise<void> {
+    if (loadTfliteModel) {
+      const baseOptions: BaseOptions = options.baseOptions || {};
 
-    // Validate that exactly one model is configured
-    if (options.baseOptions?.modelAssetBuffer &&
-        options.baseOptions?.modelAssetPath) {
-      throw new Error(
-          'Cannot set both baseOptions.modelAssetPath and baseOptions.modelAssetBuffer');
-    } else if (!(this.baseOptions.getModelAsset()?.hasFileContent() ||
-                 this.baseOptions.getModelAsset()?.hasFileName() ||
-                 options.baseOptions?.modelAssetBuffer ||
-                 options.baseOptions?.modelAssetPath)) {
-      throw new Error(
-          'Either baseOptions.modelAssetPath or baseOptions.modelAssetBuffer must be set');
+      // Validate that exactly one model is configured
+      if (options.baseOptions?.modelAssetBuffer &&
+          options.baseOptions?.modelAssetPath) {
+        throw new Error(
+            'Cannot set both baseOptions.modelAssetPath and baseOptions.modelAssetBuffer');
+      } else if (!(this.baseOptions.getModelAsset()?.hasFileContent() ||
+                   this.baseOptions.getModelAsset()?.hasFileName() ||
+                   options.baseOptions?.modelAssetBuffer ||
+                   options.baseOptions?.modelAssetPath)) {
+        throw new Error(
+            'Either baseOptions.modelAssetPath or baseOptions.modelAssetBuffer must be set');
+      }
+
+      this.setAcceleration(baseOptions);
+      if (baseOptions.modelAssetPath) {
+        // We don't use `await` here since we want to apply most settings
+        // synchronously.
+        return fetch(baseOptions.modelAssetPath.toString())
+            .then(response => {
+              if (!response.ok) {
+                throw new Error(`Failed to fetch model: ${
+                    baseOptions.modelAssetPath} (${response.status})`);
+              } else {
+                return response.arrayBuffer();
+              }
+            })
+            .then(buffer => {
+              try {
+                // Try to delete file as we cannot overwite an existing file
+                // using our current API.
+                this.graphRunner.wasmModule.FS_unlink('/model.dat');
+              } catch {
+              }
+              // TODO: Consider passing the model to the graph as an
+              // input side packet as this might reduce copies.
+              this.graphRunner.wasmModule.FS_createDataFile(
+                  '/', 'model.dat', new Uint8Array(buffer),
+                  /* canRead= */ true, /* canWrite= */ false,
+                  /* canOwn= */ false);
+              this.setExternalFile('/model.dat');
+              this.refreshGraph();
+              this.onGraphRefreshed();
+            });
+      } else {
+        this.setExternalFile(baseOptions.modelAssetBuffer);
+      }
     }
 
-    this.setAcceleration(baseOptions);
-    if (baseOptions.modelAssetPath) {
-      // We don't use `await` here since we want to apply most settings
-      // synchronously.
-      return fetch(baseOptions.modelAssetPath.toString())
-          .then(response => {
-            if (!response.ok) {
-              throw new Error(`Failed to fetch model: ${
-                  baseOptions.modelAssetPath} (${response.status})`);
-            } else {
-              return response.arrayBuffer();
-            }
-          })
-          .then(buffer => {
-            try {
-              // Try to delete file as we cannot overwite an existing file using
-              // our current API.
-              this.graphRunner.wasmModule.FS_unlink('/model.dat');
-            } catch {
-            }
-            // TODO: Consider passing the model to the graph as an
-            // input side packet as this might reduce copies.
-            this.graphRunner.wasmModule.FS_createDataFile(
-                '/', 'model.dat', new Uint8Array(buffer),
-                /* canRead= */ true, /* canWrite= */ false,
-                /* canOwn= */ false);
-            this.setExternalFile('/model.dat');
-            this.refreshGraph();
-            this.onGraphRefreshed();
-          });
-    } else {
-      // Apply the setting synchronously.
-      this.setExternalFile(baseOptions.modelAssetBuffer);
-      this.refreshGraph();
-      this.onGraphRefreshed();
-      return Promise.resolve();
-    }
+    // If there is no model to download, we can apply the setting synchronously.
+    this.refreshGraph();
+    this.onGraphRefreshed();
+    return Promise.resolve();
   }
 
   /** Appliest the current options to the MediaPipe graph. */

mediapipe/tasks/web/core/wasm_fileset.d.ts

@@ -22,4 +22,6 @@ export declare interface WasmFileset {
   wasmLoaderPath: string;
   /** The path to the Wasm binary. */
   wasmBinaryPath: string;
+  /** The optional path to the asset loader script. */
+  assetLoaderPath?: string;
 }

mediapipe/tasks/web/vision/core/vision_task_runner.ts

@@ -70,7 +70,8 @@ export abstract class VisionTaskRunner extends TaskRunner {
    * @param imageStreamName the name of the input image stream.
    * @param normRectStreamName the name of the input normalized rect image
    *     stream used to provide (mandatory) rotation and (optional)
-   *     region-of-interest.
+   *     region-of-interest. `null` if the graph does not support normalized
+   *     rects.
    * @param roiAllowed Whether this task supports Region-Of-Interest
    *     pre-processing
    *
@@ -79,13 +80,20 @@ export abstract class VisionTaskRunner extends TaskRunner {
   constructor(
       protected override readonly graphRunner: VisionGraphRunner,
       private readonly imageStreamName: string,
-      private readonly normRectStreamName: string,
+      private readonly normRectStreamName: string|null,
       private readonly roiAllowed: boolean) {
     super(graphRunner);
   }
 
-  /** Configures the shared options of a vision task. */
-  override applyOptions(options: VisionTaskOptions): Promise<void> {
+  /**
+   * Configures the shared options of a vision task.
+   *
+   * @param options The options for the task.
+   * @param loadTfliteModel Whether to load the model specified in
+   *     `options.baseOptions`.
+   */
+  override applyOptions(options: VisionTaskOptions, loadTfliteModel = true):
+      Promise<void> {
     if ('runningMode' in options) {
       const useStreamMode =
           !!options.runningMode && options.runningMode !== 'IMAGE';
@@ -98,7 +106,7 @@ export abstract class VisionTaskRunner extends TaskRunner {
       }
     }
 
-    return super.applyOptions(options);
+    return super.applyOptions(options, loadTfliteModel);
   }
 
   /** Sends a single image to the graph and awaits results. */
@@ -209,11 +217,13 @@ export abstract class VisionTaskRunner extends TaskRunner {
       imageSource: ImageSource,
       imageProcessingOptions: ImageProcessingOptions|undefined,
       timestamp: number): void {
-    const normalizedRect =
-        this.convertToNormalizedRect(imageSource, imageProcessingOptions);
-    this.graphRunner.addProtoToStream(
-        normalizedRect.serializeBinary(), 'mediapipe.NormalizedRect',
-        this.normRectStreamName, timestamp);
+    if (this.normRectStreamName) {
+      const normalizedRect =
+          this.convertToNormalizedRect(imageSource, imageProcessingOptions);
+      this.graphRunner.addProtoToStream(
+          normalizedRect.serializeBinary(), 'mediapipe.NormalizedRect',
+          this.normRectStreamName, timestamp);
+    }
     this.graphRunner.addGpuBufferAsImageToStream(
         imageSource, this.imageStreamName, timestamp ?? performance.now());
     this.finishProcessing();

mediapipe/tasks/web/vision/face_detector/face_detector_test.ts

@@ -191,7 +191,7 @@ describe('FaceDetector', () => {
         categoryName: '',
         displayName: '',
       }],
-      boundingBox: {originX: 0, originY: 0, width: 0, height: 0},
+      boundingBox: {originX: 0, originY: 0, width: 0, height: 0, angle: 0},
       keypoints: []
     });
   });

mediapipe/tasks/web/vision/object_detector/object_detector_test.ts

@@ -210,7 +210,7 @@ describe('ObjectDetector', () => {
         categoryName: '',
         displayName: '',
       }],
-      boundingBox: {originX: 0, originY: 0, width: 0, height: 0},
+      boundingBox: {originX: 0, originY: 0, width: 0, height: 0, angle: 0},
       keypoints: []
     });
   });

mediapipe/util/BUILD

@@ -152,6 +152,7 @@ cc_library(
     visibility = ["//visibility:public"],
     deps = [
         "//mediapipe/framework/formats:landmark_cc_proto",
+        "//mediapipe/framework/port:logging",
         "//mediapipe/framework/port:opencv_core",
         "//mediapipe/framework/port:opencv_imgproc",
     ],

mediapipe/util/pose_util.cc

@@ -1,5 +1,6 @@
 #include "mediapipe/util/pose_util.h"
 
+#include "mediapipe/framework/port/logging.h"
 #include "mediapipe/framework/port/opencv_imgproc_inc.h"
 
 namespace {
@@ -192,7 +193,7 @@ void DrawPose(const mediapipe::NormalizedLandmarkList& pose, bool flip_y,
 }
 
 void DrawFace(const mediapipe::NormalizedLandmarkList& face, bool flip_y,
-              bool draw_nose, bool color_style, bool reverse_color,
+              bool draw_nose, int color_style, bool reverse_color,
               int draw_line_width, cv::Mat* image) {
   const int target_width = image->cols;
   const int target_height = image->rows;
@@ -202,16 +203,26 @@ void DrawFace(const mediapipe::NormalizedLandmarkList& face, bool flip_y,
                            (flip_y ? 1.0f - lm.y() : lm.y()) * target_height);
   }
 
-  cv::Scalar kFaceOvalColor = kWhiteColor;
-  cv::Scalar kLipsColor = kWhiteColor;
-  cv::Scalar kLeftEyeColor = kGreenColor;
-  cv::Scalar kLeftEyebrowColor = kGreenColor;
-  cv::Scalar kLeftEyeIrisColor = kGreenColor;
-  cv::Scalar kRightEyeColor = kRedColor;
-  cv::Scalar kRightEyebrowColor = kRedColor;
-  cv::Scalar kRightEyeIrisColor = kRedColor;
-  cv::Scalar kNoseColor = kWhiteColor;
-  if (color_style) {
+  cv::Scalar kFaceOvalColor;
+  cv::Scalar kLipsColor;
+  cv::Scalar kLeftEyeColor;
+  cv::Scalar kLeftEyebrowColor;
+  cv::Scalar kLeftEyeIrisColor;
+  cv::Scalar kRightEyeColor;
+  cv::Scalar kRightEyebrowColor;
+  cv::Scalar kRightEyeIrisColor;
+  cv::Scalar kNoseColor;
+  if (color_style == 0) {
+    kFaceOvalColor = kWhiteColor;
+    kLipsColor = kWhiteColor;
+    kLeftEyeColor = kGreenColor;
+    kLeftEyebrowColor = kGreenColor;
+    kLeftEyeIrisColor = kGreenColor;
+    kRightEyeColor = kRedColor;
+    kRightEyebrowColor = kRedColor;
+    kRightEyeIrisColor = kRedColor;
+    kNoseColor = kWhiteColor;
+  } else if (color_style == 1) {
     kFaceOvalColor = kWhiteColor;
     kLipsColor = kBlueColor;
     kLeftEyeColor = kCyanColor;
@@ -221,6 +232,18 @@ void DrawFace(const mediapipe::NormalizedLandmarkList& face, bool flip_y,
     kRightEyebrowColor = kRedColor;
     kRightEyeIrisColor = kRedColor;
     kNoseColor = kYellowColor;
+  } else if (color_style == 2) {
+    kFaceOvalColor = kWhiteColor;
+    kLipsColor = kBlueColor;
+    kLeftEyeColor = kCyanColor;
+    kLeftEyebrowColor = kGreenColor;
+    kLeftEyeIrisColor = kRedColor;
+    kRightEyeColor = kCyanColor;
+    kRightEyebrowColor = kGreenColor;
+    kRightEyeIrisColor = kRedColor;
+    kNoseColor = kYellowColor;
+  } else {
+    LOG(ERROR) << "color_style not supported.";
   }
 
   if (reverse_color) {

mediapipe/util/pose_util.h

@@ -24,7 +24,7 @@ void DrawPose(const mediapipe::NormalizedLandmarkList& pose, bool flip_y,
               cv::Mat* image);
 
 void DrawFace(const mediapipe::NormalizedLandmarkList& face, bool flip_y,
-              bool draw_nose, bool color_style, bool reverse_color,
+              bool draw_nose, int color_style, bool reverse_color,
               int draw_line_width, cv::Mat* image);
 
 }  // namespace mediapipe

mediapipe/util/sequence/BUILD

@@ -72,7 +72,6 @@ cc_test(
         "//mediapipe/framework/formats:location",
         "//mediapipe/framework/port:gtest_main",
         "//mediapipe/framework/port:opencv_imgcodecs",
-        "//mediapipe/framework/port:status",
         "@org_tensorflow//tensorflow/core:protos_all_cc",
     ],
 )

mediapipe/util/tflite/tflite_gpu_runner.cc

@@ -234,6 +234,11 @@ absl::Status TFLiteGPURunner::InitializeOpenCL(
   MP_RETURN_IF_ERROR(
       cl::NewInferenceEnvironment(env_options, &cl_environment_, &properties));
 
+  if (serialized_model_.empty() &&
+      opencl_init_from_serialized_model_is_forced_) {
+    ASSIGN_OR_RETURN(serialized_model_, GetSerializedModel());
+  }
+
   // Try to initialize from serialized model first.
   if (!serialized_model_.empty()) {
     absl::Status init_status = InitializeOpenCLFromSerializedModel(builder);
@@ -270,7 +275,6 @@ absl::Status TFLiteGPURunner::InitializeOpenCLFromSerializedModel(
 }
 
 absl::StatusOr<std::vector<uint8_t>> TFLiteGPURunner::GetSerializedModel() {
-  RET_CHECK(runner_) << "Runner is in invalid state.";
   if (serialized_model_used_) {
     return serialized_model_;
   }

mediapipe/util/tflite/tflite_gpu_runner.h

@@ -62,6 +62,9 @@ class TFLiteGPURunner {
 
   void ForceOpenGL() { opengl_is_forced_ = true; }
   void ForceOpenCL() { opencl_is_forced_ = true; }
+  void ForceOpenCLInitFromSerializedModel() {
+    opencl_init_from_serialized_model_is_forced_ = true;
+  }
 
   absl::Status BindSSBOToInputTensor(GLuint ssbo_id, int input_id);
   absl::Status BindSSBOToOutputTensor(GLuint ssbo_id, int output_id);
@@ -141,6 +144,7 @@ class TFLiteGPURunner {
 
   bool opencl_is_forced_ = false;
   bool opengl_is_forced_ = false;
+  bool opencl_init_from_serialized_model_is_forced_ = false;
 };
 
 }  // namespace gpu

platform_mappings

@@ -0,0 +1,64 @@
+# This file allows automatically mapping flags such as '--cpu' to the more
+# modern Bazel platforms (https://bazel.build/concepts/platforms).
+
+# In particular, Bazel platforms lack support for Apple for now if no such
+# mapping is put into place. It's inspired from:
+# https://github.com/bazelbuild/rules_apple/issues/1764
+
+platforms:
+  @build_bazel_apple_support//platforms:macos_x86_64
+    --cpu=darwin_x86_64
+
+  @build_bazel_apple_support//platforms:macos_arm64
+    --cpu=darwin_arm64
+
+  @build_bazel_apple_support//platforms:ios_i386
+    --cpu=ios_i386
+
+  @build_bazel_apple_support//platforms:ios_x86_64
+    --cpu=ios_x86_64
+
+  @build_bazel_apple_support//platforms:ios_sim_arm64
+    --cpu=ios_sim_arm64
+
+  @build_bazel_apple_support//platforms:ios_armv7
+    --cpu=ios_armv7
+
+  @build_bazel_apple_support//platforms:ios_arm64
+    --cpu=ios_arm64
+
+  @build_bazel_apple_support//platforms:ios_arm64e
+    --cpu=ios_arm64e
+
+flags:
+  --cpu=darwin_x86_64
+  --apple_platform_type=macos
+    @build_bazel_apple_support//platforms:macos_x86_64
+
+  --cpu=darwin_arm64
+  --apple_platform_type=macos
+    @build_bazel_apple_support//platforms:macos_arm64
+
+  --cpu=ios_i386
+  --apple_platform_type=ios
+    @build_bazel_apple_support//platforms:ios_i386
+
+  --cpu=ios_x86_64
+  --apple_platform_type=ios
+    @build_bazel_apple_support//platforms:ios_x86_64
+
+  --cpu=ios_sim_arm64
+  --apple_platform_type=ios
+    @build_bazel_apple_support//platforms:ios_sim_arm64
+
+  --cpu=ios_armv7
+  --apple_platform_type=ios
+    @build_bazel_apple_support//platforms:ios_armv7
+
+  --cpu=ios_arm64
+  --apple_platform_type=ios
+    @build_bazel_apple_support//platforms:ios_arm64
+
+  --cpu=ios_arm64e
+  --apple_platform_type=ios
+    @build_bazel_apple_support//platforms:ios_arm64e

third_party/BUILD

@@ -378,3 +378,10 @@ java_library(
         "@maven//:com_google_auto_value_auto_value_annotations",
     ],
 )
+
+java_proto_library(
+    name = "any_java_proto",
+    deps = [
+        "@com_google_protobuf//:any_proto",
+    ],
+)

third_party/com_github_glog_glog.diff

@@ -1,19 +1,8 @@
 diff --git a/src/logging.cc b/src/logging.cc
-index 0b5e6ee..be5a506 100644
+index 4028ccc..483e639 100644
 --- a/src/logging.cc
 +++ b/src/logging.cc
-@@ -67,6 +67,10 @@
- # include "stacktrace.h"
- #endif
-
-+#ifdef __ANDROID__
-+#include <android/log.h>
-+#endif
-+
- using std::string;
- using std::vector;
- using std::setw;
-@@ -1279,6 +1283,23 @@ ostream& LogMessage::stream() {
+@@ -1743,6 +1743,23 @@ ostream& LogMessage::stream() {
    return data_->stream_;
  }
 
@@ -37,7 +26,7 @@ index 0b5e6ee..be5a506 100644
  // Flush buffered message, called by the destructor, or any other function
  // that needs to synchronize the log.
  void LogMessage::Flush() {
-@@ -1313,6 +1334,12 @@ void LogMessage::Flush() {
+@@ -1779,6 +1796,12 @@ void LogMessage::Flush() {
    }
    LogDestination::WaitForSinks(data_);
 

third_party/com_github_glog_glog_f2cf2e1bd040fd15016af53598db0cb9b16a6655.diff

@@ -1,45 +0,0 @@
-https://github.com/google/glog/pull/342
-
-diff --git a/CONTRIBUTORS b/CONTRIBUTORS
-index d63f62d1..aa0dd4a8 100644
---- a/CONTRIBUTORS
-+++ b/CONTRIBUTORS
-@@ -26,6 +26,7 @@ Abhishek Dasgupta <[email protected]>
- Abhishek Parmar <[email protected]>
- Andrew Schwartzmeyer <[email protected]>
- Andy Ying <[email protected]>
-+Bret McKee <[email protected]>
- Brian Silverman <[email protected]>
- Fumitoshi Ukai <[email protected]>
- Guillaume Dumont <[email protected]>
-diff --git a/src/glog/logging.h.in b/src/glog/logging.h.in
-index 9968b96d..f6dccb29 100644
---- a/src/glog/logging.h.in
-+++ b/src/glog/logging.h.in
-@@ -649,6 +649,10 @@ void MakeCheckOpValueString(std::ostream* os, const signed char& v);
- template <> GOOGLE_GLOG_DLL_DECL
- void MakeCheckOpValueString(std::ostream* os, const unsigned char& v);
- 
-+// Provide printable value for nullptr_t
-+template <> GOOGLE_GLOG_DLL_DECL
-+void MakeCheckOpValueString(std::ostream* os, const std::nullptr_t& v);
-+
- // Build the error message string. Specify no inlining for code size.
- template <typename T1, typename T2>
- std::string* MakeCheckOpString(const T1& v1, const T2& v2, const char* exprtext)
-diff --git a/src/logging.cc b/src/logging.cc
-index 0c86cf62..256655e5 100644
---- a/src/logging.cc
-+++ b/src/logging.cc
-@@ -2163,6 +2163,11 @@ void MakeCheckOpValueString(std::ostream* os, const unsigned char& v) {
-   }
- }
- 
-+template <>
-+void MakeCheckOpValueString(std::ostream* os, const std::nullptr_t& v) {
-+  (*os) << "nullptr";
-+}
-+
- void InitGoogleLogging(const char* argv0) {
-   glog_internal_namespace_::InitGoogleLoggingUtilities(argv0);
- }

third_party/halide.BUILD

@@ -42,7 +42,7 @@ cc_library(
 cc_library(
     name = "lib_halide_static",
     srcs = select({
-        "@halide//:halide_config_windows_x86_64": [
+        "@mediapipe//mediapipe:windows": [
             "bin/Release/Halide.dll",
             "lib/Release/Halide.lib",
         ],

third_party/halide/BUILD.bazel

@@ -28,13 +28,13 @@ halide_library_runtimes()
         name = target_name,
         actual = select(
             {
-                ":halide_config_linux_x86_64": "@linux_halide//:%s" % target_name,
-                ":halide_config_macos_x86_64": "@macos_x86_64_halide//:%s" % target_name,
-                ":halide_config_macos_arm64": "@macos_arm_64_halide//:%s" % target_name,
-                ":halide_config_windows_x86_64": "@windows_halide//:%s" % target_name,
-                # deliberately no //condition:default clause here
+                "@mediapipe//mediapipe:macos_x86_64": "@macos_x86_64_halide//:%s" % target_name,
+                "@mediapipe//mediapipe:macos_arm64": "@macos_arm_64_halide//:%s" % target_name,
+                "@mediapipe//mediapipe:windows": "@windows_halide//:%s" % target_name,
+                # Assume Linux x86_64 by default.
+                # TODO: add mediapipe configs for linux to avoid assuming it's the default.
+                "//conditions:default": "@linux_halide//:%s" % target_name,
             },
-            no_match_error = "Compiling Halide code requires that the build host is one of Linux x86-64, Windows x86-64, macOS x86-64, or macOS arm64.",
         ),
     )
     for target_name in [

third_party/halide/halide.bzl

@@ -82,22 +82,22 @@ def halide_runtime_linkopts():
 # Map of halide-target-base -> config_settings
 _HALIDE_TARGET_CONFIG_SETTINGS_MAP = {
     # Android
-    "arm-32-android": ["@halide//:halide_config_android_arm"],
-    "arm-64-android": ["@halide//:halide_config_android_arm64"],
-    "x86-32-android": ["@halide//:halide_config_android_x86_32"],
-    "x86-64-android": ["@halide//:halide_config_android_x86_64"],
+    "arm-32-android": ["@mediapipe//mediapipe:android_arm"],
+    "arm-64-android": ["@mediapipe//mediapipe:android_arm64"],
+    "x86-32-android": ["@mediapipe//mediapipe:android_x86"],
+    "x86-64-android": ["@mediapipe//mediapipe:android_x86_64"],
     # iOS
-    "arm-32-ios": ["@halide//:halide_config_ios_arm"],
-    "arm-64-ios": ["@halide//:halide_config_ios_arm64"],
+    "arm-32-ios": ["@mediapipe//mediapipe:ios_armv7"],
+    "arm-64-ios": ["@mediapipe//mediapipe:ios_arm64", "@mediapipe//mediapipe:ios_arm64e"],
     # OSX (or iOS simulator)
-    "x86-32-osx": ["@halide//:halide_config_macos_x86_32", "@halide//:halide_config_ios_x86_32"],
-    "x86-64-osx": ["@halide//:halide_config_macos_x86_64", "@halide//:halide_config_ios_x86_64"],
-    "arm-64-osx": ["@halide//:halide_config_macos_arm64"],
+    "x86-32-osx": ["@mediapipe//mediapipe:ios_i386"],
+    "x86-64-osx": ["@mediapipe//mediapipe:macos_x86_64", "@mediapipe//mediapipe:ios_x86_64"],
+    "arm-64-osx": ["@mediapipe//mediapipe:macos_arm64"],
     # Windows
-    "x86-64-windows": ["@halide//:halide_config_windows_x86_64"],
+    "x86-64-windows": ["@mediapipe//mediapipe:windows"],
     # Linux
-    "x86-64-linux": ["@halide//:halide_config_linux_x86_64"],
-    # deliberately nothing here using //conditions:default
+    # TODO: add mediapipe configs for linux to avoid assuming it's the default.
+    "x86-64-linux": ["//conditions:default"],
 }
 
 _HALIDE_TARGET_MAP_DEFAULT = {
@@ -618,19 +618,6 @@ def _standard_library_runtime_names():
     return collections.uniq([_halide_library_runtime_target_name(f) for f in _standard_library_runtime_features()])
 
 def halide_library_runtimes(compatible_with = []):
-    # Note that we don't use all of these combinations
-    # (and some are invalid), but that's ok.
-    for cpu in ["arm", "arm64", "x86_32", "x86_64"]:
-        for os in ["android", "linux", "windows", "ios", "macos"]:
-            native.config_setting(
-                name = "halide_config_%s_%s" % (os, cpu),
-                constraint_values = [
-                    "@platforms//os:%s" % os,
-                    "@platforms//cpu:%s" % cpu,
-                ],
-                visibility = ["//visibility:public"],
-            )
-
     unused = [
         _define_halide_library_runtime(f, compatible_with = compatible_with)
         for f in _standard_library_runtime_features()

yarn.lock

@@ -1130,9 +1130,9 @@ which@^2.0.1:
     isexe "^2.0.0"
 
 word-wrap@~1.2.3:
-  version "1.2.3"
-  resolved "https://registry.yarnpkg.com/word-wrap/-/word-wrap-1.2.3.tgz#610636f6b1f703891bd34771ccb17fb93b47079c"
-  integrity sha512-Hz/mrNwitNRh/HUAtM/VT/5VH+ygD6DV7mYKZAtHOrbs8U7lvPS6xf7EJKMF0uW1KJCl0H701g3ZGus+muE5vQ==
+  version "1.2.4"
+  resolved "https://registry.yarnpkg.com/word-wrap/-/word-wrap-1.2.4.tgz#cb4b50ec9aca570abd1f52f33cd45b6c61739a9f"
+  integrity sha512-2V81OA4ugVo5pRo46hAoD2ivUJx8jXmWXfUkY4KFNw0hEptvN0QfH3K4nHiwzGeKl5rFKedV48QVoqYavy4YpA==
 
 wrap-ansi@^7.0.0:
   version "7.0.0"