Merge branch 'google:master' into text-classifier-python

.gitignore

@@ -2,5 +2,6 @@ bazel-*
 mediapipe/MediaPipe.xcodeproj
 mediapipe/MediaPipe.tulsiproj/*.tulsiconf-user
 mediapipe/provisioning_profile.mobileprovision
+node_modules/
 .configure.bazelrc
 .user.bazelrc

BUILD.bazel

@@ -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.
@@ -14,4 +14,9 @@
 
 licenses(["notice"])
 
-exports_files(["LICENSE"])
+exports_files([
+    "LICENSE",
+    "tsconfig.json",
+    "package.json",
+    "yarn.lock",
+])

WORKSPACE

@@ -501,5 +501,48 @@ libedgetpu_dependencies()
 load("@coral_crosstool//:configure.bzl", "cc_crosstool")
 cc_crosstool(name = "crosstool")
 
+
+# Node dependencies
+http_archive(
+    name = "build_bazel_rules_nodejs",
+    sha256 = "5aae76dced38f784b58d9776e4ab12278bc156a9ed2b1d9fcd3e39921dc88fda",
+    urls = ["https://github.com/bazelbuild/rules_nodejs/releases/download/5.7.1/rules_nodejs-5.7.1.tar.gz"],
+)
+
+load("@build_bazel_rules_nodejs//:repositories.bzl", "build_bazel_rules_nodejs_dependencies")
+build_bazel_rules_nodejs_dependencies()
+
+# fetches nodejs, npm, and yarn
+load("@build_bazel_rules_nodejs//:index.bzl", "node_repositories", "yarn_install")
+node_repositories()
+yarn_install(
+    name = "npm",
+    package_json = "//:package.json",
+    yarn_lock = "//:yarn.lock",
+)
+
+# Protobuf for Node dependencies
+http_archive(
+    name = "rules_proto_grpc",
+    sha256 = "bbe4db93499f5c9414926e46f9e35016999a4e9f6e3522482d3760dc61011070",
+    strip_prefix = "rules_proto_grpc-4.2.0",
+    urls = ["https://github.com/rules-proto-grpc/rules_proto_grpc/archive/4.2.0.tar.gz"],
+)
+
+http_archive(
+    name = "com_google_protobuf_javascript",
+    sha256 = "35bca1729532b0a77280bf28ab5937438e3dcccd6b31a282d9ae84c896b6f6e3",
+    strip_prefix = "protobuf-javascript-3.21.2",
+    urls = ["https://github.com/protocolbuffers/protobuf-javascript/archive/refs/tags/v3.21.2.tar.gz"],
+)
+
+load("@rules_proto_grpc//:repositories.bzl", "rules_proto_grpc_toolchains", "rules_proto_grpc_repos")
+rules_proto_grpc_toolchains()
+rules_proto_grpc_repos()
+
+load("@rules_proto//proto:repositories.bzl", "rules_proto_dependencies", "rules_proto_toolchains")
+rules_proto_dependencies()
+rules_proto_toolchains()
+
 load("//third_party:external_files.bzl", "external_files")
 external_files()

docs/getting_started/python.md

@@ -141,3 +141,4 @@ Nvidia Jetson and Raspberry Pi, please read
     ```bash
     (mp_env)mediapipe$ python3 setup.py bdist_wheel
     ```
+7. Exit from the MediaPipe repo directory and launch the Python interpreter.

mediapipe/calculators/core/BUILD

@@ -936,6 +936,7 @@ cc_test(
         "//mediapipe/framework/tool:simulation_clock",
         "//mediapipe/framework/tool:simulation_clock_executor",
         "//mediapipe/framework/tool:sink",
+        "//mediapipe/util:packet_test_util",
         "@com_google_absl//absl/time",
     ],
 )

mediapipe/calculators/core/flow_limiter_calculator.cc

@@ -18,7 +18,6 @@
 
 #include "mediapipe/calculators/core/flow_limiter_calculator.pb.h"
 #include "mediapipe/framework/calculator_framework.h"
-#include "mediapipe/framework/port/ret_check.h"
 #include "mediapipe/framework/port/status.h"
 #include "mediapipe/util/header_util.h"
 
@@ -68,7 +67,7 @@ constexpr char kOptionsTag[] = "OPTIONS";
 // FlowLimiterCalculator provides limited support for multiple input streams.
 // The first input stream is treated as the main input stream and successive
 // input streams are treated as auxiliary input streams.  The auxiliary input
-// streams are limited to timestamps passed on the main input stream.
+// streams are limited to timestamps allowed by the "ALLOW" stream.
 //
 class FlowLimiterCalculator : public CalculatorBase {
  public:
@@ -100,64 +99,11 @@ class FlowLimiterCalculator : public CalculatorBase {
           cc->InputSidePackets().Tag(kMaxInFlightTag).Get<int>());
     }
     input_queues_.resize(cc->Inputs().NumEntries(""));
+    allowed_[Timestamp::Unset()] = true;
     RET_CHECK_OK(CopyInputHeadersToOutputs(cc->Inputs(), &(cc->Outputs())));
     return absl::OkStatus();
   }
 
-  // Returns true if an additional frame can be released for processing.
-  // The "ALLOW" output stream indicates this condition at each input frame.
-  bool ProcessingAllowed() {
-    return frames_in_flight_.size() < options_.max_in_flight();
-  }
-
-  // Outputs a packet indicating whether a frame was sent or dropped.
-  void SendAllow(bool allow, Timestamp ts, CalculatorContext* cc) {
-    if (cc->Outputs().HasTag(kAllowTag)) {
-      cc->Outputs().Tag(kAllowTag).AddPacket(MakePacket<bool>(allow).At(ts));
-    }
-  }
-
-  // Sets the timestamp bound or closes an output stream.
-  void SetNextTimestampBound(Timestamp bound, OutputStream* stream) {
-    if (bound > Timestamp::Max()) {
-      stream->Close();
-    } else {
-      stream->SetNextTimestampBound(bound);
-    }
-  }
-
-  // Returns true if a certain timestamp is being processed.
-  bool IsInFlight(Timestamp timestamp) {
-    return std::find(frames_in_flight_.begin(), frames_in_flight_.end(),
-                     timestamp) != frames_in_flight_.end();
-  }
-
-  // Releases input packets up to the latest settled input timestamp.
-  void ProcessAuxiliaryInputs(CalculatorContext* cc) {
-    Timestamp settled_bound = cc->Outputs().Get("", 0).NextTimestampBound();
-    for (int i = 1; i < cc->Inputs().NumEntries(""); ++i) {
-      // Release settled frames from each input queue.
-      while (!input_queues_[i].empty() &&
-             input_queues_[i].front().Timestamp() < settled_bound) {
-        Packet packet = input_queues_[i].front();
-        input_queues_[i].pop_front();
-        if (IsInFlight(packet.Timestamp())) {
-          cc->Outputs().Get("", i).AddPacket(packet);
-        }
-      }
-
-      // Propagate each input timestamp bound.
-      if (!input_queues_[i].empty()) {
-        Timestamp bound = input_queues_[i].front().Timestamp();
-        SetNextTimestampBound(bound, &cc->Outputs().Get("", i));
-      } else {
-        Timestamp bound =
-            cc->Inputs().Get("", i).Value().Timestamp().NextAllowedInStream();
-        SetNextTimestampBound(bound, &cc->Outputs().Get("", i));
-      }
-    }
-  }
-
   // Releases input packets allowed by the max_in_flight constraint.
   absl::Status Process(CalculatorContext* cc) final {
     options_ = tool::RetrieveOptions(options_, cc->Inputs());
@@ -224,13 +170,97 @@ class FlowLimiterCalculator : public CalculatorBase {
     }
 
     ProcessAuxiliaryInputs(cc);
+
+    // Discard old ALLOW ranges.
+    Timestamp input_bound = InputTimestampBound(cc);
+    auto first_range = std::prev(allowed_.upper_bound(input_bound));
+    allowed_.erase(allowed_.begin(), first_range);
     return absl::OkStatus();
   }
 
+  int LedgerSize() {
+    int result = frames_in_flight_.size() + allowed_.size();
+    for (const auto& queue : input_queues_) {
+      result += queue.size();
+    }
+    return result;
+  }
+
+ private:
+  // Returns true if an additional frame can be released for processing.
+  // The "ALLOW" output stream indicates this condition at each input frame.
+  bool ProcessingAllowed() {
+    return frames_in_flight_.size() < options_.max_in_flight();
+  }
+
+  // Outputs a packet indicating whether a frame was sent or dropped.
+  void SendAllow(bool allow, Timestamp ts, CalculatorContext* cc) {
+    if (cc->Outputs().HasTag(kAllowTag)) {
+      cc->Outputs().Tag(kAllowTag).AddPacket(MakePacket<bool>(allow).At(ts));
+    }
+    allowed_[ts] = allow;
+  }
+
+  // Returns true if a timestamp falls within a range of allowed timestamps.
+  bool IsAllowed(Timestamp timestamp) {
+    auto it = allowed_.upper_bound(timestamp);
+    return std::prev(it)->second;
+  }
+
+  // Sets the timestamp bound or closes an output stream.
+  void SetNextTimestampBound(Timestamp bound, OutputStream* stream) {
+    if (bound > Timestamp::Max()) {
+      stream->Close();
+    } else {
+      stream->SetNextTimestampBound(bound);
+    }
+  }
+
+  // Returns the lowest unprocessed input Timestamp.
+  Timestamp InputTimestampBound(CalculatorContext* cc) {
+    Timestamp result = Timestamp::Done();
+    for (int i = 0; i < input_queues_.size(); ++i) {
+      auto& queue = input_queues_[i];
+      auto& stream = cc->Inputs().Get("", i);
+      Timestamp bound = queue.empty()
+                            ? stream.Value().Timestamp().NextAllowedInStream()
+                            : queue.front().Timestamp();
+      result = std::min(result, bound);
+    }
+    return result;
+  }
+
+  // Releases input packets up to the latest settled input timestamp.
+  void ProcessAuxiliaryInputs(CalculatorContext* cc) {
+    Timestamp settled_bound = cc->Outputs().Get("", 0).NextTimestampBound();
+    for (int i = 1; i < cc->Inputs().NumEntries(""); ++i) {
+      // Release settled frames from each input queue.
+      while (!input_queues_[i].empty() &&
+             input_queues_[i].front().Timestamp() < settled_bound) {
+        Packet packet = input_queues_[i].front();
+        input_queues_[i].pop_front();
+        if (IsAllowed(packet.Timestamp())) {
+          cc->Outputs().Get("", i).AddPacket(packet);
+        }
+      }
+
+      // Propagate each input timestamp bound.
+      if (!input_queues_[i].empty()) {
+        Timestamp bound = input_queues_[i].front().Timestamp();
+        SetNextTimestampBound(bound, &cc->Outputs().Get("", i));
+      } else {
+        Timestamp bound =
+            cc->Inputs().Get("", i).Value().Timestamp().NextAllowedInStream();
+        SetNextTimestampBound(bound, &cc->Outputs().Get("", i));
+      }
+    }
+  }
+
  private:
   FlowLimiterCalculatorOptions options_;
   std::vector<std::deque<Packet>> input_queues_;
   std::deque<Timestamp> frames_in_flight_;
+  std::map<Timestamp, bool> allowed_;
 };
 REGISTER_CALCULATOR(FlowLimiterCalculator);
 

mediapipe/calculators/core/flow_limiter_calculator_test.cc

@@ -15,6 +15,7 @@
 #include <algorithm>
 #include <memory>
 #include <string>
+#include <utility>
 #include <vector>
 
 #include "absl/time/clock.h"
@@ -32,6 +33,7 @@
 #include "mediapipe/framework/tool/simulation_clock.h"
 #include "mediapipe/framework/tool/simulation_clock_executor.h"
 #include "mediapipe/framework/tool/sink.h"
+#include "mediapipe/util/packet_test_util.h"
 
 namespace mediapipe {
 
@@ -77,6 +79,77 @@ std::vector<T> PacketValues(const std::vector<Packet>& packets) {
   return result;
 }
 
+template <typename T>
+std::vector<Packet> MakePackets(std::vector<std::pair<Timestamp, T>> contents) {
+  std::vector<Packet> result;
+  for (auto& entry : contents) {
+    result.push_back(MakePacket<T>(entry.second).At(entry.first));
+  }
+  return result;
+}
+
+std::string SourceString(Timestamp t) {
+  return (t.IsSpecialValue())
+             ? t.DebugString()
+             : absl::StrCat("Timestamp(", t.DebugString(), ")");
+}
+
+template <typename PacketContainer, typename PacketContent>
+class PacketsEqMatcher
+    : public ::testing::MatcherInterface<const PacketContainer&> {
+ public:
+  PacketsEqMatcher(PacketContainer packets) : packets_(packets) {}
+  void DescribeTo(::std::ostream* os) const override {
+    *os << "The expected packet contents: \n";
+    Print(packets_, os);
+  }
+  bool MatchAndExplain(
+      const PacketContainer& value,
+      ::testing::MatchResultListener* listener) const override {
+    if (!Equals(packets_, value)) {
+      if (listener->IsInterested()) {
+        *listener << "The actual packet contents: \n";
+        Print(value, listener->stream());
+      }
+      return false;
+    }
+    return true;
+  }
+
+ private:
+  bool Equals(const PacketContainer& c1, const PacketContainer& c2) const {
+    if (c1.size() != c2.size()) {
+      return false;
+    }
+    for (auto i1 = c1.begin(), i2 = c2.begin(); i1 != c1.end(); ++i1, ++i2) {
+      Packet p1 = *i1, p2 = *i2;
+      if (p1.Timestamp() != p2.Timestamp() ||
+          p1.Get<PacketContent>() != p2.Get<PacketContent>()) {
+        return false;
+      }
+    }
+    return true;
+  }
+  void Print(const PacketContainer& packets, ::std::ostream* os) const {
+    for (auto it = packets.begin(); it != packets.end(); ++it) {
+      const Packet& packet = *it;
+      *os << (it == packets.begin() ? "{" : "") << "{"
+          << SourceString(packet.Timestamp()) << ", "
+          << packet.Get<PacketContent>() << "}"
+          << (std::next(it) == packets.end() ? "}" : ", ");
+    }
+  }
+
+  const PacketContainer packets_;
+};
+
+template <typename PacketContainer, typename PacketContent>
+::testing::Matcher<const PacketContainer&> PackestEq(
+    const PacketContainer& packets) {
+  return MakeMatcher(
+      new PacketsEqMatcher<PacketContainer, PacketContent>(packets));
+}
+
 // A Calculator::Process callback function.
 typedef std::function<absl::Status(const InputStreamShardSet&,
                                    OutputStreamShardSet*)>
@@ -651,11 +724,12 @@ TEST_F(FlowLimiterCalculatorTest, TwoInputStreams) {
       input_packets_[17], input_packets_[19], input_packets_[20],
   };
   EXPECT_EQ(out_1_packets_, expected_output);
-  // Exactly the timestamps released by FlowLimiterCalculator for in_1_sampled.
+  // The timestamps released by FlowLimiterCalculator for in_1_sampled,
+  // plus input_packets_[21].
   std::vector<Packet> expected_output_2 = {
       input_packets_[0],  input_packets_[2],  input_packets_[4],
       input_packets_[14], input_packets_[17], input_packets_[19],
-      input_packets_[20],
+      input_packets_[20], input_packets_[21],
   };
   EXPECT_EQ(out_2_packets, expected_output_2);
 }
@@ -665,6 +739,9 @@ TEST_F(FlowLimiterCalculatorTest, TwoInputStreams) {
 // The processing time "sleep_time" is reduced from 22ms to 12ms to create
 // the same frame rate as FlowLimiterCalculatorTest::TwoInputStreams.
 TEST_F(FlowLimiterCalculatorTest, ZeroQueue) {
+  auto BoolPackestEq = PackestEq<std::vector<Packet>, bool>;
+  auto IntPackestEq = PackestEq<std::vector<Packet>, int>;
+
   // Configure the test.
   SetUpInputData();
   SetUpSimulationClock();
@@ -699,11 +776,10 @@ TEST_F(FlowLimiterCalculatorTest, ZeroQueue) {
         }
       )pb");
 
-  auto limiter_options = ParseTextProtoOrDie<FlowLimiterCalculatorOptions>(R"pb(
-    max_in_flight: 1
-    max_in_queue: 0
-    in_flight_timeout: 100000  # 100 ms
-  )pb");
+  auto limiter_options = ParseTextProtoOrDie<FlowLimiterCalculatorOptions>(
+      R"pb(
+        max_in_flight: 1 max_in_queue: 0 in_flight_timeout: 100000  # 100 ms
+      )pb");
   std::map<std::string, Packet> side_packets = {
       {"limiter_options",
        MakePacket<FlowLimiterCalculatorOptions>(limiter_options)},
@@ -759,13 +835,131 @@ TEST_F(FlowLimiterCalculatorTest, ZeroQueue) {
       input_packets_[0],  input_packets_[2],  input_packets_[15],
       input_packets_[17], input_packets_[19],
   };
-  EXPECT_EQ(out_1_packets_, expected_output);
+  EXPECT_THAT(out_1_packets_, IntPackestEq(expected_output));
   // Exactly the timestamps released by FlowLimiterCalculator for in_1_sampled.
   std::vector<Packet> expected_output_2 = {
       input_packets_[0],  input_packets_[2],  input_packets_[4],
       input_packets_[15], input_packets_[17], input_packets_[19],
   };
-  EXPECT_EQ(out_2_packets, expected_output_2);
+  EXPECT_THAT(out_2_packets, IntPackestEq(expected_output_2));
+
+  // Validate the ALLOW stream output.
+  std::vector<Packet> expected_allow = MakePackets<bool>(  //
+      {{Timestamp(0), true},       {Timestamp(10000), false},
+       {Timestamp(20000), true},   {Timestamp(30000), false},
+       {Timestamp(40000), true},   {Timestamp(50000), false},
+       {Timestamp(60000), false},  {Timestamp(70000), false},
+       {Timestamp(80000), false},  {Timestamp(90000), false},
+       {Timestamp(100000), false}, {Timestamp(110000), false},
+       {Timestamp(120000), false}, {Timestamp(130000), false},
+       {Timestamp(140000), false}, {Timestamp(150000), true},
+       {Timestamp(160000), false}, {Timestamp(170000), true},
+       {Timestamp(180000), false}, {Timestamp(190000), true},
+       {Timestamp(200000), false}});
+  EXPECT_THAT(allow_packets_, BoolPackestEq(expected_allow));
+}
+
+// Shows how FlowLimiterCalculator releases auxiliary input packets.
+// In this test, auxiliary input packets arrive at twice the primary rate.
+TEST_F(FlowLimiterCalculatorTest, AuxiliaryInputs) {
+  auto BoolPackestEq = PackestEq<std::vector<Packet>, bool>;
+  auto IntPackestEq = PackestEq<std::vector<Packet>, int>;
+
+  // Configure the test.
+  SetUpInputData();
+  SetUpSimulationClock();
+  CalculatorGraphConfig graph_config =
+      ParseTextProtoOrDie<CalculatorGraphConfig>(R"pb(
+        input_stream: 'in_1'
+        input_stream: 'in_2'
+        node {
+          calculator: 'FlowLimiterCalculator'
+          input_side_packet: 'OPTIONS:limiter_options'
+          input_stream: 'in_1'
+          input_stream: 'in_2'
+          input_stream: 'FINISHED:out_1'
+          input_stream_info: { tag_index: 'FINISHED' back_edge: true }
+          output_stream: 'in_1_sampled'
+          output_stream: 'in_2_sampled'
+          output_stream: 'ALLOW:allow'
+        }
+        node {
+          calculator: 'SleepCalculator'
+          input_side_packet: 'WARMUP_TIME:warmup_time'
+          input_side_packet: 'SLEEP_TIME:sleep_time'
+          input_side_packet: 'CLOCK:clock'
+          input_stream: 'PACKET:in_1_sampled'
+          output_stream: 'PACKET:out_1'
+        }
+      )pb");
+
+  auto limiter_options = ParseTextProtoOrDie<FlowLimiterCalculatorOptions>(
+      R"pb(
+        max_in_flight: 1 max_in_queue: 0 in_flight_timeout: 1000000  # 1s
+      )pb");
+  std::map<std::string, Packet> side_packets = {
+      {"limiter_options",
+       MakePacket<FlowLimiterCalculatorOptions>(limiter_options)},
+      {"warmup_time", MakePacket<int64>(22000)},
+      {"sleep_time", MakePacket<int64>(22000)},
+      {"clock", MakePacket<mediapipe::Clock*>(clock_)},
+  };
+
+  // Start the graph.
+  MP_ASSERT_OK(graph_.Initialize(graph_config));
+  MP_EXPECT_OK(graph_.ObserveOutputStream("out_1", [this](Packet p) {
+    out_1_packets_.push_back(p);
+    return absl::OkStatus();
+  }));
+  std::vector<Packet> out_2_packets;
+  MP_EXPECT_OK(graph_.ObserveOutputStream("in_2_sampled", [&](Packet p) {
+    out_2_packets.push_back(p);
+    return absl::OkStatus();
+  }));
+  MP_EXPECT_OK(graph_.ObserveOutputStream("allow", [this](Packet p) {
+    allow_packets_.push_back(p);
+    return absl::OkStatus();
+  }));
+  simulation_clock_->ThreadStart();
+  MP_ASSERT_OK(graph_.StartRun(side_packets));
+
+  // Add packets 2,4,6,8 to stream in_1 and 1..9 to stream in_2.
+  clock_->Sleep(absl::Microseconds(10000));
+  for (int i = 1; i < 10; ++i) {
+    if (i % 2 == 0) {
+      MP_EXPECT_OK(graph_.AddPacketToInputStream("in_1", input_packets_[i]));
+    }
+    MP_EXPECT_OK(graph_.AddPacketToInputStream("in_2", input_packets_[i]));
+    clock_->Sleep(absl::Microseconds(10000));
+  }
+
+  // Finish the graph run.
+  MP_EXPECT_OK(graph_.CloseAllPacketSources());
+  clock_->Sleep(absl::Microseconds(40000));
+  MP_EXPECT_OK(graph_.WaitUntilDone());
+  simulation_clock_->ThreadFinish();
+
+  // Validate the output.
+  // Input packets 4 and 8 are dropped due to max_in_flight.
+  std::vector<Packet> expected_output = {
+      input_packets_[2],
+      input_packets_[6],
+  };
+  EXPECT_THAT(out_1_packets_, IntPackestEq(expected_output));
+  // Packets following input packets 2 and 6, and not input packets 4 and 8.
+  std::vector<Packet> expected_output_2 = {
+      input_packets_[1], input_packets_[2], input_packets_[3],
+      input_packets_[6], input_packets_[7],
+  };
+  EXPECT_THAT(out_2_packets, IntPackestEq(expected_output_2));
+
+  // Validate the ALLOW stream output.
+  std::vector<Packet> expected_allow =
+      MakePackets<bool>({{Timestamp(20000), 1},
+                         {Timestamp(40000), 0},
+                         {Timestamp(60000), 1},
+                         {Timestamp(80000), 0}});
+  EXPECT_THAT(allow_packets_, BoolPackestEq(expected_allow));
 }
 
 }  // anonymous namespace

mediapipe/calculators/tensor/inference_calculator_gl_advanced.cc

@@ -25,12 +25,12 @@
 #include "mediapipe/gpu/gl_calculator_helper.h"
 #include "mediapipe/util/tflite/tflite_gpu_runner.h"
 
-#if defined(MEDIAPIPE_ANDROID)
+#if defined(MEDIAPIPE_ANDROID) || defined(MEDIAPIPE_CHROMIUMOS)
 #include "mediapipe/framework/deps/file_path.h"
 #include "mediapipe/util/android/file/base/file.h"
 #include "mediapipe/util/android/file/base/filesystem.h"
 #include "mediapipe/util/android/file/base/helpers.h"
-#endif  // MEDIAPIPE_ANDROID
+#endif  // defined(MEDIAPIPE_ANDROID) || defined(MEDIAPIPE_CHROMIUMOS)
 
 #define PERFETTO_TRACK_EVENT_NAMESPACE mediapipe
 
@@ -231,7 +231,7 @@ InferenceCalculatorGlAdvancedImpl::GpuInferenceRunner::InitTFLiteGPURunner(
   return tflite_gpu_runner_->Build();
 }
 
-#if defined(MEDIAPIPE_ANDROID)
+#if defined(MEDIAPIPE_ANDROID) || defined(MEDIAPIPE_CHROMIUMOS)
 absl::Status InferenceCalculatorGlAdvancedImpl::OnDiskCacheHelper::Init(
     const mediapipe::InferenceCalculatorOptions& options,
     const mediapipe::InferenceCalculatorOptions::Delegate::Gpu&
@@ -318,7 +318,7 @@ InferenceCalculatorGlAdvancedImpl::OnDiskCacheHelper::SaveGpuCaches(
     tflite::gpu::TFLiteGPURunner* gpu_runner) const {
   return absl::OkStatus();
 }
-#endif  // MEDIAPIPE_ANDROID
+#endif  // defined(MEDIAPIPE_ANDROID) || defined(MEDIAPIPE_CHROMIUMOS)
 
 absl::Status InferenceCalculatorGlAdvancedImpl::UpdateContract(
     CalculatorContract* cc) {

mediapipe/framework/deps/BUILD

@@ -15,7 +15,7 @@
 # Description:
 #   The dependencies of mediapipe.
 
-load("//mediapipe/framework/port:build_config.bzl", "mediapipe_cc_proto_library")
+load("//mediapipe/framework/port:build_config.bzl", "mediapipe_proto_library")
 load("@bazel_skylib//:bzl_library.bzl", "bzl_library")
 
 licenses(["notice"])
@@ -38,7 +38,7 @@ bzl_library(
     visibility = ["//mediapipe/framework:__subpackages__"],
 )
 
-proto_library(
+mediapipe_proto_library(
     name = "proto_descriptor_proto",
     srcs = ["proto_descriptor.proto"],
     visibility = [
@@ -47,13 +47,6 @@ proto_library(
     ],
 )
 
-mediapipe_cc_proto_library(
-    name = "proto_descriptor_cc_proto",
-    srcs = ["proto_descriptor.proto"],
-    visibility = ["//mediapipe/framework:__subpackages__"],
-    deps = [":proto_descriptor_proto"],
-)
-
 cc_library(
     name = "aligned_malloc_and_free",
     hdrs = ["aligned_malloc_and_free.h"],

mediapipe/framework/port/build_config.bzl

@@ -4,6 +4,9 @@
 """.bzl file for mediapipe open source build configs."""
 
 load("@com_google_protobuf//:protobuf.bzl", "cc_proto_library", "py_proto_library")
+load("@npm//@bazel/typescript:index.bzl", "ts_project")
+load("@rules_proto//proto:defs.bzl", "proto_library")
+load("@rules_proto_grpc//js:defs.bzl", "js_proto_library")
 load("//mediapipe/framework/tool:mediapipe_graph.bzl", "mediapipe_options_library")
 
 def provided_args(**kwargs):
@@ -71,7 +74,7 @@ def mediapipe_proto_library(
       def_jspb_proto: define the jspb_proto_library target
       def_options_lib: define the mediapipe_options_library target
     """
-    _ignore = [def_portable_proto, def_objc_proto, def_java_proto, def_jspb_proto, portable_deps]
+    _ignore = [def_portable_proto, def_objc_proto, def_java_proto, portable_deps]  # buildifier: disable=unused-variable
 
     # The proto_library targets for the compiled ".proto" source files.
     proto_deps = [":" + name]
@@ -119,6 +122,24 @@ def mediapipe_proto_library(
             compatible_with = compatible_with,
         ))
 
+    if def_jspb_proto:
+        js_deps = replace_deps(deps, "_proto", "_jspb_proto", False)
+        proto_library(
+            name = replace_suffix(name, "_proto", "_lib_proto"),
+            srcs = srcs,
+            deps = deps,
+        )
+        js_proto_library(
+            name = replace_suffix(name, "_proto", "_jspb_proto"),
+            protos = [replace_suffix(name, "_proto", "_lib_proto")],
+            output_mode = "NO_PREFIX_FLAT",
+            # Need to specify this to work around bug in js_proto_library()
+            # https://github.com/bazelbuild/rules_nodejs/issues/3503
+            legacy_path = "unused",
+            deps = js_deps,
+            visibility = visibility,
+        )
+
     if def_options_lib:
         cc_deps = replace_deps(deps, "_proto", "_cc_proto")
         mediapipe_options_library(**provided_args(
@@ -182,3 +203,35 @@ def mediapipe_cc_proto_library(name, srcs, visibility = None, deps = [], cc_deps
         default_runtime = "@com_google_protobuf//:protobuf",
         alwayslink = 1,
     ))
+
+def mediapipe_ts_library(
+        name,
+        srcs,
+        visibility = None,
+        deps = [],
+        testonly = 0,
+        allow_unoptimized_namespaces = False):
+    """Generate ts_project for MediaPipe open source version.
+
+    Args:
+      name: the name of the cc_proto_library.
+      srcs: the .proto files of the cc_proto_library for Bazel use.
+      visibility: visibility of this target.
+      deps: a list of dependency labels for Bazel use; must be cc_proto_library.
+      testonly: test only or not.
+      allow_unoptimized_namespaces: ignored, used only internally
+    """
+    _ignore = [allow_unoptimized_namespaces]  # buildifier: disable=unused-variable
+
+    ts_project(**provided_args(
+        name = name,
+        srcs = srcs,
+        visibility = visibility,
+        deps = deps + [
+            "@npm//@types/offscreencanvas",
+            "@npm//@types/google-protobuf",
+        ],
+        testonly = testonly,
+        declaration = True,
+        tsconfig = "//:tsconfig.json",
+    ))

mediapipe/model_maker/python/core/tasks/custom_model.py

@@ -52,6 +52,7 @@ class CustomModel(abc.ABC):
     """Prints a summary of the model."""
     self._model.summary()
 
+# TODO: Remove this method when all tasks use Metadata writer
   def export_tflite(
       self,
       export_dir: str,
@@ -62,7 +63,7 @@ class CustomModel(abc.ABC):
 
     Args:
       export_dir: The directory to save exported files.
-      tflite_filename: File name to save tflite model. The full export path is
+      tflite_filename: File name to save TFLite model. The full export path is
         {export_dir}/{tflite_filename}.
       quantization_config: The configuration for model quantization.
       preprocess: A callable to preprocess the representative dataset for
@@ -73,11 +74,11 @@ class CustomModel(abc.ABC):
       tf.io.gfile.makedirs(export_dir)
 
     tflite_filepath = os.path.join(export_dir, tflite_filename)
-    # TODO: Populate metadata to the exported TFLite model.
-    model_util.export_tflite(
+    tflite_model = model_util.convert_to_tflite(
         model=self._model,
-        tflite_filepath=tflite_filepath,
         quantization_config=quantization_config,
         preprocess=preprocess)
+    model_util.save_tflite(
+        tflite_model=tflite_model, tflite_file=tflite_filepath)
     tf.compat.v1.logging.info(
         'TensorFlow Lite model exported successfully: %s' % tflite_filepath)

mediapipe/model_maker/python/core/utils/model_util.py

@@ -89,28 +89,25 @@ def get_steps_per_epoch(steps_per_epoch: Optional[int] = None,
     return len(train_data) // batch_size
 
 
-def export_tflite(
+def convert_to_tflite(
     model: tf.keras.Model,
-    tflite_filepath: str,
     quantization_config: Optional[quantization.QuantizationConfig] = None,
     supported_ops: Tuple[tf.lite.OpsSet,
                          ...] = (tf.lite.OpsSet.TFLITE_BUILTINS,),
-    preprocess: Optional[Callable[..., bool]] = None):
-  """Converts the model to tflite format and saves it.
+    preprocess: Optional[Callable[..., bool]] = None) -> bytearray:
+  """Converts the input Keras model to TFLite format.
 
   Args:
-    model: model to be converted to tflite.
-    tflite_filepath: File path to save tflite model.
+    model: Keras model to be converted to TFLite.
     quantization_config: Configuration for post-training quantization.
     supported_ops: A list of supported ops in the converted TFLite file.
     preprocess: A callable to preprocess the representative dataset for
       quantization. The callable takes three arguments in order: feature, label,
       and is_training.
-  """
-  if tflite_filepath is None:
-    raise ValueError(
-        "TFLite filepath couldn't be None when exporting to tflite.")
 
+  Returns:
+    bytearray of TFLite model
+  """
   with tempfile.TemporaryDirectory() as temp_dir:
     save_path = os.path.join(temp_dir, 'saved_model')
     model.save(save_path, include_optimizer=False, save_format='tf')
@@ -122,9 +119,22 @@ def export_tflite(
 
     converter.target_spec.supported_ops = supported_ops
     tflite_model = converter.convert()
+    return tflite_model
 
-  with tf.io.gfile.GFile(tflite_filepath, 'wb') as f:
+
+def save_tflite(tflite_model: bytearray, tflite_file: str) -> None:
+  """Saves TFLite file to tflite_file.
+
+  Args:
+    tflite_model: A valid flatbuffer representing the TFLite model.
+    tflite_file: File path to save TFLite model.
+  """
+  if tflite_file is None:
+    raise ValueError("TFLite filepath can't be None when exporting to TFLite.")
+  with tf.io.gfile.GFile(tflite_file, 'wb') as f:
     f.write(tflite_model)
+  tf.compat.v1.logging.info(
+      'TensorFlow Lite model exported successfully to: %s' % tflite_file)
 
 
 class WarmUp(tf.keras.optimizers.schedules.LearningRateSchedule):
@@ -176,14 +186,12 @@ class WarmUp(tf.keras.optimizers.schedules.LearningRateSchedule):
 class LiteRunner(object):
   """A runner to do inference with the TFLite model."""
 
-  def __init__(self, tflite_filepath: str):
-    """Initializes Lite runner with tflite model file.
+  def __init__(self, tflite_model: bytearray):
+    """Initializes Lite runner from TFLite model buffer.
 
     Args:
-      tflite_filepath: File path to the TFLite model.
+      tflite_model: A valid flatbuffer representing the TFLite model.
     """
-    with tf.io.gfile.GFile(tflite_filepath, 'rb') as f:
-      tflite_model = f.read()
     self.interpreter = tf.lite.Interpreter(model_content=tflite_model)
     self.interpreter.allocate_tensors()
     self.input_details = self.interpreter.get_input_details()
@@ -250,9 +258,9 @@ class LiteRunner(object):
     return output_tensors
 
 
-def get_lite_runner(tflite_filepath: str) -> 'LiteRunner':
-  """Returns a `LiteRunner` from file path to TFLite model."""
-  lite_runner = LiteRunner(tflite_filepath)
+def get_lite_runner(tflite_buffer: bytearray) -> 'LiteRunner':
+  """Returns a `LiteRunner` from flatbuffer of the TFLite model."""
+  lite_runner = LiteRunner(tflite_buffer)
   return lite_runner
 
 

mediapipe/model_maker/python/core/utils/model_util_test.py

@@ -95,13 +95,12 @@ class ModelUtilTest(tf.test.TestCase, parameterized.TestCase):
             'name': 'test'
         })
 
-  def test_export_tflite(self):
+  def test_convert_to_tflite(self):
     input_dim = 4
     model = test_util.build_model(input_shape=[input_dim], num_classes=2)
-    tflite_file = os.path.join(self.get_temp_dir(), 'model.tflite')
-    model_util.export_tflite(model, tflite_file)
+    tflite_model = model_util.convert_to_tflite(model)
     test_util.test_tflite(
-        keras_model=model, tflite_file=tflite_file, size=[1, input_dim])
+        keras_model=model, tflite_model=tflite_model, size=[1, input_dim])
 
   @parameterized.named_parameters(
       dict(
@@ -118,25 +117,32 @@ class ModelUtilTest(tf.test.TestCase, parameterized.TestCase):
           testcase_name='float16_quantize',
           config=quantization.QuantizationConfig.for_float16(),
           model_size=1468))
-  def test_export_tflite_quantized(self, config, model_size):
+  def test_convert_to_tflite_quantized(self, config, model_size):
     input_dim = 16
     num_classes = 2
     max_input_value = 5
     model = test_util.build_model(
         input_shape=[input_dim], num_classes=num_classes)
-    tflite_file = os.path.join(self.get_temp_dir(), 'model_quantized.tflite')
 
-    model_util.export_tflite(
-        model=model, tflite_filepath=tflite_file, quantization_config=config)
+    tflite_model = model_util.convert_to_tflite(
+        model=model, quantization_config=config)
     self.assertTrue(
         test_util.test_tflite(
             keras_model=model,
-            tflite_file=tflite_file,
+            tflite_model=tflite_model,
             size=[1, input_dim],
             high=max_input_value,
             atol=1e-00))
-    self.assertNear(os.path.getsize(tflite_file), model_size, 300)
+    self.assertNear(len(tflite_model), model_size, 300)
 
+  def test_save_tflite(self):
+    input_dim = 4
+    model = test_util.build_model(input_shape=[input_dim], num_classes=2)
+    tflite_model = model_util.convert_to_tflite(model)
+    tflite_file = os.path.join(self.get_temp_dir(), 'model.tflite')
+    model_util.save_tflite(tflite_model=tflite_model, tflite_file=tflite_file)
+    test_util.test_tflite_file(
+        keras_model=model, tflite_file=tflite_file, size=[1, input_dim])
 
 if __name__ == '__main__':
   tf.test.main()

mediapipe/model_maker/python/core/utils/test_util.py

@@ -79,13 +79,13 @@ def build_model(input_shape: List[int], num_classes: int) -> tf.keras.Model:
   return model
 
 
-def is_same_output(tflite_file: str,
+def is_same_output(tflite_model: bytearray,
                    keras_model: tf.keras.Model,
                    input_tensors: Union[List[tf.Tensor], tf.Tensor],
                    atol: float = 1e-04) -> bool:
   """Returns if the output of TFLite model and keras model are identical."""
   # Gets output from lite model.
-  lite_runner = model_util.get_lite_runner(tflite_file)
+  lite_runner = model_util.get_lite_runner(tflite_model)
   lite_output = lite_runner.run(input_tensors)
 
   # Gets output from keras model.
@@ -95,12 +95,41 @@ def is_same_output(tflite_file: str,
 
 
 def test_tflite(keras_model: tf.keras.Model,
-                tflite_file: str,
+                tflite_model: bytearray,
                 size: Union[int, List[int]],
                 high: float = 1,
                 atol: float = 1e-04) -> bool:
   """Verifies if the output of TFLite model and TF Keras model are identical.
 
+  Args:
+    keras_model: Input TensorFlow Keras model.
+    tflite_model: Input TFLite model flatbuffer.
+    size: Size of the input tesnor.
+    high: Higher boundary of the values in input tensors.
+    atol: Absolute tolerance of the difference between the outputs of Keras
+      model and TFLite model.
+
+  Returns:
+    True if the output of TFLite model and TF Keras model are identical.
+    Otherwise, False.
+  """
+  random_input = create_random_sample(size=size, high=high)
+  random_input = tf.convert_to_tensor(random_input)
+
+  return is_same_output(
+      tflite_model=tflite_model,
+      keras_model=keras_model,
+      input_tensors=random_input,
+      atol=atol)
+
+
+def test_tflite_file(keras_model: tf.keras.Model,
+                     tflite_file: bytearray,
+                     size: Union[int, List[int]],
+                     high: float = 1,
+                     atol: float = 1e-04) -> bool:
+  """Verifies if the output of TFLite model and TF Keras model are identical.
+
   Args:
     keras_model: Input TensorFlow Keras model.
     tflite_file: Input TFLite model file.
@@ -113,11 +142,6 @@ def test_tflite(keras_model: tf.keras.Model,
     True if the output of TFLite model and TF Keras model are identical.
     Otherwise, False.
   """
-  random_input = create_random_sample(size=size, high=high)
-  random_input = tf.convert_to_tensor(random_input)
-
-  return is_same_output(
-      tflite_file=tflite_file,
-      keras_model=keras_model,
-      input_tensors=random_input,
-      atol=atol)
+  with tf.io.gfile.GFile(tflite_file, "rb") as f:
+    tflite_model = f.read()
+  return test_tflite(keras_model, tflite_model, size, high, atol)

mediapipe/model_maker/python/internal/README.md

@@ -1,4 +0,0 @@
-# MediaPipe Model Maker Internal Library
-
-This directory contains model maker library for internal users and experimental
-purposes.

mediapipe/model_maker/python/internal/__init__.py

@@ -1 +0,0 @@
-"""Model maker internal library."""

mediapipe/model_maker/python/vision/image_classifier/BUILD

@@ -81,6 +81,8 @@ py_library(
         "//mediapipe/model_maker/python/core/utils:model_util",
         "//mediapipe/model_maker/python/core/utils:quantization",
         "//mediapipe/model_maker/python/vision/core:image_preprocessing",
+        "//mediapipe/tasks/python/metadata/metadata_writers:image_classifier",
+        "//mediapipe/tasks/python/metadata/metadata_writers:metadata_writer",
     ],
 )
 
@@ -88,7 +90,11 @@ py_library(
     name = "image_classifier_test_lib",
     testonly = 1,
     srcs = ["image_classifier_test.py"],
-    deps = [":image_classifier_import"],
+    data = ["//mediapipe/model_maker/python/vision/image_classifier/testdata"],
+    deps = [
+        ":image_classifier_import",
+        "//mediapipe/tasks/python/test:test_utils",
+    ],
 )
 
 py_test(

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

@@ -12,6 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 """APIs to train image classifier model."""
+import os
 
 from typing import List, Optional
 
@@ -26,6 +27,8 @@ from mediapipe.model_maker.python.vision.core import image_preprocessing
 from mediapipe.model_maker.python.vision.image_classifier import hyperparameters as hp
 from mediapipe.model_maker.python.vision.image_classifier import model_spec as ms
 from mediapipe.model_maker.python.vision.image_classifier import train_image_classifier_lib
+from mediapipe.tasks.python.metadata.metadata_writers import image_classifier as image_classifier_writer
+from mediapipe.tasks.python.metadata.metadata_writers import metadata_writer
 
 
 class ImageClassifier(classifier.Classifier):
@@ -156,15 +159,32 @@ class ImageClassifier(classifier.Classifier):
       self,
       model_name: str = 'model.tflite',
       quantization_config: Optional[quantization.QuantizationConfig] = None):
-    """Converts the model to the requested formats and exports to a file.
+    """Converts and saves the model to a TFLite file with metadata included.
+
+    Note that only the TFLite file is needed for deployment. This function also
+    saves a metadata.json file to the same directory as the TFLite file which
+    can be used to interpret the metadata content in the TFLite file.
 
     Args:
-      model_name: File name to save tflite model. The full export path is
-        {export_dir}/{tflite_filename}.
+      model_name: File name to save TFLite model with metadata. The full export
+        path is {self._hparams.model_dir}/{model_name}.
       quantization_config: The configuration for model quantization.
     """
-    super().export_tflite(
-        self._hparams.model_dir,
-        model_name,
-        quantization_config,
+    if not tf.io.gfile.exists(self._hparams.model_dir):
+      tf.io.gfile.makedirs(self._hparams.model_dir)
+    tflite_file = os.path.join(self._hparams.model_dir, model_name)
+    metadata_file = os.path.join(self._hparams.model_dir, 'metadata.json')
+
+    tflite_model = model_util.convert_to_tflite(
+        model=self._model,
+        quantization_config=quantization_config,
         preprocess=self._preprocess)
+    writer = image_classifier_writer.MetadataWriter.create(
+        tflite_model,
+        self._model_spec.mean_rgb,
+        self._model_spec.stddev_rgb,
+        labels=metadata_writer.Labels().add(self._label_names))
+    tflite_model_with_metadata, metadata_json = writer.populate()
+    model_util.save_tflite(tflite_model_with_metadata, tflite_file)
+    with open(metadata_file, 'w') as f:
+      f.write(metadata_json)

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

@@ -12,6 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import filecmp
 import os
 
 from absl.testing import parameterized
@@ -19,6 +20,7 @@ import numpy as np
 import tensorflow as tf
 
 from mediapipe.model_maker.python.vision import image_classifier
+from mediapipe.tasks.python.test import test_utils
 
 
 def _fill_image(rgb, image_size):
@@ -86,7 +88,7 @@ class ImageClassifierTest(tf.test.TestCase, parameterized.TestCase):
         validation_data=self.test_data)
     self._test_accuracy(model)
 
-  def test_efficientnetlite0_model_with_model_maker_retraining_lib(self):
+  def test_efficientnetlite0_model_train_and_export(self):
     hparams = image_classifier.HParams(
         train_epochs=1, batch_size=1, shuffle=True)
     model = image_classifier.ImageClassifier.create(
@@ -96,6 +98,19 @@ class ImageClassifierTest(tf.test.TestCase, parameterized.TestCase):
         validation_data=self.test_data)
     self._test_accuracy(model)
 
+    # Test export_model
+    model.export_model()
+    output_metadata_file = os.path.join(hparams.model_dir, 'metadata.json')
+    output_tflite_file = os.path.join(hparams.model_dir, 'model.tflite')
+    expected_metadata_file = test_utils.get_test_data_path('metadata.json')
+
+    self.assertTrue(os.path.exists(output_tflite_file))
+    self.assertGreater(os.path.getsize(output_tflite_file), 0)
+
+    self.assertTrue(os.path.exists(output_metadata_file))
+    self.assertGreater(os.path.getsize(output_metadata_file), 0)
+    self.assertTrue(filecmp.cmp(output_metadata_file, expected_metadata_file))
+
   def _test_accuracy(self, model, threshold=0.0):
     _, accuracy = model.evaluate(self.test_data)
     self.assertGreaterEqual(accuracy, threshold)

mediapipe/model_maker/python/vision/image_classifier/testdata/BUILD

@@ -0,0 +1,23 @@
+# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+#
+# 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.
+
+package(
+    default_visibility = ["//mediapipe/model_maker/python/vision/image_classifier:__subpackages__"],
+    licenses = ["notice"],  # Apache 2.0
+)
+
+filegroup(
+    name = "testdata",
+    srcs = ["metadata.json"],
+)

mediapipe/model_maker/python/vision/image_classifier/testdata/metadata.json

@@ -0,0 +1,68 @@
+{
+  "name": "ImageClassifier",
+  "description": "Identify the most prominent object in the image from a known set of categories.",
+  "subgraph_metadata": [
+    {
+      "input_tensor_metadata": [
+        {
+          "name": "image",
+          "description": "Input image to be processed.",
+          "content": {
+            "content_properties_type": "ImageProperties",
+            "content_properties": {
+              "color_space": "RGB"
+            }
+          },
+          "process_units": [
+            {
+              "options_type": "NormalizationOptions",
+              "options": {
+                "mean": [
+                  0.0
+                ],
+                "std": [
+                  255.0
+                ]
+              }
+            }
+          ],
+          "stats": {
+            "max": [
+              1.0
+            ],
+            "min": [
+              0.0
+            ]
+          }
+        }
+      ],
+      "output_tensor_metadata": [
+        {
+          "name": "score",
+          "description": "Score of the labels respectively.",
+          "content": {
+            "content_properties_type": "FeatureProperties",
+            "content_properties": {
+            }
+          },
+          "stats": {
+            "max": [
+              1.0
+            ],
+            "min": [
+              0.0
+            ]
+          },
+          "associated_files": [
+            {
+              "name": "labels.txt",
+              "description": "Labels for categories that the model can recognize.",
+              "type": "TENSOR_AXIS_LABELS"
+            }
+          ]
+        }
+      ]
+    }
+  ],
+  "min_parser_version": "1.0.0"
+}

mediapipe/python/BUILD

@@ -87,6 +87,7 @@ cc_library(
 cc_library(
     name = "builtin_task_graphs",
     deps = [
+        "//mediapipe/tasks/cc/vision/gesture_recognizer:gesture_recognizer_graph",
         "//mediapipe/tasks/cc/vision/image_classifier:image_classifier_graph",
         "//mediapipe/tasks/cc/vision/image_segmenter:image_segmenter_graph",
         "//mediapipe/tasks/cc/vision/object_detector:object_detector_graph",

mediapipe/python/pybind/packet_creator.cc

@@ -602,8 +602,11 @@ void PublicPacketCreators(pybind11::module* m) {
       // TODO: Should take "const Eigen::Ref<const Eigen::MatrixXf>&"
       // as the input argument. Investigate why bazel non-optimized mode
       // triggers a memory allocation bug in Eigen::internal::aligned_free().
-      [](const Eigen::MatrixXf& matrix) {
+      [](const Eigen::MatrixXf& matrix, bool transpose) {
         // MakePacket copies the data.
+        if (transpose) {
+          return MakePacket<Matrix>(matrix.transpose());
+        }
         return MakePacket<Matrix>(matrix);
       },
       R"doc(Create a MediaPipe Matrix Packet from a 2d numpy float ndarray.
@@ -613,6 +616,8 @@ void PublicPacketCreators(pybind11::module* m) {
 
   Args:
     matrix: A 2d numpy float ndarray.
+    transpose: A boolean to indicate if the input matrix needs to be transposed.
+      Default to False.
 
   Returns:
     A MediaPipe Matrix Packet.
@@ -625,6 +630,7 @@ void PublicPacketCreators(pybind11::module* m) {
         np.array([[.1, .2, .3], [.4, .5, .6]])
     matrix = mp.packet_getter.get_matrix(packet)
 )doc",
+      py::arg("matrix"), py::arg("transpose") = false,
       py::return_value_policy::move);
 }  // NOLINT(readability/fn_size)
 

mediapipe/tasks/cc/audio/audio_classifier/BUILD

@@ -65,6 +65,7 @@ cc_library(
         "//mediapipe/tasks/cc/audio/core:audio_task_api_factory",
         "//mediapipe/tasks/cc/audio/core:base_audio_task_api",
         "//mediapipe/tasks/cc/audio/core:running_mode",
+        "//mediapipe/tasks/cc/components/containers:classification_result",
         "//mediapipe/tasks/cc/components/containers/proto:classifications_cc_proto",
         "//mediapipe/tasks/cc/components/processors:classifier_options",
         "//mediapipe/tasks/cc/components/processors/proto:classifier_options_cc_proto",

mediapipe/tasks/cc/audio/audio_classifier/audio_classifier.cc

@@ -18,12 +18,14 @@ limitations under the License.
 #include <map>
 #include <memory>
 #include <utility>
+#include <vector>
 
 #include "absl/status/statusor.h"
 #include "mediapipe/framework/api2/builder.h"
 #include "mediapipe/framework/formats/matrix.h"
 #include "mediapipe/tasks/cc/audio/audio_classifier/proto/audio_classifier_graph_options.pb.h"
 #include "mediapipe/tasks/cc/audio/core/audio_task_api_factory.h"
+#include "mediapipe/tasks/cc/components/containers/classification_result.h"
 #include "mediapipe/tasks/cc/components/containers/proto/classifications.pb.h"
 #include "mediapipe/tasks/cc/components/processors/classifier_options.h"
 #include "mediapipe/tasks/cc/components/processors/proto/classifier_options.pb.h"
@@ -38,12 +40,16 @@ namespace audio_classifier {
 
 namespace {
 
+using ::mediapipe::tasks::components::containers::ConvertToClassificationResult;
 using ::mediapipe::tasks::components::containers::proto::ClassificationResult;
 
 constexpr char kAudioStreamName[] = "audio_in";
 constexpr char kAudioTag[] = "AUDIO";
-constexpr char kClassificationResultStreamName[] = "classification_result_out";
-constexpr char kClassificationResultTag[] = "CLASSIFICATION_RESULT";
+constexpr char kClassificationsTag[] = "CLASSIFICATIONS";
+constexpr char kClassificationsName[] = "classifications_out";
+constexpr char kTimestampedClassificationsTag[] = "TIMESTAMPED_CLASSIFICATIONS";
+constexpr char kTimestampedClassificationsName[] =
+    "timestamped_classifications_out";
 constexpr char kSampleRateName[] = "sample_rate_in";
 constexpr char kSampleRateTag[] = "SAMPLE_RATE";
 constexpr char kSubgraphTypeName[] =
@@ -63,9 +69,11 @@ CalculatorGraphConfig CreateGraphConfig(
   }
   subgraph.GetOptions<proto::AudioClassifierGraphOptions>().Swap(
       options_proto.get());
-  subgraph.Out(kClassificationResultTag)
-          .SetName(kClassificationResultStreamName) >>
-      graph.Out(kClassificationResultTag);
+  subgraph.Out(kClassificationsTag).SetName(kClassificationsName) >>
+      graph.Out(kClassificationsTag);
+  subgraph.Out(kTimestampedClassificationsTag)
+          .SetName(kTimestampedClassificationsName) >>
+      graph.Out(kTimestampedClassificationsTag);
   return graph.GetConfig();
 }
 
@@ -91,13 +99,30 @@ ConvertAudioClassifierOptionsToProto(AudioClassifierOptions* options) {
   return options_proto;
 }
 
-absl::StatusOr<ClassificationResult> ConvertOutputPackets(
+absl::StatusOr<std::vector<AudioClassifierResult>> ConvertOutputPackets(
     absl::StatusOr<tasks::core::PacketMap> status_or_packets) {
   if (!status_or_packets.ok()) {
     return status_or_packets.status();
   }
-  return status_or_packets.value()[kClassificationResultStreamName]
-      .Get<ClassificationResult>();
+  auto classification_results =
+      status_or_packets.value()[kTimestampedClassificationsName]
+          .Get<std::vector<ClassificationResult>>();
+  std::vector<AudioClassifierResult> results;
+  results.reserve(classification_results.size());
+  for (const auto& classification_result : classification_results) {
+    results.emplace_back(ConvertToClassificationResult(classification_result));
+  }
+  return results;
+}
+
+absl::StatusOr<AudioClassifierResult> ConvertAsyncOutputPackets(
+    absl::StatusOr<tasks::core::PacketMap> status_or_packets) {
+  if (!status_or_packets.ok()) {
+    return status_or_packets.status();
+  }
+  return ConvertToClassificationResult(
+      status_or_packets.value()[kClassificationsName]
+          .Get<ClassificationResult>());
 }
 }  // namespace
 
@@ -118,7 +143,7 @@ absl::StatusOr<std::unique_ptr<AudioClassifier>> AudioClassifier::Create(
     auto result_callback = options->result_callback;
     packets_callback =
         [=](absl::StatusOr<tasks::core::PacketMap> status_or_packets) {
-          result_callback(ConvertOutputPackets(status_or_packets));
+          result_callback(ConvertAsyncOutputPackets(status_or_packets));
         };
   }
   return core::AudioTaskApiFactory::Create<AudioClassifier,
@@ -128,7 +153,7 @@ absl::StatusOr<std::unique_ptr<AudioClassifier>> AudioClassifier::Create(
       std::move(packets_callback));
 }
 
-absl::StatusOr<ClassificationResult> AudioClassifier::Classify(
+absl::StatusOr<std::vector<AudioClassifierResult>> AudioClassifier::Classify(
     Matrix audio_clip, double audio_sample_rate) {
   return ConvertOutputPackets(ProcessAudioClip(
       {{kAudioStreamName, MakePacket<Matrix>(std::move(audio_clip))},

mediapipe/tasks/cc/audio/audio_classifier/audio_classifier.h

@@ -18,12 +18,13 @@ limitations under the License.
 
 #include <memory>
 #include <utility>
+#include <vector>
 
 #include "absl/status/statusor.h"
 #include "mediapipe/framework/formats/matrix.h"
 #include "mediapipe/tasks/cc/audio/core/base_audio_task_api.h"
 #include "mediapipe/tasks/cc/audio/core/running_mode.h"
-#include "mediapipe/tasks/cc/components/containers/proto/classifications.pb.h"
+#include "mediapipe/tasks/cc/components/containers/classification_result.h"
 #include "mediapipe/tasks/cc/components/processors/classifier_options.h"
 #include "mediapipe/tasks/cc/core/base_options.h"
 
@@ -32,6 +33,10 @@ namespace tasks {
 namespace audio {
 namespace audio_classifier {
 
+// Alias the shared ClassificationResult struct as result type.
+using AudioClassifierResult =
+    ::mediapipe::tasks::components::containers::ClassificationResult;
+
 // The options for configuring a mediapipe audio classifier task.
 struct AudioClassifierOptions {
   // Base options for configuring Task library, such as specifying the TfLite
@@ -59,9 +64,8 @@ struct AudioClassifierOptions {
   // The user-defined result callback for processing audio stream data.
   // The result callback should only be specified when the running mode is set
   // to RunningMode::AUDIO_STREAM.
-  std::function<void(
-      absl::StatusOr<components::containers::proto::ClassificationResult>)>
-      result_callback = nullptr;
+  std::function<void(absl::StatusOr<AudioClassifierResult>)> result_callback =
+      nullptr;
 };
 
 // Performs audio classification on audio clips or audio stream.
@@ -117,23 +121,36 @@ class AudioClassifier : tasks::audio::core::BaseAudioTaskApi {
   // required to provide the corresponding audio sample rate along with the
   // input audio clips.
   //
-  // For each audio clip, the output classifications are grouped in a
-  // ClassificationResult object that has three dimensions:
-  //   Classification head:
-  //     The prediction heads targeting different audio classification tasks
-  //     such as audio event classification and bird sound classification.
-  //   Classification timestamp:
-  //     The start time (in milliseconds) of each audio clip that is sent to the
-  //     model for audio classification. As the audio classification models take
-  //     a fixed number of audio samples, long audio clips will be framed to
-  //     multiple buffers (with the desired number of audio samples) during
-  //     preprocessing.
-  //   Classification category:
-  //     The list of the classification categories that model predicts per
-  //     framed audio clip.
+  // The input audio clip may be longer than what the model is able to process
+  // in a single inference. When this occurs, the input audio clip is split into
+  // multiple chunks starting at different timestamps. For this reason, this
+  // function returns a vector of ClassificationResult objects, each associated
+  // with a timestamp corresponding to the start (in milliseconds) of the chunk
+  // data that was classified, e.g:
+  //
+  // ClassificationResult #0 (first chunk of data):
+  //  timestamp_ms: 0 (starts at 0ms)
+  //  classifications #0 (single head model):
+  //   category #0:
+  //    category_name: "Speech"
+  //    score: 0.6
+  //   category #1:
+  //    category_name: "Music"
+  //    score: 0.2
+  // ClassificationResult #1 (second chunk of data):
+  //  timestamp_ms: 800 (starts at 800ms)
+  //  classifications #0 (single head model):
+  //   category #0:
+  //    category_name: "Speech"
+  //    score: 0.5
+  //   category #1:
+  //    category_name: "Silence"
+  //    score: 0.1
+  //  ...
+  //
   // TODO: Use `sample_rate` in AudioClassifierOptions by default
   // and makes `audio_sample_rate` optional.
-  absl::StatusOr<components::containers::proto::ClassificationResult> Classify(
+  absl::StatusOr<std::vector<AudioClassifierResult>> Classify(
       mediapipe::Matrix audio_clip, double audio_sample_rate);
 
   // Sends audio data (a block in a continuous audio stream) to perform audio
@@ -147,17 +164,10 @@ class AudioClassifier : tasks::audio::core::BaseAudioTaskApi {
   // milliseconds) to indicate the start time of the input audio block. The
   // timestamps must be monotonically increasing.
   //
-  // The output classifications are grouped in a ClassificationResult object
-  // that has three dimensions:
-  //   Classification head:
-  //     The prediction heads targeting different audio classification tasks
-  //     such as audio event classification and bird sound classification.
-  //   Classification timestamp :
-  //     The start time (in milliseconds) of the framed audio block that is sent
-  //     to the model for audio classification.
-  //   Classification category:
-  //     The list of the classification categories that model predicts per
-  //     framed audio clip.
+  // The input audio block may be longer than what the model is able to process
+  // in a single inference. When this occurs, the input audio block is split
+  // into multiple chunks. For this reason, the callback may be called multiple
+  // times (once per chunk) for each call to this function.
   absl::Status ClassifyAsync(mediapipe::Matrix audio_block, int64 timestamp_ms);
 
   // Shuts down the AudioClassifier when all works are done.

mediapipe/tasks/cc/audio/audio_classifier/audio_classifier_graph.cc

@@ -16,6 +16,7 @@ limitations under the License.
 #include <stdint.h>
 
 #include <utility>
+#include <vector>
 
 #include "absl/status/status.h"
 #include "absl/status/statusor.h"
@@ -57,12 +58,20 @@ using ::mediapipe::tasks::components::containers::proto::ClassificationResult;
 
 constexpr char kAtPrestreamTag[] = "AT_PRESTREAM";
 constexpr char kAudioTag[] = "AUDIO";
-constexpr char kClassificationResultTag[] = "CLASSIFICATION_RESULT";
+constexpr char kClassificationsTag[] = "CLASSIFICATIONS";
+constexpr char kTimestampedClassificationsTag[] = "TIMESTAMPED_CLASSIFICATIONS";
 constexpr char kPacketTag[] = "PACKET";
 constexpr char kSampleRateTag[] = "SAMPLE_RATE";
 constexpr char kTensorsTag[] = "TENSORS";
 constexpr char kTimestampsTag[] = "TIMESTAMPS";
 
+// Struct holding the different output streams produced by the audio classifier
+// graph.
+struct AudioClassifierOutputStreams {
+  Source<ClassificationResult> classifications;
+  Source<std::vector<ClassificationResult>> timestamped_classifications;
+};
+
 absl::Status SanityCheckOptions(
     const proto::AudioClassifierGraphOptions& options) {
   if (options.base_options().use_stream_mode() &&
@@ -124,16 +133,20 @@ void ConfigureAudioToTensorCalculator(
 //     series stream header with sample rate info.
 //
 // Outputs:
-//   CLASSIFICATION_RESULT - ClassificationResult
-//     The aggregated classification result object that has 3 dimensions:
-//     (classification head, classification timestamp, classification category).
+//   CLASSIFICATIONS - ClassificationResult @Optional
+//     The classification results aggregated by head. Only produces results if
+//     the graph if the 'use_stream_mode' option is true.
+//   TIMESTAMPED_CLASSIFICATIONS - std::vector<ClassificationResult> @Optional
+//     The classification result aggregated by timestamp, then by head. Only
+//     produces results if the graph if the 'use_stream_mode' option is false.
 //
 // Example:
 // node {
 //   calculator: "mediapipe.tasks.audio.audio_classifier.AudioClassifierGraph"
 //   input_stream: "AUDIO:audio_in"
 //   input_stream: "SAMPLE_RATE:sample_rate_in"
-//   output_stream: "CLASSIFICATION_RESULT:classification_result_out"
+//   output_stream: "CLASSIFICATIONS:classifications"
+//   output_stream: "TIMESTAMPED_CLASSIFICATIONS:timestamped_classifications"
 //   options {
 //     [mediapipe.tasks.audio.audio_classifier.proto.AudioClassifierGraphOptions.ext]
 //     {
@@ -162,7 +175,7 @@ class AudioClassifierGraph : public core::ModelTaskGraph {
             .base_options()
             .use_stream_mode();
     ASSIGN_OR_RETURN(
-        auto classification_result_out,
+        auto output_streams,
         BuildAudioClassificationTask(
             sc->Options<proto::AudioClassifierGraphOptions>(), *model_resources,
             graph[Input<Matrix>(kAudioTag)],
@@ -170,8 +183,11 @@ class AudioClassifierGraph : public core::ModelTaskGraph {
                 ? absl::nullopt
                 : absl::make_optional(graph[Input<double>(kSampleRateTag)]),
             graph));
-    classification_result_out >>
-        graph[Output<ClassificationResult>(kClassificationResultTag)];
+    output_streams.classifications >>
+        graph[Output<ClassificationResult>(kClassificationsTag)];
+    output_streams.timestamped_classifications >>
+        graph[Output<std::vector<ClassificationResult>>(
+            kTimestampedClassificationsTag)];
     return graph.GetConfig();
   }
 
@@ -187,7 +203,7 @@ class AudioClassifierGraph : public core::ModelTaskGraph {
   // audio_in: (mediapipe::Matrix) stream to run audio classification on.
   // sample_rate_in: (double) optional stream of the input audio sample rate.
   // graph: the mediapipe builder::Graph instance to be updated.
-  absl::StatusOr<Source<ClassificationResult>> BuildAudioClassificationTask(
+  absl::StatusOr<AudioClassifierOutputStreams> BuildAudioClassificationTask(
       const proto::AudioClassifierGraphOptions& task_options,
       const core::ModelResources& model_resources, Source<Matrix> audio_in,
       absl::optional<Source<double>> sample_rate_in, Graph& graph) {
@@ -250,16 +266,20 @@ class AudioClassifierGraph : public core::ModelTaskGraph {
     inference.Out(kTensorsTag) >> postprocessing.In(kTensorsTag);
 
     // Time aggregation is only needed for performing audio classification on
-    // audio files. Disables time aggregration by not connecting the
+    // audio files. Disables timestamp aggregation by not connecting the
     // "TIMESTAMPS" streams.
     if (!use_stream_mode) {
       audio_to_tensor.Out(kTimestampsTag) >> postprocessing.In(kTimestampsTag);
     }
 
-    // Outputs the aggregated classification result as the subgraph output
-    // stream.
-    return postprocessing[Output<ClassificationResult>(
-        kClassificationResultTag)];
+    // Output both streams as graph output streams/
+    return AudioClassifierOutputStreams{
+        /*classifications=*/postprocessing[Output<ClassificationResult>(
+            kClassificationsTag)],
+        /*timestamped_classifications=*/
+        postprocessing[Output<std::vector<ClassificationResult>>(
+            kTimestampedClassificationsTag)],
+    };
   }
 };
 

mediapipe/tasks/cc/audio/audio_classifier/audio_classifier_test.cc

@@ -32,13 +32,11 @@ limitations under the License.
 #include "mediapipe/framework/formats/matrix.h"
 #include "mediapipe/framework/port/gmock.h"
 #include "mediapipe/framework/port/gtest.h"
-#include "mediapipe/framework/port/parse_text_proto.h"
-#include "mediapipe/framework/port/status_matchers.h"
 #include "mediapipe/tasks/cc/audio/core/running_mode.h"
 #include "mediapipe/tasks/cc/audio/utils/test_utils.h"
 #include "mediapipe/tasks/cc/common.h"
-#include "mediapipe/tasks/cc/components/containers/proto/category.pb.h"
-#include "mediapipe/tasks/cc/components/containers/proto/classifications.pb.h"
+#include "mediapipe/tasks/cc/components/containers/category.h"
+#include "mediapipe/tasks/cc/components/containers/classification_result.h"
 #include "tensorflow/lite/core/shims/cc/shims_test_util.h"
 
 namespace mediapipe {
@@ -49,7 +47,6 @@ namespace {
 
 using ::absl::StatusOr;
 using ::mediapipe::file::JoinPath;
-using ::mediapipe::tasks::components::containers::proto::ClassificationResult;
 using ::testing::HasSubstr;
 using ::testing::Optional;
 
@@ -73,95 +70,86 @@ Matrix GetAudioData(absl::string_view filename) {
   return matrix_mapping.matrix();
 }
 
-void CheckSpeechClassificationResult(const ClassificationResult& result) {
-  EXPECT_THAT(result.classifications_size(), testing::Eq(1));
-  EXPECT_EQ(result.classifications(0).head_name(), "scores");
-  EXPECT_EQ(result.classifications(0).head_index(), 0);
-  EXPECT_THAT(result.classifications(0).entries_size(), testing::Eq(5));
+void CheckSpeechResult(const std::vector<AudioClassifierResult>& result,
+                       int expected_num_categories = 521) {
+  EXPECT_EQ(result.size(), 5);
+  // Ignore last result, which operates on a too small chunk to return relevant
+  // results.
   std::vector<int64> timestamps_ms = {0, 975, 1950, 2925};
   for (int i = 0; i < timestamps_ms.size(); i++) {
-    EXPECT_THAT(result.classifications(0).entries(0).categories_size(),
-                testing::Eq(521));
-    const auto* top_category =
-        &result.classifications(0).entries(0).categories(0);
-    EXPECT_THAT(top_category->category_name(), testing::Eq("Speech"));
-    EXPECT_GT(top_category->score(), 0.9f);
-    EXPECT_EQ(result.classifications(0).entries(i).timestamp_ms(),
-              timestamps_ms[i]);
+    EXPECT_EQ(result[i].timestamp_ms, timestamps_ms[i]);
+    EXPECT_EQ(result[i].classifications.size(), 1);
+    auto classifications = result[i].classifications[0];
+    EXPECT_EQ(classifications.head_index, 0);
+    EXPECT_EQ(classifications.head_name, "scores");
+    EXPECT_EQ(classifications.categories.size(), expected_num_categories);
+    auto category = classifications.categories[0];
+    EXPECT_EQ(category.index, 0);
+    EXPECT_EQ(category.category_name, "Speech");
+    EXPECT_GT(category.score, 0.9f);
   }
 }
 
-void CheckTwoHeadsClassificationResult(const ClassificationResult& result) {
-  EXPECT_THAT(result.classifications_size(), testing::Eq(2));
-  // Checks classification head #1.
-  EXPECT_EQ(result.classifications(0).head_name(), "yamnet_classification");
-  EXPECT_EQ(result.classifications(0).head_index(), 0);
-  EXPECT_THAT(result.classifications(0).entries(0).categories_size(),
-              testing::Eq(521));
-  const auto* top_category =
-      &result.classifications(0).entries(0).categories(0);
-  EXPECT_THAT(top_category->category_name(),
-              testing::Eq("Environmental noise"));
-  EXPECT_GT(top_category->score(), 0.5f);
-  EXPECT_EQ(result.classifications(0).entries(0).timestamp_ms(), 0);
-  if (result.classifications(0).entries_size() == 2) {
-    top_category = &result.classifications(0).entries(1).categories(0);
-    EXPECT_THAT(top_category->category_name(), testing::Eq("Silence"));
-    EXPECT_GT(top_category->score(), 0.99f);
-    EXPECT_EQ(result.classifications(0).entries(1).timestamp_ms(), 975);
+void CheckTwoHeadsResult(const std::vector<AudioClassifierResult>& result) {
+  EXPECT_GE(result.size(), 1);
+  EXPECT_LE(result.size(), 2);
+  // Check first result.
+  EXPECT_EQ(result[0].timestamp_ms, 0);
+  EXPECT_EQ(result[0].classifications.size(), 2);
+  // Check first head.
+  EXPECT_EQ(result[0].classifications[0].head_index, 0);
+  EXPECT_EQ(result[0].classifications[0].head_name, "yamnet_classification");
+  EXPECT_EQ(result[0].classifications[0].categories.size(), 521);
+  EXPECT_EQ(result[0].classifications[0].categories[0].index, 508);
+  EXPECT_EQ(result[0].classifications[0].categories[0].category_name,
+            "Environmental noise");
+  EXPECT_GT(result[0].classifications[0].categories[0].score, 0.5f);
+  // Check second head.
+  EXPECT_EQ(result[0].classifications[1].head_index, 1);
+  EXPECT_EQ(result[0].classifications[1].head_name, "bird_classification");
+  EXPECT_EQ(result[0].classifications[1].categories.size(), 5);
+  EXPECT_EQ(result[0].classifications[1].categories[0].index, 4);
+  EXPECT_EQ(result[0].classifications[1].categories[0].category_name,
+            "Chestnut-crowned Antpitta");
+  EXPECT_GT(result[0].classifications[1].categories[0].score, 0.9f);
+  // Check second result, if present.
+  if (result.size() == 2) {
+    EXPECT_EQ(result[1].timestamp_ms, 975);
+    EXPECT_EQ(result[1].classifications.size(), 2);
+    // Check first head.
+    EXPECT_EQ(result[1].classifications[0].head_index, 0);
+    EXPECT_EQ(result[1].classifications[0].head_name, "yamnet_classification");
+    EXPECT_EQ(result[1].classifications[0].categories.size(), 521);
+    EXPECT_EQ(result[1].classifications[0].categories[0].index, 494);
+    EXPECT_EQ(result[1].classifications[0].categories[0].category_name,
+              "Silence");
+    EXPECT_GT(result[1].classifications[0].categories[0].score, 0.99f);
+    // Check second head.
+    EXPECT_EQ(result[1].classifications[1].head_index, 1);
+    EXPECT_EQ(result[1].classifications[1].head_name, "bird_classification");
+    EXPECT_EQ(result[1].classifications[1].categories.size(), 5);
+    EXPECT_EQ(result[1].classifications[1].categories[0].index, 1);
+    EXPECT_EQ(result[1].classifications[1].categories[0].category_name,
+              "White-breasted Wood-Wren");
+    EXPECT_GT(result[1].classifications[1].categories[0].score, 0.99f);
   }
-  // Checks classification head #2.
-  EXPECT_EQ(result.classifications(1).head_name(), "bird_classification");
-  EXPECT_EQ(result.classifications(1).head_index(), 1);
-  EXPECT_THAT(result.classifications(1).entries(0).categories_size(),
-              testing::Eq(5));
-  top_category = &result.classifications(1).entries(0).categories(0);
-  EXPECT_THAT(top_category->category_name(),
-              testing::Eq("Chestnut-crowned Antpitta"));
-  EXPECT_GT(top_category->score(), 0.9f);
-  EXPECT_EQ(result.classifications(1).entries(0).timestamp_ms(), 0);
 }
 
-ClassificationResult GenerateSpeechClassificationResult() {
-  return ParseTextProtoOrDie<ClassificationResult>(
-      R"pb(classifications {
-             head_index: 0
-             head_name: "scores"
-             entries {
-               categories { index: 0 score: 0.94140625 category_name: "Speech" }
-               timestamp_ms: 0
-             }
-             entries {
-               categories { index: 0 score: 0.9921875 category_name: "Speech" }
-               timestamp_ms: 975
-             }
-             entries {
-               categories { index: 0 score: 0.98828125 category_name: "Speech" }
-               timestamp_ms: 1950
-             }
-             entries {
-               categories { index: 0 score: 0.99609375 category_name: "Speech" }
-               timestamp_ms: 2925
-             }
-             entries {
-               # categories are filtered out due to the low scores.
-               timestamp_ms: 3900
-             }
-           })pb");
-}
-
-void CheckStreamingModeClassificationResult(
-    std::vector<ClassificationResult> outputs) {
-  ASSERT_TRUE(outputs.size() == 5 || outputs.size() == 6);
-  auto expected_results = GenerateSpeechClassificationResult();
-  for (int i = 0; i < outputs.size() - 1; ++i) {
-    EXPECT_THAT(outputs[i].classifications(0).entries(0),
-                EqualsProto(expected_results.classifications(0).entries(i)));
+void CheckStreamingModeResults(std::vector<AudioClassifierResult> outputs) {
+  EXPECT_EQ(outputs.size(), 5);
+  // Ignore last result, which operates on a too small chunk to return relevant
+  // results.
+  for (int i = 0; i < outputs.size() - 1; i++) {
+    EXPECT_FALSE(outputs[i].timestamp_ms.has_value());
+    EXPECT_EQ(outputs[i].classifications.size(), 1);
+    EXPECT_EQ(outputs[i].classifications[0].head_index, 0);
+    EXPECT_EQ(outputs[i].classifications[0].head_name, "scores");
+    EXPECT_EQ(outputs[i].classifications[0].categories.size(), 1);
+    EXPECT_EQ(outputs[i].classifications[0].categories[0].index, 0);
+    EXPECT_EQ(outputs[i].classifications[0].categories[0].category_name,
+              "Speech");
+    EXPECT_GT(outputs[i].classifications[0].categories[0].score, 0.9f);
   }
-  int last_elem_index = outputs.size() - 1;
-  EXPECT_EQ(
-      mediapipe::Timestamp::Done().Value() / 1000,
-      outputs[last_elem_index].classifications(0).entries(0).timestamp_ms());
 }
 
 class CreateFromOptionsTest : public tflite_shims::testing::Test {};
@@ -264,7 +252,7 @@ TEST_F(CreateFromOptionsTest, FailsWithUnnecessaryCallback) {
   options->base_options.model_asset_path =
       JoinPath("./", kTestDataDirectory, kModelWithoutMetadata);
   options->result_callback =
-      [](absl::StatusOr<ClassificationResult> status_or_result) {};
+      [](absl::StatusOr<AudioClassifierResult> status_or_result) {};
   StatusOr<std::unique_ptr<AudioClassifier>> audio_classifier_or =
       AudioClassifier::Create(std::move(options));
 
@@ -284,7 +272,7 @@ TEST_F(CreateFromOptionsTest, FailsWithMissingDefaultInputAudioSampleRate) {
       JoinPath("./", kTestDataDirectory, kModelWithoutMetadata);
   options->running_mode = core::RunningMode::AUDIO_STREAM;
   options->result_callback =
-      [](absl::StatusOr<ClassificationResult> status_or_result) {};
+      [](absl::StatusOr<AudioClassifierResult> status_or_result) {};
   StatusOr<std::unique_ptr<AudioClassifier>> audio_classifier_or =
       AudioClassifier::Create(std::move(options));
 
@@ -310,7 +298,7 @@ TEST_F(ClassifyTest, Succeeds) {
       auto result, audio_classifier->Classify(std::move(audio_buffer),
                                               /*audio_sample_rate=*/16000));
   MP_ASSERT_OK(audio_classifier->Close());
-  CheckSpeechClassificationResult(result);
+  CheckSpeechResult(result);
 }
 
 TEST_F(ClassifyTest, SucceedsWithResampling) {
@@ -324,7 +312,7 @@ TEST_F(ClassifyTest, SucceedsWithResampling) {
       auto result, audio_classifier->Classify(std::move(audio_buffer),
                                               /*audio_sample_rate=*/48000));
   MP_ASSERT_OK(audio_classifier->Close());
-  CheckSpeechClassificationResult(result);
+  CheckSpeechResult(result);
 }
 
 TEST_F(ClassifyTest, SucceedsWithInputsAtDifferentSampleRates) {
@@ -339,13 +327,13 @@ TEST_F(ClassifyTest, SucceedsWithInputsAtDifferentSampleRates) {
       auto result_16k_hz,
       audio_classifier->Classify(std::move(audio_buffer_16k_hz),
                                  /*audio_sample_rate=*/16000));
-  CheckSpeechClassificationResult(result_16k_hz);
+  CheckSpeechResult(result_16k_hz);
   MP_ASSERT_OK_AND_ASSIGN(
       auto result_48k_hz,
       audio_classifier->Classify(std::move(audio_buffer_48k_hz),
                                  /*audio_sample_rate=*/48000));
   MP_ASSERT_OK(audio_classifier->Close());
-  CheckSpeechClassificationResult(result_48k_hz);
+  CheckSpeechResult(result_48k_hz);
 }
 
 TEST_F(ClassifyTest, SucceedsWithInsufficientData) {
@@ -361,15 +349,16 @@ TEST_F(ClassifyTest, SucceedsWithInsufficientData) {
   MP_ASSERT_OK_AND_ASSIGN(
       auto result, audio_classifier->Classify(std::move(zero_matrix), 16000));
   MP_ASSERT_OK(audio_classifier->Close());
-  EXPECT_THAT(result.classifications_size(), testing::Eq(1));
-  EXPECT_THAT(result.classifications(0).entries_size(), testing::Eq(1));
-  EXPECT_THAT(result.classifications(0).entries(0).categories_size(),
-              testing::Eq(521));
-  EXPECT_THAT(
-      result.classifications(0).entries(0).categories(0).category_name(),
-      testing::Eq("Silence"));
-  EXPECT_THAT(result.classifications(0).entries(0).categories(0).score(),
-              testing::FloatEq(.800781f));
+  EXPECT_EQ(result.size(), 1);
+  EXPECT_EQ(result[0].timestamp_ms, 0);
+  EXPECT_EQ(result[0].classifications.size(), 1);
+  EXPECT_EQ(result[0].classifications[0].head_index, 0);
+  EXPECT_EQ(result[0].classifications[0].head_name, "scores");
+  EXPECT_EQ(result[0].classifications[0].categories.size(), 521);
+  EXPECT_EQ(result[0].classifications[0].categories[0].index, 494);
+  EXPECT_EQ(result[0].classifications[0].categories[0].category_name,
+            "Silence");
+  EXPECT_FLOAT_EQ(result[0].classifications[0].categories[0].score, 0.800781f);
 }
 
 TEST_F(ClassifyTest, SucceedsWithMultiheadsModel) {
@@ -383,7 +372,7 @@ TEST_F(ClassifyTest, SucceedsWithMultiheadsModel) {
       auto result, audio_classifier->Classify(std::move(audio_buffer),
                                               /*audio_sample_rate=*/16000));
   MP_ASSERT_OK(audio_classifier->Close());
-  CheckTwoHeadsClassificationResult(result);
+  CheckTwoHeadsResult(result);
 }
 
 TEST_F(ClassifyTest, SucceedsWithMultiheadsModelAndResampling) {
@@ -397,7 +386,7 @@ TEST_F(ClassifyTest, SucceedsWithMultiheadsModelAndResampling) {
       auto result, audio_classifier->Classify(std::move(audio_buffer),
                                               /*audio_sample_rate=*/44100));
   MP_ASSERT_OK(audio_classifier->Close());
-  CheckTwoHeadsClassificationResult(result);
+  CheckTwoHeadsResult(result);
 }
 
 TEST_F(ClassifyTest,
@@ -413,13 +402,13 @@ TEST_F(ClassifyTest,
       auto result_44k_hz,
       audio_classifier->Classify(std::move(audio_buffer_44k_hz),
                                  /*audio_sample_rate=*/44100));
-  CheckTwoHeadsClassificationResult(result_44k_hz);
+  CheckTwoHeadsResult(result_44k_hz);
   MP_ASSERT_OK_AND_ASSIGN(
       auto result_16k_hz,
       audio_classifier->Classify(std::move(audio_buffer_16k_hz),
                                  /*audio_sample_rate=*/16000));
   MP_ASSERT_OK(audio_classifier->Close());
-  CheckTwoHeadsClassificationResult(result_16k_hz);
+  CheckTwoHeadsResult(result_16k_hz);
 }
 
 TEST_F(ClassifyTest, SucceedsWithMaxResultOption) {
@@ -428,14 +417,13 @@ TEST_F(ClassifyTest, SucceedsWithMaxResultOption) {
   options->base_options.model_asset_path =
       JoinPath("./", kTestDataDirectory, kModelWithMetadata);
   options->classifier_options.max_results = 1;
-  options->classifier_options.score_threshold = 0.35f;
   MP_ASSERT_OK_AND_ASSIGN(std::unique_ptr<AudioClassifier> audio_classifier,
                           AudioClassifier::Create(std::move(options)));
   MP_ASSERT_OK_AND_ASSIGN(
       auto result, audio_classifier->Classify(std::move(audio_buffer),
                                               /*audio_sample_rate=*/48000));
   MP_ASSERT_OK(audio_classifier->Close());
-  EXPECT_THAT(result, EqualsProto(GenerateSpeechClassificationResult()));
+  CheckSpeechResult(result, /*expected_num_categories=*/1);
 }
 
 TEST_F(ClassifyTest, SucceedsWithScoreThresholdOption) {
@@ -450,7 +438,7 @@ TEST_F(ClassifyTest, SucceedsWithScoreThresholdOption) {
       auto result, audio_classifier->Classify(std::move(audio_buffer),
                                               /*audio_sample_rate=*/48000));
   MP_ASSERT_OK(audio_classifier->Close());
-  EXPECT_THAT(result, EqualsProto(GenerateSpeechClassificationResult()));
+  CheckSpeechResult(result, /*expected_num_categories=*/1);
 }
 
 TEST_F(ClassifyTest, SucceedsWithCategoryAllowlist) {
@@ -466,7 +454,7 @@ TEST_F(ClassifyTest, SucceedsWithCategoryAllowlist) {
       auto result, audio_classifier->Classify(std::move(audio_buffer),
                                               /*audio_sample_rate=*/48000));
   MP_ASSERT_OK(audio_classifier->Close());
-  EXPECT_THAT(result, EqualsProto(GenerateSpeechClassificationResult()));
+  CheckSpeechResult(result, /*expected_num_categories=*/1);
 }
 
 TEST_F(ClassifyTest, SucceedsWithCategoryDenylist) {
@@ -482,16 +470,16 @@ TEST_F(ClassifyTest, SucceedsWithCategoryDenylist) {
       auto result, audio_classifier->Classify(std::move(audio_buffer),
                                               /*audio_sample_rate=*/48000));
   MP_ASSERT_OK(audio_classifier->Close());
-  // All categroies with the "Speech" label are filtered out.
-  EXPECT_THAT(result, EqualsProto(R"pb(classifications {
-                                         head_index: 0
-                                         head_name: "scores"
-                                         entries { timestamp_ms: 0 }
-                                         entries { timestamp_ms: 975 }
-                                         entries { timestamp_ms: 1950 }
-                                         entries { timestamp_ms: 2925 }
-                                         entries { timestamp_ms: 3900 }
-                                       })pb"));
+  // All categories with the "Speech" label are filtered out.
+  std::vector<int64> timestamps_ms = {0, 975, 1950, 2925};
+  for (int i = 0; i < timestamps_ms.size(); i++) {
+    EXPECT_EQ(result[i].timestamp_ms, timestamps_ms[i]);
+    EXPECT_EQ(result[i].classifications.size(), 1);
+    auto classifications = result[i].classifications[0];
+    EXPECT_EQ(classifications.head_index, 0);
+    EXPECT_EQ(classifications.head_name, "scores");
+    EXPECT_TRUE(classifications.categories.empty());
+  }
 }
 
 class ClassifyAsyncTest : public tflite_shims::testing::Test {};
@@ -506,9 +494,9 @@ TEST_F(ClassifyAsyncTest, Succeeds) {
   options->classifier_options.score_threshold = 0.3f;
   options->running_mode = core::RunningMode::AUDIO_STREAM;
   options->sample_rate = kSampleRateHz;
-  std::vector<ClassificationResult> outputs;
+  std::vector<AudioClassifierResult> outputs;
   options->result_callback =
-      [&outputs](absl::StatusOr<ClassificationResult> status_or_result) {
+      [&outputs](absl::StatusOr<AudioClassifierResult> status_or_result) {
         MP_ASSERT_OK_AND_ASSIGN(outputs.emplace_back(), status_or_result);
       };
   MP_ASSERT_OK_AND_ASSIGN(std::unique_ptr<AudioClassifier> audio_classifier,
@@ -523,7 +511,7 @@ TEST_F(ClassifyAsyncTest, Succeeds) {
     start_col += kYamnetNumOfAudioSamples * 3;
   }
   MP_ASSERT_OK(audio_classifier->Close());
-  CheckStreamingModeClassificationResult(outputs);
+  CheckStreamingModeResults(outputs);
 }
 
 TEST_F(ClassifyAsyncTest, SucceedsWithNonDeterministicNumAudioSamples) {
@@ -536,9 +524,9 @@ TEST_F(ClassifyAsyncTest, SucceedsWithNonDeterministicNumAudioSamples) {
   options->classifier_options.score_threshold = 0.3f;
   options->running_mode = core::RunningMode::AUDIO_STREAM;
   options->sample_rate = kSampleRateHz;
-  std::vector<ClassificationResult> outputs;
+  std::vector<AudioClassifierResult> outputs;
   options->result_callback =
-      [&outputs](absl::StatusOr<ClassificationResult> status_or_result) {
+      [&outputs](absl::StatusOr<AudioClassifierResult> status_or_result) {
         MP_ASSERT_OK_AND_ASSIGN(outputs.emplace_back(), status_or_result);
       };
   MP_ASSERT_OK_AND_ASSIGN(std::unique_ptr<AudioClassifier> audio_classifier,
@@ -555,7 +543,7 @@ TEST_F(ClassifyAsyncTest, SucceedsWithNonDeterministicNumAudioSamples) {
     start_col += num_samples;
   }
   MP_ASSERT_OK(audio_classifier->Close());
-  CheckStreamingModeClassificationResult(outputs);
+  CheckStreamingModeResults(outputs);
 }
 
 }  // namespace

mediapipe/tasks/cc/components/BUILD

@@ -40,6 +40,7 @@ cc_library(
         "//mediapipe/calculators/image:image_properties_calculator",
         "//mediapipe/calculators/tensor:image_to_tensor_calculator",
         "//mediapipe/calculators/tensor:image_to_tensor_calculator_cc_proto",
+        "//mediapipe/calculators/tensor:inference_calculator_cc_proto",
         "//mediapipe/framework:calculator_framework",
         "//mediapipe/framework/api2:builder",
         "//mediapipe/framework/api2:port",
@@ -60,41 +61,6 @@ cc_library(
 
 # TODO: Enable this test
 
-cc_library(
-    name = "embedder_options",
-    srcs = ["embedder_options.cc"],
-    hdrs = ["embedder_options.h"],
-    deps = ["//mediapipe/tasks/cc/components/proto:embedder_options_cc_proto"],
-)
-
-cc_library(
-    name = "embedding_postprocessing_graph",
-    srcs = ["embedding_postprocessing_graph.cc"],
-    hdrs = ["embedding_postprocessing_graph.h"],
-    deps = [
-        "//mediapipe/calculators/tensor:tensors_dequantization_calculator",
-        "//mediapipe/framework:calculator_framework",
-        "//mediapipe/framework/api2:builder",
-        "//mediapipe/framework/api2:port",
-        "//mediapipe/framework/formats:tensor",
-        "//mediapipe/framework/tool:options_map",
-        "//mediapipe/tasks/cc:common",
-        "//mediapipe/tasks/cc/components/calculators:tensors_to_embeddings_calculator",
-        "//mediapipe/tasks/cc/components/calculators:tensors_to_embeddings_calculator_cc_proto",
-        "//mediapipe/tasks/cc/components/containers/proto:embeddings_cc_proto",
-        "//mediapipe/tasks/cc/components/proto:embedder_options_cc_proto",
-        "//mediapipe/tasks/cc/components/proto:embedding_postprocessing_graph_options_cc_proto",
-        "//mediapipe/tasks/cc/components/utils:source_or_node_output",
-        "//mediapipe/tasks/cc/core:model_resources",
-        "//mediapipe/tasks/cc/metadata:metadata_extractor",
-        "@com_google_absl//absl/status",
-        "@com_google_absl//absl/status:statusor",
-        "@com_google_absl//absl/strings:str_format",
-        "@org_tensorflow//tensorflow/lite/schema:schema_fbs",
-    ],
-    alwayslink = 1,
-)
-
 # TODO: Investigate rewriting the build rule to only link
 # the Bert Preprocessor if it's needed.
 cc_library(

mediapipe/tasks/cc/components/calculators/BUILD

@@ -163,7 +163,7 @@ mediapipe_proto_library(
     deps = [
         "//mediapipe/framework:calculator_options_proto",
         "//mediapipe/framework:calculator_proto",
-        "//mediapipe/tasks/cc/components/proto:embedder_options_proto",
+        "//mediapipe/tasks/cc/components/processors/proto:embedder_options_proto",
     ],
 )
 
@@ -178,7 +178,7 @@ cc_library(
         "//mediapipe/framework/formats:tensor",
         "//mediapipe/framework/port:ret_check",
         "//mediapipe/tasks/cc/components/containers/proto:embeddings_cc_proto",
-        "//mediapipe/tasks/cc/components/proto:embedder_options_cc_proto",
+        "//mediapipe/tasks/cc/components/processors/proto:embedder_options_cc_proto",
         "@com_google_absl//absl/status",
         "@com_google_absl//absl/strings:str_format",
     ],

mediapipe/tasks/cc/components/calculators/tensors_to_embeddings_calculator.cc

@@ -26,14 +26,14 @@
 #include "mediapipe/framework/port/ret_check.h"
 #include "mediapipe/tasks/cc/components/calculators/tensors_to_embeddings_calculator.pb.h"
 #include "mediapipe/tasks/cc/components/containers/proto/embeddings.pb.h"
-#include "mediapipe/tasks/cc/components/proto/embedder_options.pb.h"
+#include "mediapipe/tasks/cc/components/processors/proto/embedder_options.pb.h"
 
 namespace mediapipe {
 namespace api2 {
 
 namespace {
 
-using ::mediapipe::tasks::components::containers::proto::EmbeddingEntry;
+using ::mediapipe::tasks::components::containers::proto::Embedding;
 using ::mediapipe::tasks::components::containers::proto::EmbeddingResult;
 
 // Computes the inverse L2 norm of the provided array of values. Returns 1.0 in
@@ -66,7 +66,7 @@ float GetInverseL2Norm(const float* values, int size) {
 class TensorsToEmbeddingsCalculator : public Node {
  public:
   static constexpr Input<std::vector<Tensor>> kTensorsIn{"TENSORS"};
-  static constexpr Output<EmbeddingResult> kEmbeddingsOut{"EMBEDDING_RESULT"};
+  static constexpr Output<EmbeddingResult> kEmbeddingsOut{"EMBEDDINGS"};
   MEDIAPIPE_NODE_CONTRACT(kTensorsIn, kEmbeddingsOut);
 
   absl::Status Open(CalculatorContext* cc) override;
@@ -77,8 +77,8 @@ class TensorsToEmbeddingsCalculator : public Node {
   bool quantize_;
   std::vector<std::string> head_names_;
 
-  void FillFloatEmbeddingEntry(const Tensor& tensor, EmbeddingEntry* entry);
-  void FillQuantizedEmbeddingEntry(const Tensor& tensor, EmbeddingEntry* entry);
+  void FillFloatEmbedding(const Tensor& tensor, Embedding* embedding);
+  void FillQuantizedEmbedding(const Tensor& tensor, Embedding* embedding);
 };
 
 absl::Status TensorsToEmbeddingsCalculator::Open(CalculatorContext* cc) {
@@ -104,42 +104,42 @@ absl::Status TensorsToEmbeddingsCalculator::Process(CalculatorContext* cc) {
   for (int i = 0; i < tensors.size(); ++i) {
     const auto& tensor = tensors[i];
     RET_CHECK(tensor.element_type() == Tensor::ElementType::kFloat32);
-    auto* embeddings = result.add_embeddings();
-    embeddings->set_head_index(i);
+    auto* embedding = result.add_embeddings();
+    embedding->set_head_index(i);
     if (!head_names_.empty()) {
-      embeddings->set_head_name(head_names_[i]);
+      embedding->set_head_name(head_names_[i]);
     }
     if (quantize_) {
-      FillQuantizedEmbeddingEntry(tensor, embeddings->add_entries());
+      FillQuantizedEmbedding(tensor, embedding);
     } else {
-      FillFloatEmbeddingEntry(tensor, embeddings->add_entries());
+      FillFloatEmbedding(tensor, embedding);
     }
   }
   kEmbeddingsOut(cc).Send(result);
   return absl::OkStatus();
 }
 
-void TensorsToEmbeddingsCalculator::FillFloatEmbeddingEntry(
-    const Tensor& tensor, EmbeddingEntry* entry) {
+void TensorsToEmbeddingsCalculator::FillFloatEmbedding(const Tensor& tensor,
+                                                       Embedding* embedding) {
   int size = tensor.shape().num_elements();
   auto tensor_view = tensor.GetCpuReadView();
   const float* tensor_buffer = tensor_view.buffer<float>();
   float inv_l2_norm =
       l2_normalize_ ? GetInverseL2Norm(tensor_buffer, size) : 1.0f;
-  auto* float_embedding = entry->mutable_float_embedding();
+  auto* float_embedding = embedding->mutable_float_embedding();
   for (int i = 0; i < size; ++i) {
     float_embedding->add_values(tensor_buffer[i] * inv_l2_norm);
   }
 }
 
-void TensorsToEmbeddingsCalculator::FillQuantizedEmbeddingEntry(
-    const Tensor& tensor, EmbeddingEntry* entry) {
+void TensorsToEmbeddingsCalculator::FillQuantizedEmbedding(
+    const Tensor& tensor, Embedding* embedding) {
   int size = tensor.shape().num_elements();
   auto tensor_view = tensor.GetCpuReadView();
   const float* tensor_buffer = tensor_view.buffer<float>();
   float inv_l2_norm =
       l2_normalize_ ? GetInverseL2Norm(tensor_buffer, size) : 1.0f;
-  auto* values = entry->mutable_quantized_embedding()->mutable_values();
+  auto* values = embedding->mutable_quantized_embedding()->mutable_values();
   values->resize(size);
   for (int i = 0; i < size; ++i) {
     // Normalize.

mediapipe/tasks/cc/components/calculators/tensors_to_embeddings_calculator.proto

@@ -18,7 +18,7 @@ syntax = "proto2";
 package mediapipe;
 
 import "mediapipe/framework/calculator.proto";
-import "mediapipe/tasks/cc/components/proto/embedder_options.proto";
+import "mediapipe/tasks/cc/components/processors/proto/embedder_options.proto";
 
 message TensorsToEmbeddingsCalculatorOptions {
   extend mediapipe.CalculatorOptions {
@@ -27,8 +27,8 @@ message TensorsToEmbeddingsCalculatorOptions {
 
   // The embedder options defining whether to L2-normalize or scalar-quantize
   // the outputs.
-  optional mediapipe.tasks.components.proto.EmbedderOptions embedder_options =
-      1;
+  optional mediapipe.tasks.components.processors.proto.EmbedderOptions
+      embedder_options = 1;
 
   // The embedder head names.
   repeated string head_names = 2;

mediapipe/tasks/cc/components/calculators/tensors_to_embeddings_calculator_test.cc

@@ -55,7 +55,7 @@ TEST(TensorsToEmbeddingsCalculatorTest, FailsWithInvalidHeadNamesNumber) {
   CalculatorRunner runner(ParseTextProtoOrDie<Node>(R"pb(
     calculator: "TensorsToEmbeddingsCalculator"
     input_stream: "TENSORS:tensors"
-    output_stream: "EMBEDDING_RESULT:embeddings"
+    output_stream: "EMBEDDINGS:embeddings"
     options {
       [mediapipe.TensorsToEmbeddingsCalculatorOptions.ext] { head_names: "foo" }
     }
@@ -73,7 +73,7 @@ TEST(TensorsToEmbeddingsCalculatorTest, SucceedsWithoutHeadNames) {
   CalculatorRunner runner(ParseTextProtoOrDie<Node>(R"pb(
     calculator: "TensorsToEmbeddingsCalculator"
     input_stream: "TENSORS:tensors"
-    output_stream: "EMBEDDING_RESULT:embeddings"
+    output_stream: "EMBEDDINGS:embeddings"
     options {
       [mediapipe.TensorsToEmbeddingsCalculatorOptions.ext] {
         embedder_options { l2_normalize: false quantize: false }
@@ -84,28 +84,24 @@ TEST(TensorsToEmbeddingsCalculatorTest, SucceedsWithoutHeadNames) {
   BuildGraph(&runner, {{0.1, 0.2}, {-0.2, -0.3}});
   MP_ASSERT_OK(runner.Run());
 
-  const EmbeddingResult& result = runner.Outputs()
-                                      .Get("EMBEDDING_RESULT", 0)
-                                      .packets[0]
-                                      .Get<EmbeddingResult>();
-  EXPECT_THAT(
-      result,
-      EqualsProto(ParseTextProtoOrDie<EmbeddingResult>(
-          R"pb(embeddings {
-                 entries { float_embedding { values: 0.1 values: 0.2 } }
-                 head_index: 0
-               }
-               embeddings {
-                 entries { float_embedding { values: -0.2 values: -0.3 } }
-                 head_index: 1
-               })pb")));
+  const EmbeddingResult& result =
+      runner.Outputs().Get("EMBEDDINGS", 0).packets[0].Get<EmbeddingResult>();
+  EXPECT_THAT(result, EqualsProto(ParseTextProtoOrDie<EmbeddingResult>(
+                          R"pb(embeddings {
+                                 float_embedding { values: 0.1 values: 0.2 }
+                                 head_index: 0
+                               }
+                               embeddings {
+                                 float_embedding { values: -0.2 values: -0.3 }
+                                 head_index: 1
+                               })pb")));
 }
 
 TEST(TensorsToEmbeddingsCalculatorTest, SucceedsWithHeadNames) {
   CalculatorRunner runner(ParseTextProtoOrDie<Node>(R"pb(
     calculator: "TensorsToEmbeddingsCalculator"
     input_stream: "TENSORS:tensors"
-    output_stream: "EMBEDDING_RESULT:embeddings"
+    output_stream: "EMBEDDINGS:embeddings"
     options {
       [mediapipe.TensorsToEmbeddingsCalculatorOptions.ext] {
         embedder_options { l2_normalize: false quantize: false }
@@ -118,30 +114,26 @@ TEST(TensorsToEmbeddingsCalculatorTest, SucceedsWithHeadNames) {
   BuildGraph(&runner, {{0.1, 0.2}, {-0.2, -0.3}});
   MP_ASSERT_OK(runner.Run());
 
-  const EmbeddingResult& result = runner.Outputs()
-                                      .Get("EMBEDDING_RESULT", 0)
-                                      .packets[0]
-                                      .Get<EmbeddingResult>();
-  EXPECT_THAT(
-      result,
-      EqualsProto(ParseTextProtoOrDie<EmbeddingResult>(
-          R"pb(embeddings {
-                 entries { float_embedding { values: 0.1 values: 0.2 } }
-                 head_index: 0
-                 head_name: "foo"
-               }
-               embeddings {
-                 entries { float_embedding { values: -0.2 values: -0.3 } }
-                 head_index: 1
-                 head_name: "bar"
-               })pb")));
+  const EmbeddingResult& result =
+      runner.Outputs().Get("EMBEDDINGS", 0).packets[0].Get<EmbeddingResult>();
+  EXPECT_THAT(result, EqualsProto(ParseTextProtoOrDie<EmbeddingResult>(
+                          R"pb(embeddings {
+                                 float_embedding { values: 0.1 values: 0.2 }
+                                 head_index: 0
+                                 head_name: "foo"
+                               }
+                               embeddings {
+                                 float_embedding { values: -0.2 values: -0.3 }
+                                 head_index: 1
+                                 head_name: "bar"
+                               })pb")));
 }
 
 TEST(TensorsToEmbeddingsCalculatorTest, SucceedsWithNormalization) {
   CalculatorRunner runner(ParseTextProtoOrDie<Node>(R"pb(
     calculator: "TensorsToEmbeddingsCalculator"
     input_stream: "TENSORS:tensors"
-    output_stream: "EMBEDDING_RESULT:embeddings"
+    output_stream: "EMBEDDINGS:embeddings"
     options {
       [mediapipe.TensorsToEmbeddingsCalculatorOptions.ext] {
         embedder_options { l2_normalize: true quantize: false }
@@ -152,23 +144,17 @@ TEST(TensorsToEmbeddingsCalculatorTest, SucceedsWithNormalization) {
   BuildGraph(&runner, {{0.1, 0.2}, {-0.2, -0.3}});
   MP_ASSERT_OK(runner.Run());
 
-  const EmbeddingResult& result = runner.Outputs()
-                                      .Get("EMBEDDING_RESULT", 0)
-                                      .packets[0]
-                                      .Get<EmbeddingResult>();
+  const EmbeddingResult& result =
+      runner.Outputs().Get("EMBEDDINGS", 0).packets[0].Get<EmbeddingResult>();
   EXPECT_THAT(
       result,
       EqualsProto(ParseTextProtoOrDie<EmbeddingResult>(
           R"pb(embeddings {
-                 entries {
-                   float_embedding { values: 0.44721356 values: 0.8944271 }
-                 }
+                 float_embedding { values: 0.44721356 values: 0.8944271 }
                  head_index: 0
                }
                embeddings {
-                 entries {
-                   float_embedding { values: -0.5547002 values: -0.8320503 }
-                 }
+                 float_embedding { values: -0.5547002 values: -0.8320503 }
                  head_index: 1
                })pb")));
 }
@@ -177,7 +163,7 @@ TEST(TensorsToEmbeddingsCalculatorTest, SucceedsWithQuantization) {
   CalculatorRunner runner(ParseTextProtoOrDie<Node>(R"pb(
     calculator: "TensorsToEmbeddingsCalculator"
     input_stream: "TENSORS:tensors"
-    output_stream: "EMBEDDING_RESULT:embeddings"
+    output_stream: "EMBEDDINGS:embeddings"
     options {
       [mediapipe.TensorsToEmbeddingsCalculatorOptions.ext] {
         embedder_options { l2_normalize: false quantize: true }
@@ -188,22 +174,16 @@ TEST(TensorsToEmbeddingsCalculatorTest, SucceedsWithQuantization) {
   BuildGraph(&runner, {{0.1, 0.2}, {-0.2, -0.3}});
   MP_ASSERT_OK(runner.Run());
 
-  const EmbeddingResult& result = runner.Outputs()
-                                      .Get("EMBEDDING_RESULT", 0)
-                                      .packets[0]
-                                      .Get<EmbeddingResult>();
+  const EmbeddingResult& result =
+      runner.Outputs().Get("EMBEDDINGS", 0).packets[0].Get<EmbeddingResult>();
   EXPECT_THAT(result,
               EqualsProto(ParseTextProtoOrDie<EmbeddingResult>(
                   R"pb(embeddings {
-                         entries {
-                           quantized_embedding { values: "\x0d\x1a" }  # 13,26
-                         }
+                         quantized_embedding { values: "\x0d\x1a" }  # 13,26
                          head_index: 0
                        }
                        embeddings {
-                         entries {
-                           quantized_embedding { values: "\xe6\xda" }  # -26,-38
-                         }
+                         quantized_embedding { values: "\xe6\xda" }  # -26,-38
                          head_index: 1
                        })pb")));
 }
@@ -213,7 +193,7 @@ TEST(TensorsToEmbeddingsCalculatorTest,
   CalculatorRunner runner(ParseTextProtoOrDie<Node>(R"pb(
     calculator: "TensorsToEmbeddingsCalculator"
     input_stream: "TENSORS:tensors"
-    output_stream: "EMBEDDING_RESULT:embeddings"
+    output_stream: "EMBEDDINGS:embeddings"
     options {
       [mediapipe.TensorsToEmbeddingsCalculatorOptions.ext] {
         embedder_options { l2_normalize: true quantize: true }
@@ -224,25 +204,18 @@ TEST(TensorsToEmbeddingsCalculatorTest,
   BuildGraph(&runner, {{0.1, 0.2}, {-0.2, -0.3}});
   MP_ASSERT_OK(runner.Run());
 
-  const EmbeddingResult& result = runner.Outputs()
-                                      .Get("EMBEDDING_RESULT", 0)
-                                      .packets[0]
-                                      .Get<EmbeddingResult>();
-  EXPECT_THAT(
-      result,
-      EqualsProto(ParseTextProtoOrDie<EmbeddingResult>(
-          R"pb(embeddings {
-                 entries {
-                   quantized_embedding { values: "\x39\x72" }  # 57,114
-                 }
-                 head_index: 0
-               }
-               embeddings {
-                 entries {
-                   quantized_embedding { values: "\xb9\x95" }  # -71,-107
-                 }
-                 head_index: 1
-               })pb")));
+  const EmbeddingResult& result =
+      runner.Outputs().Get("EMBEDDINGS", 0).packets[0].Get<EmbeddingResult>();
+  EXPECT_THAT(result,
+              EqualsProto(ParseTextProtoOrDie<EmbeddingResult>(
+                  R"pb(embeddings {
+                         quantized_embedding { values: "\x39\x72" }  # 57,114
+                         head_index: 0
+                       }
+                       embeddings {
+                         quantized_embedding { values: "\xb9\x95" }  # -71,-107
+                         head_index: 1
+                       })pb")));
 }
 
 }  // namespace

mediapipe/tasks/cc/components/containers/BUILD

@@ -49,3 +49,12 @@ cc_library(
         "//mediapipe/tasks/cc/components/containers/proto:classifications_cc_proto",
     ],
 )
+
+cc_library(
+    name = "embedding_result",
+    srcs = ["embedding_result.cc"],
+    hdrs = ["embedding_result.h"],
+    deps = [
+        "//mediapipe/tasks/cc/components/containers/proto:embeddings_cc_proto",
+    ],
+)

mediapipe/tasks/cc/components/containers/embedding_result.cc

@@ -0,0 +1,57 @@
+/* Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+
+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 "mediapipe/tasks/cc/components/containers/embedding_result.h"
+
+#include <iterator>
+#include <optional>
+#include <string>
+#include <vector>
+
+#include "mediapipe/tasks/cc/components/containers/proto/embeddings.pb.h"
+
+namespace mediapipe::tasks::components::containers {
+
+Embedding ConvertToEmbedding(const proto::Embedding& proto) {
+  Embedding embedding;
+  if (proto.has_float_embedding()) {
+    embedding.float_embedding = {
+        std::make_move_iterator(proto.float_embedding().values().begin()),
+        std::make_move_iterator(proto.float_embedding().values().end())};
+  } else {
+    embedding.quantized_embedding = {
+        std::make_move_iterator(proto.quantized_embedding().values().begin()),
+        std::make_move_iterator(proto.quantized_embedding().values().end())};
+  }
+  embedding.head_index = proto.head_index();
+  if (proto.has_head_name()) {
+    embedding.head_name = proto.head_name();
+  }
+  return embedding;
+}
+
+EmbeddingResult ConvertToEmbeddingResult(const proto::EmbeddingResult& proto) {
+  EmbeddingResult embedding_result;
+  embedding_result.embeddings.reserve(proto.embeddings_size());
+  for (const auto& embedding : proto.embeddings()) {
+    embedding_result.embeddings.push_back(ConvertToEmbedding(embedding));
+  }
+  if (proto.has_timestamp_ms()) {
+    embedding_result.timestamp_ms = proto.timestamp_ms();
+  }
+  return embedding_result;
+}
+
+}  // namespace mediapipe::tasks::components::containers

mediapipe/tasks/cc/components/containers/embedding_result.h

@@ -0,0 +1,72 @@
+/* Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+
+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_COMPONENTS_CONTAINERS_EMBEDDING_RESULT_H_
+#define MEDIAPIPE_TASKS_CC_COMPONENTS_CONTAINERS_EMBEDDING_RESULT_H_
+
+#include <optional>
+#include <string>
+#include <vector>
+
+#include "mediapipe/tasks/cc/components/containers/proto/embeddings.pb.h"
+
+namespace mediapipe::tasks::components::containers {
+
+// Embedding result for a given embedder head.
+//
+// One and only one of the two 'float_embedding' and 'quantized_embedding' will
+// contain data, based on whether or not the embedder was configured to perform
+// scalar quantization.
+struct Embedding {
+  // Floating-point embedding. Empty if the embedder was configured to perform
+  // scalar-quantization.
+  std::vector<float> float_embedding;
+  // Scalar-quantized embedding. Empty if the embedder was not configured to
+  // perform scalar quantization.
+  std::string quantized_embedding;
+  // The index of the embedder head (i.e. output tensor) this embedding comes
+  // from. This is useful for multi-head models.
+  int head_index;
+  // The optional name of the embedder head, as provided in the TFLite Model
+  // Metadata [1] if present. This is useful for multi-head models.
+  //
+  // [1]: https://www.tensorflow.org/lite/convert/metadata
+  std::optional<std::string> head_name = std::nullopt;
+};
+
+// Defines embedding results of a model.
+struct EmbeddingResult {
+  // The embedding results for each head of the model.
+  std::vector<Embedding> embeddings;
+  // The optional timestamp (in milliseconds) of the start of the chunk of data
+  // corresponding to these results.
+  //
+  // This is only used for embedding extraction on time series (e.g. audio
+  // embedding). In these use cases, the amount of data to process might
+  // exceed the maximum size that the model can process: to solve this, the
+  // input data is split into multiple chunks starting at different timestamps.
+  std::optional<int64_t> timestamp_ms = std::nullopt;
+};
+
+// Utility function to convert from Embedding proto to Embedding struct.
+Embedding ConvertToEmbedding(const proto::Embedding& proto);
+
+// Utility function to convert from EmbeddingResult proto to EmbeddingResult
+// struct.
+EmbeddingResult ConvertToEmbeddingResult(const proto::EmbeddingResult& proto);
+
+}  // namespace mediapipe::tasks::components::containers
+
+#endif  // MEDIAPIPE_TASKS_CC_COMPONENTS_CONTAINERS_EMBEDDING_RESULT_H_

mediapipe/tasks/cc/components/containers/proto/embeddings.proto

@@ -30,30 +30,31 @@ message QuantizedEmbedding {
   optional bytes values = 1;
 }
 
-// Floating-point or scalar-quantized embedding with an optional timestamp.
-message EmbeddingEntry {
-  // The actual embedding, either floating-point or scalar-quantized.
+// Embedding result for a given embedder head.
+message Embedding {
+  // The actual embedding, either floating-point or quantized.
   oneof embedding {
     FloatEmbedding float_embedding = 1;
     QuantizedEmbedding quantized_embedding = 2;
   }
-  // The optional timestamp (in milliseconds) associated to the embedding entry.
-  // This is useful for time series use cases, e.g. audio embedding.
-  optional int64 timestamp_ms = 3;
-}
-
-// Embeddings for a given embedder head.
-message Embeddings {
-  repeated EmbeddingEntry entries = 1;
   // The index of the embedder head that produced this embedding. This is useful
   // for multi-head models.
-  optional int32 head_index = 2;
+  optional int32 head_index = 3;
   // The name of the embedder head, which is the corresponding tensor metadata
   // name (if any). This is useful for multi-head models.
-  optional string head_name = 3;
+  optional string head_name = 4;
 }
 
-// Contains one set of results per embedder head.
+// Embedding results for a given embedder model.
 message EmbeddingResult {
-  repeated Embeddings embeddings = 1;
+  // The embedding results for each model head, i.e. one for each output tensor.
+  repeated Embedding embeddings = 1;
+  // The optional timestamp (in milliseconds) of the start of the chunk of data
+  // corresponding to these results.
+  //
+  // This is only used for embedding extraction on time series (e.g. audio
+  // embedding). In these use cases, the amount of data to process might
+  // exceed the maximum size that the model can process: to solve this, the
+  // input data is split into multiple chunks starting at different timestamps.
+  optional int64 timestamp_ms = 2;
 }

mediapipe/tasks/cc/components/image_preprocessing.cc

@@ -24,6 +24,7 @@ limitations under the License.
 #include "absl/status/statusor.h"
 #include "mediapipe/calculators/image/image_clone_calculator.pb.h"
 #include "mediapipe/calculators/tensor/image_to_tensor_calculator.pb.h"
+#include "mediapipe/calculators/tensor/inference_calculator.pb.h"
 #include "mediapipe/framework/api2/builder.h"
 #include "mediapipe/framework/api2/port.h"
 #include "mediapipe/framework/calculator_framework.h"

mediapipe/tasks/cc/components/processors/BUILD

@@ -62,3 +62,38 @@ cc_library(
     ],
     alwayslink = 1,
 )
+
+cc_library(
+    name = "embedder_options",
+    srcs = ["embedder_options.cc"],
+    hdrs = ["embedder_options.h"],
+    deps = ["//mediapipe/tasks/cc/components/processors/proto:embedder_options_cc_proto"],
+)
+
+cc_library(
+    name = "embedding_postprocessing_graph",
+    srcs = ["embedding_postprocessing_graph.cc"],
+    hdrs = ["embedding_postprocessing_graph.h"],
+    deps = [
+        "//mediapipe/calculators/tensor:tensors_dequantization_calculator",
+        "//mediapipe/framework:calculator_framework",
+        "//mediapipe/framework/api2:builder",
+        "//mediapipe/framework/api2:port",
+        "//mediapipe/framework/formats:tensor",
+        "//mediapipe/framework/tool:options_map",
+        "//mediapipe/tasks/cc:common",
+        "//mediapipe/tasks/cc/components/calculators:tensors_to_embeddings_calculator",
+        "//mediapipe/tasks/cc/components/calculators:tensors_to_embeddings_calculator_cc_proto",
+        "//mediapipe/tasks/cc/components/containers/proto:embeddings_cc_proto",
+        "//mediapipe/tasks/cc/components/processors/proto:embedder_options_cc_proto",
+        "//mediapipe/tasks/cc/components/processors/proto:embedding_postprocessing_graph_options_cc_proto",
+        "//mediapipe/tasks/cc/components/utils:source_or_node_output",
+        "//mediapipe/tasks/cc/core:model_resources",
+        "//mediapipe/tasks/cc/metadata:metadata_extractor",
+        "@com_google_absl//absl/status",
+        "@com_google_absl//absl/status:statusor",
+        "@com_google_absl//absl/strings:str_format",
+        "@org_tensorflow//tensorflow/lite/schema:schema_fbs",
+    ],
+    alwayslink = 1,
+)

mediapipe/tasks/cc/components/processors/embedder_options.cc

@@ -13,22 +13,24 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
-#include "mediapipe/tasks/cc/components/embedder_options.h"
+#include "mediapipe/tasks/cc/components/processors/embedder_options.h"
 
-#include "mediapipe/tasks/cc/components/proto/embedder_options.pb.h"
+#include "mediapipe/tasks/cc/components/processors/proto/embedder_options.pb.h"
 
 namespace mediapipe {
 namespace tasks {
 namespace components {
+namespace processors {
 
-tasks::components::proto::EmbedderOptions ConvertEmbedderOptionsToProto(
+proto::EmbedderOptions ConvertEmbedderOptionsToProto(
     EmbedderOptions* embedder_options) {
-  tasks::components::proto::EmbedderOptions options_proto;
+  proto::EmbedderOptions options_proto;
   options_proto.set_l2_normalize(embedder_options->l2_normalize);
   options_proto.set_quantize(embedder_options->quantize);
   return options_proto;
 }
 
+}  // namespace processors
 }  // namespace components
 }  // namespace tasks
 }  // namespace mediapipe

mediapipe/tasks/cc/components/processors/embedder_options.h

@@ -13,14 +13,15 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
-#ifndef MEDIAPIPE_TASKS_CC_COMPONENTS_EMBEDDER_OPTIONS_H_
-#define MEDIAPIPE_TASKS_CC_COMPONENTS_EMBEDDER_OPTIONS_H_
+#ifndef MEDIAPIPE_TASKS_CC_COMPONENTS_PROCESSORS_EMBEDDER_OPTIONS_H_
+#define MEDIAPIPE_TASKS_CC_COMPONENTS_PROCESSORS_EMBEDDER_OPTIONS_H_
 
-#include "mediapipe/tasks/cc/components/proto/embedder_options.pb.h"
+#include "mediapipe/tasks/cc/components/processors/proto/embedder_options.pb.h"
 
 namespace mediapipe {
 namespace tasks {
 namespace components {
+namespace processors {
 
 // Embedder options for MediaPipe C++ embedding extraction tasks.
 struct EmbedderOptions {
@@ -37,11 +38,12 @@ struct EmbedderOptions {
   bool quantize;
 };
 
-tasks::components::proto::EmbedderOptions ConvertEmbedderOptionsToProto(
+proto::EmbedderOptions ConvertEmbedderOptionsToProto(
     EmbedderOptions* embedder_options);
 
+}  // namespace processors
 }  // namespace components
 }  // namespace tasks
 }  // namespace mediapipe
 
-#endif  // MEDIAPIPE_TASKS_CC_COMPONENTS_EMBEDDER_OPTIONS_H_
+#endif  // MEDIAPIPE_TASKS_CC_COMPONENTS_PROCESSORS_EMBEDDER_OPTIONS_H_

mediapipe/tasks/cc/components/processors/embedding_postprocessing_graph.cc

@@ -13,7 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
-#include "mediapipe/tasks/cc/components/embedding_postprocessing_graph.h"
+#include "mediapipe/tasks/cc/components/processors/embedding_postprocessing_graph.h"
 
 #include <string>
 #include <vector>
@@ -29,8 +29,8 @@ limitations under the License.
 #include "mediapipe/tasks/cc/common.h"
 #include "mediapipe/tasks/cc/components/calculators/tensors_to_embeddings_calculator.pb.h"
 #include "mediapipe/tasks/cc/components/containers/proto/embeddings.pb.h"
-#include "mediapipe/tasks/cc/components/proto/embedder_options.pb.h"
-#include "mediapipe/tasks/cc/components/proto/embedding_postprocessing_graph_options.pb.h"
+#include "mediapipe/tasks/cc/components/processors/proto/embedder_options.pb.h"
+#include "mediapipe/tasks/cc/components/processors/proto/embedding_postprocessing_graph_options.pb.h"
 #include "mediapipe/tasks/cc/components/utils/source_or_node_output.h"
 #include "mediapipe/tasks/cc/core/model_resources.h"
 #include "mediapipe/tasks/cc/metadata/metadata_extractor.h"
@@ -39,6 +39,7 @@ limitations under the License.
 namespace mediapipe {
 namespace tasks {
 namespace components {
+namespace processors {
 
 namespace {
 
@@ -49,13 +50,12 @@ using ::mediapipe::api2::builder::GenericNode;
 using ::mediapipe::api2::builder::Graph;
 using ::mediapipe::api2::builder::Source;
 using ::mediapipe::tasks::components::containers::proto::EmbeddingResult;
-using ::mediapipe::tasks::components::proto::EmbedderOptions;
 using ::mediapipe::tasks::core::ModelResources;
 using TensorsSource =
     ::mediapipe::tasks::SourceOrNodeOutput<std::vector<Tensor>>;
 
 constexpr char kTensorsTag[] = "TENSORS";
-constexpr char kEmbeddingResultTag[] = "EMBEDDING_RESULT";
+constexpr char kEmbeddingsTag[] = "EMBEDDINGS";
 
 // Identifies whether or not the model has quantized outputs, and performs
 // sanity checks.
@@ -144,7 +144,7 @@ absl::StatusOr<std::vector<std::string>> GetHeadNames(
 
 absl::Status ConfigureEmbeddingPostprocessing(
     const ModelResources& model_resources,
-    const EmbedderOptions& embedder_options,
+    const proto::EmbedderOptions& embedder_options,
     proto::EmbeddingPostprocessingGraphOptions* options) {
   ASSIGN_OR_RETURN(bool has_quantized_outputs,
                    HasQuantizedOutputs(model_resources));
@@ -188,7 +188,7 @@ class EmbeddingPostprocessingGraph : public mediapipe::Subgraph {
         BuildEmbeddingPostprocessing(
             sc->Options<proto::EmbeddingPostprocessingGraphOptions>(),
             graph[Input<std::vector<Tensor>>(kTensorsTag)], graph));
-    embedding_result_out >> graph[Output<EmbeddingResult>(kEmbeddingResultTag)];
+    embedding_result_out >> graph[Output<EmbeddingResult>(kEmbeddingsTag)];
     return graph.GetConfig();
   }
 
@@ -220,13 +220,13 @@ class EmbeddingPostprocessingGraph : public mediapipe::Subgraph {
         .GetOptions<mediapipe::TensorsToEmbeddingsCalculatorOptions>()
         .CopyFrom(options.tensors_to_embeddings_options());
     dequantized_tensors >> tensors_to_embeddings_node.In(kTensorsTag);
-    return tensors_to_embeddings_node[Output<EmbeddingResult>(
-        kEmbeddingResultTag)];
+    return tensors_to_embeddings_node[Output<EmbeddingResult>(kEmbeddingsTag)];
   }
 };
 REGISTER_MEDIAPIPE_GRAPH(
-    ::mediapipe::tasks::components::EmbeddingPostprocessingGraph);
+    ::mediapipe::tasks::components::processors::EmbeddingPostprocessingGraph);
 
+}  // namespace processors
 }  // namespace components
 }  // namespace tasks
 }  // namespace mediapipe

mediapipe/tasks/cc/components/processors/embedding_postprocessing_graph.h

@@ -13,17 +13,18 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
-#ifndef MEDIAPIPE_TASKS_CC_COMPONENTS_EMBEDDING_POSTPROCESSING_GRAPH_H_
-#define MEDIAPIPE_TASKS_CC_COMPONENTS_EMBEDDING_POSTPROCESSING_GRAPH_H_
+#ifndef MEDIAPIPE_TASKS_CC_COMPONENTS_PROCESSORS_EMBEDDING_POSTPROCESSING_GRAPH_H_
+#define MEDIAPIPE_TASKS_CC_COMPONENTS_PROCESSORS_EMBEDDING_POSTPROCESSING_GRAPH_H_
 
 #include "absl/status/status.h"
-#include "mediapipe/tasks/cc/components/proto/embedder_options.pb.h"
-#include "mediapipe/tasks/cc/components/proto/embedding_postprocessing_graph_options.pb.h"
+#include "mediapipe/tasks/cc/components/processors/proto/embedder_options.pb.h"
+#include "mediapipe/tasks/cc/components/processors/proto/embedding_postprocessing_graph_options.pb.h"
 #include "mediapipe/tasks/cc/core/model_resources.h"
 
 namespace mediapipe {
 namespace tasks {
 namespace components {
+namespace processors {
 
 // Configures an EmbeddingPostprocessingGraph using the provided model resources
 // and EmbedderOptions.
@@ -44,18 +45,19 @@ namespace components {
 //     The output tensors of an InferenceCalculator, to convert into
 //     EmbeddingResult objects. Expected to be of type kFloat32 or kUInt8.
 // Outputs:
-//   EMBEDDING_RESULT - EmbeddingResult
+//   EMBEDDINGS - EmbeddingResult
 //     The output EmbeddingResult.
 //
 // TODO: add support for additional optional "TIMESTAMPS" input for
 // embeddings aggregation.
 absl::Status ConfigureEmbeddingPostprocessing(
     const tasks::core::ModelResources& model_resources,
-    const tasks::components::proto::EmbedderOptions& embedder_options,
+    const proto::EmbedderOptions& embedder_options,
     proto::EmbeddingPostprocessingGraphOptions* options);
 
+}  // namespace processors
 }  // namespace components
 }  // namespace tasks
 }  // namespace mediapipe
 
-#endif  // MEDIAPIPE_TASKS_CC_COMPONENTS_EMBEDDING_POSTPROCESSING_GRAPH_H_
+#endif  // MEDIAPIPE_TASKS_CC_COMPONENTS_PROCESSORS_EMBEDDING_POSTPROCESSING_GRAPH_H_

mediapipe/tasks/cc/components/processors/embedding_postprocessing_graph_test.cc

@@ -13,7 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
-#include "mediapipe/tasks/cc/components/embedding_postprocessing_graph.h"
+#include "mediapipe/tasks/cc/components/processors/embedding_postprocessing_graph.h"
 
 #include <memory>
 
@@ -25,8 +25,8 @@ limitations under the License.
 #include "mediapipe/framework/port/gtest.h"
 #include "mediapipe/framework/port/parse_text_proto.h"
 #include "mediapipe/framework/port/status_matchers.h"
-#include "mediapipe/tasks/cc/components/proto/embedder_options.pb.h"
-#include "mediapipe/tasks/cc/components/proto/embedding_postprocessing_graph_options.pb.h"
+#include "mediapipe/tasks/cc/components/processors/proto/embedder_options.pb.h"
+#include "mediapipe/tasks/cc/components/processors/proto/embedding_postprocessing_graph_options.pb.h"
 #include "mediapipe/tasks/cc/core/model_resources.h"
 #include "mediapipe/tasks/cc/core/proto/external_file.pb.h"
 #include "tensorflow/lite/core/shims/cc/shims_test_util.h"
@@ -34,12 +34,10 @@ limitations under the License.
 namespace mediapipe {
 namespace tasks {
 namespace components {
+namespace processors {
 namespace {
 
 using ::mediapipe::file::JoinPath;
-using ::mediapipe::tasks::components::proto::EmbedderOptions;
-using ::mediapipe::tasks::components::proto::
-    EmbeddingPostprocessingGraphOptions;
 using ::mediapipe::tasks::core::ModelResources;
 
 constexpr char kTestDataDirectory[] = "/mediapipe/tasks/testdata/";
@@ -69,68 +67,72 @@ TEST_F(ConfigureTest, SucceedsWithQuantizedModelWithMetadata) {
   MP_ASSERT_OK_AND_ASSIGN(
       auto model_resources,
       CreateModelResourcesForModel(kQuantizedImageClassifierWithMetadata));
-  EmbedderOptions options_in;
+  proto::EmbedderOptions options_in;
   options_in.set_l2_normalize(true);
 
-  EmbeddingPostprocessingGraphOptions options_out;
+  proto::EmbeddingPostprocessingGraphOptions options_out;
   MP_ASSERT_OK(ConfigureEmbeddingPostprocessing(*model_resources, options_in,
                                                 &options_out));
 
   EXPECT_THAT(
       options_out,
-      EqualsProto(ParseTextProtoOrDie<EmbeddingPostprocessingGraphOptions>(
-          R"pb(tensors_to_embeddings_options {
-                 embedder_options { l2_normalize: true }
-                 head_names: "probability"
-               }
-               has_quantized_outputs: true)pb")));
+      EqualsProto(
+          ParseTextProtoOrDie<proto::EmbeddingPostprocessingGraphOptions>(
+              R"pb(tensors_to_embeddings_options {
+                     embedder_options { l2_normalize: true }
+                     head_names: "probability"
+                   }
+                   has_quantized_outputs: true)pb")));
 }
 
 TEST_F(ConfigureTest, SucceedsWithQuantizedModelWithoutMetadata) {
   MP_ASSERT_OK_AND_ASSIGN(
       auto model_resources,
       CreateModelResourcesForModel(kQuantizedImageClassifierWithoutMetadata));
-  EmbedderOptions options_in;
+  proto::EmbedderOptions options_in;
   options_in.set_quantize(true);
 
-  EmbeddingPostprocessingGraphOptions options_out;
+  proto::EmbeddingPostprocessingGraphOptions options_out;
   MP_ASSERT_OK(ConfigureEmbeddingPostprocessing(*model_resources, options_in,
                                                 &options_out));
 
   EXPECT_THAT(
       options_out,
-      EqualsProto(ParseTextProtoOrDie<EmbeddingPostprocessingGraphOptions>(
-          R"pb(tensors_to_embeddings_options {
-                 embedder_options { quantize: true }
-               }
-               has_quantized_outputs: true)pb")));
+      EqualsProto(
+          ParseTextProtoOrDie<proto::EmbeddingPostprocessingGraphOptions>(
+              R"pb(tensors_to_embeddings_options {
+                     embedder_options { quantize: true }
+                   }
+                   has_quantized_outputs: true)pb")));
 }
 
 TEST_F(ConfigureTest, SucceedsWithFloatModelWithMetadata) {
   MP_ASSERT_OK_AND_ASSIGN(auto model_resources,
                           CreateModelResourcesForModel(kMobileNetV3Embedder));
-  EmbedderOptions options_in;
+  proto::EmbedderOptions options_in;
   options_in.set_quantize(true);
   options_in.set_l2_normalize(true);
 
-  EmbeddingPostprocessingGraphOptions options_out;
+  proto::EmbeddingPostprocessingGraphOptions options_out;
   MP_ASSERT_OK(ConfigureEmbeddingPostprocessing(*model_resources, options_in,
                                                 &options_out));
 
   EXPECT_THAT(
       options_out,
-      EqualsProto(ParseTextProtoOrDie<EmbeddingPostprocessingGraphOptions>(
-          R"pb(tensors_to_embeddings_options {
-                 embedder_options { quantize: true l2_normalize: true }
-                 head_names: "feature"
-               }
-               has_quantized_outputs: false)pb")));
+      EqualsProto(
+          ParseTextProtoOrDie<proto::EmbeddingPostprocessingGraphOptions>(
+              R"pb(tensors_to_embeddings_options {
+                     embedder_options { quantize: true l2_normalize: true }
+                     head_names: "feature"
+                   }
+                   has_quantized_outputs: false)pb")));
 }
 
 // TODO: add E2E Postprocessing tests once timestamp aggregation is
 // supported.
 
 }  // namespace
+}  // namespace processors
 }  // namespace components
 }  // namespace tasks
 }  // namespace mediapipe

mediapipe/tasks/cc/components/processors/proto/BUILD

@@ -34,3 +34,18 @@ mediapipe_proto_library(
         "//mediapipe/tasks/cc/components/calculators:score_calibration_calculator_proto",
     ],
 )
+
+mediapipe_proto_library(
+    name = "embedder_options_proto",
+    srcs = ["embedder_options.proto"],
+)
+
+mediapipe_proto_library(
+    name = "embedding_postprocessing_graph_options_proto",
+    srcs = ["embedding_postprocessing_graph_options.proto"],
+    deps = [
+        "//mediapipe/framework:calculator_options_proto",
+        "//mediapipe/framework:calculator_proto",
+        "//mediapipe/tasks/cc/components/calculators:tensors_to_embeddings_calculator_proto",
+    ],
+)

mediapipe/tasks/cc/components/processors/proto/embedder_options.proto

@@ -15,7 +15,10 @@ limitations under the License.
 
 syntax = "proto2";
 
-package mediapipe.tasks.components.proto;
+package mediapipe.tasks.components.processors.proto;
+
+option java_package = "com.google.mediapipe.tasks.components.processors.proto";
+option java_outer_classname = "EmbedderOptionsProto";
 
 // Shared options used by all embedding extraction tasks.
 message EmbedderOptions {

mediapipe/tasks/cc/components/processors/proto/embedding_postprocessing_graph_options.proto

@@ -15,7 +15,7 @@ limitations under the License.
 
 syntax = "proto2";
 
-package mediapipe.tasks.components.proto;
+package mediapipe.tasks.components.processors.proto;
 
 import "mediapipe/framework/calculator.proto";
 import "mediapipe/tasks/cc/components/calculators/tensors_to_embeddings_calculator.proto";

mediapipe/tasks/cc/components/proto/BUILD

@@ -23,21 +23,6 @@ mediapipe_proto_library(
     srcs = ["segmenter_options.proto"],
 )
 
-mediapipe_proto_library(
-    name = "embedder_options_proto",
-    srcs = ["embedder_options.proto"],
-)
-
-mediapipe_proto_library(
-    name = "embedding_postprocessing_graph_options_proto",
-    srcs = ["embedding_postprocessing_graph_options.proto"],
-    deps = [
-        "//mediapipe/framework:calculator_options_proto",
-        "//mediapipe/framework:calculator_proto",
-        "//mediapipe/tasks/cc/components/calculators:tensors_to_embeddings_calculator_proto",
-    ],
-)
-
 mediapipe_proto_library(
     name = "text_preprocessing_graph_options_proto",
     srcs = ["text_preprocessing_graph_options.proto"],

mediapipe/tasks/cc/components/utils/BUILD

@@ -26,7 +26,7 @@ cc_library(
     hdrs = ["cosine_similarity.h"],
     deps = [
         "//mediapipe/tasks/cc:common",
-        "//mediapipe/tasks/cc/components/containers/proto:embeddings_cc_proto",
+        "//mediapipe/tasks/cc/components/containers:embedding_result",
         "@com_google_absl//absl/status",
         "@com_google_absl//absl/status:statusor",
         "@com_google_absl//absl/strings:str_format",
@@ -39,7 +39,7 @@ cc_test(
     deps = [
         ":cosine_similarity",
         "//mediapipe/framework/port:gtest_main",
-        "//mediapipe/tasks/cc/components/containers/proto:embeddings_cc_proto",
+        "//mediapipe/tasks/cc/components/containers:embedding_result",
     ],
 )
 

mediapipe/tasks/cc/components/utils/cosine_similarity.cc

@@ -21,7 +21,7 @@ limitations under the License.
 #include "absl/status/statusor.h"
 #include "absl/strings/str_format.h"
 #include "mediapipe/tasks/cc/common.h"
-#include "mediapipe/tasks/cc/components/containers/proto/embeddings.pb.h"
+#include "mediapipe/tasks/cc/components/containers/embedding_result.h"
 
 namespace mediapipe {
 namespace tasks {
@@ -30,7 +30,7 @@ namespace utils {
 
 namespace {
 
-using ::mediapipe::tasks::components::containers::proto::EmbeddingEntry;
+using ::mediapipe::tasks::components::containers::Embedding;
 
 template <typename T>
 absl::StatusOr<double> ComputeCosineSimilarity(const T& u, const T& v,
@@ -66,39 +66,35 @@ absl::StatusOr<double> ComputeCosineSimilarity(const T& u, const T& v,
 // an L2-norm of 0.
 //
 // [1]: https://en.wikipedia.org/wiki/Cosine_similarity
-absl::StatusOr<double> CosineSimilarity(const EmbeddingEntry& u,
-                                        const EmbeddingEntry& v) {
-  if (u.has_float_embedding() && v.has_float_embedding()) {
-    if (u.float_embedding().values().size() !=
-        v.float_embedding().values().size()) {
+absl::StatusOr<double> CosineSimilarity(const Embedding& u,
+                                        const Embedding& v) {
+  if (!u.float_embedding.empty() && !v.float_embedding.empty()) {
+    if (u.float_embedding.size() != v.float_embedding.size()) {
       return CreateStatusWithPayload(
           absl::StatusCode::kInvalidArgument,
           absl::StrFormat("Cannot compute cosine similarity between embeddings "
                           "of different sizes (%d vs. %d)",
-                          u.float_embedding().values().size(),
-                          v.float_embedding().values().size()),
+                          u.float_embedding.size(), v.float_embedding.size()),
           MediaPipeTasksStatus::kInvalidArgumentError);
     }
-    return ComputeCosineSimilarity(u.float_embedding().values().data(),
-                                   v.float_embedding().values().data(),
-                                   u.float_embedding().values().size());
+    return ComputeCosineSimilarity(u.float_embedding.data(),
+                                   v.float_embedding.data(),
+                                   u.float_embedding.size());
   }
-  if (u.has_quantized_embedding() && v.has_quantized_embedding()) {
-    if (u.quantized_embedding().values().size() !=
-        v.quantized_embedding().values().size()) {
+  if (!u.quantized_embedding.empty() && !v.quantized_embedding.empty()) {
+    if (u.quantized_embedding.size() != v.quantized_embedding.size()) {
       return CreateStatusWithPayload(
           absl::StatusCode::kInvalidArgument,
           absl::StrFormat("Cannot compute cosine similarity between embeddings "
                           "of different sizes (%d vs. %d)",
-                          u.quantized_embedding().values().size(),
-                          v.quantized_embedding().values().size()),
+                          u.quantized_embedding.size(),
+                          v.quantized_embedding.size()),
           MediaPipeTasksStatus::kInvalidArgumentError);
     }
-    return ComputeCosineSimilarity(reinterpret_cast<const int8_t*>(
-                                       u.quantized_embedding().values().data()),
-                                   reinterpret_cast<const int8_t*>(
-                                       v.quantized_embedding().values().data()),
-                                   u.quantized_embedding().values().size());
+    return ComputeCosineSimilarity(
+        reinterpret_cast<const int8_t*>(u.quantized_embedding.data()),
+        reinterpret_cast<const int8_t*>(v.quantized_embedding.data()),
+        u.quantized_embedding.size());
   }
   return CreateStatusWithPayload(
       absl::StatusCode::kInvalidArgument,

mediapipe/tasks/cc/components/utils/cosine_similarity.h

@@ -17,22 +17,20 @@ limitations under the License.
 #define MEDIAPIPE_TASKS_CC_COMPONENTS_UTILS_COSINE_SIMILARITY_H_
 
 #include "absl/status/statusor.h"
-#include "mediapipe/tasks/cc/components/containers/proto/embeddings.pb.h"
+#include "mediapipe/tasks/cc/components/containers/embedding_result.h"
 
 namespace mediapipe {
 namespace tasks {
 namespace components {
 namespace utils {
 
-// Utility function to compute cosine similarity [1] between two embedding
-// entries. May return an InvalidArgumentError if e.g. the feature vectors are
-// of different types (quantized vs. float), have different sizes, or have a
-// an L2-norm of 0.
+// Utility function to compute cosine similarity [1] between two embeddings. May
+// return an InvalidArgumentError if e.g. the embeddings are of different types
+// (quantized vs. float), have different sizes, or have a an L2-norm of 0.
 //
 // [1]: https://en.wikipedia.org/wiki/Cosine_similarity
-absl::StatusOr<double> CosineSimilarity(
-    const containers::proto::EmbeddingEntry& u,
-    const containers::proto::EmbeddingEntry& v);
+absl::StatusOr<double> CosineSimilarity(const containers::Embedding& u,
+                                        const containers::Embedding& v);
 
 }  // namespace utils
 }  // namespace components

mediapipe/tasks/cc/components/utils/cosine_similarity_test.cc

@@ -22,7 +22,7 @@ limitations under the License.
 #include "mediapipe/framework/port/gmock.h"
 #include "mediapipe/framework/port/gtest.h"
 #include "mediapipe/framework/port/status_matchers.h"
-#include "mediapipe/tasks/cc/components/containers/proto/embeddings.pb.h"
+#include "mediapipe/tasks/cc/components/containers/embedding_result.h"
 
 namespace mediapipe {
 namespace tasks {
@@ -30,29 +30,27 @@ namespace components {
 namespace utils {
 namespace {
 
-using ::mediapipe::tasks::components::containers::proto::EmbeddingEntry;
+using ::mediapipe::tasks::components::containers::Embedding;
 using ::testing::HasSubstr;
 
-// Helper function to generate float EmbeddingEntry.
-EmbeddingEntry BuildFloatEntry(std::vector<float> values) {
-  EmbeddingEntry entry;
-  for (const float value : values) {
-    entry.mutable_float_embedding()->add_values(value);
-  }
-  return entry;
+// Helper function to generate float Embedding.
+Embedding BuildFloatEmbedding(std::vector<float> values) {
+  Embedding embedding;
+  embedding.float_embedding = values;
+  return embedding;
 }
 
-// Helper function to generate quantized EmbeddingEntry.
-EmbeddingEntry BuildQuantizedEntry(std::vector<int8_t> values) {
-  EmbeddingEntry entry;
-  entry.mutable_quantized_embedding()->set_values(
-      reinterpret_cast<uint8_t*>(values.data()), values.size());
-  return entry;
+// Helper function to generate quantized Embedding.
+Embedding BuildQuantizedEmbedding(std::vector<int8_t> values) {
+  Embedding embedding;
+  uint8_t* data = reinterpret_cast<uint8_t*>(values.data());
+  embedding.quantized_embedding = {data, data + values.size()};
+  return embedding;
 }
 
 TEST(CosineSimilarity, FailsWithQuantizedAndFloatEmbeddings) {
-  auto u = BuildFloatEntry({0.1, 0.2});
-  auto v = BuildQuantizedEntry({0, 1});
+  auto u = BuildFloatEmbedding({0.1, 0.2});
+  auto v = BuildQuantizedEmbedding({0, 1});
 
   auto status = CosineSimilarity(u, v);
 
@@ -63,8 +61,8 @@ TEST(CosineSimilarity, FailsWithQuantizedAndFloatEmbeddings) {
 }
 
 TEST(CosineSimilarity, FailsWithZeroNorm) {
-  auto u = BuildFloatEntry({0.1, 0.2});
-  auto v = BuildFloatEntry({0.0, 0.0});
+  auto u = BuildFloatEmbedding({0.1, 0.2});
+  auto v = BuildFloatEmbedding({0.0, 0.0});
 
   auto status = CosineSimilarity(u, v);
 
@@ -75,8 +73,8 @@ TEST(CosineSimilarity, FailsWithZeroNorm) {
 }
 
 TEST(CosineSimilarity, FailsWithDifferentSizes) {
-  auto u = BuildFloatEntry({0.1, 0.2});
-  auto v = BuildFloatEntry({0.1, 0.2, 0.3});
+  auto u = BuildFloatEmbedding({0.1, 0.2});
+  auto v = BuildFloatEmbedding({0.1, 0.2, 0.3});
 
   auto status = CosineSimilarity(u, v);
 
@@ -87,8 +85,8 @@ TEST(CosineSimilarity, FailsWithDifferentSizes) {
 }
 
 TEST(CosineSimilarity, SucceedsWithFloatEntries) {
-  auto u = BuildFloatEntry({1.0, 0.0, 0.0, 0.0});
-  auto v = BuildFloatEntry({0.5, 0.5, 0.5, 0.5});
+  auto u = BuildFloatEmbedding({1.0, 0.0, 0.0, 0.0});
+  auto v = BuildFloatEmbedding({0.5, 0.5, 0.5, 0.5});
 
   MP_ASSERT_OK_AND_ASSIGN(auto result, CosineSimilarity(u, v));
 
@@ -96,8 +94,8 @@ TEST(CosineSimilarity, SucceedsWithFloatEntries) {
 }
 
 TEST(CosineSimilarity, SucceedsWithQuantizedEntries) {
-  auto u = BuildQuantizedEntry({127, 0, 0, 0});
-  auto v = BuildQuantizedEntry({-128, 0, 0, 0});
+  auto u = BuildQuantizedEmbedding({127, 0, 0, 0});
+  auto v = BuildQuantizedEmbedding({-128, 0, 0, 0});
 
   MP_ASSERT_OK_AND_ASSIGN(auto result, CosineSimilarity(u, v));
 

mediapipe/tasks/cc/vision/gesture_recognizer/gesture_recognizer_graph.cc

@@ -273,11 +273,12 @@ class GestureRecognizerGraph : public core::ModelTaskGraph {
         hand_gesture_subgraph[Output<std::vector<ClassificationList>>(
             kHandGesturesTag)];
 
-    return {{.gesture = hand_gestures,
-             .handedness = handedness,
-             .hand_landmarks = hand_landmarks,
-             .hand_world_landmarks = hand_world_landmarks,
-             .image = hand_landmarker_graph[Output<Image>(kImageTag)]}};
+    return GestureRecognizerOutputs{
+        /*gesture=*/hand_gestures,
+        /*handedness=*/handedness,
+        /*hand_landmarks=*/hand_landmarks,
+        /*hand_world_landmarks=*/hand_world_landmarks,
+        /*image=*/hand_landmarker_graph[Output<Image>(kImageTag)]};
   }
 };
 

mediapipe/tasks/cc/vision/hand_landmarker/calculators/hand_landmarks_deduplication_calculator.cc

@@ -144,10 +144,10 @@ Rect CalculateBound(const NormalizedLandmarkList& list) {
   }
 
   // Populate normalized non rotated face bounding box
-  return {.left = bounding_box_left,
-          .top = bounding_box_top,
-          .right = bounding_box_right,
-          .bottom = bounding_box_bottom};
+  return Rect{/*left=*/bounding_box_left,
+              /*top=*/bounding_box_top,
+              /*right=*/bounding_box_right,
+              /*bottom=*/bounding_box_bottom};
 }
 
 // Uses IoU and distance of some corresponding hand landmarks to detect

mediapipe/tasks/cc/vision/image_embedder/BUILD

@@ -26,12 +26,12 @@ cc_library(
         "//mediapipe/framework/api2:port",
         "//mediapipe/framework/formats:image",
         "//mediapipe/framework/formats:rect_cc_proto",
-        "//mediapipe/tasks/cc/components:embedding_postprocessing_graph",
         "//mediapipe/tasks/cc/components:image_preprocessing",
         "//mediapipe/tasks/cc/components:image_preprocessing_options_cc_proto",
         "//mediapipe/tasks/cc/components/calculators:tensors_to_embeddings_calculator",
         "//mediapipe/tasks/cc/components/containers/proto:embeddings_cc_proto",
-        "//mediapipe/tasks/cc/components/proto:embedding_postprocessing_graph_options_cc_proto",
+        "//mediapipe/tasks/cc/components/processors:embedding_postprocessing_graph",
+        "//mediapipe/tasks/cc/components/processors/proto:embedding_postprocessing_graph_options_cc_proto",
         "//mediapipe/tasks/cc/core:model_task_graph",
         "//mediapipe/tasks/cc/vision/image_embedder/proto:image_embedder_graph_options_cc_proto",
         "@com_google_absl//absl/status:statusor",
@@ -49,9 +49,10 @@ cc_library(
         "//mediapipe/framework/formats:image",
         "//mediapipe/framework/formats:rect_cc_proto",
         "//mediapipe/framework/tool:options_map",
-        "//mediapipe/tasks/cc/components:embedder_options",
+        "//mediapipe/tasks/cc/components/containers:embedding_result",
         "//mediapipe/tasks/cc/components/containers/proto:embeddings_cc_proto",
-        "//mediapipe/tasks/cc/components/proto:embedder_options_cc_proto",
+        "//mediapipe/tasks/cc/components/processors:embedder_options",
+        "//mediapipe/tasks/cc/components/processors/proto:embedder_options_cc_proto",
         "//mediapipe/tasks/cc/components/utils:cosine_similarity",
         "//mediapipe/tasks/cc/core:base_options",
         "//mediapipe/tasks/cc/core:task_runner",

mediapipe/tasks/cc/vision/image_embedder/image_embedder.cc

@@ -21,9 +21,10 @@ limitations under the License.
 #include "mediapipe/framework/api2/builder.h"
 #include "mediapipe/framework/formats/rect.pb.h"
 #include "mediapipe/framework/tool/options_map.h"
+#include "mediapipe/tasks/cc/components/containers/embedding_result.h"
 #include "mediapipe/tasks/cc/components/containers/proto/embeddings.pb.h"
-#include "mediapipe/tasks/cc/components/embedder_options.h"
-#include "mediapipe/tasks/cc/components/proto/embedder_options.pb.h"
+#include "mediapipe/tasks/cc/components/processors/embedder_options.h"
+#include "mediapipe/tasks/cc/components/processors/proto/embedder_options.pb.h"
 #include "mediapipe/tasks/cc/components/utils/cosine_similarity.h"
 #include "mediapipe/tasks/cc/core/base_options.h"
 #include "mediapipe/tasks/cc/core/proto/base_options.pb.h"
@@ -41,8 +42,8 @@ namespace image_embedder {
 
 namespace {
 
-constexpr char kEmbeddingResultStreamName[] = "embedding_result_out";
-constexpr char kEmbeddingResultTag[] = "EMBEDDING_RESULT";
+constexpr char kEmbeddingsStreamName[] = "embeddings_out";
+constexpr char kEmbeddingsTag[] = "EMBEDDINGS";
 constexpr char kImageInStreamName[] = "image_in";
 constexpr char kImageOutStreamName[] = "image_out";
 constexpr char kImageTag[] = "IMAGE";
@@ -53,7 +54,7 @@ constexpr char kGraphTypeName[] =
     "mediapipe.tasks.vision.image_embedder.ImageEmbedderGraph";
 constexpr int kMicroSecondsPerMilliSecond = 1000;
 
-using ::mediapipe::tasks::components::containers::proto::EmbeddingEntry;
+using ::mediapipe::tasks::components::containers::ConvertToEmbeddingResult;
 using ::mediapipe::tasks::components::containers::proto::EmbeddingResult;
 using ::mediapipe::tasks::core::PacketMap;
 using ::mediapipe::tasks::vision::image_embedder::proto::
@@ -71,13 +72,13 @@ CalculatorGraphConfig CreateGraphConfig(
   graph.In(kNormRectTag).SetName(kNormRectStreamName);
   auto& task_graph = graph.AddNode(kGraphTypeName);
   task_graph.GetOptions<ImageEmbedderGraphOptions>().Swap(options_proto.get());
-  task_graph.Out(kEmbeddingResultTag).SetName(kEmbeddingResultStreamName) >>
-      graph.Out(kEmbeddingResultTag);
+  task_graph.Out(kEmbeddingsTag).SetName(kEmbeddingsStreamName) >>
+      graph.Out(kEmbeddingsTag);
   task_graph.Out(kImageTag).SetName(kImageOutStreamName) >>
       graph.Out(kImageTag);
   if (enable_flow_limiting) {
     return tasks::core::AddFlowLimiterCalculator(
-        graph, task_graph, {kImageTag, kNormRectTag}, kEmbeddingResultTag);
+        graph, task_graph, {kImageTag, kNormRectTag}, kEmbeddingsTag);
   }
   graph.In(kImageTag) >> task_graph.In(kImageTag);
   graph.In(kNormRectTag) >> task_graph.In(kNormRectTag);
@@ -95,8 +96,8 @@ std::unique_ptr<ImageEmbedderGraphOptions> ConvertImageEmbedderOptionsToProto(
   options_proto->mutable_base_options()->set_use_stream_mode(
       options->running_mode != core::RunningMode::IMAGE);
   auto embedder_options_proto =
-      std::make_unique<tasks::components::proto::EmbedderOptions>(
-          components::ConvertEmbedderOptionsToProto(
+      std::make_unique<components::processors::proto::EmbedderOptions>(
+          components::processors::ConvertEmbedderOptionsToProto(
               &(options->embedder_options)));
   options_proto->mutable_embedder_options()->Swap(embedder_options_proto.get());
   return options_proto;
@@ -121,9 +122,10 @@ absl::StatusOr<std::unique_ptr<ImageEmbedder>> ImageEmbedder::Create(
             return;
           }
           Packet embedding_result_packet =
-              status_or_packets.value()[kEmbeddingResultStreamName];
+              status_or_packets.value()[kEmbeddingsStreamName];
           Packet image_packet = status_or_packets.value()[kImageOutStreamName];
-          result_callback(embedding_result_packet.Get<EmbeddingResult>(),
+          result_callback(ConvertToEmbeddingResult(
+                              embedding_result_packet.Get<EmbeddingResult>()),
                           image_packet.Get<Image>(),
                           embedding_result_packet.Timestamp().Value() /
                               kMicroSecondsPerMilliSecond);
@@ -138,7 +140,7 @@ absl::StatusOr<std::unique_ptr<ImageEmbedder>> ImageEmbedder::Create(
       std::move(packets_callback));
 }
 
-absl::StatusOr<EmbeddingResult> ImageEmbedder::Embed(
+absl::StatusOr<ImageEmbedderResult> ImageEmbedder::Embed(
     Image image,
     std::optional<core::ImageProcessingOptions> image_processing_options) {
   if (image.UsesGpu()) {
@@ -155,10 +157,11 @@ absl::StatusOr<EmbeddingResult> ImageEmbedder::Embed(
           {{kImageInStreamName, MakePacket<Image>(std::move(image))},
            {kNormRectStreamName,
             MakePacket<NormalizedRect>(std::move(norm_rect))}}));
-  return output_packets[kEmbeddingResultStreamName].Get<EmbeddingResult>();
+  return ConvertToEmbeddingResult(
+      output_packets[kEmbeddingsStreamName].Get<EmbeddingResult>());
 }
 
-absl::StatusOr<EmbeddingResult> ImageEmbedder::EmbedForVideo(
+absl::StatusOr<ImageEmbedderResult> ImageEmbedder::EmbedForVideo(
     Image image, int64 timestamp_ms,
     std::optional<core::ImageProcessingOptions> image_processing_options) {
   if (image.UsesGpu()) {
@@ -178,7 +181,8 @@ absl::StatusOr<EmbeddingResult> ImageEmbedder::EmbedForVideo(
            {kNormRectStreamName,
             MakePacket<NormalizedRect>(std::move(norm_rect))
                 .At(Timestamp(timestamp_ms * kMicroSecondsPerMilliSecond))}}));
-  return output_packets[kEmbeddingResultStreamName].Get<EmbeddingResult>();
+  return ConvertToEmbeddingResult(
+      output_packets[kEmbeddingsStreamName].Get<EmbeddingResult>());
 }
 
 absl::Status ImageEmbedder::EmbedAsync(
@@ -202,7 +206,8 @@ absl::Status ImageEmbedder::EmbedAsync(
 }
 
 absl::StatusOr<double> ImageEmbedder::CosineSimilarity(
-    const EmbeddingEntry& u, const EmbeddingEntry& v) {
+    const components::containers::Embedding& u,
+    const components::containers::Embedding& v) {
   return components::utils::CosineSimilarity(u, v);
 }
 

mediapipe/tasks/cc/vision/image_embedder/image_embedder.h

@@ -21,8 +21,8 @@ limitations under the License.
 
 #include "absl/status/statusor.h"
 #include "mediapipe/framework/formats/image.h"
-#include "mediapipe/tasks/cc/components/containers/proto/embeddings.pb.h"
-#include "mediapipe/tasks/cc/components/embedder_options.h"
+#include "mediapipe/tasks/cc/components/containers/embedding_result.h"
+#include "mediapipe/tasks/cc/components/processors/embedder_options.h"
 #include "mediapipe/tasks/cc/core/base_options.h"
 #include "mediapipe/tasks/cc/vision/core/base_vision_task_api.h"
 #include "mediapipe/tasks/cc/vision/core/image_processing_options.h"
@@ -33,6 +33,10 @@ namespace tasks {
 namespace vision {
 namespace image_embedder {
 
+// Alias the shared EmbeddingResult struct as result typo.
+using ImageEmbedderResult =
+    ::mediapipe::tasks::components::containers::EmbeddingResult;
+
 // The options for configuring a MediaPipe image embedder task.
 struct ImageEmbedderOptions {
   // Base options for configuring MediaPipe Tasks, such as specifying the model
@@ -50,14 +54,12 @@ struct ImageEmbedderOptions {
 
   // Options for configuring the embedder behavior, such as L2-normalization or
   // scalar-quantization.
-  components::EmbedderOptions embedder_options;
+  components::processors::EmbedderOptions embedder_options;
 
   // The user-defined result callback for processing live stream data.
   // The result callback should only be specified when the running mode is set
   // to RunningMode::LIVE_STREAM.
-  std::function<void(
-      absl::StatusOr<components::containers::proto::EmbeddingResult>,
-      const Image&, int64)>
+  std::function<void(absl::StatusOr<ImageEmbedderResult>, const Image&, int64)>
       result_callback = nullptr;
 };
 
@@ -104,7 +106,7 @@ class ImageEmbedder : core::BaseVisionTaskApi {
   // running mode.
   //
   // The image can be of any size with format RGB or RGBA.
-  absl::StatusOr<components::containers::proto::EmbeddingResult> Embed(
+  absl::StatusOr<ImageEmbedderResult> Embed(
       mediapipe::Image image,
       std::optional<core::ImageProcessingOptions> image_processing_options =
           std::nullopt);
@@ -127,7 +129,7 @@ class ImageEmbedder : core::BaseVisionTaskApi {
   // The image can be of any size with format RGB or RGBA. It's required to
   // provide the video frame's timestamp (in milliseconds). The input timestamps
   // must be monotonically increasing.
-  absl::StatusOr<components::containers::proto::EmbeddingResult> EmbedForVideo(
+  absl::StatusOr<ImageEmbedderResult> EmbedForVideo(
       mediapipe::Image image, int64 timestamp_ms,
       std::optional<core::ImageProcessingOptions> image_processing_options =
           std::nullopt);
@@ -168,15 +170,15 @@ class ImageEmbedder : core::BaseVisionTaskApi {
   // Shuts down the ImageEmbedder when all works are done.
   absl::Status Close() { return runner_->Close(); }
 
-  // Utility function to compute cosine similarity [1] between two embedding
-  // entries. May return an InvalidArgumentError if e.g. the feature vectors are
-  // of different types (quantized vs. float), have different sizes, or have a
-  // an L2-norm of 0.
+  // Utility function to compute cosine similarity [1] between two embeddings.
+  // May return an InvalidArgumentError if e.g. the embeddings are of different
+  // types (quantized vs. float), have different sizes, or have a an L2-norm of
+  // 0.
   //
   // [1]: https://en.wikipedia.org/wiki/Cosine_similarity
   static absl::StatusOr<double> CosineSimilarity(
-      const components::containers::proto::EmbeddingEntry& u,
-      const components::containers::proto::EmbeddingEntry& v);
+      const components::containers::Embedding& u,
+      const components::containers::Embedding& v);
 };
 
 }  // namespace image_embedder

mediapipe/tasks/cc/vision/image_embedder/image_embedder_graph.cc

@@ -20,10 +20,10 @@ limitations under the License.
 #include "mediapipe/framework/formats/image.h"
 #include "mediapipe/framework/formats/rect.pb.h"
 #include "mediapipe/tasks/cc/components/containers/proto/embeddings.pb.h"
-#include "mediapipe/tasks/cc/components/embedding_postprocessing_graph.h"
 #include "mediapipe/tasks/cc/components/image_preprocessing.h"
 #include "mediapipe/tasks/cc/components/image_preprocessing_options.pb.h"
-#include "mediapipe/tasks/cc/components/proto/embedding_postprocessing_graph_options.pb.h"
+#include "mediapipe/tasks/cc/components/processors/embedding_postprocessing_graph.h"
+#include "mediapipe/tasks/cc/components/processors/proto/embedding_postprocessing_graph_options.pb.h"
 #include "mediapipe/tasks/cc/core/model_task_graph.h"
 #include "mediapipe/tasks/cc/vision/image_embedder/proto/image_embedder_graph_options.pb.h"
 
@@ -40,10 +40,8 @@ using ::mediapipe::api2::builder::GenericNode;
 using ::mediapipe::api2::builder::Graph;
 using ::mediapipe::api2::builder::Source;
 using ::mediapipe::tasks::components::containers::proto::EmbeddingResult;
-using ::mediapipe::tasks::components::proto::
-    EmbeddingPostprocessingGraphOptions;
 
-constexpr char kEmbeddingResultTag[] = "EMBEDDING_RESULT";
+constexpr char kEmbeddingsTag[] = "EMBEDDINGS";
 constexpr char kImageTag[] = "IMAGE";
 constexpr char kNormRectTag[] = "NORM_RECT";
 constexpr char kTensorsTag[] = "TENSORS";
@@ -67,7 +65,7 @@ struct ImageEmbedderOutputStreams {
 //     Describes region of image to perform embedding extraction on.
 //     @Optional: rect covering the whole image is used if not specified.
 // Outputs:
-//   EMBEDDING_RESULT - EmbeddingResult
+//   EMBEDDINGS - EmbeddingResult
 //     The embedding result.
 //   IMAGE - Image
 //     The image that embedding extraction runs on.
@@ -76,7 +74,7 @@ struct ImageEmbedderOutputStreams {
 // node {
 //   calculator: "mediapipe.tasks.vision.image_embedder.ImageEmbedderGraph"
 //   input_stream: "IMAGE:image_in"
-//   output_stream: "EMBEDDING_RESULT:embedding_result_out"
+//   output_stream: "EMBEDDINGS:embedding_result_out"
 //   output_stream: "IMAGE:image_out"
 //   options {
 //     [mediapipe.tasks.vision.image_embedder.proto.ImageEmbedderOptions.ext]
@@ -107,7 +105,7 @@ class ImageEmbedderGraph : public core::ModelTaskGraph {
             graph[Input<Image>(kImageTag)],
             graph[Input<NormalizedRect>::Optional(kNormRectTag)], graph));
     output_streams.embedding_result >>
-        graph[Output<EmbeddingResult>(kEmbeddingResultTag)];
+        graph[Output<EmbeddingResult>(kEmbeddingsTag)];
     output_streams.image >> graph[Output<Image>(kImageTag)];
     return graph.GetConfig();
   }
@@ -152,16 +150,17 @@ class ImageEmbedderGraph : public core::ModelTaskGraph {
     // Adds postprocessing calculators and connects its input stream to the
     // inference results.
     auto& postprocessing = graph.AddNode(
-        "mediapipe.tasks.components.EmbeddingPostprocessingGraph");
-    MP_RETURN_IF_ERROR(components::ConfigureEmbeddingPostprocessing(
+        "mediapipe.tasks.components.processors.EmbeddingPostprocessingGraph");
+    MP_RETURN_IF_ERROR(components::processors::ConfigureEmbeddingPostprocessing(
         model_resources, task_options.embedder_options(),
-        &postprocessing.GetOptions<EmbeddingPostprocessingGraphOptions>()));
+        &postprocessing.GetOptions<components::processors::proto::
+                                       EmbeddingPostprocessingGraphOptions>()));
     inference.Out(kTensorsTag) >> postprocessing.In(kTensorsTag);
 
     // Outputs the embedding results.
     return ImageEmbedderOutputStreams{
         /*embedding_result=*/postprocessing[Output<EmbeddingResult>(
-            kEmbeddingResultTag)],
+            kEmbeddingsTag)],
         /*image=*/preprocessing[Output<Image>(kImageTag)]};
   }
 };

mediapipe/tasks/cc/vision/image_embedder/image_embedder_test.cc

@@ -26,7 +26,7 @@ limitations under the License.
 #include "mediapipe/framework/port/gmock.h"
 #include "mediapipe/framework/port/gtest.h"
 #include "mediapipe/framework/port/status_matchers.h"
-#include "mediapipe/tasks/cc/components/containers/proto/embeddings.pb.h"
+#include "mediapipe/tasks/cc/components/containers/embedding_result.h"
 #include "mediapipe/tasks/cc/vision/core/running_mode.h"
 #include "mediapipe/tasks/cc/vision/utils/image_utils.h"
 #include "tensorflow/lite/core/api/op_resolver.h"
@@ -42,7 +42,6 @@ namespace {
 
 using ::mediapipe::file::JoinPath;
 using ::mediapipe::tasks::components::containers::Rect;
-using ::mediapipe::tasks::components::containers::proto::EmbeddingResult;
 using ::mediapipe::tasks::vision::core::ImageProcessingOptions;
 using ::testing::HasSubstr;
 using ::testing::Optional;
@@ -54,18 +53,14 @@ constexpr double kSimilarityTolerancy = 1e-6;
 
 // Utility function to check the sizes, head_index and head_names of a result
 // procuded by kMobileNetV3Embedder.
-void CheckMobileNetV3Result(const EmbeddingResult& result, bool quantized) {
-  EXPECT_EQ(result.embeddings().size(), 1);
-  EXPECT_EQ(result.embeddings(0).head_index(), 0);
-  EXPECT_EQ(result.embeddings(0).head_name(), "feature");
-  EXPECT_EQ(result.embeddings(0).entries().size(), 1);
+void CheckMobileNetV3Result(const ImageEmbedderResult& result, bool quantized) {
+  EXPECT_EQ(result.embeddings.size(), 1);
+  EXPECT_EQ(result.embeddings[0].head_index, 0);
+  EXPECT_EQ(result.embeddings[0].head_name, "feature");
   if (quantized) {
-    EXPECT_EQ(
-        result.embeddings(0).entries(0).quantized_embedding().values().size(),
-        1024);
+    EXPECT_EQ(result.embeddings[0].quantized_embedding.size(), 1024);
   } else {
-    EXPECT_EQ(result.embeddings(0).entries(0).float_embedding().values().size(),
-              1024);
+    EXPECT_EQ(result.embeddings[0].float_embedding.size(), 1024);
   }
 }
 
@@ -154,7 +149,7 @@ TEST_F(CreateTest, FailsWithIllegalCallbackInImageOrVideoMode) {
     options->base_options.model_asset_path =
         JoinPath("./", kTestDataDirectory, kMobileNetV3Embedder);
     options->running_mode = running_mode;
-    options->result_callback = [](absl::StatusOr<EmbeddingResult>,
+    options->result_callback = [](absl::StatusOr<ImageEmbedderResult>,
                                   const Image& image, int64 timestamp_ms) {};
 
     auto image_embedder = ImageEmbedder::Create(std::move(options));
@@ -231,19 +226,18 @@ TEST_F(ImageModeTest, SucceedsWithoutL2Normalization) {
                       JoinPath("./", kTestDataDirectory, "burger_crop.jpg")));
 
   // Extract both embeddings.
-  MP_ASSERT_OK_AND_ASSIGN(const EmbeddingResult& image_result,
+  MP_ASSERT_OK_AND_ASSIGN(const ImageEmbedderResult& image_result,
                           image_embedder->Embed(image));
-  MP_ASSERT_OK_AND_ASSIGN(const EmbeddingResult& crop_result,
+  MP_ASSERT_OK_AND_ASSIGN(const ImageEmbedderResult& crop_result,
                           image_embedder->Embed(crop));
 
   // Check results.
   CheckMobileNetV3Result(image_result, false);
   CheckMobileNetV3Result(crop_result, false);
   // CheckCosineSimilarity.
-  MP_ASSERT_OK_AND_ASSIGN(
-      double similarity,
-      ImageEmbedder::CosineSimilarity(image_result.embeddings(0).entries(0),
-                                      crop_result.embeddings(0).entries(0)));
+  MP_ASSERT_OK_AND_ASSIGN(double similarity, ImageEmbedder::CosineSimilarity(
+                                                 image_result.embeddings[0],
+                                                 crop_result.embeddings[0]));
   double expected_similarity = 0.925519;
   EXPECT_LE(abs(similarity - expected_similarity), kSimilarityTolerancy);
 }
@@ -264,19 +258,18 @@ TEST_F(ImageModeTest, SucceedsWithL2Normalization) {
                       JoinPath("./", kTestDataDirectory, "burger_crop.jpg")));
 
   // Extract both embeddings.
-  MP_ASSERT_OK_AND_ASSIGN(const EmbeddingResult& image_result,
+  MP_ASSERT_OK_AND_ASSIGN(const ImageEmbedderResult& image_result,
                           image_embedder->Embed(image));
-  MP_ASSERT_OK_AND_ASSIGN(const EmbeddingResult& crop_result,
+  MP_ASSERT_OK_AND_ASSIGN(const ImageEmbedderResult& crop_result,
                           image_embedder->Embed(crop));
 
   // Check results.
   CheckMobileNetV3Result(image_result, false);
   CheckMobileNetV3Result(crop_result, false);
   // CheckCosineSimilarity.
-  MP_ASSERT_OK_AND_ASSIGN(
-      double similarity,
-      ImageEmbedder::CosineSimilarity(image_result.embeddings(0).entries(0),
-                                      crop_result.embeddings(0).entries(0)));
+  MP_ASSERT_OK_AND_ASSIGN(double similarity, ImageEmbedder::CosineSimilarity(
+                                                 image_result.embeddings[0],
+                                                 crop_result.embeddings[0]));
   double expected_similarity = 0.925519;
   EXPECT_LE(abs(similarity - expected_similarity), kSimilarityTolerancy);
 }
@@ -297,19 +290,18 @@ TEST_F(ImageModeTest, SucceedsWithQuantization) {
                       JoinPath("./", kTestDataDirectory, "burger_crop.jpg")));
 
   // Extract both embeddings.
-  MP_ASSERT_OK_AND_ASSIGN(const EmbeddingResult& image_result,
+  MP_ASSERT_OK_AND_ASSIGN(const ImageEmbedderResult& image_result,
                           image_embedder->Embed(image));
-  MP_ASSERT_OK_AND_ASSIGN(const EmbeddingResult& crop_result,
+  MP_ASSERT_OK_AND_ASSIGN(const ImageEmbedderResult& crop_result,
                           image_embedder->Embed(crop));
 
   // Check results.
   CheckMobileNetV3Result(image_result, true);
   CheckMobileNetV3Result(crop_result, true);
   // CheckCosineSimilarity.
-  MP_ASSERT_OK_AND_ASSIGN(
-      double similarity,
-      ImageEmbedder::CosineSimilarity(image_result.embeddings(0).entries(0),
-                                      crop_result.embeddings(0).entries(0)));
+  MP_ASSERT_OK_AND_ASSIGN(double similarity, ImageEmbedder::CosineSimilarity(
+                                                 image_result.embeddings[0],
+                                                 crop_result.embeddings[0]));
   double expected_similarity = 0.926791;
   EXPECT_LE(abs(similarity - expected_similarity), kSimilarityTolerancy);
 }
@@ -333,19 +325,18 @@ TEST_F(ImageModeTest, SucceedsWithRegionOfInterest) {
 
   // Extract both embeddings.
   MP_ASSERT_OK_AND_ASSIGN(
-      const EmbeddingResult& image_result,
+      const ImageEmbedderResult& image_result,
       image_embedder->Embed(image, image_processing_options));
-  MP_ASSERT_OK_AND_ASSIGN(const EmbeddingResult& crop_result,
+  MP_ASSERT_OK_AND_ASSIGN(const ImageEmbedderResult& crop_result,
                           image_embedder->Embed(crop));
 
   // Check results.
   CheckMobileNetV3Result(image_result, false);
   CheckMobileNetV3Result(crop_result, false);
   // CheckCosineSimilarity.
-  MP_ASSERT_OK_AND_ASSIGN(
-      double similarity,
-      ImageEmbedder::CosineSimilarity(image_result.embeddings(0).entries(0),
-                                      crop_result.embeddings(0).entries(0)));
+  MP_ASSERT_OK_AND_ASSIGN(double similarity, ImageEmbedder::CosineSimilarity(
+                                                 image_result.embeddings[0],
+                                                 crop_result.embeddings[0]));
   double expected_similarity = 0.999931;
   EXPECT_LE(abs(similarity - expected_similarity), kSimilarityTolerancy);
 }
@@ -367,20 +358,19 @@ TEST_F(ImageModeTest, SucceedsWithRotation) {
   image_processing_options.rotation_degrees = -90;
 
   // Extract both embeddings.
-  MP_ASSERT_OK_AND_ASSIGN(const EmbeddingResult& image_result,
+  MP_ASSERT_OK_AND_ASSIGN(const ImageEmbedderResult& image_result,
                           image_embedder->Embed(image));
   MP_ASSERT_OK_AND_ASSIGN(
-      const EmbeddingResult& rotated_result,
+      const ImageEmbedderResult& rotated_result,
       image_embedder->Embed(rotated, image_processing_options));
 
   // Check results.
   CheckMobileNetV3Result(image_result, false);
   CheckMobileNetV3Result(rotated_result, false);
   // CheckCosineSimilarity.
-  MP_ASSERT_OK_AND_ASSIGN(
-      double similarity,
-      ImageEmbedder::CosineSimilarity(image_result.embeddings(0).entries(0),
-                                      rotated_result.embeddings(0).entries(0)));
+  MP_ASSERT_OK_AND_ASSIGN(double similarity, ImageEmbedder::CosineSimilarity(
+                                                 image_result.embeddings[0],
+                                                 rotated_result.embeddings[0]));
   double expected_similarity = 0.572265;
   EXPECT_LE(abs(similarity - expected_similarity), kSimilarityTolerancy);
 }
@@ -403,20 +393,19 @@ TEST_F(ImageModeTest, SucceedsWithRegionOfInterestAndRotation) {
                                                   /*rotation_degrees=*/-90};
 
   // Extract both embeddings.
-  MP_ASSERT_OK_AND_ASSIGN(const EmbeddingResult& crop_result,
+  MP_ASSERT_OK_AND_ASSIGN(const ImageEmbedderResult& crop_result,
                           image_embedder->Embed(crop));
   MP_ASSERT_OK_AND_ASSIGN(
-      const EmbeddingResult& rotated_result,
+      const ImageEmbedderResult& rotated_result,
       image_embedder->Embed(rotated, image_processing_options));
 
   // Check results.
   CheckMobileNetV3Result(crop_result, false);
   CheckMobileNetV3Result(rotated_result, false);
   // CheckCosineSimilarity.
-  MP_ASSERT_OK_AND_ASSIGN(
-      double similarity,
-      ImageEmbedder::CosineSimilarity(crop_result.embeddings(0).entries(0),
-                                      rotated_result.embeddings(0).entries(0)));
+  MP_ASSERT_OK_AND_ASSIGN(double similarity, ImageEmbedder::CosineSimilarity(
+                                                 crop_result.embeddings[0],
+                                                 rotated_result.embeddings[0]));
   double expected_similarity = 0.62838;
   EXPECT_LE(abs(similarity - expected_similarity), kSimilarityTolerancy);
 }
@@ -487,16 +476,16 @@ TEST_F(VideoModeTest, Succeeds) {
   MP_ASSERT_OK_AND_ASSIGN(std::unique_ptr<ImageEmbedder> image_embedder,
                           ImageEmbedder::Create(std::move(options)));
 
-  EmbeddingResult previous_results;
+  ImageEmbedderResult previous_results;
   for (int i = 0; i < iterations; ++i) {
     MP_ASSERT_OK_AND_ASSIGN(auto results,
                             image_embedder->EmbedForVideo(image, i));
     CheckMobileNetV3Result(results, false);
     if (i > 0) {
-      MP_ASSERT_OK_AND_ASSIGN(double similarity,
-                              ImageEmbedder::CosineSimilarity(
-                                  results.embeddings(0).entries(0),
-                                  previous_results.embeddings(0).entries(0)));
+      MP_ASSERT_OK_AND_ASSIGN(
+          double similarity,
+          ImageEmbedder::CosineSimilarity(results.embeddings[0],
+                                          previous_results.embeddings[0]));
       double expected_similarity = 1.000000;
       EXPECT_LE(abs(similarity - expected_similarity), kSimilarityTolerancy);
     }
@@ -515,7 +504,7 @@ TEST_F(LiveStreamModeTest, FailsWithCallingWrongMethod) {
   options->base_options.model_asset_path =
       JoinPath("./", kTestDataDirectory, kMobileNetV3Embedder);
   options->running_mode = core::RunningMode::LIVE_STREAM;
-  options->result_callback = [](absl::StatusOr<EmbeddingResult>,
+  options->result_callback = [](absl::StatusOr<ImageEmbedderResult>,
                                 const Image& image, int64 timestamp_ms) {};
   MP_ASSERT_OK_AND_ASSIGN(std::unique_ptr<ImageEmbedder> image_embedder,
                           ImageEmbedder::Create(std::move(options)));
@@ -546,7 +535,7 @@ TEST_F(LiveStreamModeTest, FailsWithOutOfOrderInputTimestamps) {
   options->base_options.model_asset_path =
       JoinPath("./", kTestDataDirectory, kMobileNetV3Embedder);
   options->running_mode = core::RunningMode::LIVE_STREAM;
-  options->result_callback = [](absl::StatusOr<EmbeddingResult>,
+  options->result_callback = [](absl::StatusOr<ImageEmbedderResult>,
                                 const Image& image, int64 timestamp_ms) {};
   MP_ASSERT_OK_AND_ASSIGN(std::unique_ptr<ImageEmbedder> image_embedder,
                           ImageEmbedder::Create(std::move(options)));
@@ -564,7 +553,7 @@ TEST_F(LiveStreamModeTest, FailsWithOutOfOrderInputTimestamps) {
 }
 
 struct LiveStreamModeResults {
-  EmbeddingResult embedding_result;
+  ImageEmbedderResult embedding_result;
   std::pair<int, int> image_size;
   int64 timestamp_ms;
 };
@@ -580,7 +569,7 @@ TEST_F(LiveStreamModeTest, Succeeds) {
       JoinPath("./", kTestDataDirectory, kMobileNetV3Embedder);
   options->running_mode = core::RunningMode::LIVE_STREAM;
   options->result_callback =
-      [&results](absl::StatusOr<EmbeddingResult> embedding_result,
+      [&results](absl::StatusOr<ImageEmbedderResult> embedding_result,
                  const Image& image, int64 timestamp_ms) {
         MP_ASSERT_OK(embedding_result.status());
         results.push_back(
@@ -612,8 +601,8 @@ TEST_F(LiveStreamModeTest, Succeeds) {
       MP_ASSERT_OK_AND_ASSIGN(
           double similarity,
           ImageEmbedder::CosineSimilarity(
-              result.embedding_result.embeddings(0).entries(0),
-              results[i - 1].embedding_result.embeddings(0).entries(0)));
+              result.embedding_result.embeddings[0],
+              results[i - 1].embedding_result.embeddings[0]));
       double expected_similarity = 1.000000;
       EXPECT_LE(abs(similarity - expected_similarity), kSimilarityTolerancy);
     }

mediapipe/tasks/cc/vision/image_embedder/proto/BUILD

@@ -24,7 +24,7 @@ mediapipe_proto_library(
     deps = [
         "//mediapipe/framework:calculator_options_proto",
         "//mediapipe/framework:calculator_proto",
-        "//mediapipe/tasks/cc/components/proto:embedder_options_proto",
+        "//mediapipe/tasks/cc/components/processors/proto:embedder_options_proto",
         "//mediapipe/tasks/cc/core/proto:base_options_proto",
     ],
 )

mediapipe/tasks/cc/vision/image_embedder/proto/image_embedder_graph_options.proto

@@ -18,7 +18,7 @@ syntax = "proto2";
 package mediapipe.tasks.vision.image_embedder.proto;
 
 import "mediapipe/framework/calculator.proto";
-import "mediapipe/tasks/cc/components/proto/embedder_options.proto";
+import "mediapipe/tasks/cc/components/processors/proto/embedder_options.proto";
 import "mediapipe/tasks/cc/core/proto/base_options.proto";
 
 message ImageEmbedderGraphOptions {
@@ -31,5 +31,5 @@ message ImageEmbedderGraphOptions {
 
   // Options for configuring the embedder behavior, such as normalization or
   // quantization.
-  optional components.proto.EmbedderOptions embedder_options = 2;
+  optional components.processors.proto.EmbedderOptions embedder_options = 2;
 }

mediapipe/tasks/cc/vision/image_segmenter/image_segmenter_graph.cc

@@ -287,10 +287,9 @@ class ImageSegmenterGraph : public core::ModelTaskGraph {
             tensor_to_images[Output<Image>::Multiple(kSegmentationTag)][i]));
       }
     }
-    return {{
-        .segmented_masks = segmented_masks,
-        .image = preprocessing[Output<Image>(kImageTag)],
-    }};
+    return ImageSegmenterOutputs{
+        /*segmented_masks=*/segmented_masks,
+        /*image=*/preprocessing[Output<Image>(kImageTag)]};
   }
 };
 

mediapipe/tasks/java/com/google/mediapipe/tasks/mediapipe_tasks_aar.bzl

@@ -23,6 +23,7 @@ _CORE_TASKS_JAVA_PROTO_LITE_TARGETS = [
     "//mediapipe/tasks/cc/components/containers/proto:embeddings_java_proto_lite",
     "//mediapipe/tasks/cc/components/containers/proto:landmarks_detection_result_java_proto_lite",
     "//mediapipe/tasks/cc/components/processors/proto:classifier_options_java_proto_lite",
+    "//mediapipe/tasks/cc/components/processors/proto:embedder_options_java_proto_lite",
     "//mediapipe/tasks/cc/core/proto:acceleration_java_proto_lite",
     "//mediapipe/tasks/cc/core/proto:base_options_java_proto_lite",
     "//mediapipe/tasks/cc/core/proto:external_file_java_proto_lite",

mediapipe/tasks/python/components/containers/BUILD

@@ -18,6 +18,11 @@ package(default_visibility = ["//mediapipe/tasks:internal"])
 
 licenses(["notice"])
 
+py_library(
+    name = "audio_data",
+    srcs = ["audio_data.py"],
+)
+
 py_library(
     name = "bounding_box",
     srcs = ["bounding_box.py"],
@@ -36,6 +41,29 @@ py_library(
     ],
 )
 
+py_library(
+    name = "landmark",
+    srcs = ["landmark.py"],
+    deps = [
+        "//mediapipe/framework/formats:landmark_py_pb2",
+        "//mediapipe/tasks/python/core:optional_dependencies",
+    ],
+)
+
+py_library(
+    name = "landmark_detection_result",
+    srcs = ["landmark_detection_result.py"],
+    deps = [
+        ":landmark",
+        ":rect",
+        "//mediapipe/framework/formats:classification_py_pb2",
+        "//mediapipe/framework/formats:landmark_py_pb2",
+        "//mediapipe/tasks/cc/components/containers/proto:landmarks_detection_result_py_pb2",
+        "//mediapipe/tasks/python/components/containers:category",
+        "//mediapipe/tasks/python/core:optional_dependencies",
+    ],
+)
+
 py_library(
     name = "category",
     srcs = ["category.py"],

mediapipe/tasks/python/components/containers/audio_data.py

@@ -0,0 +1,109 @@
+# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+#
+# 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.
+"""MediaPipe audio data."""
+
+import dataclasses
+from typing import Optional
+
+import numpy as np
+
+
+@dataclasses.dataclass
+class AudioFormat:
+  """Audio format metadata.
+
+  Attributes:
+    num_channels: the number of channels of the audio data.
+    sample_rate: the audio sample rate.
+  """
+  num_channels: int = 1
+  sample_rate: Optional[float] = None
+
+
+class AudioData(object):
+  """MediaPipe Tasks' audio container."""
+
+  def __init__(
+      self, buffer_length: int,
+      audio_format: AudioFormat = AudioFormat()) -> None:
+    """Initializes the `AudioData` object.
+
+    Args:
+      buffer_length: the length of the audio buffer.
+      audio_format: the audio format metadata.
+    """
+    self._audio_format = audio_format
+    self._buffer = np.zeros([buffer_length, self._audio_format.num_channels],
+                            dtype=np.float32)
+
+  def clear(self):
+    """Clears the internal buffer and fill it with zeros."""
+    self._buffer.fill(0)
+
+  def load_from_array(self,
+                      src: np.ndarray,
+                      offset: int = 0,
+                      size: int = -1) -> None:
+    """Loads the audio data from a NumPy array.
+
+    Args:
+      src: A NumPy source array contains the input audio.
+      offset: An optional offset for loading a slice of the `src` array to the
+        buffer.
+      size: An optional size parameter denoting the number of samples to load
+        from the `src` array.
+
+    Raises:
+      ValueError: If the input array has an incorrect shape or if
+        `offset` + `size` exceeds the length of the `src` array.
+    """
+    if src.shape[1] != self._audio_format.num_channels:
+      raise ValueError(f"Input audio contains an invalid number of channels. "
+                       f"Expect {self._audio_format.num_channels}.")
+
+    if size < 0:
+      size = len(src)
+
+    if offset + size > len(src):
+      raise ValueError(
+          f"Index out of range. offset {offset} + size {size} should be <= "
+          f"src's length: {len(src)}")
+
+    if len(src) >= len(self._buffer):
+      # If the internal buffer is shorter than the load target (src), copy
+      # values from the end of the src array to the internal buffer.
+      new_offset = offset + size - len(self._buffer)
+      new_size = len(self._buffer)
+      self._buffer = src[new_offset:new_offset + new_size].copy()
+    else:
+      # Shift the internal buffer backward and add the incoming data to the end
+      # of the buffer.
+      shift = size
+      self._buffer = np.roll(self._buffer, -shift, axis=0)
+      self._buffer[-shift:, :] = src[offset:offset + size].copy()
+
+  @property
+  def audio_format(self) -> AudioFormat:
+    """Gets the audio format of the audio."""
+    return self._audio_format
+
+  @property
+  def buffer_length(self) -> int:
+    """Gets the sample count of the audio."""
+    return self._buffer.shape[0]
+
+  @property
+  def buffer(self) -> np.ndarray:
+    """Gets the internal buffer."""
+    return self._buffer

mediapipe/tasks/python/components/containers/category.py

@@ -14,7 +14,7 @@
 """Category data class."""
 
 import dataclasses
-from typing import Any
+from typing import Any, Optional
 
 from mediapipe.tasks.cc.components.containers.proto import category_pb2
 from mediapipe.tasks.python.core.optional_dependencies import doc_controls
@@ -39,10 +39,10 @@ class Category:
     category_name: The label of this category object.
   """
 
-  index: int
-  score: float
-  display_name: str
-  category_name: str
+  index: Optional[int] = None
+  score: Optional[float] = None
+  display_name: Optional[str] = None
+  category_name: Optional[str] = None
 
   @doc_controls.do_not_generate_docs
   def to_pb2(self) -> _CategoryProto:

mediapipe/tasks/python/components/containers/landmark.py

@@ -0,0 +1,122 @@
+# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+#
+# 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.
+"""Landmark data class."""
+
+import dataclasses
+from typing import Optional
+
+from mediapipe.framework.formats import landmark_pb2
+from mediapipe.tasks.python.core.optional_dependencies import doc_controls
+
+_LandmarkProto = landmark_pb2.Landmark
+_NormalizedLandmarkProto = landmark_pb2.NormalizedLandmark
+
+
+@dataclasses.dataclass
+class Landmark:
+  """A landmark that can have 1 to 3 dimensions.
+
+  Use x for 1D points, (x, y) for 2D points and (x, y, z) for 3D points.
+
+  Attributes:
+    x: The x coordinate.
+    y: The y coordinate.
+    z: The z coordinate.
+    visibility: Landmark visibility. Should stay unset if not supported. Float
+      score of whether landmark is visible or occluded by other objects.
+      Landmark considered as invisible also if it is not present on the screen
+      (out of scene bounds). Depending on the model, visibility value is either
+      a sigmoid or an argument of sigmoid.
+    presence: Landmark presence. Should stay unset if not supported. Float score
+      of whether landmark is present on the scene (located within scene bounds).
+      Depending on the model, presence value is either a result of sigmoid or an
+      argument of sigmoid function to get landmark presence probability.
+  """
+
+  x: Optional[float] = None
+  y: Optional[float] = None
+  z: Optional[float] = None
+  visibility: Optional[float] = None
+  presence: Optional[float] = None
+
+  @doc_controls.do_not_generate_docs
+  def to_pb2(self) -> _LandmarkProto:
+    """Generates a Landmark protobuf object."""
+    return _LandmarkProto(
+        x=self.x,
+        y=self.y,
+        z=self.z,
+        visibility=self.visibility,
+        presence=self.presence)
+
+  @classmethod
+  @doc_controls.do_not_generate_docs
+  def create_from_pb2(cls, pb2_obj: _LandmarkProto) -> 'Landmark':
+    """Creates a `Landmark` object from the given protobuf object."""
+    return Landmark(
+        x=pb2_obj.x,
+        y=pb2_obj.y,
+        z=pb2_obj.z,
+        visibility=pb2_obj.visibility,
+        presence=pb2_obj.presence)
+
+
+@dataclasses.dataclass
+class NormalizedLandmark:
+  """A normalized version of above Landmark proto.
+
+  All coordinates should be within [0, 1].
+
+  Attributes:
+    x: The normalized x coordinate.
+    y: The normalized y coordinate.
+    z: The normalized z coordinate.
+    visibility: Landmark visibility. Should stay unset if not supported. Float
+      score of whether landmark is visible or occluded by other objects.
+      Landmark considered as invisible also if it is not present on the screen
+      (out of scene bounds). Depending on the model, visibility value is either
+      a sigmoid or an argument of sigmoid.
+    presence: Landmark presence. Should stay unset if not supported. Float score
+      of whether landmark is present on the scene (located within scene bounds).
+      Depending on the model, presence value is either a result of sigmoid or an
+      argument of sigmoid function to get landmark presence probability.
+  """
+
+  x: Optional[float] = None
+  y: Optional[float] = None
+  z: Optional[float] = None
+  visibility: Optional[float] = None
+  presence: Optional[float] = None
+
+  @doc_controls.do_not_generate_docs
+  def to_pb2(self) -> _NormalizedLandmarkProto:
+    """Generates a NormalizedLandmark protobuf object."""
+    return _NormalizedLandmarkProto(
+        x=self.x,
+        y=self.y,
+        z=self.z,
+        visibility=self.visibility,
+        presence=self.presence)
+
+  @classmethod
+  @doc_controls.do_not_generate_docs
+  def create_from_pb2(
+      cls, pb2_obj: _NormalizedLandmarkProto) -> 'NormalizedLandmark':
+    """Creates a `NormalizedLandmark` object from the given protobuf object."""
+    return NormalizedLandmark(
+        x=pb2_obj.x,
+        y=pb2_obj.y,
+        z=pb2_obj.z,
+        visibility=pb2_obj.visibility,
+        presence=pb2_obj.presence)

mediapipe/tasks/python/components/containers/landmark_detection_result.py

@@ -0,0 +1,96 @@
+# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+#
+# 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.
+"""Landmarks Detection Result data class."""
+
+import dataclasses
+from typing import Optional, List
+
+from mediapipe.framework.formats import classification_pb2
+from mediapipe.framework.formats import landmark_pb2
+from mediapipe.tasks.cc.components.containers.proto import landmarks_detection_result_pb2
+from mediapipe.tasks.python.components.containers import category as category_module
+from mediapipe.tasks.python.components.containers import landmark as landmark_module
+from mediapipe.tasks.python.components.containers import rect as rect_module
+from mediapipe.tasks.python.core.optional_dependencies import doc_controls
+
+_LandmarksDetectionResultProto = landmarks_detection_result_pb2.LandmarksDetectionResult
+_ClassificationProto = classification_pb2.Classification
+_ClassificationListProto = classification_pb2.ClassificationList
+_LandmarkListProto = landmark_pb2.LandmarkList
+_NormalizedLandmarkListProto = landmark_pb2.NormalizedLandmarkList
+_NormalizedRect = rect_module.NormalizedRect
+_Category = category_module.Category
+_NormalizedLandmark = landmark_module.NormalizedLandmark
+_Landmark = landmark_module.Landmark
+
+
+@dataclasses.dataclass
+class LandmarksDetectionResult:
+  """Represents the landmarks detection result.
+
+  Attributes: landmarks : A list of `NormalizedLandmark` objects. categories : A
+  list of `Category` objects. world_landmarks : A list of `Landmark` objects.
+  rect : A `NormalizedRect` object.
+  """
+
+  landmarks: Optional[List[_NormalizedLandmark]]
+  categories: Optional[List[_Category]]
+  world_landmarks: Optional[List[_Landmark]]
+  rect: _NormalizedRect
+
+  @doc_controls.do_not_generate_docs
+  def to_pb2(self) -> _LandmarksDetectionResultProto:
+    """Generates a LandmarksDetectionResult protobuf object."""
+
+    classifications = _ClassificationListProto()
+    for category in self.categories:
+      classifications.classification.append(
+          _ClassificationProto(
+              index=category.index,
+              score=category.score,
+              label=category.category_name,
+              display_name=category.display_name))
+
+    return _LandmarksDetectionResultProto(
+        landmarks=_NormalizedLandmarkListProto(self.landmarks),
+        classifications=classifications,
+        world_landmarks=_LandmarkListProto(self.world_landmarks),
+        rect=self.rect.to_pb2())
+
+  @classmethod
+  @doc_controls.do_not_generate_docs
+  def create_from_pb2(
+      cls,
+      pb2_obj: _LandmarksDetectionResultProto) -> 'LandmarksDetectionResult':
+    """Creates a `LandmarksDetectionResult` object from the given protobuf object.
+    """
+    categories = []
+    for classification in pb2_obj.classifications.classification:
+      categories.append(
+          category_module.Category(
+              score=classification.score,
+              index=classification.index,
+              category_name=classification.label,
+              display_name=classification.display_name))
+    return LandmarksDetectionResult(
+        landmarks=[
+            _NormalizedLandmark.create_from_pb2(landmark)
+            for landmark in pb2_obj.landmarks.landmark
+        ],
+        categories=categories,
+        world_landmarks=[
+            _Landmark.create_from_pb2(landmark)
+            for landmark in pb2_obj.world_landmarks.landmark
+        ],
+        rect=_NormalizedRect.create_from_pb2(pb2_obj.rect))

mediapipe/tasks/python/components/containers/rect.py

@@ -19,80 +19,49 @@ from typing import Any, Optional
 from mediapipe.framework.formats import rect_pb2
 from mediapipe.tasks.python.core.optional_dependencies import doc_controls
 
-_RectProto = rect_pb2.Rect
 _NormalizedRectProto = rect_pb2.NormalizedRect
 
 
 @dataclasses.dataclass
 class Rect:
-  """A rectangle with rotation in image coordinates.
+  """A rectangle, used as part of detection results or as input region-of-interest.
 
-  Attributes: x_center : The X coordinate of the top-left corner, in pixels.
-  y_center : The Y coordinate of the top-left corner, in pixels.
-    width: The width of the rectangle, in pixels.
-    height: The height of the rectangle, in pixels.
-    rotation: Rotation angle is clockwise in radians.
-    rect_id:  Optional unique id to help associate different rectangles to each
-    other.
+  The coordinates are normalized wrt the image dimensions, i.e. generally in
+  [0,1] but they may exceed these bounds if describing a region overlapping the
+  image. The origin is on the top-left corner of the image.
+
+  Attributes:
+    left: The X coordinate of the left side of the rectangle.
+    top: The Y coordinate of the top of the rectangle.
+    right: The X coordinate of the right side of the rectangle.
+    bottom: The Y coordinate of the bottom of the rectangle.
   """
 
-  x_center: int
-  y_center: int
-  width: int
-  height: int
-  rotation: Optional[float] = 0.0
-  rect_id: Optional[int] = None
-
-  @doc_controls.do_not_generate_docs
-  def to_pb2(self) -> _RectProto:
-    """Generates a Rect protobuf object."""
-    return _RectProto(
-        x_center=self.x_center,
-        y_center=self.y_center,
-        width=self.width,
-        height=self.height,
-    )
-
-  @classmethod
-  @doc_controls.do_not_generate_docs
-  def create_from_pb2(cls, pb2_obj: _RectProto) -> 'Rect':
-    """Creates a `Rect` object from the given protobuf object."""
-    return Rect(
-        x_center=pb2_obj.x_center,
-        y_center=pb2_obj.y_center,
-        width=pb2_obj.width,
-        height=pb2_obj.height)
-
-  def __eq__(self, other: Any) -> bool:
-    """Checks if this object is equal to the given object.
-
-    Args:
-      other: The object to be compared with.
-
-    Returns:
-      True if the objects are equal.
-    """
-    if not isinstance(other, Rect):
-      return False
-
-    return self.to_pb2().__eq__(other.to_pb2())
+  left: float
+  top: float
+  right: float
+  bottom: float
 
 
 @dataclasses.dataclass
 class NormalizedRect:
   """A rectangle with rotation in normalized coordinates.
 
-  The values of box
+  Location of the center of the rectangle in image coordinates. The (0.0, 0.0)
+  point is at the (top, left) corner.
 
-    center location and size are within [0, 1].
+  The values of box center location and size are within [0, 1].
 
-  Attributes: x_center : The X normalized coordinate of the top-left corner.
-  y_center : The Y normalized coordinate of the top-left corner.
+  Attributes:
+    x_center: The normalized X coordinate of the rectangle, in image
+      coordinates.
+    y_center: The normalized Y coordinate of the rectangle, in image
+      coordinates.
     width: The width of the rectangle.
     height: The height of the rectangle.
     rotation: Rotation angle is clockwise in radians.
-    rect_id:  Optional unique id to help associate different rectangles to each
-    other.
+    rect_id: Optional unique id to help associate different rectangles to each
+      other.
   """
 
   x_center: float

mediapipe/tasks/python/components/processors/__init__.py

@@ -0,0 +1,13 @@
+# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+#
+# 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.

mediapipe/tasks/python/test/BUILD

@@ -24,7 +24,7 @@ py_library(
     srcs = ["test_utils.py"],
     srcs_version = "PY3",
     visibility = [
-        "//mediapipe/model_maker/python/vision/gesture_recognizer:__pkg__",
+        "//mediapipe/model_maker/python:__subpackages__",
         "//mediapipe/tasks:internal",
     ],
     deps = [

mediapipe/tasks/python/test/vision/BUILD

@@ -53,6 +53,7 @@ py_test(
         "//mediapipe/tasks/python/core:base_options",
         "//mediapipe/tasks/python/test:test_utils",
         "//mediapipe/tasks/python/vision:image_classifier",
+        "//mediapipe/tasks/python/vision/core:image_processing_options",
         "//mediapipe/tasks/python/vision/core:vision_task_running_mode",
     ],
 )

mediapipe/tasks/python/test/vision/image_classifier_test.py

@@ -30,9 +30,10 @@ from mediapipe.tasks.python.components.processors import classifier_options
 from mediapipe.tasks.python.core import base_options as base_options_module
 from mediapipe.tasks.python.test import test_utils
 from mediapipe.tasks.python.vision import image_classifier
+from mediapipe.tasks.python.vision.core import image_processing_options as image_processing_options_module
 from mediapipe.tasks.python.vision.core import vision_task_running_mode
 
-_NormalizedRect = rect.NormalizedRect
+_Rect = rect.Rect
 _BaseOptions = base_options_module.BaseOptions
 _ClassifierOptions = classifier_options.ClassifierOptions
 _Category = category.Category
@@ -43,6 +44,7 @@ _Image = image.Image
 _ImageClassifier = image_classifier.ImageClassifier
 _ImageClassifierOptions = image_classifier.ImageClassifierOptions
 _RUNNING_MODE = vision_task_running_mode.VisionTaskRunningMode
+_ImageProcessingOptions = image_processing_options_module.ImageProcessingOptions
 
 _MODEL_FILE = 'mobilenet_v2_1.0_224.tflite'
 _IMAGE_FILE = 'burger.jpg'
@@ -227,11 +229,11 @@ class ImageClassifierTest(parameterized.TestCase):
       test_image = _Image.create_from_file(
           test_utils.get_test_data_path(
               os.path.join(_TEST_DATA_DIR, 'multi_objects.jpg')))
-      # NormalizedRect around the soccer ball.
-      roi = _NormalizedRect(
-          x_center=0.532, y_center=0.521, width=0.164, height=0.427)
+      # Region-of-interest around the soccer ball.
+      roi = _Rect(left=0.45, top=0.3075, right=0.614, bottom=0.7345)
+      image_processing_options = _ImageProcessingOptions(roi)
       # Performs image classification on the input.
-      image_result = classifier.classify(test_image, roi)
+      image_result = classifier.classify(test_image, image_processing_options)
       # Comparing results.
       test_utils.assert_proto_equals(self, image_result.to_pb2(),
                                      _generate_soccer_ball_results(0).to_pb2())
@@ -417,12 +419,12 @@ class ImageClassifierTest(parameterized.TestCase):
       test_image = _Image.create_from_file(
           test_utils.get_test_data_path(
               os.path.join(_TEST_DATA_DIR, 'multi_objects.jpg')))
-      # NormalizedRect around the soccer ball.
-      roi = _NormalizedRect(
-          x_center=0.532, y_center=0.521, width=0.164, height=0.427)
+      # Region-of-interest around the soccer ball.
+      roi = _Rect(left=0.45, top=0.3075, right=0.614, bottom=0.7345)
+      image_processing_options = _ImageProcessingOptions(roi)
       for timestamp in range(0, 300, 30):
         classification_result = classifier.classify_for_video(
-            test_image, timestamp, roi)
+            test_image, timestamp, image_processing_options)
         test_utils.assert_proto_equals(
             self, classification_result.to_pb2(),
             _generate_soccer_ball_results(timestamp).to_pb2())
@@ -491,9 +493,9 @@ class ImageClassifierTest(parameterized.TestCase):
     test_image = _Image.create_from_file(
         test_utils.get_test_data_path(
             os.path.join(_TEST_DATA_DIR, 'multi_objects.jpg')))
-    # NormalizedRect around the soccer ball.
-    roi = _NormalizedRect(
-        x_center=0.532, y_center=0.521, width=0.164, height=0.427)
+    # Region-of-interest around the soccer ball.
+    roi = _Rect(left=0.45, top=0.3075, right=0.614, bottom=0.7345)
+    image_processing_options = _ImageProcessingOptions(roi)
     observed_timestamp_ms = -1
 
     def check_result(result: _ClassificationResult, output_image: _Image,
@@ -514,7 +516,8 @@ class ImageClassifierTest(parameterized.TestCase):
         result_callback=check_result)
     with _ImageClassifier.create_from_options(options) as classifier:
       for timestamp in range(0, 300, 30):
-        classifier.classify_async(test_image, timestamp, roi)
+        classifier.classify_async(test_image, timestamp,
+                                  image_processing_options)
 
 
 if __name__ == '__main__':

mediapipe/tasks/python/test/vision/image_segmenter_test.py

@@ -33,8 +33,8 @@ from mediapipe.tasks.python.vision.core import vision_task_running_mode
 _BaseOptions = base_options_module.BaseOptions
 _Image = image_module.Image
 _ImageFormat = image_frame.ImageFormat
-_OutputType = image_segmenter.OutputType
-_Activation = image_segmenter.Activation
+_OutputType = image_segmenter.ImageSegmenterOptions.OutputType
+_Activation = image_segmenter.ImageSegmenterOptions.Activation
 _ImageSegmenter = image_segmenter.ImageSegmenter
 _ImageSegmenterOptions = image_segmenter.ImageSegmenterOptions
 _RUNNING_MODE = vision_task_running_mode.VisionTaskRunningMode

mediapipe/tasks/python/vision/BUILD

@@ -55,6 +55,7 @@ py_library(
         "//mediapipe/tasks/python/core:optional_dependencies",
         "//mediapipe/tasks/python/core:task_info",
         "//mediapipe/tasks/python/vision/core:base_vision_task_api",
+        "//mediapipe/tasks/python/vision/core:image_processing_options",
         "//mediapipe/tasks/python/vision/core:vision_task_running_mode",
     ],
 )
@@ -77,3 +78,27 @@ py_library(
         "//mediapipe/tasks/python/vision/core:vision_task_running_mode",
     ],
 )
+
+py_library(
+    name = "gesture_recognizer",
+    srcs = [
+        "gesture_recognizer.py",
+    ],
+    deps = [
+        "//mediapipe/framework/formats:classification_py_pb2",
+        "//mediapipe/framework/formats:landmark_py_pb2",
+        "//mediapipe/python:_framework_bindings",
+        "//mediapipe/python:packet_creator",
+        "//mediapipe/python:packet_getter",
+        "//mediapipe/tasks/cc/vision/gesture_recognizer/proto:gesture_recognizer_graph_options_py_pb2",
+        "//mediapipe/tasks/python/components/containers:category",
+        "//mediapipe/tasks/python/components/containers:landmark",
+        "//mediapipe/tasks/python/components/processors:classifier_options",
+        "//mediapipe/tasks/python/core:base_options",
+        "//mediapipe/tasks/python/core:optional_dependencies",
+        "//mediapipe/tasks/python/core:task_info",
+        "//mediapipe/tasks/python/vision/core:base_vision_task_api",
+        "//mediapipe/tasks/python/vision/core:image_processing_options",
+        "//mediapipe/tasks/python/vision/core:vision_task_running_mode",
+    ],
+)

mediapipe/tasks/python/vision/__init__.py

@@ -15,17 +15,25 @@
 """MediaPipe Tasks Vision API."""
 
 import mediapipe.tasks.python.vision.core
+import mediapipe.tasks.python.vision.gesture_recognizer
 import mediapipe.tasks.python.vision.image_classifier
+import mediapipe.tasks.python.vision.image_segmenter
 import mediapipe.tasks.python.vision.object_detector
 
+GestureRecognizer = gesture_recognizer.GestureRecognizer
+GestureRecognizerOptions = gesture_recognizer.GestureRecognizerOptions
 ImageClassifier = image_classifier.ImageClassifier
 ImageClassifierOptions = image_classifier.ImageClassifierOptions
+ImageSegmenter = image_segmenter.ImageSegmenter
+ImageSegmenterOptions = image_segmenter.ImageSegmenterOptions
 ObjectDetector = object_detector.ObjectDetector
 ObjectDetectorOptions = object_detector.ObjectDetectorOptions
 RunningMode = core.vision_task_running_mode.VisionTaskRunningMode
 
 # Remove unnecessary modules to avoid duplication in API docs.
 del core
+del gesture_recognizer
 del image_classifier
+del image_segmenter
 del object_detector
 del mediapipe

mediapipe/tasks/python/vision/core/BUILD

@@ -23,15 +23,25 @@ py_library(
     srcs = ["vision_task_running_mode.py"],
 )
 
+py_library(
+    name = "image_processing_options",
+    srcs = ["image_processing_options.py"],
+    deps = [
+        "//mediapipe/tasks/python/components/containers:rect",
+    ],
+)
+
 py_library(
     name = "base_vision_task_api",
     srcs = [
         "base_vision_task_api.py",
     ],
     deps = [
+        ":image_processing_options",
         ":vision_task_running_mode",
         "//mediapipe/framework:calculator_py_pb2",
         "//mediapipe/python:_framework_bindings",
+        "//mediapipe/tasks/python/components/containers:rect",
         "//mediapipe/tasks/python/core:optional_dependencies",
     ],
 )

mediapipe/tasks/python/vision/core/base_vision_task_api.py

@@ -13,17 +13,22 @@
 # limitations under the License.
 """MediaPipe vision task base api."""
 
+import math
 from typing import Callable, Mapping, Optional
 
 from mediapipe.framework import calculator_pb2
 from mediapipe.python._framework_bindings import packet as packet_module
 from mediapipe.python._framework_bindings import task_runner as task_runner_module
+from mediapipe.tasks.python.components.containers import rect as rect_module
 from mediapipe.tasks.python.core.optional_dependencies import doc_controls
+from mediapipe.tasks.python.vision.core import image_processing_options as image_processing_options_module
 from mediapipe.tasks.python.vision.core import vision_task_running_mode as running_mode_module
 
 _TaskRunner = task_runner_module.TaskRunner
 _Packet = packet_module.Packet
+_NormalizedRect = rect_module.NormalizedRect
 _RunningMode = running_mode_module.VisionTaskRunningMode
+_ImageProcessingOptions = image_processing_options_module.ImageProcessingOptions
 
 
 class BaseVisionTaskApi(object):
@@ -122,6 +127,49 @@ class BaseVisionTaskApi(object):
           + self._running_mode.name)
     self._runner.send(inputs)
 
+  def convert_to_normalized_rect(self,
+                                 options: _ImageProcessingOptions,
+                                 roi_allowed: bool = True) -> _NormalizedRect:
+    """Converts from ImageProcessingOptions to NormalizedRect, performing sanity checks on-the-fly.
+
+    If the input ImageProcessingOptions is not present, returns a default
+    NormalizedRect covering the whole image with rotation set to 0. If
+    'roi_allowed' is false, an error will be returned if the input
+    ImageProcessingOptions has its 'region_of_interest' field set.
+
+    Args:
+      options: Options for image processing.
+      roi_allowed: Indicates if the `region_of_interest` field is allowed to be
+        set. By default, it's set to True.
+
+    Returns:
+      A normalized rect proto that repesents the image processing options.
+    """
+    normalized_rect = _NormalizedRect(
+        rotation=0, x_center=0.5, y_center=0.5, width=1, height=1)
+    if options is None:
+      return normalized_rect
+
+    if options.rotation_degrees % 90 != 0:
+      raise ValueError('Expected rotation to be a multiple of 90°.')
+
+    # Convert to radians counter-clockwise.
+    normalized_rect.rotation = -options.rotation_degrees * math.pi / 180.0
+
+    if options.region_of_interest:
+      if not roi_allowed:
+        raise ValueError("This task doesn't support region-of-interest.")
+      roi = options.region_of_interest
+      if roi.left >= roi.right or roi.top >= roi.bottom:
+        raise ValueError('Expected Rect with left < right and top < bottom.')
+      if roi.left < 0 or roi.top < 0 or roi.right > 1 or roi.bottom > 1:
+        raise ValueError('Expected Rect values to be in [0,1].')
+      normalized_rect.x_center = (roi.left + roi.right) / 2.0
+      normalized_rect.y_center = (roi.top + roi.bottom) / 2.0
+      normalized_rect.width = roi.right - roi.left
+      normalized_rect.height = roi.bottom - roi.top
+    return normalized_rect
+
   def close(self) -> None:
     """Shuts down the mediapipe vision task instance.
 

mediapipe/tasks/python/vision/core/image_processing_options.py

@@ -0,0 +1,39 @@
+# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+#
+# 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.
+"""MediaPipe vision options for image processing."""
+
+import dataclasses
+from typing import Optional
+
+from mediapipe.tasks.python.components.containers import rect as rect_module
+
+
+@dataclasses.dataclass
+class ImageProcessingOptions:
+  """Options for image processing.
+
+  If both region-of-interest and rotation are specified, the crop around the
+  region-of-interest is extracted first, then the specified rotation is applied
+   to the crop.
+
+  Attributes:
+    region_of_interest: The optional region-of-interest to crop from the image.
+      If not specified, the full image is used. Coordinates must be in [0,1]
+      with 'left' < 'right' and 'top' < 'bottom'.
+    rotation_degrees: The rotation to apply to the image (or cropped
+      region-of-interest), in degrees clockwise. The rotation must be a multiple
+      (positive or negative) of 90°.
+  """
+  region_of_interest: Optional[rect_module.Rect] = None
+  rotation_degrees: int = 0

mediapipe/tasks/python/vision/gesture_recognizer.py

@@ -0,0 +1,426 @@
+# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+#
+# 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.
+"""MediaPipe gesture recognizer task."""
+
+import dataclasses
+from typing import Callable, Mapping, Optional, List
+
+from mediapipe.framework.formats import classification_pb2
+from mediapipe.framework.formats import landmark_pb2
+from mediapipe.python import packet_creator
+from mediapipe.python import packet_getter
+from mediapipe.python._framework_bindings import image as image_module
+from mediapipe.python._framework_bindings import packet as packet_module
+from mediapipe.tasks.cc.vision.gesture_recognizer.proto import gesture_recognizer_graph_options_pb2
+from mediapipe.tasks.python.components.containers import category as category_module
+from mediapipe.tasks.python.components.containers import landmark as landmark_module
+from mediapipe.tasks.python.components.processors import classifier_options
+from mediapipe.tasks.python.core import base_options as base_options_module
+from mediapipe.tasks.python.core import task_info as task_info_module
+from mediapipe.tasks.python.core.optional_dependencies import doc_controls
+from mediapipe.tasks.python.vision.core import base_vision_task_api
+from mediapipe.tasks.python.vision.core import image_processing_options as image_processing_options_module
+from mediapipe.tasks.python.vision.core import vision_task_running_mode as running_mode_module
+
+_BaseOptions = base_options_module.BaseOptions
+_GestureRecognizerGraphOptionsProto = gesture_recognizer_graph_options_pb2.GestureRecognizerGraphOptions
+_ClassifierOptions = classifier_options.ClassifierOptions
+_RunningMode = running_mode_module.VisionTaskRunningMode
+_ImageProcessingOptions = image_processing_options_module.ImageProcessingOptions
+_TaskInfo = task_info_module.TaskInfo
+
+_IMAGE_IN_STREAM_NAME = 'image_in'
+_IMAGE_OUT_STREAM_NAME = 'image_out'
+_IMAGE_TAG = 'IMAGE'
+_NORM_RECT_STREAM_NAME = 'norm_rect_in'
+_NORM_RECT_TAG = 'NORM_RECT'
+_HAND_GESTURE_STREAM_NAME = 'hand_gestures'
+_HAND_GESTURE_TAG = 'HAND_GESTURES'
+_HANDEDNESS_STREAM_NAME = 'handedness'
+_HANDEDNESS_TAG = 'HANDEDNESS'
+_HAND_LANDMARKS_STREAM_NAME = 'landmarks'
+_HAND_LANDMARKS_TAG = 'LANDMARKS'
+_HAND_WORLD_LANDMARKS_STREAM_NAME = 'world_landmarks'
+_HAND_WORLD_LANDMARKS_TAG = 'WORLD_LANDMARKS'
+_TASK_GRAPH_NAME = 'mediapipe.tasks.vision.gesture_recognizer.GestureRecognizerGraph'
+_MICRO_SECONDS_PER_MILLISECOND = 1000
+_GESTURE_DEFAULT_INDEX = -1
+
+
+@dataclasses.dataclass
+class GestureRecognitionResult:
+  """The gesture recognition result from GestureRecognizer, where each vector element represents a single hand detected in the image.
+
+  Attributes:
+    gestures: Recognized hand gestures of detected hands. Note that the index of
+      the gesture is always -1, because the raw indices from multiple gesture
+      classifiers cannot consolidate to a meaningful index.
+    handedness: Classification of handedness.
+    hand_landmarks: Detected hand landmarks in normalized image coordinates.
+    hand_world_landmarks: Detected hand landmarks in world coordinates.
+  """
+
+  gestures: List[List[category_module.Category]]
+  handedness: List[List[category_module.Category]]
+  hand_landmarks: List[List[landmark_module.NormalizedLandmark]]
+  hand_world_landmarks: List[List[landmark_module.Landmark]]
+
+
+def _build_recognition_result(
+    output_packets: Mapping[str,
+                            packet_module.Packet]) -> GestureRecognitionResult:
+  """Consturcts a `GestureRecognitionResult` from output packets."""
+  gestures_proto_list = packet_getter.get_proto_list(
+      output_packets[_HAND_GESTURE_STREAM_NAME])
+  handedness_proto_list = packet_getter.get_proto_list(
+      output_packets[_HANDEDNESS_STREAM_NAME])
+  hand_landmarks_proto_list = packet_getter.get_proto_list(
+      output_packets[_HAND_LANDMARKS_STREAM_NAME])
+  hand_world_landmarks_proto_list = packet_getter.get_proto_list(
+      output_packets[_HAND_WORLD_LANDMARKS_STREAM_NAME])
+
+  gesture_results = []
+  for proto in gestures_proto_list:
+    gesture_categories = []
+    gesture_classifications = classification_pb2.ClassificationList()
+    gesture_classifications.MergeFrom(proto)
+    for gesture in gesture_classifications.classification:
+      gesture_categories.append(
+          category_module.Category(
+              index=_GESTURE_DEFAULT_INDEX,
+              score=gesture.score,
+              display_name=gesture.display_name,
+              category_name=gesture.label))
+    gesture_results.append(gesture_categories)
+
+  handedness_results = []
+  for proto in handedness_proto_list:
+    handedness_categories = []
+    handedness_classifications = classification_pb2.ClassificationList()
+    handedness_classifications.MergeFrom(proto)
+    for handedness in handedness_classifications.classification:
+      handedness_categories.append(
+          category_module.Category(
+              index=handedness.index,
+              score=handedness.score,
+              display_name=handedness.display_name,
+              category_name=handedness.label))
+    handedness_results.append(handedness_categories)
+
+  hand_landmarks_results = []
+  for proto in hand_landmarks_proto_list:
+    hand_landmarks = landmark_pb2.NormalizedLandmarkList()
+    hand_landmarks.MergeFrom(proto)
+    hand_landmarks_results.append([
+        landmark_module.NormalizedLandmark.create_from_pb2(hand_landmark)
+        for hand_landmark in hand_landmarks.landmark
+    ])
+
+  hand_world_landmarks_results = []
+  for proto in hand_world_landmarks_proto_list:
+    hand_world_landmarks = landmark_pb2.LandmarkList()
+    hand_world_landmarks.MergeFrom(proto)
+    hand_world_landmarks_results.append([
+        landmark_module.Landmark.create_from_pb2(hand_world_landmark)
+        for hand_world_landmark in hand_world_landmarks.landmark
+    ])
+
+  return GestureRecognitionResult(gesture_results, handedness_results,
+                                  hand_landmarks_results,
+                                  hand_world_landmarks_results)
+
+
+@dataclasses.dataclass
+class GestureRecognizerOptions:
+  """Options for the gesture recognizer task.
+
+  Attributes:
+    base_options: Base options for the hand gesture recognizer task.
+    running_mode: The running mode of the task. Default to the image mode.
+      Gesture recognizer task has three running modes: 1) The image mode for
+      recognizing hand gestures on single image inputs. 2) The video mode for
+      recognizing hand gestures on the decoded frames of a video. 3) The live
+      stream mode for recognizing hand gestures on a live stream of input data,
+      such as from camera.
+    num_hands: The maximum number of hands can be detected by the recognizer.
+    min_hand_detection_confidence: The minimum confidence score for the hand
+      detection to be considered successful.
+    min_hand_presence_confidence: The minimum confidence score of hand presence
+      score in the hand landmark detection.
+    min_tracking_confidence: The minimum confidence score for the hand tracking
+      to be considered successful.
+    canned_gesture_classifier_options: Options for configuring the canned
+      gestures classifier, such as score threshold, allow list and deny list of
+      gestures. The categories for canned gesture classifiers are: ["None",
+      "Closed_Fist", "Open_Palm", "Pointing_Up", "Thumb_Down", "Thumb_Up",
+      "Victory", "ILoveYou"]. Note this option is subject to change.
+    custom_gesture_classifier_options: Options for configuring the custom
+      gestures classifier, such as score threshold, allow list and deny list of
+      gestures. Note this option is subject to change.
+    result_callback: The user-defined result callback for processing live stream
+      data. The result callback should only be specified when the running mode
+      is set to the live stream mode.
+  """
+  base_options: _BaseOptions
+  running_mode: _RunningMode = _RunningMode.IMAGE
+  num_hands: Optional[int] = 1
+  min_hand_detection_confidence: Optional[float] = 0.5
+  min_hand_presence_confidence: Optional[float] = 0.5
+  min_tracking_confidence: Optional[float] = 0.5
+  canned_gesture_classifier_options: Optional[
+      _ClassifierOptions] = _ClassifierOptions()
+  custom_gesture_classifier_options: Optional[
+      _ClassifierOptions] = _ClassifierOptions()
+  result_callback: Optional[Callable[
+      [GestureRecognitionResult, image_module.Image, int], None]] = None
+
+  @doc_controls.do_not_generate_docs
+  def to_pb2(self) -> _GestureRecognizerGraphOptionsProto:
+    """Generates an GestureRecognizerOptions protobuf object."""
+    base_options_proto = self.base_options.to_pb2()
+    base_options_proto.use_stream_mode = False if self.running_mode == _RunningMode.IMAGE else True
+
+    # Initialize gesture recognizer options from base options.
+    gesture_recognizer_options_proto = _GestureRecognizerGraphOptionsProto(
+        base_options=base_options_proto)
+    # Configure hand detector and hand landmarker options.
+    hand_landmarker_options_proto = gesture_recognizer_options_proto.hand_landmarker_graph_options
+    hand_landmarker_options_proto.min_tracking_confidence = self.min_tracking_confidence
+    hand_landmarker_options_proto.hand_detector_graph_options.num_hands = self.num_hands
+    hand_landmarker_options_proto.hand_detector_graph_options.min_detection_confidence = self.min_hand_detection_confidence
+    hand_landmarker_options_proto.hand_landmarks_detector_graph_options.min_detection_confidence = self.min_hand_presence_confidence
+
+    # Configure hand gesture recognizer options.
+    hand_gesture_recognizer_options_proto = gesture_recognizer_options_proto.hand_gesture_recognizer_graph_options
+    hand_gesture_recognizer_options_proto.canned_gesture_classifier_graph_options.classifier_options.CopyFrom(
+        self.canned_gesture_classifier_options.to_pb2())
+    hand_gesture_recognizer_options_proto.custom_gesture_classifier_graph_options.classifier_options.CopyFrom(
+        self.custom_gesture_classifier_options.to_pb2())
+
+    return gesture_recognizer_options_proto
+
+
+class GestureRecognizer(base_vision_task_api.BaseVisionTaskApi):
+  """Class that performs gesture recognition on images."""
+
+  @classmethod
+  def create_from_model_path(cls, model_path: str) -> 'GestureRecognizer':
+    """Creates an `GestureRecognizer` object from a TensorFlow Lite model and the default `GestureRecognizerOptions`.
+
+    Note that the created `GestureRecognizer` instance is in image mode, for
+    recognizing hand gestures on single image inputs.
+
+    Args:
+      model_path: Path to the model.
+
+    Returns:
+      `GestureRecognizer` object that's created from the model file and the
+      default `GestureRecognizerOptions`.
+
+    Raises:
+      ValueError: If failed to create `GestureRecognizer` object from the
+        provided file such as invalid file path.
+      RuntimeError: If other types of error occurred.
+    """
+    base_options = _BaseOptions(model_asset_path=model_path)
+    options = GestureRecognizerOptions(
+        base_options=base_options, running_mode=_RunningMode.IMAGE)
+    return cls.create_from_options(options)
+
+  @classmethod
+  def create_from_options(
+      cls, options: GestureRecognizerOptions) -> 'GestureRecognizer':
+    """Creates the `GestureRecognizer` object from gesture recognizer options.
+
+    Args:
+      options: Options for the gesture recognizer task.
+
+    Returns:
+      `GestureRecognizer` object that's created from `options`.
+
+    Raises:
+      ValueError: If failed to create `GestureRecognizer` object from
+        `GestureRecognizerOptions` such as missing the model.
+      RuntimeError: If other types of error occurred.
+    """
+
+    def packets_callback(output_packets: Mapping[str, packet_module.Packet]):
+      if output_packets[_IMAGE_OUT_STREAM_NAME].is_empty():
+        return
+
+      image = packet_getter.get_image(output_packets[_IMAGE_OUT_STREAM_NAME])
+
+      if output_packets[_HAND_GESTURE_STREAM_NAME].is_empty():
+        empty_packet = output_packets[_HAND_GESTURE_STREAM_NAME]
+        options.result_callback(
+            GestureRecognitionResult([], [], [], []), image,
+            empty_packet.timestamp.value // _MICRO_SECONDS_PER_MILLISECOND)
+        return
+
+      gesture_recognition_result = _build_recognition_result(output_packets)
+      timestamp = output_packets[_HAND_GESTURE_STREAM_NAME].timestamp
+      options.result_callback(gesture_recognition_result, image,
+                              timestamp.value // _MICRO_SECONDS_PER_MILLISECOND)
+
+    task_info = _TaskInfo(
+        task_graph=_TASK_GRAPH_NAME,
+        input_streams=[
+            ':'.join([_IMAGE_TAG, _IMAGE_IN_STREAM_NAME]),
+            ':'.join([_NORM_RECT_TAG, _NORM_RECT_STREAM_NAME]),
+        ],
+        output_streams=[
+            ':'.join([_HAND_GESTURE_TAG, _HAND_GESTURE_STREAM_NAME]),
+            ':'.join([_HANDEDNESS_TAG, _HANDEDNESS_STREAM_NAME]),
+            ':'.join([_HAND_LANDMARKS_TAG,
+                      _HAND_LANDMARKS_STREAM_NAME]), ':'.join([
+                          _HAND_WORLD_LANDMARKS_TAG,
+                          _HAND_WORLD_LANDMARKS_STREAM_NAME
+                      ]), ':'.join([_IMAGE_TAG, _IMAGE_OUT_STREAM_NAME])
+        ],
+        task_options=options)
+    return cls(
+        task_info.generate_graph_config(
+            enable_flow_limiting=options.running_mode ==
+            _RunningMode.LIVE_STREAM), options.running_mode,
+        packets_callback if options.result_callback else None)
+
+  def recognize(
+      self,
+      image: image_module.Image,
+      image_processing_options: Optional[_ImageProcessingOptions] = None
+  ) -> GestureRecognitionResult:
+    """Performs hand gesture recognition on the given image.
+
+    Only use this method when the GestureRecognizer is created with the image
+    running mode.
+
+    The image can be of any size with format RGB or RGBA.
+    TODO: Describes how the input image will be preprocessed after the yuv
+    support is implemented.
+
+    Args:
+      image: MediaPipe Image.
+      image_processing_options: Options for image processing.
+
+    Returns:
+      The hand gesture recognition results.
+
+    Raises:
+      ValueError: If any of the input arguments is invalid.
+      RuntimeError: If gesture recognition failed to run.
+    """
+    normalized_rect = self.convert_to_normalized_rect(
+        image_processing_options, roi_allowed=False)
+    output_packets = self._process_image_data({
+        _IMAGE_IN_STREAM_NAME:
+            packet_creator.create_image(image),
+        _NORM_RECT_STREAM_NAME:
+            packet_creator.create_proto(normalized_rect.to_pb2())
+    })
+
+    if output_packets[_HAND_GESTURE_STREAM_NAME].is_empty():
+      return GestureRecognitionResult([], [], [], [])
+
+    return _build_recognition_result(output_packets)
+
+  def recognize_for_video(
+      self,
+      image: image_module.Image,
+      timestamp_ms: int,
+      image_processing_options: Optional[_ImageProcessingOptions] = None
+  ) -> GestureRecognitionResult:
+    """Performs gesture recognition on the provided video frame.
+
+    Only use this method when the GestureRecognizer is created with the video
+    running mode.
+
+    Only use this method when the GestureRecognizer is created with the video
+    running mode. It's required to provide the video frame's timestamp (in
+    milliseconds) along with the video frame. The input timestamps should be
+    monotonically increasing for adjacent calls of this method.
+
+    Args:
+      image: MediaPipe Image.
+      timestamp_ms: The timestamp of the input video frame in milliseconds.
+      image_processing_options: Options for image processing.
+
+    Returns:
+      The hand gesture recognition results.
+
+    Raises:
+      ValueError: If any of the input arguments is invalid.
+      RuntimeError: If gesture recognition failed to run.
+    """
+    normalized_rect = self.convert_to_normalized_rect(
+        image_processing_options, roi_allowed=False)
+    output_packets = self._process_video_data({
+        _IMAGE_IN_STREAM_NAME:
+            packet_creator.create_image(image).at(
+                timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND),
+        _NORM_RECT_STREAM_NAME:
+            packet_creator.create_proto(normalized_rect.to_pb2()).at(
+                timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND)
+    })
+
+    if output_packets[_HAND_GESTURE_STREAM_NAME].is_empty():
+      return GestureRecognitionResult([], [], [], [])
+
+    return _build_recognition_result(output_packets)
+
+  def recognize_async(
+      self,
+      image: image_module.Image,
+      timestamp_ms: int,
+      image_processing_options: Optional[_ImageProcessingOptions] = None
+  ) -> None:
+    """Sends live image data to perform gesture recognition.
+
+    The results will be available via the "result_callback" provided in the
+    GestureRecognizerOptions. Only use this method when the GestureRecognizer
+    is created with the live stream running mode.
+
+    Only use this method when the GestureRecognizer is created with the live
+    stream running mode. The input timestamps should be monotonically increasing
+    for adjacent calls of this method. This method will return immediately after
+    the input image is accepted. The results will be available via the
+    `result_callback` provided in the `GestureRecognizerOptions`. The
+    `recognize_async` method is designed to process live stream data such as
+    camera input. To lower the overall latency, gesture recognizer may drop the
+    input images if needed. In other words, it's not guaranteed to have output
+    per input image.
+
+    The `result_callback` provides:
+      - The hand gesture recognition results.
+      - The input image that the gesture recognizer runs on.
+      - The input timestamp in milliseconds.
+
+    Args:
+      image: MediaPipe Image.
+      timestamp_ms: The timestamp of the input image in milliseconds.
+      image_processing_options: Options for image processing.
+
+    Raises:
+      ValueError: If the current input timestamp is smaller than what the
+      gesture recognizer has already processed.
+    """
+    normalized_rect = self.convert_to_normalized_rect(
+        image_processing_options, roi_allowed=False)
+    self._send_live_stream_data({
+        _IMAGE_IN_STREAM_NAME:
+            packet_creator.create_image(image).at(
+                timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND),
+        _NORM_RECT_STREAM_NAME:
+            packet_creator.create_proto(normalized_rect.to_pb2()).at(
+                timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND)
+    })

mediapipe/tasks/python/vision/image_classifier.py

@@ -30,6 +30,7 @@ from mediapipe.tasks.python.core import base_options as base_options_module
 from mediapipe.tasks.python.core import task_info as task_info_module
 from mediapipe.tasks.python.core.optional_dependencies import doc_controls
 from mediapipe.tasks.python.vision.core import base_vision_task_api
+from mediapipe.tasks.python.vision.core import image_processing_options as image_processing_options_module
 from mediapipe.tasks.python.vision.core import vision_task_running_mode
 
 _NormalizedRect = rect.NormalizedRect
@@ -37,6 +38,7 @@ _BaseOptions = base_options_module.BaseOptions
 _ImageClassifierGraphOptionsProto = image_classifier_graph_options_pb2.ImageClassifierGraphOptions
 _ClassifierOptions = classifier_options.ClassifierOptions
 _RunningMode = vision_task_running_mode.VisionTaskRunningMode
+_ImageProcessingOptions = image_processing_options_module.ImageProcessingOptions
 _TaskInfo = task_info_module.TaskInfo
 
 _CLASSIFICATION_RESULT_OUT_STREAM_NAME = 'classification_result_out'
@@ -44,17 +46,12 @@ _CLASSIFICATION_RESULT_TAG = 'CLASSIFICATION_RESULT'
 _IMAGE_IN_STREAM_NAME = 'image_in'
 _IMAGE_OUT_STREAM_NAME = 'image_out'
 _IMAGE_TAG = 'IMAGE'
-_NORM_RECT_NAME = 'norm_rect_in'
+_NORM_RECT_STREAM_NAME = 'norm_rect_in'
 _NORM_RECT_TAG = 'NORM_RECT'
 _TASK_GRAPH_NAME = 'mediapipe.tasks.vision.image_classifier.ImageClassifierGraph'
 _MICRO_SECONDS_PER_MILLISECOND = 1000
 
 
-def _build_full_image_norm_rect() -> _NormalizedRect:
-  # Builds a NormalizedRect covering the entire image.
-  return _NormalizedRect(x_center=0.5, y_center=0.5, width=1, height=1)
-
-
 @dataclasses.dataclass
 class ImageClassifierOptions:
   """Options for the image classifier task.
@@ -156,7 +153,7 @@ class ImageClassifier(base_vision_task_api.BaseVisionTaskApi):
         task_graph=_TASK_GRAPH_NAME,
         input_streams=[
             ':'.join([_IMAGE_TAG, _IMAGE_IN_STREAM_NAME]),
-            ':'.join([_NORM_RECT_TAG, _NORM_RECT_NAME]),
+            ':'.join([_NORM_RECT_TAG, _NORM_RECT_STREAM_NAME]),
         ],
         output_streams=[
             ':'.join([
@@ -171,17 +168,16 @@ class ImageClassifier(base_vision_task_api.BaseVisionTaskApi):
             _RunningMode.LIVE_STREAM), options.running_mode,
         packets_callback if options.result_callback else None)
 
-  # TODO: Replace _NormalizedRect with ImageProcessingOption
   def classify(
       self,
       image: image_module.Image,
-      roi: Optional[_NormalizedRect] = None
+      image_processing_options: Optional[_ImageProcessingOptions] = None
   ) -> classifications.ClassificationResult:
     """Performs image classification on the provided MediaPipe Image.
 
     Args:
       image: MediaPipe Image.
-      roi: The region of interest.
+      image_processing_options: Options for image processing.
 
     Returns:
       A classification result object that contains a list of classifications.
@@ -190,10 +186,12 @@ class ImageClassifier(base_vision_task_api.BaseVisionTaskApi):
       ValueError: If any of the input arguments is invalid.
       RuntimeError: If image classification failed to run.
     """
-    norm_rect = roi if roi is not None else _build_full_image_norm_rect()
+    normalized_rect = self.convert_to_normalized_rect(image_processing_options)
     output_packets = self._process_image_data({
-        _IMAGE_IN_STREAM_NAME: packet_creator.create_image(image),
-        _NORM_RECT_NAME: packet_creator.create_proto(norm_rect.to_pb2())
+        _IMAGE_IN_STREAM_NAME:
+            packet_creator.create_image(image),
+        _NORM_RECT_STREAM_NAME:
+            packet_creator.create_proto(normalized_rect.to_pb2())
     })
 
     classification_result_proto = classifications_pb2.ClassificationResult()
@@ -210,7 +208,7 @@ class ImageClassifier(base_vision_task_api.BaseVisionTaskApi):
       self,
       image: image_module.Image,
       timestamp_ms: int,
-      roi: Optional[_NormalizedRect] = None
+      image_processing_options: Optional[_ImageProcessingOptions] = None
   ) -> classifications.ClassificationResult:
     """Performs image classification on the provided video frames.
 
@@ -222,7 +220,7 @@ class ImageClassifier(base_vision_task_api.BaseVisionTaskApi):
     Args:
       image: MediaPipe Image.
       timestamp_ms: The timestamp of the input video frame in milliseconds.
-      roi: The region of interest.
+      image_processing_options: Options for image processing.
 
     Returns:
       A classification result object that contains a list of classifications.
@@ -231,13 +229,13 @@ class ImageClassifier(base_vision_task_api.BaseVisionTaskApi):
       ValueError: If any of the input arguments is invalid.
       RuntimeError: If image classification failed to run.
     """
-    norm_rect = roi if roi is not None else _build_full_image_norm_rect()
+    normalized_rect = self.convert_to_normalized_rect(image_processing_options)
     output_packets = self._process_video_data({
         _IMAGE_IN_STREAM_NAME:
             packet_creator.create_image(image).at(
                 timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND),
-        _NORM_RECT_NAME:
-            packet_creator.create_proto(norm_rect.to_pb2()).at(
+        _NORM_RECT_STREAM_NAME:
+            packet_creator.create_proto(normalized_rect.to_pb2()).at(
                 timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND)
     })
 
@@ -251,10 +249,12 @@ class ImageClassifier(base_vision_task_api.BaseVisionTaskApi):
         for classification in classification_result_proto.classifications
     ])
 
-  def classify_async(self,
-                     image: image_module.Image,
-                     timestamp_ms: int,
-                     roi: Optional[_NormalizedRect] = None) -> None:
+  def classify_async(
+      self,
+      image: image_module.Image,
+      timestamp_ms: int,
+      image_processing_options: Optional[_ImageProcessingOptions] = None
+  ) -> None:
     """Sends live image data (an Image with a unique timestamp) to perform image classification.
 
     Only use this method when the ImageClassifier is created with the live
@@ -275,18 +275,18 @@ class ImageClassifier(base_vision_task_api.BaseVisionTaskApi):
     Args:
       image: MediaPipe Image.
       timestamp_ms: The timestamp of the input image in milliseconds.
-      roi: The region of interest.
+      image_processing_options: Options for image processing.
 
     Raises:
       ValueError: If the current input timestamp is smaller than what the image
         classifier has already processed.
     """
-    norm_rect = roi if roi is not None else _build_full_image_norm_rect()
+    normalized_rect = self.convert_to_normalized_rect(image_processing_options)
     self._send_live_stream_data({
         _IMAGE_IN_STREAM_NAME:
             packet_creator.create_image(image).at(
                 timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND),
-        _NORM_RECT_NAME:
-            packet_creator.create_proto(norm_rect.to_pb2()).at(
+        _NORM_RECT_STREAM_NAME:
+            packet_creator.create_proto(normalized_rect.to_pb2()).at(
                 timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND)
     })

mediapipe/tasks/python/vision/image_segmenter.py

@@ -44,18 +44,6 @@ _TASK_GRAPH_NAME = 'mediapipe.tasks.vision.ImageSegmenterGraph'
 _MICRO_SECONDS_PER_MILLISECOND = 1000
 
 
-class OutputType(enum.Enum):
-  UNSPECIFIED = 0
-  CATEGORY_MASK = 1
-  CONFIDENCE_MASK = 2
-
-
-class Activation(enum.Enum):
-  NONE = 0
-  SIGMOID = 1
-  SOFTMAX = 2
-
-
 @dataclasses.dataclass
 class ImageSegmenterOptions:
   """Options for the image segmenter task.
@@ -74,6 +62,17 @@ class ImageSegmenterOptions:
       data. The result callback should only be specified when the running mode
       is set to the live stream mode.
   """
+
+  class OutputType(enum.Enum):
+    UNSPECIFIED = 0
+    CATEGORY_MASK = 1
+    CONFIDENCE_MASK = 2
+
+  class Activation(enum.Enum):
+    NONE = 0
+    SIGMOID = 1
+    SOFTMAX = 2
+
   base_options: _BaseOptions
   running_mode: _RunningMode = _RunningMode.IMAGE
   output_type: Optional[OutputType] = OutputType.CATEGORY_MASK

mediapipe/tasks/web/components/containers/BUILD

@@ -0,0 +1,21 @@
+# This package contains options shared by all MediaPipe Tasks for Web.
+
+load("//mediapipe/framework/port:build_config.bzl", "mediapipe_ts_library")
+
+package(default_visibility = ["//mediapipe/tasks:internal"])
+
+mediapipe_ts_library(
+    name = "category",
+    srcs = ["category.d.ts"],
+)
+
+mediapipe_ts_library(
+    name = "classifications",
+    srcs = ["classifications.d.ts"],
+    deps = [":category"],
+)
+
+mediapipe_ts_library(
+    name = "landmark",
+    srcs = ["landmark.d.ts"],
+)

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

@@ -0,0 +1,38 @@
+/**
+ * Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+ *
+ * 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.
+ */
+
+/** A classification category. */
+export interface Category {
+  /** The probability score of this label category. */
+  score: number;
+
+  /** The index of the category in the corresponding label file. */
+  index: number;
+
+  /**
+   * The label of this category object. Defaults to an empty string if there is
+   * no category.
+   */
+  categoryName: string;
+
+  /**
+   * The display name of the label, which may be translated for different
+   * locales. For example, a label, "apple", may be translated into Spanish for
+   * display purpose, so that the `display_name` is "manzana". Defaults to an
+   * empty string if there is no display name.
+   */
+  displayName: string;
+}

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

@@ -0,0 +1,51 @@
+/**
+ * Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+ *
+ * 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 {Category} from '../../../../tasks/web/components/containers/category';
+
+/** List of predicted categories with an optional timestamp. */
+export interface ClassificationEntry {
+  /**
+   * The array of predicted categories, usually sorted by descending scores,
+   * e.g., from high to low probability.
+   */
+  categories: Category[];
+
+  /**
+   * The optional timestamp (in milliseconds) associated to the classification
+   * entry. This is useful for time series use cases, e.g., audio
+   * classification.
+   */
+  timestampMs?: number;
+}
+
+/** Classifications for a given classifier head. */
+export interface Classifications {
+  /** A list of classification entries. */
+  entries: ClassificationEntry[];
+
+  /**
+   * The index of the classifier head these categories refer to. This is
+   * useful for multi-head models.
+   */
+  headIndex: number;
+
+  /**
+   * The name of the classifier head, which is the corresponding tensor
+   * metadata name.
+   */
+  headName: string;
+}

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

@@ -0,0 +1,35 @@
+/**
+ * Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+ *
+ * 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.
+ */
+
+/**
+ * Landmark represents a point in 3D space with x, y, z coordinates. If
+ * normalized is true, the landmark coordinates is normalized respect to the
+ * dimension of image, and the coordinates values are in the range of [0,1].
+ * Otherwise, it represenet a point in world coordinates.
+ */
+export class Landmark {
+  /** The x coordinates of the landmark. */
+  x: number;
+
+  /** The y coordinates of the landmark. */
+  y: number;
+
+  /** The z coordinates of the landmark. */
+  z: number;
+
+  /** Whether this landmark is normalized with respect to the image size. */
+  normalized: boolean;
+}

mediapipe/tasks/web/components/processors/BUILD

@@ -0,0 +1,33 @@
+# This package contains options shared by all MediaPipe Tasks for Web.
+
+load("//mediapipe/framework/port:build_config.bzl", "mediapipe_ts_library")
+
+package(default_visibility = ["//mediapipe/tasks:internal"])
+
+mediapipe_ts_library(
+    name = "classifier_options",
+    srcs = ["classifier_options.ts"],
+    deps = [
+        "//mediapipe/tasks/cc/components/processors/proto:classifier_options_jspb_proto",
+        "//mediapipe/tasks/web/core:classifier_options",
+    ],
+)
+
+mediapipe_ts_library(
+    name = "classifier_result",
+    srcs = ["classifier_result.ts"],
+    deps = [
+        "//mediapipe/tasks/cc/components/containers/proto:classifications_jspb_proto",
+        "//mediapipe/tasks/web/components/containers:classifications",
+    ],
+)
+
+mediapipe_ts_library(
+    name = "base_options",
+    srcs = ["base_options.ts"],
+    deps = [
+        "//mediapipe/tasks/cc/core/proto:base_options_jspb_proto",
+        "//mediapipe/tasks/cc/core/proto:external_file_jspb_proto",
+        "//mediapipe/tasks/web/core",
+    ],
+)

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

@@ -0,0 +1,50 @@
+/**
+ * Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+ *
+ * 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 {BaseOptions as BaseOptionsProto} from '../../../../tasks/cc/core/proto/base_options_pb';
+import {ExternalFile} from '../../../../tasks/cc/core/proto/external_file_pb';
+import {BaseOptions} from '../../../../tasks/web/core/base_options';
+
+// The OSS JS API does not support the builder pattern.
+// tslint:disable:jspb-use-builder-pattern
+
+/**
+ * Converts a BaseOptions API object to its Protobuf representation.
+ * @throws If neither a model assset path or buffer is provided
+ */
+export async function convertBaseOptionsToProto(baseOptions: BaseOptions):
+    Promise<BaseOptionsProto> {
+  if (baseOptions.modelAssetPath && baseOptions.modelAssetBuffer) {
+    throw new Error(
+        'Cannot set both baseOptions.modelAssetPath and baseOptions.modelAssetBuffer');
+  }
+  if (!baseOptions.modelAssetPath && !baseOptions.modelAssetBuffer) {
+    throw new Error(
+        'Either baseOptions.modelAssetPath or baseOptions.modelAssetBuffer must be set');
+  }
+
+  let modelAssetBuffer = baseOptions.modelAssetBuffer;
+  if (!modelAssetBuffer) {
+    const response = await fetch(baseOptions.modelAssetPath!.toString());
+    modelAssetBuffer = new Uint8Array(await response.arrayBuffer());
+  }
+
+  const proto = new BaseOptionsProto();
+  const externalFile = new ExternalFile();
+  externalFile.setFileContent(modelAssetBuffer);
+  proto.setModelAsset(externalFile);
+  return proto;
+}

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

@@ -0,0 +1,62 @@
+/**
+ * Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+ *
+ * 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 {ClassifierOptions as ClassifierOptionsProto} from '../../../../tasks/cc/components/processors/proto/classifier_options_pb';
+import {ClassifierOptions} from '../../../../tasks/web/core/classifier_options';
+
+/**
+ * Converts a ClassifierOptions object to its Proto representation, optionally
+ * based on existing definition.
+ * @param options The options object to convert to a Proto. Only options that
+ *     are expliclty provided are set.
+ * @param baseOptions A base object that options can be merged into.
+ */
+export function convertClassifierOptionsToProto(
+    options: ClassifierOptions,
+    baseOptions?: ClassifierOptionsProto): ClassifierOptionsProto {
+  const classifierOptions =
+      baseOptions ? baseOptions.clone() : new ClassifierOptionsProto();
+  if (options.displayNamesLocale) {
+    classifierOptions.setDisplayNamesLocale(options.displayNamesLocale);
+  } else if (options.displayNamesLocale === undefined) {
+    classifierOptions.clearDisplayNamesLocale();
+  }
+
+  if (options.maxResults) {
+    classifierOptions.setMaxResults(options.maxResults);
+  } else if ('maxResults' in options) {  // Check for undefined
+    classifierOptions.clearMaxResults();
+  }
+
+  if (options.scoreThreshold) {
+    classifierOptions.setScoreThreshold(options.scoreThreshold);
+  } else if ('scoreThreshold' in options) {  // Check for undefined
+    classifierOptions.clearScoreThreshold();
+  }
+
+  if (options.categoryAllowlist) {
+    classifierOptions.setCategoryAllowlistList(options.categoryAllowlist);
+  } else if ('categoryAllowlist' in options) {  // Check for undefined
+    classifierOptions.clearCategoryAllowlistList();
+  }
+
+  if (options.categoryDenylist) {
+    classifierOptions.setCategoryDenylistList(options.categoryDenylist);
+  } else if ('categoryDenylist' in options) {  // Check for undefined
+    classifierOptions.clearCategoryDenylistList();
+  }
+  return classifierOptions;
+}

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

@@ -0,0 +1,61 @@
+/**
+ * Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+ *
+ * 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 {ClassificationEntry as ClassificationEntryProto, ClassificationResult} from '../../../../tasks/cc/components/containers/proto/classifications_pb';
+import {ClassificationEntry, Classifications} from '../../../../tasks/web/components/containers/classifications';
+
+const DEFAULT_INDEX = -1;
+const DEFAULT_SCORE = 0.0;
+
+/**
+ * Converts a ClassificationEntry proto to the ClassificationEntry result
+ * type.
+ */
+function convertFromClassificationEntryProto(source: ClassificationEntryProto):
+    ClassificationEntry {
+  const categories = source.getCategoriesList().map(category => {
+    return {
+      index: category.getIndex() ?? DEFAULT_INDEX,
+      score: category.getScore() ?? DEFAULT_SCORE,
+      displayName: category.getDisplayName() ?? '',
+      categoryName: category.getCategoryName() ?? '',
+    };
+  });
+
+  return {
+    categories,
+    timestampMs: source.getTimestampMs(),
+  };
+}
+
+/**
+ * Converts a ClassificationResult proto to a list of classifications.
+ */
+export function convertFromClassificationResultProto(
+    classificationResult: ClassificationResult) : Classifications[] {
+  const result: Classifications[] = [];
+  for (const classificationsProto of
+           classificationResult.getClassificationsList()) {
+    const classifications: Classifications = {
+      entries: classificationsProto.getEntriesList().map(
+          entry => convertFromClassificationEntryProto(entry)),
+      headIndex: classificationsProto.getHeadIndex() ?? DEFAULT_INDEX,
+      headName: classificationsProto.getHeadName() ?? '',
+    };
+    result.push(classifications);
+  }
+  return result;
+}

mediapipe/tasks/web/core/BUILD

@@ -0,0 +1,21 @@
+# This package contains options shared by all MediaPipe Tasks for Web.
+
+load("//mediapipe/framework/port:build_config.bzl", "mediapipe_ts_library")
+
+package(default_visibility = ["//mediapipe/tasks:internal"])
+
+mediapipe_ts_library(
+    name = "core",
+    srcs = [
+        "base_options.d.ts",
+        "wasm_loader_options.d.ts",
+    ],
+)
+
+mediapipe_ts_library(
+    name = "classifier_options",
+    srcs = [
+        "classifier_options.d.ts",
+    ],
+    deps = [":core"],
+)

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

@@ -0,0 +1,31 @@
+/**
+ * Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+ *
+ * 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.
+ */
+
+// Placeholder for internal dependency on trusted resource url
+
+/** Options to configure MediaPipe Tasks in general. */
+export interface BaseOptions {
+  /**
+   * The model path to the model asset file. Only one of `modelAssetPath` or
+   * `modelAssetBuffer` can be set.
+   */
+  modelAssetPath?: string;
+  /**
+   * A buffer containing the model aaset. Only one of `modelAssetPath` or
+   * `modelAssetBuffer` can be set.
+   */
+  modelAssetBuffer?: Uint8Array;
+}

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

@@ -0,0 +1,52 @@
+/**
+ * Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+ *
+ * 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 {BaseOptions} from '../../../tasks/web/core/base_options';
+
+/** Options to configure the Mediapipe Classifier Task. */
+export interface ClassifierOptions {
+  /** Options to configure the loading of the model assets. */
+  baseOptions?: BaseOptions;
+
+  /**
+   * The locale to use for display names specified through the TFLite Model
+   * Metadata, if any. Defaults to English.
+   */
+  displayNamesLocale?: string|undefined;
+
+  /** The maximum number of top-scored detection results to return. */
+  maxResults?: number|undefined;
+
+  /**
+   * Overrides the value provided in the model metadata. Results below this
+   * value are rejected.
+   */
+  scoreThreshold?: number|undefined;
+
+  /**
+   * Allowlist of category names. If non-empty, detection results whose category
+   * name is not in this set will be filtered out. Duplicate or unknown category
+   * names are ignored. Mutually exclusive with `categoryDenylist`.
+   */
+  categoryAllowlist?: string[]|undefined;
+
+  /**
+   * Denylist of category names. If non-empty, detection results whose category
+   * name is in this set will be filtered out. Duplicate or unknown category
+   * names are ignored. Mutually exclusive with `categoryAllowlist`.
+   */
+  categoryDenylist?: string[]|undefined;
+}

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

@@ -0,0 +1,25 @@
+/**
+ * Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+ *
+ * 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.
+ */
+
+// Placeholder for internal dependency on trusted resource url
+
+/** An object containing the locations of all Wasm assets */
+export interface WasmLoaderOptions {
+  /** The path to the Wasm loader script. */
+  wasmLoaderPath: string;
+  /** The path to the Wasm binary. */
+  wasmBinaryPath: string;
+}

package.json

@@ -0,0 +1,15 @@
+{
+  "name": "medipipe-dev",
+  "version": "0.0.0-alphga",
+  "description": "MediaPipe GitHub repo",
+  "devDependencies": {
+    "@bazel/typescript": "^5.7.1",
+    "@types/google-protobuf": "^3.15.6",
+    "@types/offscreencanvas": "^2019.7.0",
+    "google-protobuf": "^3.21.2",
+    "protobufjs": "^7.1.2",
+    "protobufjs-cli": "^1.0.2",
+    "ts-protoc-gen": "^0.15.0",
+    "typescript": "^4.8.4"
+  }
+}

requirements.txt

@@ -1,5 +1,6 @@
 absl-py
 attrs>=19.1.0
+flatbuffers>=2.0
 matplotlib
 numpy
 opencv-contrib-python

setup.py

@@ -129,6 +129,15 @@ def _add_mp_init_files():
   mp_dir_init_file.close()
 
 
+def _copy_to_build_lib_dir(build_lib, file):
+  """Copy a file from bazel-bin to the build lib dir."""
+  dst = os.path.join(build_lib + '/', file)
+  dst_dir = os.path.dirname(dst)
+  if not os.path.exists(dst_dir):
+    os.makedirs(dst_dir)
+  shutil.copyfile(os.path.join('bazel-bin/', file), dst)
+
+
 class GeneratePyProtos(build_ext.build_ext):
   """Generate MediaPipe Python protobuf files by Protocol Compiler."""
 
@@ -259,7 +268,7 @@ class BuildModules(build_ext.build_ext):
     ]
     if subprocess.call(fetch_model_command) != 0:
       sys.exit(-1)
-    self._copy_to_build_lib_dir(external_file)
+    _copy_to_build_lib_dir(self.build_lib, external_file)
 
   def _generate_binary_graph(self, binary_graph_target):
     """Generate binary graph for a particular MediaPipe binary graph target."""
@@ -277,15 +286,27 @@ class BuildModules(build_ext.build_ext):
       bazel_command.append('--define=OPENCV=source')
     if subprocess.call(bazel_command) != 0:
       sys.exit(-1)
-    self._copy_to_build_lib_dir(binary_graph_target + '.binarypb')
+    _copy_to_build_lib_dir(self.build_lib, binary_graph_target + '.binarypb')
 
-  def _copy_to_build_lib_dir(self, file):
-    """Copy a file from bazel-bin to the build lib dir."""
-    dst = os.path.join(self.build_lib + '/', file)
-    dst_dir = os.path.dirname(dst)
-    if not os.path.exists(dst_dir):
-      os.makedirs(dst_dir)
-    shutil.copyfile(os.path.join('bazel-bin/', file), dst)
+
+class GenerateMetadataSchema(build_ext.build_ext):
+  """Generate metadata python schema files."""
+
+  def run(self):
+    for target in ['metadata_schema_py', 'schema_py']:
+      bazel_command = [
+          'bazel',
+          'build',
+          '--compilation_mode=opt',
+          '--define=MEDIAPIPE_DISABLE_GPU=1',
+          '--action_env=PYTHON_BIN_PATH=' + _normalize_path(sys.executable),
+          '//mediapipe/tasks/metadata:' + target,
+      ]
+      if subprocess.call(bazel_command) != 0:
+        sys.exit(-1)
+      _copy_to_build_lib_dir(
+          self.build_lib,
+          'mediapipe/tasks/metadata/' + target + '_generated.py')
 
 
 class BazelExtension(setuptools.Extension):
@@ -375,6 +396,7 @@ class BuildPy(build_py.build_py):
     build_ext_obj = self.distribution.get_command_obj('build_ext')
     build_ext_obj.link_opencv = self.link_opencv
     self.run_command('gen_protos')
+    self.run_command('generate_metadata_schema')
     self.run_command('build_modules')
     self.run_command('build_ext')
     build_py.build_py.run(self)
@@ -434,18 +456,25 @@ setuptools.setup(
     author_email='mediapipe@google.com',
     long_description=_get_long_description(),
     long_description_content_type='text/markdown',
-    packages=setuptools.find_packages(exclude=['mediapipe.examples.desktop.*']),
+    packages=setuptools.find_packages(
+        exclude=['mediapipe.examples.desktop.*', 'mediapipe.model_maker.*']),
     install_requires=_parse_requirements('requirements.txt'),
     cmdclass={
         'build_py': BuildPy,
-        'gen_protos': GeneratePyProtos,
         'build_modules': BuildModules,
         'build_ext': BuildExtension,
+        'generate_metadata_schema': GenerateMetadataSchema,
+        'gen_protos': GeneratePyProtos,
         'install': Install,
         'restore': Restore,
     },
     ext_modules=[
         BazelExtension('//mediapipe/python:_framework_bindings'),
+        BazelExtension(
+            '//mediapipe/tasks/cc/metadata/python:_pywrap_metadata_version'),
+        BazelExtension(
+            '//mediapipe/tasks/python/metadata/flatbuffers_lib:_pywrap_flatbuffers'
+        ),
     ],
     zip_safe=False,
     include_package_data=True,

tsconfig.json

@@ -0,0 +1,47 @@
+{
+  "compilerOptions": {
+    "target": "es2017",
+    "module": "commonjs",
+    "lib": ["ES2017", "dom"],
+    "declaration": true,
+    "moduleResolution": "node",
+    "esModuleInterop": true,
+    "noImplicitAny": true,
+    "inlineSourceMap": true,
+    "inlineSources": true,
+    "strict": true,
+    "types": ["@types/offscreencanvas"],
+    "rootDirs": [
+      ".",
+      "./bazel-out/host/bin",
+      "./bazel-out/darwin-dbg/bin",
+      "./bazel-out/darwin-fastbuild/bin",
+      "./bazel-out/darwin-opt/bin",
+      "./bazel-out/darwin_arm64-dbg/bin",
+      "./bazel-out/darwin_arm64-fastbuild/bin",
+      "./bazel-out/darwin_arm64-opt/bin",
+      "./bazel-out/k8-dbg/bin",
+      "./bazel-out/k8-fastbuild/bin",
+      "./bazel-out/k8-opt/bin",
+      "./bazel-out/x64_windows-dbg/bin",
+      "./bazel-out/x64_windows-fastbuild/bin",
+      "./bazel-out/x64_windows-opt/bin",
+      "./bazel-out/darwin-dbg/bin",
+      "./bazel-out/darwin-fastbuild/bin",
+      "./bazel-out/darwin-opt/bin",
+      "./bazel-out/k8-dbg/bin",
+      "./bazel-out/k8-fastbuild/bin",
+      "./bazel-out/k8-opt/bin",
+      "./bazel-out/x64_windows-dbg/bin",
+      "./bazel-out/x64_windows-fastbuild/bin",
+      "./bazel-out/x64_windows-opt/bin",
+      "./bazel-out/k8-fastbuild-ST-4a519fd6d3e4/bin"
+    ]
+  },
+  "exclude": [
+    "./_bazel_bin",
+    "./_bazel_buildbot",
+    "./_bazel_out",
+    "./_bazel_testlogs"
+  ]
+}

yarn.lock

@@ -0,0 +1,626 @@
+# THIS IS AN AUTOGENERATED FILE. DO NOT EDIT THIS FILE DIRECTLY.
+# yarn lockfile v1
+
+
+"@babel/parser@^7.9.4":
+  version "7.20.1"
+  resolved "https://registry.yarnpkg.com/@babel/parser/-/parser-7.20.1.tgz#3e045a92f7b4623cafc2425eddcb8cf2e54f9cc5"
+  integrity sha512-hp0AYxaZJhxULfM1zyp7Wgr+pSUKBcP3M+PHnSzWGdXOzg/kHWIgiUWARvubhUKGOEw3xqY4x+lyZ9ytBVcELw==
+
+"@bazel/typescript@^5.7.1":
+  version "5.7.1"
+  resolved "https://registry.yarnpkg.com/@bazel/typescript/-/typescript-5.7.1.tgz#e585bcdc54a4ccb23d99c3e1206abf4853cf0682"
+  integrity sha512-MAnAtFxA2znadm81+rbYXcyWX1DEF/urzZ1F4LBq+w27EQ4PGyqIqCM5om7JcoSZJwjjMoBJc3SflRsMrZZ6+g==
+  dependencies:
+    "@bazel/worker" "5.7.1"
+    semver "5.6.0"
+    source-map-support "0.5.9"
+    tsutils "3.21.0"
+
+"@bazel/worker@5.7.1":
+  version "5.7.1"
+  resolved "https://registry.yarnpkg.com/@bazel/worker/-/worker-5.7.1.tgz#2c4a9bd0e0ef75e496aec9599ff64a87307e7dad"
+  integrity sha512-UndmQVRqK0t0NMNl8I1P5XmxzdPvMA0X6jufszpfwy5gyzjOxeiOIzmC0ALCOx78CuJqOB/8WOI1pwTRmhd0tg==
+  dependencies:
+    google-protobuf "^3.6.1"
+
+"@protobufjs/aspromise@^1.1.1", "@protobufjs/aspromise@^1.1.2":
+  version "1.1.2"
+  resolved "https://registry.yarnpkg.com/@protobufjs/aspromise/-/aspromise-1.1.2.tgz#9b8b0cc663d669a7d8f6f5d0893a14d348f30fbf"
+  integrity sha512-j+gKExEuLmKwvz3OgROXtrJ2UG2x8Ch2YZUxahh+s1F2HZ+wAceUNLkvy6zKCPVRkU++ZWQrdxsUeQXmcg4uoQ==
+
+"@protobufjs/base64@^1.1.2":
+  version "1.1.2"
+  resolved "https://registry.yarnpkg.com/@protobufjs/base64/-/base64-1.1.2.tgz#4c85730e59b9a1f1f349047dbf24296034bb2735"
+  integrity sha512-AZkcAA5vnN/v4PDqKyMR5lx7hZttPDgClv83E//FMNhR2TMcLUhfRUBHCmSl0oi9zMgDDqRUJkSxO3wm85+XLg==
+
+"@protobufjs/codegen@^2.0.4":
+  version "2.0.4"
+  resolved "https://registry.yarnpkg.com/@protobufjs/codegen/-/codegen-2.0.4.tgz#7ef37f0d010fb028ad1ad59722e506d9262815cb"
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+
+"@protobufjs/eventemitter@^1.1.0":
+  version "1.1.0"
+  resolved "https://registry.yarnpkg.com/@protobufjs/eventemitter/-/eventemitter-1.1.0.tgz#355cbc98bafad5978f9ed095f397621f1d066b70"
+  integrity sha512-j9ednRT81vYJ9OfVuXG6ERSTdEL1xVsNgqpkxMsbIabzSo3goCjDIveeGv5d03om39ML71RdmrGNjG5SReBP/Q==
+
+"@protobufjs/fetch@^1.1.0":
+  version "1.1.0"
+  resolved "https://registry.yarnpkg.com/@protobufjs/fetch/-/fetch-1.1.0.tgz#ba99fb598614af65700c1619ff06d454b0d84c45"
+  integrity sha512-lljVXpqXebpsijW71PZaCYeIcE5on1w5DlQy5WH6GLbFryLUrBD4932W/E2BSpfRJWseIL4v/KPgBFxDOIdKpQ==
+  dependencies:
+    "@protobufjs/aspromise" "^1.1.1"
+    "@protobufjs/inquire" "^1.1.0"
+
+"@protobufjs/float@^1.0.2":
+  version "1.0.2"
+  resolved "https://registry.yarnpkg.com/@protobufjs/float/-/float-1.0.2.tgz#5e9e1abdcb73fc0a7cb8b291df78c8cbd97b87d1"
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+
+"@protobufjs/inquire@^1.1.0":
+  version "1.1.0"
+  resolved "https://registry.yarnpkg.com/@protobufjs/inquire/-/inquire-1.1.0.tgz#ff200e3e7cf2429e2dcafc1140828e8cc638f089"
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+
+"@protobufjs/path@^1.1.2":
+  version "1.1.2"
+  resolved "https://registry.yarnpkg.com/@protobufjs/path/-/path-1.1.2.tgz#6cc2b20c5c9ad6ad0dccfd21ca7673d8d7fbf68d"
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+
+"@protobufjs/pool@^1.1.0":
+  version "1.1.0"
+  resolved "https://registry.yarnpkg.com/@protobufjs/pool/-/pool-1.1.0.tgz#09fd15f2d6d3abfa9b65bc366506d6ad7846ff54"
+  integrity sha512-0kELaGSIDBKvcgS4zkjz1PeddatrjYcmMWOlAuAPwAeccUrPHdUqo/J6LiymHHEiJT5NrF1UVwxY14f+fy4WQw==
+
+"@protobufjs/utf8@^1.1.0":
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+  resolved "https://registry.yarnpkg.com/@protobufjs/utf8/-/utf8-1.1.0.tgz#a777360b5b39a1a2e5106f8e858f2fd2d060c570"
+  integrity sha512-Vvn3zZrhQZkkBE8LSuW3em98c0FwgO4nxzv6OdSxPKJIEKY2bGbHn+mhGIPerzI4twdxaP8/0+06HBpwf345Lw==
+
+"@types/google-protobuf@^3.15.6":
+  version "3.15.6"
+  resolved "https://registry.yarnpkg.com/@types/google-protobuf/-/google-protobuf-3.15.6.tgz#674a69493ef2c849b95eafe69167ea59079eb504"
+  integrity sha512-pYVNNJ+winC4aek+lZp93sIKxnXt5qMkuKmaqS3WGuTq0Bw1ZDYNBgzG5kkdtwcv+GmYJGo3yEg6z2cKKAiEdw==
+
+"@types/linkify-it@*":
+  version "3.0.2"
+  resolved "https://registry.yarnpkg.com/@types/linkify-it/-/linkify-it-3.0.2.tgz#fd2cd2edbaa7eaac7e7f3c1748b52a19143846c9"
+  integrity sha512-HZQYqbiFVWufzCwexrvh694SOim8z2d+xJl5UNamcvQFejLY/2YUtzXHYi3cHdI7PMlS8ejH2slRAOJQ32aNbA==
+
+"@types/markdown-it@^12.2.3":
+  version "12.2.3"
+  resolved "https://registry.yarnpkg.com/@types/markdown-it/-/markdown-it-12.2.3.tgz#0d6f6e5e413f8daaa26522904597be3d6cd93b51"
+  integrity sha512-GKMHFfv3458yYy+v/N8gjufHO6MSZKCOXpZc5GXIWWy8uldwfmPn98vp81gZ5f9SVw8YYBctgfJ22a2d7AOMeQ==
+  dependencies:
+    "@types/linkify-it" "*"
+    "@types/mdurl" "*"
+
+"@types/mdurl@*":
+  version "1.0.2"
+  resolved "https://registry.yarnpkg.com/@types/mdurl/-/mdurl-1.0.2.tgz#e2ce9d83a613bacf284c7be7d491945e39e1f8e9"
+  integrity sha512-eC4U9MlIcu2q0KQmXszyn5Akca/0jrQmwDRgpAMJai7qBWq4amIQhZyNau4VYGtCeALvW1/NtjzJJ567aZxfKA==
+
+"@types/node@>=13.7.0":
+  version "18.11.9"
+  resolved "https://registry.yarnpkg.com/@types/node/-/node-18.11.9.tgz#02d013de7058cea16d36168ef2fc653464cfbad4"
+  integrity sha512-CRpX21/kGdzjOpFsZSkcrXMGIBWMGNIHXXBVFSH+ggkftxg+XYP20TESbh+zFvFj3EQOl5byk0HTRn1IL6hbqg==
+
+"@types/offscreencanvas@^2019.7.0":
+  version "2019.7.0"
+  resolved "https://registry.yarnpkg.com/@types/offscreencanvas/-/offscreencanvas-2019.7.0.tgz#e4a932069db47bb3eabeb0b305502d01586fa90d"
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+
+acorn-jsx@^5.3.2:
+  version "5.3.2"
+  resolved "https://registry.yarnpkg.com/acorn-jsx/-/acorn-jsx-5.3.2.tgz#7ed5bb55908b3b2f1bc55c6af1653bada7f07937"
+  integrity sha512-rq9s+JNhf0IChjtDXxllJ7g41oZk5SlXtp0LHwyA5cejwn7vKmKp4pPri6YEePv2PU65sAsegbXtIinmDFDXgQ==
+
+acorn@^8.8.0:
+  version "8.8.1"
+  resolved "https://registry.yarnpkg.com/acorn/-/acorn-8.8.1.tgz#0a3f9cbecc4ec3bea6f0a80b66ae8dd2da250b73"
+  integrity sha512-7zFpHzhnqYKrkYdUjF1HI1bzd0VygEGX8lFk4k5zVMqHEoES+P+7TKI+EvLO9WVMJ8eekdO0aDEK044xTXwPPA==
+
+ansi-styles@^4.1.0:
+  version "4.3.0"
+  resolved "https://registry.yarnpkg.com/ansi-styles/-/ansi-styles-4.3.0.tgz#edd803628ae71c04c85ae7a0906edad34b648937"
+  integrity sha512-zbB9rCJAT1rbjiVDb2hqKFHNYLxgtk8NURxZ3IZwD3F6NtxbXZQCnnSi1Lkx+IDohdPlFp222wVALIheZJQSEg==
+  dependencies:
+    color-convert "^2.0.1"
+
+argparse@^2.0.1:
+  version "2.0.1"
+  resolved "https://registry.yarnpkg.com/argparse/-/argparse-2.0.1.tgz#246f50f3ca78a3240f6c997e8a9bd1eac49e4b38"
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+
+balanced-match@^1.0.0:
+  version "1.0.2"
+  resolved "https://registry.yarnpkg.com/balanced-match/-/balanced-match-1.0.2.tgz#e83e3a7e3f300b34cb9d87f615fa0cbf357690ee"
+  integrity sha512-3oSeUO0TMV67hN1AmbXsK4yaqU7tjiHlbxRDZOpH0KW9+CeX4bRAaX0Anxt0tx2MrpRpWwQaPwIlISEJhYU5Pw==
+
+bluebird@^3.7.2:
+  version "3.7.2"
+  resolved "https://registry.yarnpkg.com/bluebird/-/bluebird-3.7.2.tgz#9f229c15be272454ffa973ace0dbee79a1b0c36f"
+  integrity sha512-XpNj6GDQzdfW+r2Wnn7xiSAd7TM3jzkxGXBGTtWKuSXv1xUV+azxAm8jdWZN06QTQk+2N2XB9jRDkvbmQmcRtg==
+
+brace-expansion@^1.1.7:
+  version "1.1.11"
+  resolved "https://registry.yarnpkg.com/brace-expansion/-/brace-expansion-1.1.11.tgz#3c7fcbf529d87226f3d2f52b966ff5271eb441dd"
+  integrity sha512-iCuPHDFgrHX7H2vEI/5xpz07zSHB00TpugqhmYtVmMO6518mCuRMoOYFldEBl0g187ufozdaHgWKcYFb61qGiA==
+  dependencies:
+    balanced-match "^1.0.0"
+    concat-map "0.0.1"
+
+brace-expansion@^2.0.1:
+  version "2.0.1"
+  resolved "https://registry.yarnpkg.com/brace-expansion/-/brace-expansion-2.0.1.tgz#1edc459e0f0c548486ecf9fc99f2221364b9a0ae"
+  integrity sha512-XnAIvQ8eM+kC6aULx6wuQiwVsnzsi9d3WxzV3FpWTGA19F621kwdbsAcFKXgKUHZWsy+mY6iL1sHTxWEFCytDA==
+  dependencies:
+    balanced-match "^1.0.0"
+
+buffer-from@^1.0.0:
+  version "1.1.2"
+  resolved "https://registry.yarnpkg.com/buffer-from/-/buffer-from-1.1.2.tgz#2b146a6fd72e80b4f55d255f35ed59a3a9a41bd5"
+  integrity sha512-E+XQCRwSbaaiChtv6k6Dwgc+bx+Bs6vuKJHHl5kox/BaKbhiXzqQOwK4cO22yElGp2OCmjwVhT3HmxgyPGnJfQ==
+
+catharsis@^0.9.0:
+  version "0.9.0"
+  resolved "https://registry.yarnpkg.com/catharsis/-/catharsis-0.9.0.tgz#40382a168be0e6da308c277d3a2b3eb40c7d2121"
+  integrity sha512-prMTQVpcns/tzFgFVkVp6ak6RykZyWb3gu8ckUpd6YkTlacOd3DXGJjIpD4Q6zJirizvaiAjSSHlOsA+6sNh2A==
+  dependencies:
+    lodash "^4.17.15"
+
+chalk@^4.0.0:
+  version "4.1.2"
+  resolved "https://registry.yarnpkg.com/chalk/-/chalk-4.1.2.tgz#aac4e2b7734a740867aeb16bf02aad556a1e7a01"
+  integrity sha512-oKnbhFyRIXpUuez8iBMmyEa4nbj4IOQyuhc/wy9kY7/WVPcwIO9VA668Pu8RkO7+0G76SLROeyw9CpQ061i4mA==
+  dependencies:
+    ansi-styles "^4.1.0"
+    supports-color "^7.1.0"
+
+color-convert@^2.0.1:
+  version "2.0.1"
+  resolved "https://registry.yarnpkg.com/color-convert/-/color-convert-2.0.1.tgz#72d3a68d598c9bdb3af2ad1e84f21d896abd4de3"
+  integrity sha512-RRECPsj7iu/xb5oKYcsFHSppFNnsj/52OVTRKb4zP5onXwVF3zVmmToNcOfGC+CRDpfK/U584fMg38ZHCaElKQ==
+  dependencies:
+    color-name "~1.1.4"
+
+color-name@~1.1.4:
+  version "1.1.4"
+  resolved "https://registry.yarnpkg.com/color-name/-/color-name-1.1.4.tgz#c2a09a87acbde69543de6f63fa3995c826c536a2"
+  integrity sha512-dOy+3AuW3a2wNbZHIuMZpTcgjGuLU/uBL/ubcZF9OXbDo8ff4O8yVp5Bf0efS8uEoYo5q4Fx7dY9OgQGXgAsQA==
+
+concat-map@0.0.1:
+  version "0.0.1"
+  resolved "https://registry.yarnpkg.com/concat-map/-/concat-map-0.0.1.tgz#d8a96bd77fd68df7793a73036a3ba0d5405d477b"
+  integrity sha512-/Srv4dswyQNBfohGpz9o6Yb3Gz3SrUDqBH5rTuhGR7ahtlbYKnVxw2bCFMRljaA7EXHaXZ8wsHdodFvbkhKmqg==
+
+deep-is@~0.1.3:
+  version "0.1.4"
+  resolved "https://registry.yarnpkg.com/deep-is/-/deep-is-0.1.4.tgz#a6f2dce612fadd2ef1f519b73551f17e85199831"
+  integrity sha512-oIPzksmTg4/MriiaYGO+okXDT7ztn/w3Eptv/+gSIdMdKsJo0u4CfYNFJPy+4SKMuCqGw2wxnA+URMg3t8a/bQ==
+
+entities@~2.1.0:
+  version "2.1.0"
+  resolved "https://registry.yarnpkg.com/entities/-/entities-2.1.0.tgz#992d3129cf7df6870b96c57858c249a120f8b8b5"
+  integrity sha512-hCx1oky9PFrJ611mf0ifBLBRW8lUUVRlFolb5gWRfIELabBlbp9xZvrqZLZAs+NxFnbfQoeGd8wDkygjg7U85w==
+
+escape-string-regexp@^2.0.0:
+  version "2.0.0"
+  resolved "https://registry.yarnpkg.com/escape-string-regexp/-/escape-string-regexp-2.0.0.tgz#a30304e99daa32e23b2fd20f51babd07cffca344"
+  integrity sha512-UpzcLCXolUWcNu5HtVMHYdXJjArjsF9C0aNnquZYY4uW/Vu0miy5YoWvbV345HauVvcAUnpRuhMMcqTcGOY2+w==
+
+escodegen@^1.13.0:
+  version "1.14.3"
+  resolved "https://registry.yarnpkg.com/escodegen/-/escodegen-1.14.3.tgz#4e7b81fba61581dc97582ed78cab7f0e8d63f503"
+  integrity sha512-qFcX0XJkdg+PB3xjZZG/wKSuT1PnQWx57+TVSjIMmILd2yC/6ByYElPwJnslDsuWuSAp4AwJGumarAAmJch5Kw==
+  dependencies:
+    esprima "^4.0.1"
+    estraverse "^4.2.0"
+    esutils "^2.0.2"
+    optionator "^0.8.1"
+  optionalDependencies:
+    source-map "~0.6.1"
+
+eslint-visitor-keys@^3.3.0:
+  version "3.3.0"
+  resolved "https://registry.yarnpkg.com/eslint-visitor-keys/-/eslint-visitor-keys-3.3.0.tgz#f6480fa6b1f30efe2d1968aa8ac745b862469826"
+  integrity sha512-mQ+suqKJVyeuwGYHAdjMFqjCyfl8+Ldnxuyp3ldiMBFKkvytrXUZWaiPCEav8qDHKty44bD+qV1IP4T+w+xXRA==
+
+espree@^9.0.0:
+  version "9.4.0"
+  resolved "https://registry.yarnpkg.com/espree/-/espree-9.4.0.tgz#cd4bc3d6e9336c433265fc0aa016fc1aaf182f8a"
+  integrity sha512-DQmnRpLj7f6TgN/NYb0MTzJXL+vJF9h3pHy4JhCIs3zwcgez8xmGg3sXHcEO97BrmO2OSvCwMdfdlyl+E9KjOw==
+  dependencies:
+    acorn "^8.8.0"
+    acorn-jsx "^5.3.2"
+    eslint-visitor-keys "^3.3.0"
+
+esprima@^4.0.1:
+  version "4.0.1"
+  resolved "https://registry.yarnpkg.com/esprima/-/esprima-4.0.1.tgz#13b04cdb3e6c5d19df91ab6987a8695619b0aa71"
+  integrity sha512-eGuFFw7Upda+g4p+QHvnW0RyTX/SVeJBDM/gCtMARO0cLuT2HcEKnTPvhjV6aGeqrCB/sbNop0Kszm0jsaWU4A==
+
+estraverse@^4.2.0:
+  version "4.3.0"
+  resolved "https://registry.yarnpkg.com/estraverse/-/estraverse-4.3.0.tgz#398ad3f3c5a24948be7725e83d11a7de28cdbd1d"
+  integrity sha512-39nnKffWz8xN1BU/2c79n9nB9HDzo0niYUqx6xyqUnyoAnQyyWpOTdZEeiCch8BBu515t4wp9ZmgVfVhn9EBpw==
+
+estraverse@^5.1.0:
+  version "5.3.0"
+  resolved "https://registry.yarnpkg.com/estraverse/-/estraverse-5.3.0.tgz#2eea5290702f26ab8fe5370370ff86c965d21123"
+  integrity sha512-MMdARuVEQziNTeJD8DgMqmhwR11BRQ/cBP+pLtYdSTnf3MIO8fFeiINEbX36ZdNlfU/7A9f3gUw49B3oQsvwBA==
+
+esutils@^2.0.2:
+  version "2.0.3"
+  resolved "https://registry.yarnpkg.com/esutils/-/esutils-2.0.3.tgz#74d2eb4de0b8da1293711910d50775b9b710ef64"
+  integrity sha512-kVscqXk4OCp68SZ0dkgEKVi6/8ij300KBWTJq32P/dYeWTSwK41WyTxalN1eRmA5Z9UU/LX9D7FWSmV9SAYx6g==
+
+fast-levenshtein@~2.0.6:
+  version "2.0.6"
+  resolved "https://registry.yarnpkg.com/fast-levenshtein/-/fast-levenshtein-2.0.6.tgz#3d8a5c66883a16a30ca8643e851f19baa7797917"
+  integrity sha512-DCXu6Ifhqcks7TZKY3Hxp3y6qphY5SJZmrWMDrKcERSOXWQdMhU9Ig/PYrzyw/ul9jOIyh0N4M0tbC5hodg8dw==
+
+fs.realpath@^1.0.0:
+  version "1.0.0"
+  resolved "https://registry.yarnpkg.com/fs.realpath/-/fs.realpath-1.0.0.tgz#1504ad2523158caa40db4a2787cb01411994ea4f"
+  integrity sha512-OO0pH2lK6a0hZnAdau5ItzHPI6pUlvI7jMVnxUQRtw4owF2wk8lOSabtGDCTP4Ggrg2MbGnWO9X8K1t4+fGMDw==
+
+glob@^7.1.3:
+  version "7.2.3"
+  resolved "https://registry.yarnpkg.com/glob/-/glob-7.2.3.tgz#b8df0fb802bbfa8e89bd1d938b4e16578ed44f2b"
+  integrity sha512-nFR0zLpU2YCaRxwoCJvL6UvCH2JFyFVIvwTLsIf21AuHlMskA1hhTdk+LlYJtOlYt9v6dvszD2BGRqBL+iQK9Q==
+  dependencies:
+    fs.realpath "^1.0.0"
+    inflight "^1.0.4"
+    inherits "2"
+    minimatch "^3.1.1"
+    once "^1.3.0"
+    path-is-absolute "^1.0.0"
+
+glob@^8.0.0:
+  version "8.0.3"
+  resolved "https://registry.yarnpkg.com/glob/-/glob-8.0.3.tgz#415c6eb2deed9e502c68fa44a272e6da6eeca42e"
+  integrity sha512-ull455NHSHI/Y1FqGaaYFaLGkNMMJbavMrEGFXG/PGrg6y7sutWHUHrz6gy6WEBH6akM1M414dWKCNs+IhKdiQ==
+  dependencies:
+    fs.realpath "^1.0.0"
+    inflight "^1.0.4"
+    inherits "2"
+    minimatch "^5.0.1"
+    once "^1.3.0"
+
+google-protobuf@^3.15.5, google-protobuf@^3.21.2, google-protobuf@^3.6.1:
+  version "3.21.2"
+  resolved "https://registry.yarnpkg.com/google-protobuf/-/google-protobuf-3.21.2.tgz#4580a2bea8bbb291ee579d1fefb14d6fa3070ea4"
+  integrity sha512-3MSOYFO5U9mPGikIYCzK0SaThypfGgS6bHqrUGXG3DPHCrb+txNqeEcns1W0lkGfk0rCyNXm7xB9rMxnCiZOoA==
+
+graceful-fs@^4.1.9:
+  version "4.2.10"
+  resolved "https://registry.yarnpkg.com/graceful-fs/-/graceful-fs-4.2.10.tgz#147d3a006da4ca3ce14728c7aefc287c367d7a6c"
+  integrity sha512-9ByhssR2fPVsNZj478qUUbKfmL0+t5BDVyjShtyZZLiK7ZDAArFFfopyOTj0M05wE2tJPisA4iTnnXl2YoPvOA==
+
+has-flag@^4.0.0:
+  version "4.0.0"
+  resolved "https://registry.yarnpkg.com/has-flag/-/has-flag-4.0.0.tgz#944771fd9c81c81265c4d6941860da06bb59479b"
+  integrity sha512-EykJT/Q1KjTWctppgIAgfSO0tKVuZUjhgMr17kqTumMl6Afv3EISleU7qZUzoXDFTAHTDC4NOoG/ZxU3EvlMPQ==
+
+inflight@^1.0.4:
+  version "1.0.6"
+  resolved "https://registry.yarnpkg.com/inflight/-/inflight-1.0.6.tgz#49bd6331d7d02d0c09bc910a1075ba8165b56df9"
+  integrity sha512-k92I/b08q4wvFscXCLvqfsHCrjrF7yiXsQuIVvVE7N82W3+aqpzuUdBbfhWcy/FZR3/4IgflMgKLOsvPDrGCJA==
+  dependencies:
+    once "^1.3.0"
+    wrappy "1"
+
+inherits@2:
+  version "2.0.4"
+  resolved "https://registry.yarnpkg.com/inherits/-/inherits-2.0.4.tgz#0fa2c64f932917c3433a0ded55363aae37416b7c"
+  integrity sha512-k/vGaX4/Yla3WzyMCvTQOXYeIHvqOKtnqBduzTHpzpQZzAskKMhZ2K+EnBiSM9zGSoIFeMpXKxa4dYeZIQqewQ==
+
+js2xmlparser@^4.0.2:
+  version "4.0.2"
+  resolved "https://registry.yarnpkg.com/js2xmlparser/-/js2xmlparser-4.0.2.tgz#2a1fdf01e90585ef2ae872a01bc169c6a8d5e60a"
+  integrity sha512-6n4D8gLlLf1n5mNLQPRfViYzu9RATblzPEtm1SthMX1Pjao0r9YI9nw7ZIfRxQMERS87mcswrg+r/OYrPRX6jA==
+  dependencies:
+    xmlcreate "^2.0.4"
+
+jsdoc@^3.6.3:
+  version "3.6.11"
+  resolved "https://registry.yarnpkg.com/jsdoc/-/jsdoc-3.6.11.tgz#8bbb5747e6f579f141a5238cbad4e95e004458ce"
+  integrity sha512-8UCU0TYeIYD9KeLzEcAu2q8N/mx9O3phAGl32nmHlE0LpaJL71mMkP4d+QE5zWfNt50qheHtOZ0qoxVrsX5TUg==
+  dependencies:
+    "@babel/parser" "^7.9.4"
+    "@types/markdown-it" "^12.2.3"
+    bluebird "^3.7.2"
+    catharsis "^0.9.0"
+    escape-string-regexp "^2.0.0"
+    js2xmlparser "^4.0.2"
+    klaw "^3.0.0"
+    markdown-it "^12.3.2"
+    markdown-it-anchor "^8.4.1"
+    marked "^4.0.10"
+    mkdirp "^1.0.4"
+    requizzle "^0.2.3"
+    strip-json-comments "^3.1.0"
+    taffydb "2.6.2"
+    underscore "~1.13.2"
+
+klaw@^3.0.0:
+  version "3.0.0"
+  resolved "https://registry.yarnpkg.com/klaw/-/klaw-3.0.0.tgz#b11bec9cf2492f06756d6e809ab73a2910259146"
+  integrity sha512-0Fo5oir+O9jnXu5EefYbVK+mHMBeEVEy2cmctR1O1NECcCkPRreJKrS6Qt/j3KC2C148Dfo9i3pCmCMsdqGr0g==
+  dependencies:
+    graceful-fs "^4.1.9"
+
+levn@~0.3.0:
+  version "0.3.0"
+  resolved "https://registry.yarnpkg.com/levn/-/levn-0.3.0.tgz#3b09924edf9f083c0490fdd4c0bc4421e04764ee"
+  integrity sha512-0OO4y2iOHix2W6ujICbKIaEQXvFQHue65vUG3pb5EUomzPI90z9hsA1VsO/dbIIpC53J8gxM9Q4Oho0jrCM/yA==
+  dependencies:
+    prelude-ls "~1.1.2"
+    type-check "~0.3.2"
+
+linkify-it@^3.0.1:
+  version "3.0.3"
+  resolved "https://registry.yarnpkg.com/linkify-it/-/linkify-it-3.0.3.tgz#a98baf44ce45a550efb4d49c769d07524cc2fa2e"
+  integrity sha512-ynTsyrFSdE5oZ/O9GEf00kPngmOfVwazR5GKDq6EYfhlpFug3J2zybX56a2PRRpc9P+FuSoGNAwjlbDs9jJBPQ==
+  dependencies:
+    uc.micro "^1.0.1"
+
+lodash@^4.17.14, lodash@^4.17.15:
+  version "4.17.21"
+  resolved "https://registry.yarnpkg.com/lodash/-/lodash-4.17.21.tgz#679591c564c3bffaae8454cf0b3df370c3d6911c"
+  integrity sha512-v2kDEe57lecTulaDIuNTPy3Ry4gLGJ6Z1O3vE1krgXZNrsQ+LFTGHVxVjcXPs17LhbZVGedAJv8XZ1tvj5FvSg==
+
+long@^5.0.0:
+  version "5.2.0"
+  resolved "https://registry.yarnpkg.com/long/-/long-5.2.0.tgz#2696dadf4b4da2ce3f6f6b89186085d94d52fd61"
+  integrity sha512-9RTUNjK60eJbx3uz+TEGF7fUr29ZDxR5QzXcyDpeSfeH28S9ycINflOgOlppit5U+4kNTe83KQnMEerw7GmE8w==
+
+lru-cache@^6.0.0:
+  version "6.0.0"
+  resolved "https://registry.yarnpkg.com/lru-cache/-/lru-cache-6.0.0.tgz#6d6fe6570ebd96aaf90fcad1dafa3b2566db3a94"
+  integrity sha512-Jo6dJ04CmSjuznwJSS3pUeWmd/H0ffTlkXXgwZi+eq1UCmqQwCh+eLsYOYCwY991i2Fah4h1BEMCx4qThGbsiA==
+  dependencies:
+    yallist "^4.0.0"
+
+markdown-it-anchor@^8.4.1:
+  version "8.6.5"
+  resolved "https://registry.yarnpkg.com/markdown-it-anchor/-/markdown-it-anchor-8.6.5.tgz#30c4bc5bbff327f15ce3c429010ec7ba75e7b5f8"
+  integrity sha512-PI1qEHHkTNWT+X6Ip9w+paonfIQ+QZP9sCeMYi47oqhH+EsW8CrJ8J7CzV19QVOj6il8ATGbK2nTECj22ZHGvQ==
+
+markdown-it@^12.3.2:
+  version "12.3.2"
+  resolved "https://registry.yarnpkg.com/markdown-it/-/markdown-it-12.3.2.tgz#bf92ac92283fe983fe4de8ff8abfb5ad72cd0c90"
+  integrity sha512-TchMembfxfNVpHkbtriWltGWc+m3xszaRD0CZup7GFFhzIgQqxIfn3eGj1yZpfuflzPvfkt611B2Q/Bsk1YnGg==
+  dependencies:
+    argparse "^2.0.1"
+    entities "~2.1.0"
+    linkify-it "^3.0.1"
+    mdurl "^1.0.1"
+    uc.micro "^1.0.5"
+
+marked@^4.0.10:
+  version "4.2.1"
+  resolved "https://registry.yarnpkg.com/marked/-/marked-4.2.1.tgz#eaa32594e45b4e58c02e4d118531fd04345de3b4"
+  integrity sha512-VK1/jNtwqDLvPktNpL0Fdg3qoeUZhmRsuiIjPEy/lHwXW4ouLoZfO4XoWd4ClDt+hupV1VLpkZhEovjU0W/kqA==
+
+mdurl@^1.0.1:
+  version "1.0.1"
+  resolved "https://registry.yarnpkg.com/mdurl/-/mdurl-1.0.1.tgz#fe85b2ec75a59037f2adfec100fd6c601761152e"
+  integrity sha512-/sKlQJCBYVY9Ers9hqzKou4H6V5UWc/M59TH2dvkt+84itfnq7uFOMLpOiOS4ujvHP4etln18fmIxA5R5fll0g==
+
+minimatch@^3.1.1:
+  version "3.1.2"
+  resolved "https://registry.yarnpkg.com/minimatch/-/minimatch-3.1.2.tgz#19cd194bfd3e428f049a70817c038d89ab4be35b"
+  integrity sha512-J7p63hRiAjw1NDEww1W7i37+ByIrOWO5XQQAzZ3VOcL0PNybwpfmV/N05zFAzwQ9USyEcX6t3UO+K5aqBQOIHw==
+  dependencies:
+    brace-expansion "^1.1.7"
+
+minimatch@^5.0.1:
+  version "5.1.0"
+  resolved "https://registry.yarnpkg.com/minimatch/-/minimatch-5.1.0.tgz#1717b464f4971b144f6aabe8f2d0b8e4511e09c7"
+  integrity sha512-9TPBGGak4nHfGZsPBohm9AWg6NoT7QTCehS3BIJABslyZbzxfV78QM2Y6+i741OPZIafFAaiiEMh5OyIrJPgtg==
+  dependencies:
+    brace-expansion "^2.0.1"
+
+minimist@^1.2.0:
+  version "1.2.7"
+  resolved "https://registry.yarnpkg.com/minimist/-/minimist-1.2.7.tgz#daa1c4d91f507390437c6a8bc01078e7000c4d18"
+  integrity sha512-bzfL1YUZsP41gmu/qjrEk0Q6i2ix/cVeAhbCbqH9u3zYutS1cLg00qhrD0M2MVdCcx4Sc0UpP2eBWo9rotpq6g==
+
+mkdirp@^1.0.4:
+  version "1.0.4"
+  resolved "https://registry.yarnpkg.com/mkdirp/-/mkdirp-1.0.4.tgz#3eb5ed62622756d79a5f0e2a221dfebad75c2f7e"
+  integrity sha512-vVqVZQyf3WLx2Shd0qJ9xuvqgAyKPLAiqITEtqW0oIUjzo3PePDd6fW9iFz30ef7Ysp/oiWqbhszeGWW2T6Gzw==
+
+once@^1.3.0:
+  version "1.4.0"
+  resolved "https://registry.yarnpkg.com/once/-/once-1.4.0.tgz#583b1aa775961d4b113ac17d9c50baef9dd76bd1"
+  integrity sha512-lNaJgI+2Q5URQBkccEKHTQOPaXdUxnZZElQTZY0MFUAuaEqe1E+Nyvgdz/aIyNi6Z9MzO5dv1H8n58/GELp3+w==
+  dependencies:
+    wrappy "1"
+
+optionator@^0.8.1:
+  version "0.8.3"
+  resolved "https://registry.yarnpkg.com/optionator/-/optionator-0.8.3.tgz#84fa1d036fe9d3c7e21d99884b601167ec8fb495"
+  integrity sha512-+IW9pACdk3XWmmTXG8m3upGUJst5XRGzxMRjXzAuJ1XnIFNvfhjjIuYkDvysnPQ7qzqVzLt78BCruntqRhWQbA==
+  dependencies:
+    deep-is "~0.1.3"
+    fast-levenshtein "~2.0.6"
+    levn "~0.3.0"
+    prelude-ls "~1.1.2"
+    type-check "~0.3.2"
+    word-wrap "~1.2.3"
+
+path-is-absolute@^1.0.0:
+  version "1.0.1"
+  resolved "https://registry.yarnpkg.com/path-is-absolute/-/path-is-absolute-1.0.1.tgz#174b9268735534ffbc7ace6bf53a5a9e1b5c5f5f"
+  integrity sha512-AVbw3UJ2e9bq64vSaS9Am0fje1Pa8pbGqTTsmXfaIiMpnr5DlDhfJOuLj9Sf95ZPVDAUerDfEk88MPmPe7UCQg==
+
+prelude-ls@~1.1.2:
+  version "1.1.2"
+  resolved "https://registry.yarnpkg.com/prelude-ls/-/prelude-ls-1.1.2.tgz#21932a549f5e52ffd9a827f570e04be62a97da54"
+  integrity sha512-ESF23V4SKG6lVSGZgYNpbsiaAkdab6ZgOxe52p7+Kid3W3u3bxR4Vfd/o21dmN7jSt0IwgZ4v5MUd26FEtXE9w==
+
+protobufjs-cli@^1.0.2:
+  version "1.0.2"
+  resolved "https://registry.yarnpkg.com/protobufjs-cli/-/protobufjs-cli-1.0.2.tgz#905fc49007cf4aaf3c45d5f250eb294eedeea062"
+  integrity sha512-cz9Pq9p/Zs7okc6avH20W7QuyjTclwJPgqXG11jNaulfS3nbVisID8rC+prfgq0gbZE0w9LBFd1OKFF03kgFzg==
+  dependencies:
+    chalk "^4.0.0"
+    escodegen "^1.13.0"
+    espree "^9.0.0"
+    estraverse "^5.1.0"
+    glob "^8.0.0"
+    jsdoc "^3.6.3"
+    minimist "^1.2.0"
+    semver "^7.1.2"
+    tmp "^0.2.1"
+    uglify-js "^3.7.7"
+
+protobufjs@^7.1.2:
+  version "7.1.2"
+  resolved "https://registry.yarnpkg.com/protobufjs/-/protobufjs-7.1.2.tgz#a0cf6aeaf82f5625bffcf5a38b7cd2a7de05890c"
+  integrity sha512-4ZPTPkXCdel3+L81yw3dG6+Kq3umdWKh7Dc7GW/CpNk4SX3hK58iPCWeCyhVTDrbkNeKrYNZ7EojM5WDaEWTLQ==
+  dependencies:
+    "@protobufjs/aspromise" "^1.1.2"
+    "@protobufjs/base64" "^1.1.2"
+    "@protobufjs/codegen" "^2.0.4"
+    "@protobufjs/eventemitter" "^1.1.0"
+    "@protobufjs/fetch" "^1.1.0"
+    "@protobufjs/float" "^1.0.2"
+    "@protobufjs/inquire" "^1.1.0"
+    "@protobufjs/path" "^1.1.2"
+    "@protobufjs/pool" "^1.1.0"
+    "@protobufjs/utf8" "^1.1.0"
+    "@types/node" ">=13.7.0"
+    long "^5.0.0"
+
+requizzle@^0.2.3:
+  version "0.2.3"
+  resolved "https://registry.yarnpkg.com/requizzle/-/requizzle-0.2.3.tgz#4675c90aacafb2c036bd39ba2daa4a1cb777fded"
+  integrity sha512-YanoyJjykPxGHii0fZP0uUPEXpvqfBDxWV7s6GKAiiOsiqhX6vHNyW3Qzdmqp/iq/ExbhaGbVrjB4ruEVSM4GQ==
+  dependencies:
+    lodash "^4.17.14"
+
+rimraf@^3.0.0:
+  version "3.0.2"
+  resolved "https://registry.yarnpkg.com/rimraf/-/rimraf-3.0.2.tgz#f1a5402ba6220ad52cc1282bac1ae3aa49fd061a"
+  integrity sha512-JZkJMZkAGFFPP2YqXZXPbMlMBgsxzE8ILs4lMIX/2o0L9UBw9O/Y3o6wFw/i9YLapcUJWwqbi3kdxIPdC62TIA==
+  dependencies:
+    glob "^7.1.3"
+
+semver@5.6.0:
+  version "5.6.0"
+  resolved "https://registry.yarnpkg.com/semver/-/semver-5.6.0.tgz#7e74256fbaa49c75aa7c7a205cc22799cac80004"
+  integrity sha512-RS9R6R35NYgQn++fkDWaOmqGoj4Ek9gGs+DPxNUZKuwE183xjJroKvyo1IzVFeXvUrvmALy6FWD5xrdJT25gMg==
+
+semver@^7.1.2:
+  version "7.3.8"
+  resolved "https://registry.yarnpkg.com/semver/-/semver-7.3.8.tgz#07a78feafb3f7b32347d725e33de7e2a2df67798"
+  integrity sha512-NB1ctGL5rlHrPJtFDVIVzTyQylMLu9N9VICA6HSFJo8MCGVTMW6gfpicwKmmK/dAjTOrqu5l63JJOpDSrAis3A==
+  dependencies:
+    lru-cache "^6.0.0"
+
+source-map-support@0.5.9:
+  version "0.5.9"
+  resolved "https://registry.yarnpkg.com/source-map-support/-/source-map-support-0.5.9.tgz#41bc953b2534267ea2d605bccfa7bfa3111ced5f"
+  integrity sha512-gR6Rw4MvUlYy83vP0vxoVNzM6t8MUXqNuRsuBmBHQDu1Fh6X015FrLdgoDKcNdkwGubozq0P4N0Q37UyFVr1EA==
+  dependencies:
+    buffer-from "^1.0.0"
+    source-map "^0.6.0"
+
+source-map@^0.6.0, source-map@~0.6.1:
+  version "0.6.1"
+  resolved "https://registry.yarnpkg.com/source-map/-/source-map-0.6.1.tgz#74722af32e9614e9c287a8d0bbde48b5e2f1a263"
+  integrity sha512-UjgapumWlbMhkBgzT7Ykc5YXUT46F0iKu8SGXq0bcwP5dz/h0Plj6enJqjz1Zbq2l5WaqYnrVbwWOWMyF3F47g==
+
+strip-json-comments@^3.1.0:
+  version "3.1.1"
+  resolved "https://registry.yarnpkg.com/strip-json-comments/-/strip-json-comments-3.1.1.tgz#31f1281b3832630434831c310c01cccda8cbe006"
+  integrity sha512-6fPc+R4ihwqP6N/aIv2f1gMH8lOVtWQHoqC4yK6oSDVVocumAsfCqjkXnqiYMhmMwS/mEHLp7Vehlt3ql6lEig==
+
+supports-color@^7.1.0:
+  version "7.2.0"
+  resolved "https://registry.yarnpkg.com/supports-color/-/supports-color-7.2.0.tgz#1b7dcdcb32b8138801b3e478ba6a51caa89648da"
+  integrity sha512-qpCAvRl9stuOHveKsn7HncJRvv501qIacKzQlO/+Lwxc9+0q2wLyv4Dfvt80/DPn2pqOBsJdDiogXGR9+OvwRw==
+  dependencies:
+    has-flag "^4.0.0"
+
+taffydb@2.6.2:
+  version "2.6.2"
+  resolved "https://registry.yarnpkg.com/taffydb/-/taffydb-2.6.2.tgz#7cbcb64b5a141b6a2efc2c5d2c67b4e150b2a268"
+  integrity sha512-y3JaeRSplks6NYQuCOj3ZFMO3j60rTwbuKCvZxsAraGYH2epusatvZ0baZYA01WsGqJBq/Dl6vOrMUJqyMj8kA==
+
+tmp@^0.2.1:
+  version "0.2.1"
+  resolved "https://registry.yarnpkg.com/tmp/-/tmp-0.2.1.tgz#8457fc3037dcf4719c251367a1af6500ee1ccf14"
+  integrity sha512-76SUhtfqR2Ijn+xllcI5P1oyannHNHByD80W1q447gU3mp9G9PSpGdWmjUOHRDPiHYacIk66W7ubDTuPF3BEtQ==
+  dependencies:
+    rimraf "^3.0.0"
+
+ts-protoc-gen@^0.15.0:
+  version "0.15.0"
+  resolved "https://registry.yarnpkg.com/ts-protoc-gen/-/ts-protoc-gen-0.15.0.tgz#2fec5930b46def7dcc9fa73c060d770b7b076b7b"
+  integrity sha512-TycnzEyrdVDlATJ3bWFTtra3SCiEP0W0vySXReAuEygXCUr1j2uaVyL0DhzjwuUdQoW5oXPwk6oZWeA0955V+g==
+  dependencies:
+    google-protobuf "^3.15.5"
+
+tslib@^1.8.1:
+  version "1.14.1"
+  resolved "https://registry.yarnpkg.com/tslib/-/tslib-1.14.1.tgz#cf2d38bdc34a134bcaf1091c41f6619e2f672d00"
+  integrity sha512-Xni35NKzjgMrwevysHTCArtLDpPvye8zV/0E4EyYn43P7/7qvQwPh9BGkHewbMulVntbigmcT7rdX3BNo9wRJg==
+
+tsutils@3.21.0:
+  version "3.21.0"
+  resolved "https://registry.yarnpkg.com/tsutils/-/tsutils-3.21.0.tgz#b48717d394cea6c1e096983eed58e9d61715b623"
+  integrity sha512-mHKK3iUXL+3UF6xL5k0PEhKRUBKPBCv/+RkEOpjRWxxx27KKRBmmA60A9pgOUvMi8GKhRMPEmjBRPzs2W7O1OA==
+  dependencies:
+    tslib "^1.8.1"
+
+type-check@~0.3.2:
+  version "0.3.2"
+  resolved "https://registry.yarnpkg.com/type-check/-/type-check-0.3.2.tgz#5884cab512cf1d355e3fb784f30804b2b520db72"
+  integrity sha512-ZCmOJdvOWDBYJlzAoFkC+Q0+bUyEOS1ltgp1MGU03fqHG+dbi9tBFU2Rd9QKiDZFAYrhPh2JUf7rZRIuHRKtOg==
+  dependencies:
+    prelude-ls "~1.1.2"
+
+typescript@^4.8.4:
+  version "4.8.4"
+  resolved "https://registry.yarnpkg.com/typescript/-/typescript-4.8.4.tgz#c464abca159669597be5f96b8943500b238e60e6"
+  integrity sha512-QCh+85mCy+h0IGff8r5XWzOVSbBO+KfeYrMQh7NJ58QujwcE22u+NUSmUxqF+un70P9GXKxa2HCNiTTMJknyjQ==
+
+uc.micro@^1.0.1, uc.micro@^1.0.5:
+  version "1.0.6"
+  resolved "https://registry.yarnpkg.com/uc.micro/-/uc.micro-1.0.6.tgz#9c411a802a409a91fc6cf74081baba34b24499ac"
+  integrity sha512-8Y75pvTYkLJW2hWQHXxoqRgV7qb9B+9vFEtidML+7koHUFapnVJAZ6cKs+Qjz5Aw3aZWHMC6u0wJE3At+nSGwA==
+
+uglify-js@^3.7.7:
+  version "3.17.4"
+  resolved "https://registry.yarnpkg.com/uglify-js/-/uglify-js-3.17.4.tgz#61678cf5fa3f5b7eb789bb345df29afb8257c22c"
+  integrity sha512-T9q82TJI9e/C1TAxYvfb16xO120tMVFZrGA3f9/P4424DNu6ypK103y0GPFVa17yotwSyZW5iYXgjYHkGrJW/g==
+
+underscore@~1.13.2:
+  version "1.13.6"
+  resolved "https://registry.yarnpkg.com/underscore/-/underscore-1.13.6.tgz#04786a1f589dc6c09f761fc5f45b89e935136441"
+  integrity sha512-+A5Sja4HP1M08MaXya7p5LvjuM7K6q/2EaC0+iovj/wOcMsTzMvDFbasi/oSapiwOlt252IqsKqPjCl7huKS0A==
+
+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==
+
+wrappy@1:
+  version "1.0.2"
+  resolved "https://registry.yarnpkg.com/wrappy/-/wrappy-1.0.2.tgz#b5243d8f3ec1aa35f1364605bc0d1036e30ab69f"
+  integrity sha512-l4Sp/DRseor9wL6EvV2+TuQn63dMkPjZ/sp9XkghTEbV9KlPS1xUsZ3u7/IQO4wxtcFB4bgpQPRcR3QCvezPcQ==
+
+xmlcreate@^2.0.4:
+  version "2.0.4"
+  resolved "https://registry.yarnpkg.com/xmlcreate/-/xmlcreate-2.0.4.tgz#0c5ab0f99cdd02a81065fa9cd8f8ae87624889be"
+  integrity sha512-nquOebG4sngPmGPICTS5EnxqhKbCmz5Ox5hsszI2T6U5qdrJizBc+0ilYSEjTSzU0yZcmvppztXe/5Al5fUwdg==
+
+yallist@^4.0.0:
+  version "4.0.0"
+  resolved "https://registry.yarnpkg.com/yallist/-/yallist-4.0.0.tgz#9bb92790d9c0effec63be73519e11a35019a3a72"
+  integrity sha512-3wdGidZyq5PB084XLES5TpOSRA3wjXAlIWMhum2kRcv/41Sn2emQ0dycQW4uZXLejwKvg6EsvbdlVL+FYEct7A==