Merge branch 'google:master' into pose-landmarker-python

mediapipe/calculators/core/constant_side_packet_calculator.cc

@@ -78,7 +78,7 @@ class ConstantSidePacketCalculator : public CalculatorBase {
       } else if (packet_options.has_string_value()) {
         packet.Set<std::string>();
       } else if (packet_options.has_uint64_value()) {
-        packet.Set<uint64>();
+        packet.Set<uint64_t>();
       } else if (packet_options.has_classification_list_value()) {
         packet.Set<ClassificationList>();
       } else if (packet_options.has_landmark_list_value()) {
@@ -112,7 +112,7 @@ class ConstantSidePacketCalculator : public CalculatorBase {
       } else if (packet_options.has_string_value()) {
         packet.Set(MakePacket<std::string>(packet_options.string_value()));
       } else if (packet_options.has_uint64_value()) {
-        packet.Set(MakePacket<uint64>(packet_options.uint64_value()));
+        packet.Set(MakePacket<uint64_t>(packet_options.uint64_value()));
       } else if (packet_options.has_classification_list_value()) {
         packet.Set(MakePacket<ClassificationList>(
             packet_options.classification_list_value()));

mediapipe/calculators/core/gate_calculator_test.cc

@@ -35,14 +35,14 @@ class GateCalculatorTest : public ::testing::Test {
   }
 
   // Use this when ALLOW/DISALLOW input is provided as a side packet.
-  void RunTimeStep(int64 timestamp, bool stream_payload) {
+  void RunTimeStep(int64_t timestamp, bool stream_payload) {
     runner_->MutableInputs()->Get("", 0).packets.push_back(
         MakePacket<bool>(stream_payload).At(Timestamp(timestamp)));
     MP_ASSERT_OK(runner_->Run()) << "Calculator execution failed.";
   }
 
   // Use this when ALLOW/DISALLOW input is provided as an input stream.
-  void RunTimeStep(int64 timestamp, const std::string& control_tag,
+  void RunTimeStep(int64_t timestamp, const std::string& control_tag,
                    bool control) {
     runner_->MutableInputs()->Get("", 0).packets.push_back(
         MakePacket<bool>(true).At(Timestamp(timestamp)));
@@ -134,9 +134,9 @@ TEST_F(GateCalculatorTest, AllowByALLOWOptionToTrue) {
         }
   )");
 
-  constexpr int64 kTimestampValue0 = 42;
+  constexpr int64_t kTimestampValue0 = 42;
   RunTimeStep(kTimestampValue0, true);
-  constexpr int64 kTimestampValue1 = 43;
+  constexpr int64_t kTimestampValue1 = 43;
   RunTimeStep(kTimestampValue1, false);
 
   const std::vector<Packet>& output = runner()->Outputs().Get("", 0).packets;
@@ -159,9 +159,9 @@ TEST_F(GateCalculatorTest, DisallowByALLOWOptionSetToFalse) {
         }
   )");
 
-  constexpr int64 kTimestampValue0 = 42;
+  constexpr int64_t kTimestampValue0 = 42;
   RunTimeStep(kTimestampValue0, true);
-  constexpr int64 kTimestampValue1 = 43;
+  constexpr int64_t kTimestampValue1 = 43;
   RunTimeStep(kTimestampValue1, false);
 
   const std::vector<Packet>& output = runner()->Outputs().Get("", 0).packets;
@@ -175,9 +175,9 @@ TEST_F(GateCalculatorTest, DisallowByALLOWOptionNotSet) {
         output_stream: "test_output"
   )");
 
-  constexpr int64 kTimestampValue0 = 42;
+  constexpr int64_t kTimestampValue0 = 42;
   RunTimeStep(kTimestampValue0, true);
-  constexpr int64 kTimestampValue1 = 43;
+  constexpr int64_t kTimestampValue1 = 43;
   RunTimeStep(kTimestampValue1, false);
 
   const std::vector<Packet>& output = runner()->Outputs().Get("", 0).packets;
@@ -193,9 +193,9 @@ TEST_F(GateCalculatorTest, AllowByALLOWSidePacketSetToTrue) {
   )");
   runner()->MutableSidePackets()->Tag(kAllowTag) = Adopt(new bool(true));
 
-  constexpr int64 kTimestampValue0 = 42;
+  constexpr int64_t kTimestampValue0 = 42;
   RunTimeStep(kTimestampValue0, true);
-  constexpr int64 kTimestampValue1 = 43;
+  constexpr int64_t kTimestampValue1 = 43;
   RunTimeStep(kTimestampValue1, false);
 
   const std::vector<Packet>& output = runner()->Outputs().Get("", 0).packets;
@@ -215,9 +215,9 @@ TEST_F(GateCalculatorTest, AllowByDisallowSidePacketSetToFalse) {
   )");
   runner()->MutableSidePackets()->Tag(kDisallowTag) = Adopt(new bool(false));
 
-  constexpr int64 kTimestampValue0 = 42;
+  constexpr int64_t kTimestampValue0 = 42;
   RunTimeStep(kTimestampValue0, true);
-  constexpr int64 kTimestampValue1 = 43;
+  constexpr int64_t kTimestampValue1 = 43;
   RunTimeStep(kTimestampValue1, false);
 
   const std::vector<Packet>& output = runner()->Outputs().Get("", 0).packets;
@@ -237,9 +237,9 @@ TEST_F(GateCalculatorTest, DisallowByALLOWSidePacketSetToFalse) {
   )");
   runner()->MutableSidePackets()->Tag(kAllowTag) = Adopt(new bool(false));
 
-  constexpr int64 kTimestampValue0 = 42;
+  constexpr int64_t kTimestampValue0 = 42;
   RunTimeStep(kTimestampValue0, true);
-  constexpr int64 kTimestampValue1 = 43;
+  constexpr int64_t kTimestampValue1 = 43;
   RunTimeStep(kTimestampValue1, false);
 
   const std::vector<Packet>& output = runner()->Outputs().Get("", 0).packets;
@@ -255,9 +255,9 @@ TEST_F(GateCalculatorTest, DisallowByDISALLOWSidePacketSetToTrue) {
   )");
   runner()->MutableSidePackets()->Tag(kDisallowTag) = Adopt(new bool(true));
 
-  constexpr int64 kTimestampValue0 = 42;
+  constexpr int64_t kTimestampValue0 = 42;
   RunTimeStep(kTimestampValue0, true);
-  constexpr int64 kTimestampValue1 = 43;
+  constexpr int64_t kTimestampValue1 = 43;
   RunTimeStep(kTimestampValue1, false);
 
   const std::vector<Packet>& output = runner()->Outputs().Get("", 0).packets;
@@ -272,13 +272,13 @@ TEST_F(GateCalculatorTest, Allow) {
         output_stream: "test_output"
   )");
 
-  constexpr int64 kTimestampValue0 = 42;
+  constexpr int64_t kTimestampValue0 = 42;
   RunTimeStep(kTimestampValue0, "ALLOW", true);
-  constexpr int64 kTimestampValue1 = 43;
+  constexpr int64_t kTimestampValue1 = 43;
   RunTimeStep(kTimestampValue1, "ALLOW", false);
-  constexpr int64 kTimestampValue2 = 44;
+  constexpr int64_t kTimestampValue2 = 44;
   RunTimeStep(kTimestampValue2, "ALLOW", true);
-  constexpr int64 kTimestampValue3 = 45;
+  constexpr int64_t kTimestampValue3 = 45;
   RunTimeStep(kTimestampValue3, "ALLOW", false);
 
   const std::vector<Packet>& output = runner()->Outputs().Get("", 0).packets;
@@ -297,13 +297,13 @@ TEST_F(GateCalculatorTest, Disallow) {
         output_stream: "test_output"
   )");
 
-  constexpr int64 kTimestampValue0 = 42;
+  constexpr int64_t kTimestampValue0 = 42;
   RunTimeStep(kTimestampValue0, "DISALLOW", true);
-  constexpr int64 kTimestampValue1 = 43;
+  constexpr int64_t kTimestampValue1 = 43;
   RunTimeStep(kTimestampValue1, "DISALLOW", false);
-  constexpr int64 kTimestampValue2 = 44;
+  constexpr int64_t kTimestampValue2 = 44;
   RunTimeStep(kTimestampValue2, "DISALLOW", true);
-  constexpr int64 kTimestampValue3 = 45;
+  constexpr int64_t kTimestampValue3 = 45;
   RunTimeStep(kTimestampValue3, "DISALLOW", false);
 
   const std::vector<Packet>& output = runner()->Outputs().Get("", 0).packets;
@@ -323,13 +323,13 @@ TEST_F(GateCalculatorTest, AllowWithStateChange) {
         output_stream: "STATE_CHANGE:state_changed"
   )");
 
-  constexpr int64 kTimestampValue0 = 42;
+  constexpr int64_t kTimestampValue0 = 42;
   RunTimeStep(kTimestampValue0, "ALLOW", false);
-  constexpr int64 kTimestampValue1 = 43;
+  constexpr int64_t kTimestampValue1 = 43;
   RunTimeStep(kTimestampValue1, "ALLOW", true);
-  constexpr int64 kTimestampValue2 = 44;
+  constexpr int64_t kTimestampValue2 = 44;
   RunTimeStep(kTimestampValue2, "ALLOW", true);
-  constexpr int64 kTimestampValue3 = 45;
+  constexpr int64_t kTimestampValue3 = 45;
   RunTimeStep(kTimestampValue3, "ALLOW", false);
 
   const std::vector<Packet>& output =
@@ -379,13 +379,13 @@ TEST_F(GateCalculatorTest, DisallowWithStateChange) {
         output_stream: "STATE_CHANGE:state_changed"
   )");
 
-  constexpr int64 kTimestampValue0 = 42;
+  constexpr int64_t kTimestampValue0 = 42;
   RunTimeStep(kTimestampValue0, "DISALLOW", true);
-  constexpr int64 kTimestampValue1 = 43;
+  constexpr int64_t kTimestampValue1 = 43;
   RunTimeStep(kTimestampValue1, "DISALLOW", false);
-  constexpr int64 kTimestampValue2 = 44;
+  constexpr int64_t kTimestampValue2 = 44;
   RunTimeStep(kTimestampValue2, "DISALLOW", false);
-  constexpr int64 kTimestampValue3 = 45;
+  constexpr int64_t kTimestampValue3 = 45;
   RunTimeStep(kTimestampValue3, "DISALLOW", true);
 
   const std::vector<Packet>& output =
@@ -432,7 +432,7 @@ TEST_F(GateCalculatorTest, DisallowInitialNoStateTransition) {
         output_stream: "STATE_CHANGE:state_changed"
   )");
 
-  constexpr int64 kTimestampValue0 = 42;
+  constexpr int64_t kTimestampValue0 = 42;
   RunTimeStep(kTimestampValue0, "DISALLOW", false);
 
   const std::vector<Packet>& output =
@@ -450,7 +450,7 @@ TEST_F(GateCalculatorTest, AllowInitialNoStateTransition) {
         output_stream: "STATE_CHANGE:state_changed"
   )");
 
-  constexpr int64 kTimestampValue0 = 42;
+  constexpr int64_t kTimestampValue0 = 42;
   RunTimeStep(kTimestampValue0, "ALLOW", true);
 
   const std::vector<Packet>& output =

mediapipe/calculators/core/string_to_int_calculator.cc

@@ -64,16 +64,16 @@ REGISTER_CALCULATOR(StringToIntCalculator);
 using StringToUintCalculator = StringToIntCalculatorTemplate<unsigned int>;
 REGISTER_CALCULATOR(StringToUintCalculator);
 
-using StringToInt32Calculator = StringToIntCalculatorTemplate<int32>;
+using StringToInt32Calculator = StringToIntCalculatorTemplate<int32_t>;
 REGISTER_CALCULATOR(StringToInt32Calculator);
 
-using StringToUint32Calculator = StringToIntCalculatorTemplate<uint32>;
+using StringToUint32Calculator = StringToIntCalculatorTemplate<uint32_t>;
 REGISTER_CALCULATOR(StringToUint32Calculator);
 
-using StringToInt64Calculator = StringToIntCalculatorTemplate<int64>;
+using StringToInt64Calculator = StringToIntCalculatorTemplate<int64_t>;
 REGISTER_CALCULATOR(StringToInt64Calculator);
 
-using StringToUint64Calculator = StringToIntCalculatorTemplate<uint64>;
+using StringToUint64Calculator = StringToIntCalculatorTemplate<uint64_t>;
 REGISTER_CALCULATOR(StringToUint64Calculator);
 
 }  // namespace mediapipe

mediapipe/calculators/image/warp_affine_calculator.cc

@@ -166,7 +166,7 @@ class WarpAffineRunnerHolder<mediapipe::Image> {
       const ImageFrame image_frame(frame_ptr->Format(), frame_ptr->Width(),
                                    frame_ptr->Height(), frame_ptr->WidthStep(),
                                    const_cast<uint8_t*>(frame_ptr->PixelData()),
-                                   [](uint8* data){});
+                                   [](uint8_t* data){});
       ASSIGN_OR_RETURN(auto result,
                        runner->Run(image_frame, matrix, size, border_mode));
       return mediapipe::Image(std::make_shared<ImageFrame>(std::move(result)));

mediapipe/framework/stream_handler/fixed_size_input_stream_handler.cc

@@ -131,9 +131,9 @@ class FixedSizeInputStreamHandler : public DefaultInputStreamHandler {
       ABSL_EXCLUSIVE_LOCKS_REQUIRED(erase_mutex_) {
     // Record the most recent first kept timestamp on any stream.
     for (const auto& stream : input_stream_managers_) {
-      int32 queue_size = (stream->QueueSize() >= trigger_queue_size_)
+      int32_t queue_size = (stream->QueueSize() >= trigger_queue_size_)
-                             ? target_queue_size_
+                               ? target_queue_size_
-                             : trigger_queue_size_ - 1;
+                               : trigger_queue_size_ - 1;
       if (stream->QueueSize() > queue_size) {
         kept_timestamp_ = std::max(
             kept_timestamp_, stream->GetMinTimestampAmongNLatest(queue_size + 1)
@@ -214,8 +214,8 @@ class FixedSizeInputStreamHandler : public DefaultInputStreamHandler {
   }
 
  private:
-  int32 trigger_queue_size_;
+  int32_t trigger_queue_size_;
-  int32 target_queue_size_;
+  int32_t target_queue_size_;
   bool fixed_min_size_;
   // Indicates that GetNodeReadiness has returned kReadyForProcess once, and
   // the corresponding call to FillInputSet has not yet completed.

mediapipe/gpu/gl_context_webgl.cc

@@ -67,53 +67,14 @@ absl::Status GlContext::CreateContextInternal(
   // TODO: Investigate this option in more detail, esp. on Safari.
   attrs.preserveDrawingBuffer = 0;
 
-  // Since the Emscripten canvas target finding function is visible from here,
+  // Quick patch for -s DISABLE_DEPRECATED_FIND_EVENT_TARGET_BEHAVIOR so it also
-  // we hijack findCanvasEventTarget directly for enforcing old Module.canvas
+  // looks for our #canvas target in Module.canvas, where we expect it to be.
-  // behavior if the user desires, falling back to the new DOM element CSS
+  // -s OFFSCREENCANVAS_SUPPORT=1 will no longer work with this under the new
-  // selector behavior next if that is specified, and finally just allowing the
+  // event target behavior, but it was never supposed to be tapping into our
-  // lookup to proceed on a null target.
+  // canvas anyways. See b/278155946 for more background.
-  // TODO: Ensure this works with all options (in particular,
+  EM_ASM({ specialHTMLTargets["#canvas"] = Module.canvas; });
-  //   multithreading options, like the special-case combination of USE_PTHREADS
-  //   and OFFSCREEN_FRAMEBUFFER)
-  // clang-format off
-  EM_ASM(
-    let init_once = true;
-    if (init_once) {
-      const cachedFindCanvasEventTarget = findCanvasEventTarget;
-
-      if (typeof cachedFindCanvasEventTarget !== 'function') {
-        if (typeof console !== 'undefined') {
-          console.error('Expected Emscripten global function '
-              + '"findCanvasEventTarget" not found. WebGL context creation '
-              + 'may fail.');
-        }
-        return;
-      }
-
-      findCanvasEventTarget = function(target) {
-        if (target == 0) {
-          if (Module && Module.canvas) {
-            return Module.canvas;
-          } else if (Module && Module.canvasCssSelector) {
-            return cachedFindCanvasEventTarget(Module.canvasCssSelector);
-          }
-          if (typeof console !== 'undefined') {
-            console.warn('Module properties canvas and canvasCssSelector not ' +
-                         'found during WebGL context creation.');
-          }
-        }
-        // We still go through with the find attempt, although for most use
-        // cases it will not succeed, just in case the user does want to fall-
-        // back.
-        return cachedFindCanvasEventTarget(target);
-      };  // NOLINT: Necessary semicolon.
-      init_once = false;
-    }
-  );
-  // clang-format on
-
   EMSCRIPTEN_WEBGL_CONTEXT_HANDLE context_handle =
-      emscripten_webgl_create_context(nullptr, &attrs);
+      emscripten_webgl_create_context("#canvas", &attrs);
 
   // Check for failure
   if (context_handle <= 0) {

mediapipe/gpu/gl_texture_buffer.cc

@@ -64,7 +64,7 @@ std::unique_ptr<GlTextureBuffer> GlTextureBuffer::Create(
   int actual_ws = image_frame.WidthStep();
   int alignment = 0;
   std::unique_ptr<ImageFrame> temp;
-  const uint8* data = image_frame.PixelData();
+  const uint8_t* data = image_frame.PixelData();
 
   // Let's see if the pixel data is tightly aligned to one of the alignments
   // supported by OpenGL, preferring 4 if possible since it's the default.

mediapipe/gpu/gpu_buffer_storage_yuv_image.cc

@@ -167,7 +167,7 @@ GpuBufferStorageYuvImage::GpuBufferStorageYuvImage(int width, int height,
                                                    GpuBufferFormat format) {
   libyuv::FourCC fourcc = FourCCForGpuBufferFormat(format);
   int y_stride = std::ceil(1.0f * width / kDefaultDataAligment);
-  auto y_data = std::make_unique<uint8[]>(y_stride * height);
+  auto y_data = std::make_unique<uint8_t[]>(y_stride * height);
   switch (fourcc) {
     case libyuv::FOURCC_NV12:
     case libyuv::FOURCC_NV21: {
@@ -175,7 +175,7 @@ GpuBufferStorageYuvImage::GpuBufferStorageYuvImage(int width, int height,
       int uv_width = 2 * std::ceil(0.5f * width);
       int uv_height = std::ceil(0.5f * height);
       int uv_stride = std::ceil(1.0f * uv_width / kDefaultDataAligment);
-      auto uv_data = std::make_unique<uint8[]>(uv_stride * uv_height);
+      auto uv_data = std::make_unique<uint8_t[]>(uv_stride * uv_height);
       yuv_image_ = std::make_shared<YUVImage>(
           fourcc, std::move(y_data), y_stride, std::move(uv_data), uv_stride,
           nullptr, 0, width, height);
@@ -187,8 +187,8 @@ GpuBufferStorageYuvImage::GpuBufferStorageYuvImage(int width, int height,
       int uv_width = std::ceil(0.5f * width);
       int uv_height = std::ceil(0.5f * height);
       int uv_stride = std::ceil(1.0f * uv_width / kDefaultDataAligment);
-      auto u_data = std::make_unique<uint8[]>(uv_stride * uv_height);
+      auto u_data = std::make_unique<uint8_t[]>(uv_stride * uv_height);
-      auto v_data = std::make_unique<uint8[]>(uv_stride * uv_height);
+      auto v_data = std::make_unique<uint8_t[]>(uv_stride * uv_height);
       yuv_image_ = std::make_shared<YUVImage>(
           fourcc, std::move(y_data), y_stride, std::move(u_data), uv_stride,
           std::move(v_data), uv_stride, width, height);

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

@@ -16,6 +16,7 @@ import csv
 import filecmp
 import os
 import tempfile
+import unittest
 from unittest import mock as unittest_mock
 
 import tensorflow as tf
@@ -24,6 +25,7 @@ from mediapipe.model_maker.python.text import text_classifier
 from mediapipe.tasks.python.test import test_utils
 
 
+@unittest.skip('b/275624089')
 class TextClassifierTest(tf.test.TestCase):
 
   _AVERAGE_WORD_EMBEDDING_JSON_FILE = (

mediapipe/model_maker/python/vision/object_detector/BUILD

@@ -175,11 +175,7 @@ py_test(
     data = [":testdata"],
     tags = ["requires-net:external"],
     deps = [
-        ":dataset",
+        ":object_detector_import",
-        ":hyperparameters",
-        ":model_spec",
-        ":object_detector",
-        ":object_detector_options",
         "//mediapipe/tasks/python/test:test_utils",
     ],
 )

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

@@ -19,11 +19,7 @@ from unittest import mock as unittest_mock
 from absl.testing import parameterized
 import tensorflow as tf
 
-from mediapipe.model_maker.python.vision.object_detector import dataset
+from mediapipe.model_maker.python.vision import object_detector
-from mediapipe.model_maker.python.vision.object_detector import hyperparameters
-from mediapipe.model_maker.python.vision.object_detector import model_spec as ms
-from mediapipe.model_maker.python.vision.object_detector import object_detector
-from mediapipe.model_maker.python.vision.object_detector import object_detector_options
 from mediapipe.tasks.python.test import test_utils as task_test_utils
 
 
@@ -33,7 +29,7 @@ class ObjectDetectorTest(tf.test.TestCase, parameterized.TestCase):
     super().setUp()
     dataset_folder = task_test_utils.get_test_data_path('coco_data')
     cache_dir = self.create_tempdir()
-    self.data = dataset.Dataset.from_coco_folder(
+    self.data = object_detector.Dataset.from_coco_folder(
         dataset_folder, cache_dir=cache_dir
     )
     # Mock tempfile.gettempdir() to be unique for each test to avoid race
@@ -48,15 +44,16 @@ class ObjectDetectorTest(tf.test.TestCase, parameterized.TestCase):
     self.addCleanup(mock_gettempdir.stop)
 
   def test_object_detector(self):
-    hparams = hyperparameters.HParams(
+    hparams = object_detector.HParams(
         epochs=1,
         batch_size=2,
         learning_rate=0.9,
         shuffle=False,
         export_dir=self.create_tempdir(),
     )
-    options = object_detector_options.ObjectDetectorOptions(
+    options = object_detector.ObjectDetectorOptions(
-        supported_model=ms.SupportedModels.MOBILENET_V2, hparams=hparams
+        supported_model=object_detector.SupportedModels.MOBILENET_V2,
+        hparams=hparams,
     )
     # Test `create``
     model = object_detector.ObjectDetector.create(
@@ -79,7 +76,7 @@ class ObjectDetectorTest(tf.test.TestCase, parameterized.TestCase):
     self.assertGreater(os.path.getsize(output_metadata_file), 0)
 
     # Test `quantization_aware_training`
-    qat_hparams = hyperparameters.QATHParams(
+    qat_hparams = object_detector.QATHParams(
         learning_rate=0.9,
         batch_size=2,
         epochs=1,

mediapipe/modules/objectron/calculators/frame_annotation_tracker.cc

@@ -24,8 +24,8 @@ namespace mediapipe {
 
 void FrameAnnotationTracker::AddDetectionResult(
     const FrameAnnotation& frame_annotation) {
-  const int64 time_us =
+  const int64_t time_us =
-      static_cast<int64>(std::round(frame_annotation.timestamp()));
+      static_cast<int64_t>(std::round(frame_annotation.timestamp()));
   for (const auto& object_annotation : frame_annotation.annotations()) {
     detected_objects_[time_us + object_annotation.object_id()] =
         object_annotation;
@@ -37,7 +37,7 @@ FrameAnnotation FrameAnnotationTracker::ConsolidateTrackingResult(
     absl::flat_hash_set<int>* cancel_object_ids) {
   CHECK(cancel_object_ids != nullptr);
   FrameAnnotation frame_annotation;
-  std::vector<int64> keys_to_be_deleted;
+  std::vector<int64_t> keys_to_be_deleted;
   for (const auto& detected_obj : detected_objects_) {
     const int object_id = detected_obj.second.object_id();
     if (cancel_object_ids->contains(object_id)) {

mediapipe/tasks/cc/core/BUILD

@@ -78,6 +78,7 @@ cc_library(
     hdrs = ["mediapipe_builtin_op_resolver.h"],
     deps = [
         "//mediapipe/tasks/cc/text/custom_ops/ragged:ragged_tensor_to_tensor_tflite",
+        "//mediapipe/tasks/cc/text/custom_ops/sentencepiece:sentencepiece_tokenizer_tflite",
         "//mediapipe/tasks/cc/text/language_detector/custom_ops:kmeans_embedding_lookup",
         "//mediapipe/tasks/cc/text/language_detector/custom_ops:ngram_hash",
         "//mediapipe/util/tflite/operations:landmarks_to_transform_matrix",

mediapipe/tasks/cc/core/mediapipe_builtin_op_resolver.cc

@@ -16,6 +16,7 @@ limitations under the License.
 #include "mediapipe/tasks/cc/core/mediapipe_builtin_op_resolver.h"
 
 #include "mediapipe/tasks/cc/text/custom_ops/ragged/ragged_tensor_to_tensor_tflite.h"
+#include "mediapipe/tasks/cc/text/custom_ops/sentencepiece/sentencepiece_tokenizer_tflite.h"
 #include "mediapipe/tasks/cc/text/language_detector/custom_ops/kmeans_embedding_lookup.h"
 #include "mediapipe/tasks/cc/text/language_detector/custom_ops/ngram_hash.h"
 #include "mediapipe/util/tflite/operations/landmarks_to_transform_matrix.h"
@@ -51,6 +52,8 @@ MediaPipeBuiltinOpResolver::MediaPipeBuiltinOpResolver() {
   AddCustom("KmeansEmbeddingLookup",
             mediapipe::tflite_operations::Register_KmeansEmbeddingLookup());
   // For the UniversalSentenceEncoder model.
+  AddCustom("TFSentencepieceTokenizeOp",
+            mediapipe::tflite_operations::Register_SENTENCEPIECE_TOKENIZER());
   AddCustom("RaggedTensorToTensor",
             mediapipe::tflite_operations::Register_RAGGED_TENSOR_TO_TENSOR());
 }

mediapipe/tasks/cc/text/custom_ops/sentencepiece/BUILD

@@ -18,6 +18,13 @@ package(default_visibility = ["//mediapipe/tasks:internal"])
 
 licenses(["notice"])
 
+filegroup(
+    name = "testdata",
+    srcs = glob([
+        "testdata/**",
+    ]),
+)
+
 filegroup(
     name = "config_fbs",
     srcs = ["config.fbs"],
@@ -80,3 +87,86 @@ cc_test(
         "//mediapipe/framework/port:gtest_main",
     ],
 )
+
+cc_library(
+    name = "sentencepiece_constants",
+    hdrs = ["sentencepiece_constants.h"],
+)
+
+cc_library(
+    name = "model_converter",
+    srcs = [
+        "model_converter.cc",
+    ],
+    hdrs = [
+        "model_converter.h",
+    ],
+    deps = [
+        ":config",
+        ":double_array_trie_builder",
+        ":encoder_config",
+        ":sentencepiece_constants",
+        "@com_google_absl//absl/status",
+        "@com_google_absl//absl/status:statusor",
+        "@com_google_sentencepiece//src:sentencepiece_model_cc_proto",
+    ],
+)
+
+cc_library(
+    name = "optimized_encoder",
+    srcs = [
+        "optimized_encoder.cc",
+    ],
+    hdrs = [
+        "optimized_encoder.h",
+    ],
+    deps = [
+        ":double_array_trie",
+        ":encoder_config",
+        ":utils",
+    ],
+)
+
+cc_library(
+    name = "sentencepiece_tokenizer_tflite",
+    srcs = ["sentencepiece_tokenizer_tflite.cc"],
+    hdrs = ["sentencepiece_tokenizer_tflite.h"],
+    visibility = [
+        "//visibility:public",
+    ],
+    deps =
+        [
+            ":optimized_encoder",
+            "@flatbuffers",
+            "@org_tensorflow//tensorflow/lite:framework",
+            "@org_tensorflow//tensorflow/lite:string_util",
+            "@org_tensorflow//tensorflow/lite/c:common",
+            "@org_tensorflow//tensorflow/lite/kernels:builtin_ops",
+            "@org_tensorflow//tensorflow/lite/kernels:kernel_util",
+            "@org_tensorflow//tensorflow/lite/kernels/internal:tensor",
+        ],
+)
+
+cc_test(
+    name = "optimized_encoder_test",
+    srcs = [
+        "optimized_encoder_test.cc",
+    ],
+    data = [
+        ":testdata",
+    ],
+    deps = [
+        ":double_array_trie_builder",
+        ":encoder_config",
+        ":model_converter",
+        ":optimized_encoder",
+        "//mediapipe/framework/deps:file_path",
+        "//mediapipe/framework/port:gtest_main",
+        "@com_google_absl//absl/flags:flag",
+        "@com_google_absl//absl/status",
+        "@com_google_absl//absl/strings:str_format",
+        "@com_google_sentencepiece//src:sentencepiece_cc_proto",
+        "@com_google_sentencepiece//src:sentencepiece_processor",
+        "@org_tensorflow//tensorflow/core:lib",
+    ],
+)

mediapipe/tasks/cc/text/custom_ops/sentencepiece/model_converter.cc

@@ -0,0 +1,131 @@
+/* Copyright 2023 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/text/custom_ops/sentencepiece/model_converter.h"
+
+#include "absl/status/status.h"
+#include "absl/status/statusor.h"
+#include "mediapipe/tasks/cc/text/custom_ops/sentencepiece/double_array_trie_builder.h"
+#include "mediapipe/tasks/cc/text/custom_ops/sentencepiece/encoder_config_generated.h"
+#include "mediapipe/tasks/cc/text/custom_ops/sentencepiece/sentencepiece_constants.h"
+#include "src/sentencepiece_model.pb.h"
+
+namespace mediapipe::tflite_operations::sentencepiece {
+
+std::tuple<std::vector<uint32_t>, std::vector<int8_t>>
+DecodePrecompiledCharsmap(
+    const ::sentencepiece::NormalizerSpec& normalizer_spec) {
+  // This function "undoes" encoding done by
+  // sentencepiece::normalizer::Normalizer::EncodePrecompiledCharsMap.
+  const char* precompiled_map = normalizer_spec.precompiled_charsmap().data();
+  const uint32_t trie_size =
+      *reinterpret_cast<const uint32_t*>(precompiled_map);
+  const uint32_t* trie_ptr =
+      reinterpret_cast<const uint32_t*>(precompiled_map + sizeof(uint32_t));
+  const int8_t* normalized_ptr = reinterpret_cast<const int8_t*>(
+      precompiled_map + sizeof(uint32_t) + trie_size);
+  const int normalized_size = normalizer_spec.precompiled_charsmap().length() -
+                              sizeof(uint32_t) - trie_size;
+  return std::make_tuple(
+      std::vector<uint32_t>(trie_ptr, trie_ptr + trie_size / sizeof(uint32_t)),
+      std::vector<int8_t>(normalized_ptr, normalized_ptr + normalized_size));
+}
+
+absl::StatusOr<std::string> ConvertSentencepieceModelToFlatBuffer(
+    const std::string& model_config_str, int encoding_offset) {
+  ::sentencepiece::ModelProto model_config;
+  if (!model_config.ParseFromString(model_config_str)) {
+    return absl::InvalidArgumentError(
+        "Invalid configuration, can't parse SentencePiece model config " +
+        model_config.InitializationErrorString());
+  }
+  // Convert sentencepieces.
+  std::vector<std::string> pieces;
+  pieces.reserve(model_config.pieces_size());
+  std::vector<float> scores;
+  scores.reserve(model_config.pieces_size());
+  std::vector<int> ids;
+  ids.reserve(model_config.pieces_size());
+  float min_score = 0.0;
+  int index = 0;
+  for (const auto& piece : model_config.pieces()) {
+    switch (piece.type()) {
+      case ::sentencepiece::ModelProto::SentencePiece::NORMAL:
+      case ::sentencepiece::ModelProto::SentencePiece::USER_DEFINED:
+        pieces.push_back(piece.piece());
+        ids.push_back(index);
+        if (piece.score() < min_score) {
+          min_score = piece.score();
+        }
+        break;
+      case ::sentencepiece::ModelProto::SentencePiece::UNKNOWN:
+      case ::sentencepiece::ModelProto::SentencePiece::CONTROL:
+        // Ignore unknown and control codes.
+        break;
+      default:
+        return absl::InvalidArgumentError("Invalid SentencePiece piece type " +
+                                          piece.piece());
+    }
+    scores.push_back(piece.score());
+    ++index;
+  }
+  flatbuffers::FlatBufferBuilder builder(1024);
+  const auto pieces_trie_vector = builder.CreateVector(BuildTrie(pieces, ids));
+  const auto pieces_score_vector = builder.CreateVector(scores);
+  TrieBuilder pieces_trie_builder(builder);
+  pieces_trie_builder.add_nodes(pieces_trie_vector);
+  const auto pieces_trie_fbs = pieces_trie_builder.Finish();
+
+  // Converting normalization.
+  const auto normalization =
+      DecodePrecompiledCharsmap(model_config.normalizer_spec());
+  const auto normalization_trie = std::get<0>(normalization);
+  const auto normalization_strings = std::get<1>(normalization);
+  const auto normalization_trie_vector =
+      builder.CreateVector(normalization_trie);
+  TrieBuilder normalization_trie_builder(builder);
+  normalization_trie_builder.add_nodes(normalization_trie_vector);
+  const auto normalization_trie_fbs = normalization_trie_builder.Finish();
+  const auto normalization_strings_fbs =
+      builder.CreateVector(normalization_strings);
+
+  EncoderConfigBuilder ecb(builder);
+  ecb.add_version(EncoderVersion::EncoderVersion_SENTENCE_PIECE);
+  ecb.add_start_code(model_config.trainer_spec().bos_id());
+  ecb.add_end_code(model_config.trainer_spec().eos_id());
+  ecb.add_unknown_code(model_config.trainer_spec().unk_id());
+  ecb.add_unknown_penalty(min_score - kUnkPenalty);
+  ecb.add_encoding_offset(encoding_offset);
+  ecb.add_pieces(pieces_trie_fbs);
+  ecb.add_pieces_scores(pieces_score_vector);
+  ecb.add_remove_extra_whitespaces(
+      model_config.normalizer_spec().remove_extra_whitespaces());
+  ecb.add_add_dummy_prefix(model_config.normalizer_spec().add_dummy_prefix());
+  ecb.add_escape_whitespaces(
+      model_config.normalizer_spec().escape_whitespaces());
+  ecb.add_normalized_prefixes(normalization_trie_fbs);
+  ecb.add_normalized_replacements(normalization_strings_fbs);
+  FinishEncoderConfigBuffer(builder, ecb.Finish());
+  return std::string(reinterpret_cast<const char*>(builder.GetBufferPointer()),
+                     builder.GetSize());
+}
+
+std::string ConvertSentencepieceModel(const std::string& model_string) {
+  const auto result = ConvertSentencepieceModelToFlatBuffer(model_string);
+  assert(result.status().ok());
+  return result.value();
+}
+
+}  // namespace mediapipe::tflite_operations::sentencepiece

mediapipe/tasks/cc/text/custom_ops/sentencepiece/model_converter.h

@@ -0,0 +1,33 @@
+/* Copyright 2023 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_TEXT_CUSTOM_OPS_SENTENCEPIECE_MODEL_CONVERTER_H_
+#define MEDIAPIPE_TASKS_CC_TEXT_CUSTOM_OPS_SENTENCEPIECE_MODEL_CONVERTER_H_
+
+#include <string>
+
+#include "absl/status/statusor.h"
+
+namespace mediapipe::tflite_operations::sentencepiece {
+
+// Converts Sentencepiece configuration to flatbuffer format.
+// encoding_offset is used by some encoders that combine different encodings.
+absl::StatusOr<std::string> ConvertSentencepieceModelToFlatBuffer(
+    const std::string& model_config_str, int encoding_offset = 0);
+std::string ConvertSentencepieceModel(const std::string& model_string);
+
+}  // namespace mediapipe::tflite_operations::sentencepiece
+
+#endif  // MEDIAPIPE_TASKS_CC_TEXT_CUSTOM_OPS_SENTENCEPIECE_MODEL_CONVERTER_H_

mediapipe/tasks/cc/text/custom_ops/sentencepiece/optimized_encoder.cc

@@ -0,0 +1,236 @@
+/* Copyright 2023 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/text/custom_ops/sentencepiece/optimized_encoder.h"
+
+#include <algorithm>
+#include <tuple>
+
+#include "mediapipe/tasks/cc/text/custom_ops/sentencepiece/double_array_trie.h"
+#include "mediapipe/tasks/cc/text/custom_ops/sentencepiece/encoder_config_generated.h"
+#include "mediapipe/tasks/cc/text/custom_ops/sentencepiece/utils.h"
+
+namespace mediapipe::tflite_operations::sentencepiece {
+namespace {
+
+const char kSpaceSymbol[] = "\xe2\x96\x81";
+
+template <typename processing_callback>
+std::tuple<std::string, std::vector<int>> process_string(
+    const std::string& input, const std::vector<int>& offsets,
+    const processing_callback& pc) {
+  std::string result_string;
+  result_string.reserve(input.size());
+  std::vector<int> result_offsets;
+  result_offsets.reserve(offsets.size());
+  for (int i = 0, j = 0; i < input.size();) {
+    auto result = pc(input.data() + i, input.size() - i);
+    auto consumed = std::get<0>(result);
+    auto new_string = std::get<1>(result);
+    if (consumed == 0) {
+      // Skip the current byte and move forward.
+      result_string.push_back(input[i]);
+      result_offsets.push_back(offsets[j]);
+      i++;
+      j++;
+      continue;
+    }
+    result_string.append(new_string.data(), new_string.length());
+    for (int i = 0; i < new_string.length(); ++i) {
+      result_offsets.push_back(offsets[j]);
+    }
+    j += consumed;
+    i += consumed;
+  }
+  return std::make_tuple(result_string, result_offsets);
+}
+
+inline char is_whitespace(char c) {
+  return c == ' ' || c == '\t' || c == '\r' || c == '\n';
+}
+
+std::tuple<int, utils::string_view> remove_extra_whitespaces(const char* data,
+                                                             int len) {
+  if (len == 0 || !is_whitespace(*data)) {
+    return std::make_tuple(0, utils::string_view(nullptr, 0));
+  }
+  int num_consumed = 1;
+  for (; num_consumed < len && is_whitespace(data[num_consumed]);
+       ++num_consumed) {
+  }
+  return num_consumed > 1
+             ? std::make_tuple(num_consumed, utils::string_view(" ", 1))
+             : std::make_tuple(0, utils::string_view(nullptr, 0));
+}
+
+std::tuple<int, utils::string_view> find_replacement(
+    const char* data, int len, const DoubleArrayTrie& dat,
+    const flatbuffers::Vector<int8_t>& replacements) {
+  const auto max_match = dat.LongestPrefixMatch(utils::string_view(data, len));
+  if (!max_match.empty()) {
+    // Because flatbuffer byte is signed char which is not the same as char,
+    // there is the reinterpret_cast here.
+    const char* replaced_string_ptr =
+        reinterpret_cast<const char*>(replacements.data() + max_match.id);
+    return std::make_tuple(max_match.match_length,
+                           utils::string_view(replaced_string_ptr));
+  }
+  return std::make_tuple(0, utils::string_view(nullptr, 0));
+}
+}  // namespace
+
+std::tuple<std::string, std::vector<int>> NormalizeString(
+    const std::string& in_string, const EncoderConfig& config) {
+  std::vector<int> output_offsets;
+  std::string result = in_string;
+  output_offsets.reserve(in_string.length());
+  for (int i = 0; i < in_string.length(); ++i) {
+    output_offsets.push_back(i);
+  }
+  if (in_string.empty()) {
+    return std::make_tuple(result, output_offsets);
+  }
+  if (config.add_dummy_prefix()) {
+    result.insert(result.begin(), ' ');
+    output_offsets.insert(output_offsets.begin(), 0);
+  }
+  // Greedely replace normalized_prefixes with normalized_replacements
+  if (config.normalized_prefixes() != nullptr &&
+      config.normalized_replacements() != nullptr) {
+    const DoubleArrayTrie normalized_prefixes_matcher(
+        config.normalized_prefixes()->nodes());
+    const auto norm_replace = [&config, &normalized_prefixes_matcher](
+                                  const char* data, int len) {
+      return find_replacement(data, len, normalized_prefixes_matcher,
+                              *config.normalized_replacements());
+    };
+    std::tie(result, output_offsets) =
+        process_string(result, output_offsets, norm_replace);
+  }
+  if (config.remove_extra_whitespaces()) {
+    std::tie(result, output_offsets) =
+        process_string(result, output_offsets, remove_extra_whitespaces);
+    if (!result.empty() && is_whitespace(result.back())) {
+      result.pop_back();
+      output_offsets.pop_back();
+    }
+  }
+  if (config.escape_whitespaces()) {
+    const auto replace_whitespaces = [](const char* data, int len) {
+      if (len > 0 && is_whitespace(*data)) {
+        return std::make_tuple(1, utils::string_view(kSpaceSymbol));
+      }
+      return std::make_tuple(0, utils::string_view(nullptr, 0));
+    };
+    std::tie(result, output_offsets) =
+        process_string(result, output_offsets, replace_whitespaces);
+  }
+
+  return std::make_tuple(result, output_offsets);
+}
+
+EncoderResult EncodeNormalizedString(const std::string& str,
+                                     const std::vector<int>& offsets,
+                                     const EncoderConfig& config, bool add_bos,
+                                     bool add_eos, bool reverse) {
+  const DoubleArrayTrie piece_matcher(config.pieces()->nodes());
+  const flatbuffers::Vector<float>* piece_scores = config.pieces_scores();
+  const int unknown_code = config.unknown_code();
+  const float unknown_penalty = config.unknown_penalty();
+  struct LatticeElement {
+    float score = 0;
+    int code = -1;
+    int prev_position = -1;
+    LatticeElement(float score_, int code_, int prev_position_)
+        : score(score_), code(code_), prev_position(prev_position_) {}
+    LatticeElement() {}
+  };
+  const int length = str.length();
+  std::vector<LatticeElement> lattice(length + 1);
+  for (int i = 0; i < length; ++i) {
+    if (i > 0 && lattice[i].prev_position < 0) {
+      // This state is unreachable.
+      continue;
+    }
+    if (unknown_code >= 0) {
+      // Put unknown code.
+      const float penalized_score = lattice[i].score + unknown_penalty;
+      const int pos = i + 1;
+      LatticeElement& current_element = lattice[pos];
+      if (current_element.prev_position < 0 ||
+          current_element.score < penalized_score) {
+        current_element = LatticeElement(
+            penalized_score, unknown_code,
+            // If the current state is already reached by unknown code, merge
+            // states.
+            lattice[i].code == unknown_code ? lattice[i].prev_position : i);
+      }
+    }
+    auto lattice_update = [&lattice, i,
+                           piece_scores](const DoubleArrayTrie::Match& m) {
+      LatticeElement& target_element = lattice[i + m.match_length];
+      const float score = lattice[i].score + (*piece_scores)[m.id];
+      if (target_element.prev_position < 0 || target_element.score < score) {
+        target_element = LatticeElement(score, m.id, i);
+      }
+    };
+    piece_matcher.IteratePrefixMatches(
+        utils::string_view(str.data() + i, length - i), lattice_update);
+  }
+
+  EncoderResult result;
+  if (add_eos) {
+    result.codes.push_back(config.end_code());
+    result.offsets.push_back(length);
+  }
+  if (lattice[length].prev_position >= 0) {
+    for (int pos = length; pos > 0;) {
+      auto code = lattice[pos].code;
+      if (code != config.unknown_code()) {
+        code += config.encoding_offset();
+      }
+      result.codes.push_back(code);
+      pos = lattice[pos].prev_position;
+      result.offsets.push_back(offsets[pos]);
+    }
+  }
+  if (add_bos) {
+    result.codes.push_back(config.start_code());
+    result.offsets.push_back(0);
+  }
+  if (!reverse) {
+    std::reverse(result.codes.begin(), result.codes.end());
+    std::reverse(result.offsets.begin(), result.offsets.end());
+  }
+  return result;
+}
+
+EncoderResult EncodeString(const std::string& string, const void* config_buffer,
+                           bool add_bos, bool add_eos, bool reverse) {
+  // Get the config from the buffer.
+  const EncoderConfig* config = GetEncoderConfig(config_buffer);
+  if (config->version() != EncoderVersion::EncoderVersion_SENTENCE_PIECE) {
+    EncoderResult result;
+    result.type = EncoderResultType::WRONG_CONFIG;
+    return result;
+  }
+  std::string normalized_string;
+  std::vector<int> offsets;
+  std::tie(normalized_string, offsets) = NormalizeString(string, *config);
+  return EncodeNormalizedString(normalized_string, offsets, *config, add_bos,
+                                add_eos, reverse);
+}
+
+}  // namespace mediapipe::tflite_operations::sentencepiece

mediapipe/tasks/cc/text/custom_ops/sentencepiece/optimized_encoder.h

@@ -0,0 +1,46 @@
+/* Copyright 2023 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_TEXT_CUSTOM_OPS_SENTENCEPIECE_OPTIMIZED_ENCODER_H_
+#define MEDIAPIPE_TASKS_CC_TEXT_CUSTOM_OPS_SENTENCEPIECE_OPTIMIZED_ENCODER_H_
+
+// Sentencepiece encoder optimized with memmapped model.
+
+#include <string>
+#include <tuple>
+#include <vector>
+
+#include "mediapipe/tasks/cc/text/custom_ops/sentencepiece/encoder_config_generated.h"
+
+namespace mediapipe::tflite_operations::sentencepiece {
+
+enum class EncoderResultType { SUCCESS = 0, WRONG_CONFIG = 1 };
+
+struct EncoderResult {
+  EncoderResultType type = EncoderResultType::SUCCESS;
+  std::vector<int> codes;
+  std::vector<int> offsets;
+};
+std::tuple<std::string, std::vector<int>> NormalizeString(
+    const std::string& in_string, const EncoderConfig& config);
+
+// Encodes one string and returns ids and offsets. Takes the configuration as a
+// type-erased buffer.
+EncoderResult EncodeString(const std::string& string, const void* config_buffer,
+                           bool add_bos, bool add_eos, bool reverse);
+
+}  // namespace mediapipe::tflite_operations::sentencepiece
+
+#endif  // MEDIAPIPE_TASKS_CC_TEXT_CUSTOM_OPS_SENTENCEPIECE_OPTIMIZED_ENCODER_H_

mediapipe/tasks/cc/text/custom_ops/sentencepiece/optimized_encoder_test.cc

@@ -0,0 +1,171 @@
+/* Copyright 2023 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/text/custom_ops/sentencepiece/optimized_encoder.h"
+
+#include <fstream>
+
+#include "absl/flags/flag.h"
+#include "absl/status/status.h"
+#include "absl/strings/str_format.h"
+#include "mediapipe/framework/deps/file_path.h"
+#include "mediapipe/framework/port/gmock.h"
+#include "mediapipe/framework/port/gtest.h"
+#include "mediapipe/tasks/cc/text/custom_ops/sentencepiece/double_array_trie_builder.h"
+#include "mediapipe/tasks/cc/text/custom_ops/sentencepiece/encoder_config_generated.h"
+#include "mediapipe/tasks/cc/text/custom_ops/sentencepiece/model_converter.h"
+#include "src/sentencepiece.pb.h"
+#include "src/sentencepiece_processor.h"
+#include "tensorflow/core/platform/env.h"
+
+namespace mediapipe::tflite_operations::sentencepiece {
+
+namespace internal {
+
+tensorflow::Status TFReadFileToString(const std::string& filepath,
+                                      std::string* data) {
+  return tensorflow::ReadFileToString(tensorflow::Env::Default(), filepath,
+                                      data);
+}
+
+absl::Status StdReadFileToString(const std::string& filepath,
+                                 std::string* data) {
+  std::ifstream infile(filepath);
+  if (!infile.is_open()) {
+    return absl::NotFoundError(
+        absl::StrFormat("Error when opening %s", filepath));
+  }
+  std::string contents((std::istreambuf_iterator<char>(infile)),
+                       (std::istreambuf_iterator<char>()));
+  data->append(contents);
+  infile.close();
+  return absl::OkStatus();
+}
+}  // namespace internal
+
+namespace {
+
+using ::mediapipe::file::JoinPath;
+
+static char kConfigFilePath[] =
+    "/mediapipe/tasks/cc/text/custom_ops/"
+    "sentencepiece/testdata/sentencepiece.model";
+
+TEST(OptimizedEncoder, NormalizeStringWhitestpaces) {
+  flatbuffers::FlatBufferBuilder builder(1024);
+  EncoderConfigBuilder ecb(builder);
+  ecb.add_remove_extra_whitespaces(true);
+  ecb.add_add_dummy_prefix(true);
+  ecb.add_escape_whitespaces(true);
+  FinishEncoderConfigBuffer(builder, ecb.Finish());
+  const EncoderConfig* config = GetEncoderConfig(builder.GetBufferPointer());
+  {
+    const auto result = NormalizeString("x  y", *config);
+    const auto res_string = std::get<0>(result);
+    const auto offsets = std::get<1>(result);
+    EXPECT_EQ(res_string, "\xe2\x96\x81x\xe2\x96\x81y");
+    EXPECT_THAT(offsets, ::testing::ElementsAre(0, 0, 0, 0, 1, 1, 1, 3));
+  }
+  {
+    const auto result = NormalizeString("\tx  y\n", *config);
+    const auto res_string = std::get<0>(result);
+    const auto offsets = std::get<1>(result);
+    EXPECT_EQ(res_string, "\xe2\x96\x81x\xe2\x96\x81y");
+    EXPECT_THAT(offsets, ::testing::ElementsAre(0, 0, 0, 1, 2, 2, 2, 4));
+  }
+}
+
+TEST(OptimizedEncoder, NormalizeStringReplacement) {
+  flatbuffers::FlatBufferBuilder builder(1024);
+  const std::vector<std::string> norm_prefixes = {"A", "AA", "AAA", "AAAA"};
+  const char norm_replacements[] = "A1\0A2\0A3\0A4";
+  const auto trie_vector =
+      builder.CreateVector(BuildTrie(norm_prefixes, {0, 3, 6, 9}));
+  const auto norm_r = builder.CreateVector<int8_t>(
+      reinterpret_cast<const signed char*>(norm_replacements),
+      sizeof(norm_replacements));
+  TrieBuilder trie_builder(builder);
+  trie_builder.add_nodes(trie_vector);
+  const auto norm_p = trie_builder.Finish();
+  EncoderConfigBuilder ecb(builder);
+  ecb.add_remove_extra_whitespaces(false);
+  ecb.add_normalized_prefixes(norm_p);
+  ecb.add_normalized_replacements(norm_r);
+  FinishEncoderConfigBuffer(builder, ecb.Finish());
+  const EncoderConfig* config = GetEncoderConfig(builder.GetBufferPointer());
+  {
+    const auto result = NormalizeString("ABAABAAABAAAA", *config);
+    const auto res_string = std::get<0>(result);
+    const auto offsets = std::get<1>(result);
+    EXPECT_EQ(res_string, "A1BA2BA3BA4");
+    EXPECT_THAT(offsets,
+                ::testing::ElementsAre(0, 0, 1, 2, 2, 4, 5, 5, 8, 9, 9));
+  }
+}
+
+TEST(OptimizedEncoder, NormalizeStringWhitespacesRemove) {
+  flatbuffers::FlatBufferBuilder builder(1024);
+  const std::vector<std::string> norm_prefixes = {"A", "AA", "AAA", "AAAA",
+                                                  "X"};
+  const char norm_replacements[] = "A1\0A2\0A3\0A4\0 ";
+  const auto trie_vector =
+      builder.CreateVector(BuildTrie(norm_prefixes, {0, 3, 6, 9, 12}));
+  const auto norm_r = builder.CreateVector<int8_t>(
+      reinterpret_cast<const signed char*>(norm_replacements),
+      sizeof(norm_replacements));
+  TrieBuilder trie_builder(builder);
+  trie_builder.add_nodes(trie_vector);
+  const auto norm_p = trie_builder.Finish();
+  EncoderConfigBuilder ecb(builder);
+  ecb.add_remove_extra_whitespaces(true);
+  ecb.add_normalized_prefixes(norm_p);
+  ecb.add_normalized_replacements(norm_r);
+  FinishEncoderConfigBuffer(builder, ecb.Finish());
+  const EncoderConfig* config = GetEncoderConfig(builder.GetBufferPointer());
+  {
+    const auto result = NormalizeString("XXABAABAAABAAAA", *config);
+    const auto res_string = std::get<0>(result);
+    const auto offsets = std::get<1>(result);
+    EXPECT_EQ(res_string, " A1BA2BA3BA4");
+    EXPECT_THAT(offsets,
+                ::testing::ElementsAre(0, 2, 2, 3, 4, 4, 6, 7, 7, 10, 11, 11));
+  }
+}
+
+TEST(OptimizedEncoder, ConfigConverter) {
+  std::string config;
+  auto status =
+      internal::TFReadFileToString(JoinPath("./", kConfigFilePath), &config);
+  ASSERT_TRUE(status.ok());
+
+  ::sentencepiece::SentencePieceProcessor processor;
+  ASSERT_TRUE(processor.LoadFromSerializedProto(config).ok());
+  const auto converted_model = ConvertSentencepieceModel(config);
+  const std::string test_string("Hello world!\\xF0\\x9F\\x8D\\x95");
+  const auto encoded =
+      EncodeString(test_string, converted_model.data(), false, false, false);
+  ASSERT_EQ(encoded.codes.size(), encoded.offsets.size());
+
+  ::sentencepiece::SentencePieceText reference_encoded;
+  ASSERT_TRUE(processor.Encode(test_string, &reference_encoded).ok());
+  EXPECT_EQ(encoded.codes.size(), reference_encoded.pieces_size());
+  for (int i = 0; i < encoded.codes.size(); ++i) {
+    EXPECT_EQ(encoded.codes[i], reference_encoded.pieces(i).id());
+    EXPECT_EQ(encoded.offsets[i], reference_encoded.pieces(i).begin());
+  }
+}
+
+}  // namespace
+}  // namespace mediapipe::tflite_operations::sentencepiece

mediapipe/tasks/cc/text/custom_ops/sentencepiece/sentencepiece_constants.h

@@ -0,0 +1,38 @@
+/* Copyright 2023 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_TEXT_CUSTOM_OPS_SENTENCEPIECE_SENTENCEPIECE_CONSTANTS_H_
+#define MEDIAPIPE_TASKS_CC_TEXT_CUSTOM_OPS_SENTENCEPIECE_SENTENCEPIECE_CONSTANTS_H_
+
+namespace mediapipe::tflite_operations::sentencepiece {
+
+// The constant is copied from
+// https://github.com/google/sentencepiece/blob/master/src/unigram_model.cc
+constexpr float kUnkPenalty = 10.0;
+
+// These constants are copied from
+// https://github.com/google/sentencepiece/blob/master/src/sentencepiece_processor.cc
+//
+// Replaces white space with U+2581 (LOWER ONE EIGHT BLOCK).
+constexpr char kSpaceSymbol[] = "\xe2\x96\x81";
+
+// Encodes <unk> into U+2047 (DOUBLE QUESTION MARK),
+// since this character can be useful both for user and
+// developer. We can easily figure out that <unk> is emitted.
+constexpr char kDefaultUnknownSymbol[] = " \xE2\x81\x87 ";
+
+}  // namespace mediapipe::tflite_operations::sentencepiece
+
+#endif  // MEDIAPIPE_TASKS_CC_TEXT_CUSTOM_OPS_SENTENCEPIECE_SENTENCEPIECE_CONSTANTS_H_

mediapipe/tasks/cc/text/custom_ops/sentencepiece/sentencepiece_tokenizer_tflite.cc

@@ -0,0 +1,129 @@
+/* Copyright 2023 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/text/custom_ops/sentencepiece/sentencepiece_tokenizer_tflite.h"
+
+#include "flatbuffers/flexbuffers.h"
+#include "mediapipe/tasks/cc/text/custom_ops/sentencepiece/optimized_encoder.h"
+#include "tensorflow/lite/c/common.h"
+#include "tensorflow/lite/context.h"
+#include "tensorflow/lite/kernels/internal/tensor.h"
+#include "tensorflow/lite/kernels/kernel_util.h"
+#include "tensorflow/lite/model.h"
+#include "tensorflow/lite/string_util.h"
+
+namespace mediapipe::tflite_operations {
+namespace sentencepiece::tokenizer {
+namespace {
+
+using ::tflite::SetTensorToDynamic;
+
+constexpr int kSPModelIndex = 0;
+constexpr int kInputIndex = 1;
+constexpr int kAddBOSInput = 4;
+constexpr int kAddEOSInput = 5;
+constexpr int kReverseInput = 6;
+
+constexpr int kOutputValuesInd = 0;
+constexpr int kOutputSplitsInd = 1;
+
+TfLiteIntArray* CreateSizeArray(const std::initializer_list<int>& sizes) {
+  TfLiteIntArray* array_size = TfLiteIntArrayCreate(sizes.size());
+  int index = 0;
+  for (const int size : sizes) {
+    array_size->data[index++] = size;
+  }
+  return array_size;
+}
+}  // namespace
+
+// Initializes text encoder object from serialized parameters.
+void* Initialize(TfLiteContext* /*context*/, const char* /*buffer*/,
+                 size_t /*length*/) {
+  return nullptr;
+}
+void Free(TfLiteContext* /*context*/, void* /*buffer*/) {}
+
+TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
+  // TODO: Add checks for input and output tensors.
+  TfLiteTensor& output_values =
+      context->tensors[node->outputs->data[kOutputValuesInd]];
+  SetTensorToDynamic(&output_values);
+
+  TfLiteTensor& output_splits =
+      context->tensors[node->outputs->data[kOutputSplitsInd]];
+  SetTensorToDynamic(&output_splits);
+  return kTfLiteOk;
+}
+
+TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
+  const TfLiteTensor& model_tensor =
+      context->tensors[node->inputs->data[kSPModelIndex]];
+  const auto model_buffer_data = model_tensor.data.data;
+  const TfLiteTensor& input_text =
+      context->tensors[node->inputs->data[kInputIndex]];
+
+  const TfLiteTensor add_bos_tensor =
+      context->tensors[node->inputs->data[kAddBOSInput]];
+  const bool add_bos = add_bos_tensor.data.b[0];
+  const TfLiteTensor add_eos_tensor =
+      context->tensors[node->inputs->data[kAddEOSInput]];
+  const bool add_eos = add_eos_tensor.data.b[0];
+  const TfLiteTensor reverse_tensor =
+      context->tensors[node->inputs->data[kReverseInput]];
+  const bool reverse = reverse_tensor.data.b[0];
+
+  std::vector<int32> encoded;
+  std::vector<int32> splits;
+  const int num_strings = tflite::GetStringCount(&input_text);
+  for (int i = 0; i < num_strings; ++i) {
+    const auto strref = tflite::GetString(&input_text, i);
+    const auto res = EncodeString(std::string(strref.str, strref.len),
+                                  model_buffer_data, add_bos, add_eos, reverse);
+    TF_LITE_ENSURE_MSG(context, res.type == EncoderResultType::SUCCESS,
+                       "Sentencepiece conversion failed");
+    std::copy(res.codes.begin(), res.codes.end(), std::back_inserter(encoded));
+    splits.emplace_back(encoded.size());
+  }
+
+  TfLiteTensor& output_values =
+      context->tensors[node->outputs->data[kOutputValuesInd]];
+  TF_LITE_ENSURE_OK(context,
+                    context->ResizeTensor(
+                        context, &output_values,
+                        CreateSizeArray({static_cast<int>(encoded.size())})));
+  int32_t* output_values_flat = output_values.data.i32;
+  std::copy(encoded.begin(), encoded.end(), output_values_flat);
+  TfLiteTensor& output_splits =
+      context->tensors[node->outputs->data[kOutputSplitsInd]];
+  TF_LITE_ENSURE_OK(
+      context, context->ResizeTensor(
+                   context, &output_splits,
+                   CreateSizeArray({static_cast<int>(splits.size() + 1)})));
+  int32_t* output_splits_flat = output_splits.data.i32;
+  *output_splits_flat = 0;
+  std::copy(splits.begin(), splits.end(), output_splits_flat + 1);
+  return kTfLiteOk;
+}
+}  // namespace sentencepiece::tokenizer
+
+TfLiteRegistration* Register_SENTENCEPIECE_TOKENIZER() {
+  static TfLiteRegistration r = {
+      sentencepiece::tokenizer::Initialize, sentencepiece::tokenizer::Free,
+      sentencepiece::tokenizer::Prepare, sentencepiece::tokenizer::Eval};
+  return &r;
+}
+
+}  // namespace mediapipe::tflite_operations

mediapipe/tasks/cc/text/custom_ops/sentencepiece/sentencepiece_tokenizer_tflite.h

@@ -0,0 +1,27 @@
+/* Copyright 2023 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_TEXT_CUSTOM_OPS_SENTENCEPIECE_SENTENCEPIECE_TOKENIZER_TFLITE_H_
+#define MEDIAPIPE_TASKS_CC_TEXT_CUSTOM_OPS_SENTENCEPIECE_SENTENCEPIECE_TOKENIZER_TFLITE_H_
+
+#include "tensorflow/lite/kernels/register.h"
+
+namespace mediapipe::tflite_operations {
+
+TfLiteRegistration* Register_SENTENCEPIECE_TOKENIZER();
+
+}  // namespace mediapipe::tflite_operations
+
+#endif  // MEDIAPIPE_TASKS_CC_TEXT_CUSTOM_OPS_SENTENCEPIECE_SENTENCEPIECE_TOKENIZER_TFLITE_H_

mediapipe/tasks/cc/text/text_embedder/text_embedder_test.cc

@@ -39,6 +39,8 @@ constexpr char kMobileBert[] = "mobilebert_embedding_with_metadata.tflite";
 // Embedding model with regex preprocessing.
 constexpr char kRegexOneEmbeddingModel[] =
     "regex_one_embedding_with_metadata.tflite";
+constexpr char kUniversalSentenceEncoderModel[] =
+    "universal_sentence_encoder_qa_with_metadata.tflite";
 
 // Tolerance for embedding vector coordinate values.
 constexpr float kEpsilon = 1e-4;
@@ -147,6 +149,35 @@ TEST_F(EmbedderTest, SucceedsWithQuantization) {
   MP_ASSERT_OK(text_embedder->Close());
 }
 
+TEST(EmbedTest, SucceedsWithUniversalSentenceEncoderModel) {
+  auto options = std::make_unique<TextEmbedderOptions>();
+  options->base_options.model_asset_path =
+      JoinPath("./", kTestDataDirectory, kUniversalSentenceEncoderModel);
+  MP_ASSERT_OK_AND_ASSIGN(std::unique_ptr<TextEmbedder> text_embedder,
+                          TextEmbedder::Create(std::move(options)));
+
+  MP_ASSERT_OK_AND_ASSIGN(
+      auto result0,
+      text_embedder->Embed("it's a charming and often affecting journey"));
+  ASSERT_EQ(result0.embeddings.size(), 1);
+  ASSERT_EQ(result0.embeddings[0].float_embedding.size(), 100);
+  ASSERT_NEAR(result0.embeddings[0].float_embedding[0], 1.422951f, kEpsilon);
+
+  MP_ASSERT_OK_AND_ASSIGN(
+      auto result1, text_embedder->Embed("what a great and fantastic trip"));
+  ASSERT_EQ(result1.embeddings.size(), 1);
+  ASSERT_EQ(result1.embeddings[0].float_embedding.size(), 100);
+  ASSERT_NEAR(result1.embeddings[0].float_embedding[0], 1.404664f, kEpsilon);
+
+  // Check cosine similarity.
+  MP_ASSERT_OK_AND_ASSIGN(
+      double similarity, TextEmbedder::CosineSimilarity(result0.embeddings[0],
+                                                        result1.embeddings[0]));
+  ASSERT_NEAR(similarity, 0.851961, kSimilarityTolerancy);
+
+  MP_ASSERT_OK(text_embedder->Close());
+}
+
 TEST_F(EmbedderTest, SucceedsWithMobileBertAndDifferentThemes) {
   auto options = std::make_unique<TextEmbedderOptions>();
   options->base_options.model_asset_path =
@@ -178,5 +209,31 @@ TEST_F(EmbedderTest, SucceedsWithMobileBertAndDifferentThemes) {
   MP_ASSERT_OK(text_embedder->Close());
 }
 
+TEST_F(EmbedderTest, SucceedsWithUSEAndDifferentThemes) {
+  auto options = std::make_unique<TextEmbedderOptions>();
+  options->base_options.model_asset_path =
+      JoinPath("./", kTestDataDirectory, kUniversalSentenceEncoderModel);
+  MP_ASSERT_OK_AND_ASSIGN(std::unique_ptr<TextEmbedder> text_embedder,
+                          TextEmbedder::Create(std::move(options)));
+
+  MP_ASSERT_OK_AND_ASSIGN(
+      TextEmbedderResult result0,
+      text_embedder->Embed("When you go to this restaurant, they hold the "
+                           "pancake upside-down before they hand it "
+                           "to you. It's a great gimmick."));
+  MP_ASSERT_OK_AND_ASSIGN(
+      TextEmbedderResult result1,
+      text_embedder->Embed(
+          "Let's make a plan to steal the declaration of independence."));
+
+  // Check cosine similarity.
+  MP_ASSERT_OK_AND_ASSIGN(
+      double similarity, TextEmbedder::CosineSimilarity(result0.embeddings[0],
+                                                        result1.embeddings[0]));
+  EXPECT_NEAR(similarity, 0.780334, kSimilarityTolerancy);
+
+  MP_ASSERT_OK(text_embedder->Close());
+}
+
 }  // namespace
 }  // namespace mediapipe::tasks::text::text_embedder

mediapipe/tasks/cc/vision/face_stylizer/BUILD

@@ -23,18 +23,12 @@ cc_library(
     srcs = ["face_stylizer_graph.cc"],
     deps = [
         "//mediapipe/calculators/core:split_vector_calculator_cc_proto",
-        "//mediapipe/calculators/image:image_cropping_calculator",
+        "//mediapipe/calculators/image:image_clone_calculator_cc_proto",
-        "//mediapipe/calculators/image:image_cropping_calculator_cc_proto",
-        "//mediapipe/calculators/image:warp_affine_calculator",
-        "//mediapipe/calculators/image:warp_affine_calculator_cc_proto",
         "//mediapipe/calculators/tensor:image_to_tensor_calculator_cc_proto",
         "//mediapipe/calculators/tensor:inference_calculator",
         "//mediapipe/calculators/util:detections_to_rects_calculator",
         "//mediapipe/calculators/util:face_to_rect_calculator",
-        "//mediapipe/calculators/util:from_image_calculator",
-        "//mediapipe/calculators/util:inverse_matrix_calculator",
         "//mediapipe/calculators/util:landmarks_to_detection_calculator_cc_proto",
-        "//mediapipe/calculators/util:to_image_calculator",
         "//mediapipe/framework/api2:builder",
         "//mediapipe/framework/api2:port",
         "//mediapipe/framework/formats:image",
@@ -53,7 +47,6 @@ cc_library(
         "//mediapipe/tasks/cc/vision/face_landmarker:face_landmarker_graph",
         "//mediapipe/tasks/cc/vision/face_landmarker/proto:face_landmarker_graph_options_cc_proto",
         "//mediapipe/tasks/cc/vision/face_landmarker/proto:face_landmarks_detector_graph_options_cc_proto",
-        "//mediapipe/tasks/cc/vision/face_stylizer/calculators:strip_rotation_calculator",
         "//mediapipe/tasks/cc/vision/face_stylizer/calculators:tensors_to_image_calculator",
         "//mediapipe/tasks/cc/vision/face_stylizer/calculators:tensors_to_image_calculator_cc_proto",
         "//mediapipe/tasks/cc/vision/face_stylizer/proto:face_stylizer_graph_options_cc_proto",

mediapipe/tasks/cc/vision/face_stylizer/face_stylizer.h

@@ -84,9 +84,7 @@ class FaceStylizer : tasks::vision::core::BaseVisionTaskApi {
   // The input image can be of any size with format RGB or RGBA.
   // When no face is detected on the input image, the method returns a
   // std::nullopt. Otherwise, returns the stylized image of the most visible
-  // face. To ensure that the output image has reasonable quality, the stylized
+  // face. The stylized output image size is the same as the model output size.
-  // output image size is the smaller of the model output size and the size of
-  // the 'region_of_interest' specified in 'image_processing_options'.
   absl::StatusOr<std::optional<mediapipe::Image>> Stylize(
       mediapipe::Image image,
       std::optional<core::ImageProcessingOptions> image_processing_options =
@@ -111,9 +109,7 @@ class FaceStylizer : tasks::vision::core::BaseVisionTaskApi {
   // must be monotonically increasing.
   // When no face is detected on the input image, the method returns a
   // std::nullopt. Otherwise, returns the stylized image of the most visible
-  // face. To ensure that the output image has reasonable quality, the stylized
+  // face. The stylized output image size is the same as the model output size.
-  // output image size is the smaller of the model output size and the size of
-  // the 'region_of_interest' specified in 'image_processing_options'.
   absl::StatusOr<std::optional<mediapipe::Image>> StylizeForVideo(
       mediapipe::Image image, int64_t timestamp_ms,
       std::optional<core::ImageProcessingOptions> image_processing_options =
@@ -143,10 +139,8 @@ class FaceStylizer : tasks::vision::core::BaseVisionTaskApi {
   // The "result_callback" provides:
   //   - When no face is detected on the input image, the method returns a
   //     std::nullopt. Otherwise, returns the stylized image of the most visible
-  //     face. To ensure that the output image has reasonable quality, the
+  //     face. The stylized output image size is the same as the model output
-  //     stylized output image size is the smaller of the model output size and
+  //     size.
-  //     the size of the 'region_of_interest' specified in
-  //     'image_processing_options'.
   //   - The input timestamp in milliseconds.
   absl::Status StylizeAsync(mediapipe::Image image, int64_t timestamp_ms,
                             std::optional<core::ImageProcessingOptions>

mediapipe/tasks/cc/vision/face_stylizer/face_stylizer_graph.cc

@@ -19,8 +19,7 @@ limitations under the License.
 #include "absl/memory/memory.h"
 #include "absl/status/statusor.h"
 #include "mediapipe/calculators/core/split_vector_calculator.pb.h"
-#include "mediapipe/calculators/image/image_cropping_calculator.pb.h"
+#include "mediapipe/calculators/image/image_clone_calculator.pb.h"
-#include "mediapipe/calculators/image/warp_affine_calculator.pb.h"
 #include "mediapipe/calculators/tensor/image_to_tensor_calculator.pb.h"
 #include "mediapipe/calculators/util/landmarks_to_detection_calculator.pb.h"
 #include "mediapipe/framework/api2/builder.h"
@@ -326,7 +325,6 @@ class FaceStylizerGraph : public core::ModelTaskGraph {
     image_in >> preprocessing.In(kImageTag);
     face_rect >> preprocessing.In(kNormRectTag);
     auto preprocessed_tensors = preprocessing.Out(kTensorsTag);
-    auto transform_matrix = preprocessing.Out(kMatrixTag);
 
     // Adds inference subgraph and connects its input stream to the output
     // tensors produced by the ImageToTensorCalculator.
@@ -344,53 +342,12 @@ class FaceStylizerGraph : public core::ModelTaskGraph {
     model_output_tensors >> tensors_to_image.In(kTensorsTag);
     auto tensor_image = tensors_to_image.Out(kImageTag);
 
-    auto& inverse_matrix = graph.AddNode("InverseMatrixCalculator");
+    auto& image_converter = graph.AddNode("ImageCloneCalculator");
-    transform_matrix >> inverse_matrix.In(kMatrixTag);
+    image_converter.GetOptions<mediapipe::ImageCloneCalculatorOptions>()
-    auto inverse_transform_matrix = inverse_matrix.Out(kMatrixTag);
+        .set_output_on_gpu(false);
+    tensor_image >> image_converter.In("");
 
-    auto& warp_affine = graph.AddNode("WarpAffineCalculator");
+    return {{/*stylized_image=*/image_converter.Out("").Cast<Image>(),
-    auto& warp_affine_options =
-        warp_affine.GetOptions<WarpAffineCalculatorOptions>();
-    warp_affine_options.set_border_mode(
-        WarpAffineCalculatorOptions::BORDER_ZERO);
-    warp_affine_options.set_gpu_origin(mediapipe::GpuOrigin_Mode_TOP_LEFT);
-    tensor_image >> warp_affine.In(kImageTag);
-    inverse_transform_matrix >> warp_affine.In(kMatrixTag);
-    image_size >> warp_affine.In(kOutputSizeTag);
-    auto image_to_crop = warp_affine.Out(kImageTag);
-
-    // The following calculators are for cropping and resizing the output image
-    // based on the roi and the model output size. As the WarpAffineCalculator
-    // rotates the image based on the transform matrix, the rotation info in the
-    // rect proto is stripped to prevent the ImageCroppingCalculator from
-    // performing extra rotation.
-    auto& strip_rotation =
-        graph.AddNode("mediapipe.tasks.StripRotationCalculator");
-    face_rect >> strip_rotation.In(kNormRectTag);
-    auto norm_rect_no_rotation = strip_rotation.Out(kNormRectTag);
-    auto& from_image = graph.AddNode("FromImageCalculator");
-    image_to_crop >> from_image.In(kImageTag);
-    auto& image_cropping = graph.AddNode("ImageCroppingCalculator");
-    auto& image_cropping_opts =
-        image_cropping.GetOptions<ImageCroppingCalculatorOptions>();
-    image_cropping_opts.set_output_max_width(
-        image_to_tensor_options.output_tensor_width());
-    image_cropping_opts.set_output_max_height(
-        image_to_tensor_options.output_tensor_height());
-    norm_rect_no_rotation >> image_cropping.In(kNormRectTag);
-    auto& to_image = graph.AddNode("ToImageCalculator");
-    // ImageCroppingCalculator currently doesn't support mediapipe::Image, the
-    // graph selects its cpu or gpu path based on the image preprocessing
-    // backend.
-    if (use_gpu) {
-      from_image.Out(kImageGpuTag) >> image_cropping.In(kImageGpuTag);
-      image_cropping.Out(kImageGpuTag) >> to_image.In(kImageGpuTag);
-    } else {
-      from_image.Out(kImageCpuTag) >> image_cropping.In(kImageTag);
-      image_cropping.Out(kImageTag) >> to_image.In(kImageCpuTag);
-    }
-
-    return {{/*stylized_image=*/to_image.Out(kImageTag).Cast<Image>(),
              /*original_image=*/preprocessing.Out(kImageTag).Cast<Image>()}};
   }
 };

mediapipe/tasks/cc/vision/pose_landmarker/BUILD

@@ -100,6 +100,7 @@ cc_library(
         "//mediapipe/util:graph_builder_utils",
         "@com_google_absl//absl/status:statusor",
     ],
+    alwayslink = 1,
 )
 
 cc_library(

mediapipe/tasks/ios/components/containers/sources/MPPClassificationResult.h

@@ -90,7 +90,7 @@ NS_SWIFT_NAME(ClassificationResult)
  * 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.
  */
-@property(nonatomic, readonly) NSInteger timestampMs;
+@property(nonatomic, readonly) NSInteger timestampInMilliseconds;
 
 /**
  * Initializes a new `MPPClassificationResult` with the given array of classifications and time
@@ -98,14 +98,15 @@ NS_SWIFT_NAME(ClassificationResult)
  *
  * @param classifications An Array of `MPPClassifications` objects containing the predicted
  * categories for each head of the model.
- * @param timestampMs The timestamp (in milliseconds) of the start of the chunk of data
+ * @param timestampInMilliseconds The timestamp (in milliseconds) of the start of the chunk of data
  * corresponding to these results.
  *
  * @return An instance of `MPPClassificationResult` initialized with the given array of
- * classifications and timestampMs.
+ * classifications and timestamp (in milliseconds).
  */
 - (instancetype)initWithClassifications:(NSArray<MPPClassifications *> *)classifications
-                            timestampMs:(NSInteger)timestampMs NS_DESIGNATED_INITIALIZER;
+                timestampInMilliseconds:(NSInteger)timestampInMilliseconds
+    NS_DESIGNATED_INITIALIZER;
 
 - (instancetype)init NS_UNAVAILABLE;
 

mediapipe/tasks/ios/components/containers/sources/MPPClassificationResult.m

@@ -38,11 +38,11 @@
 @implementation MPPClassificationResult
 
 - (instancetype)initWithClassifications:(NSArray<MPPClassifications *> *)classifications
-                            timestampMs:(NSInteger)timestampMs {
+                timestampInMilliseconds:(NSInteger)timestampInMilliseconds {
   self = [super init];
   if (self) {
     _classifications = classifications;
-    _timestampMs = timestampMs;
+    _timestampInMilliseconds = timestampInMilliseconds;
   }
 
   return self;

mediapipe/tasks/ios/components/containers/sources/MPPEmbeddingResult.h

@@ -33,7 +33,7 @@ NS_SWIFT_NAME(EmbeddingResult)
  * 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.
  */
-@property(nonatomic, readonly) NSInteger timestampMs;
+@property(nonatomic, readonly) NSInteger timestampInMilliseconds;
 
 /**
  * Initializes a new `MPPEmbedding` with the given array of embeddings and timestamp (in
@@ -41,14 +41,14 @@ NS_SWIFT_NAME(EmbeddingResult)
  *
  * @param embeddings An Array of `MPPEmbedding` objects containing the embedding results for each
  * head of the model.
- * @param timestampMs The optional timestamp (in milliseconds) of the start of the chunk of data
+ * @param timestampInMilliseconds The optional timestamp (in milliseconds) of the start of the chunk
- * corresponding to these results. Pass `0` if timestamp is absent.
+ * of data corresponding to these results. Pass `0` if timestamp is absent.
  *
  * @return An instance of `MPPEmbeddingResult` initialized with the given array of embeddings and
- * timestampMs.
+ * timestamp (in milliseconds).
  */
 - (instancetype)initWithEmbeddings:(NSArray<MPPEmbedding *> *)embeddings
-                       timestampMs:(NSInteger)timestampMs NS_DESIGNATED_INITIALIZER;
+           timestampInMilliseconds:(NSInteger)timestampInMilliseconds NS_DESIGNATED_INITIALIZER;
 
 - (instancetype)init NS_UNAVAILABLE;
 

mediapipe/tasks/ios/components/containers/sources/MPPEmbeddingResult.m

@@ -17,11 +17,11 @@
 @implementation MPPEmbeddingResult
 
 - (instancetype)initWithEmbeddings:(NSArray<MPPEmbedding *> *)embeddings
-                       timestampMs:(NSInteger)timestampMs {
+           timestampInMilliseconds:(NSInteger)timestampInMilliseconds {
   self = [super init];
   if (self) {
     _embeddings = embeddings;
-    _timestampMs = timestampMs;
+    _timestampInMilliseconds = timestampInMilliseconds;
   }
 
   return self;

mediapipe/tasks/ios/components/containers/utils/sources/MPPClassificationResult+Helpers.mm

@@ -55,13 +55,13 @@ using ClassificationResultProto =
     [classifications addObject:[MPPClassifications classificationsWithProto:classificationsProto]];
   }
 
-  NSInteger timestampMs = 0;
+  NSInteger timestampInMilliseconds = 0;
   if (classificationResultProto.has_timestamp_ms()) {
-    timestampMs = (NSInteger)classificationResultProto.timestamp_ms();
+    timestampInMilliseconds = (NSInteger)classificationResultProto.timestamp_ms();
   }
 
   return [[MPPClassificationResult alloc] initWithClassifications:classifications
-                                                      timestampMs:timestampMs];
+                                          timestampInMilliseconds:timestampInMilliseconds];
   ;
 }
 

mediapipe/tasks/ios/components/containers/utils/sources/MPPEmbeddingResult+Helpers.mm

@@ -31,12 +31,13 @@ using EmbeddingResultProto = ::mediapipe::tasks::components::containers::proto::
     [embeddings addObject:[MPPEmbedding embeddingWithProto:embeddingProto]];
   }
 
-  NSInteger timestampMs = 0;
+  NSInteger timestampInMilliseconds = 0;
   if (embeddingResultProto.has_timestamp_ms()) {
-    timestampMs = (NSInteger)embeddingResultProto.timestamp_ms();
+    timestampInMilliseconds = (NSInteger)embeddingResultProto.timestamp_ms();
   }
 
-  return [[MPPEmbeddingResult alloc] initWithEmbeddings:embeddings timestampMs:timestampMs];
+  return [[MPPEmbeddingResult alloc] initWithEmbeddings:embeddings
+                                timestampInMilliseconds:timestampInMilliseconds];
 }
 
 @end

mediapipe/tasks/ios/core/sources/MPPTaskResult.h

@@ -26,11 +26,12 @@ NS_SWIFT_NAME(TaskResult)
 /**
  * Timestamp that is associated with the task result object.
  */
-@property(nonatomic, assign, readonly) NSInteger timestampMs;
+@property(nonatomic, assign, readonly) NSInteger timestampInMilliseconds;
 
 - (instancetype)init NS_UNAVAILABLE;
 
-- (instancetype)initWithTimestampMs:(NSInteger)timestampMs NS_DESIGNATED_INITIALIZER;
+- (instancetype)initWithTimestampInMilliseconds:(NSInteger)timestampInMilliseconds
+    NS_DESIGNATED_INITIALIZER;
 
 @end
 

mediapipe/tasks/ios/core/sources/MPPTaskResult.m

@@ -16,16 +16,16 @@
 
 @implementation MPPTaskResult
 
-- (instancetype)initWithTimestampMs:(NSInteger)timestampMs {
+- (instancetype)initWithTimestampInMilliseconds:(NSInteger)timestampInMilliseconds {
   self = [super init];
   if (self) {
-    _timestampMs = timestampMs;
+    _timestampInMilliseconds = timestampInMilliseconds;
   }
   return self;
 }
 
 - (id)copyWithZone:(NSZone *)zone {
-  return [[MPPTaskResult alloc] initWithTimestampMs:self.timestampMs];
+  return [[MPPTaskResult alloc] initWithTimestampInMilliseconds:self.timestampInMilliseconds];
 }
 
 @end

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

@@ -487,7 +487,7 @@ static NSString *const kExpectedErrorDomain = @"com.google.mediapipe.tasks";
 
   NSError *liveStreamApiCallError;
   XCTAssertFalse([imageClassifier classifyAsyncImage:image
-                                         timestampMs:0
+                             timestampInMilliseconds:0
                                                error:&liveStreamApiCallError]);
 
   NSError *expectedLiveStreamApiCallError =
@@ -501,7 +501,9 @@ static NSString *const kExpectedErrorDomain = @"com.google.mediapipe.tasks";
   AssertEqualErrors(liveStreamApiCallError, expectedLiveStreamApiCallError);
 
   NSError *videoApiCallError;
-  XCTAssertFalse([imageClassifier classifyVideoFrame:image timestampMs:0 error:&videoApiCallError]);
+  XCTAssertFalse([imageClassifier classifyVideoFrame:image
+                             timestampInMilliseconds:0
+                                               error:&videoApiCallError]);
 
   NSError *expectedVideoApiCallError =
       [NSError errorWithDomain:kExpectedErrorDomain
@@ -524,7 +526,7 @@ static NSString *const kExpectedErrorDomain = @"com.google.mediapipe.tasks";
 
   NSError *liveStreamApiCallError;
   XCTAssertFalse([imageClassifier classifyAsyncImage:image
-                                         timestampMs:0
+                             timestampInMilliseconds:0
                                                error:&liveStreamApiCallError]);
 
   NSError *expectedLiveStreamApiCallError =
@@ -575,7 +577,9 @@ static NSString *const kExpectedErrorDomain = @"com.google.mediapipe.tasks";
   AssertEqualErrors(imageApiCallError, expectedImageApiCallError);
 
   NSError *videoApiCallError;
-  XCTAssertFalse([imageClassifier classifyVideoFrame:image timestampMs:0 error:&videoApiCallError]);
+  XCTAssertFalse([imageClassifier classifyVideoFrame:image
+                             timestampInMilliseconds:0
+                                               error:&videoApiCallError]);
 
   NSError *expectedVideoApiCallError =
       [NSError errorWithDomain:kExpectedErrorDomain
@@ -601,7 +605,7 @@ static NSString *const kExpectedErrorDomain = @"com.google.mediapipe.tasks";
 
   for (int i = 0; i < 3; i++) {
     MPPImageClassifierResult *imageClassifierResult = [imageClassifier classifyVideoFrame:image
-                                                                              timestampMs:i
+                                                                  timestampInMilliseconds:i
                                                                                     error:nil];
     [self assertImageClassifierResult:imageClassifierResult
            hasExpectedCategoriesCount:maxResults
@@ -630,10 +634,10 @@ static NSString *const kExpectedErrorDomain = @"com.google.mediapipe.tasks";
 
   MPPImage *image = [self imageWithFileInfo:kBurgerImage];
 
-  XCTAssertTrue([imageClassifier classifyAsyncImage:image timestampMs:1 error:nil]);
+  XCTAssertTrue([imageClassifier classifyAsyncImage:image timestampInMilliseconds:1 error:nil]);
 
   NSError *error;
-  XCTAssertFalse([imageClassifier classifyAsyncImage:image timestampMs:0 error:&error]);
+  XCTAssertFalse([imageClassifier classifyAsyncImage:image timestampInMilliseconds:0 error:&error]);
 
   NSError *expectedError =
       [NSError errorWithDomain:kExpectedErrorDomain
@@ -668,7 +672,7 @@ static NSString *const kExpectedErrorDomain = @"com.google.mediapipe.tasks";
   MPPImage *image = [self imageWithFileInfo:kBurgerImage];
 
   for (int i = 0; i < 3; i++) {
-    XCTAssertTrue([imageClassifier classifyAsyncImage:image timestampMs:i error:nil]);
+    XCTAssertTrue([imageClassifier classifyAsyncImage:image timestampInMilliseconds:i error:nil]);
   }
 }
 

mediapipe/tasks/ios/text/text_classifier/sources/MPPTextClassifierResult.h

@@ -31,13 +31,13 @@ NS_SWIFT_NAME(TextClassifierResult)
  *
  * @param classificationResult The `MPPClassificationResult` instance containing one set of results
  * per classifier head.
- * @param timestampMs The timestamp for this result.
+ * @param timestampInMilliseconds The timestamp (in milliseconds) for this result.
  *
  * @return An instance of `MPPTextClassifierResult` initialized with the given
  * `MPPClassificationResult` and timestamp (in milliseconds).
  */
 - (instancetype)initWithClassificationResult:(MPPClassificationResult *)classificationResult
-                                 timestampMs:(NSInteger)timestampMs;
+                     timestampInMilliseconds:(NSInteger)timestampInMilliseconds;
 
 @end
 

mediapipe/tasks/ios/text/text_classifier/sources/MPPTextClassifierResult.m

@@ -17,8 +17,8 @@
 @implementation MPPTextClassifierResult
 
 - (instancetype)initWithClassificationResult:(MPPClassificationResult *)classificationResult
-                                 timestampMs:(NSInteger)timestampMs {
+                                 timestampInMilliseconds:(NSInteger)timestampInMilliseconds {
-  self = [super initWithTimestampMs:timestampMs];
+  self = [super initWithTimestampInMilliseconds:timestampInMilliseconds];
   if (self) {
     _classificationResult = classificationResult;
   }

mediapipe/tasks/ios/text/text_classifier/utils/sources/MPPTextClassifierResult+Helpers.mm

@@ -35,7 +35,7 @@ using ::mediapipe::Packet;
 
   return [[MPPTextClassifierResult alloc]
       initWithClassificationResult:classificationResult
-                       timestampMs:(NSInteger)(packet.Timestamp().Value() /
+           timestampInMilliseconds:(NSInteger)(packet.Timestamp().Value() /
                                                kMicroSecondsPerMilliSecond)];
 }
 

mediapipe/tasks/ios/text/text_embedder/sources/MPPTextEmbedderResult.h

@@ -31,13 +31,13 @@ NS_SWIFT_NAME(TextEmbedderResult)
  *
  * @param embeddingResult The `MPPEmbeddingResult` instance containing one set of results
  * per classifier head.
- * @param timestampMs The timestamp for this result.
+ * @param timestampInMilliseconds The timestamp (in millisecondss) for this result.
  *
  * @return An instance of `MPPTextEmbedderResult` initialized with the given
  * `MPPEmbeddingResult` and timestamp (in milliseconds).
  */
 - (instancetype)initWithEmbeddingResult:(MPPEmbeddingResult *)embeddingResult
-                            timestampMs:(NSInteger)timestampMs;
+                timestampInMilliseconds:(NSInteger)timestampInMilliseconds;
 
 - (instancetype)init NS_UNAVAILABLE;
 

mediapipe/tasks/ios/text/text_embedder/sources/MPPTextEmbedderResult.m

@@ -17,8 +17,8 @@
 @implementation MPPTextEmbedderResult
 
 - (instancetype)initWithEmbeddingResult:(MPPEmbeddingResult *)embeddingResult
-                            timestampMs:(NSInteger)timestampMs {
+                timestampInMilliseconds:(NSInteger)timestampInMilliseconds {
-  self = [super initWithTimestampMs:timestampMs];
+  self = [super initWithTimestampInMilliseconds:timestampInMilliseconds];
   if (self) {
     _embeddingResult = embeddingResult;
   }

mediapipe/tasks/ios/text/text_embedder/utils/sources/MPPTextEmbedderResult+Helpers.mm

@@ -34,7 +34,7 @@ using ::mediapipe::Packet;
 
   return [[MPPTextEmbedderResult alloc]
       initWithEmbeddingResult:embeddingResult
-                  timestampMs:(NSInteger)(packet.Timestamp().Value() /
+      timestampInMilliseconds:(NSInteger)(packet.Timestamp().Value() /
                                           kMicroSecondsPerMilliSecond)];
 }
 

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

@@ -41,7 +41,7 @@
  * timestamp.
  *
  * @param image The image to send to the MediaPipe graph.
- * @param timestampMs The timestamp (in milliseconds) to assign to the packet.
+ * @param timestampInMilliseconds The timestamp (in milliseconds) to assign to the packet.
  * @param error Pointer to the memory location where errors if any should be saved. If @c NULL, no
  *    error will be saved.
  *
@@ -49,7 +49,7 @@
  *    occurred during the conversion.
  */
 + (mediapipe::Packet)createPacketWithMPPImage:(MPPImage *)image
-                                  timestampMs:(NSInteger)timestampMs
+                      timestampInMilliseconds:(NSInteger)timestampInMilliseconds
                                         error:(NSError **)error;
 
 /**
@@ -66,11 +66,11 @@
  * specified timestamp.
  *
  * @param image The `NormalizedRect` to send to the MediaPipe graph.
- * @param timestampMs The timestamp (in milliseconds) to assign to the packet.
+ * @param timestampInMilliseconds The timestamp (in milliseconds) to assign to the packet.
  *
  * @return The MediaPipe packet containing the normalized rect.
  */
 + (mediapipe::Packet)createPacketWithNormalizedRect:(mediapipe::NormalizedRect &)normalizedRect
-                                        timestampMs:(NSInteger)timestampMs;
+                            timestampInMilliseconds:(NSInteger)timestampInMilliseconds;
 
 @end

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

@@ -42,7 +42,7 @@ using ::mediapipe::Timestamp;
 }
 
 + (Packet)createPacketWithMPPImage:(MPPImage *)image
-                       timestampMs:(NSInteger)timestampMs
+           timestampInMilliseconds:(NSInteger)timestampInMilliseconds
                              error:(NSError **)error {
   std::unique_ptr<ImageFrame> imageFrame = [image imageFrameWithError:error];
 
@@ -51,7 +51,7 @@ using ::mediapipe::Timestamp;
   }
 
   return MakePacket<Image>(std::move(imageFrame))
-      .At(Timestamp(int64(timestampMs * kMicroSecondsPerMilliSecond)));
+      .At(Timestamp(int64(timestampInMilliseconds * kMicroSecondsPerMilliSecond)));
 }
 
 + (Packet)createPacketWithNormalizedRect:(NormalizedRect &)normalizedRect {
@@ -59,9 +59,9 @@ using ::mediapipe::Timestamp;
 }
 
 + (Packet)createPacketWithNormalizedRect:(NormalizedRect &)normalizedRect
-                             timestampMs:(NSInteger)timestampMs {
+                 timestampInMilliseconds:(NSInteger)timestampInMilliseconds {
   return MakePacket<NormalizedRect>(std::move(normalizedRect))
-      .At(Timestamp(int64(timestampMs * kMicroSecondsPerMilliSecond)));
+      .At(Timestamp(int64(timestampInMilliseconds * kMicroSecondsPerMilliSecond)));
 }
 
 @end

mediapipe/tasks/ios/vision/gesture_recognizer/sources/MPPGestureRecognizerResult.m

@@ -21,7 +21,7 @@
                        handedness:(NSArray<NSArray<MPPCategory *> *> *)handedness
                          gestures:(NSArray<NSArray<MPPCategory *> *> *)gestures
           timestampInMilliseconds:(NSInteger)timestampInMilliseconds {
-  self = [super initWithTimestampMs:timestampInMilliseconds];
+  self = [super initWithTimestampInMilliseconds:timestampInMilliseconds];
   if (self) {
     _landmarks = landmarks;
     _worldLandmarks = worldLandmarks;

mediapipe/tasks/ios/vision/image_classifier/sources/MPPImageClassifier.h

@@ -122,17 +122,17 @@ NS_SWIFT_NAME(ImageClassifier)
  * `MPPRunningModeVideo`.
  *
  * @param image The `MPPImage` on which image classification is to be performed.
- * @param timestampMs The video frame's timestamp (in milliseconds). The input timestamps must be
+ * @param timestampInMilliseconds The video frame's timestamp (in milliseconds). The input
- * monotonically increasing.
+ * timestamps must be monotonically increasing.
  * @param error An optional error parameter populated when there is an error in performing image
  * classification on the input video frame.
  *
  * @return  An `MPPImageClassifierResult` object that contains a list of image classifications.
  */
 - (nullable MPPImageClassifierResult *)classifyVideoFrame:(MPPImage *)image
-                                              timestampMs:(NSInteger)timestampMs
+                                  timestampInMilliseconds:(NSInteger)timestampInMilliseconds
                                                     error:(NSError **)error
-    NS_SWIFT_NAME(classify(videoFrame:timestampMs:));
+    NS_SWIFT_NAME(classify(videoFrame:timestampInMilliseconds:));
 
 /**
  * Performs image classification on the provided video frame of type `MPPImage` cropped to the
@@ -145,8 +145,8 @@ NS_SWIFT_NAME(ImageClassifier)
  *
  * @param image A live stream image data of type `MPPImage` on which image classification is to be
  * performed.
- * @param timestampMs The video frame's timestamp (in milliseconds). The input timestamps must be
+ * @param timestampInMilliseconds The video frame's timestamp (in milliseconds). The input
- * monotonically increasing.
+ * timestamps must be monotonically increasing.
  * @param roi A `CGRect` specifying the region of interest within the video frame of type
  * `MPPImage`, on which image classification should be performed.
  * @param error An optional error parameter populated when there is an error in performing image
@@ -155,10 +155,10 @@ NS_SWIFT_NAME(ImageClassifier)
  * @return  An `MPPImageClassifierResult` object that contains a list of image classifications.
  */
 - (nullable MPPImageClassifierResult *)classifyVideoFrame:(MPPImage *)image
-                                              timestampMs:(NSInteger)timestampMs
+                                  timestampInMilliseconds:(NSInteger)timestampInMilliseconds
                                          regionOfInterest:(CGRect)roi
                                                     error:(NSError **)error
-    NS_SWIFT_NAME(classify(videoFrame:timestampMs:regionOfInterest:));
+    NS_SWIFT_NAME(classify(videoFrame:timestampInMilliseconds:regionOfInterest:));
 
 /**
  * Sends live stream image data of type `MPPImage` to perform image classification using the whole
@@ -172,16 +172,17 @@ NS_SWIFT_NAME(ImageClassifier)
  *
  * @param image A live stream image data of type `MPPImage` on which image classification is to be
  * performed.
- * @param timestampMs The timestamp (in milliseconds) which indicates when the input image is sent
+ * @param timestampInMilliseconds The timestamp (in milliseconds) which indicates when the input
- * to the image classifier. The input timestamps must be monotonically increasing.
+ * image is sent to the image classifier. The input timestamps must be monotonically increasing.
  * @param error An optional error parameter populated when there is an error in performing image
  * classification on the input live stream image data.
  *
  * @return `YES` if the image was sent to the task successfully, otherwise `NO`.
  */
 - (BOOL)classifyAsyncImage:(MPPImage *)image
-               timestampMs:(NSInteger)timestampMs
+    timestampInMilliseconds:(NSInteger)timestampInMilliseconds
-                     error:(NSError **)error NS_SWIFT_NAME(classifyAsync(image:timestampMs:));
+                      error:(NSError **)error
+    NS_SWIFT_NAME(classifyAsync(image:timestampInMilliseconds:));
 
 /**
  * Sends live stream image data of type `MPPImage` to perform image classification, cropped to the
@@ -195,8 +196,8 @@ NS_SWIFT_NAME(ImageClassifier)
  *
  * @param image A live stream image data of type `MPPImage` on which image classification is to be
  * performed.
- * @param timestampMs The timestamp (in milliseconds) which indicates when the input image is sent
+ * @param timestampInMilliseconds The timestamp (in milliseconds) which indicates when the input
- * to the image classifier. The input timestamps must be monotonically increasing.
+ * image is sent to the image classifier. The input timestamps must be monotonically increasing.
  * @param roi A `CGRect` specifying the region of interest within the given live stream image data
  * of type `MPPImage`, on which image classification should be performed.
  * @param error An optional error parameter populated when there is an error in performing image
@@ -205,10 +206,10 @@ NS_SWIFT_NAME(ImageClassifier)
  * @return `YES` if the image was sent to the task successfully, otherwise `NO`.
  */
 - (BOOL)classifyAsyncImage:(MPPImage *)image
-               timestampMs:(NSInteger)timestampMs
+    timestampInMilliseconds:(NSInteger)timestampInMilliseconds
-          regionOfInterest:(CGRect)roi
+           regionOfInterest:(CGRect)roi
-                     error:(NSError **)error
+                      error:(NSError **)error
-    NS_SWIFT_NAME(classifyAsync(image:timestampMs:regionOfInterest:));
+    NS_SWIFT_NAME(classifyAsync(image:timestampInMilliseconds:regionOfInterest:));
 
 - (instancetype)init NS_UNAVAILABLE;
 

mediapipe/tasks/ios/vision/image_classifier/sources/MPPImageClassifier.mm

@@ -149,7 +149,7 @@ static NSString *const kTaskGraphName =
 }
 
 - (std::optional<PacketMap>)inputPacketMapWithMPPImage:(MPPImage *)image
-                                           timestampMs:(NSInteger)timestampMs
+                               timestampInMilliseconds:(NSInteger)timestampInMilliseconds
                                       regionOfInterest:(CGRect)roi
                                                  error:(NSError **)error {
   std::optional<NormalizedRect> rect =
@@ -162,14 +162,15 @@ static NSString *const kTaskGraphName =
   }
 
   Packet imagePacket = [MPPVisionPacketCreator createPacketWithMPPImage:image
-                                                            timestampMs:timestampMs
+                                                timestampInMilliseconds:timestampInMilliseconds
                                                                   error:error];
   if (imagePacket.IsEmpty()) {
     return std::nullopt;
   }
 
-  Packet normalizedRectPacket = [MPPVisionPacketCreator createPacketWithNormalizedRect:rect.value()
+  Packet normalizedRectPacket =
-                                                                           timestampMs:timestampMs];
+      [MPPVisionPacketCreator createPacketWithNormalizedRect:rect.value()
+                                     timestampInMilliseconds:timestampInMilliseconds];
 
   PacketMap inputPacketMap = InputPacketMap(imagePacket, normalizedRectPacket);
   return inputPacketMap;
@@ -180,11 +181,11 @@ static NSString *const kTaskGraphName =
 }
 
 - (nullable MPPImageClassifierResult *)classifyVideoFrame:(MPPImage *)image
-                                              timestampMs:(NSInteger)timestampMs
+                                  timestampInMilliseconds:(NSInteger)timestampInMilliseconds
                                          regionOfInterest:(CGRect)roi
                                                     error:(NSError **)error {
   std::optional<PacketMap> inputPacketMap = [self inputPacketMapWithMPPImage:image
-                                                                 timestampMs:timestampMs
+                                                     timestampInMilliseconds:timestampInMilliseconds
                                                             regionOfInterest:roi
                                                                        error:error];
   if (!inputPacketMap.has_value()) {
@@ -204,20 +205,20 @@ static NSString *const kTaskGraphName =
 }
 
 - (nullable MPPImageClassifierResult *)classifyVideoFrame:(MPPImage *)image
-                                              timestampMs:(NSInteger)timestampMs
+                                  timestampInMilliseconds:(NSInteger)timestampInMilliseconds
                                                     error:(NSError **)error {
   return [self classifyVideoFrame:image
-                      timestampMs:timestampMs
+          timestampInMilliseconds:timestampInMilliseconds
                  regionOfInterest:CGRectZero
                             error:error];
 }
 
 - (BOOL)classifyAsyncImage:(MPPImage *)image
-               timestampMs:(NSInteger)timestampMs
+    timestampInMilliseconds:(NSInteger)timestampInMilliseconds
-          regionOfInterest:(CGRect)roi
+           regionOfInterest:(CGRect)roi
-                     error:(NSError **)error {
+                      error:(NSError **)error {
   std::optional<PacketMap> inputPacketMap = [self inputPacketMapWithMPPImage:image
-                                                                 timestampMs:timestampMs
+                                                     timestampInMilliseconds:timestampInMilliseconds
                                                             regionOfInterest:roi
                                                                        error:error];
   if (!inputPacketMap.has_value()) {
@@ -228,10 +229,10 @@ static NSString *const kTaskGraphName =
 }
 
 - (BOOL)classifyAsyncImage:(MPPImage *)image
-               timestampMs:(NSInteger)timestampMs
+    timestampInMilliseconds:(NSInteger)timestampInMilliseconds
-                     error:(NSError **)error {
+                      error:(NSError **)error {
   return [self classifyAsyncImage:image
-                      timestampMs:timestampMs
+          timestampInMilliseconds:timestampInMilliseconds
                  regionOfInterest:CGRectZero
                             error:error];
 }

mediapipe/tasks/ios/vision/image_classifier/sources/MPPImageClassifierResult.h

@@ -31,13 +31,13 @@ NS_SWIFT_NAME(ImageClassifierResult)
  *
  * @param classificationResult The `MPPClassificationResult` instance containing one set of results
  * per classifier head.
- * @param timestampMs The timestamp for this result.
+ * @param timestampInMilliseconds The timestamp (in milliseconds) for this result.
  *
  * @return An instance of `MPPImageClassifierResult` initialized with the given
  * `MPPClassificationResult` and timestamp (in milliseconds).
  */
 - (instancetype)initWithClassificationResult:(MPPClassificationResult *)classificationResult
-                                 timestampMs:(NSInteger)timestampMs;
+                     timestampInMilliseconds:(NSInteger)timestampInMilliseconds;
 
 @end
 

mediapipe/tasks/ios/vision/image_classifier/sources/MPPImageClassifierResult.m

@@ -17,8 +17,8 @@
 @implementation MPPImageClassifierResult
 
 - (instancetype)initWithClassificationResult:(MPPClassificationResult *)classificationResult
-                                 timestampMs:(NSInteger)timestampMs {
+                     timestampInMilliseconds:(NSInteger)timestampInMilliseconds {
-  self = [super initWithTimestampMs:timestampMs];
+  self = [super initWithTimestampInMilliseconds:timestampInMilliseconds];
   if (self) {
     _classificationResult = classificationResult;
   }

mediapipe/tasks/ios/vision/image_classifier/utils/sources/MPPImageClassifierResult+Helpers.mm

@@ -34,7 +34,7 @@ using ::mediapipe::Packet;
 
   return [[MPPImageClassifierResult alloc]
       initWithClassificationResult:classificationResult
-                       timestampMs:(NSInteger)(packet.Timestamp().Value() /
+           timestampInMilliseconds:(NSInteger)(packet.Timestamp().Value() /
                                                kMicroSecondsPerMilliSecond)];
 }
 

mediapipe/tasks/ios/vision/object_detector/sources/MPPObjectDetectionResult.h

@@ -36,13 +36,13 @@ NS_SWIFT_NAME(ObjectDetectionResult)
  * @param detections An array of `MPPDetection` objects each of which has a bounding box that is
  * expressed in the unrotated input frame of reference coordinates system, i.e. in `[0,image_width)
  * x [0,image_height)`, which are the dimensions of the underlying image data.
- * @param timestampMs The timestamp for this result.
+ * @param timestampInMilliseconds The timestamp (in milliseconds) for this result.
  *
  * @return An instance of `MPPObjectDetectionResult` initialized with the given array of detections
  * and timestamp (in milliseconds).
  */
 - (instancetype)initWithDetections:(NSArray<MPPDetection *> *)detections
-                       timestampMs:(NSInteger)timestampMs;
+           timestampInMilliseconds:(NSInteger)timestampInMilliseconds;
 
 @end
 

mediapipe/tasks/ios/vision/object_detector/sources/MPPObjectDetectionResult.m

@@ -17,8 +17,8 @@
 @implementation MPPObjectDetectionResult
 
 - (instancetype)initWithDetections:(NSArray<MPPDetection *> *)detections
-                       timestampMs:(NSInteger)timestampMs {
+           timestampInMilliseconds:(NSInteger)timestampInMilliseconds {
-  self = [super initWithTimestampMs:timestampMs];
+  self = [super initWithTimestampInMilliseconds:timestampInMilliseconds];
   if (self) {
     _detections = detections;
   }

mediapipe/tasks/ios/vision/object_detector/sources/MPPObjectDetector.h

@@ -138,8 +138,8 @@ NS_SWIFT_NAME(ObjectDetector)
  * `MPPRunningModeVideo`.
  *
  * @param image The `MPPImage` on which object detection is to be performed.
- * @param timestampMs The video frame's timestamp (in milliseconds). The input timestamps must be
+ * @param timestampInMilliseconds The video frame's timestamp (in milliseconds). The input
- * monotonically increasing.
+ * timestamps must be monotonically increasing.
  * @param error An optional error parameter populated when there is an error in performing object
  * detection on the input image.
  *
@@ -149,9 +149,9 @@ NS_SWIFT_NAME(ObjectDetector)
  * image data.
  */
 - (nullable MPPObjectDetectionResult *)detectInVideoFrame:(MPPImage *)image
-                                              timestampMs:(NSInteger)timestampMs
+                                  timestampInMilliseconds:(NSInteger)timestampInMilliseconds
                                                     error:(NSError **)error
-    NS_SWIFT_NAME(detect(videoFrame:timestampMs:));
+    NS_SWIFT_NAME(detect(videoFrame:timestampInMilliseconds:));
 
 /**
  * Performs object detection on the provided video frame of type `MPPImage` cropped to the
@@ -164,8 +164,8 @@ NS_SWIFT_NAME(ObjectDetector)
  *
  * @param image A live stream image data of type `MPPImage` on which object detection is to be
  * performed.
- * @param timestampMs The video frame's timestamp (in milliseconds). The input timestamps must be
+ * @param timestampInMilliseconds The video frame's timestamp (in milliseconds). The input
- * monotonically increasing.
+ * timestamps must be monotonically increasing.
  * @param roi A `CGRect` specifying the region of interest within the given `MPPImage`, on which
  * object detection should be performed.
  *
@@ -178,10 +178,10 @@ NS_SWIFT_NAME(ObjectDetector)
  * image data.
  */
 - (nullable MPPObjectDetectionResult *)detectInVideoFrame:(MPPImage *)image
-                                              timestampMs:(NSInteger)timestampMs
+                                  timestampInMilliseconds:(NSInteger)timestampInMilliseconds
                                          regionOfInterest:(CGRect)roi
                                                     error:(NSError **)error
-    NS_SWIFT_NAME(detect(videoFrame:timestampMs:regionOfInterest:));
+    NS_SWIFT_NAME(detect(videoFrame:timestampInMilliseconds:regionOfInterest:));
 
 /**
  * Sends live stream image data of type `MPPImage` to perform object detection using the whole
@@ -195,16 +195,17 @@ NS_SWIFT_NAME(ObjectDetector)
  *
  * @param image A live stream image data of type `MPPImage` on which object detection is to be
  * performed.
- * @param timestampMs The timestamp (in milliseconds) which indicates when the input image is sent
+ * @param timestampInMilliseconds The timestamp (in milliseconds) which indicates when the input
- * to the object detector. The input timestamps must be monotonically increasing.
+ * image is sent to the object detector. The input timestamps must be monotonically increasing.
  * @param error An optional error parameter populated when there is an error in performing object
  * detection on the input live stream image data.
  *
  * @return `YES` if the image was sent to the task successfully, otherwise `NO`.
  */
 - (BOOL)detectAsyncInImage:(MPPImage *)image
-               timestampMs:(NSInteger)timestampMs
+    timestampInMilliseconds:(NSInteger)timestampInMilliseconds
-                     error:(NSError **)error NS_SWIFT_NAME(detectAsync(image:timestampMs:));
+                      error:(NSError **)error
+    NS_SWIFT_NAME(detectAsync(image:timestampInMilliseconds:));
 
 /**
  * Sends live stream image data of type `MPPImage` to perform object detection, cropped to the
@@ -218,8 +219,8 @@ NS_SWIFT_NAME(ObjectDetector)
  *
  * @param image A live stream image data of type `MPPImage` on which object detection is to be
  * performed.
- * @param timestampMs The timestamp (in milliseconds) which indicates when the input image is sent
+ * @param timestampInMilliseconds The timestamp (in milliseconds) which indicates when the input
- * to the object detector. The input timestamps must be monotonically increasing.
+ * image is sent to the object detector. The input timestamps must be monotonically increasing.
  * @param roi A `CGRect` specifying the region of interest within the given live stream image data
  * of type `MPPImage`, on which iobject detection should be performed.
  * @param error An optional error parameter populated when there is an error in performing object
@@ -228,10 +229,10 @@ NS_SWIFT_NAME(ObjectDetector)
  * @return `YES` if the image was sent to the task successfully, otherwise `NO`.
  */
 - (BOOL)detectAsyncInImage:(MPPImage *)image
-               timestampMs:(NSInteger)timestampMs
+    timestampInMilliseconds:(NSInteger)timestampInMilliseconds
-          regionOfInterest:(CGRect)roi
+           regionOfInterest:(CGRect)roi
-                     error:(NSError **)error
+                      error:(NSError **)error
-    NS_SWIFT_NAME(detectAsync(image:timestampMs:regionOfInterest:));
+    NS_SWIFT_NAME(detectAsync(image:timestampInMilliseconds:regionOfInterest:));
 
 - (instancetype)init NS_UNAVAILABLE;
 

mediapipe/tasks/ios/vision/object_detector/sources/MPPObjectDetector.mm

@@ -157,7 +157,7 @@ static NSString *const kTaskGraphName = @"mediapipe.tasks.vision.ObjectDetectorG
 }
 
 - (std::optional<PacketMap>)inputPacketMapWithMPPImage:(MPPImage *)image
-                                           timestampMs:(NSInteger)timestampMs
+                               timestampInMilliseconds:(NSInteger)timestampInMilliseconds
                                       regionOfInterest:(CGRect)roi
                                                  error:(NSError **)error {
   std::optional<NormalizedRect> rect =
@@ -170,14 +170,15 @@ static NSString *const kTaskGraphName = @"mediapipe.tasks.vision.ObjectDetectorG
   }
 
   Packet imagePacket = [MPPVisionPacketCreator createPacketWithMPPImage:image
-                                                            timestampMs:timestampMs
+                                                timestampInMilliseconds:timestampInMilliseconds
                                                                   error:error];
   if (imagePacket.IsEmpty()) {
     return std::nullopt;
   }
 
-  Packet normalizedRectPacket = [MPPVisionPacketCreator createPacketWithNormalizedRect:rect.value()
+  Packet normalizedRectPacket =
-                                                                           timestampMs:timestampMs];
+      [MPPVisionPacketCreator createPacketWithNormalizedRect:rect.value()
+                                     timestampInMilliseconds:timestampInMilliseconds];
 
   PacketMap inputPacketMap = InputPacketMap(imagePacket, normalizedRectPacket);
   return inputPacketMap;
@@ -188,11 +189,11 @@ static NSString *const kTaskGraphName = @"mediapipe.tasks.vision.ObjectDetectorG
 }
 
 - (nullable MPPObjectDetectionResult *)detectInVideoFrame:(MPPImage *)image
-                                              timestampMs:(NSInteger)timestampMs
+                                  timestampInMilliseconds:(NSInteger)timestampInMilliseconds
                                          regionOfInterest:(CGRect)roi
                                                     error:(NSError **)error {
   std::optional<PacketMap> inputPacketMap = [self inputPacketMapWithMPPImage:image
-                                                                 timestampMs:timestampMs
+                                                     timestampInMilliseconds:timestampInMilliseconds
                                                             regionOfInterest:roi
                                                                        error:error];
   if (!inputPacketMap.has_value()) {
@@ -212,20 +213,20 @@ static NSString *const kTaskGraphName = @"mediapipe.tasks.vision.ObjectDetectorG
 }
 
 - (nullable MPPObjectDetectionResult *)detectInVideoFrame:(MPPImage *)image
-                                              timestampMs:(NSInteger)timestampMs
+                                  timestampInMilliseconds:(NSInteger)timestampInMilliseconds
                                                     error:(NSError **)error {
   return [self detectInVideoFrame:image
-                      timestampMs:timestampMs
+          timestampInMilliseconds:timestampInMilliseconds
                  regionOfInterest:CGRectZero
                             error:error];
 }
 
 - (BOOL)detectAsyncInImage:(MPPImage *)image
-               timestampMs:(NSInteger)timestampMs
+    timestampInMilliseconds:(NSInteger)timestampInMilliseconds
-          regionOfInterest:(CGRect)roi
+           regionOfInterest:(CGRect)roi
-                     error:(NSError **)error {
+                      error:(NSError **)error {
   std::optional<PacketMap> inputPacketMap = [self inputPacketMapWithMPPImage:image
-                                                                 timestampMs:timestampMs
+                                                     timestampInMilliseconds:timestampInMilliseconds
                                                             regionOfInterest:roi
                                                                        error:error];
   if (!inputPacketMap.has_value()) {
@@ -236,10 +237,10 @@ static NSString *const kTaskGraphName = @"mediapipe.tasks.vision.ObjectDetectorG
 }
 
 - (BOOL)detectAsyncInImage:(MPPImage *)image
-               timestampMs:(NSInteger)timestampMs
+    timestampInMilliseconds:(NSInteger)timestampInMilliseconds
-                     error:(NSError **)error {
+                      error:(NSError **)error {
   return [self detectAsyncInImage:image
-                      timestampMs:timestampMs
+          timestampInMilliseconds:timestampInMilliseconds
                  regionOfInterest:CGRectZero
                             error:error];
 }

mediapipe/tasks/ios/vision/object_detector/utils/sources/MPPObjectDetectionResult+Helpers.mm

@@ -38,8 +38,9 @@ using ::mediapipe::Packet;
   }
 
   return [[MPPObjectDetectionResult alloc]
-      initWithDetections:detections
+           initWithDetections:detections
-             timestampMs:(NSInteger)(packet.Timestamp().Value() / kMicroSecondsPerMilliSecond)];
+      timestampInMilliseconds:(NSInteger)(packet.Timestamp().Value() /
+                                          kMicroSecondsPerMilliSecond)];
 }
 
 @end

mediapipe/tasks/java/com/google/mediapipe/tasks/vision/facestylizer/FaceStylizer.java

@@ -198,9 +198,9 @@ public final class FaceStylizer extends BaseVisionTaskApi {
    *   <li>{@link android.graphics.Bitmap.Config#ARGB_8888}
    * </ul>
    *
-   * <p>The image can be of any size. To ensure that the output image has reasonable quality, the
+   * <p>The input image can be of any size. The output image is the stylized image with the most
-   * size of the stylized output is based the model output size and can be smaller than the input
+   * visible face. The stylized output image size is the same as the model output size. When no face
-   * image.
+   * is detected on the input image, returns {@code Optional.empty()}.
    *
    * @param image a MediaPipe {@link MPImage} object for processing.
    * @throws MediaPipeException if there is an internal error. Or if {@link FaceStylizer} is created
@@ -220,9 +220,9 @@ public final class FaceStylizer extends BaseVisionTaskApi {
    *   <li>{@link android.graphics.Bitmap.Config#ARGB_8888}
    * </ul>
    *
-   * <p>The input image can be of any size. To ensure that the output image has reasonable quality,
+   * <p>The input image can be of any size. The output image is the stylized image with the most
-   * the stylized output image size is the smaller of the model output size and the size of the
+   * visible face. The stylized output image size is the same as the model output size. When no face
-   * {@link ImageProcessingOptions#regionOfInterest} specified in {@code imageProcessingOptions}.
+   * is detected on the input image, returns {@code Optional.empty()}.
    *
    * @param image a MediaPipe {@link MPImage} object for processing.
    * @param imageProcessingOptions the {@link ImageProcessingOptions} specifying how to process the
@@ -256,9 +256,9 @@ public final class FaceStylizer extends BaseVisionTaskApi {
    *   <li>{@link android.graphics.Bitmap.Config#ARGB_8888}
    * </ul>
    *
-   * <p>The image can be of any size. To ensure that the output image has reasonable quality, the
+   * <p>The input image can be of any size. The output image is the stylized image with the most
-   * size of the stylized output is based the model output size and can be smaller than the input
+   * visible face. The stylized output image size is the same as the model output size. When no face
-   * image.
+   * is detected on the input image, returns {@code Optional.empty()}.
    *
    * @param image a MediaPipe {@link MPImage} object for processing.
    * @throws IllegalArgumentException if the {@link ImageProcessingOptions} specify a
@@ -281,9 +281,9 @@ public final class FaceStylizer extends BaseVisionTaskApi {
    *   <li>{@link android.graphics.Bitmap.Config#ARGB_8888}
    * </ul>
    *
-   * <p>The input image can be of any size. To ensure that the output image has reasonable quality,
+   * <p>The input image can be of any size. The output image is the stylized image with the most
-   * the stylized output image size is the smaller of the model output size and the size of the
+   * visible face. The stylized output image size is the same as the model output size. When no face
-   * {@link ImageProcessingOptions#regionOfInterest} specified in {@code imageProcessingOptions}.
+   * is detected on the input image, returns {@code Optional.empty()}.
    *
    * @param image a MediaPipe {@link MPImage} object for processing.
    * @param imageProcessingOptions the {@link ImageProcessingOptions} specifying how to process the
@@ -320,9 +320,9 @@ public final class FaceStylizer extends BaseVisionTaskApi {
    *   <li>{@link android.graphics.Bitmap.Config#ARGB_8888}
    * </ul>
    *
-   * <p>The image can be of any size. To ensure that the output image has reasonable quality, the
+   * <p>The input image can be of any size. The output image is the stylized image with the most
-   * size of the stylized output is based the model output size and can be smaller than the input
+   * visible face. The stylized output image size is the same as the model output size. When no face
-   * image.
+   * is detected on the input image, returns {@code Optional.empty()}.
    *
    * @param image a MediaPipe {@link MPImage} object for processing.
    * @param timestampMs the input timestamp (in milliseconds).
@@ -346,9 +346,9 @@ public final class FaceStylizer extends BaseVisionTaskApi {
    *   <li>{@link android.graphics.Bitmap.Config#ARGB_8888}
    * </ul>
    *
-   * <p>The input image can be of any size. To ensure that the output image has reasonable quality,
+   * <p>The input image can be of any size. The output image is the stylized image with the most
-   * the stylized output image size is the smaller of the model output size and the size of the
+   * visible face. The stylized output image size is the same as the model output size. When no face
-   * {@link ImageProcessingOptions#regionOfInterest} specified in {@code imageProcessingOptions}.
+   * is detected on the input image, returns {@code Optional.empty()}. *
    *
    * @param image a MediaPipe {@link MPImage} object for processing.
    * @param imageProcessingOptions the {@link ImageProcessingOptions} specifying how to process the
@@ -387,9 +387,9 @@ public final class FaceStylizer extends BaseVisionTaskApi {
    *   <li>{@link android.graphics.Bitmap.Config#ARGB_8888}
    * </ul>
    *
-   * <p>The image can be of any size. To ensure that the output image has reasonable quality, the
+   * <p>The input image can be of any size. The output image is the stylized image with the most
-   * size of the stylized output is based the model output size and can be smaller than the input
+   * visible face. The stylized output image size is the same as the model output size. When no face
-   * image.
+   * is detected on the input image, returns {@code Optional.empty()}.
    *
    * @param image a MediaPipe {@link MPImage} object for processing.
    * @param timestampMs the input timestamp (in milliseconds).
@@ -414,9 +414,9 @@ public final class FaceStylizer extends BaseVisionTaskApi {
    *   <li>{@link android.graphics.Bitmap.Config#ARGB_8888}
    * </ul>
    *
-   * <p>The input image can be of any size. To ensure that the output image has reasonable quality,
+   * <p>The input image can be of any size. The output image is the stylized image with the most
-   * the stylized output image size is the smaller of the model output size and the size of the
+   * visible face. The stylized output image size is the same as the model output size. When no face
-   * {@link ImageProcessingOptions#regionOfInterest} specified in {@code imageProcessingOptions}.
+   * is detected on the input image, returns {@code Optional.empty()}.
    *
    * @param image a MediaPipe {@link MPImage} object for processing.
    * @param timestampMs the input timestamp (in milliseconds).
@@ -445,9 +445,9 @@ public final class FaceStylizer extends BaseVisionTaskApi {
    *
    * <p>{@link FaceStylizer} supports the following color space types:
    *
-   * <p>The image can be of any size. To ensure that the output image has reasonable quality, the
+   * <p>The input image can be of any size. The output image is the stylized image with the most
-   * size of the stylized output is based the model output * size and can be smaller than the input
+   * visible face. The stylized output image size is the same as the model output size. When no face
-   * image.
+   * is detected on the input image, returns {@code Optional.empty()}.
    *
    * <ul>
    *   <li>{@link android.graphics.Bitmap.Config#ARGB_8888}
@@ -475,9 +475,9 @@ public final class FaceStylizer extends BaseVisionTaskApi {
    *   <li>{@link android.graphics.Bitmap.Config#ARGB_8888}
    * </ul>
    *
-   * <p>The input image can be of any size. To ensure that the output image has reasonable quality,
+   * <p>The input image can be of any size. The output image is the stylized image with the most
-   * the stylized output image size is the smaller of the model output size and the size of the
+   * visible face. The stylized output image size is the same as the model output size. When no face
-   * {@link ImageProcessingOptions#regionOfInterest} specified in {@code imageProcessingOptions}.
+   * is detected on the input image, returns {@code Optional.empty()}.
    *
    * @param image a MediaPipe {@link MPImage} object for processing.
    * @param imageProcessingOptions the {@link ImageProcessingOptions} specifying how to process the

mediapipe/tasks/java/com/google/mediapipe/tasks/vision/interactivesegmenter/InteractiveSegmenter.java

@@ -94,15 +94,7 @@ public final class InteractiveSegmenter extends BaseVisionTaskApi {
               "IMAGE:" + IMAGE_IN_STREAM_NAME,
               "ROI:" + ROI_IN_STREAM_NAME,
               "NORM_RECT:" + NORM_RECT_IN_STREAM_NAME));
-  private static final List<String> OUTPUT_STREAMS =
+  private static final int IMAGE_OUT_STREAM_INDEX = 0;
-      Collections.unmodifiableList(
-          Arrays.asList(
-              "GROUPED_SEGMENTATION:segmented_mask_out",
-              "IMAGE:image_out",
-              "SEGMENTATION:0:segmentation"));
-  private static final int GROUPED_SEGMENTATION_OUT_STREAM_INDEX = 0;
-  private static final int IMAGE_OUT_STREAM_INDEX = 1;
-  private static final int SEGMENTATION_OUT_STREAM_INDEX = 2;
   private static final String TASK_GRAPH_NAME =
       "mediapipe.tasks.vision.interactive_segmenter.InteractiveSegmenterGraph";
   private static final String TENSORS_TO_SEGMENTATION_CALCULATOR_NAME =
@@ -123,6 +115,21 @@ public final class InteractiveSegmenter extends BaseVisionTaskApi {
    */
   public static InteractiveSegmenter createFromOptions(
       Context context, InteractiveSegmenterOptions segmenterOptions) {
+    if (!segmenterOptions.outputConfidenceMasks() && !segmenterOptions.outputCategoryMask()) {
+      throw new IllegalArgumentException(
+          "At least one of `outputConfidenceMasks` and `outputCategoryMask` must be set.");
+    }
+    List<String> outputStreams = new ArrayList<>();
+    outputStreams.add("IMAGE:image_out");
+    if (segmenterOptions.outputConfidenceMasks()) {
+      outputStreams.add("CONFIDENCE_MASKS:confidence_masks");
+    }
+    final int confidenceMasksOutStreamIndex = outputStreams.size() - 1;
+    if (segmenterOptions.outputCategoryMask()) {
+      outputStreams.add("CATEGORY_MASK:category_mask");
+    }
+    final int categoryMaskOutStreamIndex = outputStreams.size() - 1;
+
     // TODO: Consolidate OutputHandler and TaskRunner.
     OutputHandler<ImageSegmenterResult, MPImage> handler = new OutputHandler<>();
     handler.setOutputPacketConverter(
@@ -130,52 +137,72 @@ public final class InteractiveSegmenter extends BaseVisionTaskApi {
           @Override
           public ImageSegmenterResult convertToTaskResult(List<Packet> packets)
               throws MediaPipeException {
-            if (packets.get(GROUPED_SEGMENTATION_OUT_STREAM_INDEX).isEmpty()) {
+            if (packets.get(IMAGE_OUT_STREAM_INDEX).isEmpty()) {
               return ImageSegmenterResult.create(
                   Optional.empty(),
                   Optional.empty(),
-                  packets.get(GROUPED_SEGMENTATION_OUT_STREAM_INDEX).getTimestamp());
+                  packets.get(IMAGE_OUT_STREAM_INDEX).getTimestamp());
             }
-            List<MPImage> segmentedMasks = new ArrayList<>();
+            // If resultListener is not provided, the resulted MPImage is deep copied from
-            int width = PacketGetter.getImageWidth(packets.get(SEGMENTATION_OUT_STREAM_INDEX));
+            // mediapipe graph. If provided, the result MPImage is wrapping the mediapipe packet
-            int height = PacketGetter.getImageHeight(packets.get(SEGMENTATION_OUT_STREAM_INDEX));
+            // memory.
-            int imageFormat =
+            boolean copyImage = !segmenterOptions.resultListener().isPresent();
-                segmenterOptions.outputType()
+            Optional<List<MPImage>> confidenceMasks = Optional.empty();
-                        == InteractiveSegmenterOptions.OutputType.CONFIDENCE_MASK
+            if (segmenterOptions.outputConfidenceMasks()) {
-                    ? MPImage.IMAGE_FORMAT_VEC32F1
+              confidenceMasks = Optional.of(new ArrayList<>());
-                    : MPImage.IMAGE_FORMAT_ALPHA;
+              int width =
-            int imageListSize =
+                  PacketGetter.getImageWidthFromImageList(
-                PacketGetter.getImageListSize(packets.get(GROUPED_SEGMENTATION_OUT_STREAM_INDEX));
+                      packets.get(confidenceMasksOutStreamIndex));
-            ByteBuffer[] buffersArray = new ByteBuffer[imageListSize];
+              int height =
-            // If resultListener is not provided, the resulted MPImage is deep copied from mediapipe
+                  PacketGetter.getImageHeightFromImageList(
-            // graph. If provided, the result MPImage is wrapping the mediapipe packet memory.
+                      packets.get(confidenceMasksOutStreamIndex));
-            if (!segmenterOptions.resultListener().isPresent()) {
+              int imageListSize =
-              for (int i = 0; i < imageListSize; i++) {
+                  PacketGetter.getImageListSize(packets.get(confidenceMasksOutStreamIndex));
-                buffersArray[i] =
+              ByteBuffer[] buffersArray = new ByteBuffer[imageListSize];
-                    ByteBuffer.allocateDirect(
+              // confidence masks are float type image.
-                        width * height * (imageFormat == MPImage.IMAGE_FORMAT_VEC32F1 ? 4 : 1));
+              final int numBytes = 4;
+              if (copyImage) {
+                for (int i = 0; i < imageListSize; i++) {
+                  buffersArray[i] = ByteBuffer.allocateDirect(width * height * numBytes);
+                }
+              }
+              if (!PacketGetter.getImageList(
+                  packets.get(confidenceMasksOutStreamIndex), buffersArray, copyImage)) {
+                throw new MediaPipeException(
+                    MediaPipeException.StatusCode.INTERNAL.ordinal(),
+                    "There is an error getting confidence masks.");
+              }
+              for (ByteBuffer buffer : buffersArray) {
+                ByteBufferImageBuilder builder =
+                    new ByteBufferImageBuilder(buffer, width, height, MPImage.IMAGE_FORMAT_VEC32F1);
+                confidenceMasks.get().add(builder.build());
               }
             }
-            if (!PacketGetter.getImageList(
+            Optional<MPImage> categoryMask = Optional.empty();
-                packets.get(GROUPED_SEGMENTATION_OUT_STREAM_INDEX),
+            if (segmenterOptions.outputCategoryMask()) {
-                buffersArray,
+              int width = PacketGetter.getImageWidth(packets.get(categoryMaskOutStreamIndex));
-                !segmenterOptions.resultListener().isPresent())) {
+              int height = PacketGetter.getImageHeight(packets.get(categoryMaskOutStreamIndex));
-              throw new MediaPipeException(
+              ByteBuffer buffer;
-                  MediaPipeException.StatusCode.INTERNAL.ordinal(),
+              if (copyImage) {
-                  "There is an error getting segmented masks. It usually results from incorrect"
+                buffer = ByteBuffer.allocateDirect(width * height);
-                      + " options of unsupported OutputType of given model.");
+                if (!PacketGetter.getImageData(packets.get(categoryMaskOutStreamIndex), buffer)) {
-            }
+                  throw new MediaPipeException(
-            for (ByteBuffer buffer : buffersArray) {
+                      MediaPipeException.StatusCode.INTERNAL.ordinal(),
+                      "There is an error getting category mask.");
+                }
+              } else {
+                buffer = PacketGetter.getImageDataDirectly(packets.get(categoryMaskOutStreamIndex));
+              }
               ByteBufferImageBuilder builder =
-                  new ByteBufferImageBuilder(buffer, width, height, imageFormat);
+                  new ByteBufferImageBuilder(buffer, width, height, MPImage.IMAGE_FORMAT_ALPHA);
-              segmentedMasks.add(builder.build());
+              categoryMask = Optional.of(builder.build());
             }
 
             return ImageSegmenterResult.create(
-                Optional.of(segmentedMasks),
+                confidenceMasks,
-                Optional.empty(),
+                categoryMask,
                 BaseVisionTaskApi.generateResultTimestampMs(
-                    RunningMode.IMAGE, packets.get(GROUPED_SEGMENTATION_OUT_STREAM_INDEX)));
+                    RunningMode.IMAGE, packets.get(IMAGE_OUT_STREAM_INDEX)));
           }
 
           @Override
@@ -195,7 +222,7 @@ public final class InteractiveSegmenter extends BaseVisionTaskApi {
                 .setTaskRunningModeName(RunningMode.IMAGE.name())
                 .setTaskGraphName(TASK_GRAPH_NAME)
                 .setInputStreams(INPUT_STREAMS)
-                .setOutputStreams(OUTPUT_STREAMS)
+                .setOutputStreams(outputStreams)
                 .setTaskOptions(segmenterOptions)
                 .setEnableFlowLimiting(false)
                 .build(),
@@ -394,8 +421,11 @@ public final class InteractiveSegmenter extends BaseVisionTaskApi {
       /** Sets the base options for the image segmenter task. */
       public abstract Builder setBaseOptions(BaseOptions value);
 
-      /** The output type from image segmenter. */
+      /** Sets whether to output confidence masks. Default to true. */
-      public abstract Builder setOutputType(OutputType value);
+      public abstract Builder setOutputConfidenceMasks(boolean value);
+
+      /** Sets whether to output category mask. Default to false. */
+      public abstract Builder setOutputCategoryMask(boolean value);
 
       /**
        * Sets an optional {@link ResultListener} to receive the segmentation results when the graph
@@ -417,25 +447,18 @@ public final class InteractiveSegmenter extends BaseVisionTaskApi {
 
     abstract BaseOptions baseOptions();
 
-    abstract OutputType outputType();
+    abstract boolean outputConfidenceMasks();
+
+    abstract boolean outputCategoryMask();
 
     abstract Optional<ResultListener<ImageSegmenterResult, MPImage>> resultListener();
 
     abstract Optional<ErrorListener> errorListener();
 
-    /** The output type of segmentation results. */
-    public enum OutputType {
-      // Gives a single output mask where each pixel represents the class which
-      // the pixel in the original image was predicted to belong to.
-      CATEGORY_MASK,
-      // Gives a list of output masks where, for each mask, each pixel represents
-      // the prediction confidence, usually in the [0, 1] range.
-      CONFIDENCE_MASK
-    }
-
     public static Builder builder() {
       return new AutoValue_InteractiveSegmenter_InteractiveSegmenterOptions.Builder()
-          .setOutputType(OutputType.CATEGORY_MASK);
+          .setOutputConfidenceMasks(true)
+          .setOutputCategoryMask(false);
     }
 
     /**
@@ -454,14 +477,6 @@ public final class InteractiveSegmenter extends BaseVisionTaskApi {
 
       SegmenterOptionsProto.SegmenterOptions.Builder segmenterOptionsBuilder =
           SegmenterOptionsProto.SegmenterOptions.newBuilder();
-      if (outputType() == OutputType.CONFIDENCE_MASK) {
-        segmenterOptionsBuilder.setOutputType(
-            SegmenterOptionsProto.SegmenterOptions.OutputType.CONFIDENCE_MASK);
-      } else if (outputType() == OutputType.CATEGORY_MASK) {
-        segmenterOptionsBuilder.setOutputType(
-            SegmenterOptionsProto.SegmenterOptions.OutputType.CATEGORY_MASK);
-      }
-
       taskOptionsBuilder.setSegmenterOptions(segmenterOptionsBuilder);
       return CalculatorOptions.newBuilder()
           .setExtension(

mediapipe/tasks/javatests/com/google/mediapipe/tasks/vision/facestylizer/FaceStylizerTest.java

@@ -234,8 +234,8 @@ public class FaceStylizerTest {
       FaceStylizerResult actualResult = faceStylizer.stylize(inputImage);
       MPImage stylizedImage = actualResult.stylizedImage().get();
       assertThat(stylizedImage).isNotNull();
-      assertThat(stylizedImage.getWidth()).isEqualTo(83);
+      assertThat(stylizedImage.getWidth()).isEqualTo(modelImageSize);
-      assertThat(stylizedImage.getHeight()).isEqualTo(83);
+      assertThat(stylizedImage.getHeight()).isEqualTo(modelImageSize);
     }
 
     @Test

mediapipe/tasks/javatests/com/google/mediapipe/tasks/vision/interactivesegmenter/InteractiveSegmenterTest.java

@@ -53,18 +53,15 @@ public class InteractiveSegmenterTest {
       InteractiveSegmenterOptions options =
           InteractiveSegmenterOptions.builder()
               .setBaseOptions(BaseOptions.builder().setModelAssetPath(DEEPLAB_MODEL_FILE).build())
-              .setOutputType(InteractiveSegmenterOptions.OutputType.CATEGORY_MASK)
+              .setOutputConfidenceMasks(false)
+              .setOutputCategoryMask(true)
               .build();
       InteractiveSegmenter imageSegmenter =
           InteractiveSegmenter.createFromOptions(
               ApplicationProvider.getApplicationContext(), options);
       MPImage image = getImageFromAsset(inputImageName);
       ImageSegmenterResult actualResult = imageSegmenter.segment(image, roi);
-      // TODO update to correct category mask output.
+      assertThat(actualResult.categoryMask().isPresent()).isTrue();
-      // After InteractiveSegmenter updated according to (b/276519300), update this to use
-      // categoryMask field instead of confidenceMasks.
-      List<MPImage> segmentations = actualResult.confidenceMasks().get();
-      assertThat(segmentations.size()).isEqualTo(1);
     }
 
     @Test
@@ -75,15 +72,17 @@ public class InteractiveSegmenterTest {
       InteractiveSegmenterOptions options =
           InteractiveSegmenterOptions.builder()
               .setBaseOptions(BaseOptions.builder().setModelAssetPath(DEEPLAB_MODEL_FILE).build())
-              .setOutputType(InteractiveSegmenterOptions.OutputType.CONFIDENCE_MASK)
+              .setOutputConfidenceMasks(true)
+              .setOutputCategoryMask(false)
               .build();
       InteractiveSegmenter imageSegmenter =
           InteractiveSegmenter.createFromOptions(
               ApplicationProvider.getApplicationContext(), options);
       ImageSegmenterResult actualResult =
           imageSegmenter.segment(getImageFromAsset(inputImageName), roi);
-      List<MPImage> segmentations = actualResult.confidenceMasks().get();
+      assertThat(actualResult.confidenceMasks().isPresent()).isTrue();
-      assertThat(segmentations.size()).isEqualTo(2);
+      List<MPImage> confidenceMasks = actualResult.confidenceMasks().get();
+      assertThat(confidenceMasks.size()).isEqualTo(2);
     }
   }
 

mediapipe/tasks/python/core/pybind/task_runner.cc

@@ -204,6 +204,11 @@ This can be useful for resetting a stateful task graph to process new data.
 Raises:
   RuntimeError: The underlying medipaipe graph fails to reset and restart.
 )doc");
+
+  task_runner.def(
+      "get_graph_config",
+      [](TaskRunner* self) { return self->GetGraphConfig(); },
+      R"doc(Returns the canonicalized CalculatorGraphConfig of the underlying graph.)doc");
 }
 
 }  // namespace python

mediapipe/tasks/python/test/text/text_embedder_test.py

@@ -32,6 +32,7 @@ _TextEmbedderOptions = text_embedder.TextEmbedderOptions
 
 _BERT_MODEL_FILE = 'mobilebert_embedding_with_metadata.tflite'
 _REGEX_MODEL_FILE = 'regex_one_embedding_with_metadata.tflite'
+_USE_MODEL_FILE = 'universal_sentence_encoder_qa_with_metadata.tflite'
 _TEST_DATA_DIR = 'mediapipe/tasks/testdata/text'
 # Tolerance for embedding vector coordinate values.
 _EPSILON = 1e-4
@@ -138,6 +139,24 @@ class TextEmbedderTest(parameterized.TestCase):
           16,
           (0.549632, 0.552879),
       ),
+      (
+          False,
+          False,
+          _USE_MODEL_FILE,
+          ModelFileType.FILE_NAME,
+          0.851961,
+          100,
+          (1.422951, 1.404664),
+      ),
+      (
+          True,
+          False,
+          _USE_MODEL_FILE,
+          ModelFileType.FILE_CONTENT,
+          0.851961,
+          100,
+          (0.127049, 0.125416),
+      ),
   )
   def test_embed(self, l2_normalize, quantize, model_name, model_file_type,
                  expected_similarity, expected_size, expected_first_values):
@@ -213,6 +232,24 @@ class TextEmbedderTest(parameterized.TestCase):
           16,
           (0.549632, 0.552879),
       ),
+      (
+          False,
+          False,
+          _USE_MODEL_FILE,
+          ModelFileType.FILE_NAME,
+          0.851961,
+          100,
+          (1.422951, 1.404664),
+      ),
+      (
+          True,
+          False,
+          _USE_MODEL_FILE,
+          ModelFileType.FILE_CONTENT,
+          0.851961,
+          100,
+          (0.127049, 0.125416),
+      ),
   )
   def test_embed_in_context(self, l2_normalize, quantize, model_name,
                             model_file_type, expected_similarity, expected_size,
@@ -251,6 +288,7 @@ class TextEmbedderTest(parameterized.TestCase):
   @parameterized.parameters(
       # TODO: The similarity should likely be lower
       (_BERT_MODEL_FILE, 0.980880),
+      (_USE_MODEL_FILE, 0.780334),
   )
   def test_embed_with_different_themes(self, model_file, expected_similarity):
     # Creates embedder.

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

@@ -15,7 +15,6 @@
 
 import enum
 import os
-from typing import List
 from unittest import mock
 
 from absl.testing import absltest
@@ -30,11 +29,10 @@ from mediapipe.tasks.python.test import test_utils
 from mediapipe.tasks.python.vision import image_segmenter
 from mediapipe.tasks.python.vision.core import vision_task_running_mode
 
+ImageSegmenterResult = image_segmenter.ImageSegmenterResult
 _BaseOptions = base_options_module.BaseOptions
 _Image = image_module.Image
 _ImageFormat = image_frame.ImageFormat
-_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
@@ -42,11 +40,33 @@ _RUNNING_MODE = vision_task_running_mode.VisionTaskRunningMode
 _MODEL_FILE = 'deeplabv3.tflite'
 _IMAGE_FILE = 'segmentation_input_rotation0.jpg'
 _SEGMENTATION_FILE = 'segmentation_golden_rotation0.png'
+_CAT_IMAGE = 'cat.jpg'
+_CAT_MASK = 'cat_mask.jpg'
 _MASK_MAGNIFICATION_FACTOR = 10
 _MASK_SIMILARITY_THRESHOLD = 0.98
 _TEST_DATA_DIR = 'mediapipe/tasks/testdata/vision'
 
 
+def _calculate_soft_iou(m1, m2):
+  intersection_sum = np.sum(m1 * m2)
+  union_sum = np.sum(m1 * m1) + np.sum(m2 * m2) - intersection_sum
+
+  if union_sum > 0:
+    return intersection_sum / union_sum
+  else:
+    return 0
+
+
+def _similar_to_float_mask(actual_mask, expected_mask, similarity_threshold):
+  actual_mask = actual_mask.numpy_view()
+  expected_mask = expected_mask.numpy_view() / 255.0
+
+  return (
+      actual_mask.shape == expected_mask.shape
+      and _calculate_soft_iou(actual_mask, expected_mask) > similarity_threshold
+  )
+
+
 def _similar_to_uint8_mask(actual_mask, expected_mask):
   actual_mask_pixels = actual_mask.numpy_view().flatten()
   expected_mask_pixels = expected_mask.numpy_view().flatten()
@@ -56,8 +76,9 @@ def _similar_to_uint8_mask(actual_mask, expected_mask):
 
   for index in range(num_pixels):
     consistent_pixels += (
-        actual_mask_pixels[index] *
+        actual_mask_pixels[index] * _MASK_MAGNIFICATION_FACTOR
-        _MASK_MAGNIFICATION_FACTOR == expected_mask_pixels[index])
+        == expected_mask_pixels[index]
+    )
 
   return consistent_pixels / num_pixels >= _MASK_SIMILARITY_THRESHOLD
 
@@ -73,16 +94,27 @@ class ImageSegmenterTest(parameterized.TestCase):
     super().setUp()
     # Load the test input image.
     self.test_image = _Image.create_from_file(
-        test_utils.get_test_data_path(
+        test_utils.get_test_data_path(os.path.join(_TEST_DATA_DIR, _IMAGE_FILE))
-            os.path.join(_TEST_DATA_DIR, _IMAGE_FILE)))
+    )
     # Loads ground truth segmentation file.
     gt_segmentation_data = cv2.imread(
         test_utils.get_test_data_path(
-            os.path.join(_TEST_DATA_DIR, _SEGMENTATION_FILE)),
+            os.path.join(_TEST_DATA_DIR, _SEGMENTATION_FILE)
-        cv2.IMREAD_GRAYSCALE)
+        ),
+        cv2.IMREAD_GRAYSCALE,
+    )
     self.test_seg_image = _Image(_ImageFormat.GRAY8, gt_segmentation_data)
     self.model_path = test_utils.get_test_data_path(
-        os.path.join(_TEST_DATA_DIR, _MODEL_FILE))
+        os.path.join(_TEST_DATA_DIR, _MODEL_FILE)
+    )
+
+  def _load_segmentation_mask(self, file_path: str):
+    # Loads ground truth segmentation file.
+    gt_segmentation_data = cv2.imread(
+        test_utils.get_test_data_path(os.path.join(_TEST_DATA_DIR, file_path)),
+        cv2.IMREAD_GRAYSCALE,
+    )
+    return _Image(_ImageFormat.GRAY8, gt_segmentation_data)
 
   def test_create_from_file_succeeds_with_valid_model_path(self):
     # Creates with default option and valid model file successfully.
@@ -98,9 +130,11 @@ class ImageSegmenterTest(parameterized.TestCase):
 
   def test_create_from_options_fails_with_invalid_model_path(self):
     with self.assertRaisesRegex(
-        RuntimeError, 'Unable to open file at /path/to/invalid/model.tflite'):
+        RuntimeError, 'Unable to open file at /path/to/invalid/model.tflite'
+    ):
       base_options = _BaseOptions(
-          model_asset_path='/path/to/invalid/model.tflite')
+          model_asset_path='/path/to/invalid/model.tflite'
+      )
       options = _ImageSegmenterOptions(base_options=base_options)
       _ImageSegmenter.create_from_options(options)
 
@@ -112,8 +146,9 @@ class ImageSegmenterTest(parameterized.TestCase):
       segmenter = _ImageSegmenter.create_from_options(options)
       self.assertIsInstance(segmenter, _ImageSegmenter)
 
-  @parameterized.parameters((ModelFileType.FILE_NAME,),
+  @parameterized.parameters(
-                            (ModelFileType.FILE_CONTENT,))
+      (ModelFileType.FILE_NAME,), (ModelFileType.FILE_CONTENT,)
+  )
   def test_segment_succeeds_with_category_mask(self, model_file_type):
     # Creates segmenter.
     if model_file_type is ModelFileType.FILE_NAME:
@@ -127,22 +162,27 @@ class ImageSegmenterTest(parameterized.TestCase):
       raise ValueError('model_file_type is invalid.')
 
     options = _ImageSegmenterOptions(
-        base_options=base_options, output_type=_OutputType.CATEGORY_MASK)
+        base_options=base_options,
+        output_category_mask=True,
+        output_confidence_masks=False,
+    )
     segmenter = _ImageSegmenter.create_from_options(options)
 
     # Performs image segmentation on the input.
-    category_masks = segmenter.segment(self.test_image)
+    segmentation_result = segmenter.segment(self.test_image)
-    self.assertLen(category_masks, 1)
+    category_mask = segmentation_result.category_mask
-    category_mask = category_masks[0]
     result_pixels = category_mask.numpy_view().flatten()
 
     # Check if data type of `category_mask` is correct.
     self.assertEqual(result_pixels.dtype, np.uint8)
 
     self.assertTrue(
-        _similar_to_uint8_mask(category_masks[0], self.test_seg_image),
+        _similar_to_uint8_mask(category_mask, self.test_seg_image),
-        f'Number of pixels in the candidate mask differing from that of the '
+        (
-        f'ground truth mask exceeds {_MASK_SIMILARITY_THRESHOLD}.')
+            'Number of pixels in the candidate mask differing from that of the'
+            f' ground truth mask exceeds {_MASK_SIMILARITY_THRESHOLD}.'
+        ),
+    )
 
     # Closes the segmenter explicitly when the segmenter is not used in
     # a context.
@@ -152,74 +192,46 @@ class ImageSegmenterTest(parameterized.TestCase):
     # Creates segmenter.
     base_options = _BaseOptions(model_asset_path=self.model_path)
 
-    # Run segmentation on the model in CATEGORY_MASK mode.
+    # Load the cat image.
-    options = _ImageSegmenterOptions(
+    test_image = _Image.create_from_file(
-        base_options=base_options, output_type=_OutputType.CATEGORY_MASK)
+        test_utils.get_test_data_path(os.path.join(_TEST_DATA_DIR, _CAT_IMAGE))
-    segmenter = _ImageSegmenter.create_from_options(options)
+    )
-    category_masks = segmenter.segment(self.test_image)
-    category_mask = category_masks[0].numpy_view()
 
     # Run segmentation on the model in CONFIDENCE_MASK mode.
     options = _ImageSegmenterOptions(
         base_options=base_options,
-        output_type=_OutputType.CONFIDENCE_MASK,
+        output_category_mask=False,
-        activation=_Activation.SOFTMAX)
+        output_confidence_masks=True,
-    segmenter = _ImageSegmenter.create_from_options(options)
+    )
-    confidence_masks = segmenter.segment(self.test_image)
 
-    # Check if confidence mask shape is correct.
-    self.assertLen(
-        confidence_masks, 21,
-        'Number of confidence masks must match with number of categories.')
-
-    # Gather the confidence masks in a single array `confidence_mask_array`.
-    confidence_mask_array = np.array(
-        [confidence_mask.numpy_view() for confidence_mask in confidence_masks])
-
-    # Check if data type of `confidence_masks` are correct.
-    self.assertEqual(confidence_mask_array.dtype, np.float32)
-
-    # Compute the category mask from the created confidence mask.
-    calculated_category_mask = np.argmax(confidence_mask_array, axis=0)
-    self.assertListEqual(
-        calculated_category_mask.tolist(), category_mask.tolist(),
-        'Confidence mask does not match with the category mask.')
-
-    # Closes the segmenter explicitly when the segmenter is not used in
-    # a context.
-    segmenter.close()
-
-  @parameterized.parameters((ModelFileType.FILE_NAME),
-                            (ModelFileType.FILE_CONTENT))
-  def test_segment_in_context(self, model_file_type):
-    if model_file_type is ModelFileType.FILE_NAME:
-      base_options = _BaseOptions(model_asset_path=self.model_path)
-    elif model_file_type is ModelFileType.FILE_CONTENT:
-      with open(self.model_path, 'rb') as f:
-        model_contents = f.read()
-      base_options = _BaseOptions(model_asset_buffer=model_contents)
-    else:
-      # Should never happen
-      raise ValueError('model_file_type is invalid.')
-
-    options = _ImageSegmenterOptions(
-        base_options=base_options, output_type=_OutputType.CATEGORY_MASK)
     with _ImageSegmenter.create_from_options(options) as segmenter:
-      # Performs image segmentation on the input.
+      segmentation_result = segmenter.segment(test_image)
-      category_masks = segmenter.segment(self.test_image)
+      confidence_masks = segmentation_result.confidence_masks
-      self.assertLen(category_masks, 1)
+
+      # Check if confidence mask shape is correct.
+      self.assertLen(
+          confidence_masks,
+          21,
+          'Number of confidence masks must match with number of categories.',
+      )
+
+      # Loads ground truth segmentation file.
+      expected_mask = self._load_segmentation_mask(_CAT_MASK)
 
       self.assertTrue(
-          _similar_to_uint8_mask(category_masks[0], self.test_seg_image),
+          _similar_to_float_mask(
-          f'Number of pixels in the candidate mask differing from that of the '
+              confidence_masks[8], expected_mask, _MASK_SIMILARITY_THRESHOLD
-          f'ground truth mask exceeds {_MASK_SIMILARITY_THRESHOLD}.')
+          )
+      )
 
   def test_missing_result_callback(self):
     options = _ImageSegmenterOptions(
         base_options=_BaseOptions(model_asset_path=self.model_path),
-        running_mode=_RUNNING_MODE.LIVE_STREAM)
+        running_mode=_RUNNING_MODE.LIVE_STREAM,
-    with self.assertRaisesRegex(ValueError,
+    )
-                                r'result callback must be provided'):
+    with self.assertRaisesRegex(
+        ValueError, r'result callback must be provided'
+    ):
       with _ImageSegmenter.create_from_options(options) as unused_segmenter:
         pass
 
@@ -228,130 +240,236 @@ class ImageSegmenterTest(parameterized.TestCase):
     options = _ImageSegmenterOptions(
         base_options=_BaseOptions(model_asset_path=self.model_path),
         running_mode=running_mode,
-        result_callback=mock.MagicMock())
+        result_callback=mock.MagicMock(),
-    with self.assertRaisesRegex(ValueError,
+    )
-                                r'result callback should not be provided'):
+    with self.assertRaisesRegex(
+        ValueError, r'result callback should not be provided'
+    ):
       with _ImageSegmenter.create_from_options(options) as unused_segmenter:
         pass
 
   def test_calling_segment_for_video_in_image_mode(self):
     options = _ImageSegmenterOptions(
         base_options=_BaseOptions(model_asset_path=self.model_path),
-        running_mode=_RUNNING_MODE.IMAGE)
+        running_mode=_RUNNING_MODE.IMAGE,
+    )
     with _ImageSegmenter.create_from_options(options) as segmenter:
-      with self.assertRaisesRegex(ValueError,
+      with self.assertRaisesRegex(
-                                  r'not initialized with the video mode'):
+          ValueError, r'not initialized with the video mode'
+      ):
         segmenter.segment_for_video(self.test_image, 0)
 
   def test_calling_segment_async_in_image_mode(self):
     options = _ImageSegmenterOptions(
         base_options=_BaseOptions(model_asset_path=self.model_path),
-        running_mode=_RUNNING_MODE.IMAGE)
+        running_mode=_RUNNING_MODE.IMAGE,
+    )
     with _ImageSegmenter.create_from_options(options) as segmenter:
-      with self.assertRaisesRegex(ValueError,
+      with self.assertRaisesRegex(
-                                  r'not initialized with the live stream mode'):
+          ValueError, r'not initialized with the live stream mode'
+      ):
         segmenter.segment_async(self.test_image, 0)
 
   def test_calling_segment_in_video_mode(self):
     options = _ImageSegmenterOptions(
         base_options=_BaseOptions(model_asset_path=self.model_path),
-        running_mode=_RUNNING_MODE.VIDEO)
+        running_mode=_RUNNING_MODE.VIDEO,
+    )
     with _ImageSegmenter.create_from_options(options) as segmenter:
-      with self.assertRaisesRegex(ValueError,
+      with self.assertRaisesRegex(
-                                  r'not initialized with the image mode'):
+          ValueError, r'not initialized with the image mode'
+      ):
         segmenter.segment(self.test_image)
 
   def test_calling_segment_async_in_video_mode(self):
     options = _ImageSegmenterOptions(
         base_options=_BaseOptions(model_asset_path=self.model_path),
-        running_mode=_RUNNING_MODE.VIDEO)
+        running_mode=_RUNNING_MODE.VIDEO,
+    )
     with _ImageSegmenter.create_from_options(options) as segmenter:
-      with self.assertRaisesRegex(ValueError,
+      with self.assertRaisesRegex(
-                                  r'not initialized with the live stream mode'):
+          ValueError, r'not initialized with the live stream mode'
+      ):
         segmenter.segment_async(self.test_image, 0)
 
   def test_segment_for_video_with_out_of_order_timestamp(self):
     options = _ImageSegmenterOptions(
         base_options=_BaseOptions(model_asset_path=self.model_path),
-        running_mode=_RUNNING_MODE.VIDEO)
+        running_mode=_RUNNING_MODE.VIDEO,
+    )
     with _ImageSegmenter.create_from_options(options) as segmenter:
       unused_result = segmenter.segment_for_video(self.test_image, 1)
       with self.assertRaisesRegex(
-          ValueError, r'Input timestamp must be monotonically increasing'):
+          ValueError, r'Input timestamp must be monotonically increasing'
+      ):
         segmenter.segment_for_video(self.test_image, 0)
 
-  def test_segment_for_video(self):
+  def test_segment_for_video_in_category_mask_mode(self):
     options = _ImageSegmenterOptions(
         base_options=_BaseOptions(model_asset_path=self.model_path),
-        output_type=_OutputType.CATEGORY_MASK,
+        output_category_mask=True,
-        running_mode=_RUNNING_MODE.VIDEO)
+        output_confidence_masks=False,
+        running_mode=_RUNNING_MODE.VIDEO,
+    )
     with _ImageSegmenter.create_from_options(options) as segmenter:
       for timestamp in range(0, 300, 30):
-        category_masks = segmenter.segment_for_video(self.test_image, timestamp)
+        segmentation_result = segmenter.segment_for_video(
-        self.assertLen(category_masks, 1)
+            self.test_image, timestamp
+        )
+        category_mask = segmentation_result.category_mask
         self.assertTrue(
-            _similar_to_uint8_mask(category_masks[0], self.test_seg_image),
+            _similar_to_uint8_mask(category_mask, self.test_seg_image),
-            f'Number of pixels in the candidate mask differing from that of the '
+            (
-            f'ground truth mask exceeds {_MASK_SIMILARITY_THRESHOLD}.')
+                'Number of pixels in the candidate mask differing from that of'
+                f' the ground truth mask exceeds {_MASK_SIMILARITY_THRESHOLD}.'
+            ),
+        )
+
+  def test_segment_for_video_in_confidence_mask_mode(self):
+    # Load the cat image.
+    test_image = _Image.create_from_file(
+        test_utils.get_test_data_path(os.path.join(_TEST_DATA_DIR, _CAT_IMAGE))
+    )
+
+    options = _ImageSegmenterOptions(
+        base_options=_BaseOptions(model_asset_path=self.model_path),
+        running_mode=_RUNNING_MODE.VIDEO,
+        output_category_mask=False,
+        output_confidence_masks=True,
+    )
+    with _ImageSegmenter.create_from_options(options) as segmenter:
+      for timestamp in range(0, 300, 30):
+        segmentation_result = segmenter.segment_for_video(test_image, timestamp)
+        confidence_masks = segmentation_result.confidence_masks
+
+        # Check if confidence mask shape is correct.
+        self.assertLen(
+            confidence_masks,
+            21,
+            'Number of confidence masks must match with number of categories.',
+        )
+
+        # Loads ground truth segmentation file.
+        expected_mask = self._load_segmentation_mask(_CAT_MASK)
+        self.assertTrue(
+            _similar_to_float_mask(
+                confidence_masks[8], expected_mask, _MASK_SIMILARITY_THRESHOLD
+            )
+        )
 
   def test_calling_segment_in_live_stream_mode(self):
     options = _ImageSegmenterOptions(
         base_options=_BaseOptions(model_asset_path=self.model_path),
         running_mode=_RUNNING_MODE.LIVE_STREAM,
-        result_callback=mock.MagicMock())
+        result_callback=mock.MagicMock(),
+    )
     with _ImageSegmenter.create_from_options(options) as segmenter:
-      with self.assertRaisesRegex(ValueError,
+      with self.assertRaisesRegex(
-                                  r'not initialized with the image mode'):
+          ValueError, r'not initialized with the image mode'
+      ):
         segmenter.segment(self.test_image)
 
   def test_calling_segment_for_video_in_live_stream_mode(self):
     options = _ImageSegmenterOptions(
         base_options=_BaseOptions(model_asset_path=self.model_path),
         running_mode=_RUNNING_MODE.LIVE_STREAM,
-        result_callback=mock.MagicMock())
+        result_callback=mock.MagicMock(),
+    )
     with _ImageSegmenter.create_from_options(options) as segmenter:
-      with self.assertRaisesRegex(ValueError,
+      with self.assertRaisesRegex(
-                                  r'not initialized with the video mode'):
+          ValueError, r'not initialized with the video mode'
+      ):
         segmenter.segment_for_video(self.test_image, 0)
 
   def test_segment_async_calls_with_illegal_timestamp(self):
     options = _ImageSegmenterOptions(
         base_options=_BaseOptions(model_asset_path=self.model_path),
         running_mode=_RUNNING_MODE.LIVE_STREAM,
-        result_callback=mock.MagicMock())
+        result_callback=mock.MagicMock(),
+    )
     with _ImageSegmenter.create_from_options(options) as segmenter:
       segmenter.segment_async(self.test_image, 100)
       with self.assertRaisesRegex(
-          ValueError, r'Input timestamp must be monotonically increasing'):
+          ValueError, r'Input timestamp must be monotonically increasing'
+      ):
         segmenter.segment_async(self.test_image, 0)
 
-  def test_segment_async_calls(self):
+  def test_segment_async_calls_in_category_mask_mode(self):
     observed_timestamp_ms = -1
 
-    def check_result(result: List[image_module.Image], output_image: _Image,
+    def check_result(
-                     timestamp_ms: int):
+        result: ImageSegmenterResult, output_image: _Image, timestamp_ms: int
+    ):
       # Get the output category mask.
-      category_mask = result[0]
+      category_mask = result.category_mask
       self.assertEqual(output_image.width, self.test_image.width)
       self.assertEqual(output_image.height, self.test_image.height)
       self.assertEqual(output_image.width, self.test_seg_image.width)
       self.assertEqual(output_image.height, self.test_seg_image.height)
       self.assertTrue(
           _similar_to_uint8_mask(category_mask, self.test_seg_image),
-          f'Number of pixels in the candidate mask differing from that of the '
+          (
-          f'ground truth mask exceeds {_MASK_SIMILARITY_THRESHOLD}.')
+              'Number of pixels in the candidate mask differing from that of'
+              f' the ground truth mask exceeds {_MASK_SIMILARITY_THRESHOLD}.'
+          ),
+      )
       self.assertLess(observed_timestamp_ms, timestamp_ms)
       self.observed_timestamp_ms = timestamp_ms
 
     options = _ImageSegmenterOptions(
         base_options=_BaseOptions(model_asset_path=self.model_path),
-        output_type=_OutputType.CATEGORY_MASK,
+        output_category_mask=True,
+        output_confidence_masks=False,
         running_mode=_RUNNING_MODE.LIVE_STREAM,
-        result_callback=check_result)
+        result_callback=check_result,
+    )
     with _ImageSegmenter.create_from_options(options) as segmenter:
       for timestamp in range(0, 300, 30):
         segmenter.segment_async(self.test_image, timestamp)
 
+  def test_segment_async_calls_in_confidence_mask_mode(self):
+    # Load the cat image.
+    test_image = _Image.create_from_file(
+        test_utils.get_test_data_path(os.path.join(_TEST_DATA_DIR, _CAT_IMAGE))
+    )
+
+    # Loads ground truth segmentation file.
+    expected_mask = self._load_segmentation_mask(_CAT_MASK)
+    observed_timestamp_ms = -1
+
+    def check_result(
+        result: ImageSegmenterResult, output_image: _Image, timestamp_ms: int
+    ):
+      # Get the output category mask.
+      confidence_masks = result.confidence_masks
+
+      # Check if confidence mask shape is correct.
+      self.assertLen(
+          confidence_masks,
+          21,
+          'Number of confidence masks must match with number of categories.',
+      )
+      self.assertEqual(output_image.width, test_image.width)
+      self.assertEqual(output_image.height, test_image.height)
+      self.assertTrue(
+          _similar_to_float_mask(
+              confidence_masks[8], expected_mask, _MASK_SIMILARITY_THRESHOLD
+          )
+      )
+      self.assertLess(observed_timestamp_ms, timestamp_ms)
+      self.observed_timestamp_ms = timestamp_ms
+
+    options = _ImageSegmenterOptions(
+        base_options=_BaseOptions(model_asset_path=self.model_path),
+        running_mode=_RUNNING_MODE.LIVE_STREAM,
+        output_category_mask=False,
+        output_confidence_masks=True,
+        result_callback=check_result,
+    )
+    with _ImageSegmenter.create_from_options(options) as segmenter:
+      for timestamp in range(0, 300, 30):
+        segmenter.segment_async(test_image, timestamp)
+
 
 if __name__ == '__main__':
   absltest.main()

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

@@ -30,12 +30,12 @@ from mediapipe.tasks.python.test import test_utils
 from mediapipe.tasks.python.vision import interactive_segmenter
 from mediapipe.tasks.python.vision.core import image_processing_options as image_processing_options_module
 
+InteractiveSegmenterResult = interactive_segmenter.InteractiveSegmenterResult
 _BaseOptions = base_options_module.BaseOptions
 _Image = image_module.Image
 _ImageFormat = image_frame.ImageFormat
 _NormalizedKeypoint = keypoint_module.NormalizedKeypoint
 _Rect = rect.Rect
-_OutputType = interactive_segmenter.InteractiveSegmenterOptions.OutputType
 _InteractiveSegmenter = interactive_segmenter.InteractiveSegmenter
 _InteractiveSegmenterOptions = interactive_segmenter.InteractiveSegmenterOptions
 _RegionOfInterest = interactive_segmenter.RegionOfInterest
@@ -200,15 +200,16 @@ class InteractiveSegmenterTest(parameterized.TestCase):
       raise ValueError('model_file_type is invalid.')
 
     options = _InteractiveSegmenterOptions(
-        base_options=base_options, output_type=_OutputType.CATEGORY_MASK
+        base_options=base_options,
+        output_category_mask=True,
+        output_confidence_masks=False,
     )
     segmenter = _InteractiveSegmenter.create_from_options(options)
 
     # Performs image segmentation on the input.
     roi = _RegionOfInterest(format=roi_format, keypoint=keypoint)
-    category_masks = segmenter.segment(self.test_image, roi)
+    segmentation_result = segmenter.segment(self.test_image, roi)
-    self.assertLen(category_masks, 1)
+    category_mask = segmentation_result.category_mask
-    category_mask = category_masks[0]
     result_pixels = category_mask.numpy_view().flatten()
 
     # Check if data type of `category_mask` is correct.
@@ -219,7 +220,7 @@ class InteractiveSegmenterTest(parameterized.TestCase):
 
     self.assertTrue(
         _similar_to_uint8_mask(
-            category_masks[0], test_seg_image, similarity_threshold
+            category_mask, test_seg_image, similarity_threshold
         ),
         (
             'Number of pixels in the candidate mask differing from that of the'
@@ -254,12 +255,15 @@ class InteractiveSegmenterTest(parameterized.TestCase):
 
     # Run segmentation on the model in CONFIDENCE_MASK mode.
     options = _InteractiveSegmenterOptions(
-        base_options=base_options, output_type=_OutputType.CONFIDENCE_MASK
+        base_options=base_options,
+        output_category_mask=False,
+        output_confidence_masks=True,
     )
 
     with _InteractiveSegmenter.create_from_options(options) as segmenter:
       # Perform segmentation
-      confidence_masks = segmenter.segment(self.test_image, roi)
+      segmentation_result = segmenter.segment(self.test_image, roi)
+      confidence_masks = segmentation_result.confidence_masks
 
       # Check if confidence mask shape is correct.
       self.assertLen(
@@ -287,15 +291,18 @@ class InteractiveSegmenterTest(parameterized.TestCase):
 
     # Run segmentation on the model in CONFIDENCE_MASK mode.
     options = _InteractiveSegmenterOptions(
-        base_options=base_options, output_type=_OutputType.CONFIDENCE_MASK
+        base_options=base_options,
+        output_category_mask=False,
+        output_confidence_masks=True,
     )
 
     with _InteractiveSegmenter.create_from_options(options) as segmenter:
       # Perform segmentation
       image_processing_options = _ImageProcessingOptions(rotation_degrees=-90)
-      confidence_masks = segmenter.segment(
+      segmentation_result = segmenter.segment(
           self.test_image, roi, image_processing_options
       )
+      confidence_masks = segmentation_result.confidence_masks
 
       # Check if confidence mask shape is correct.
       self.assertLen(
@@ -314,7 +321,9 @@ class InteractiveSegmenterTest(parameterized.TestCase):
 
     # Run segmentation on the model in CONFIDENCE_MASK mode.
     options = _InteractiveSegmenterOptions(
-        base_options=base_options, output_type=_OutputType.CONFIDENCE_MASK
+        base_options=base_options,
+        output_category_mask=False,
+        output_confidence_masks=True,
     )
 
     with self.assertRaisesRegex(

mediapipe/tasks/python/vision/__init__.py

@@ -32,6 +32,7 @@ FaceDetectorResult = face_detector.FaceDetectorResult
 FaceLandmarker = face_landmarker.FaceLandmarker
 FaceLandmarkerOptions = face_landmarker.FaceLandmarkerOptions
 FaceLandmarkerResult = face_landmarker.FaceLandmarkerResult
+FaceLandmarksConnections = face_landmarker.FaceLandmarksConnections
 FaceStylizer = face_stylizer.FaceStylizer
 FaceStylizerOptions = face_stylizer.FaceStylizerOptions
 GestureRecognizer = gesture_recognizer.GestureRecognizer

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

@@ -208,6 +208,11 @@ class BaseVisionTaskApi(object):
     """
     self._runner.close()
 
+  def get_graph_config(self) -> calculator_pb2.CalculatorGraphConfig:
+    """Returns the canonicalized CalculatorGraphConfig of the underlying graph.
+    """
+    return self._runner.get_graph_config()
+
   def __enter__(self):
     """Return `self` upon entering the runtime context."""
     return self

mediapipe/tasks/python/vision/face_stylizer.py

@@ -176,16 +176,13 @@ class FaceStylizer(base_vision_task_api.BaseVisionTaskApi):
     Only use this method when the FaceStylizer is created with the image
     running mode.
 
-    To ensure that the output image has reasonable quality, the stylized output
-    image size is the smaller of the model output size and the size of the
-    `region_of_interest` specified in `image_processing_options`.
-
     Args:
       image: MediaPipe Image.
       image_processing_options: Options for image processing.
 
     Returns:
-      The stylized image of the most visible face. None if no face is detected
+      The stylized image of the most visible face. The stylized output image
+      size is the same as the model output size. None if no face is detected
       on the input image.
 
     Raises:
@@ -217,17 +214,14 @@ class FaceStylizer(base_vision_task_api.BaseVisionTaskApi):
     milliseconds) along with the video frame. The input timestamps should be
     monotonically increasing for adjacent calls of this method.
 
-    To ensure that the output image has reasonable quality, the stylized output
-    image size is the smaller of the model output size and the size of the
-    `region_of_interest` specified in `image_processing_options`.
-
     Args:
       image: MediaPipe Image.
       timestamp_ms: The timestamp of the input video frame in milliseconds.
       image_processing_options: Options for image processing.
 
     Returns:
-      The stylized image of the most visible face. None if no face is detected
+      The stylized image of the most visible face. The stylized output image
+      size is the same as the model output size. None if no face is detected
       on the input image.
 
     Raises:
@@ -266,12 +260,9 @@ class FaceStylizer(base_vision_task_api.BaseVisionTaskApi):
     images if needed. In other words, it's not guaranteed to have output per
     input image.
 
-    To ensure that the stylized image has reasonable quality, the stylized
-    output image size is the smaller of the model output size and the size of
-    the `region_of_interest` specified in `image_processing_options`.
-
     The `result_callback` provides:
-      - The stylized image of the most visible face. None if no face is detected
+      - The stylized image of the most visible face. The stylized output image
+        size is the same as the model output size. None if no face is detected
         on the input image.
       - The input image that the face stylizer runs on.
       - The input timestamp in milliseconds.

mediapipe/tasks/python/vision/image_segmenter.py

@@ -14,7 +14,6 @@
 """MediaPipe image segmenter task."""
 
 import dataclasses
-import enum
 from typing import Callable, List, Mapping, Optional
 
 from mediapipe.python import packet_creator
@@ -31,7 +30,6 @@ 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
 
-ImageSegmenterResult = List[image_module.Image]
 _NormalizedRect = rect.NormalizedRect
 _BaseOptions = base_options_module.BaseOptions
 _SegmenterOptionsProto = segmenter_options_pb2.SegmenterOptions
@@ -42,8 +40,10 @@ _RunningMode = vision_task_running_mode.VisionTaskRunningMode
 _ImageProcessingOptions = image_processing_options_module.ImageProcessingOptions
 _TaskInfo = task_info_module.TaskInfo
 
-_SEGMENTATION_OUT_STREAM_NAME = 'segmented_mask_out'
+_CONFIDENCE_MASKS_STREAM_NAME = 'confidence_masks'
-_SEGMENTATION_TAG = 'GROUPED_SEGMENTATION'
+_CONFIDENCE_MASKS_TAG = 'CONFIDENCE_MASKS'
+_CATEGORY_MASK_STREAM_NAME = 'category_mask'
+_CATEGORY_MASK_TAG = 'CATEGORY_MASK'
 _IMAGE_IN_STREAM_NAME = 'image_in'
 _IMAGE_OUT_STREAM_NAME = 'image_out'
 _IMAGE_TAG = 'IMAGE'
@@ -53,6 +53,21 @@ _TASK_GRAPH_NAME = 'mediapipe.tasks.vision.image_segmenter.ImageSegmenterGraph'
 _MICRO_SECONDS_PER_MILLISECOND = 1000
 
 
+@dataclasses.dataclass
+class ImageSegmenterResult:
+  """Output result of ImageSegmenter.
+
+  confidence_masks: multiple masks of float image where, for each mask, each
+  pixel represents the prediction confidence, usually in the [0, 1] range.
+
+  category_mask: a category mask of uint8 image where each pixel represents the
+  class which the pixel in the original image was predicted to belong to.
+  """
+
+  confidence_masks: Optional[List[image_module.Image]] = None
+  category_mask: Optional[image_module.Image] = None
+
+
 @dataclasses.dataclass
 class ImageSegmenterOptions:
   """Options for the image segmenter task.
@@ -64,28 +79,17 @@ class ImageSegmenterOptions:
       objects on single image inputs. 2) The video mode for segmenting objects
       on the decoded frames of a video. 3) The live stream mode for segmenting
       objects on a live stream of input data, such as from camera.
-    output_type: The output mask type allows specifying the type of
+    output_confidence_masks: Whether to output confidence masks.
-      post-processing to perform on the raw model results.
+    output_category_mask: Whether to output category mask.
-    activation: Activation function to apply to input tensor.
     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.
   """
 
-  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
+  output_confidence_masks: bool = True
-  activation: Optional[Activation] = Activation.NONE
+  output_category_mask: bool = False
   result_callback: Optional[
       Callable[[ImageSegmenterResult, image_module.Image, int], None]
   ] = None
@@ -97,9 +101,7 @@ class ImageSegmenterOptions:
     base_options_proto.use_stream_mode = (
         False if self.running_mode == _RunningMode.IMAGE else True
     )
-    segmenter_options_proto = _SegmenterOptionsProto(
+    segmenter_options_proto = _SegmenterOptionsProto()
-        output_type=self.output_type.value, activation=self.activation.value
-    )
     return _ImageSegmenterGraphOptionsProto(
         base_options=base_options_proto,
         segmenter_options=segmenter_options_proto,
@@ -177,27 +179,48 @@ class ImageSegmenter(base_vision_task_api.BaseVisionTaskApi):
     def packets_callback(output_packets: Mapping[str, packet.Packet]):
       if output_packets[_IMAGE_OUT_STREAM_NAME].is_empty():
         return
-      segmentation_result = packet_getter.get_image_list(
+
-          output_packets[_SEGMENTATION_OUT_STREAM_NAME]
+      segmentation_result = ImageSegmenterResult()
-      )
+
+      if options.output_confidence_masks:
+        segmentation_result.confidence_masks = packet_getter.get_image_list(
+            output_packets[_CONFIDENCE_MASKS_STREAM_NAME]
+        )
+
+      if options.output_category_mask:
+        segmentation_result.category_mask = packet_getter.get_image(
+            output_packets[_CATEGORY_MASK_STREAM_NAME]
+        )
+
       image = packet_getter.get_image(output_packets[_IMAGE_OUT_STREAM_NAME])
-      timestamp = output_packets[_SEGMENTATION_OUT_STREAM_NAME].timestamp
+      timestamp = output_packets[_IMAGE_OUT_STREAM_NAME].timestamp
       options.result_callback(
           segmentation_result,
           image,
           timestamp.value // _MICRO_SECONDS_PER_MILLISECOND,
       )
 
+    output_streams = [
+        ':'.join([_IMAGE_TAG, _IMAGE_OUT_STREAM_NAME]),
+    ]
+
+    if options.output_confidence_masks:
+      output_streams.append(
+          ':'.join([_CONFIDENCE_MASKS_TAG, _CONFIDENCE_MASKS_STREAM_NAME])
+      )
+
+    if options.output_category_mask:
+      output_streams.append(
+          ':'.join([_CATEGORY_MASK_TAG, _CATEGORY_MASK_STREAM_NAME])
+      )
+
     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=[
+        output_streams=output_streams,
-            ':'.join([_SEGMENTATION_TAG, _SEGMENTATION_OUT_STREAM_NAME]),
-            ':'.join([_IMAGE_TAG, _IMAGE_OUT_STREAM_NAME]),
-        ],
         task_options=options,
     )
     return cls(
@@ -240,9 +263,18 @@ class ImageSegmenter(base_vision_task_api.BaseVisionTaskApi):
             normalized_rect.to_pb2()
         ),
     })
-    segmentation_result = packet_getter.get_image_list(
+    segmentation_result = ImageSegmenterResult()
-        output_packets[_SEGMENTATION_OUT_STREAM_NAME]
+
-    )
+    if _CONFIDENCE_MASKS_STREAM_NAME in output_packets:
+      segmentation_result.confidence_masks = packet_getter.get_image_list(
+          output_packets[_CONFIDENCE_MASKS_STREAM_NAME]
+      )
+
+    if _CATEGORY_MASK_STREAM_NAME in output_packets:
+      segmentation_result.category_mask = packet_getter.get_image(
+          output_packets[_CATEGORY_MASK_STREAM_NAME]
+      )
+
     return segmentation_result
 
   def segment_for_video(
@@ -285,9 +317,18 @@ class ImageSegmenter(base_vision_task_api.BaseVisionTaskApi):
             normalized_rect.to_pb2()
         ).at(timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND),
     })
-    segmentation_result = packet_getter.get_image_list(
+    segmentation_result = ImageSegmenterResult()
-        output_packets[_SEGMENTATION_OUT_STREAM_NAME]
+
-    )
+    if _CONFIDENCE_MASKS_STREAM_NAME in output_packets:
+      segmentation_result.confidence_masks = packet_getter.get_image_list(
+          output_packets[_CONFIDENCE_MASKS_STREAM_NAME]
+      )
+
+    if _CATEGORY_MASK_STREAM_NAME in output_packets:
+      segmentation_result.category_mask = packet_getter.get_image(
+          output_packets[_CATEGORY_MASK_STREAM_NAME]
+      )
+
     return segmentation_result
 
   def segment_async(

mediapipe/tasks/python/vision/interactive_segmenter.py

@@ -41,8 +41,10 @@ _RunningMode = vision_task_running_mode.VisionTaskRunningMode
 _ImageProcessingOptions = image_processing_options_module.ImageProcessingOptions
 _TaskInfo = task_info_module.TaskInfo
 
-_SEGMENTATION_OUT_STREAM_NAME = 'segmented_mask_out'
+_CONFIDENCE_MASKS_STREAM_NAME = 'confidence_masks'
-_SEGMENTATION_TAG = 'GROUPED_SEGMENTATION'
+_CONFIDENCE_MASKS_TAG = 'CONFIDENCE_MASKS'
+_CATEGORY_MASK_STREAM_NAME = 'category_mask'
+_CATEGORY_MASK_TAG = 'CATEGORY_MASK'
 _IMAGE_IN_STREAM_NAME = 'image_in'
 _IMAGE_OUT_STREAM_NAME = 'image_out'
 _ROI_STREAM_NAME = 'roi_in'
@@ -55,32 +57,41 @@ _TASK_GRAPH_NAME = (
 )
 
 
+@dataclasses.dataclass
+class InteractiveSegmenterResult:
+  """Output result of InteractiveSegmenter.
+
+  confidence_masks: multiple masks of float image where, for each mask, each
+  pixel represents the prediction confidence, usually in the [0, 1] range.
+
+  category_mask: a category mask of uint8 image where each pixel represents the
+  class which the pixel in the original image was predicted to belong to.
+  """
+
+  confidence_masks: Optional[List[image_module.Image]] = None
+  category_mask: Optional[image_module.Image] = None
+
+
 @dataclasses.dataclass
 class InteractiveSegmenterOptions:
   """Options for the interactive segmenter task.
 
   Attributes:
     base_options: Base options for the interactive segmenter task.
-    output_type: The output mask type allows specifying the type of
+    output_confidence_masks: Whether to output confidence masks.
-      post-processing to perform on the raw model results.
+    output_category_mask: Whether to output category mask.
   """
 
-  class OutputType(enum.Enum):
-    UNSPECIFIED = 0
-    CATEGORY_MASK = 1
-    CONFIDENCE_MASK = 2
-
   base_options: _BaseOptions
-  output_type: Optional[OutputType] = OutputType.CATEGORY_MASK
+  output_confidence_masks: bool = True
+  output_category_mask: bool = False
 
   @doc_controls.do_not_generate_docs
   def to_pb2(self) -> _ImageSegmenterGraphOptionsProto:
     """Generates an InteractiveSegmenterOptions protobuf object."""
     base_options_proto = self.base_options.to_pb2()
     base_options_proto.use_stream_mode = False
-    segmenter_options_proto = _SegmenterOptionsProto(
+    segmenter_options_proto = _SegmenterOptionsProto()
-        output_type=self.output_type.value
-    )
     return _ImageSegmenterGraphOptionsProto(
         base_options=base_options_proto,
         segmenter_options=segmenter_options_proto,
@@ -192,6 +203,20 @@ class InteractiveSegmenter(base_vision_task_api.BaseVisionTaskApi):
       RuntimeError: If other types of error occurred.
     """
 
+    output_streams = [
+        ':'.join([_IMAGE_TAG, _IMAGE_OUT_STREAM_NAME]),
+    ]
+
+    if options.output_confidence_masks:
+      output_streams.append(
+          ':'.join([_CONFIDENCE_MASKS_TAG, _CONFIDENCE_MASKS_STREAM_NAME])
+      )
+
+    if options.output_category_mask:
+      output_streams.append(
+          ':'.join([_CATEGORY_MASK_TAG, _CATEGORY_MASK_STREAM_NAME])
+      )
+
     task_info = _TaskInfo(
         task_graph=_TASK_GRAPH_NAME,
         input_streams=[
@@ -199,10 +224,7 @@ class InteractiveSegmenter(base_vision_task_api.BaseVisionTaskApi):
             ':'.join([_ROI_TAG, _ROI_STREAM_NAME]),
             ':'.join([_NORM_RECT_TAG, _NORM_RECT_STREAM_NAME]),
         ],
-        output_streams=[
+        output_streams=output_streams,
-            ':'.join([_SEGMENTATION_TAG, _SEGMENTATION_OUT_STREAM_NAME]),
-            ':'.join([_IMAGE_TAG, _IMAGE_OUT_STREAM_NAME]),
-        ],
         task_options=options,
     )
     return cls(
@@ -216,7 +238,7 @@ class InteractiveSegmenter(base_vision_task_api.BaseVisionTaskApi):
       image: image_module.Image,
       roi: RegionOfInterest,
       image_processing_options: Optional[_ImageProcessingOptions] = None,
-  ) -> List[image_module.Image]:
+  ) -> InteractiveSegmenterResult:
     """Performs the actual segmentation task on the provided MediaPipe Image.
 
     The image can be of any size with format RGB.
@@ -248,7 +270,16 @@ class InteractiveSegmenter(base_vision_task_api.BaseVisionTaskApi):
             normalized_rect.to_pb2()
         ),
     })
-    segmentation_result = packet_getter.get_image_list(
+    segmentation_result = InteractiveSegmenterResult()
-        output_packets[_SEGMENTATION_OUT_STREAM_NAME]
+
-    )
+    if _CONFIDENCE_MASKS_STREAM_NAME in output_packets:
+      segmentation_result.confidence_masks = packet_getter.get_image_list(
+          output_packets[_CONFIDENCE_MASKS_STREAM_NAME]
+      )
+
+    if _CATEGORY_MASK_STREAM_NAME in output_packets:
+      segmentation_result.category_mask = packet_getter.get_image(
+          output_packets[_CATEGORY_MASK_STREAM_NAME]
+      )
+
     return segmentation_result

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

@@ -59,13 +59,12 @@ export function drawCategoryMask(
   const isFloatArray = image instanceof Float32Array;
   for (let i = 0; i < image.length; i++) {
     const colorIndex = isFloatArray ? Math.round(image[i] * 255) : image[i];
-    const color = COLOR_MAP[colorIndex];
+    let color = COLOR_MAP[colorIndex % COLOR_MAP.length];
 
-    // When we're given a confidence mask by accident, we just log and return.
-    // TODO: We should fix this.
     if (!color) {
+      // TODO: We should fix this.
       console.warn('No color for ', colorIndex);
-      return;
+      color = COLOR_MAP[colorIndex % COLOR_MAP.length];
     }
 
     rgbaArray[4 * i] = color[0];

mediapipe/tasks/web/vision/core/types.d.ts

@@ -25,17 +25,6 @@ import {NormalizedKeypoint} from '../../../../tasks/web/components/containers/ke
  */
 export type SegmentationMask = Uint8ClampedArray|Float32Array|WebGLTexture;
 
-/**
- * A callback that receives the computed masks from the segmentation tasks. The
- * callback either receives a single element array with a category mask (as a
- * `[Uint8ClampedArray]`) or multiple confidence masks (as a `Float32Array[]`).
- * The returned data is only valid for the duration of the callback. If
- * asynchronous processing is needed, all data needs to be copied before the
- * callback returns.
- */
-export type SegmentationMaskCallback =
-    (masks: SegmentationMask[], width: number, height: number) => void;
-
 /**
  * A callback that receives an `ImageData` object from a Vision task. The
  * lifetime of the underlying data is limited to the duration of the callback.

mediapipe/tasks/web/vision/face_landmarker/face_landmarks_connections.ts

@@ -19,7 +19,7 @@ import {Connection} from '../../../../tasks/web/vision/core/types';
 // tslint:disable:class-as-namespace Using for easier import by 3P users
 
 /**
- * A class containing the Pairs of landmark indices to be rendered with
+ * A class containing the pairs of landmark indices to be rendered with
  * connections.
  */
 export class FaceLandmarksConnections {

mediapipe/tasks/web/vision/face_stylizer/face_stylizer.ts

@@ -129,10 +129,6 @@ export class FaceStylizer extends VisionTaskRunner {
    * synchronously once the callback returns. Only use this method when the
    * FaceStylizer is created with the image running mode.
    *
-   * The input image can be of any size. To ensure that the output image has
-   * reasonable quality, the stylized output image size is determined by the
-   * model output size.
-   *
    * @param image An image to process.
    * @param callback The callback that is invoked with the stylized image. The
    *    lifetime of the returned data is only guaranteed for the duration of the
@@ -153,11 +149,6 @@ export class FaceStylizer extends VisionTaskRunner {
    *  If both are specified, the crop around the region-of-interest is extracted
    *  first, then the specified rotation is applied to the crop.
    *
-   * The input image can be of any size. To ensure that the output image has
-   * reasonable quality, the stylized output image size is the smaller of the
-   * model output size and the size of the 'regionOfInterest' specified in
-   * 'imageProcessingOptions'.
-   *
    * @param image An image to process.
    * @param imageProcessingOptions the `ImageProcessingOptions` specifying how
    *    to process the input image before running inference.
@@ -192,9 +183,6 @@ export class FaceStylizer extends VisionTaskRunner {
    * frame's timestamp (in milliseconds). The input timestamps must be
    * monotonically increasing.
    *
-   * To ensure that the output image has reasonable quality, the stylized
-   * output image size is determined by the model output size.
-   *
    * @param videoFrame A video frame to process.
    * @param timestamp The timestamp of the current frame, in ms.
    * @param callback The callback that is invoked with the stylized image. The
@@ -221,10 +209,6 @@ export class FaceStylizer extends VisionTaskRunner {
    * frame's timestamp (in milliseconds). The input timestamps must be
    * monotonically increasing.
    *
-   * To ensure that the output image has reasonable quality, the stylized
-   * output image size is the smaller of the model output size and the size of
-   * the 'regionOfInterest' specified in 'imageProcessingOptions'.
-   *
    * @param videoFrame A video frame to process.
    * @param imageProcessingOptions the `ImageProcessingOptions` specifying how
    *    to process the input image before running inference.
@@ -278,8 +262,12 @@ export class FaceStylizer extends VisionTaskRunner {
 
     this.graphRunner.attachImageListener(
         STYLIZED_IMAGE_STREAM, (image, timestamp) => {
-          const imageData = this.convertToImageData(image);
+          if (image.data instanceof WebGLTexture) {
-          this.userCallback(imageData, image.width, image.height);
+            this.userCallback(image.data, image.width, image.height);
+          } else {
+            const imageData = this.convertToImageData(image);
+            this.userCallback(imageData, image.width, image.height);
+          }
           this.setLatestOutputTimestamp(timestamp);
         });
     this.graphRunner.attachEmptyPacketListener(

mediapipe/tasks/web/vision/gesture_recognizer/BUILD

@@ -34,6 +34,7 @@ mediapipe_ts_library(
         "//mediapipe/tasks/web/core:classifier_options",
         "//mediapipe/tasks/web/vision/core:image_processing_options",
         "//mediapipe/tasks/web/vision/core:vision_task_runner",
+        "//mediapipe/tasks/web/vision/hand_landmarker:hand_landmarks_connections",
         "//mediapipe/web/graph_runner:graph_runner_ts",
     ],
 )

mediapipe/tasks/web/vision/gesture_recognizer/gesture_recognizer.ts

@@ -31,6 +31,7 @@ import {convertClassifierOptionsToProto} from '../../../../tasks/web/components/
 import {WasmFileset} from '../../../../tasks/web/core/wasm_fileset';
 import {ImageProcessingOptions} from '../../../../tasks/web/vision/core/image_processing_options';
 import {VisionGraphRunner, VisionTaskRunner} from '../../../../tasks/web/vision/core/vision_task_runner';
+import {HAND_CONNECTIONS} from '../../../../tasks/web/vision/hand_landmarker/hand_landmarks_connections';
 import {ImageSource, WasmModule} from '../../../../web/graph_runner/graph_runner';
 // Placeholder for internal dependency on trusted resource url
 
@@ -72,6 +73,12 @@ export class GestureRecognizer extends VisionTaskRunner {
   private readonly handGestureRecognizerGraphOptions:
       HandGestureRecognizerGraphOptions;
 
+  /**
+   * An array containing the pairs of hand landmark indices to be rendered with
+   * connections.
+   */
+  static HAND_CONNECTIONS = HAND_CONNECTIONS;
+
   /**
    * Initializes the Wasm runtime and creates a new gesture recognizer from the
    * provided options.

mediapipe/tasks/web/vision/hand_landmarker/BUILD

@@ -16,6 +16,7 @@ mediapipe_ts_library(
     visibility = ["//visibility:public"],
     deps = [
         ":hand_landmarker_types",
+        ":hand_landmarks_connections",
         "//mediapipe/framework:calculator_jspb_proto",
         "//mediapipe/framework:calculator_options_jspb_proto",
         "//mediapipe/framework/formats:classification_jspb_proto",
@@ -72,3 +73,9 @@ jasmine_node_test(
     tags = ["nomsan"],
     deps = [":hand_landmarker_test_lib"],
 )
+
+mediapipe_ts_library(
+    name = "hand_landmarks_connections",
+    srcs = ["hand_landmarks_connections.ts"],
+    deps = ["//mediapipe/tasks/web/vision/core:types"],
+)

mediapipe/tasks/web/vision/hand_landmarker/hand_landmarker.ts

@@ -27,6 +27,7 @@ import {Landmark, NormalizedLandmark} from '../../../../tasks/web/components/con
 import {WasmFileset} from '../../../../tasks/web/core/wasm_fileset';
 import {ImageProcessingOptions} from '../../../../tasks/web/vision/core/image_processing_options';
 import {VisionGraphRunner, VisionTaskRunner} from '../../../../tasks/web/vision/core/vision_task_runner';
+import {HAND_CONNECTIONS} from '../../../../tasks/web/vision/hand_landmarker/hand_landmarks_connections';
 import {ImageSource, WasmModule} from '../../../../web/graph_runner/graph_runner';
 // Placeholder for internal dependency on trusted resource url
 
@@ -63,6 +64,12 @@ export class HandLandmarker extends VisionTaskRunner {
       HandLandmarksDetectorGraphOptions;
   private readonly handDetectorGraphOptions: HandDetectorGraphOptions;
 
+  /**
+   * An array containing the pairs of hand landmark indices to be rendered with
+   * connections.
+   */
+  static HAND_CONNECTIONS = HAND_CONNECTIONS;
+
   /**
    * Initializes the Wasm runtime and creates a new `HandLandmarker` from the
    * provided options.

mediapipe/tasks/web/vision/hand_landmarker/hand_landmarks_connections.ts

@@ -0,0 +1,31 @@
+/**
+ * Copyright 2023 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 {Connection} from '../../../../tasks/web/vision/core/types';
+
+/**
+ * An array containing the pairs of hand landmark indices to be rendered with
+ * connections.
+ */
+export const HAND_CONNECTIONS: Connection[] = [
+  {start: 0, end: 1},   {start: 1, end: 2},   {start: 2, end: 3},
+  {start: 3, end: 4},   {start: 0, end: 5},   {start: 5, end: 6},
+  {start: 6, end: 7},   {start: 7, end: 8},   {start: 5, end: 9},
+  {start: 9, end: 10},  {start: 10, end: 11}, {start: 11, end: 12},
+  {start: 9, end: 13},  {start: 13, end: 14}, {start: 14, end: 15},
+  {start: 15, end: 16}, {start: 13, end: 17}, {start: 0, end: 17},
+  {start: 17, end: 18}, {start: 18, end: 19}, {start: 19, end: 20}
+];

mediapipe/tasks/web/vision/image_segmenter/BUILD

@@ -29,7 +29,10 @@ mediapipe_ts_library(
 
 mediapipe_ts_declaration(
     name = "image_segmenter_types",
-    srcs = ["image_segmenter_options.d.ts"],
+    srcs = [
+        "image_segmenter_options.d.ts",
+        "image_segmenter_result.d.ts",
+    ],
     deps = [
         "//mediapipe/tasks/web/core",
         "//mediapipe/tasks/web/core:classifier_options",

mediapipe/tasks/web/vision/image_segmenter/image_segmenter.ts

@@ -22,33 +22,48 @@ import {ImageSegmenterGraphOptions as ImageSegmenterGraphOptionsProto} from '../
 import {SegmenterOptions as SegmenterOptionsProto} from '../../../../tasks/cc/vision/image_segmenter/proto/segmenter_options_pb';
 import {WasmFileset} from '../../../../tasks/web/core/wasm_fileset';
 import {ImageProcessingOptions} from '../../../../tasks/web/vision/core/image_processing_options';
-import {SegmentationMask, SegmentationMaskCallback} from '../../../../tasks/web/vision/core/types';
+import {SegmentationMask} from '../../../../tasks/web/vision/core/types';
 import {VisionGraphRunner, VisionTaskRunner} from '../../../../tasks/web/vision/core/vision_task_runner';
 import {LabelMapItem} from '../../../../util/label_map_pb';
 import {ImageSource, WasmModule} from '../../../../web/graph_runner/graph_runner';
 // Placeholder for internal dependency on trusted resource url
 
 import {ImageSegmenterOptions} from './image_segmenter_options';
+import {ImageSegmenterResult} from './image_segmenter_result';
 
 export * from './image_segmenter_options';
-export {SegmentationMask, SegmentationMaskCallback};
+export * from './image_segmenter_result';
+export {SegmentationMask};
 export {ImageSource};  // Used in the public API
 
 const IMAGE_STREAM = 'image_in';
 const NORM_RECT_STREAM = 'norm_rect';
-const GROUPED_SEGMENTATIONS_STREAM = 'segmented_masks';
+const CONFIDENCE_MASKS_STREAM = 'confidence_masks';
+const CATEGORY_MASK_STREAM = 'category_mask';
 const IMAGE_SEGMENTER_GRAPH =
     'mediapipe.tasks.vision.image_segmenter.ImageSegmenterGraph';
 const TENSORS_TO_SEGMENTATION_CALCULATOR_NAME =
     'mediapipe.tasks.TensorsToSegmentationCalculator';
+const DEFAULT_OUTPUT_CATEGORY_MASK = false;
+const DEFAULT_OUTPUT_CONFIDENCE_MASKS = true;
 
 // The OSS JS API does not support the builder pattern.
 // tslint:disable:jspb-use-builder-pattern
 
+/**
+ * A callback that receives the computed masks from the image segmenter. The
+ * returned data is only valid for the duration of the callback. If
+ * asynchronous processing is needed, all data needs to be copied before the
+ * callback returns.
+ */
+export type ImageSegmenterCallack = (result: ImageSegmenterResult) => void;
+
 /** Performs image segmentation on images. */
 export class ImageSegmenter extends VisionTaskRunner {
-  private userCallback: SegmentationMaskCallback = () => {};
+  private result: ImageSegmenterResult = {width: 0, height: 0};
   private labels: string[] = [];
+  private outputCategoryMask = DEFAULT_OUTPUT_CATEGORY_MASK;
+  private outputConfidenceMasks = DEFAULT_OUTPUT_CONFIDENCE_MASKS;
   private readonly options: ImageSegmenterGraphOptionsProto;
   private readonly segmenterOptions: SegmenterOptionsProto;
 
@@ -109,7 +124,6 @@ export class ImageSegmenter extends VisionTaskRunner {
     this.options.setBaseOptions(new BaseOptionsProto());
   }
 
-
   protected override get baseOptions(): BaseOptionsProto {
     return this.options.getBaseOptions()!;
   }
@@ -137,12 +151,14 @@ export class ImageSegmenter extends VisionTaskRunner {
       this.options.clearDisplayNamesLocale();
     }
 
-    if (options.outputType === 'CONFIDENCE_MASK') {
+    if ('outputCategoryMask' in options) {
-      this.segmenterOptions.setOutputType(
+      this.outputCategoryMask =
-          SegmenterOptionsProto.OutputType.CONFIDENCE_MASK);
+          options.outputCategoryMask ?? DEFAULT_OUTPUT_CATEGORY_MASK;
-    } else {
+    }
-      this.segmenterOptions.setOutputType(
+
-          SegmenterOptionsProto.OutputType.CATEGORY_MASK);
+    if ('outputConfidenceMasks' in options) {
+      this.outputConfidenceMasks =
+          options.outputConfidenceMasks ?? DEFAULT_OUTPUT_CONFIDENCE_MASKS;
     }
 
     return super.applyOptions(options);
@@ -192,7 +208,7 @@ export class ImageSegmenter extends VisionTaskRunner {
    *    lifetime of the returned data is only guaranteed for the duration of the
    *    callback.
    */
-  segment(image: ImageSource, callback: SegmentationMaskCallback): void;
+  segment(image: ImageSource, callback: ImageSegmenterCallack): void;
   /**
    * Performs image segmentation on the provided single image and invokes the
    * callback with the response. The method returns synchronously once the
@@ -208,22 +224,77 @@ export class ImageSegmenter extends VisionTaskRunner {
    */
   segment(
       image: ImageSource, imageProcessingOptions: ImageProcessingOptions,
-      callback: SegmentationMaskCallback): void;
+      callback: ImageSegmenterCallack): void;
   segment(
       image: ImageSource,
       imageProcessingOptionsOrCallback: ImageProcessingOptions|
-      SegmentationMaskCallback,
+      ImageSegmenterCallack,
-      callback?: SegmentationMaskCallback): void {
+      callback?: ImageSegmenterCallack): void {
     const imageProcessingOptions =
         typeof imageProcessingOptionsOrCallback !== 'function' ?
         imageProcessingOptionsOrCallback :
         {};
-
+    const userCallback =
-    this.userCallback = typeof imageProcessingOptionsOrCallback === 'function' ?
+        typeof imageProcessingOptionsOrCallback === 'function' ?
         imageProcessingOptionsOrCallback :
         callback!;
+
+    this.reset();
     this.processImageData(image, imageProcessingOptions);
-    this.userCallback = () => {};
+    userCallback(this.result);
+  }
+
+  /**
+   * Performs image segmentation on the provided video frame and invokes the
+   * callback with the response. The method returns synchronously once the
+   * callback returns. Only use this method when the ImageSegmenter is
+   * created with running mode `video`.
+   *
+   * @param videoFrame A video frame to process.
+   * @param timestamp The timestamp of the current frame, in ms.
+   * @param callback The callback that is invoked with the segmented masks. The
+   *    lifetime of the returned data is only guaranteed for the duration of the
+   *    callback.
+   */
+  segmentForVideo(
+      videoFrame: ImageSource, timestamp: number,
+      callback: ImageSegmenterCallack): void;
+  /**
+   * Performs image segmentation on the provided video frame and invokes the
+   * callback with the response. The method returns synchronously once the
+   * callback returns. Only use this method when the ImageSegmenter is
+   * created with running mode `video`.
+   *
+   * @param videoFrame A video frame to process.
+   * @param imageProcessingOptions the `ImageProcessingOptions` specifying how
+   *    to process the input image before running inference.
+   * @param timestamp The timestamp of the current frame, in ms.
+   * @param callback The callback that is invoked with the segmented masks. The
+   *    lifetime of the returned data is only guaranteed for the duration of the
+   *    callback.
+   */
+  segmentForVideo(
+      videoFrame: ImageSource, imageProcessingOptions: ImageProcessingOptions,
+      timestamp: number, callback: ImageSegmenterCallack): void;
+  segmentForVideo(
+      videoFrame: ImageSource,
+      timestampOrImageProcessingOptions: number|ImageProcessingOptions,
+      timestampOrCallback: number|ImageSegmenterCallack,
+      callback?: ImageSegmenterCallack): void {
+    const imageProcessingOptions =
+        typeof timestampOrImageProcessingOptions !== 'number' ?
+        timestampOrImageProcessingOptions :
+        {};
+    const timestamp = typeof timestampOrImageProcessingOptions === 'number' ?
+        timestampOrImageProcessingOptions :
+        timestampOrCallback as number;
+    const userCallback = typeof timestampOrCallback === 'function' ?
+        timestampOrCallback :
+        callback!;
+
+    this.reset();
+    this.processVideoData(videoFrame, imageProcessingOptions, timestamp);
+    userCallback(this.result);
   }
 
   /**
@@ -241,56 +312,8 @@ export class ImageSegmenter extends VisionTaskRunner {
     return this.labels;
   }
 
-  /**
+  private reset(): void {
-   * Performs image segmentation on the provided video frame and invokes the
+    this.result = {width: 0, height: 0};
-   * callback with the response. The method returns synchronously once the
-   * callback returns. Only use this method when the ImageSegmenter is
-   * created with running mode `video`.
-   *
-   * @param videoFrame A video frame to process.
-   * @param timestamp The timestamp of the current frame, in ms.
-   * @param callback The callback that is invoked with the segmented masks. The
-   *    lifetime of the returned data is only guaranteed for the duration of the
-   *    callback.
-   */
-  segmentForVideo(
-      videoFrame: ImageSource, timestamp: number,
-      callback: SegmentationMaskCallback): void;
-  /**
-   * Performs image segmentation on the provided video frame and invokes the
-   * callback with the response. The method returns synchronously once the
-   * callback returns. Only use this method when the ImageSegmenter is
-   * created with running mode `video`.
-   *
-   * @param videoFrame A video frame to process.
-   * @param imageProcessingOptions the `ImageProcessingOptions` specifying how
-   *    to process the input image before running inference.
-   * @param timestamp The timestamp of the current frame, in ms.
-   * @param callback The callback that is invoked with the segmented masks. The
-   *    lifetime of the returned data is only guaranteed for the duration of the
-   *    callback.
-   */
-  segmentForVideo(
-      videoFrame: ImageSource, imageProcessingOptions: ImageProcessingOptions,
-      timestamp: number, callback: SegmentationMaskCallback): void;
-  segmentForVideo(
-      videoFrame: ImageSource,
-      timestampOrImageProcessingOptions: number|ImageProcessingOptions,
-      timestampOrCallback: number|SegmentationMaskCallback,
-      callback?: SegmentationMaskCallback): void {
-    const imageProcessingOptions =
-        typeof timestampOrImageProcessingOptions !== 'number' ?
-        timestampOrImageProcessingOptions :
-        {};
-    const timestamp = typeof timestampOrImageProcessingOptions === 'number' ?
-        timestampOrImageProcessingOptions :
-        timestampOrCallback as number;
-
-    this.userCallback = typeof timestampOrCallback === 'function' ?
-        timestampOrCallback :
-        callback!;
-    this.processVideoData(videoFrame, imageProcessingOptions, timestamp);
-    this.userCallback = () => {};
   }
 
   /** Updates the MediaPipe graph configuration. */
@@ -298,7 +321,6 @@ export class ImageSegmenter extends VisionTaskRunner {
     const graphConfig = new CalculatorGraphConfig();
     graphConfig.addInputStream(IMAGE_STREAM);
     graphConfig.addInputStream(NORM_RECT_STREAM);
-    graphConfig.addOutputStream(GROUPED_SEGMENTATIONS_STREAM);
 
     const calculatorOptions = new CalculatorOptions();
     calculatorOptions.setExtension(
@@ -308,26 +330,47 @@ export class ImageSegmenter extends VisionTaskRunner {
     segmenterNode.setCalculator(IMAGE_SEGMENTER_GRAPH);
     segmenterNode.addInputStream('IMAGE:' + IMAGE_STREAM);
     segmenterNode.addInputStream('NORM_RECT:' + NORM_RECT_STREAM);
-    segmenterNode.addOutputStream(
-        'GROUPED_SEGMENTATION:' + GROUPED_SEGMENTATIONS_STREAM);
     segmenterNode.setOptions(calculatorOptions);
 
     graphConfig.addNode(segmenterNode);
 
-    this.graphRunner.attachImageVectorListener(
+    if (this.outputConfidenceMasks) {
-        GROUPED_SEGMENTATIONS_STREAM, (masks, timestamp) => {
+      graphConfig.addOutputStream(CONFIDENCE_MASKS_STREAM);
-          if (masks.length === 0) {
+      segmenterNode.addOutputStream(
-            this.userCallback([], 0, 0);
+          'CONFIDENCE_MASKS:' + CONFIDENCE_MASKS_STREAM);
-          } else {
+
-            this.userCallback(
+      this.graphRunner.attachImageVectorListener(
-                masks.map(m => m.data), masks[0].width, masks[0].height);
+          CONFIDENCE_MASKS_STREAM, (masks, timestamp) => {
-          }
+            this.result.confidenceMasks = masks.map(m => m.data);
-          this.setLatestOutputTimestamp(timestamp);
+            if (masks.length >= 0) {
-        });
+              this.result.width = masks[0].width;
-    this.graphRunner.attachEmptyPacketListener(
+              this.result.height = masks[0].height;
-        GROUPED_SEGMENTATIONS_STREAM, timestamp => {
+            }
-          this.setLatestOutputTimestamp(timestamp);
+
-        });
+            this.setLatestOutputTimestamp(timestamp);
+          });
+      this.graphRunner.attachEmptyPacketListener(
+          CONFIDENCE_MASKS_STREAM, timestamp => {
+            this.setLatestOutputTimestamp(timestamp);
+          });
+    }
+
+    if (this.outputCategoryMask) {
+      graphConfig.addOutputStream(CATEGORY_MASK_STREAM);
+      segmenterNode.addOutputStream('CATEGORY_MASK:' + CATEGORY_MASK_STREAM);
+
+      this.graphRunner.attachImageListener(
+          CATEGORY_MASK_STREAM, (mask, timestamp) => {
+            this.result.categoryMask = mask.data;
+            this.result.width = mask.width;
+            this.result.height = mask.height;
+            this.setLatestOutputTimestamp(timestamp);
+          });
+      this.graphRunner.attachEmptyPacketListener(
+          CATEGORY_MASK_STREAM, timestamp => {
+            this.setLatestOutputTimestamp(timestamp);
+          });
+    }
 
     const binaryGraph = graphConfig.serializeBinary();
     this.setGraph(new Uint8Array(binaryGraph), /* isBinary= */ true);

mediapipe/tasks/web/vision/image_segmenter/image_segmenter_options.d.ts

@@ -24,18 +24,9 @@ export interface ImageSegmenterOptions extends VisionTaskOptions {
    */
   displayNamesLocale?: string|undefined;
 
-  /**
+  /** Whether to output confidence masks. Defaults to true. */
-   * The output type of segmentation results.
+  outputConfidenceMasks?: boolean|undefined;
-   *
+
-   * The two supported modes are:
+  /** Whether to output the category masks. Defaults to false. */
-   * - Category Mask:   Gives a single output mask where each pixel represents
+  outputCategoryMask?: boolean|undefined;
-   *                    the class which the pixel in the original image was
-   *                    predicted to belong to.
-   * - Confidence Mask: Gives a list of output masks (one for each class). For
-   *                    each mask, the pixel represents the prediction
-   *                    confidence, usually in the [0.0, 0.1] range.
-   *
-   * Defaults to `CATEGORY_MASK`.
-   */
-  outputType?: 'CATEGORY_MASK'|'CONFIDENCE_MASK'|undefined;
 }

mediapipe/tasks/web/vision/image_segmenter/image_segmenter_result.d.ts

@@ -0,0 +1,37 @@
+/**
+ * Copyright 2023 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.
+ */
+
+/** The output result of ImageSegmenter. */
+export declare interface ImageSegmenterResult {
+  /**
+   * Multiple masks as Float32Arrays or WebGLTextures where, for each mask, each
+   * pixel represents the prediction confidence, usually in the [0, 1] range.
+   */
+  confidenceMasks?: Float32Array[]|WebGLTexture[];
+
+  /**
+   * A category mask as a Uint8ClampedArray or WebGLTexture where each
+   * pixel represents the class which the pixel in the original image was
+   * predicted to belong to.
+   */
+  categoryMask?: Uint8ClampedArray|WebGLTexture;
+
+  /** The width of the masks. */
+  width: number;
+
+  /** The height of the masks. */
+  height: number;
+}

mediapipe/tasks/web/vision/image_segmenter/image_segmenter_test.ts

@@ -18,7 +18,7 @@ import 'jasmine';
 
 // Placeholder for internal dependency on encodeByteArray
 import {CalculatorGraphConfig} from '../../../../framework/calculator_pb';
-import {addJasmineCustomFloatEqualityTester, createSpyWasmModule, MediapipeTasksFake, SpyWasmModule, verifyGraph, verifyListenersRegistered} from '../../../../tasks/web/core/task_runner_test_utils';
+import {addJasmineCustomFloatEqualityTester, createSpyWasmModule, MediapipeTasksFake, SpyWasmModule, verifyGraph} from '../../../../tasks/web/core/task_runner_test_utils';
 import {WasmImage} from '../../../../web/graph_runner/graph_runner_image_lib';
 
 import {ImageSegmenter} from './image_segmenter';
@@ -30,7 +30,9 @@ class ImageSegmenterFake extends ImageSegmenter implements MediapipeTasksFake {
   graph: CalculatorGraphConfig|undefined;
 
   fakeWasmModule: SpyWasmModule;
-  imageVectorListener:
+  categoryMaskListener:
+      ((images: WasmImage, timestamp: number) => void)|undefined;
+  confidenceMasksListener:
       ((images: WasmImage[], timestamp: number) => void)|undefined;
 
   constructor() {
@@ -38,11 +40,16 @@ class ImageSegmenterFake extends ImageSegmenter implements MediapipeTasksFake {
     this.fakeWasmModule =
         this.graphRunner.wasmModule as unknown as SpyWasmModule;
 
-    this.attachListenerSpies[0] =
+    this.attachListenerSpies[0] = spyOn(this.graphRunner, 'attachImageListener')
+                                      .and.callFake((stream, listener) => {
+                                        expect(stream).toEqual('category_mask');
+                                        this.categoryMaskListener = listener;
+                                      });
+    this.attachListenerSpies[1] =
         spyOn(this.graphRunner, 'attachImageVectorListener')
             .and.callFake((stream, listener) => {
-              expect(stream).toEqual('segmented_masks');
+              expect(stream).toEqual('confidence_masks');
-              this.imageVectorListener = listener;
+              this.confidenceMasksListener = listener;
             });
     spyOn(this.graphRunner, 'setGraph').and.callFake(binaryGraph => {
       this.graph = CalculatorGraphConfig.deserializeBinary(binaryGraph);
@@ -63,17 +70,18 @@ describe('ImageSegmenter', () => {
 
   it('initializes graph', async () => {
     verifyGraph(imageSegmenter);
-    verifyListenersRegistered(imageSegmenter);
+
+    // Verify default options
+    expect(imageSegmenter.categoryMaskListener).not.toBeDefined();
+    expect(imageSegmenter.confidenceMasksListener).toBeDefined();
   });
 
   it('reloads graph when settings are changed', async () => {
     await imageSegmenter.setOptions({displayNamesLocale: 'en'});
     verifyGraph(imageSegmenter, ['displayNamesLocale', 'en']);
-    verifyListenersRegistered(imageSegmenter);
 
     await imageSegmenter.setOptions({displayNamesLocale: 'de'});
     verifyGraph(imageSegmenter, ['displayNamesLocale', 'de']);
-    verifyListenersRegistered(imageSegmenter);
   });
 
   it('can use custom models', async () => {
@@ -100,9 +108,11 @@ describe('ImageSegmenter', () => {
   });
 
   it('merges options', async () => {
-    await imageSegmenter.setOptions({outputType: 'CATEGORY_MASK'});
+    await imageSegmenter.setOptions(
+        {baseOptions: {modelAssetBuffer: new Uint8Array([])}});
     await imageSegmenter.setOptions({displayNamesLocale: 'en'});
-    verifyGraph(imageSegmenter, [['segmenterOptions', 'outputType'], 1]);
+    verifyGraph(
+        imageSegmenter, [['baseOptions', 'modelAsset', 'fileContent'], '']);
     verifyGraph(imageSegmenter, ['displayNamesLocale', 'en']);
   });
 
@@ -115,22 +125,13 @@ describe('ImageSegmenter', () => {
       defaultValue: unknown;
     }
 
-    const testCases: TestCase[] = [
+    const testCases: TestCase[] = [{
-      {
+      optionName: 'displayNamesLocale',
-        optionName: 'displayNamesLocale',
+      fieldPath: ['displayNamesLocale'],
-        fieldPath: ['displayNamesLocale'],
+      userValue: 'en',
-        userValue: 'en',
+      graphValue: 'en',
-        graphValue: 'en',
+      defaultValue: 'en'
-        defaultValue: 'en'
+    }];
-      },
-      {
-        optionName: 'outputType',
-        fieldPath: ['segmenterOptions', 'outputType'],
-        userValue: 'CONFIDENCE_MASK',
-        graphValue: 2,
-        defaultValue: 1
-      },
-    ];
 
     for (const testCase of testCases) {
       it(`can set ${testCase.optionName}`, async () => {
@@ -158,27 +159,31 @@ describe('ImageSegmenter', () => {
     }).toThrowError('This task doesn\'t support region-of-interest.');
   });
 
-  it('supports category masks', (done) => {
+  it('supports category mask', async () => {
     const mask = new Uint8ClampedArray([1, 2, 3, 4]);
 
+    await imageSegmenter.setOptions(
+        {outputCategoryMask: true, outputConfidenceMasks: false});
+
     // Pass the test data to our listener
     imageSegmenter.fakeWasmModule._waitUntilIdle.and.callFake(() => {
-      verifyListenersRegistered(imageSegmenter);
+      expect(imageSegmenter.categoryMaskListener).toBeDefined();
-      imageSegmenter.imageVectorListener!(
+      imageSegmenter.categoryMaskListener!
-          [
+          ({data: mask, width: 2, height: 2},
-            {data: mask, width: 2, height: 2},
+           /* timestamp= */ 1337);
-          ],
-          /* timestamp= */ 1337);
     });
 
     // Invoke the image segmenter
-    imageSegmenter.segment({} as HTMLImageElement, (masks, width, height) => {
+
-      expect(imageSegmenter.fakeWasmModule._waitUntilIdle).toHaveBeenCalled();
+    return new Promise<void>(resolve => {
-      expect(masks).toHaveSize(1);
+      imageSegmenter.segment({} as HTMLImageElement, result => {
-      expect(masks[0]).toEqual(mask);
+        expect(imageSegmenter.fakeWasmModule._waitUntilIdle).toHaveBeenCalled();
-      expect(width).toEqual(2);
+        expect(result.categoryMask).toEqual(mask);
-      expect(height).toEqual(2);
+        expect(result.confidenceMasks).not.toBeDefined();
-      done();
+        expect(result.width).toEqual(2);
+        expect(result.height).toEqual(2);
+        resolve();
+      });
     });
   });
 
@@ -186,12 +191,13 @@ describe('ImageSegmenter', () => {
     const mask1 = new Float32Array([0.1, 0.2, 0.3, 0.4]);
     const mask2 = new Float32Array([0.5, 0.6, 0.7, 0.8]);
 
-    await imageSegmenter.setOptions({outputType: 'CONFIDENCE_MASK'});
+    await imageSegmenter.setOptions(
+        {outputCategoryMask: false, outputConfidenceMasks: true});
 
     // Pass the test data to our listener
     imageSegmenter.fakeWasmModule._waitUntilIdle.and.callFake(() => {
-      verifyListenersRegistered(imageSegmenter);
+      expect(imageSegmenter.confidenceMasksListener).toBeDefined();
-      imageSegmenter.imageVectorListener!(
+      imageSegmenter.confidenceMasksListener!(
           [
             {data: mask1, width: 2, height: 2},
             {data: mask2, width: 2, height: 2},
@@ -201,13 +207,49 @@ describe('ImageSegmenter', () => {
 
     return new Promise<void>(resolve => {
       // Invoke the image segmenter
-      imageSegmenter.segment({} as HTMLImageElement, (masks, width, height) => {
+      imageSegmenter.segment({} as HTMLImageElement, result => {
         expect(imageSegmenter.fakeWasmModule._waitUntilIdle).toHaveBeenCalled();
-        expect(masks).toHaveSize(2);
+        expect(result.categoryMask).not.toBeDefined();
-        expect(masks[0]).toEqual(mask1);
+        expect(result.confidenceMasks).toEqual([mask1, mask2]);
-        expect(masks[1]).toEqual(mask2);
+        expect(result.width).toEqual(2);
-        expect(width).toEqual(2);
+        expect(result.height).toEqual(2);
-        expect(height).toEqual(2);
+        resolve();
+      });
+    });
+  });
+
+  it('supports combined category and confidence masks', async () => {
+    const categoryMask = new Uint8ClampedArray([1, 0]);
+    const confidenceMask1 = new Float32Array([0.0, 1.0]);
+    const confidenceMask2 = new Float32Array([1.0, 0.0]);
+
+    await imageSegmenter.setOptions(
+        {outputCategoryMask: true, outputConfidenceMasks: true});
+
+    // Pass the test data to our listener
+    imageSegmenter.fakeWasmModule._waitUntilIdle.and.callFake(() => {
+      expect(imageSegmenter.categoryMaskListener).toBeDefined();
+      expect(imageSegmenter.confidenceMasksListener).toBeDefined();
+      imageSegmenter.categoryMaskListener!
+          ({data: categoryMask, width: 1, height: 1}, 1337);
+      imageSegmenter.confidenceMasksListener!(
+          [
+            {data: confidenceMask1, width: 1, height: 1},
+            {data: confidenceMask2, width: 1, height: 1},
+          ],
+          1337);
+    });
+
+    return new Promise<void>(resolve => {
+      // Invoke the image segmenter
+      imageSegmenter.segment({} as HTMLImageElement, result => {
+        expect(imageSegmenter.fakeWasmModule._waitUntilIdle).toHaveBeenCalled();
+        expect(result.categoryMask).toEqual(categoryMask);
+        expect(result.confidenceMasks).toEqual([
+          confidenceMask1, confidenceMask2
+        ]);
+        expect(result.width).toEqual(1);
+        expect(result.height).toEqual(1);
         resolve();
       });
     });

mediapipe/tasks/web/vision/interactive_segmenter/BUILD

@@ -30,7 +30,10 @@ mediapipe_ts_library(
 
 mediapipe_ts_declaration(
     name = "interactive_segmenter_types",
-    srcs = ["interactive_segmenter_options.d.ts"],
+    srcs = [
+        "interactive_segmenter_options.d.ts",
+        "interactive_segmenter_result.d.ts",
+    ],
     deps = [
         "//mediapipe/tasks/web/core",
         "//mediapipe/tasks/web/core:classifier_options",

mediapipe/tasks/web/vision/interactive_segmenter/interactive_segmenter.ts

@@ -21,7 +21,7 @@ import {ImageSegmenterGraphOptions as ImageSegmenterGraphOptionsProto} from '../
 import {SegmenterOptions as SegmenterOptionsProto} from '../../../../tasks/cc/vision/image_segmenter/proto/segmenter_options_pb';
 import {WasmFileset} from '../../../../tasks/web/core/wasm_fileset';
 import {ImageProcessingOptions} from '../../../../tasks/web/vision/core/image_processing_options';
-import {RegionOfInterest, SegmentationMask, SegmentationMaskCallback} from '../../../../tasks/web/vision/core/types';
+import {RegionOfInterest, SegmentationMask} from '../../../../tasks/web/vision/core/types';
 import {VisionGraphRunner, VisionTaskRunner} from '../../../../tasks/web/vision/core/vision_task_runner';
 import {Color as ColorProto} from '../../../../util/color_pb';
 import {RenderAnnotation as RenderAnnotationProto, RenderData as RenderDataProto} from '../../../../util/render_data_pb';
@@ -29,21 +29,35 @@ import {ImageSource, WasmModule} from '../../../../web/graph_runner/graph_runner
 // Placeholder for internal dependency on trusted resource url
 
 import {InteractiveSegmenterOptions} from './interactive_segmenter_options';
+import {InteractiveSegmenterResult} from './interactive_segmenter_result';
 
 export * from './interactive_segmenter_options';
-export {SegmentationMask, SegmentationMaskCallback, RegionOfInterest};
+export * from './interactive_segmenter_result';
+export {SegmentationMask, RegionOfInterest};
 export {ImageSource};
 
 const IMAGE_IN_STREAM = 'image_in';
 const NORM_RECT_IN_STREAM = 'norm_rect_in';
 const ROI_IN_STREAM = 'roi_in';
-const IMAGE_OUT_STREAM = 'image_out';
+const CONFIDENCE_MASKS_STREAM = 'confidence_masks';
+const CATEGORY_MASK_STREAM = 'category_mask';
 const IMAGEA_SEGMENTER_GRAPH =
     'mediapipe.tasks.vision.interactive_segmenter.InteractiveSegmenterGraph';
+const DEFAULT_OUTPUT_CATEGORY_MASK = false;
+const DEFAULT_OUTPUT_CONFIDENCE_MASKS = true;
 
 // The OSS JS API does not support the builder pattern.
 // tslint:disable:jspb-use-builder-pattern
 
+/**
+ * A callback that receives the computed masks from the interactive segmenter.
+ * The returned data is only valid for the duration of the callback. If
+ * asynchronous processing is needed, all data needs to be copied before the
+ * callback returns.
+ */
+export type InteractiveSegmenterCallack =
+    (result: InteractiveSegmenterResult) => void;
+
 /**
  * Performs interactive segmentation on images.
  *
@@ -69,7 +83,9 @@ const IMAGEA_SEGMENTER_GRAPH =
  *   - batch is always 1
  */
 export class InteractiveSegmenter extends VisionTaskRunner {
-  private userCallback: SegmentationMaskCallback = () => {};
+  private result: InteractiveSegmenterResult = {width: 0, height: 0};
+  private outputCategoryMask = DEFAULT_OUTPUT_CATEGORY_MASK;
+  private outputConfidenceMasks = DEFAULT_OUTPUT_CONFIDENCE_MASKS;
   private readonly options: ImageSegmenterGraphOptionsProto;
   private readonly segmenterOptions: SegmenterOptionsProto;
 
@@ -154,12 +170,14 @@ export class InteractiveSegmenter extends VisionTaskRunner {
    * @return A Promise that resolves when the settings have been applied.
    */
   override setOptions(options: InteractiveSegmenterOptions): Promise<void> {
-    if (options.outputType === 'CONFIDENCE_MASK') {
+    if ('outputCategoryMask' in options) {
-      this.segmenterOptions.setOutputType(
+      this.outputCategoryMask =
-          SegmenterOptionsProto.OutputType.CONFIDENCE_MASK);
+          options.outputCategoryMask ?? DEFAULT_OUTPUT_CATEGORY_MASK;
-    } else {
+    }
-      this.segmenterOptions.setOutputType(
+
-          SegmenterOptionsProto.OutputType.CATEGORY_MASK);
+    if ('outputConfidenceMasks' in options) {
+      this.outputConfidenceMasks =
+          options.outputConfidenceMasks ?? DEFAULT_OUTPUT_CONFIDENCE_MASKS;
     }
 
     return super.applyOptions(options);
@@ -184,7 +202,7 @@ export class InteractiveSegmenter extends VisionTaskRunner {
    */
   segment(
       image: ImageSource, roi: RegionOfInterest,
-      callback: SegmentationMaskCallback): void;
+      callback: InteractiveSegmenterCallack): void;
   /**
    * Performs interactive segmentation on the provided single image and invokes
    * the callback with the response. The `roi` parameter is used to represent a
@@ -213,24 +231,29 @@ export class InteractiveSegmenter extends VisionTaskRunner {
   segment(
       image: ImageSource, roi: RegionOfInterest,
       imageProcessingOptions: ImageProcessingOptions,
-      callback: SegmentationMaskCallback): void;
+      callback: InteractiveSegmenterCallack): void;
   segment(
       image: ImageSource, roi: RegionOfInterest,
       imageProcessingOptionsOrCallback: ImageProcessingOptions|
-      SegmentationMaskCallback,
+      InteractiveSegmenterCallack,
-      callback?: SegmentationMaskCallback): void {
+      callback?: InteractiveSegmenterCallack): void {
     const imageProcessingOptions =
         typeof imageProcessingOptionsOrCallback !== 'function' ?
         imageProcessingOptionsOrCallback :
         {};
-
+    const userCallback =
-    this.userCallback = typeof imageProcessingOptionsOrCallback === 'function' ?
+        typeof imageProcessingOptionsOrCallback === 'function' ?
         imageProcessingOptionsOrCallback :
         callback!;
 
+    this.reset();
     this.processRenderData(roi, this.getSynctheticTimestamp());
     this.processImageData(image, imageProcessingOptions);
-    this.userCallback = () => {};
+    userCallback(this.result);
+  }
+
+  private reset(): void {
+    this.result = {width: 0, height: 0};
   }
 
   /** Updates the MediaPipe graph configuration. */
@@ -239,7 +262,6 @@ export class InteractiveSegmenter extends VisionTaskRunner {
     graphConfig.addInputStream(IMAGE_IN_STREAM);
     graphConfig.addInputStream(ROI_IN_STREAM);
     graphConfig.addInputStream(NORM_RECT_IN_STREAM);
-    graphConfig.addOutputStream(IMAGE_OUT_STREAM);
 
     const calculatorOptions = new CalculatorOptions();
     calculatorOptions.setExtension(
@@ -250,24 +272,47 @@ export class InteractiveSegmenter extends VisionTaskRunner {
     segmenterNode.addInputStream('IMAGE:' + IMAGE_IN_STREAM);
     segmenterNode.addInputStream('ROI:' + ROI_IN_STREAM);
     segmenterNode.addInputStream('NORM_RECT:' + NORM_RECT_IN_STREAM);
-    segmenterNode.addOutputStream('GROUPED_SEGMENTATION:' + IMAGE_OUT_STREAM);
     segmenterNode.setOptions(calculatorOptions);
 
     graphConfig.addNode(segmenterNode);
 
-    this.graphRunner.attachImageVectorListener(
+    if (this.outputConfidenceMasks) {
-        IMAGE_OUT_STREAM, (masks, timestamp) => {
+      graphConfig.addOutputStream(CONFIDENCE_MASKS_STREAM);
-          if (masks.length === 0) {
+      segmenterNode.addOutputStream(
-            this.userCallback([], 0, 0);
+          'CONFIDENCE_MASKS:' + CONFIDENCE_MASKS_STREAM);
-          } else {
+
-            this.userCallback(
+      this.graphRunner.attachImageVectorListener(
-                masks.map(m => m.data), masks[0].width, masks[0].height);
+          CONFIDENCE_MASKS_STREAM, (masks, timestamp) => {
-          }
+            this.result.confidenceMasks = masks.map(m => m.data);
-          this.setLatestOutputTimestamp(timestamp);
+            if (masks.length >= 0) {
-        });
+              this.result.width = masks[0].width;
-    this.graphRunner.attachEmptyPacketListener(IMAGE_OUT_STREAM, timestamp => {
+              this.result.height = masks[0].height;
-      this.setLatestOutputTimestamp(timestamp);
+            }
-    });
+
+            this.setLatestOutputTimestamp(timestamp);
+          });
+      this.graphRunner.attachEmptyPacketListener(
+          CONFIDENCE_MASKS_STREAM, timestamp => {
+            this.setLatestOutputTimestamp(timestamp);
+          });
+    }
+
+    if (this.outputCategoryMask) {
+      graphConfig.addOutputStream(CATEGORY_MASK_STREAM);
+      segmenterNode.addOutputStream('CATEGORY_MASK:' + CATEGORY_MASK_STREAM);
+
+      this.graphRunner.attachImageListener(
+          CATEGORY_MASK_STREAM, (mask, timestamp) => {
+            this.result.categoryMask = mask.data;
+            this.result.width = mask.width;
+            this.result.height = mask.height;
+            this.setLatestOutputTimestamp(timestamp);
+          });
+      this.graphRunner.attachEmptyPacketListener(
+          CATEGORY_MASK_STREAM, timestamp => {
+            this.setLatestOutputTimestamp(timestamp);
+          });
+    }
 
     const binaryGraph = graphConfig.serializeBinary();
     this.setGraph(new Uint8Array(binaryGraph), /* isBinary= */ true);

mediapipe/tasks/web/vision/interactive_segmenter/interactive_segmenter_options.d.ts

@@ -19,18 +19,9 @@ import {TaskRunnerOptions} from '../../../../tasks/web/core/task_runner_options'
 
 /** Options to configure the MediaPipe Interactive Segmenter Task */
 export interface InteractiveSegmenterOptions extends TaskRunnerOptions {
-  /**
+  /** Whether to output confidence masks. Defaults to true. */
-   * The output type of segmentation results.
+  outputConfidenceMasks?: boolean|undefined;
-   *
+
-   * The two supported modes are:
+  /** Whether to output the category masks. Defaults to false. */
-   * - Category Mask:   Gives a single output mask where each pixel represents
+  outputCategoryMask?: boolean|undefined;
-   *                    the class which the pixel in the original image was
-   *                    predicted to belong to.
-   * - Confidence Mask: Gives a list of output masks (one for each class). For
-   *                    each mask, the pixel represents the prediction
-   *                    confidence, usually in the [0.0, 0.1] range.
-   *
-   * Defaults to `CATEGORY_MASK`.
-   */
-  outputType?: 'CATEGORY_MASK'|'CONFIDENCE_MASK'|undefined;
 }

mediapipe/tasks/web/vision/interactive_segmenter/interactive_segmenter_result.d.ts

@@ -0,0 +1,37 @@
+/**
+ * Copyright 2023 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.
+ */
+
+/** The output result of InteractiveSegmenter. */
+export declare interface InteractiveSegmenterResult {
+  /**
+   * Multiple masks as Float32Arrays or WebGLTextures where, for each mask, each
+   * pixel represents the prediction confidence, usually in the [0, 1] range.
+   */
+  confidenceMasks?: Float32Array[]|WebGLTexture[];
+
+  /**
+   * A category mask as a Uint8ClampedArray or WebGLTexture where each
+   * pixel represents the class which the pixel in the original image was
+   * predicted to belong to.
+   */
+  categoryMask?: Uint8ClampedArray|WebGLTexture;
+
+  /** The width of the masks. */
+  width: number;
+
+  /** The height of the masks. */
+  height: number;
+}

mediapipe/tasks/web/vision/interactive_segmenter/interactive_segmenter_test.ts

@@ -18,7 +18,7 @@ import 'jasmine';
 
 // Placeholder for internal dependency on encodeByteArray
 import {CalculatorGraphConfig} from '../../../../framework/calculator_pb';
-import {addJasmineCustomFloatEqualityTester, createSpyWasmModule, MediapipeTasksFake, SpyWasmModule, verifyGraph, verifyListenersRegistered} from '../../../../tasks/web/core/task_runner_test_utils';
+import {addJasmineCustomFloatEqualityTester, createSpyWasmModule, MediapipeTasksFake, SpyWasmModule, verifyGraph} from '../../../../tasks/web/core/task_runner_test_utils';
 import {RenderData as RenderDataProto} from '../../../../util/render_data_pb';
 import {WasmImage} from '../../../../web/graph_runner/graph_runner_image_lib';
 
@@ -37,7 +37,9 @@ class InteractiveSegmenterFake extends InteractiveSegmenter implements
   graph: CalculatorGraphConfig|undefined;
 
   fakeWasmModule: SpyWasmModule;
-  imageVectorListener:
+  categoryMaskListener:
+      ((images: WasmImage, timestamp: number) => void)|undefined;
+  confidenceMasksListener:
       ((images: WasmImage[], timestamp: number) => void)|undefined;
   lastRoi?: RenderDataProto;
 
@@ -46,11 +48,16 @@ class InteractiveSegmenterFake extends InteractiveSegmenter implements
     this.fakeWasmModule =
         this.graphRunner.wasmModule as unknown as SpyWasmModule;
 
-    this.attachListenerSpies[0] =
+    this.attachListenerSpies[0] = spyOn(this.graphRunner, 'attachImageListener')
+                                      .and.callFake((stream, listener) => {
+                                        expect(stream).toEqual('category_mask');
+                                        this.categoryMaskListener = listener;
+                                      });
+    this.attachListenerSpies[1] =
         spyOn(this.graphRunner, 'attachImageVectorListener')
             .and.callFake((stream, listener) => {
-              expect(stream).toEqual('image_out');
+              expect(stream).toEqual('confidence_masks');
-              this.imageVectorListener = listener;
+              this.confidenceMasksListener = listener;
             });
     spyOn(this.graphRunner, 'setGraph').and.callFake(binaryGraph => {
       this.graph = CalculatorGraphConfig.deserializeBinary(binaryGraph);
@@ -79,17 +86,21 @@ describe('InteractiveSegmenter', () => {
 
   it('initializes graph', async () => {
     verifyGraph(interactiveSegmenter);
-    verifyListenersRegistered(interactiveSegmenter);
+
+    // Verify default options
+    expect(interactiveSegmenter.categoryMaskListener).not.toBeDefined();
+    expect(interactiveSegmenter.confidenceMasksListener).toBeDefined();
   });
 
   it('reloads graph when settings are changed', async () => {
-    await interactiveSegmenter.setOptions({outputType: 'CATEGORY_MASK'});
+    await interactiveSegmenter.setOptions(
-    verifyGraph(interactiveSegmenter, [['segmenterOptions', 'outputType'], 1]);
+        {outputConfidenceMasks: true, outputCategoryMask: false});
-    verifyListenersRegistered(interactiveSegmenter);
+    expect(interactiveSegmenter.categoryMaskListener).not.toBeDefined();
+    expect(interactiveSegmenter.confidenceMasksListener).toBeDefined();
 
-    await interactiveSegmenter.setOptions({outputType: 'CONFIDENCE_MASK'});
+    await interactiveSegmenter.setOptions(
-    verifyGraph(interactiveSegmenter, [['segmenterOptions', 'outputType'], 2]);
+        {outputConfidenceMasks: false, outputCategoryMask: true});
-    verifyListenersRegistered(interactiveSegmenter);
+    expect(interactiveSegmenter.categoryMaskListener).toBeDefined();
   });
 
   it('can use custom models', async () => {
@@ -115,23 +126,6 @@ describe('InteractiveSegmenter', () => {
         ]);
   });
 
-
-  describe('setOptions()', () => {
-    const fieldPath = ['segmenterOptions', 'outputType'];
-
-    it(`can set outputType`, async () => {
-      await interactiveSegmenter.setOptions({outputType: 'CONFIDENCE_MASK'});
-      verifyGraph(interactiveSegmenter, [fieldPath, 2]);
-    });
-
-    it(`can clear outputType`, async () => {
-      await interactiveSegmenter.setOptions({outputType: 'CONFIDENCE_MASK'});
-      verifyGraph(interactiveSegmenter, [fieldPath, 2]);
-      await interactiveSegmenter.setOptions({outputType: undefined});
-      verifyGraph(interactiveSegmenter, [fieldPath, 1]);
-    });
-  });
-
   it('doesn\'t support region of interest', () => {
     expect(() => {
       interactiveSegmenter.segment(
@@ -153,60 +147,99 @@ describe('InteractiveSegmenter', () => {
     interactiveSegmenter.segment({} as HTMLImageElement, ROI, () => {});
   });
 
-  it('supports category masks', (done) => {
+  it('supports category mask', async () => {
     const mask = new Uint8ClampedArray([1, 2, 3, 4]);
 
+    await interactiveSegmenter.setOptions(
+        {outputCategoryMask: true, outputConfidenceMasks: false});
+
     // Pass the test data to our listener
     interactiveSegmenter.fakeWasmModule._waitUntilIdle.and.callFake(() => {
-      verifyListenersRegistered(interactiveSegmenter);
+      expect(interactiveSegmenter.categoryMaskListener).toBeDefined();
-      interactiveSegmenter.imageVectorListener!(
+      interactiveSegmenter.categoryMaskListener!
-          [
+          ({data: mask, width: 2, height: 2},
-            {data: mask, width: 2, height: 2},
+           /* timestamp= */ 1337);
-          ],
-          /* timestamp= */ 1337);
     });
 
     // Invoke the image segmenter
-    interactiveSegmenter.segment(
+    return new Promise<void>(resolve => {
-        {} as HTMLImageElement, ROI, (masks, width, height) => {
+      interactiveSegmenter.segment({} as HTMLImageElement, ROI, result => {
-          expect(interactiveSegmenter.fakeWasmModule._waitUntilIdle)
+        expect(interactiveSegmenter.fakeWasmModule._waitUntilIdle)
-              .toHaveBeenCalled();
+            .toHaveBeenCalled();
-          expect(masks).toHaveSize(1);
+        expect(result.categoryMask).toEqual(mask);
-          expect(masks[0]).toEqual(mask);
+        expect(result.confidenceMasks).not.toBeDefined();
-          expect(width).toEqual(2);
+        expect(result.width).toEqual(2);
-          expect(height).toEqual(2);
+        expect(result.height).toEqual(2);
-          done();
+        resolve();
-        });
+      });
+    });
   });
 
   it('supports confidence masks', async () => {
     const mask1 = new Float32Array([0.1, 0.2, 0.3, 0.4]);
     const mask2 = new Float32Array([0.5, 0.6, 0.7, 0.8]);
 
-    await interactiveSegmenter.setOptions({outputType: 'CONFIDENCE_MASK'});
+    await interactiveSegmenter.setOptions(
+        {outputCategoryMask: false, outputConfidenceMasks: true});
 
     // Pass the test data to our listener
     interactiveSegmenter.fakeWasmModule._waitUntilIdle.and.callFake(() => {
-      verifyListenersRegistered(interactiveSegmenter);
+      expect(interactiveSegmenter.confidenceMasksListener).toBeDefined();
-      interactiveSegmenter.imageVectorListener!(
+      interactiveSegmenter.confidenceMasksListener!(
           [
             {data: mask1, width: 2, height: 2},
             {data: mask2, width: 2, height: 2},
           ],
           1337);
     });
+    return new Promise<void>(resolve => {
+      // Invoke the image segmenter
+      interactiveSegmenter.segment({} as HTMLImageElement, ROI, result => {
+        expect(interactiveSegmenter.fakeWasmModule._waitUntilIdle)
+            .toHaveBeenCalled();
+        expect(result.categoryMask).not.toBeDefined();
+        expect(result.confidenceMasks).toEqual([mask1, mask2]);
+        expect(result.width).toEqual(2);
+        expect(result.height).toEqual(2);
+        resolve();
+      });
+    });
+  });
+
+  it('supports combined category and confidence masks', async () => {
+    const categoryMask = new Uint8ClampedArray([1, 0]);
+    const confidenceMask1 = new Float32Array([0.0, 1.0]);
+    const confidenceMask2 = new Float32Array([1.0, 0.0]);
+
+    await interactiveSegmenter.setOptions(
+        {outputCategoryMask: true, outputConfidenceMasks: true});
+
+    // Pass the test data to our listener
+    interactiveSegmenter.fakeWasmModule._waitUntilIdle.and.callFake(() => {
+      expect(interactiveSegmenter.categoryMaskListener).toBeDefined();
+      expect(interactiveSegmenter.confidenceMasksListener).toBeDefined();
+      interactiveSegmenter.categoryMaskListener!
+          ({data: categoryMask, width: 1, height: 1}, 1337);
+      interactiveSegmenter.confidenceMasksListener!(
+          [
+            {data: confidenceMask1, width: 1, height: 1},
+            {data: confidenceMask2, width: 1, height: 1},
+          ],
+          1337);
+    });
 
     return new Promise<void>(resolve => {
       // Invoke the image segmenter
       interactiveSegmenter.segment(
-          {} as HTMLImageElement, ROI, (masks, width, height) => {
+          {} as HTMLImageElement, ROI, result => {
             expect(interactiveSegmenter.fakeWasmModule._waitUntilIdle)
                 .toHaveBeenCalled();
-            expect(masks).toHaveSize(2);
+            expect(result.categoryMask).toEqual(categoryMask);
-            expect(masks[0]).toEqual(mask1);
+            expect(result.confidenceMasks).toEqual([
-            expect(masks[1]).toEqual(mask2);
+              confidenceMask1, confidenceMask2
-            expect(width).toEqual(2);
+            ]);
-            expect(height).toEqual(2);
+            expect(result.width).toEqual(1);
+            expect(result.height).toEqual(1);
             resolve();
           });
     });

mediapipe/util/annotation_renderer.cc

@@ -56,8 +56,8 @@ bool NormalizedtoPixelCoordinates(double normalized_x, double normalized_y,
     VLOG(1) << "Normalized coordinates must be between 0.0 and 1.0";
   }
 
-  *x_px = static_cast<int32>(round(normalized_x * image_width));
+  *x_px = static_cast<int32_t>(round(normalized_x * image_width));
-  *y_px = static_cast<int32>(round(normalized_y * image_height));
+  *y_px = static_cast<int32_t>(round(normalized_y * image_height));
 
   return true;
 }

mediapipe/util/cpu_util.cc

@@ -43,7 +43,7 @@ ABSL_FLAG(std::string, system_cpu_max_freq_file,
 namespace mediapipe {
 namespace {
 
-constexpr uint32 kBufferLength = 64;
+constexpr uint32_t kBufferLength = 64;
 
 absl::StatusOr<std::string> GetFilePath(int cpu) {
   if (!absl::StrContains(absl::GetFlag(FLAGS_system_cpu_max_freq_file), "$0")) {
@@ -54,7 +54,7 @@ absl::StatusOr<std::string> GetFilePath(int cpu) {
   return absl::Substitute(absl::GetFlag(FLAGS_system_cpu_max_freq_file), cpu);
 }
 
-absl::StatusOr<uint64> GetCpuMaxFrequency(int cpu) {
+absl::StatusOr<uint64_t> GetCpuMaxFrequency(int cpu) {
   auto path_or_status = GetFilePath(cpu);
   if (!path_or_status.ok()) {
     return path_or_status.status();
@@ -65,7 +65,7 @@ absl::StatusOr<uint64> GetCpuMaxFrequency(int cpu) {
     char buffer[kBufferLength];
     file.getline(buffer, kBufferLength);
     file.close();
-    uint64 frequency;
+    uint64_t frequency;
     if (absl::SimpleAtoi(buffer, &frequency)) {
       return frequency;
     } else {
@@ -79,7 +79,7 @@ absl::StatusOr<uint64> GetCpuMaxFrequency(int cpu) {
 }
 
 std::set<int> InferLowerOrHigherCoreIds(bool lower) {
-  std::vector<std::pair<int, uint64>> cpu_freq_pairs;
+  std::vector<std::pair<int, uint64_t>> cpu_freq_pairs;
   for (int cpu = 0; cpu < NumCPUCores(); ++cpu) {
     auto freq_or_status = GetCpuMaxFrequency(cpu);
     if (freq_or_status.ok()) {
@@ -90,12 +90,12 @@ std::set<int> InferLowerOrHigherCoreIds(bool lower) {
     return {};
   }
 
-  absl::c_sort(cpu_freq_pairs, [lower](const std::pair<int, uint64>& left,
+  absl::c_sort(cpu_freq_pairs, [lower](const std::pair<int, uint64_t>& left,
-                                       const std::pair<int, uint64>& right) {
+                                       const std::pair<int, uint64_t>& right) {
     return (lower && left.second < right.second) ||
            (!lower && left.second > right.second);
   });
-  uint64 edge_freq = cpu_freq_pairs[0].second;
+  uint64_t edge_freq = cpu_freq_pairs[0].second;
 
   std::set<int> inferred_cores;
   for (const auto& cpu_freq_pair : cpu_freq_pairs) {

mediapipe/util/image_frame_util.cc

@@ -89,12 +89,12 @@ void ImageFrameToYUVImage(const ImageFrame& image_frame, YUVImage* yuv_image) {
   const int uv_stride = (uv_width + 15) & ~15;
   const int y_size = y_stride * height;
   const int uv_size = uv_stride * uv_height;
-  uint8* data =
+  uint8_t* data =
-      reinterpret_cast<uint8*>(aligned_malloc(y_size + uv_size * 2, 16));
+      reinterpret_cast<uint8_t*>(aligned_malloc(y_size + uv_size * 2, 16));
   std::function<void()> deallocate = [data]() { aligned_free(data); };
-  uint8* y = data;
+  uint8_t* y = data;
-  uint8* u = y + y_size;
+  uint8_t* u = y + y_size;
-  uint8* v = u + uv_size;
+  uint8_t* v = u + uv_size;
   yuv_image->Initialize(libyuv::FOURCC_I420, deallocate,  //
                         y, y_stride,                      //
                         u, uv_stride,                     //
@@ -123,10 +123,11 @@ void ImageFrameToYUVNV12Image(const ImageFrame& image_frame,
   const int uv_stride = y_stride;
   const int uv_height = (height + 1) / 2;
   const int uv_size = uv_stride * uv_height;
-  uint8* data = reinterpret_cast<uint8*>(aligned_malloc(y_size + uv_size, 16));
+  uint8_t* data =
+      reinterpret_cast<uint8_t*>(aligned_malloc(y_size + uv_size, 16));
   std::function<void()> deallocate = [data] { aligned_free(data); };
-  uint8* y = data;
+  uint8_t* y = data;
-  uint8* uv = y + y_size;
+  uint8_t* uv = y + y_size;
   yuv_nv12_image->Initialize(libyuv::FOURCC_NV12, deallocate, y, y_stride, uv,
                              uv_stride, nullptr, 0, width, height);
   const int rv = libyuv::I420ToNV12(
@@ -210,44 +211,44 @@ void YUVImageToImageFrameFromFormat(const YUVImage& yuv_image,
   }
 }
 
-void SrgbToMpegYCbCr(const uint8 r, const uint8 g, const uint8 b,  //
+void SrgbToMpegYCbCr(const uint8_t r, const uint8_t g, const uint8_t b,  //
-                     uint8* y, uint8* cb, uint8* cr) {
+                     uint8_t* y, uint8_t* cb, uint8_t* cr) {
   // ITU-R BT.601 conversion from sRGB to YCbCr.
   // FastIntRound is used rather than SafeRound since the possible
   // range of values is [16,235] for Y and [16,240] for Cb and Cr and we
   // don't care about the rounding direction for values exactly between
   // two integers.
-  *y = static_cast<uint8>(
+  *y = static_cast<uint8_t>(
       mediapipe::MathUtil::FastIntRound(16.0 +                 //
                                         65.481 * r / 255.0 +   //
                                         128.553 * g / 255.0 +  //
                                         24.966 * b / 255.0));
-  *cb = static_cast<uint8>(
+  *cb = static_cast<uint8_t>(
       mediapipe::MathUtil::FastIntRound(128.0 +                //
                                         -37.797 * r / 255.0 +  //
                                         -74.203 * g / 255.0 +  //
                                         112.0 * b / 255.0));
-  *cr = static_cast<uint8>(
+  *cr = static_cast<uint8_t>(
       mediapipe::MathUtil::FastIntRound(128.0 +                //
                                         112.0 * r / 255.0 +    //
                                         -93.786 * g / 255.0 +  //
                                         -18.214 * b / 255.0));
 }
 
-void MpegYCbCrToSrgb(const uint8 y, const uint8 cb, const uint8 cr,  //
+void MpegYCbCrToSrgb(const uint8_t y, const uint8_t cb, const uint8_t cr,  //
-                     uint8* r, uint8* g, uint8* b) {
+                     uint8_t* r, uint8_t* g, uint8_t* b) {
   // ITU-R BT.601 conversion from YCbCr to sRGB
   // Use SafeRound since many MPEG YCbCr values do not correspond directly
   // to an sRGB value.
-  *r = mediapipe::MathUtil::SafeRound<uint8, double>(  //
+  *r = mediapipe::MathUtil::SafeRound<uint8_t, double>(  //
-      255.0 / 219.0 * (y - 16.0) +                     //
+      255.0 / 219.0 * (y - 16.0) +                       //
       255.0 / 112.0 * 0.701 * (cr - 128.0));
-  *g = mediapipe::MathUtil::SafeRound<uint8, double>(
+  *g = mediapipe::MathUtil::SafeRound<uint8_t, double>(
       255.0 / 219.0 * (y - 16.0) -                            //
       255.0 / 112.0 * 0.886 * 0.114 / 0.587 * (cb - 128.0) -  //
       255.0 / 112.0 * 0.701 * 0.299 / 0.587 * (cr - 128.0));
-  *b = mediapipe::MathUtil::SafeRound<uint8, double>(  //
+  *b = mediapipe::MathUtil::SafeRound<uint8_t, double>(  //
-      255.0 / 219.0 * (y - 16.0) +                     //
+      255.0 / 219.0 * (y - 16.0) +                       //
       255.0 / 112.0 * 0.886 * (cb - 128.0));
 }
 
@@ -260,15 +261,15 @@ void MpegYCbCrToSrgb(const uint8 y, const uint8 cb, const uint8 cr,  //
 
 cv::Mat GetSrgbToLinearRgb16Lut() {
   cv::Mat lut(1, 256, CV_16UC1);
-  uint16* ptr = lut.ptr<uint16>();
+  uint16_t* ptr = lut.ptr<uint16_t>();
   constexpr double kUint8Max = 255.0;
   constexpr double kUint16Max = 65535.0;
   for (int i = 0; i < 256; ++i) {
     if (i < 0.04045 * kUint8Max) {
-      ptr[i] = static_cast<uint16>(
+      ptr[i] = static_cast<uint16_t>(
           (static_cast<double>(i) / kUint8Max / 12.92) * kUint16Max + .5);
     } else {
-      ptr[i] = static_cast<uint16>(
+      ptr[i] = static_cast<uint16_t>(
           pow((static_cast<double>(i) / kUint8Max + 0.055) / 1.055, 2.4) *
               kUint16Max +
           .5);
@@ -279,15 +280,15 @@ cv::Mat GetSrgbToLinearRgb16Lut() {
 
 cv::Mat GetLinearRgb16ToSrgbLut() {
   cv::Mat lut(1, 65536, CV_8UC1);
-  uint8* ptr = lut.ptr<uint8>();
+  uint8_t* ptr = lut.ptr<uint8_t>();
   constexpr double kUint8Max = 255.0;
   constexpr double kUint16Max = 65535.0;
   for (int i = 0; i < 65536; ++i) {
     if (i < 0.0031308 * kUint16Max) {
-      ptr[i] = static_cast<uint8>(
+      ptr[i] = static_cast<uint8_t>(
           (static_cast<double>(i) / kUint16Max * 12.92) * kUint8Max + .5);
     } else {
-      ptr[i] = static_cast<uint8>(
+      ptr[i] = static_cast<uint8_t>(
           (1.055 * pow(static_cast<double>(i) / kUint16Max, 1.0 / 2.4) - .055) *
               kUint8Max +
           .5);
@@ -306,13 +307,13 @@ void LinearRgb16ToSrgb(const cv::Mat& source, cv::Mat* destination) {
   destination->create(source.size(), CV_8UC(source.channels()));
 
   static const cv::Mat kLut = GetLinearRgb16ToSrgbLut();
-  const uint8* lookup_table_ptr = kLut.ptr<uint8>();
+  const uint8_t* lookup_table_ptr = kLut.ptr<uint8_t>();
   const int num_channels = source.channels();
   for (int row = 0; row < source.rows; ++row) {
     for (int col = 0; col < source.cols; ++col) {
       for (int channel = 0; channel < num_channels; ++channel) {
-        uint8* ptr = destination->ptr<uint8>(row);
+        uint8_t* ptr = destination->ptr<uint8_t>(row);
-        const uint16* ptr16 = source.ptr<uint16>(row);
+        const uint16_t* ptr16 = source.ptr<uint16_t>(row);
         ptr[col * num_channels + channel] =
             lookup_table_ptr[ptr16[col * num_channels + channel]];
       }

mediapipe/util/image_test_utils.cc

@@ -43,14 +43,14 @@ mediapipe::ImageFormat::Format GetImageFormat(int image_channels) {
 
 Packet MakeImageFramePacket(cv::Mat input, int timestamp) {
   ImageFrame input_image(GetImageFormat(input.channels()), input.cols,
-                         input.rows, input.step, input.data, [](uint8*) {});
+                         input.rows, input.step, input.data, [](uint8_t*) {});
   return MakePacket<ImageFrame>(std::move(input_image)).At(Timestamp(0));
 }
 
 Packet MakeImagePacket(cv::Mat input, int timestamp) {
   mediapipe::Image input_image(std::make_shared<mediapipe::ImageFrame>(
       GetImageFormat(input.channels()), input.cols, input.rows, input.step,
-      input.data, [](uint8*) {}));
+      input.data, [](uint8_t*) {}));
   return MakePacket<mediapipe::Image>(std::move(input_image)).At(Timestamp(0));
 }
 

mediapipe/util/label_map_util.cc

@@ -25,7 +25,7 @@
 
 namespace mediapipe {
 
-absl::StatusOr<proto_ns::Map<int64, LabelMapItem>> BuildLabelMapFromFiles(
+absl::StatusOr<proto_ns::Map<int64_t, LabelMapItem>> BuildLabelMapFromFiles(
     absl::string_view labels_file_contents,
     absl::string_view display_names_file) {
   if (labels_file_contents.empty()) {
@@ -68,7 +68,7 @@ absl::StatusOr<proto_ns::Map<int64, LabelMapItem>> BuildLabelMapFromFiles(
       label_map_items[i].set_display_name(display_names[i]);
     }
   }
-  proto_ns::Map<int64, LabelMapItem> label_map;
+  proto_ns::Map<int64_t, LabelMapItem> label_map;
   for (int i = 0; i < label_map_items.size(); ++i) {
     label_map[i] = label_map_items[i];
   }

third_party/flatbuffers/BUILD.bazel

@@ -45,12 +45,16 @@ filegroup(
         "include/flatbuffers/bfbs_generator.h",
         "include/flatbuffers/buffer.h",
         "include/flatbuffers/buffer_ref.h",
+        "include/flatbuffers/code_generator.h",
         "include/flatbuffers/code_generators.h",
         "include/flatbuffers/default_allocator.h",
         "include/flatbuffers/detached_buffer.h",
         "include/flatbuffers/flatbuffer_builder.h",
         "include/flatbuffers/flatbuffers.h",
+        "include/flatbuffers/flatc.h",
+        "include/flatbuffers/flex_flat_util.h",
         "include/flatbuffers/flexbuffers.h",
+        "include/flatbuffers/grpc.h",
         "include/flatbuffers/hash.h",
         "include/flatbuffers/idl.h",
         "include/flatbuffers/minireflect.h",

third_party/flatbuffers/workspace.bzl

@@ -5,11 +5,11 @@ load("//third_party:repo.bzl", "third_party_http_archive")
 def repo():
     third_party_http_archive(
         name = "flatbuffers",
-        strip_prefix = "flatbuffers-2.0.6",
+        strip_prefix = "flatbuffers-23.1.21",
-        sha256 = "e2dc24985a85b278dd06313481a9ca051d048f9474e0f199e372fea3ea4248c9",
+        sha256 = "d84cb25686514348e615163b458ae0767001b24b42325f426fd56406fd384238",
         urls = [
-            "https://storage.googleapis.com/mirror.tensorflow.org/github.com/google/flatbuffers/archive/v2.0.6.tar.gz",
+            "https://storage.googleapis.com/mirror.tensorflow.org/github.com/google/flatbuffers/archive/v23.1.21.tar.gz",
-            "https://github.com/google/flatbuffers/archive/v2.0.6.tar.gz",
+            "https://github.com/google/flatbuffers/archive/v23.1.21.tar.gz",
         ],
         build_file = "//third_party/flatbuffers:BUILD.bazel",
         delete = ["build_defs.bzl", "BUILD.bazel"],

third_party/wasm_files.bzl

@@ -12,72 +12,72 @@ def wasm_files():
 
     http_file(
         name = "com_google_mediapipe_wasm_audio_wasm_internal_js",
-        sha256 = "0eca68e2291a548b734bcab5db4c9e6b997e852ea7e19228003b9e2a78c7c646",
+        sha256 = "b810de53d7ccf991b9c70fcdf7e88b5c3f2942ae766436f22be48159b6a7e687",
-        urls = ["https://storage.googleapis.com/mediapipe-assets/wasm/audio_wasm_internal.js?generation=1681328323089931"],
+        urls = ["https://storage.googleapis.com/mediapipe-assets/wasm/audio_wasm_internal.js?generation=1681849488227617"],
     )
 
     http_file(
         name = "com_google_mediapipe_wasm_audio_wasm_internal_wasm",
-        sha256 = "69bc95af5b783b510ec1842d6fb9594254907d8e1334799c5753164878a7dcac",
+        sha256 = "26d91147e5c6c8a92e0a4ebf59599068a3cff6108847b793ef33ac23e98eddb9",
-        urls = ["https://storage.googleapis.com/mediapipe-assets/wasm/audio_wasm_internal.wasm?generation=1681328325829340"],
+        urls = ["https://storage.googleapis.com/mediapipe-assets/wasm/audio_wasm_internal.wasm?generation=1681849491546937"],
     )
 
     http_file(
         name = "com_google_mediapipe_wasm_audio_wasm_nosimd_internal_js",
-        sha256 = "88a0176cc80d6a1eb175a5105df705cf8b8684cf13f6db0a264af0b67b65a22a",
+        sha256 = "b38e37b3024692558eaaba159921fedd3297d1a09bba1c16a06fed327845b0bd",
-        urls = ["https://storage.googleapis.com/mediapipe-assets/wasm/audio_wasm_nosimd_internal.js?generation=1681328328330829"],
+        urls = ["https://storage.googleapis.com/mediapipe-assets/wasm/audio_wasm_nosimd_internal.js?generation=1681849494099698"],
     )
 
     http_file(
         name = "com_google_mediapipe_wasm_audio_wasm_nosimd_internal_wasm",
-        sha256 = "1cc0c3db7d252801be4b090d8bbba61f308cc3dd5efe197319581d3af29495c7",
+        sha256 = "6a8e73d2e926565046e16adf1748f0f8ec5135fafe7eb8b9c83892e64c1a449a",
-        urls = ["https://storage.googleapis.com/mediapipe-assets/wasm/audio_wasm_nosimd_internal.wasm?generation=1681328331085637"],
+        urls = ["https://storage.googleapis.com/mediapipe-assets/wasm/audio_wasm_nosimd_internal.wasm?generation=1681849496451970"],
     )
 
     http_file(
         name = "com_google_mediapipe_wasm_text_wasm_internal_js",
-        sha256 = "d9cd100b6d330d36f7749fe5fc64a2cdd0abb947a0376e6140784cfb0361a4e2",
+        sha256 = "785cba67b623b1dc66dc3621e97fd6b30edccbb408184a3094d0aa68ddd5becb",
-        urls = ["https://storage.googleapis.com/mediapipe-assets/wasm/text_wasm_internal.js?generation=1681328333442454"],
+        urls = ["https://storage.googleapis.com/mediapipe-assets/wasm/text_wasm_internal.js?generation=1681849498746265"],
     )
 
     http_file(
         name = "com_google_mediapipe_wasm_text_wasm_internal_wasm",
-        sha256 = "30a2fcca630bdad6e99173ea7d0d8c5d7086aedf393d0159fa05bf9d08d4ff65",
+        sha256 = "a858b8a2e8b40e9c936b66566c5aefd396536c4e936459ab9ae7e239621adc14",
-        urls = ["https://storage.googleapis.com/mediapipe-assets/wasm/text_wasm_internal.wasm?generation=1681328335803336"],
+        urls = ["https://storage.googleapis.com/mediapipe-assets/wasm/text_wasm_internal.wasm?generation=1681849501370461"],
     )
 
     http_file(
         name = "com_google_mediapipe_wasm_text_wasm_nosimd_internal_js",
-        sha256 = "70ca2bd15c56e0ce7bb10ff2188b4a1f9eafbb657eb9424e4cab8d7b29179871",
+        sha256 = "5292f1442d5e5c037e7cffb78a8c2d71255348ca2c3bd759b314bdbedd5590c2",
-        urls = ["https://storage.googleapis.com/mediapipe-assets/wasm/text_wasm_nosimd_internal.js?generation=1681328338162884"],
+        urls = ["https://storage.googleapis.com/mediapipe-assets/wasm/text_wasm_nosimd_internal.js?generation=1681849503379116"],
     )
 
     http_file(
         name = "com_google_mediapipe_wasm_text_wasm_nosimd_internal_wasm",
-        sha256 = "8221b385905f36a769d7731a0adbe18b681bcb873561890429ca84278c67c3fd",
+        sha256 = "e44b48ab29ee1d8befec804e9a63445c56266b679d19fb476d556ca621f0e493",
-        urls = ["https://storage.googleapis.com/mediapipe-assets/wasm/text_wasm_nosimd_internal.wasm?generation=1681328340808115"],
+        urls = ["https://storage.googleapis.com/mediapipe-assets/wasm/text_wasm_nosimd_internal.wasm?generation=1681849505997020"],
     )
 
     http_file(
         name = "com_google_mediapipe_wasm_vision_wasm_internal_js",
-        sha256 = "07692acd8202adafebd35dbcd7e2b8e88a76d4a0e6b9229cb3cad59503eeddc7",
+        sha256 = "205855eba70464a92b9d00e90acac15c51a9f76192f900e697304ac6dea8f714",
-        urls = ["https://storage.googleapis.com/mediapipe-assets/wasm/vision_wasm_internal.js?generation=1681328343147709"],
+        urls = ["https://storage.googleapis.com/mediapipe-assets/wasm/vision_wasm_internal.js?generation=1681849508414277"],
     )
 
     http_file(
         name = "com_google_mediapipe_wasm_vision_wasm_internal_wasm",
-        sha256 = "03bf553fa6a768b0d70103a5e7d835b6b37371ff44e201c3392f22e0879737c3",
+        sha256 = "c0cbd0df3adb2a9cd1331d14f522d2bae9f8adc9f1b35f92cbbc4b782b190cef",
-        urls = ["https://storage.googleapis.com/mediapipe-assets/wasm/vision_wasm_internal.wasm?generation=1681328345605574"],
+        urls = ["https://storage.googleapis.com/mediapipe-assets/wasm/vision_wasm_internal.wasm?generation=1681849510936608"],
     )
 
     http_file(
         name = "com_google_mediapipe_wasm_vision_wasm_nosimd_internal_js",
-        sha256 = "36697be14f921985eac15d1447ec8a260817b05ade1c9bb3ca7e906e0f047ec0",
+        sha256 = "0969812de4d3573198fa2eba4f5b0a7e97e98f97bd4215d876543f4925e57b84",
-        urls = ["https://storage.googleapis.com/mediapipe-assets/wasm/vision_wasm_nosimd_internal.js?generation=1681328348025082"],
+        urls = ["https://storage.googleapis.com/mediapipe-assets/wasm/vision_wasm_nosimd_internal.js?generation=1681849513292639"],
     )
 
     http_file(
         name = "com_google_mediapipe_wasm_vision_wasm_nosimd_internal_wasm",
-        sha256 = "103fb145438d61cfecb2e8db3f06b43a5d77a7e3fcea940437fe272227cf2592",
+        sha256 = "f2ab62c3f8dabab0a573dadf5c105ff81a03c29c70f091f8cf273ae030c0a86f",
-        urls = ["https://storage.googleapis.com/mediapipe-assets/wasm/vision_wasm_nosimd_internal.wasm?generation=1681328350709881"],
+        urls = ["https://storage.googleapis.com/mediapipe-assets/wasm/vision_wasm_nosimd_internal.wasm?generation=1681849515999000"],
     )