Support hair segmentation model in image segmenter

PiperOrigin-RevId: 515151150

mediapipe/tasks/cc/vision/image_segmenter/BUILD

@@ -47,26 +47,35 @@ cc_library(
     srcs = ["image_segmenter_graph.cc"],
     deps = [
         "//mediapipe/calculators/core:merge_to_vector_calculator",
+        "//mediapipe/calculators/image:image_clone_calculator_cc_proto",
         "//mediapipe/calculators/image:image_properties_calculator",
+        "//mediapipe/calculators/image:image_transformation_calculator",
+        "//mediapipe/calculators/image:image_transformation_calculator_cc_proto",
         "//mediapipe/calculators/tensor:image_to_tensor_calculator",
+        "//mediapipe/calculators/tensor:image_to_tensor_calculator_cc_proto",
         "//mediapipe/calculators/tensor:inference_calculator",
+        "//mediapipe/calculators/tensor:tensor_converter_calculator",
+        "//mediapipe/calculators/tensor:tensor_converter_calculator_cc_proto",
+        "//mediapipe/calculators/util:from_image_calculator",
         "//mediapipe/framework/api2:builder",
         "//mediapipe/framework/api2:port",
         "//mediapipe/framework/formats:image",
         "//mediapipe/framework/formats:rect_cc_proto",
+        "//mediapipe/framework/formats:tensor",
         "//mediapipe/framework/port:status",
+        "//mediapipe/gpu:scale_mode_cc_proto",
         "//mediapipe/tasks/cc:common",
         "//mediapipe/tasks/cc/components/processors:image_preprocessing_graph",
         "//mediapipe/tasks/cc/components/processors/proto:image_preprocessing_graph_options_cc_proto",
         "//mediapipe/tasks/cc/core:model_resources",
         "//mediapipe/tasks/cc/core:model_task_graph",
-        "//mediapipe/tasks/cc/core/proto:acceleration_cc_proto",
         "//mediapipe/tasks/cc/core/proto:inference_subgraph_cc_proto",
         "//mediapipe/tasks/cc/metadata:metadata_extractor",
         "//mediapipe/tasks/cc/vision/image_segmenter/calculators:tensors_to_segmentation_calculator",
         "//mediapipe/tasks/cc/vision/image_segmenter/calculators:tensors_to_segmentation_calculator_cc_proto",
         "//mediapipe/tasks/cc/vision/image_segmenter/proto:image_segmenter_graph_options_cc_proto",
         "//mediapipe/tasks/cc/vision/image_segmenter/proto:segmenter_options_cc_proto",
+        "//mediapipe/tasks/cc/vision/utils:image_tensor_specs",
         "//mediapipe/tasks/metadata:metadata_schema_cc",
         "//mediapipe/util:label_map_cc_proto",
         "//mediapipe/util:label_map_util",

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

@@ -20,22 +20,26 @@ limitations under the License.
 #include "absl/status/status.h"
 #include "absl/status/statusor.h"
 #include "absl/strings/str_format.h"
+#include "mediapipe/calculators/image/image_clone_calculator.pb.h"
+#include "mediapipe/calculators/image/image_transformation_calculator.pb.h"
+#include "mediapipe/calculators/tensor/tensor_converter_calculator.pb.h"
 #include "mediapipe/framework/api2/builder.h"
 #include "mediapipe/framework/api2/port.h"
 #include "mediapipe/framework/formats/image.h"
 #include "mediapipe/framework/formats/rect.pb.h"
+#include "mediapipe/framework/formats/tensor.h"
 #include "mediapipe/framework/port/status_macros.h"
 #include "mediapipe/tasks/cc/common.h"
 #include "mediapipe/tasks/cc/components/processors/image_preprocessing_graph.h"
 #include "mediapipe/tasks/cc/components/processors/proto/image_preprocessing_graph_options.pb.h"
 #include "mediapipe/tasks/cc/core/model_resources.h"
 #include "mediapipe/tasks/cc/core/model_task_graph.h"
-#include "mediapipe/tasks/cc/core/proto/acceleration.pb.h"
 #include "mediapipe/tasks/cc/core/proto/inference_subgraph.pb.h"
 #include "mediapipe/tasks/cc/metadata/metadata_extractor.h"
 #include "mediapipe/tasks/cc/vision/image_segmenter/calculators/tensors_to_segmentation_calculator.pb.h"
 #include "mediapipe/tasks/cc/vision/image_segmenter/proto/image_segmenter_graph_options.pb.h"
 #include "mediapipe/tasks/cc/vision/image_segmenter/proto/segmenter_options.pb.h"
+#include "mediapipe/tasks/cc/vision/utils/image_tensor_specs.h"
 #include "mediapipe/tasks/metadata/metadata_schema_generated.h"
 #include "mediapipe/util/label_map.pb.h"
 #include "mediapipe/util/label_map_util.h"
@@ -59,13 +63,14 @@ using ::mediapipe::tasks::metadata::ModelMetadataExtractor;
 using ::mediapipe::tasks::vision::image_segmenter::proto::
     ImageSegmenterGraphOptions;
 using ::mediapipe::tasks::vision::image_segmenter::proto::SegmenterOptions;
-using ::tflite::Tensor;
 using ::tflite::TensorMetadata;
 using LabelItems = mediapipe::proto_ns::Map<int64, ::mediapipe::LabelMapItem>;
 
 constexpr char kSegmentationTag[] = "SEGMENTATION";
 constexpr char kGroupedSegmentationTag[] = "GROUPED_SEGMENTATION";
 constexpr char kImageTag[] = "IMAGE";
+constexpr char kImageCpuTag[] = "IMAGE_CPU";
+constexpr char kImageGpuTag[] = "IMAGE_GPU";
 constexpr char kNormRectTag[] = "NORM_RECT";
 constexpr char kTensorsTag[] = "TENSORS";
 constexpr char kOutputSizeTag[] = "OUTPUT_SIZE";
@@ -78,6 +83,13 @@ struct ImageSegmenterOutputs {
   Source<Image> image;
 };
 
+// Struct holding the image and input tensors after image preprocessing and
+// transferred to the requested device.
+struct ImageAndTensorsOnDevice {
+  Source<Image> image;
+  Source<std::vector<Tensor>> tensors;
+};
+
 }  // namespace
 
 absl::Status SanityCheckOptions(const ImageSegmenterGraphOptions& options) {
@@ -144,7 +156,8 @@ absl::Status ConfigureTensorsToSegmentationCalculator(
   return absl::OkStatus();
 }
 
-absl::StatusOr<const Tensor*> GetOutputTensor(
+// Get the output tensor from the tflite model of given model resources.
+absl::StatusOr<const tflite::Tensor*> GetOutputTensor(
     const core::ModelResources& model_resources) {
   const tflite::Model& model = *model_resources.GetTfLiteModel();
   const auto* primary_subgraph = (*model.subgraphs())[0];
@@ -153,6 +166,115 @@ absl::StatusOr<const Tensor*> GetOutputTensor(
   return output_tensor;
 }
 
+// Get the input tensor from the tflite model of given model resources.
+absl::StatusOr<const tflite::Tensor*> GetInputTensor(
+    const core::ModelResources& model_resources) {
+  const tflite::Model& model = *model_resources.GetTfLiteModel();
+  const auto* primary_subgraph = (*model.subgraphs())[0];
+  const auto* input_tensor =
+      (*primary_subgraph->tensors())[(*primary_subgraph->inputs())[0]];
+  return input_tensor;
+}
+
+// Configure the ImageTransformationCalculator according to the input tensor.
+void ConfigureImageTransformationCalculator(
+    const tflite::Tensor& tflite_input_tensor,
+    mediapipe::ImageTransformationCalculatorOptions& options) {
+  options.set_output_height(tflite_input_tensor.shape()->data()[1]);
+  options.set_output_width(tflite_input_tensor.shape()->data()[2]);
+}
+
+// Configure the TensorConverterCalculator to convert the image to tensor.
+void ConfigureTensorConverterCalculator(
+    const ImageTensorSpecs& image_tensor_specs,
+    mediapipe::TensorConverterCalculatorOptions& options) {
+  float mean = image_tensor_specs.normalization_options->mean_values[0];
+  float std = image_tensor_specs.normalization_options->std_values[0];
+  options.set_max_num_channels(4);
+  options.mutable_output_tensor_float_range()->set_min((0.0f - mean) / std);
+  options.mutable_output_tensor_float_range()->set_max((255.0f - mean) / std);
+}
+
+// Image preprocessing step to convert the given image to the input tensors for
+// the tflite model.
+absl::StatusOr<ImageAndTensorsOnDevice> ConvertImageToTensors(
+    Source<Image> image_in, Source<NormalizedRect> norm_rect_in, bool use_gpu,
+    const core::ModelResources& model_resources, Graph& graph) {
+  ASSIGN_OR_RETURN(const tflite::Tensor* tflite_input_tensor,
+                   GetInputTensor(model_resources));
+  if (tflite_input_tensor->shape()->size() != 4) {
+    return absl::InvalidArgumentError(
+        absl::StrFormat("Expect segmentation model has input image tensor to "
+                        "be 4 dims. Got input tensor with "
+                        "dims: %d",
+                        tflite_input_tensor->shape()->size()));
+  }
+  const int input_tensor_channel = tflite_input_tensor->shape()->data()[3];
+  if (input_tensor_channel != 3 && input_tensor_channel != 4) {
+    return absl::InvalidArgumentError(absl::StrFormat(
+        "Expect segmentation model has input image tensor with channels = 3 or "
+        "4. Get "
+        "channel = %d",
+        tflite_input_tensor->shape()->data()[3]));
+  } else if (input_tensor_channel == 3) {
+    // ImagePreprocessingGraph is backed by ImageToTensorCalculator which only
+    // supports Tensor with channel = 3.
+    auto& preprocessing = graph.AddNode(
+        "mediapipe.tasks.components.processors.ImagePreprocessingGraph");
+    MP_RETURN_IF_ERROR(components::processors::ConfigureImagePreprocessingGraph(
+        model_resources, use_gpu,
+        &preprocessing.GetOptions<tasks::components::processors::proto::
+                                      ImagePreprocessingGraphOptions>()));
+    image_in >> preprocessing.In(kImageTag);
+    norm_rect_in >> preprocessing.In(kNormRectTag);
+    return {{preprocessing.Out(kImageTag).Cast<Image>(),
+             preprocessing.Out(kTensorsTag).Cast<std::vector<Tensor>>()}};
+  } else {
+    // TODO Remove legacy preprocessing calculators.
+    // For segmentation model with input Tensor with channel = 4, use legacy
+    // TfLite preprocessing calculators
+
+    // Upload image to GPU if requested to use gpu.
+    auto& image_clone = graph.AddNode("ImageCloneCalculator");
+    image_clone.GetOptions<mediapipe::ImageCloneCalculatorOptions>()
+        .set_output_on_gpu(use_gpu);
+    image_in >> image_clone.In("");
+    Source<Image> image_on_device = image_clone.Out("").Cast<Image>();
+
+    // Convert from Image to legacy ImageFrame or GpuBuffer.
+    auto& from_image = graph.AddNode("FromImageCalculator");
+    image_on_device >> from_image.In(kImageTag);
+    auto image_cpu_or_gpu =
+        from_image.Out(use_gpu ? kImageGpuTag : kImageCpuTag);
+
+    // Resize the input image to the model input size.
+    auto& image_transformation = graph.AddNode("ImageTransformationCalculator");
+    ConfigureImageTransformationCalculator(
+        *tflite_input_tensor,
+        image_transformation
+            .GetOptions<mediapipe::ImageTransformationCalculatorOptions>());
+    const absl::string_view image_or_image_gpu_tag =
+        use_gpu ? kImageGpuTag : kImageTag;
+    image_cpu_or_gpu >> image_transformation.In(image_or_image_gpu_tag);
+    auto transformed_image = image_transformation.Out(image_or_image_gpu_tag);
+
+    // Convert image to mediapipe tensor.
+    auto& tensor_converter = graph.AddNode("TensorConverterCalculator");
+    ASSIGN_OR_RETURN(auto image_tensor_specs,
+                     vision::BuildInputImageTensorSpecs(model_resources));
+    ConfigureTensorConverterCalculator(
+        image_tensor_specs,
+        tensor_converter
+            .GetOptions<mediapipe::TensorConverterCalculatorOptions>());
+
+    transformed_image >> tensor_converter.In(image_or_image_gpu_tag);
+    auto tensors =
+        tensor_converter.Out(kTensorsTag).Cast<std::vector<Tensor>>();
+
+    return {{image_on_device, tensors}};
+  }
+}
+
 // An "mediapipe.tasks.vision.ImageSegmenterGraph" performs semantic
 // segmentation.
 // Two kinds of outputs are provided: SEGMENTATION and GROUPED_SEGMENTATION.
@@ -244,23 +366,17 @@ class ImageSegmenterGraph : public core::ModelTaskGraph {
 
     // Adds preprocessing calculators and connects them to the graph input image
     // stream.
-    auto& preprocessing = graph.AddNode(
-        "mediapipe.tasks.components.processors.ImagePreprocessingGraph");
     bool use_gpu =
         components::processors::DetermineImagePreprocessingGpuBackend(
             task_options.base_options().acceleration());
-    MP_RETURN_IF_ERROR(components::processors::ConfigureImagePreprocessingGraph(
+    ASSIGN_OR_RETURN(auto image_and_tensors,
-        model_resources, use_gpu,
+                     ConvertImageToTensors(image_in, norm_rect_in, use_gpu,
-        &preprocessing.GetOptions<tasks::components::processors::proto::
+                                           model_resources, graph));
-                                      ImagePreprocessingGraphOptions>()));
-    image_in >> preprocessing.In(kImageTag);
-    norm_rect_in >> preprocessing.In(kNormRectTag);
-
     // Adds inference subgraph and connects its input stream to the output
     // tensors produced by the ImageToTensorCalculator.
     auto& inference = AddInference(
         model_resources, task_options.base_options().acceleration(), graph);
-    preprocessing.Out(kTensorsTag) >> inference.In(kTensorsTag);
+    image_and_tensors.tensors >> inference.In(kTensorsTag);
 
     // Adds segmentation calculators for output streams.
     auto& tensor_to_images =
@@ -283,7 +399,7 @@ class ImageSegmenterGraph : public core::ModelTaskGraph {
       segmented_masks.push_back(
           Source<Image>(tensor_to_images[Output<Image>(kSegmentationTag)]));
     } else {
-      ASSIGN_OR_RETURN(const Tensor* output_tensor,
+      ASSIGN_OR_RETURN(const tflite::Tensor* output_tensor,
                        GetOutputTensor(model_resources));
       const int segmentation_streams_num = *output_tensor->shape()->rbegin();
       for (int i = 0; i < segmentation_streams_num; ++i) {
@@ -291,9 +407,8 @@ class ImageSegmenterGraph : public core::ModelTaskGraph {
             tensor_to_images[Output<Image>::Multiple(kSegmentationTag)][i]));
       }
     }
-    return ImageSegmenterOutputs{
+    return ImageSegmenterOutputs{/*segmented_masks=*/segmented_masks,
-        /*segmented_masks=*/segmented_masks,
+                                 /*image=*/image_and_tensors.image};
-        /*image=*/preprocessing[Output<Image>(kImageTag)]};
   }
 };
 

mediapipe/tasks/cc/vision/image_segmenter/image_segmenter_test.cc

@@ -27,8 +27,10 @@ limitations under the License.
 #include "mediapipe/framework/port/gtest.h"
 #include "mediapipe/framework/port/opencv_core_inc.h"
 #include "mediapipe/framework/port/opencv_imgcodecs_inc.h"
+#include "mediapipe/framework/port/opencv_imgproc_inc.h"
 #include "mediapipe/framework/port/status_matchers.h"
 #include "mediapipe/tasks/cc/components/containers/rect.h"
+#include "mediapipe/tasks/cc/core/base_options.h"
 #include "mediapipe/tasks/cc/core/proto/base_options.pb.h"
 #include "mediapipe/tasks/cc/core/proto/external_file.pb.h"
 #include "mediapipe/tasks/cc/vision/core/image_processing_options.h"
@@ -59,6 +61,8 @@ constexpr char kSelfie128x128WithMetadata[] = "selfie_segm_128_128_3.tflite";
 
 constexpr char kSelfie144x256WithMetadata[] = "selfie_segm_144_256_3.tflite";
 
+constexpr char kHairSegmentationWithMetadata[] = "hair_segmentation.tflite";
+
 constexpr float kGoldenMaskSimilarity = 0.98;
 
 // Magnification factor used when creating the golden category masks to make
@@ -87,7 +91,21 @@ Image GetSRGBImage(const std::string& image_path) {
   cv::Mat image_mat = cv::imread(image_path);
   mediapipe::ImageFrame image_frame(
       mediapipe::ImageFormat::SRGB, image_mat.cols, image_mat.rows,
-      image_mat.step, image_mat.data, [image_mat](uint8[]) {});
+      image_mat.step, image_mat.data, [image_mat](uint8_t[]) {});
+  Image image(std::make_shared<mediapipe::ImageFrame>(std::move(image_frame)));
+  return image;
+}
+
+Image GetSRGBAImage(const std::string& image_path) {
+  cv::Mat image_mat = cv::imread(image_path);
+  cv::cvtColor(image_mat, image_mat, cv::COLOR_BGR2RGBA);
+  std::vector<cv::Mat> channels(4);
+  cv::split(image_mat, channels);
+  channels[3].setTo(0);
+  cv::merge(channels.data(), 4, image_mat);
+  mediapipe::ImageFrame image_frame(
+      mediapipe::ImageFormat::SRGBA, image_mat.cols, image_mat.rows,
+      image_mat.step, image_mat.data, [image_mat](uint8_t[]) {});
   Image image(std::make_shared<mediapipe::ImageFrame>(std::move(image_frame)));
   return image;
 }
@@ -202,6 +220,30 @@ TEST_F(CreateFromOptionsTest, FailsWithMissingModel) {
                   MediaPipeTasksStatus::kRunnerInitializationError))));
 }
 
+TEST_F(CreateFromOptionsTest, FailsWithInputDimsTwoModel) {
+  auto options = std::make_unique<ImageSegmenterOptions>();
+  options->base_options.model_asset_path =
+      JoinPath("./", kTestDataDirectory, "dense.tflite");
+  absl::StatusOr<std::unique_ptr<ImageSegmenter>> result =
+      ImageSegmenter::Create(std::move(options));
+  EXPECT_EQ(result.status().code(), absl::StatusCode::kInvalidArgument);
+  EXPECT_THAT(result.status().message(),
+              HasSubstr("Expect segmentation model has input image tensor to "
+                        "be 4 dims."));
+}
+
+TEST_F(CreateFromOptionsTest, FailsWithInputChannelOneModel) {
+  auto options = std::make_unique<ImageSegmenterOptions>();
+  options->base_options.model_asset_path =
+      JoinPath("./", kTestDataDirectory, "conv2d_input_channel_1.tflite");
+  absl::StatusOr<std::unique_ptr<ImageSegmenter>> result =
+      ImageSegmenter::Create(std::move(options));
+  EXPECT_EQ(result.status().code(), absl::StatusCode::kInvalidArgument);
+  EXPECT_THAT(result.status().message(),
+              HasSubstr("Expect segmentation model has input image tensor with "
+                        "channels = 3 or 4."));
+}
+
 class ImageModeTest : public tflite_shims::testing::Test {};
 
 TEST_F(ImageModeTest, SucceedsWithCategoryMask) {
@@ -369,6 +411,31 @@ TEST_F(ImageModeTest, SucceedsSelfie144x256Segmentations) {
               SimilarToFloatMask(expected_mask_float, kGoldenMaskSimilarity));
 }
 
+TEST_F(ImageModeTest, SucceedsHairSegmentation) {
+  Image image =
+      GetSRGBAImage(JoinPath("./", kTestDataDirectory, "portrait.jpg"));
+  auto options = std::make_unique<ImageSegmenterOptions>();
+  options->base_options.model_asset_path =
+      JoinPath("./", kTestDataDirectory, kHairSegmentationWithMetadata);
+  options->output_type = ImageSegmenterOptions::OutputType::CONFIDENCE_MASK;
+  options->activation = ImageSegmenterOptions::Activation::SOFTMAX;
+  MP_ASSERT_OK_AND_ASSIGN(std::unique_ptr<ImageSegmenter> segmenter,
+                          ImageSegmenter::Create(std::move(options)));
+  MP_ASSERT_OK_AND_ASSIGN(auto confidence_masks, segmenter->Segment(image));
+  EXPECT_EQ(confidence_masks.size(), 2);
+
+  cv::Mat hair_mask = mediapipe::formats::MatView(
+      confidence_masks[1].GetImageFrameSharedPtr().get());
+  MP_ASSERT_OK(segmenter->Close());
+  cv::Mat expected_mask = cv::imread(
+      JoinPath("./", kTestDataDirectory, "portrait_hair_expected_mask.jpg"),
+      cv::IMREAD_GRAYSCALE);
+  cv::Mat expected_mask_float;
+  expected_mask.convertTo(expected_mask_float, CV_32FC1, 1 / 255.f);
+  EXPECT_THAT(hair_mask,
+              SimilarToFloatMask(expected_mask_float, kGoldenMaskSimilarity));
+}
+
 class VideoModeTest : public tflite_shims::testing::Test {};
 
 TEST_F(VideoModeTest, FailsWithCallingWrongMethod) {
@@ -548,8 +615,6 @@ TEST_F(LiveStreamModeTest, Succeeds) {
   }
 }
 
-// TODO: Add test for hair segmentation model.
-
 }  // namespace
 }  // namespace image_segmenter
 }  // namespace vision

mediapipe/tasks/testdata/vision/BUILD

@@ -36,7 +36,9 @@ mediapipe_files(srcs = [
     "coco_efficientdet_lite0_v1_1.0_quant_2021_09_06.tflite",
     "coco_ssd_mobilenet_v1_1.0_quant_2018_06_29.tflite",
     "coco_ssd_mobilenet_v1_1.0_quant_2018_06_29_with_dummy_score_calibration.tflite",
+    "conv2d_input_channel_1.tflite",
     "deeplabv3.tflite",
+    "dense.tflite",
     "face_detection_full_range.tflite",
     "face_detection_full_range_sparse.tflite",
     "face_detection_short_range.tflite",
@@ -44,6 +46,7 @@ mediapipe_files(srcs = [
     "face_landmark_with_attention.tflite",
     "fist.jpg",
     "fist.png",
+    "hair_segmentation.tflite",
     "hand_landmark_full.tflite",
     "hand_landmark_lite.tflite",
     "hand_landmarker.task",
@@ -64,6 +67,7 @@ mediapipe_files(srcs = [
     "pointing_up.jpg",
     "pointing_up_rotated.jpg",
     "portrait.jpg",
+    "portrait_hair_expected_mask.jpg",
     "portrait_rotated.jpg",
     "right_hands.jpg",
     "right_hands_rotated.jpg",
@@ -117,6 +121,7 @@ filegroup(
         "pointing_up.jpg",
         "pointing_up_rotated.jpg",
         "portrait.jpg",
+        "portrait_hair_expected_mask.jpg",
         "portrait_rotated.jpg",
         "right_hands.jpg",
         "right_hands_rotated.jpg",
@@ -140,12 +145,15 @@ filegroup(
         "coco_efficientdet_lite0_v1_1.0_quant_2021_09_06.tflite",
         "coco_ssd_mobilenet_v1_1.0_quant_2018_06_29.tflite",
         "coco_ssd_mobilenet_v1_1.0_quant_2018_06_29_with_dummy_score_calibration.tflite",
+        "conv2d_input_channel_1.tflite",
         "deeplabv3.tflite",
+        "dense.tflite",
         "face_detection_full_range.tflite",
         "face_detection_full_range_sparse.tflite",
         "face_detection_short_range.tflite",
         "face_landmark.tflite",
         "face_landmark_with_attention.tflite",
+        "hair_segmentation.tflite",
         "hand_landmark_full.tflite",
         "hand_landmark_lite.tflite",
         "hand_landmarker.task",

third_party/external_files.bzl

@@ -190,6 +190,12 @@ def external_files():
         urls = ["https://storage.googleapis.com/mediapipe-assets/coco_ssd_mobilenet_v1_score_calibration.json?generation=1677522739770755"],
     )
 
+    http_file(
+        name = "com_google_mediapipe_conv2d_input_channel_1_tflite",
+        sha256 = "126edac445967799f3b8b124d15483b1506f6d6cb57a501c1636eb8f2fb3734f",
+        urls = ["https://storage.googleapis.com/mediapipe-assets/conv2d_input_channel_1.tflite?generation=1678218348519744"],
+    )
+
     http_file(
         name = "com_google_mediapipe_corrupted_mobilenet_v1_0_25_224_1_default_1_tflite",
         sha256 = "f0cbeb8061f4c693e20de779ce255af923508492e8a24f6db320845a52facb51",
@@ -202,6 +208,12 @@ def external_files():
         urls = ["https://storage.googleapis.com/mediapipe-assets/deeplabv3.tflite?generation=1661875711618421"],
     )
 
+    http_file(
+        name = "com_google_mediapipe_dense_tflite",
+        sha256 = "be9323068461b1cbf412692ee916be30dcb1a5fb59a9ee875d470bc340d9e869",
+        urls = ["https://storage.googleapis.com/mediapipe-assets/dense.tflite?generation=1678218351373709"],
+    )
+
     http_file(
         name = "com_google_mediapipe_dummy_gesture_recognizer_task",
         sha256 = "18e54586bda33300d459ca140cd045f6daf43d897224ba215a16db3423eae18e",
@@ -354,8 +366,8 @@ def external_files():
 
     http_file(
         name = "com_google_mediapipe_hair_segmentation_tflite",
-        sha256 = "d2c940c4fd80edeaf38f5d7387d1b4235ee320ed120080df67c663e749e77633",
+        sha256 = "0bec40bc9ba97c4143f3d4225a935014abffea37c1f3766ae32aba3f2748e711",
-        urls = ["https://storage.googleapis.com/mediapipe-assets/hair_segmentation.tflite?generation=1661875756623461"],
+        urls = ["https://storage.googleapis.com/mediapipe-assets/hair_segmentation.tflite?generation=1678218355806671"],
     )
 
     http_file(
@@ -766,6 +778,12 @@ def external_files():
         urls = ["https://storage.googleapis.com/mediapipe-assets/pointing_up_rotated_landmarks.pbtxt?generation=1666629486774022"],
     )
 
+    http_file(
+        name = "com_google_mediapipe_portrait_expected_blendshapes_with_attention_pbtxt",
+        sha256 = "0142d56705093c3d79ea5ee79b8e9454499abee00fc059491e6ca14f5fbab862",
+        urls = ["https://storage.googleapis.com/mediapipe-assets/portrait_expected_blendshapes_with_attention.pbtxt?generation=1678218364703223"],
+    )
+
     http_file(
         name = "com_google_mediapipe_portrait_expected_detection_pbtxt",
         sha256 = "ace755f0fd0ba3b2d75e4f8bb1b08d2f65975fd5570898004540dfef735c1c3d",
@@ -780,8 +798,14 @@ def external_files():
 
     http_file(
         name = "com_google_mediapipe_portrait_expected_face_landmarks_with_attention_pbtxt",
-        sha256 = "f2ccd889654b914996e4aab0d7831a3e73d3b63d6c14f6bac4bec5cd3415bce4",
+        sha256 = "dae959456f001015278f3a1535bd03c9fa0990a3df951135645ce23293be0613",
-        urls = ["https://storage.googleapis.com/mediapipe-assets/portrait_expected_face_landmarks_with_attention.pbtxt?generation=1676415475626542"],
+        urls = ["https://storage.googleapis.com/mediapipe-assets/portrait_expected_face_landmarks_with_attention.pbtxt?generation=1678218367300928"],
+    )
+
+    http_file(
+        name = "com_google_mediapipe_portrait_hair_expected_mask_jpg",
+        sha256 = "d9ffc4f2ed0ee2d551d9239942e4dfceebf0c33a56858c84410f32ea4f0c1b2c",
+        urls = ["https://storage.googleapis.com/mediapipe-assets/portrait_hair_expected_mask.jpg?generation=1678218370120178"],
     )
 
     http_file(