landmarks_to_tensor stream utility function.

PiperOrigin-RevId: 569003241
This commit is contained in:
MediaPipe Team 2023-09-27 17:11:09 -07:00 committed by Copybara-Service
parent 8837b49026
commit da02052c70
4 changed files with 222 additions and 0 deletions

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@ -123,6 +123,38 @@ cc_test(
],
)
cc_library(
name = "landmarks_to_tensor",
srcs = ["landmarks_to_tensor.cc"],
hdrs = ["landmarks_to_tensor.h"],
deps = [
"//mediapipe/calculators/tensor:landmarks_to_tensor_calculator",
"//mediapipe/calculators/tensor:landmarks_to_tensor_calculator_cc_proto",
"//mediapipe/framework/api2:builder",
"//mediapipe/framework/api2:port",
"//mediapipe/framework/formats:landmark_cc_proto",
"//mediapipe/framework/formats:tensor",
"@com_google_absl//absl/types:span",
],
)
cc_test(
name = "landmarks_to_tensor_test",
srcs = ["landmarks_to_tensor_test.cc"],
deps = [
":landmarks_to_tensor",
"//mediapipe/calculators/tensor:landmarks_to_tensor_calculator_cc_proto",
"//mediapipe/framework:calculator_framework",
"//mediapipe/framework/api2:builder",
"//mediapipe/framework/formats:landmark_cc_proto",
"//mediapipe/framework/formats:tensor",
"//mediapipe/framework/port:gtest",
"//mediapipe/framework/port:gtest_main",
"//mediapipe/framework/port:parse_text_proto",
"//mediapipe/framework/port:status_matchers",
],
)
cc_library(
name = "landmarks_projection",
srcs = ["landmarks_projection.cc"],

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@ -0,0 +1,64 @@
#include "mediapipe/framework/api2/stream/landmarks_to_tensor.h"
#include <optional>
#include <utility>
#include <vector>
#include "mediapipe/calculators/tensor/landmarks_to_tensor_calculator.h"
#include "mediapipe/framework/api2/builder.h"
#include "mediapipe/framework/api2/port.h"
#include "mediapipe/framework/formats/landmark.pb.h"
#include "mediapipe/framework/formats/tensor.h"
namespace mediapipe::api2::builder {
namespace {
using ::mediapipe::api2::LandmarksToTensorCalculator;
template <typename LandmarkListType>
Stream<std::vector<Tensor>> InternalConvertToTensor(
Stream<LandmarkListType> landmarks,
std::optional<Stream<std::pair<int, int>>> image_size,
absl::Span<const LandmarksToTensorCalculatorOptions::Attribute> attributes,
const bool flatten, Graph& graph) {
auto& to_tensor = graph.AddNode<LandmarksToTensorCalculator>();
auto& to_tensor_options =
to_tensor.GetOptions<LandmarksToTensorCalculatorOptions>();
for (const auto& attribute : attributes) {
to_tensor_options.add_attributes(attribute);
}
to_tensor_options.set_flatten(flatten);
if constexpr (std::is_same_v<LandmarkListType, LandmarkList>) {
landmarks.ConnectTo(
to_tensor[LandmarksToTensorCalculator::kInLandmarkList]);
} else {
landmarks.ConnectTo(
to_tensor[LandmarksToTensorCalculator::kInNormLandmarkList]);
}
if (image_size.has_value()) {
image_size->ConnectTo(to_tensor[LandmarksToTensorCalculator::kImageSize]);
}
return to_tensor[LandmarksToTensorCalculator::kOutTensors];
}
} // namespace
Stream<std::vector<Tensor>> ConvertLandmarksToTensor(
Stream<LandmarkList> landmarks,
absl::Span<const LandmarksToTensorCalculatorOptions::Attribute> attributes,
const bool flatten, Graph& graph) {
return InternalConvertToTensor(landmarks, /*image_size=*/std::nullopt,
attributes, flatten, graph);
}
Stream<std::vector<Tensor>> ConvertNormalizedLandmarksToTensor(
Stream<NormalizedLandmarkList> normalized_landmarks,
Stream<std::pair<int, int>> image_size,
absl::Span<const LandmarksToTensorCalculatorOptions::Attribute> attributes,
const bool flatten, Graph& graph) {
return InternalConvertToTensor(normalized_landmarks, image_size, attributes,
flatten, graph);
}
} // namespace mediapipe::api2::builder

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@ -0,0 +1,37 @@
#ifndef MEDIAPIPE_FRAMEWORK_API2_STREAM_LANDMARKS_TO_TENSOR_H_
#define MEDIAPIPE_FRAMEWORK_API2_STREAM_LANDMARKS_TO_TENSOR_H_
#include <utility>
#include <vector>
#include "absl/types/span.h"
#include "mediapipe/calculators/tensor/landmarks_to_tensor_calculator.pb.h"
#include "mediapipe/framework/api2/builder.h"
#include "mediapipe/framework/formats/landmark.pb.h"
#include "mediapipe/framework/formats/tensor.h"
namespace mediapipe::api2::builder {
// Updates @graph to convert @landmarks to a Tensor. Values and their order are
// defined by @attributes. If @flatten is true resulting tensor will be 1D,
// otherwise tensor will be 2D with (n_landmarks, n_attributes) shape.
Stream<std::vector<Tensor>> ConvertLandmarksToTensor(
Stream<mediapipe::LandmarkList> landmarks,
absl::Span<const mediapipe::LandmarksToTensorCalculatorOptions::Attribute>
attributes,
bool flatten, Graph& graph);
// Updates @graph to convert @normalized_landmarks to a Tensor. Values and their
// order are defined by @attributes. X, Y and Z values are scaled using
// @image_size. If @flatten is true resulting tensor will be 1D, otherwise
// tensor will be 2D with (n_landmarks, n_attributes) shape.
Stream<std::vector<Tensor>> ConvertNormalizedLandmarksToTensor(
Stream<mediapipe::NormalizedLandmarkList> normalized_landmarks,
Stream<std::pair<int, int>> image_size,
absl::Span<const mediapipe::LandmarksToTensorCalculatorOptions::Attribute>
attributes,
bool flatten, Graph& graph);
} // namespace mediapipe::api2::builder
#endif // MEDIAPIPE_FRAMEWORK_API2_STREAM_LANDMARKS_TO_TENSOR_H_

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@ -0,0 +1,89 @@
#include "mediapipe/framework/api2/stream/landmarks_to_tensor.h"
#include <utility>
#include <vector>
#include "mediapipe/calculators/tensor/landmarks_to_tensor_calculator.pb.h"
#include "mediapipe/framework/api2/builder.h"
#include "mediapipe/framework/calculator_framework.h"
#include "mediapipe/framework/formats/landmark.pb.h"
#include "mediapipe/framework/formats/tensor.h"
#include "mediapipe/framework/port/gmock.h"
#include "mediapipe/framework/port/gtest.h"
#include "mediapipe/framework/port/parse_text_proto.h"
#include "mediapipe/framework/port/status_matchers.h"
namespace mediapipe::api2::builder {
namespace {
TEST(ConvertLandmarksToTensor, ConvertLandmarksToTensor) {
Graph graph;
Stream<LandmarkList> landmarks = graph.In("LANDMARKS").Cast<LandmarkList>();
Stream<std::vector<Tensor>> tensors =
ConvertLandmarksToTensor(landmarks,
{LandmarksToTensorCalculatorOptions::X,
LandmarksToTensorCalculatorOptions::Y,
LandmarksToTensorCalculatorOptions::Z},
/*flatten=*/true, graph);
tensors.SetName("tensors");
EXPECT_THAT(graph.GetConfig(),
EqualsProto(ParseTextProtoOrDie<CalculatorGraphConfig>(R"pb(
node {
calculator: "LandmarksToTensorCalculator"
input_stream: "LANDMARKS:__stream_0"
output_stream: "TENSORS:tensors"
options {
[mediapipe.LandmarksToTensorCalculatorOptions.ext] {
attributes: [ X, Y, Z ]
flatten: true
}
}
}
input_stream: "LANDMARKS:__stream_0"
)pb")));
CalculatorGraph calcualtor_graph;
MP_EXPECT_OK(calcualtor_graph.Initialize(graph.GetConfig()));
}
TEST(ConvertLandmarksToTensor, ConvertNormalizedLandmarksToTensor) {
Graph graph;
Stream<NormalizedLandmarkList> landmarks =
graph.In("LANDMARKS").Cast<NormalizedLandmarkList>();
Stream<std::pair<int, int>> image_size =
graph.In("IMAGE_SIZE").Cast<std::pair<int, int>>();
Stream<std::vector<Tensor>> tensors = ConvertNormalizedLandmarksToTensor(
landmarks, image_size,
{LandmarksToTensorCalculatorOptions::X,
LandmarksToTensorCalculatorOptions::Y,
LandmarksToTensorCalculatorOptions::Z},
/*flatten=*/false, graph);
tensors.SetName("tensors");
EXPECT_THAT(graph.GetConfig(),
EqualsProto(ParseTextProtoOrDie<CalculatorGraphConfig>(R"pb(
node {
calculator: "LandmarksToTensorCalculator"
input_stream: "IMAGE_SIZE:__stream_0"
input_stream: "NORM_LANDMARKS:__stream_1"
output_stream: "TENSORS:tensors"
options {
[mediapipe.LandmarksToTensorCalculatorOptions.ext] {
attributes: [ X, Y, Z ]
flatten: false
}
}
}
input_stream: "IMAGE_SIZE:__stream_0"
input_stream: "LANDMARKS:__stream_1"
)pb")));
CalculatorGraph calcualtor_graph;
MP_EXPECT_OK(calcualtor_graph.Initialize(graph.GetConfig()));
}
} // namespace
} // namespace mediapipe::api2::builder