diff --git a/mediapipe/calculators/tensor/BUILD b/mediapipe/calculators/tensor/BUILD index 96a29089e..e95398a9d 100644 --- a/mediapipe/calculators/tensor/BUILD +++ b/mediapipe/calculators/tensor/BUILD @@ -657,7 +657,6 @@ cc_library( }), deps = [ ":tensor_converter_calculator_cc_proto", - ":tensor_converter_cpu", "//mediapipe/framework:calculator_framework", "//mediapipe/framework:port", "//mediapipe/framework/formats:image_frame", @@ -666,7 +665,6 @@ cc_library( "//mediapipe/framework/port:ret_check", "//mediapipe/framework/port:status", "//mediapipe/framework/port:statusor", - "//mediapipe/gpu:gpu_buffer", "//mediapipe/gpu:gpu_buffer_format", "//mediapipe/gpu:gpu_origin_cc_proto", "//mediapipe/util:resource_util", @@ -676,17 +674,10 @@ cc_library( "@com_google_absl//absl/log:check", "@com_google_absl//absl/status", "@com_google_absl//absl/status:statusor", - "@com_google_absl//absl/strings", "@com_google_absl//absl/strings:str_format", ] + select({ "//mediapipe/gpu:disable_gpu": [], - "//conditions:default": [ - "tensor_converter_calculator_gpu_deps", - "//mediapipe/gpu:gl_base", - "//mediapipe/gpu:gl_calculator_helper", - "//mediapipe/gpu:gl_simple_shaders", - "//mediapipe/gpu:shader_util", - ], + "//conditions:default": ["tensor_converter_calculator_gpu_deps"], }) + select({ "//mediapipe:apple": [ "//third_party/apple_frameworks:MetalKit", @@ -696,35 +687,6 @@ cc_library( alwayslink = 1, ) -cc_library( - name = "tensor_converter_cpu", - srcs = ["tensor_converter_cpu.cc"], - hdrs = ["tensor_converter_cpu.h"], - deps = [ - "//mediapipe/framework/formats:image_frame", - "//mediapipe/framework/formats:matrix", - "//mediapipe/framework/formats:tensor", - "//mediapipe/framework/port:ret_check", - "//mediapipe/framework/port:status", - "@com_google_absl//absl/status", - "@com_google_absl//absl/status:statusor", - ], -) - -cc_test( - name = "tensor_converter_cpu_test", - srcs = ["tensor_converter_cpu_test.cc"], - deps = [ - ":tensor_converter_cpu", - "//mediapipe/framework/formats:matrix", - "//mediapipe/framework/formats:tensor", - "//mediapipe/framework/port:gtest", - "//mediapipe/framework/port:gtest_main", - "//mediapipe/framework/port:status_matchers", - "//mediapipe/util:image_test_utils", - ], -) - cc_library( name = "tensor_converter_calculator_gpu_deps", visibility = ["//visibility:private"], diff --git a/mediapipe/calculators/tensor/tensor_converter_calculator.cc b/mediapipe/calculators/tensor/tensor_converter_calculator.cc index 80cac63c7..b42cb0b17 100644 --- a/mediapipe/calculators/tensor/tensor_converter_calculator.cc +++ b/mediapipe/calculators/tensor/tensor_converter_calculator.cc @@ -14,7 +14,6 @@ #include #include -#include #include #include "absl/log/absl_check.h" @@ -22,21 +21,17 @@ #include "absl/status/status.h" #include "absl/status/statusor.h" #include "absl/strings/str_format.h" -#include "absl/strings/substitute.h" #include "mediapipe/calculators/tensor/tensor_converter_calculator.pb.h" -#include "mediapipe/calculators/tensor/tensor_converter_cpu.h" #include "mediapipe/framework/calculator_framework.h" #include "mediapipe/framework/formats/image_frame.h" #include "mediapipe/framework/formats/matrix.h" #include "mediapipe/framework/formats/tensor.h" #include "mediapipe/framework/port.h" #include "mediapipe/framework/port/ret_check.h" -#include "mediapipe/framework/port/status_macros.h" #include "mediapipe/gpu/gpu_buffer_format.h" #include "mediapipe/gpu/gpu_origin.pb.h" #if !MEDIAPIPE_DISABLE_GPU -#include "mediapipe/gpu/gl_base.h" #include "mediapipe/gpu/gpu_buffer.h" #if MEDIAPIPE_METAL_ENABLED #import @@ -99,6 +94,11 @@ absl::StatusOr ShouldFlipVertically( } } +typedef Eigen::Matrix + RowMajorMatrixXf; +typedef Eigen::Matrix + ColMajorMatrixXf; + constexpr char kImageFrameTag[] = "IMAGE"; constexpr char kGpuBufferTag[] = "IMAGE_GPU"; constexpr char kTensorsTag[] = "TENSORS"; @@ -156,6 +156,10 @@ class TensorConverterCalculator : public CalculatorBase { private: absl::Status InitGpu(CalculatorContext* cc); absl::Status LoadOptions(CalculatorContext* cc, bool use_gpu); + template + absl::Status NormalizeImage(const ImageFrame& image_frame, + bool flip_vertically, float* tensor_ptr); + absl::Status CopyMatrixToTensor(const Matrix& matrix, float* tensor_ptr); absl::Status ProcessCPU(CalculatorContext* cc); absl::Status ProcessGPU(CalculatorContext* cc); @@ -275,19 +279,46 @@ absl::Status TensorConverterCalculator::ProcessCPU(CalculatorContext* cc) { } const auto& image_frame = cc->Inputs().Tag(kImageFrameTag).Get(); - MP_ASSIGN_OR_RETURN( - Tensor output, - ConvertImageFrameToTensorOnCpu(image_frame, *output_range_, - flip_vertically_, max_num_channels_)); - output_tensors->emplace_back(std::move(output)); + const int height = image_frame.Height(); + const int width = image_frame.Width(); + const int channels = image_frame.NumberOfChannels(); + const int channels_preserved = std::min(channels, max_num_channels_); + const mediapipe::ImageFormat::Format format = image_frame.Format(); + + if (!(format == mediapipe::ImageFormat::SRGBA || + format == mediapipe::ImageFormat::SRGB || + format == mediapipe::ImageFormat::GRAY8 || + format == mediapipe::ImageFormat::VEC32F1)) + RET_CHECK_FAIL() << "Unsupported CPU input format."; + + output_tensors->emplace_back( + Tensor::ElementType::kFloat32, + Tensor::Shape{1, height, width, channels_preserved}); + auto cpu_view = output_tensors->back().GetCpuWriteView(); + + // Copy image data into tensor. + if (image_frame.ByteDepth() == 1) { + MP_RETURN_IF_ERROR(NormalizeImage(image_frame, flip_vertically_, + cpu_view.buffer())); + } else if (image_frame.ByteDepth() == 4) { + MP_RETURN_IF_ERROR(NormalizeImage(image_frame, flip_vertically_, + cpu_view.buffer())); + } else { + return absl::InternalError( + "Only byte-based (8 bit) and float (32 bit) images supported."); + } } else if (cc->Inputs().HasTag(kMatrixTag)) { if (cc->Inputs().Tag(kMatrixTag).IsEmpty()) { return absl::OkStatus(); } const auto& matrix = cc->Inputs().Tag(kMatrixTag).Get(); - MP_ASSIGN_OR_RETURN(Tensor output, - ConvertMatrixToTensorOnCpu(matrix, row_major_matrix_)); - output_tensors->emplace_back(std::move(output)); + const int height = matrix.rows(); + const int width = matrix.cols(); + const int channels = 1; + output_tensors->emplace_back(Tensor::ElementType::kFloat32, + Tensor::Shape{1, height, width, channels}); + MP_RETURN_IF_ERROR(CopyMatrixToTensor( + matrix, output_tensors->back().GetCpuWriteView().buffer())); } else { return absl::OkStatus(); } @@ -638,4 +669,67 @@ absl::Status TensorConverterCalculator::LoadOptions(CalculatorContext* cc, return absl::OkStatus(); } +template +absl::Status TensorConverterCalculator::NormalizeImage( + const ImageFrame& image_frame, bool flip_vertically, float* tensor_ptr) { + const int height = image_frame.Height(); + const int width = image_frame.Width(); + const int channels = image_frame.NumberOfChannels(); + const int channels_preserved = std::min(channels, max_num_channels_); + const int channels_ignored = channels - channels_preserved; + + if (output_range_.has_value()) { + // If the output float range is set and we are not using custom + // normalization, normalize the pixel values from [0, 255] to the specified + // output range. + RET_CHECK_NE(output_range_->first, output_range_->second); + const float scale = (output_range_->second - output_range_->first) / 255.0f; + const float bias = output_range_->first; + + for (int i = 0; i < height; ++i) { + const T* image_ptr = reinterpret_cast( + image_frame.PixelData() + + (flip_vertically ? height - 1 - i : i) * image_frame.WidthStep()); + for (int j = 0; j < width; ++j) { + for (int c = 0; c < channels_preserved; ++c) { + *tensor_ptr++ = *image_ptr++ * scale + bias; + } + image_ptr += channels_ignored; + } + } + } else { + // [0,1], scale only (bias == 0) + // Verified that there are no precision issues with 1.0f / 255.0f expression + const float scale = 1.0f / 255.0f; + for (int i = 0; i < height; ++i) { + const T* image_ptr = reinterpret_cast( + image_frame.PixelData() + + (flip_vertically ? height - 1 - i : i) * image_frame.WidthStep()); + for (int j = 0; j < width; ++j) { + for (int c = 0; c < channels_preserved; ++c) { + *tensor_ptr++ = *image_ptr++ * scale; + } + image_ptr += channels_ignored; + } + } + } + + return absl::OkStatus(); +} + +absl::Status TensorConverterCalculator::CopyMatrixToTensor(const Matrix& matrix, + float* tensor_ptr) { + if (row_major_matrix_) { + auto matrix_map = + Eigen::Map(tensor_ptr, matrix.rows(), matrix.cols()); + matrix_map = matrix; + } else { + auto matrix_map = + Eigen::Map(tensor_ptr, matrix.rows(), matrix.cols()); + matrix_map = matrix; + } + + return absl::OkStatus(); +} + } // namespace mediapipe diff --git a/mediapipe/calculators/tensor/tensor_converter_cpu.cc b/mediapipe/calculators/tensor/tensor_converter_cpu.cc deleted file mode 100644 index f72a24c31..000000000 --- a/mediapipe/calculators/tensor/tensor_converter_cpu.cc +++ /dev/null @@ -1,145 +0,0 @@ -// Copyright 2023 The MediaPipe Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// http://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#include "mediapipe/calculators/tensor/tensor_converter_cpu.h" - -#include -#include -#include - -#include "absl/status/status.h" -#include "absl/status/statusor.h" -#include "mediapipe/framework/formats/image_frame.h" -#include "mediapipe/framework/formats/matrix.h" -#include "mediapipe/framework/formats/tensor.h" -#include "mediapipe/framework/port/ret_check.h" -#include "mediapipe/framework/port/status_macros.h" - -namespace mediapipe { -namespace { - -typedef Eigen::Matrix - RowMajorMatrixXf; -typedef Eigen::Matrix - ColMajorMatrixXf; - -template -absl::Status NormalizeImage(const ImageFrame& image_frame, bool flip_vertically, - const std::pair& output_range, - int max_num_channels, float* tensor_ptr) { - const int height = image_frame.Height(); - const int width = image_frame.Width(); - const int channels = image_frame.NumberOfChannels(); - const int channels_preserved = std::min(channels, max_num_channels); - const int channels_ignored = channels - channels_preserved; - - RET_CHECK_NE(output_range.first, output_range.second); - const float scale = (output_range.second - output_range.first) / 255.0f; - const float bias = output_range.first; - - for (int i = 0; i < height; ++i) { - const T* image_ptr = reinterpret_cast( - image_frame.PixelData() + - (flip_vertically ? height - 1 - i : i) * image_frame.WidthStep()); - for (int j = 0; j < width; ++j) { - for (int c = 0; c < channels_preserved; ++c) { - *tensor_ptr++ = *image_ptr++ * scale + bias; - } - image_ptr += channels_ignored; - } - } - return absl::OkStatus(); -} - -} // namespace - -absl::Status NormalizeUInt8Image(const ImageFrame& image_frame, - bool flip_vertically, - const std::pair& output_range, - int max_num_channels, float* tensor_ptr) { - return NormalizeImage(image_frame, flip_vertically, output_range, - max_num_channels, tensor_ptr); -} - -absl::Status NormalizeFloatImage(const ImageFrame& image_frame, - bool flip_vertically, - const std::pair& output_range, - int max_num_channels, float* tensor_ptr) { - return NormalizeImage(image_frame, flip_vertically, output_range, - max_num_channels, tensor_ptr); -} - -absl::Status CopyMatrixToTensor(const Matrix& matrix, bool is_row_major_matrix, - float* tensor_ptr) { - if (is_row_major_matrix) { - auto matrix_map = - Eigen::Map(tensor_ptr, matrix.rows(), matrix.cols()); - matrix_map = matrix; - } else { - auto matrix_map = - Eigen::Map(tensor_ptr, matrix.rows(), matrix.cols()); - matrix_map = matrix; - } - return absl::OkStatus(); -} - -absl::StatusOr ConvertImageFrameToTensorOnCpu( - const ImageFrame& image_frame, const std::pair& output_range, - bool flip_vertically, int max_num_channels) { - const int height = image_frame.Height(); - const int width = image_frame.Width(); - const int channels = image_frame.NumberOfChannels(); - const int channels_preserved = std::min(channels, max_num_channels); - const mediapipe::ImageFormat::Format format = image_frame.Format(); - - if (!(format == mediapipe::ImageFormat::SRGBA || - format == mediapipe::ImageFormat::SRGB || - format == mediapipe::ImageFormat::GRAY8 || - format == mediapipe::ImageFormat::VEC32F1)) - RET_CHECK_FAIL() << "Unsupported CPU input format."; - - Tensor output_tensor(Tensor::ElementType::kFloat32, - Tensor::Shape{1, height, width, channels_preserved}); - auto cpu_view = output_tensor.GetCpuWriteView(); - - // Copy image data into tensor. - if (image_frame.ByteDepth() == 1) { - MP_RETURN_IF_ERROR(NormalizeUInt8Image(image_frame, flip_vertically, - output_range, max_num_channels, - cpu_view.buffer())); - } else if (image_frame.ByteDepth() == 4) { - MP_RETURN_IF_ERROR(NormalizeFloatImage(image_frame, flip_vertically, - output_range, max_num_channels, - cpu_view.buffer())); - } else { - return absl::InternalError( - "Only byte-based (8 bit) and float (32 bit) images supported."); - } - return output_tensor; -} - -absl::StatusOr ConvertMatrixToTensorOnCpu(const Matrix& matrix, - bool row_major_matrix) { - const int height = matrix.rows(); - const int width = matrix.cols(); - const int channels = 1; - Tensor output_tensor(Tensor::ElementType::kFloat32, - Tensor::Shape{1, height, width, channels}); - MP_RETURN_IF_ERROR( - CopyMatrixToTensor(matrix, row_major_matrix, - output_tensor.GetCpuWriteView().buffer())); - return output_tensor; -} - -} // namespace mediapipe diff --git a/mediapipe/calculators/tensor/tensor_converter_cpu.h b/mediapipe/calculators/tensor/tensor_converter_cpu.h deleted file mode 100644 index 784bade80..000000000 --- a/mediapipe/calculators/tensor/tensor_converter_cpu.h +++ /dev/null @@ -1,61 +0,0 @@ -// Copyright 2023 The MediaPipe Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// http://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#ifndef MEDIAPIPE_CALCULATORS_TENSOR_TENSOR_CONVERTER_CPU_H_ -#define MEDIAPIPE_CALCULATORS_TENSOR_TENSOR_CONVERTER_CPU_H_ - -#include - -#include "absl/status/status.h" -#include "absl/status/statusor.h" -#include "mediapipe/framework/formats/image_frame.h" -#include "mediapipe/framework/formats/matrix.h" -#include "mediapipe/framework/formats/tensor.h" - -namespace mediapipe { - -// Converts an ImageFrame to a vector of Tensors. -// @flip_vertically enables to flip the image during conversion. -// @max_num_channels can be used to reserve extra channels in the output -// tensors. -// Returns output Tensor. -absl::StatusOr ConvertImageFrameToTensorOnCpu( - const ImageFrame& image_frame, const std::pair& output_range, - bool flip_vertically, int max_num_channels); - -// Converts a Matrix to a vector of Tensors. -// @row_major_matrix defines the ordering in the input matrix. -// @max_num_channels can be used to reserve extra channels in the output -// tensors. -// Returns output Tensor. -absl::StatusOr ConvertMatrixToTensorOnCpu(const Matrix& matrix, - bool row_major_matrix); - -// For testing only below. -absl::Status NormalizeUInt8Image(const ImageFrame& image_frame, - bool flip_vertically, - const std::pair& output_range, - int max_num_channels, float* tensor_ptr); - -absl::Status NormalizeFloatImage(const ImageFrame& image_frame, - bool flip_vertically, - const std::pair& output_range, - int max_num_channels, float* tensor_ptr); - -absl::Status CopyMatrixToTensor(const Matrix& matrix, bool is_row_major_matrix, - float* tensor_ptr); - -} // namespace mediapipe - -#endif // MEDIAPIPE_CALCULATORS_TENSOR_TENSOR_CONVERTER_CPU_H_ diff --git a/mediapipe/calculators/tensor/tensor_converter_cpu_test.cc b/mediapipe/calculators/tensor/tensor_converter_cpu_test.cc deleted file mode 100644 index 478a9c6dc..000000000 --- a/mediapipe/calculators/tensor/tensor_converter_cpu_test.cc +++ /dev/null @@ -1,175 +0,0 @@ -// Copyright 2023 The MediaPipe Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// http://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#include "mediapipe/calculators/tensor/tensor_converter_cpu.h" - -#include -#include -#include - -#include "mediapipe/framework/formats/matrix.h" -#include "mediapipe/framework/formats/tensor.h" -#include "mediapipe/framework/port/gmock.h" -#include "mediapipe/framework/port/gtest.h" -#include "mediapipe/framework/port/status_matchers.h" -#include "mediapipe/util/image_test_utils.h" - -namespace mediapipe { -namespace { - -Matrix CreateTestMatrix(int num_rows, int num_columns) { - Matrix matrix(num_rows, num_columns); - for (int r = 0; r < num_rows; ++r) { - for (int c = 0; c < num_columns; ++c) { - matrix(r, c) = r * num_columns + c; - } - } - return matrix; -} - -TEST(TensorConverterCpuTest, ShouldCopyMatrixInRowMajorFormatToTensor) { - auto test_matrix = CreateTestMatrix(/* num_rows=*/3, /*num_columns=*/4); - std::vector tensor_data(test_matrix.size(), 0.0f); - - MP_EXPECT_OK(CopyMatrixToTensor(test_matrix, /*is_row_major_matrix=*/true, - tensor_data.data())); - - for (int i = 0; i < tensor_data.size(); ++i) { - const int row = i / test_matrix.cols(); - const int column = i % test_matrix.cols(); - EXPECT_FLOAT_EQ(tensor_data[i], (test_matrix)(row, column)); - } -} - -TEST(TensorConverterCpuTest, ShouldCopyMatrixInColumnMajorFormatToTensor) { - auto test_matrix = CreateTestMatrix(/*num_rows=*/3, /*num_columns=*/4); - std::vector tensor_data(test_matrix.size(), 0.0f); - - MP_EXPECT_OK(CopyMatrixToTensor(test_matrix, /*is_row_major_matrix=*/false, - tensor_data.data())); - - for (int i = 0; i < tensor_data.size(); ++i) { - const int row = i % test_matrix.rows(); - const int column = i / test_matrix.rows(); - EXPECT_FLOAT_EQ(tensor_data[i], (test_matrix)(row, column)); - } -} - -TEST(TensorConverterCpuTest, ShouldNormalizeGrey8ImageWithDefaultRange) { - auto grey8_image_frame = CreateTestGrey8ImageFrame(/*width=*/3, /*height=*/4); - std::vector tensor_data( - grey8_image_frame.Width() * grey8_image_frame.Height(), 0.0f); - - MP_EXPECT_OK(NormalizeUInt8Image(grey8_image_frame, /*flip_vertically=*/false, - {0.0f, 1.0f}, /*num_tensor_channels=*/1, - tensor_data.data())); - - for (int i = 0; i < tensor_data.size(); ++i) { - EXPECT_FLOAT_EQ( - tensor_data[i], - static_cast(grey8_image_frame.PixelData()[i]) / 255.0f); - } -} - -TEST(TensorConverterCpuTest, ShouldNormalizeGrey8ImageWithSpecifiedRange) { - auto grey8_image_frame = CreateTestGrey8ImageFrame(/*width=*/3, /*height=*/4); - std::vector tensor_data( - grey8_image_frame.Width() * grey8_image_frame.Height(), 0.0f); - const auto range = std::make_pair(2.0f, 3.0f); - - MP_EXPECT_OK( - NormalizeUInt8Image(grey8_image_frame, /*flip_vertically=*/false, range, - /*num_tensor_channels=*/1, tensor_data.data())); - - for (int i = 0; i < tensor_data.size(); ++i) { - EXPECT_FLOAT_EQ(tensor_data[i], - static_cast(grey8_image_frame.PixelData()[i]) / - 255.0f * (range.second - range.first) + - range.first); - } -} - -TEST(TensorConverterCpuTest, ShouldNormalizeGrey8ImageFlipped) { - auto grey8_image_frame = CreateTestGrey8ImageFrame(/*width=*/3, /*height=*/4); - std::vector tensor_data( - grey8_image_frame.Width() * grey8_image_frame.Height(), 0.0f); - - MP_EXPECT_OK(NormalizeUInt8Image(grey8_image_frame, /*flip_vertically=*/true, - {0.0f, 1.0f}, /*num_tensor_channels=*/1, - tensor_data.data())); - - for (int i = 0; i < tensor_data.size(); ++i) { - const int x = i % grey8_image_frame.Width(); - const int y = i / grey8_image_frame.Width(); - const int flipped_y = grey8_image_frame.Height() - y - 1; - - const int index = flipped_y * grey8_image_frame.Width() + x; - EXPECT_FLOAT_EQ( - tensor_data[index], - static_cast(grey8_image_frame.PixelData()[i]) / 255.0f); - } -} - -TEST(TensorConverterCpuTest, ShouldNormalizeFloatImageWithDefaultRange) { - auto float_image_frame = - CreateTestFloat32ImageFrame(/*width=*/3, /*height=*/4); - std::vector tensor_data( - float_image_frame.Width() * float_image_frame.Height(), 0.0f); - - MP_EXPECT_OK(NormalizeFloatImage(float_image_frame, /*flip_vertically=*/false, - {0.0f, 1.0f}, /*num_tensor_channels=*/1, - tensor_data.data())); - - for (int i = 0; i < tensor_data.size(); ++i) { - EXPECT_FLOAT_EQ(tensor_data[i], reinterpret_cast( - float_image_frame.PixelData())[i] / - 255.0f); - } -} - -TEST(TensorConverterCpuTest, ConvertImageFrameToTensorOnCpu) { - auto grey8_image_frame = CreateTestGrey8ImageFrame(/*width=*/3, /*height=*/4); - - MP_ASSERT_OK_AND_ASSIGN(Tensor output, ConvertImageFrameToTensorOnCpu( - grey8_image_frame, {0.0f, 1.0f}, - /*flip_vertically=*/false, - /*max_num_channels=*/1)); - - const auto cpu_read_view = output.GetCpuReadView(); - const float* tensor_ptr = cpu_read_view.buffer(); - for (int i = 0; i < grey8_image_frame.Width() * grey8_image_frame.Height(); - ++i) { - EXPECT_FLOAT_EQ( - tensor_ptr[i], - static_cast(grey8_image_frame.PixelData()[i]) / 255.0); - } -} - -TEST(TensorConverterCpuTest, ConvertMatrixToTensorOnCpu) { - auto test_matrix = CreateTestMatrix(/*num_rows=*/3, /*num_columns=*/4); - - MP_ASSERT_OK_AND_ASSIGN( - Tensor output, ConvertMatrixToTensorOnCpu(test_matrix, - /*row_major_matrix=*/false)); - - const auto cpu_read_view = output.GetCpuReadView(); - const float* tensor_ptr = cpu_read_view.buffer(); - for (int i = 0; i < test_matrix.size(); ++i) { - EXPECT_FLOAT_EQ(tensor_ptr[i], test_matrix.data()[i]); - } -} - -} // namespace - -} // namespace mediapipe diff --git a/mediapipe/util/image_test_utils.cc b/mediapipe/util/image_test_utils.cc index 325b308f1..9e10f40c1 100644 --- a/mediapipe/util/image_test_utils.cc +++ b/mediapipe/util/image_test_utils.cc @@ -17,34 +17,6 @@ namespace mediapipe { -namespace { - -template -ImageFrame CreateTestImageFrame(int width, int height, DataType max_value) { - ImageFrame image_frame(Format, width, height, - /*alignment_boundary=*/1); - const int num_channels = image_frame.NumberOfChannels(); - const float num_values = width * height * num_channels; - uint8_t* const data_ptr = - reinterpret_cast(image_frame.MutablePixelData()); - for (int y = 0; y < height; ++y) { - uint8_t* const row = data_ptr + image_frame.WidthStep() * y; - for (int x = 0; x < width; ++x) { - DataType* pixel = reinterpret_cast(row) + x * num_channels; - for (int c = 0; c < num_channels; ++c) { - // Fill pixel channel with a value in [0:max_value] range. - pixel[c] = - static_cast(static_cast(y * width * num_channels + - x * num_channels + c) / - num_values * max_value); - } - } - } - return image_frame; -} - -} // namespace - cv::Mat GetRgb(const std::string& path) { cv::Mat bgr = cv::imread(path); cv::Mat rgb; @@ -99,14 +71,4 @@ cv::Mat RgbaToBgr(cv::Mat rgba) { return bgra; } -ImageFrame CreateTestFloat32ImageFrame(int width, int height) { - return CreateTestImageFrame(width, height, - /*max_value=*/1.0f); -} - -ImageFrame CreateTestGrey8ImageFrame(int width, int height) { - return CreateTestImageFrame(width, height, - /*max_value=*/255); -} - } // namespace mediapipe diff --git a/mediapipe/util/image_test_utils.h b/mediapipe/util/image_test_utils.h index 49943382f..15a21c5b1 100644 --- a/mediapipe/util/image_test_utils.h +++ b/mediapipe/util/image_test_utils.h @@ -4,7 +4,6 @@ #include #include "mediapipe/framework/formats/image_format.pb.h" -#include "mediapipe/framework/formats/image_frame.h" #include "mediapipe/framework/packet.h" #include "mediapipe/framework/port/opencv_core_inc.h" @@ -31,12 +30,6 @@ Packet MakeImagePacket(cv::Mat input, int timestamp = 0); // Converts RGBA Mat to BGR. cv::Mat RgbaToBgr(cv::Mat rgba); -// Generates single-channel float32 ImageFrame with increasing [0,1] values. -ImageFrame CreateTestFloat32ImageFrame(int width, int height); - -// Generates single-channel uint8 ImageFrame with increasing [0,255] values. -ImageFrame CreateTestGrey8ImageFrame(int width, int height); - } // namespace mediapipe #endif // MEDIAPIPE_UTIL_IMAGE_TEST_UTILS_H_