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PiperOrigin-RevId: 583186277
This commit is contained in:
MediaPipe Team 2023-11-16 15:36:20 -08:00 committed by Copybara-Service
parent 7287056674
commit e7c7638833
7 changed files with 108 additions and 478 deletions
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40
mediapipe/calculators/tensor/BUILD
View File

@ -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"],

120
mediapipe/calculators/tensor/tensor_converter_calculator.cc
View File

@ -14,7 +14,6 @@
#include <cstdint>
#include <string>
#include <utility>
#include <vector>
#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 <CoreVideo/CoreVideo.h>
@ -99,6 +94,11 @@ absl::StatusOr<bool> ShouldFlipVertically(
}
}
typedef Eigen::Matrix<float, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor>
RowMajorMatrixXf;
typedef Eigen::Matrix<float, Eigen::Dynamic, Eigen::Dynamic, Eigen::ColMajor>
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 <class T>
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<ImageFrame>();
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<uint8_t>(image_frame, flip_vertically_,
cpu_view.buffer<float>()));
} else if (image_frame.ByteDepth() == 4) {
MP_RETURN_IF_ERROR(NormalizeImage<float>(image_frame, flip_vertically_,
cpu_view.buffer<float>()));
} 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<Matrix>();
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<float>()));
} else {
return absl::OkStatus();
}
@ -638,4 +669,67 @@ absl::Status TensorConverterCalculator::LoadOptions(CalculatorContext* cc,
return absl::OkStatus();
}
template <class T>
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<const T*>(
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<const T*>(
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<RowMajorMatrixXf>(tensor_ptr, matrix.rows(), matrix.cols());
matrix_map = matrix;
} else {
auto matrix_map =
Eigen::Map<ColMajorMatrixXf>(tensor_ptr, matrix.rows(), matrix.cols());
matrix_map = matrix;
}
return absl::OkStatus();
}
} // namespace mediapipe

145
mediapipe/calculators/tensor/tensor_converter_cpu.cc
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@ -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 <algorithm>
#include <cstdint>
#include <utility>
#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<float, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor>
RowMajorMatrixXf;
typedef Eigen::Matrix<float, Eigen::Dynamic, Eigen::Dynamic, Eigen::ColMajor>
ColMajorMatrixXf;
template <class T>
absl::Status NormalizeImage(const ImageFrame& image_frame, bool flip_vertically,
const std::pair<float, float>& 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<const T*>(
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<float, float>& output_range,
int max_num_channels, float* tensor_ptr) {
return NormalizeImage<uint8_t>(image_frame, flip_vertically, output_range,
max_num_channels, tensor_ptr);
}
absl::Status NormalizeFloatImage(const ImageFrame& image_frame,
bool flip_vertically,
const std::pair<float, float>& output_range,
int max_num_channels, float* tensor_ptr) {
return NormalizeImage<float>(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<RowMajorMatrixXf>(tensor_ptr, matrix.rows(), matrix.cols());
matrix_map = matrix;
} else {
auto matrix_map =
Eigen::Map<ColMajorMatrixXf>(tensor_ptr, matrix.rows(), matrix.cols());
matrix_map = matrix;
}
return absl::OkStatus();
}
absl::StatusOr<Tensor> ConvertImageFrameToTensorOnCpu(
const ImageFrame& image_frame, const std::pair<float, float>& 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<float>()));
} else if (image_frame.ByteDepth() == 4) {
MP_RETURN_IF_ERROR(NormalizeFloatImage(image_frame, flip_vertically,
output_range, max_num_channels,
cpu_view.buffer<float>()));
} else {
return absl::InternalError(
"Only byte-based (8 bit) and float (32 bit) images supported.");
}
return output_tensor;
}
absl::StatusOr<Tensor> 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<float>()));
return output_tensor;
}
} // namespace mediapipe

61
mediapipe/calculators/tensor/tensor_converter_cpu.h
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@ -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 <utility>
#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<Tensor> ConvertImageFrameToTensorOnCpu(
const ImageFrame& image_frame, const std::pair<float, float>& 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<Tensor> 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<float, float>& output_range,
int max_num_channels, float* tensor_ptr);
absl::Status NormalizeFloatImage(const ImageFrame& image_frame,
bool flip_vertically,
const std::pair<float, float>& 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_

175
mediapipe/calculators/tensor/tensor_converter_cpu_test.cc
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@ -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 <cstdint>
#include <utility>
#include <vector>
#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<float> 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<float> 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<float> 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<uint8_t>(grey8_image_frame.PixelData()[i]) / 255.0f);
}
}
TEST(TensorConverterCpuTest, ShouldNormalizeGrey8ImageWithSpecifiedRange) {
auto grey8_image_frame = CreateTestGrey8ImageFrame(/*width=*/3, /*height=*/4);
std::vector<float> 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<uint8_t>(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<float> 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<uint8_t>(grey8_image_frame.PixelData()[i]) / 255.0f);
}
}
TEST(TensorConverterCpuTest, ShouldNormalizeFloatImageWithDefaultRange) {
auto float_image_frame =
CreateTestFloat32ImageFrame(/*width=*/3, /*height=*/4);
std::vector<float> 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<const float*>(
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<float>();
for (int i = 0; i < grey8_image_frame.Width() * grey8_image_frame.Height();
++i) {
EXPECT_FLOAT_EQ(
tensor_ptr[i],
static_cast<uint8_t>(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<float>();
for (int i = 0; i < test_matrix.size(); ++i) {
EXPECT_FLOAT_EQ(tensor_ptr[i], test_matrix.data()[i]);
}
}
} // namespace
} // namespace mediapipe

38
mediapipe/util/image_test_utils.cc
View File

@ -17,34 +17,6 @@
namespace mediapipe {
namespace {
template <ImageFormat::Format Format, typename DataType>
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<uint8_t*>(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<DataType*>(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<DataType>(static_cast<float>(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<ImageFormat::VEC32F1, float>(width, height,
/*max_value=*/1.0f);
}
ImageFrame CreateTestGrey8ImageFrame(int width, int height) {
return CreateTestImageFrame<ImageFormat::GRAY8, uint8_t>(width, height,
/*max_value=*/255);
}
} // namespace mediapipe

7
mediapipe/util/image_test_utils.h
View File

@ -4,7 +4,6 @@
#include <string>
#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_