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PiperOrigin-RevId: 522255287
This commit is contained in:
MediaPipe Team 2023-04-05 23:22:52 -07:00 committed by Copybara-Service
parent 56b3cd4350
commit 12ecc8139f
3 changed files with 18 additions and 16 deletions
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19
mediapipe/calculators/tensor/image_to_tensor_converter_opencv.cc
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@ -92,13 +92,14 @@ class OpenCvProcessor : public ImageToTensorConverter {
const int dst_data_type = output_channels == 1 ? mat_gray_type_ : mat_type_; const int dst_data_type = output_channels == 1 ? mat_gray_type_ : mat_type_;
switch (tensor_type_) { switch (tensor_type_) {
case Tensor::ElementType::kInt8: case Tensor::ElementType::kInt8:
RET_CHECK_GE(output_shape.num_elements(), RET_CHECK_GE(
tensor_buffer_offset / sizeof(int8) + num_elements_per_img) output_shape.num_elements(),
tensor_buffer_offset / sizeof(int8_t) + num_elements_per_img)
<< "The buffer offset + the input image size is larger than the " << "The buffer offset + the input image size is larger than the "
"allocated tensor buffer."; "allocated tensor buffer.";
dst = cv::Mat( dst = cv::Mat(output_height, output_width, dst_data_type,
output_height, output_width, dst_data_type, buffer_view.buffer<int8_t>() +
buffer_view.buffer<int8>() + tensor_buffer_offset / sizeof(int8)); tensor_buffer_offset / sizeof(int8_t));
break; break;
case Tensor::ElementType::kFloat32: case Tensor::ElementType::kFloat32:
RET_CHECK_GE( RET_CHECK_GE(
@ -113,12 +114,12 @@ class OpenCvProcessor : public ImageToTensorConverter {
case Tensor::ElementType::kUInt8: case Tensor::ElementType::kUInt8:
RET_CHECK_GE( RET_CHECK_GE(
output_shape.num_elements(), output_shape.num_elements(),
tensor_buffer_offset / sizeof(uint8) + num_elements_per_img) tensor_buffer_offset / sizeof(uint8_t) + num_elements_per_img)
<< "The buffer offset + the input image size is larger than the " << "The buffer offset + the input image size is larger than the "
"allocated tensor buffer."; "allocated tensor buffer.";
dst = cv::Mat( dst = cv::Mat(output_height, output_width, dst_data_type,
output_height, output_width, dst_data_type, buffer_view.buffer<uint8_t>() +
buffer_view.buffer<uint8>() + tensor_buffer_offset / sizeof(uint8)); tensor_buffer_offset / sizeof(uint8_t));
break; break;
default: default:
return InvalidArgumentError( return InvalidArgumentError(

8
mediapipe/calculators/tensor/tensor_converter_calculator_test.cc
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@ -41,7 +41,7 @@ constexpr char kTransposeOptionsString[] =
using RandomEngine = std::mt19937_64; using RandomEngine = std::mt19937_64;
using testing::Eq; using testing::Eq;
const uint32 kSeed = 1234; const uint32_t kSeed = 1234;
const int kNumSizes = 8; const int kNumSizes = 8;
const int sizes[kNumSizes][2] = {{1, 1}, {12, 1}, {1, 9}, {2, 2}, const int sizes[kNumSizes][2] = {{1, 1}, {12, 1}, {1, 9}, {2, 2},
{5, 3}, {7, 13}, {16, 32}, {101, 2}}; {5, 3}, {7, 13}, {16, 32}, {101, 2}};
@ -49,7 +49,7 @@ const int sizes[kNumSizes][2] = {{1, 1}, {12, 1}, {1, 9}, {2, 2},
class TensorConverterCalculatorTest : public ::testing::Test { class TensorConverterCalculatorTest : public ::testing::Test {
protected: protected:
// Adds a packet with a matrix filled with random values in [0,1]. // Adds a packet with a matrix filled with random values in [0,1].
void AddRandomMatrix(int num_rows, int num_columns, uint32 seed, void AddRandomMatrix(int num_rows, int num_columns, uint32_t seed,
bool row_major_matrix = false) { bool row_major_matrix = false) {
RandomEngine random(kSeed); RandomEngine random(kSeed);
std::uniform_real_distribution<> uniform_dist(0, 1.0); std::uniform_real_distribution<> uniform_dist(0, 1.0);
@ -229,7 +229,7 @@ TEST_F(TensorConverterCalculatorTest, CustomDivAndSub) {
MP_ASSERT_OK(graph.StartRun({})); MP_ASSERT_OK(graph.StartRun({}));
auto input_image = absl::make_unique<ImageFrame>(ImageFormat::GRAY8, 1, 1); auto input_image = absl::make_unique<ImageFrame>(ImageFormat::GRAY8, 1, 1);
cv::Mat mat = mediapipe::formats::MatView(input_image.get()); cv::Mat mat = mediapipe::formats::MatView(input_image.get());
mat.at<uint8>(0, 0) = 200; mat.at<uint8_t>(0, 0) = 200;
MP_ASSERT_OK(graph.AddPacketToInputStream( MP_ASSERT_OK(graph.AddPacketToInputStream(
"input_image", Adopt(input_image.release()).At(Timestamp(0)))); "input_image", Adopt(input_image.release()).At(Timestamp(0))));
@ -286,7 +286,7 @@ TEST_F(TensorConverterCalculatorTest, SetOutputRange) {
MP_ASSERT_OK(graph.StartRun({})); MP_ASSERT_OK(graph.StartRun({}));
auto input_image = absl::make_unique<ImageFrame>(ImageFormat::GRAY8, 1, 1); auto input_image = absl::make_unique<ImageFrame>(ImageFormat::GRAY8, 1, 1);
cv::Mat mat = mediapipe::formats::MatView(input_image.get()); cv::Mat mat = mediapipe::formats::MatView(input_image.get());
mat.at<uint8>(0, 0) = 200; mat.at<uint8_t>(0, 0) = 200;
MP_ASSERT_OK(graph.AddPacketToInputStream( MP_ASSERT_OK(graph.AddPacketToInputStream(
"input_image", Adopt(input_image.release()).At(Timestamp(0)))); "input_image", Adopt(input_image.release()).At(Timestamp(0))));

7
mediapipe/calculators/tensor/tensors_to_classification_calculator.cc
View File

@ -84,7 +84,7 @@ class TensorsToClassificationCalculator : public Node {
private: private:
int top_k_ = 0; int top_k_ = 0;
bool sort_by_descending_score_ = false; bool sort_by_descending_score_ = false;
proto_ns::Map<int64, LabelMapItem> local_label_map_; proto_ns::Map<int64_t, LabelMapItem> local_label_map_;
bool label_map_loaded_ = false; bool label_map_loaded_ = false;
bool is_binary_classification_ = false; bool is_binary_classification_ = false;
float min_score_threshold_ = std::numeric_limits<float>::lowest(); float min_score_threshold_ = std::numeric_limits<float>::lowest();
@ -98,7 +98,8 @@ class TensorsToClassificationCalculator : public Node {
// These are used to filter out the output classification results. // These are used to filter out the output classification results.
ClassIndexSet class_index_set_; ClassIndexSet class_index_set_;
bool IsClassIndexAllowed(int class_index); bool IsClassIndexAllowed(int class_index);
const proto_ns::Map<int64, LabelMapItem>& GetLabelMap(CalculatorContext* cc); const proto_ns::Map<int64_t, LabelMapItem>& GetLabelMap(
CalculatorContext* cc);
}; };
MEDIAPIPE_REGISTER_NODE(TensorsToClassificationCalculator); MEDIAPIPE_REGISTER_NODE(TensorsToClassificationCalculator);
@ -252,7 +253,7 @@ bool TensorsToClassificationCalculator::IsClassIndexAllowed(int class_index) {
} }
} }
const proto_ns::Map<int64, LabelMapItem>& const proto_ns::Map<int64_t, LabelMapItem>&
TensorsToClassificationCalculator::GetLabelMap(CalculatorContext* cc) { TensorsToClassificationCalculator::GetLabelMap(CalculatorContext* cc) {
return !local_label_map_.empty() return !local_label_map_.empty()
? local_label_map_ ? local_label_map_