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Merge pull request #2 from google/master

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Update 0.5
This commit is contained in:
Ali Zahid Raja 2019-12-05 00:29:09 +05:00 committed by GitHub
commit 66cd04ce42
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GPG Key ID: 4AEE18F83AFDEB23
131 changed files with 2610 additions and 542 deletions
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4
Dockerfile
View File

@ -30,6 +30,7 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
unzip \
python \
python-pip \
python3-pip \
libopencv-core-dev \
libopencv-highgui-dev \
libopencv-imgproc-dev \
@ -42,9 +43,10 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
RUN pip install --upgrade setuptools
RUN pip install future
RUN pip3 install six
# Install bazel
ARG BAZEL_VERSION=0.26.1
ARG BAZEL_VERSION=1.1.0
RUN mkdir /bazel && \
wget --no-check-certificate -O /bazel/installer.sh "https://github.com/bazelbuild/bazel/releases/download/${BAZEL_VERSION}/b\
azel-${BAZEL_VERSION}-installer-linux-x86_64.sh" && \

5
README.md
View File

@ -10,11 +10,13 @@
## ML Solutions in MediaPipe
* [Hand Tracking](mediapipe/docs/hand_tracking_mobile_gpu.md)
* [Multi-hand Tracking](mediapipe/docs/multi_hand_tracking_mobile_gpu.md)
* [Face Detection](mediapipe/docs/face_detection_mobile_gpu.md)
* [Hair Segmentation](mediapipe/docs/hair_segmentation_mobile_gpu.md)
* [Object Detection](mediapipe/docs/object_detection_mobile_gpu.md)
![hand_tracking](mediapipe/docs/images/mobile/hand_tracking_3d_android_gpu_small.gif)
![multi-hand_tracking](mediapipe/docs/images/mobile/multi_hand_tracking_android_gpu_small.gif)
![face_detection](mediapipe/docs/images/mobile/face_detection_android_gpu_small.gif)
![hair_segmentation](mediapipe/docs/images/mobile/hair_segmentation_android_gpu_small.gif)
![object_detection](mediapipe/docs/images/mobile/object_detection_android_gpu_small.gif)
@ -23,7 +25,7 @@
Follow these [instructions](mediapipe/docs/install.md).
## Getting started
See mobile and desktop [examples](mediapipe/docs/examples.md).
See mobile, desktop and Google Coral [examples](mediapipe/docs/examples.md).
## Documentation
[MediaPipe Read-the-Docs](https://mediapipe.readthedocs.io/) or [docs.mediapipe.dev](https://docs.mediapipe.dev)
@ -41,6 +43,7 @@ A web-based visualizer is hosted on [viz.mediapipe.dev](https://viz.mediapipe.de
* [MediaPipe: A Framework for Building Perception Pipelines](https://arxiv.org/abs/1906.08172)
## Events
* [AI Nextcon 2020, 12-16 Feb 2020, Seattle](http://aisea20.xnextcon.com/)
* [MediaPipe Madrid Meetup, 16 Dec 2019](https://www.meetup.com/Madrid-AI-Developers-Group/events/266329088/)
* [MediaPipe London Meetup, Google 123 Building, 12 Dec 2019](https://www.meetup.com/London-AI-Tech-Talk/events/266329038)
* [ML Conference, Berlin, 11 Dec 2019](https://mlconference.ai/machine-learning-advanced-development/mediapipe-building-real-time-cross-platform-mobile-web-edge-desktop-video-audio-ml-pipelines/)

42
WORKSPACE
View File

@ -12,17 +12,21 @@ http_archive(
load("@bazel_skylib//lib:versions.bzl", "versions")
versions.check(minimum_bazel_version = "0.24.1")
# ABSL cpp library.
# ABSL cpp library lts_2019_08_08.
http_archive(
name = "com_google_absl",
# Head commit on 2019-04-12.
# TODO: Switch to the latest absl version when the problem gets
# fixed.
urls = [
"https://github.com/abseil/abseil-cpp/archive/a02f62f456f2c4a7ecf2be3104fe0c6e16fbad9a.tar.gz",
"https://github.com/abseil/abseil-cpp/archive/20190808.tar.gz",
],
sha256 = "d437920d1434c766d22e85773b899c77c672b8b4865d5dc2cd61a29fdff3cf03",
strip_prefix = "abseil-cpp-a02f62f456f2c4a7ecf2be3104fe0c6e16fbad9a",
# Remove after https://github.com/abseil/abseil-cpp/issues/326 is solved.
patches = [
"@//third_party:com_google_absl_f863b622fe13612433fdf43f76547d5edda0c93001.diff"
],
patch_args = [
"-p1",
],
strip_prefix = "abseil-cpp-20190808",
sha256 = "8100085dada279bf3ee00cd064d43b5f55e5d913be0dfe2906f06f8f28d5b37e"
)
http_archive(
@ -103,9 +107,9 @@ http_archive(
],
)
# 2019-11-12
_TENSORFLOW_GIT_COMMIT = "a5f9bcd64453ff3d1f64cb4da4786db3d2da7f82"
_TENSORFLOW_SHA256= "f2b6f2ab2ffe63e86eccd3ce4bea6b7197383d726638dfeeebcdc1e7de73f075"
# 2019-11-21
_TENSORFLOW_GIT_COMMIT = "f482488b481a799ca07e7e2d153cf47b8e91a60c"
_TENSORFLOW_SHA256= "8d9118c2ce186c7e1403f04b96982fe72c184060c7f7a93e30a28dca358694f0"
http_archive(
name = "org_tensorflow",
urls = [
@ -149,11 +153,10 @@ new_local_repository(
http_archive(
name = "android_opencv",
sha256 = "056b849842e4fa8751d09edbb64530cfa7a63c84ccd232d0ace330e27ba55d0b",
build_file = "@//third_party:opencv_android.BUILD",
strip_prefix = "OpenCV-android-sdk",
type = "zip",
url = "https://github.com/opencv/opencv/releases/download/4.1.0/opencv-4.1.0-android-sdk.zip",
url = "https://github.com/opencv/opencv/releases/download/3.4.3/opencv-3.4.3-android-sdk.zip",
)
# After OpenCV 3.2.0, the pre-compiled opencv2.framework has google protobuf symbols, which will
@ -184,13 +187,18 @@ maven_install(
artifacts = [
"androidx.annotation:annotation:aar:1.1.0",
"androidx.appcompat:appcompat:aar:1.1.0-rc01",
"androidx.camera:camera-core:aar:1.0.0-alpha06",
"androidx.camera:camera-camera2:aar:1.0.0-alpha06",
"androidx.constraintlayout:constraintlayout:aar:1.1.3",
"androidx.core:core:aar:1.1.0-rc03",
"androidx.legacy:legacy-support-v4:aar:1.0.0",
"androidx.recyclerview:recyclerview:aar:1.1.0-beta02",
"com.google.android.material:material:aar:1.0.0-rc01",
],
repositories = ["https://dl.google.com/dl/android/maven2"],
repositories = [
"https://dl.google.com/dl/android/maven2",
"https://repo1.maven.org/maven2",
],
)
maven_server(
@ -206,10 +214,10 @@ maven_jar(
)
maven_jar(
name = "androidx_concurrent_futures",
artifact = "androidx.concurrent:concurrent-futures:1.0.0-alpha03",
sha1 = "b528df95c7e2fefa2210c0c742bf3e491c1818ae",
server = "google_server",
name = "androidx_concurrent_futures",
artifact = "androidx.concurrent:concurrent-futures:1.0.0-alpha03",
sha1 = "b528df95c7e2fefa2210c0c742bf3e491c1818ae",
server = "google_server",
)
maven_jar(

1
mediapipe/calculators/core/BUILD
View File

@ -691,6 +691,7 @@ cc_library(
":split_vector_calculator_cc_proto",
"//mediapipe/framework:calculator_framework",
"//mediapipe/framework/formats:landmark_cc_proto",
"//mediapipe/framework/formats:rect_cc_proto",
"//mediapipe/framework/port:ret_check",
"//mediapipe/framework/port:status",
"//mediapipe/util:resource_util",

14
mediapipe/calculators/core/begin_loop_calculator.cc
View File

@ -21,16 +21,10 @@
namespace mediapipe {
// A calculator to process std::vector<NormalizedLandmark>.
typedef BeginLoopCalculator<std::vector<::mediapipe::NormalizedLandmark>>
BeginLoopNormalizedLandmarkCalculator;
REGISTER_CALCULATOR(BeginLoopNormalizedLandmarkCalculator);
// A calculator to process std::vector<std::vector<NormalizedLandmark>>.
typedef BeginLoopCalculator<
std::vector<std::vector<::mediapipe::NormalizedLandmark>>>
BeginLoopNormalizedLandmarksVectorCalculator;
REGISTER_CALCULATOR(BeginLoopNormalizedLandmarksVectorCalculator);
// A calculator to process std::vector<NormalizedLandmarkList>.
typedef BeginLoopCalculator<std::vector<::mediapipe::NormalizedLandmarkList>>
BeginLoopNormalizedLandmarkListVectorCalculator;
REGISTER_CALCULATOR(BeginLoopNormalizedLandmarkListVectorCalculator);
// A calculator to process std::vector<NormalizedRect>.
typedef BeginLoopCalculator<std::vector<::mediapipe::NormalizedRect>>

4
mediapipe/calculators/core/concatenate_vector_calculator.cc
View File

@ -19,7 +19,7 @@
#include "mediapipe/framework/formats/landmark.pb.h"
#include "tensorflow/lite/interpreter.h"
#if !defined(MEDIAPIPE_DISABLE_GPU) && !defined(__APPLE__)
#if !defined(MEDIAPIPE_DISABLE_GL_COMPUTE)
#include "tensorflow/lite/delegates/gpu/gl/gl_buffer.h"
#endif // !MEDIAPIPE_DISABLE_GPU
@ -50,7 +50,7 @@ typedef ConcatenateVectorCalculator<::mediapipe::NormalizedLandmark>
ConcatenateLandmarkVectorCalculator;
REGISTER_CALCULATOR(ConcatenateLandmarkVectorCalculator);
#if !defined(MEDIAPIPE_DISABLE_GPU) && !defined(__APPLE__)
#if !defined(MEDIAPIPE_DISABLE_GL_COMPUTE)
typedef ConcatenateVectorCalculator<::tflite::gpu::gl::GlBuffer>
ConcatenateGlBufferVectorCalculator;
REGISTER_CALCULATOR(ConcatenateGlBufferVectorCalculator);

11
mediapipe/calculators/core/end_loop_calculator.cc
View File

@ -26,14 +26,9 @@ typedef EndLoopCalculator<std::vector<::mediapipe::NormalizedRect>>
EndLoopNormalizedRectCalculator;
REGISTER_CALCULATOR(EndLoopNormalizedRectCalculator);
typedef EndLoopCalculator<std::vector<::mediapipe::NormalizedLandmark>>
EndLoopNormalizedLandmarkCalculator;
REGISTER_CALCULATOR(EndLoopNormalizedLandmarkCalculator);
typedef EndLoopCalculator<
std::vector<std::vector<::mediapipe::NormalizedLandmark>>>
EndLoopNormalizedLandmarksVectorCalculator;
REGISTER_CALCULATOR(EndLoopNormalizedLandmarksVectorCalculator);
typedef EndLoopCalculator<std::vector<::mediapipe::NormalizedLandmarkList>>
EndLoopNormalizedLandmarkListVectorCalculator;
REGISTER_CALCULATOR(EndLoopNormalizedLandmarkListVectorCalculator);
typedef EndLoopCalculator<std::vector<bool>> EndLoopBooleanCalculator;
REGISTER_CALCULATOR(EndLoopBooleanCalculator);

21
mediapipe/calculators/core/previous_loopback_calculator_test.cc
View File

@ -93,14 +93,19 @@ TEST(PreviousLoopbackCalculator, CorrectTimestamps) {
EXPECT_EQ(TimestampValues(in_prev), (std::vector<int64>{1}));
EXPECT_EQ(pair_values(in_prev.back()), std::make_pair(1, -1));
send_packet("in", 2);
MP_EXPECT_OK(graph_.WaitUntilIdle());
EXPECT_EQ(TimestampValues(in_prev), (std::vector<int64>{1, 2}));
EXPECT_EQ(pair_values(in_prev.back()), std::make_pair(2, 1));
send_packet("in", 5);
MP_EXPECT_OK(graph_.WaitUntilIdle());
EXPECT_EQ(TimestampValues(in_prev), (std::vector<int64>{1, 5}));
EXPECT_EQ(pair_values(in_prev.back()), std::make_pair(5, 1));
EXPECT_EQ(TimestampValues(in_prev), (std::vector<int64>{1, 2, 5}));
EXPECT_EQ(pair_values(in_prev.back()), std::make_pair(5, 2));
send_packet("in", 15);
MP_EXPECT_OK(graph_.WaitUntilIdle());
EXPECT_EQ(TimestampValues(in_prev), (std::vector<int64>{1, 5, 15}));
EXPECT_EQ(TimestampValues(in_prev), (std::vector<int64>{1, 2, 5, 15}));
EXPECT_EQ(pair_values(in_prev.back()), std::make_pair(15, 5));
MP_EXPECT_OK(graph_.CloseAllInputStreams());
@ -182,18 +187,22 @@ TEST(PreviousLoopbackCalculator, ClosesCorrectly) {
MP_EXPECT_OK(graph_.WaitUntilIdle());
EXPECT_EQ(TimestampValues(outputs), (std::vector<int64>{1}));
send_packet("in", 2);
MP_EXPECT_OK(graph_.WaitUntilIdle());
EXPECT_EQ(TimestampValues(outputs), (std::vector<int64>{1, 2}));
send_packet("in", 5);
MP_EXPECT_OK(graph_.WaitUntilIdle());
EXPECT_EQ(TimestampValues(outputs), (std::vector<int64>{1, 5}));
EXPECT_EQ(TimestampValues(outputs), (std::vector<int64>{1, 2, 5}));
send_packet("in", 15);
MP_EXPECT_OK(graph_.WaitUntilIdle());
EXPECT_EQ(TimestampValues(outputs), (std::vector<int64>{1, 5, 15}));
EXPECT_EQ(TimestampValues(outputs), (std::vector<int64>{1, 2, 5, 15}));
MP_EXPECT_OK(graph_.CloseAllInputStreams());
MP_EXPECT_OK(graph_.WaitUntilIdle());
EXPECT_EQ(TimestampValues(outputs),
(std::vector<int64>{1, 5, 15, Timestamp::Max().Value()}));
(std::vector<int64>{1, 2, 5, 15, Timestamp::Max().Value()}));
MP_EXPECT_OK(graph_.WaitUntilDone());
}

5
mediapipe/calculators/core/split_vector_calculator.cc
View File

@ -17,6 +17,7 @@
#include <vector>
#include "mediapipe/framework/formats/landmark.pb.h"
#include "mediapipe/framework/formats/rect.pb.h"
#include "tensorflow/lite/interpreter.h"
namespace mediapipe {
@ -41,4 +42,8 @@ REGISTER_CALCULATOR(SplitTfLiteTensorVectorCalculator);
typedef SplitVectorCalculator<::mediapipe::NormalizedLandmark>
SplitLandmarkVectorCalculator;
REGISTER_CALCULATOR(SplitLandmarkVectorCalculator);
typedef SplitVectorCalculator<::mediapipe::NormalizedRect>
SplitNormalizedRectVectorCalculator;
REGISTER_CALCULATOR(SplitNormalizedRectVectorCalculator);
} // namespace mediapipe

2
mediapipe/calculators/image/BUILD
View File

@ -356,13 +356,13 @@ cc_library(
"//mediapipe/framework/port:opencv_imgproc",
"//mediapipe/framework/port:ret_check",
"//mediapipe/framework/port:status",
"//mediapipe/gpu:gpu_buffer",
] + select({
"//mediapipe/gpu:disable_gpu": [],
"//conditions:default": [
"//mediapipe/gpu:gl_calculator_helper",
"//mediapipe/gpu:gl_simple_shaders",
"//mediapipe/gpu:gl_quad_renderer",
"//mediapipe/gpu:gpu_buffer",
"//mediapipe/gpu:shader_util",
],
}),

2
mediapipe/calculators/image/image_transformation_calculator.cc
View File

@ -400,7 +400,7 @@ REGISTER_CALCULATOR(ImageTransformationCalculator);
QuadRenderer* renderer = nullptr;
GlTexture src1;
#if defined(__APPLE__) && !TARGET_OS_OSX
#if defined(MEDIAPIPE_IOS)
if (input.format() == GpuBufferFormat::kBiPlanar420YpCbCr8VideoRange ||
input.format() == GpuBufferFormat::kBiPlanar420YpCbCr8FullRange) {
if (!yuv_renderer_) {

3
mediapipe/calculators/image/scale_image_calculator.proto
View File

@ -36,7 +36,8 @@ message ScaleImageCalculatorOptions {
// If ratio is positive, crop the image to this minimum and maximum
// aspect ratio (preserving the center of the frame). This is done
// before scaling.
// before scaling. The string must contain "/", so to disable cropping,
// set both to "0/1".
// For example, for a min_aspect_ratio of "9/16" and max of "16/9" the
// following cropping will occur:
// 1920x1080 (which is 16:9) is not cropped

4
mediapipe/calculators/tensorflow/tensorflow_inference_calculator.cc
View File

@ -34,7 +34,7 @@
#include "tensorflow/core/framework/tensor_shape.h"
#include "tensorflow/core/framework/tensor_util.h"
#if !defined(__ANDROID__) && !defined(__APPLE__)
#if !defined(MEDIAPIPE_MOBILE) && !defined(__APPLE__)
#include "tensorflow/core/profiler/lib/traceme.h"
#endif
@ -441,7 +441,7 @@ class TensorFlowInferenceCalculator : public CalculatorBase {
const int64 run_start_time = absl::ToUnixMicros(clock_->TimeNow());
tf::Status tf_status;
{
#if !defined(__ANDROID__) && !defined(__APPLE__)
#if !defined(MEDIAPIPE_MOBILE) && !defined(__APPLE__)
tensorflow::profiler::TraceMe trace(absl::string_view(cc->NodeName()));
#endif
tf_status = session_->Run(input_tensors, output_tensor_names,

3
mediapipe/calculators/tensorflow/tensorflow_session_from_frozen_graph_calculator.cc
View File

@ -31,8 +31,7 @@
#include "mediapipe/framework/tool/status_util.h"
#include "tensorflow/core/public/session_options.h"
#if defined(MEDIAPIPE_LITE) || defined(__ANDROID__) || \
defined(__APPLE__) && !TARGET_OS_OSX
#if defined(MEDIAPIPE_MOBILE)
#include "mediapipe/util/android/file/base/helpers.h"
#else
#include "mediapipe/framework/port/file_helpers.h"

4
mediapipe/calculators/tensorflow/tfrecord_reader_calculator.cc
View File

@ -85,7 +85,7 @@ class TFRecordReaderCalculator : public CalculatorBase {
tensorflow::io::RecordReader reader(file.get(),
tensorflow::io::RecordReaderOptions());
tensorflow::uint64 offset = 0;
std::string example_str;
tensorflow::tstring example_str;
const int target_idx =
cc->InputSidePackets().HasTag(kRecordIndex)
? cc->InputSidePackets().Tag(kRecordIndex).Get<int>()
@ -98,7 +98,7 @@ class TFRecordReaderCalculator : public CalculatorBase {
if (current_idx == target_idx) {
if (cc->OutputSidePackets().HasTag(kExampleTag)) {
tensorflow::Example tf_example;
tf_example.ParseFromString(example_str);
tf_example.ParseFromArray(example_str.data(), example_str.size());
cc->OutputSidePackets()
.Tag(kExampleTag)
.Set(MakePacket<tensorflow::Example>(std::move(tf_example)));

9
mediapipe/calculators/tflite/BUILD
View File

@ -13,12 +13,12 @@
# limitations under the License.
#
load("//mediapipe/framework/port:build_config.bzl", "mediapipe_cc_proto_library")
licenses(["notice"]) # Apache 2.0
package(default_visibility = ["//visibility:private"])
load("//mediapipe/framework/port:build_config.bzl", "mediapipe_cc_proto_library")
proto_library(
name = "ssd_anchors_calculator_proto",
srcs = ["ssd_anchors_calculator.proto"],
@ -249,6 +249,11 @@ cc_library(
"@org_tensorflow//tensorflow/lite/delegates/gpu/gl:gl_program",
"@org_tensorflow//tensorflow/lite/delegates/gpu/gl:gl_shader",
],
}) + select({
"//conditions:default": [],
"//mediapipe:android": [
"@org_tensorflow//tensorflow/lite/delegates/nnapi:nnapi_delegate",
],
}),
alwayslink = 1,
)

49
mediapipe/calculators/tflite/tflite_converter_calculator.cc
View File

@ -25,8 +25,7 @@
#include "tensorflow/lite/error_reporter.h"
#include "tensorflow/lite/interpreter.h"
#if !defined(MEDIAPIPE_DISABLE_GPU) && !defined(__EMSCRIPTEN__) && \
!defined(__APPLE__)
#if !defined(MEDIAPIPE_DISABLE_GL_COMPUTE)
#include "mediapipe/gpu/gl_calculator_helper.h"
#include "mediapipe/gpu/gpu_buffer.h"
#include "tensorflow/lite/delegates/gpu/gl/gl_buffer.h"
@ -35,7 +34,7 @@
#include "tensorflow/lite/delegates/gpu/gl_delegate.h"
#endif // !MEDIAPIPE_DISABLE_GPU
#if defined(__APPLE__) && !TARGET_OS_OSX // iOS
#if defined(MEDIAPIPE_IOS)
#import <CoreVideo/CoreVideo.h>
#import <Metal/Metal.h>
#import <MetalKit/MetalKit.h>
@ -46,10 +45,9 @@
#include "tensorflow/lite/delegates/gpu/metal_delegate.h"
#endif // iOS
#if !defined(MEDIAPIPE_DISABLE_GPU) && !defined(__EMSCRIPTEN__) && \
!defined(__APPLE__)
#if !defined(MEDIAPIPE_DISABLE_GL_COMPUTE)
typedef ::tflite::gpu::gl::GlBuffer GpuTensor;
#elif defined(__APPLE__) && !TARGET_OS_OSX // iOS
#elif defined(MEDIAPIPE_IOS)
typedef id<MTLBuffer> GpuTensor;
#endif
@ -69,8 +67,7 @@ typedef Eigen::Matrix<float, Eigen::Dynamic, Eigen::Dynamic, Eigen::ColMajor>
namespace mediapipe {
#if !defined(MEDIAPIPE_DISABLE_GPU) && !defined(__EMSCRIPTEN__) && \
!defined(__APPLE__)
#if !defined(MEDIAPIPE_DISABLE_GL_COMPUTE)
using ::tflite::gpu::gl::CreateReadWriteShaderStorageBuffer;
using ::tflite::gpu::gl::GlProgram;
using ::tflite::gpu::gl::GlShader;
@ -80,7 +77,7 @@ struct GPUData {
GlShader shader;
GlProgram program;
};
#elif defined(__APPLE__) && !TARGET_OS_OSX // iOS
#elif defined(MEDIAPIPE_IOS)
struct GPUData {
int elements = 1;
GpuTensor buffer;
@ -149,11 +146,10 @@ class TfLiteConverterCalculator : public CalculatorBase {
std::unique_ptr<tflite::Interpreter> interpreter_ = nullptr;
#if !defined(MEDIAPIPE_DISABLE_GPU) && !defined(__EMSCRIPTEN__) && \
!defined(__APPLE__)
#if !defined(MEDIAPIPE_DISABLE_GL_COMPUTE)
mediapipe::GlCalculatorHelper gpu_helper_;
std::unique_ptr<GPUData> gpu_data_out_;
#elif defined(__APPLE__) && !TARGET_OS_OSX // iOS
#elif defined(MEDIAPIPE_IOS)
MPPMetalHelper* gpu_helper_ = nullptr;
std::unique_ptr<GPUData> gpu_data_out_;
#endif
@ -202,10 +198,9 @@ REGISTER_CALCULATOR(TfLiteConverterCalculator);
#endif // !MEDIAPIPE_DISABLE_GPU
if (use_gpu) {
#if !defined(MEDIAPIPE_DISABLE_GPU) && !defined(__EMSCRIPTEN__) && \
!defined(__APPLE__)
#if !defined(MEDIAPIPE_DISABLE_GL_COMPUTE)
MP_RETURN_IF_ERROR(mediapipe::GlCalculatorHelper::UpdateContract(cc));
#elif defined(__APPLE__) && !TARGET_OS_OSX // iOS
#elif defined(MEDIAPIPE_IOS)
MP_RETURN_IF_ERROR([MPPMetalHelper updateContract:cc]);
#endif
}
@ -236,10 +231,9 @@ REGISTER_CALCULATOR(TfLiteConverterCalculator);
cc->Outputs().HasTag("TENSORS_GPU"));
// Cannot use quantization.
use_quantized_tensors_ = false;
#if !defined(MEDIAPIPE_DISABLE_GPU) && !defined(__EMSCRIPTEN__) && \
!defined(__APPLE__)
#if !defined(MEDIAPIPE_DISABLE_GL_COMPUTE)
MP_RETURN_IF_ERROR(gpu_helper_.Open(cc));
#elif defined(__APPLE__) && !TARGET_OS_OSX // iOS
#elif defined(MEDIAPIPE_IOS)
gpu_helper_ = [[MPPMetalHelper alloc] initWithCalculatorContext:cc];
RET_CHECK(gpu_helper_);
#endif
@ -270,11 +264,10 @@ REGISTER_CALCULATOR(TfLiteConverterCalculator);
}
::mediapipe::Status TfLiteConverterCalculator::Close(CalculatorContext* cc) {
#if !defined(MEDIAPIPE_DISABLE_GPU) && !defined(__EMSCRIPTEN__) && \
!defined(__APPLE__)
#if !defined(MEDIAPIPE_DISABLE_GL_COMPUTE)
gpu_helper_.RunInGlContext([this] { gpu_data_out_.reset(); });
#endif
#if defined(__APPLE__) && !TARGET_OS_OSX // iOS
#if defined(MEDIAPIPE_IOS)
gpu_data_out_.reset();
#endif
return ::mediapipe::OkStatus();
@ -390,8 +383,7 @@ REGISTER_CALCULATOR(TfLiteConverterCalculator);
::mediapipe::Status TfLiteConverterCalculator::ProcessGPU(
CalculatorContext* cc) {
#if !defined(MEDIAPIPE_DISABLE_GPU) && !defined(__EMSCRIPTEN__) && \
!defined(__APPLE__)
#if !defined(MEDIAPIPE_DISABLE_GL_COMPUTE)
// GpuBuffer to tflite::gpu::GlBuffer conversion.
const auto& input = cc->Inputs().Tag("IMAGE_GPU").Get<mediapipe::GpuBuffer>();
MP_RETURN_IF_ERROR(
@ -427,7 +419,7 @@ REGISTER_CALCULATOR(TfLiteConverterCalculator);
cc->Outputs()
.Tag("TENSORS_GPU")
.Add(output_tensors.release(), cc->InputTimestamp());
#elif defined(__APPLE__) && !TARGET_OS_OSX // iOS
#elif defined(MEDIAPIPE_IOS)
// GpuBuffer to id<MTLBuffer> conversion.
const auto& input = cc->Inputs().Tag("IMAGE_GPU").Get<mediapipe::GpuBuffer>();
{
@ -493,8 +485,7 @@ REGISTER_CALCULATOR(TfLiteConverterCalculator);
RET_CHECK_FAIL() << "Num input channels is less than desired output.";
#endif // !MEDIAPIPE_DISABLE_GPU
#if !defined(MEDIAPIPE_DISABLE_GPU) && !defined(__EMSCRIPTEN__) && \
!defined(__APPLE__)
#if !defined(MEDIAPIPE_DISABLE_GL_COMPUTE)
MP_RETURN_IF_ERROR(gpu_helper_.RunInGlContext(
[this, &include_alpha, &input, &single_channel]() -> ::mediapipe::Status {
// Device memory.
@ -538,7 +529,9 @@ REGISTER_CALCULATOR(TfLiteConverterCalculator);
&gpu_data_out_->program));
return ::mediapipe::OkStatus();
}));
#elif defined(__APPLE__) && !TARGET_OS_OSX // iOS
#elif defined(MEDIAPIPE_IOS)
RET_CHECK(include_alpha)
<< "iOS GPU inference currently accepts only RGBA input.";
@ -619,7 +612,7 @@ REGISTER_CALCULATOR(TfLiteConverterCalculator);
CHECK_GE(max_num_channels_, 1);
CHECK_LE(max_num_channels_, 4);
CHECK_NE(max_num_channels_, 2);
#if defined(__APPLE__) && !TARGET_OS_OSX // iOS
#if defined(MEDIAPIPE_IOS)
if (cc->Inputs().HasTag("IMAGE_GPU"))
// Currently on iOS, tflite gpu input tensor must be 4 channels,
// so input image must be 4 channels also (checked in InitGpu).

154
mediapipe/calculators/tflite/tflite_inference_calculator.cc
View File

@ -27,7 +27,7 @@
#include "tensorflow/lite/kernels/register.h"
#include "tensorflow/lite/model.h"
#if !defined(MEDIAPIPE_DISABLE_GPU) && !defined(MEDIAPIPE_DISABLE_GL_COMPUTE)
#if !defined(MEDIAPIPE_DISABLE_GL_COMPUTE)
#include "mediapipe/gpu/gl_calculator_helper.h"
#include "mediapipe/gpu/gpu_buffer.h"
#include "tensorflow/lite/delegates/gpu/common/shape.h"
@ -35,9 +35,9 @@
#include "tensorflow/lite/delegates/gpu/gl/gl_program.h"
#include "tensorflow/lite/delegates/gpu/gl/gl_shader.h"
#include "tensorflow/lite/delegates/gpu/gl_delegate.h"
#endif // !MEDIAPIPE_DISABLE_GPU
#endif // !MEDIAPIPE_DISABLE_GL_COMPUTE
#if defined(__APPLE__) && !TARGET_OS_OSX // iOS
#if defined(MEDIAPIPE_IOS)
#import <CoreVideo/CoreVideo.h>
#import <Metal/Metal.h>
#import <MetalKit/MetalKit.h>
@ -51,12 +51,15 @@
#include "tensorflow/lite/delegates/gpu/metal_delegate_internal.h"
#endif // iOS
#if defined(MEDIAPIPE_ANDROID)
#include "tensorflow/lite/delegates/nnapi/nnapi_delegate.h"
#endif // ANDROID
namespace {
#if !defined(MEDIAPIPE_DISABLE_GPU) && !defined(__EMSCRIPTEN__) && \
!defined(__APPLE__)
#if !defined(MEDIAPIPE_DISABLE_GL_COMPUTE)
typedef ::tflite::gpu::gl::GlBuffer GpuTensor;
#elif defined(__APPLE__) && !TARGET_OS_OSX // iOS
#elif defined(MEDIAPIPE_IOS)
typedef id<MTLBuffer> GpuTensor;
#endif
@ -64,14 +67,35 @@ typedef id<MTLBuffer> GpuTensor;
size_t RoundUp(size_t n, size_t m) { return ((n + m - 1) / m) * m; } // NOLINT
} // namespace
#if defined(MEDIAPIPE_EDGE_TPU)
#include "edgetpu.h"
// Creates and returns an Edge TPU interpreter to run the given edgetpu model.
std::unique_ptr<tflite::Interpreter> BuildEdgeTpuInterpreter(
const tflite::FlatBufferModel& model,
tflite::ops::builtin::BuiltinOpResolver* resolver,
edgetpu::EdgeTpuContext* edgetpu_context) {
resolver->AddCustom(edgetpu::kCustomOp, edgetpu::RegisterCustomOp());
std::unique_ptr<tflite::Interpreter> interpreter;
if (tflite::InterpreterBuilder(model, *resolver)(&interpreter) != kTfLiteOk) {
std::cerr << "Failed to build edge TPU interpreter." << std::endl;
}
interpreter->SetExternalContext(kTfLiteEdgeTpuContext, edgetpu_context);
interpreter->SetNumThreads(1);
if (interpreter->AllocateTensors() != kTfLiteOk) {
std::cerr << "Failed to allocate edge TPU tensors." << std::endl;
}
return interpreter;
}
#endif // MEDIAPIPE_EDGE_TPU
// TfLiteInferenceCalculator File Layout:
// * Header
// * Core
// * Aux
namespace mediapipe {
#if !defined(MEDIAPIPE_DISABLE_GPU) && !defined(__EMSCRIPTEN__) && \
!defined(__APPLE__)
#if !defined(MEDIAPIPE_DISABLE_GL_COMPUTE)
using ::tflite::gpu::gl::CopyBuffer;
using ::tflite::gpu::gl::CreateReadWriteShaderStorageBuffer;
using ::tflite::gpu::gl::GlBuffer;
@ -150,18 +174,22 @@ class TfLiteInferenceCalculator : public CalculatorBase {
std::unique_ptr<tflite::FlatBufferModel> model_;
TfLiteDelegate* delegate_ = nullptr;
#if !defined(MEDIAPIPE_DISABLE_GPU) && !defined(__EMSCRIPTEN__) && \
!defined(__APPLE__)
#if !defined(MEDIAPIPE_DISABLE_GL_COMPUTE)
mediapipe::GlCalculatorHelper gpu_helper_;
std::unique_ptr<GPUData> gpu_data_in_;
std::vector<std::unique_ptr<GPUData>> gpu_data_out_;
#elif defined(__APPLE__) && !TARGET_OS_OSX // iOS
#elif defined(MEDIAPIPE_IOS)
MPPMetalHelper* gpu_helper_ = nullptr;
std::unique_ptr<GPUData> gpu_data_in_;
std::vector<std::unique_ptr<GPUData>> gpu_data_out_;
TFLBufferConvert* converter_from_BPHWC4_ = nil;
#endif
#if defined(MEDIAPIPE_EDGE_TPU)
std::shared_ptr<edgetpu::EdgeTpuContext> edgetpu_context_ =
edgetpu::EdgeTpuManager::GetSingleton()->OpenDevice();
#endif
std::string model_path_ = "";
bool gpu_inference_ = false;
bool gpu_input_ = false;
@ -210,10 +238,9 @@ REGISTER_CALCULATOR(TfLiteInferenceCalculator);
use_gpu |= options.use_gpu();
if (use_gpu) {
#if !defined(MEDIAPIPE_DISABLE_GPU) && !defined(__EMSCRIPTEN__) && \
!defined(__APPLE__)
#if !defined(MEDIAPIPE_DISABLE_GL_COMPUTE)
MP_RETURN_IF_ERROR(mediapipe::GlCalculatorHelper::UpdateContract(cc));
#elif defined(__APPLE__) && !TARGET_OS_OSX // iOS
#elif defined(MEDIAPIPE_IOS)
MP_RETURN_IF_ERROR([MPPMetalHelper updateContract:cc]);
#endif
}
@ -253,26 +280,24 @@ REGISTER_CALCULATOR(TfLiteInferenceCalculator);
MP_RETURN_IF_ERROR(LoadModel(cc));
if (gpu_inference_) {
#if !defined(MEDIAPIPE_DISABLE_GPU) && !defined(__EMSCRIPTEN__) && \
!defined(__APPLE__)
#if !defined(MEDIAPIPE_DISABLE_GL_COMPUTE)
MP_RETURN_IF_ERROR(gpu_helper_.Open(cc));
#elif defined(__APPLE__) && !TARGET_OS_OSX // iOS
#elif defined(MEDIAPIPE_IOS)
gpu_helper_ = [[MPPMetalHelper alloc] initWithCalculatorContext:cc];
RET_CHECK(gpu_helper_);
#endif
#if !defined(MEDIAPIPE_DISABLE_GPU) && !defined(__EMSCRIPTEN__) && \
!defined(__APPLE__)
#if !defined(MEDIAPIPE_DISABLE_GL_COMPUTE)
MP_RETURN_IF_ERROR(gpu_helper_.RunInGlContext(
[this, &cc]() -> ::mediapipe::Status { return LoadDelegate(cc); }));
#else
MP_RETURN_IF_ERROR(LoadDelegate(cc));
#endif
} else {
#if defined(__EMSCRIPTEN__) || defined(MEDIAPIPE_ANDROID)
MP_RETURN_IF_ERROR(LoadDelegate(cc));
#endif // __EMSCRIPTEN__ || ANDROID
}
#if defined(__EMSCRIPTEN__)
MP_RETURN_IF_ERROR(LoadDelegate(cc));
#endif // __EMSCRIPTEN__
return ::mediapipe::OkStatus();
}
@ -280,8 +305,7 @@ REGISTER_CALCULATOR(TfLiteInferenceCalculator);
// 1. Receive pre-processed tensor inputs.
if (gpu_input_) {
// Read GPU input into SSBO.
#if !defined(MEDIAPIPE_DISABLE_GPU) && !defined(__EMSCRIPTEN__) && \
!defined(__APPLE__)
#if !defined(MEDIAPIPE_DISABLE_GL_COMPUTE)
const auto& input_tensors =
cc->Inputs().Tag("TENSORS_GPU").Get<std::vector<GpuTensor>>();
RET_CHECK_EQ(input_tensors.size(), 1);
@ -291,7 +315,7 @@ REGISTER_CALCULATOR(TfLiteInferenceCalculator);
RET_CHECK_CALL(CopyBuffer(input_tensors[0], gpu_data_in_->buffer));
return ::mediapipe::OkStatus();
}));
#elif defined(__APPLE__) && !TARGET_OS_OSX // iOS
#elif defined(MEDIAPIPE_IOS)
const auto& input_tensors =
cc->Inputs().Tag("TENSORS_GPU").Get<std::vector<GpuTensor>>();
RET_CHECK_EQ(input_tensors.size(), 1);
@ -327,14 +351,13 @@ REGISTER_CALCULATOR(TfLiteInferenceCalculator);
// 2. Run inference.
if (gpu_inference_) {
#if !defined(MEDIAPIPE_DISABLE_GPU) && !defined(__EMSCRIPTEN__) && \
!defined(__APPLE__)
#if !defined(MEDIAPIPE_DISABLE_GL_COMPUTE)
MP_RETURN_IF_ERROR(
gpu_helper_.RunInGlContext([this]() -> ::mediapipe::Status {
RET_CHECK_EQ(interpreter_->Invoke(), kTfLiteOk);
return ::mediapipe::OkStatus();
}));
#elif defined(__APPLE__) && !TARGET_OS_OSX // iOS
#elif defined(MEDIAPIPE_IOS)
RET_CHECK_EQ(interpreter_->Invoke(), kTfLiteOk);
#endif
} else {
@ -343,8 +366,7 @@ REGISTER_CALCULATOR(TfLiteInferenceCalculator);
// 3. Output processed tensors.
if (gpu_output_) {
#if !defined(MEDIAPIPE_DISABLE_GPU) && !defined(__EMSCRIPTEN__) && \
!defined(__APPLE__)
#if !defined(MEDIAPIPE_DISABLE_GL_COMPUTE)
// Output result tensors (GPU).
auto output_tensors = absl::make_unique<std::vector<GpuTensor>>();
MP_RETURN_IF_ERROR(gpu_helper_.RunInGlContext(
@ -361,7 +383,7 @@ REGISTER_CALCULATOR(TfLiteInferenceCalculator);
cc->Outputs()
.Tag("TENSORS_GPU")
.Add(output_tensors.release(), cc->InputTimestamp());
#elif defined(__APPLE__) && !TARGET_OS_OSX // iOS
#elif defined(MEDIAPIPE_IOS)
// Output result tensors (GPU).
auto output_tensors = absl::make_unique<std::vector<GpuTensor>>();
output_tensors->resize(gpu_data_out_.size());
@ -406,25 +428,29 @@ REGISTER_CALCULATOR(TfLiteInferenceCalculator);
::mediapipe::Status TfLiteInferenceCalculator::Close(CalculatorContext* cc) {
if (delegate_) {
#if !defined(MEDIAPIPE_DISABLE_GPU) && !defined(__EMSCRIPTEN__) && \
!defined(__APPLE__)
MP_RETURN_IF_ERROR(gpu_helper_.RunInGlContext([this]() -> Status {
TfLiteGpuDelegateDelete(delegate_);
if (gpu_inference_) {
#if !defined(MEDIAPIPE_DISABLE_GL_COMPUTE)
MP_RETURN_IF_ERROR(gpu_helper_.RunInGlContext([this]() -> Status {
TfLiteGpuDelegateDelete(delegate_);
gpu_data_in_.reset();
for (int i = 0; i < gpu_data_out_.size(); ++i) {
gpu_data_out_[i].reset();
}
return ::mediapipe::OkStatus();
}));
#elif defined(MEDIAPIPE_IOS)
TFLGpuDelegateDelete(delegate_);
gpu_data_in_.reset();
for (int i = 0; i < gpu_data_out_.size(); ++i) {
gpu_data_out_[i].reset();
}
return ::mediapipe::OkStatus();
}));
#elif defined(__APPLE__) && !TARGET_OS_OSX // iOS
TFLGpuDelegateDelete(delegate_);
gpu_data_in_.reset();
for (int i = 0; i < gpu_data_out_.size(); ++i) {
gpu_data_out_[i].reset();
}
#endif
}
delegate_ = nullptr;
}
#if defined(MEDIAPIPE_EDGE_TPU)
edgetpu_context_.reset();
#endif
return ::mediapipe::OkStatus();
}
@ -458,16 +484,18 @@ REGISTER_CALCULATOR(TfLiteInferenceCalculator);
model_ = tflite::FlatBufferModel::BuildFromFile(model_path_.c_str());
RET_CHECK(model_);
tflite::ops::builtin::BuiltinOpResolver op_resolver;
if (cc->InputSidePackets().HasTag("CUSTOM_OP_RESOLVER")) {
const auto& op_resolver =
cc->InputSidePackets()
.Tag("CUSTOM_OP_RESOLVER")
.Get<tflite::ops::builtin::BuiltinOpResolver>();
tflite::InterpreterBuilder(*model_, op_resolver)(&interpreter_);
} else {
const tflite::ops::builtin::BuiltinOpResolver op_resolver;
tflite::InterpreterBuilder(*model_, op_resolver)(&interpreter_);
op_resolver = cc->InputSidePackets()
.Tag("CUSTOM_OP_RESOLVER")
.Get<tflite::ops::builtin::BuiltinOpResolver>();
}
#if defined(MEDIAPIPE_EDGE_TPU)
interpreter_ =
BuildEdgeTpuInterpreter(*model_, &op_resolver, edgetpu_context_.get());
#else
tflite::InterpreterBuilder(*model_, op_resolver)(&interpreter_);
#endif // MEDIAPIPE_EDGE_TPU
RET_CHECK(interpreter_);
@ -490,8 +518,22 @@ REGISTER_CALCULATOR(TfLiteInferenceCalculator);
::mediapipe::Status TfLiteInferenceCalculator::LoadDelegate(
CalculatorContext* cc) {
#if !defined(MEDIAPIPE_DISABLE_GPU) && !defined(__EMSCRIPTEN__) && \
!defined(__APPLE__)
#if defined(MEDIAPIPE_ANDROID)
if (!gpu_inference_) {
if (cc->Options<mediapipe::TfLiteInferenceCalculatorOptions>()
.use_nnapi()) {
// Attempt to use NNAPI.
// If not supported, the default CPU delegate will be created and used.
interpreter_->SetAllowFp16PrecisionForFp32(1);
delegate_ = tflite::NnApiDelegate();
RET_CHECK_EQ(interpreter_->ModifyGraphWithDelegate(delegate_), kTfLiteOk);
}
// Return, no need for GPU delegate below.
return ::mediapipe::OkStatus();
}
#endif // ANDROID
#if !defined(MEDIAPIPE_DISABLE_GL_COMPUTE)
// Configure and create the delegate.
TfLiteGpuDelegateOptions options = TfLiteGpuDelegateOptionsDefault();
options.compile_options.precision_loss_allowed = 1;
@ -551,7 +593,7 @@ REGISTER_CALCULATOR(TfLiteInferenceCalculator);
RET_CHECK_EQ(interpreter_->ModifyGraphWithDelegate(delegate_), kTfLiteOk);
#endif // OpenGL
#if defined(__APPLE__) && !TARGET_OS_OSX // iOS
#if defined(MEDIAPIPE_IOS)
// Configure and create the delegate.
TFLGpuDelegateOptions options;
options.allow_precision_loss = false; // Must match converter, F=float/T=half

5
mediapipe/calculators/tflite/tflite_inference_calculator.proto
View File

@ -45,4 +45,9 @@ message TfLiteInferenceCalculatorOptions {
// input tensors are on CPU. For input tensors on GPU, GPU backend is always
// used.
optional bool use_gpu = 2 [default = false];
// Android only. When true, an NNAPI delegate will be used for inference.
// If NNAPI is not available, then the default CPU delegate will be used
// automatically.
optional bool use_nnapi = 3 [default = false];
}

3
mediapipe/calculators/tflite/tflite_tensors_to_classification_calculator.cc
View File

@ -24,8 +24,7 @@
#include "mediapipe/framework/port/ret_check.h"
#include "mediapipe/util/resource_util.h"
#include "tensorflow/lite/interpreter.h"
#if defined(__EMSCRIPTEN__) || defined(__ANDROID__) || \
(defined(__APPLE__) && !TARGET_OS_OSX)
#if defined(MEDIAPIPE_MOBILE)
#include "mediapipe/util/android/file/base/file.h"
#include "mediapipe/util/android/file/base/helpers.h"
#else

48
mediapipe/calculators/tflite/tflite_tensors_to_detections_calculator.cc
View File

@ -27,8 +27,7 @@
#include "mediapipe/framework/port/ret_check.h"
#include "tensorflow/lite/interpreter.h"
#if !defined(MEDIAPIPE_DISABLE_GPU) && !defined(__EMSCRIPTEN__) && \
!defined(__APPLE__)
#if !defined(MEDIAPIPE_DISABLE_GL_COMPUTE)
#include "mediapipe/gpu/gl_calculator_helper.h"
#include "tensorflow/lite/delegates/gpu/gl/gl_buffer.h"
#include "tensorflow/lite/delegates/gpu/gl/gl_program.h"
@ -36,7 +35,7 @@
#include "tensorflow/lite/delegates/gpu/gl_delegate.h"
#endif // !MEDIAPIPE_DISABLE_GPU
#if defined(__APPLE__) && !TARGET_OS_OSX // iOS
#if defined(MEDIAPIPE_IOS)
#import <CoreVideo/CoreVideo.h>
#import <Metal/Metal.h>
#import <MetalKit/MetalKit.h>
@ -56,17 +55,15 @@ constexpr int kNumCoordsPerBox = 4;
namespace mediapipe {
#if !defined(MEDIAPIPE_DISABLE_GPU) && !defined(__EMSCRIPTEN__) && \
!defined(__APPLE__)
#if !defined(MEDIAPIPE_DISABLE_GL_COMPUTE)
using ::tflite::gpu::gl::CreateReadWriteShaderStorageBuffer;
using ::tflite::gpu::gl::GlShader;
#endif
#if !defined(MEDIAPIPE_DISABLE_GPU) && !defined(__EMSCRIPTEN__) && \
!defined(__APPLE__)
#if !defined(MEDIAPIPE_DISABLE_GL_COMPUTE)
typedef ::tflite::gpu::gl::GlBuffer GpuTensor;
typedef ::tflite::gpu::gl::GlProgram GpuProgram;
#elif defined(__APPLE__) && !TARGET_OS_OSX // iOS
#elif defined(MEDIAPIPE_IOS)
typedef id<MTLBuffer> GpuTensor;
typedef id<MTLComputePipelineState> GpuProgram;
#endif
@ -183,11 +180,10 @@ class TfLiteTensorsToDetectionsCalculator : public CalculatorBase {
std::vector<Anchor> anchors_;
bool side_packet_anchors_{};
#if !defined(MEDIAPIPE_DISABLE_GPU) && !defined(__EMSCRIPTEN__) && \
!defined(__APPLE__)
#if !defined(MEDIAPIPE_DISABLE_GL_COMPUTE)
mediapipe::GlCalculatorHelper gpu_helper_;
std::unique_ptr<GPUData> gpu_data_;
#elif defined(__APPLE__) && !TARGET_OS_OSX // iOS
#elif defined(MEDIAPIPE_IOS)
MPPMetalHelper* gpu_helper_ = nullptr;
std::unique_ptr<GPUData> gpu_data_;
#endif
@ -226,10 +222,9 @@ REGISTER_CALCULATOR(TfLiteTensorsToDetectionsCalculator);
}
if (use_gpu) {
#if !defined(MEDIAPIPE_DISABLE_GPU) && !defined(__EMSCRIPTEN__) && \
!defined(__APPLE__)
#if !defined(MEDIAPIPE_DISABLE_GL_COMPUTE)
MP_RETURN_IF_ERROR(mediapipe::GlCalculatorHelper::UpdateContract(cc));
#elif defined(__APPLE__) && !TARGET_OS_OSX // iOS
#elif defined(MEDIAPIPE_IOS)
MP_RETURN_IF_ERROR([MPPMetalHelper updateContract:cc]);
#endif
}
@ -243,10 +238,9 @@ REGISTER_CALCULATOR(TfLiteTensorsToDetectionsCalculator);
if (cc->Inputs().HasTag("TENSORS_GPU")) {
gpu_input_ = true;
#if !defined(MEDIAPIPE_DISABLE_GPU) && !defined(__EMSCRIPTEN__) && \
!defined(__APPLE__)
#if !defined(MEDIAPIPE_DISABLE_GL_COMPUTE)
MP_RETURN_IF_ERROR(gpu_helper_.Open(cc));
#elif defined(__APPLE__) && !TARGET_OS_OSX // iOS
#elif defined(MEDIAPIPE_IOS)
gpu_helper_ = [[MPPMetalHelper alloc] initWithCalculatorContext:cc];
RET_CHECK(gpu_helper_);
#endif
@ -406,8 +400,7 @@ REGISTER_CALCULATOR(TfLiteTensorsToDetectionsCalculator);
}
::mediapipe::Status TfLiteTensorsToDetectionsCalculator::ProcessGPU(
CalculatorContext* cc, std::vector<Detection>* output_detections) {
#if !defined(MEDIAPIPE_DISABLE_GPU) && !defined(__EMSCRIPTEN__) && \
!defined(__APPLE__)
#if !defined(MEDIAPIPE_DISABLE_GL_COMPUTE)
const auto& input_tensors =
cc->Inputs().Tag("TENSORS_GPU").Get<std::vector<GpuTensor>>();
RET_CHECK_GE(input_tensors.size(), 2);
@ -470,7 +463,7 @@ REGISTER_CALCULATOR(TfLiteTensorsToDetectionsCalculator);
return ::mediapipe::OkStatus();
}));
#elif defined(__APPLE__) && !TARGET_OS_OSX // iOS
#elif defined(MEDIAPIPE_IOS)
const auto& input_tensors =
cc->Inputs().Tag("TENSORS_GPU").Get<std::vector<GpuTensor>>();
@ -569,12 +562,11 @@ REGISTER_CALCULATOR(TfLiteTensorsToDetectionsCalculator);
::mediapipe::Status TfLiteTensorsToDetectionsCalculator::Close(
CalculatorContext* cc) {
#if !defined(MEDIAPIPE_DISABLE_GPU) && !defined(__EMSCRIPTEN__) && \
!defined(__APPLE__)
#if !defined(MEDIAPIPE_DISABLE_GL_COMPUTE)
gpu_helper_.RunInGlContext([this] { gpu_data_.reset(); });
#elif defined(__APPLE__) && !TARGET_OS_OSX // iOS
#elif defined(MEDIAPIPE_IOS)
gpu_data_.reset();
#endif // !MEDIAPIPE_DISABLE_GPU
#endif
return ::mediapipe::OkStatus();
}
@ -723,8 +715,7 @@ Detection TfLiteTensorsToDetectionsCalculator::ConvertToDetection(
::mediapipe::Status TfLiteTensorsToDetectionsCalculator::GpuInit(
CalculatorContext* cc) {
#if !defined(MEDIAPIPE_DISABLE_GPU) && !defined(__EMSCRIPTEN__) && \
!defined(__APPLE__)
#if !defined(MEDIAPIPE_DISABLE_GL_COMPUTE)
MP_RETURN_IF_ERROR(gpu_helper_.RunInGlContext([this]()
-> ::mediapipe::Status {
gpu_data_ = absl::make_unique<GPUData>();
@ -937,8 +928,7 @@ void main() {
return ::mediapipe::OkStatus();
}));
#elif defined(__APPLE__) && !TARGET_OS_OSX // iOS
// TODO consolidate Metal and OpenGL shaders via vulkan.
#elif defined(MEDIAPIPE_IOS)
gpu_data_ = absl::make_unique<GPUData>();
id<MTLDevice> device = gpu_helper_.mtlDevice;
@ -1168,7 +1158,7 @@ kernel void scoreKernel(
CHECK_LT(num_classes_, max_wg_size) << "# classes must be <" << max_wg_size;
}
#endif // __ANDROID__ or iOS
#endif // !defined(MEDIAPIPE_DISABLE_GL_COMPUTE)
return ::mediapipe::OkStatus();
}

49
mediapipe/calculators/tflite/tflite_tensors_to_landmarks_calculator.cc
View File

@ -76,11 +76,11 @@ REGISTER_CALCULATOR(TfLiteTensorsToLandmarksCalculator);
}
if (cc->Outputs().HasTag("LANDMARKS")) {
cc->Outputs().Tag("LANDMARKS").Set<std::vector<Landmark>>();
cc->Outputs().Tag("LANDMARKS").Set<LandmarkList>();
}
if (cc->Outputs().HasTag("NORM_LANDMARKS")) {
cc->Outputs().Tag("NORM_LANDMARKS").Set<std::vector<NormalizedLandmark>>();
cc->Outputs().Tag("NORM_LANDMARKS").Set<NormalizedLandmarkList>();
}
return ::mediapipe::OkStatus();
@ -127,54 +127,55 @@ REGISTER_CALCULATOR(TfLiteTensorsToLandmarksCalculator);
const float* raw_landmarks = raw_tensor->data.f;
auto output_landmarks = absl::make_unique<std::vector<Landmark>>();
LandmarkList output_landmarks;
for (int ld = 0; ld < num_landmarks_; ++ld) {
const int offset = ld * num_dimensions;
Landmark landmark;
Landmark* landmark = output_landmarks.add_landmark();
if (options_.flip_horizontally()) {
landmark.set_x(options_.input_image_width() - raw_landmarks[offset]);
landmark->set_x(options_.input_image_width() - raw_landmarks[offset]);
} else {
landmark.set_x(raw_landmarks[offset]);
landmark->set_x(raw_landmarks[offset]);
}
if (num_dimensions > 1) {
if (options_.flip_vertically()) {
landmark.set_y(options_.input_image_height() -
raw_landmarks[offset + 1]);
landmark->set_y(options_.input_image_height() -
raw_landmarks[offset + 1]);
} else {
landmark.set_y(raw_landmarks[offset + 1]);
landmark->set_y(raw_landmarks[offset + 1]);
}
}
if (num_dimensions > 2) {
landmark.set_z(raw_landmarks[offset + 2]);
landmark->set_z(raw_landmarks[offset + 2]);
}
output_landmarks->push_back(landmark);
}
// Output normalized landmarks if required.
if (cc->Outputs().HasTag("NORM_LANDMARKS")) {
auto output_norm_landmarks =
absl::make_unique<std::vector<NormalizedLandmark>>();
for (const auto& landmark : *output_landmarks) {
NormalizedLandmark norm_landmark;
norm_landmark.set_x(static_cast<float>(landmark.x()) /
options_.input_image_width());
norm_landmark.set_y(static_cast<float>(landmark.y()) /
options_.input_image_height());
norm_landmark.set_z(landmark.z() / options_.normalize_z());
output_norm_landmarks->push_back(norm_landmark);
NormalizedLandmarkList output_norm_landmarks;
// for (const auto& landmark : output_landmarks) {
for (int i = 0; i < output_landmarks.landmark_size(); ++i) {
const Landmark& landmark = output_landmarks.landmark(i);
NormalizedLandmark* norm_landmark = output_norm_landmarks.add_landmark();
norm_landmark->set_x(static_cast<float>(landmark.x()) /
options_.input_image_width());
norm_landmark->set_y(static_cast<float>(landmark.y()) /
options_.input_image_height());
norm_landmark->set_z(landmark.z() / options_.normalize_z());
}
cc->Outputs()
.Tag("NORM_LANDMARKS")
.Add(output_norm_landmarks.release(), cc->InputTimestamp());
.AddPacket(MakePacket<NormalizedLandmarkList>(output_norm_landmarks)
.At(cc->InputTimestamp()));
}
// Output absolute landmarks.
if (cc->Outputs().HasTag("LANDMARKS")) {
cc->Outputs()
.Tag("LANDMARKS")
.Add(output_landmarks.release(), cc->InputTimestamp());
.AddPacket(MakePacket<LandmarkList>(output_landmarks)
.At(cc->InputTimestamp()));
}
return ::mediapipe::OkStatus();

39
mediapipe/calculators/tflite/tflite_tensors_to_segmentation_calculator.cc
View File

@ -28,8 +28,7 @@
#include "mediapipe/util/resource_util.h"
#include "tensorflow/lite/interpreter.h"
#if !defined(MEDIAPIPE_DISABLE_GPU) && !defined(__EMSCRIPTEN__) && \
!defined(__APPLE__)
#if !defined(MEDIAPIPE_DISABLE_GL_COMPUTE)
#include "mediapipe/gpu/gl_calculator_helper.h"
#include "mediapipe/gpu/gl_simple_shaders.h"
#include "mediapipe/gpu/shader_util.h"
@ -54,8 +53,7 @@ float Clamp(float val, float min, float max) {
namespace mediapipe {
#if !defined(MEDIAPIPE_DISABLE_GPU) && !defined(__EMSCRIPTEN__) && \
!defined(__APPLE__)
#if !defined(MEDIAPIPE_DISABLE_GL_COMPUTE)
using ::tflite::gpu::gl::CopyBuffer;
using ::tflite::gpu::gl::CreateReadWriteRgbaImageTexture;
using ::tflite::gpu::gl::CreateReadWriteShaderStorageBuffer;
@ -131,8 +129,7 @@ class TfLiteTensorsToSegmentationCalculator : public CalculatorBase {
int tensor_channels_ = 0;
bool use_gpu_ = false;
#if !defined(MEDIAPIPE_DISABLE_GPU) && !defined(__EMSCRIPTEN__) && \
!defined(__APPLE__)
#if !defined(MEDIAPIPE_DISABLE_GL_COMPUTE)
mediapipe::GlCalculatorHelper gpu_helper_;
std::unique_ptr<GlProgram> mask_program_with_prev_;
std::unique_ptr<GlProgram> mask_program_no_prev_;
@ -162,8 +159,7 @@ REGISTER_CALCULATOR(TfLiteTensorsToSegmentationCalculator);
}
// Inputs GPU.
#if !defined(MEDIAPIPE_DISABLE_GPU) && !defined(__EMSCRIPTEN__) && \
!defined(__APPLE__)
#if !defined(MEDIAPIPE_DISABLE_GL_COMPUTE)
if (cc->Inputs().HasTag("TENSORS_GPU")) {
cc->Inputs().Tag("TENSORS_GPU").Set<std::vector<GlBuffer>>();
use_gpu |= true;
@ -182,8 +178,7 @@ REGISTER_CALCULATOR(TfLiteTensorsToSegmentationCalculator);
if (cc->Outputs().HasTag("MASK")) {
cc->Outputs().Tag("MASK").Set<ImageFrame>();
}
#if !defined(MEDIAPIPE_DISABLE_GPU) && !defined(__EMSCRIPTEN__) && \
!defined(__APPLE__)
#if !defined(MEDIAPIPE_DISABLE_GL_COMPUTE)
if (cc->Outputs().HasTag("MASK_GPU")) {
cc->Outputs().Tag("MASK_GPU").Set<mediapipe::GpuBuffer>();
use_gpu |= true;
@ -191,8 +186,7 @@ REGISTER_CALCULATOR(TfLiteTensorsToSegmentationCalculator);
#endif // !MEDIAPIPE_DISABLE_GPU
if (use_gpu) {
#if !defined(MEDIAPIPE_DISABLE_GPU) && !defined(__EMSCRIPTEN__) && \
!defined(__APPLE__)
#if !defined(MEDIAPIPE_DISABLE_GL_COMPUTE)
MP_RETURN_IF_ERROR(mediapipe::GlCalculatorHelper::UpdateContract(cc));
#endif // !MEDIAPIPE_DISABLE_GPU
}
@ -205,8 +199,7 @@ REGISTER_CALCULATOR(TfLiteTensorsToSegmentationCalculator);
if (cc->Inputs().HasTag("TENSORS_GPU")) {
use_gpu_ = true;
#if !defined(MEDIAPIPE_DISABLE_GPU) && !defined(__EMSCRIPTEN__) && \
!defined(__APPLE__)
#if !defined(MEDIAPIPE_DISABLE_GL_COMPUTE)
MP_RETURN_IF_ERROR(gpu_helper_.Open(cc));
#endif // !MEDIAPIPE_DISABLE_GPU
}
@ -214,8 +207,7 @@ REGISTER_CALCULATOR(TfLiteTensorsToSegmentationCalculator);
MP_RETURN_IF_ERROR(LoadOptions(cc));
if (use_gpu_) {
#if !defined(MEDIAPIPE_DISABLE_GPU) && !defined(__EMSCRIPTEN__) && \
!defined(__APPLE__)
#if !defined(MEDIAPIPE_DISABLE_GL_COMPUTE)
MP_RETURN_IF_ERROR(
gpu_helper_.RunInGlContext([this, cc]() -> ::mediapipe::Status {
MP_RETURN_IF_ERROR(InitGpu(cc));
@ -232,8 +224,7 @@ REGISTER_CALCULATOR(TfLiteTensorsToSegmentationCalculator);
::mediapipe::Status TfLiteTensorsToSegmentationCalculator::Process(
CalculatorContext* cc) {
if (use_gpu_) {
#if !defined(MEDIAPIPE_DISABLE_GPU) && !defined(__EMSCRIPTEN__) && \
!defined(__APPLE__)
#if !defined(MEDIAPIPE_DISABLE_GL_COMPUTE)
MP_RETURN_IF_ERROR(
gpu_helper_.RunInGlContext([this, cc]() -> ::mediapipe::Status {
MP_RETURN_IF_ERROR(ProcessGpu(cc));
@ -249,8 +240,7 @@ REGISTER_CALCULATOR(TfLiteTensorsToSegmentationCalculator);
::mediapipe::Status TfLiteTensorsToSegmentationCalculator::Close(
CalculatorContext* cc) {
#if !defined(MEDIAPIPE_DISABLE_GPU) && !defined(__EMSCRIPTEN__) && \
!defined(__APPLE__)
#if !defined(MEDIAPIPE_DISABLE_GL_COMPUTE)
gpu_helper_.RunInGlContext([this] {
if (upsample_program_) glDeleteProgram(upsample_program_);
upsample_program_ = 0;
@ -377,8 +367,7 @@ REGISTER_CALCULATOR(TfLiteTensorsToSegmentationCalculator);
if (cc->Inputs().Tag("TENSORS_GPU").IsEmpty()) {
return ::mediapipe::OkStatus();
}
#if !defined(MEDIAPIPE_DISABLE_GPU) && !defined(__EMSCRIPTEN__) && \
!defined(__APPLE__)
#if !defined(MEDIAPIPE_DISABLE_GL_COMPUTE)
// Get input streams.
const auto& input_tensors =
cc->Inputs().Tag("TENSORS_GPU").Get<std::vector<GlBuffer>>();
@ -464,8 +453,7 @@ REGISTER_CALCULATOR(TfLiteTensorsToSegmentationCalculator);
}
void TfLiteTensorsToSegmentationCalculator::GlRender() {
#if !defined(MEDIAPIPE_DISABLE_GPU) && !defined(__EMSCRIPTEN__) && \
!defined(__APPLE__)
#if !defined(MEDIAPIPE_DISABLE_GL_COMPUTE)
static const GLfloat square_vertices[] = {
-1.0f, -1.0f, // bottom left
1.0f, -1.0f, // bottom right
@ -537,8 +525,7 @@ void TfLiteTensorsToSegmentationCalculator::GlRender() {
::mediapipe::Status TfLiteTensorsToSegmentationCalculator::InitGpu(
CalculatorContext* cc) {
#if !defined(MEDIAPIPE_DISABLE_GPU) && !defined(__EMSCRIPTEN__) && \
!defined(__APPLE__)
#if !defined(MEDIAPIPE_DISABLE_GL_COMPUTE)
MP_RETURN_IF_ERROR(gpu_helper_.RunInGlContext([this]()
-> ::mediapipe::Status {
// A shader to process a segmentation tensor into an output mask,

3
mediapipe/calculators/util/detection_label_id_to_text_calculator.cc
View File

@ -19,8 +19,7 @@
#include "mediapipe/framework/port/status.h"
#include "mediapipe/util/resource_util.h"
#if defined(MEDIAPIPE_LITE) || defined(__EMSCRIPTEN__) || \
defined(__ANDROID__) || (defined(__APPLE__) && !TARGET_OS_OSX)
#if defined(MEDIAPIPE_MOBILE)
#include "mediapipe/util/android/file/base/file.h"
#include "mediapipe/util/android/file/base/helpers.h"
#else

6
mediapipe/calculators/util/filter_collection_calculator.cc
View File

@ -27,8 +27,8 @@ typedef FilterCollectionCalculator<std::vector<::mediapipe::NormalizedRect>>
REGISTER_CALCULATOR(FilterNormalizedRectCollectionCalculator);
typedef FilterCollectionCalculator<
std::vector<std::vector<::mediapipe::NormalizedLandmark>>>
FilterLandmarksCollectionCalculator;
REGISTER_CALCULATOR(FilterLandmarksCollectionCalculator);
std::vector<::mediapipe::NormalizedLandmarkList>>
FilterLandmarkListCollectionCalculator;
REGISTER_CALCULATOR(FilterLandmarkListCollectionCalculator);
} // namespace mediapipe

1
mediapipe/calculators/util/labels_to_render_data_calculator.cc
View File

@ -93,6 +93,7 @@ REGISTER_CALCULATOR(LabelsToRenderDataCalculator);
}
::mediapipe::Status LabelsToRenderDataCalculator::Open(CalculatorContext* cc) {
cc->SetOffset(TimestampDiff(0));
options_ = cc->Options<LabelsToRenderDataCalculatorOptions>();
num_colors_ = options_.color_size();
label_height_px_ = std::ceil(options_.font_height_px() * kFontHeightScale);

31
mediapipe/calculators/util/landmark_letterbox_removal_calculator.cc
View File

@ -49,7 +49,7 @@ constexpr char kLetterboxPaddingTag[] = "LETTERBOX_PADDING";
// corresponding input image before letterboxing.
//
// Input:
// LANDMARKS: An std::vector<NormalizedLandmark> representing landmarks on an
// LANDMARKS: A NormalizedLandmarkList representing landmarks on an
// letterboxed image.
//
// LETTERBOX_PADDING: An std::array<float, 4> representing the letterbox
@ -57,7 +57,7 @@ constexpr char kLetterboxPaddingTag[] = "LETTERBOX_PADDING";
// image, normalized to [0.f, 1.f] by the letterboxed image dimensions.
//
// Output:
// LANDMARKS: An std::vector<NormalizedLandmark> representing landmarks with
// LANDMARKS: An NormalizedLandmarkList proto representing landmarks with
// their locations adjusted to the letterbox-removed (non-padded) image.
//
// Usage example:
@ -74,10 +74,10 @@ class LandmarkLetterboxRemovalCalculator : public CalculatorBase {
cc->Inputs().HasTag(kLetterboxPaddingTag))
<< "Missing one or more input streams.";
cc->Inputs().Tag(kLandmarksTag).Set<std::vector<NormalizedLandmark>>();
cc->Inputs().Tag(kLandmarksTag).Set<NormalizedLandmarkList>();
cc->Inputs().Tag(kLetterboxPaddingTag).Set<std::array<float, 4>>();
cc->Outputs().Tag(kLandmarksTag).Set<std::vector<NormalizedLandmark>>();
cc->Outputs().Tag(kLandmarksTag).Set<NormalizedLandmarkList>();
return ::mediapipe::OkStatus();
}
@ -94,8 +94,8 @@ class LandmarkLetterboxRemovalCalculator : public CalculatorBase {
return ::mediapipe::OkStatus();
}
const auto& input_landmarks =
cc->Inputs().Tag(kLandmarksTag).Get<std::vector<NormalizedLandmark>>();
const NormalizedLandmarkList& input_landmarks =
cc->Inputs().Tag(kLandmarksTag).Get<NormalizedLandmarkList>();
const auto& letterbox_padding =
cc->Inputs().Tag(kLetterboxPaddingTag).Get<std::array<float, 4>>();
@ -104,24 +104,23 @@ class LandmarkLetterboxRemovalCalculator : public CalculatorBase {
const float left_and_right = letterbox_padding[0] + letterbox_padding[2];
const float top_and_bottom = letterbox_padding[1] + letterbox_padding[3];
auto output_landmarks =
absl::make_unique<std::vector<NormalizedLandmark>>();
for (const auto& landmark : input_landmarks) {
NormalizedLandmark new_landmark;
NormalizedLandmarkList output_landmarks;
for (int i = 0; i < input_landmarks.landmark_size(); ++i) {
const NormalizedLandmark& landmark = input_landmarks.landmark(i);
NormalizedLandmark* new_landmark = output_landmarks.add_landmark();
const float new_x = (landmark.x() - left) / (1.0f - left_and_right);
const float new_y = (landmark.y() - top) / (1.0f - top_and_bottom);
new_landmark.set_x(new_x);
new_landmark.set_y(new_y);
new_landmark->set_x(new_x);
new_landmark->set_y(new_y);
// Keep z-coord as is.
new_landmark.set_z(landmark.z());
output_landmarks->emplace_back(new_landmark);
new_landmark->set_z(landmark.z());
}
cc->Outputs()
.Tag(kLandmarksTag)
.Add(output_landmarks.release(), cc->InputTimestamp());
.AddPacket(MakePacket<NormalizedLandmarkList>(output_landmarks)
.At(cc->InputTimestamp()));
return ::mediapipe::OkStatus();
}
};

53
mediapipe/calculators/util/landmark_letterbox_removal_calculator_test.cc
View File

@ -43,10 +43,10 @@ CalculatorGraphConfig::Node GetDefaultNode() {
TEST(LandmarkLetterboxRemovalCalculatorTest, PaddingLeftRight) {
CalculatorRunner runner(GetDefaultNode());
auto landmarks = absl::make_unique<std::vector<NormalizedLandmark>>();
landmarks->push_back(CreateLandmark(0.5f, 0.5f));
landmarks->push_back(CreateLandmark(0.2f, 0.2f));
landmarks->push_back(CreateLandmark(0.7f, 0.7f));
auto landmarks = absl::make_unique<NormalizedLandmarkList>();
*landmarks->add_landmark() = CreateLandmark(0.5f, 0.5f);
*landmarks->add_landmark() = CreateLandmark(0.2f, 0.2f);
*landmarks->add_landmark() = CreateLandmark(0.7f, 0.7f);
runner.MutableInputs()
->Tag("LANDMARKS")
.packets.push_back(
@ -61,26 +61,28 @@ TEST(LandmarkLetterboxRemovalCalculatorTest, PaddingLeftRight) {
MP_ASSERT_OK(runner.Run()) << "Calculator execution failed.";
const std::vector<Packet>& output = runner.Outputs().Tag("LANDMARKS").packets;
ASSERT_EQ(1, output.size());
const auto& output_landmarks =
output[0].Get<std::vector<NormalizedLandmark>>();
const auto& output_landmarks = output[0].Get<NormalizedLandmarkList>();
EXPECT_EQ(output_landmarks.size(), 3);
EXPECT_EQ(output_landmarks.landmark_size(), 3);
EXPECT_THAT(output_landmarks[0].x(), testing::FloatNear(0.6f, 1e-5));
EXPECT_THAT(output_landmarks[0].y(), testing::FloatNear(0.5f, 1e-5));
EXPECT_THAT(output_landmarks[1].x(), testing::FloatNear(0.0f, 1e-5));
EXPECT_THAT(output_landmarks[1].y(), testing::FloatNear(0.2f, 1e-5));
EXPECT_THAT(output_landmarks[2].x(), testing::FloatNear(1.0f, 1e-5));
EXPECT_THAT(output_landmarks[2].y(), testing::FloatNear(0.7f, 1e-5));
EXPECT_THAT(output_landmarks.landmark(0).x(), testing::FloatNear(0.6f, 1e-5));
EXPECT_THAT(output_landmarks.landmark(0).y(), testing::FloatNear(0.5f, 1e-5));
EXPECT_THAT(output_landmarks.landmark(1).x(), testing::FloatNear(0.0f, 1e-5));
EXPECT_THAT(output_landmarks.landmark(1).y(), testing::FloatNear(0.2f, 1e-5));
EXPECT_THAT(output_landmarks.landmark(2).x(), testing::FloatNear(1.0f, 1e-5));
EXPECT_THAT(output_landmarks.landmark(2).y(), testing::FloatNear(0.7f, 1e-5));
}
TEST(LandmarkLetterboxRemovalCalculatorTest, PaddingTopBottom) {
CalculatorRunner runner(GetDefaultNode());
auto landmarks = absl::make_unique<std::vector<NormalizedLandmark>>();
landmarks->push_back(CreateLandmark(0.5f, 0.5f));
landmarks->push_back(CreateLandmark(0.2f, 0.2f));
landmarks->push_back(CreateLandmark(0.7f, 0.7f));
auto landmarks = absl::make_unique<NormalizedLandmarkList>();
NormalizedLandmark* landmark = landmarks->add_landmark();
*landmark = CreateLandmark(0.5f, 0.5f);
landmark = landmarks->add_landmark();
*landmark = CreateLandmark(0.2f, 0.2f);
landmark = landmarks->add_landmark();
*landmark = CreateLandmark(0.7f, 0.7f);
runner.MutableInputs()
->Tag("LANDMARKS")
.packets.push_back(
@ -95,17 +97,16 @@ TEST(LandmarkLetterboxRemovalCalculatorTest, PaddingTopBottom) {
MP_ASSERT_OK(runner.Run()) << "Calculator execution failed.";
const std::vector<Packet>& output = runner.Outputs().Tag("LANDMARKS").packets;
ASSERT_EQ(1, output.size());
const auto& output_landmarks =
output[0].Get<std::vector<NormalizedLandmark>>();
const auto& output_landmarks = output[0].Get<NormalizedLandmarkList>();
EXPECT_EQ(output_landmarks.size(), 3);
EXPECT_EQ(output_landmarks.landmark_size(), 3);
EXPECT_THAT(output_landmarks[0].x(), testing::FloatNear(0.5f, 1e-5));
EXPECT_THAT(output_landmarks[0].y(), testing::FloatNear(0.6f, 1e-5));
EXPECT_THAT(output_landmarks[1].x(), testing::FloatNear(0.2f, 1e-5));
EXPECT_THAT(output_landmarks[1].y(), testing::FloatNear(0.0f, 1e-5));
EXPECT_THAT(output_landmarks[2].x(), testing::FloatNear(0.7f, 1e-5));
EXPECT_THAT(output_landmarks[2].y(), testing::FloatNear(1.0f, 1e-5));
EXPECT_THAT(output_landmarks.landmark(0).x(), testing::FloatNear(0.5f, 1e-5));
EXPECT_THAT(output_landmarks.landmark(0).y(), testing::FloatNear(0.6f, 1e-5));
EXPECT_THAT(output_landmarks.landmark(1).x(), testing::FloatNear(0.2f, 1e-5));
EXPECT_THAT(output_landmarks.landmark(1).y(), testing::FloatNear(0.0f, 1e-5));
EXPECT_THAT(output_landmarks.landmark(2).x(), testing::FloatNear(0.7f, 1e-5));
EXPECT_THAT(output_landmarks.landmark(2).y(), testing::FloatNear(1.0f, 1e-5));
}
} // namespace mediapipe

31
mediapipe/calculators/util/landmark_projection_calculator.cc
View File

@ -47,13 +47,13 @@ constexpr char kRectTag[] = "NORM_RECT";
// Projects normalized landmarks in a rectangle to its original coordinates. The
// rectangle must also be in normalized coordinates.
// Input:
// NORM_LANDMARKS: An std::vector<NormalizedLandmark> representing landmarks
// NORM_LANDMARKS: A NormalizedLandmarkList representing landmarks
// in a normalized rectangle.
// NORM_RECT: An NormalizedRect representing a normalized rectangle in image
// coordinates.
//
// Output:
// NORM_LANDMARKS: An std::vector<NormalizedLandmark> representing landmarks
// NORM_LANDMARKS: A NormalizedLandmarkList representing landmarks
// with their locations adjusted to the image.
//
// Usage example:
@ -70,10 +70,10 @@ class LandmarkProjectionCalculator : public CalculatorBase {
cc->Inputs().HasTag(kRectTag))
<< "Missing one or more input streams.";
cc->Inputs().Tag(kLandmarksTag).Set<std::vector<NormalizedLandmark>>();
cc->Inputs().Tag(kLandmarksTag).Set<NormalizedLandmarkList>();
cc->Inputs().Tag(kRectTag).Set<NormalizedRect>();
cc->Outputs().Tag(kLandmarksTag).Set<std::vector<NormalizedLandmark>>();
cc->Outputs().Tag(kLandmarksTag).Set<NormalizedLandmarkList>();
return ::mediapipe::OkStatus();
}
@ -92,14 +92,14 @@ class LandmarkProjectionCalculator : public CalculatorBase {
return ::mediapipe::OkStatus();
}
const auto& input_landmarks =
cc->Inputs().Tag(kLandmarksTag).Get<std::vector<NormalizedLandmark>>();
const NormalizedLandmarkList& input_landmarks =
cc->Inputs().Tag(kLandmarksTag).Get<NormalizedLandmarkList>();
const auto& input_rect = cc->Inputs().Tag(kRectTag).Get<NormalizedRect>();
auto output_landmarks =
absl::make_unique<std::vector<NormalizedLandmark>>();
for (const auto& landmark : input_landmarks) {
NormalizedLandmark new_landmark;
NormalizedLandmarkList output_landmarks;
for (int i = 0; i < input_landmarks.landmark_size(); ++i) {
const NormalizedLandmark& landmark = input_landmarks.landmark(i);
NormalizedLandmark* new_landmark = output_landmarks.add_landmark();
const float x = landmark.x() - 0.5f;
const float y = landmark.y() - 0.5f;
@ -110,17 +110,16 @@ class LandmarkProjectionCalculator : public CalculatorBase {
new_x = new_x * input_rect.width() + input_rect.x_center();
new_y = new_y * input_rect.height() + input_rect.y_center();
new_landmark.set_x(new_x);
new_landmark.set_y(new_y);
new_landmark->set_x(new_x);
new_landmark->set_y(new_y);
// Keep z-coord as is.
new_landmark.set_z(landmark.z());
output_landmarks->emplace_back(new_landmark);
new_landmark->set_z(landmark.z());
}
cc->Outputs()
.Tag(kLandmarksTag)
.Add(output_landmarks.release(), cc->InputTimestamp());
.AddPacket(MakePacket<NormalizedLandmarkList>(output_landmarks)
.At(cc->InputTimestamp()));
return ::mediapipe::OkStatus();
}
};

31
mediapipe/calculators/util/landmarks_to_detection_calculator.cc
View File

@ -28,8 +28,7 @@ namespace {
constexpr char kDetectionTag[] = "DETECTION";
constexpr char kNormalizedLandmarksTag[] = "NORM_LANDMARKS";
Detection ConvertLandmarksToDetection(
const std::vector<NormalizedLandmark>& landmarks) {
Detection ConvertLandmarksToDetection(const NormalizedLandmarkList& landmarks) {
Detection detection;
LocationData* location_data = detection.mutable_location_data();
@ -37,7 +36,8 @@ Detection ConvertLandmarksToDetection(
float x_max = std::numeric_limits<float>::min();
float y_min = std::numeric_limits<float>::max();
float y_max = std::numeric_limits<float>::min();
for (const auto& landmark : landmarks) {
for (int i = 0; i < landmarks.landmark_size(); ++i) {
const NormalizedLandmark& landmark = landmarks.landmark(i);
x_min = std::min(x_min, landmark.x());
x_max = std::max(x_max, landmark.x());
y_min = std::min(y_min, landmark.y());
@ -67,7 +67,7 @@ Detection ConvertLandmarksToDetection(
// to specify a subset of landmarks for creating the detection.
//
// Input:
// NOMR_LANDMARKS: A vector of NormalizedLandmark.
// NOMR_LANDMARKS: A NormalizedLandmarkList proto.
//
// Output:
// DETECTION: A Detection proto.
@ -95,9 +95,7 @@ REGISTER_CALCULATOR(LandmarksToDetectionCalculator);
RET_CHECK(cc->Inputs().HasTag(kNormalizedLandmarksTag));
RET_CHECK(cc->Outputs().HasTag(kDetectionTag));
// TODO: Also support converting Landmark to Detection.
cc->Inputs()
.Tag(kNormalizedLandmarksTag)
.Set<std::vector<NormalizedLandmark>>();
cc->Inputs().Tag(kNormalizedLandmarksTag).Set<NormalizedLandmarkList>();
cc->Outputs().Tag(kDetectionTag).Set<Detection>();
return ::mediapipe::OkStatus();
@ -113,19 +111,20 @@ REGISTER_CALCULATOR(LandmarksToDetectionCalculator);
::mediapipe::Status LandmarksToDetectionCalculator::Process(
CalculatorContext* cc) {
const auto& landmarks = cc->Inputs()
.Tag(kNormalizedLandmarksTag)
.Get<std::vector<NormalizedLandmark>>();
RET_CHECK_GT(landmarks.size(), 0) << "Input landmark vector is empty.";
const auto& landmarks =
cc->Inputs().Tag(kNormalizedLandmarksTag).Get<NormalizedLandmarkList>();
RET_CHECK_GT(landmarks.landmark_size(), 0)
<< "Input landmark vector is empty.";
auto detection = absl::make_unique<Detection>();
if (options_.selected_landmark_indices_size()) {
std::vector<NormalizedLandmark> subset_landmarks(
options_.selected_landmark_indices_size());
for (int i = 0; i < subset_landmarks.size(); ++i) {
RET_CHECK_LT(options_.selected_landmark_indices(i), landmarks.size())
NormalizedLandmarkList subset_landmarks;
for (int i = 0; i < options_.selected_landmark_indices_size(); ++i) {
RET_CHECK_LT(options_.selected_landmark_indices(i),
landmarks.landmark_size())
<< "Index of landmark subset is out of range.";
subset_landmarks[i] = landmarks[options_.selected_landmark_indices(i)];
*subset_landmarks.add_landmark() =
landmarks.landmark(options_.selected_landmark_indices(i));
}
*detection = ConvertLandmarksToDetection(subset_landmarks);
} else {

19
mediapipe/calculators/util/landmarks_to_floats_calculator.cc
View File

@ -48,7 +48,7 @@ constexpr char kMatrixTag[] = "MATRIX";
// Converts a vector of landmarks to a vector of floats or a matrix.
// Input:
// NORM_LANDMARKS: An std::vector<NormalizedLandmark>.
// NORM_LANDMARKS: A NormalizedLandmarkList proto.
//
// Output:
// FLOATS(optional): A vector of floats from flattened landmarks.
@ -63,7 +63,7 @@ constexpr char kMatrixTag[] = "MATRIX";
class LandmarksToFloatsCalculator : public CalculatorBase {
public:
static ::mediapipe::Status GetContract(CalculatorContract* cc) {
cc->Inputs().Tag(kLandmarksTag).Set<std::vector<NormalizedLandmark>>();
cc->Inputs().Tag(kLandmarksTag).Set<NormalizedLandmarkList>();
RET_CHECK(cc->Outputs().HasTag(kFloatsTag) ||
cc->Outputs().HasTag(kMatrixTag));
if (cc->Outputs().HasTag(kFloatsTag)) {
@ -94,11 +94,12 @@ class LandmarksToFloatsCalculator : public CalculatorBase {
}
const auto& input_landmarks =
cc->Inputs().Tag(kLandmarksTag).Get<std::vector<NormalizedLandmark>>();
cc->Inputs().Tag(kLandmarksTag).Get<NormalizedLandmarkList>();
if (cc->Outputs().HasTag(kFloatsTag)) {
auto output_floats = absl::make_unique<std::vector<float>>();
for (const auto& landmark : input_landmarks) {
for (int i = 0; i < input_landmarks.landmark_size(); ++i) {
const NormalizedLandmark& landmark = input_landmarks.landmark(i);
output_floats->emplace_back(landmark.x());
if (num_dimensions_ > 1) {
output_floats->emplace_back(landmark.y());
@ -113,14 +114,14 @@ class LandmarksToFloatsCalculator : public CalculatorBase {
.Add(output_floats.release(), cc->InputTimestamp());
} else {
auto output_matrix = absl::make_unique<Matrix>();
output_matrix->setZero(num_dimensions_, input_landmarks.size());
for (int i = 0; i < input_landmarks.size(); ++i) {
(*output_matrix)(0, i) = input_landmarks[i].x();
output_matrix->setZero(num_dimensions_, input_landmarks.landmark_size());
for (int i = 0; i < input_landmarks.landmark_size(); ++i) {
(*output_matrix)(0, i) = input_landmarks.landmark(i).x();
if (num_dimensions_ > 1) {
(*output_matrix)(1, i) = input_landmarks[i].y();
(*output_matrix)(1, i) = input_landmarks.landmark(i).y();
}
if (num_dimensions_ > 2) {
(*output_matrix)(2, i) = input_landmarks[i].z();
(*output_matrix)(2, i) = input_landmarks.landmark(i).z();
}
}
cc->Outputs()

73
mediapipe/calculators/util/landmarks_to_render_data_calculator.cc
View File

@ -46,12 +46,13 @@ inline float Remap(float x, float lo, float hi, float scale) {
return (x - lo) / (hi - lo + 1e-6) * scale;
}
template <class LandmarkType>
inline void GetMinMaxZ(const std::vector<LandmarkType>& landmarks, float* z_min,
template <class LandmarkListType, class LandmarkType>
inline void GetMinMaxZ(const LandmarkListType& landmarks, float* z_min,
float* z_max) {
*z_min = std::numeric_limits<float>::max();
*z_max = std::numeric_limits<float>::min();
for (const auto& landmark : landmarks) {
for (int i = 0; i < landmarks.landmark_size(); ++i) {
const LandmarkType& landmark = landmarks.landmark(i);
*z_min = std::min(landmark.z(), *z_min);
*z_max = std::max(landmark.z(), *z_max);
}
@ -73,7 +74,7 @@ void SetColorSizeValueFromZ(float z, float z_min, float z_max,
} // namespace
// A calculator that converts Landmark proto to RenderData proto for
// visualization. The input should be std::vector<Landmark>. It is also possible
// visualization. The input should be LandmarkList proto. It is also possible
// to specify the connections between landmarks.
//
// Example config:
@ -121,11 +122,11 @@ class LandmarksToRenderDataCalculator : public CalculatorBase {
const LandmarksToRenderDataCalculatorOptions& options, bool normalized,
int gray_val1, int gray_val2, RenderData* render_data);
template <class LandmarkType>
void AddConnections(const std::vector<LandmarkType>& landmarks,
bool normalized, RenderData* render_data);
template <class LandmarkType>
void AddConnectionsWithDepth(const std::vector<LandmarkType>& landmarks,
template <class LandmarkListType>
void AddConnections(const LandmarkListType& landmarks, bool normalized,
RenderData* render_data);
template <class LandmarkListType>
void AddConnectionsWithDepth(const LandmarkListType& landmarks,
bool normalized, float min_z, float max_z,
RenderData* render_data);
@ -144,10 +145,10 @@ REGISTER_CALCULATOR(LandmarksToRenderDataCalculator);
"normalized landmarks.";
if (cc->Inputs().HasTag(kLandmarksTag)) {
cc->Inputs().Tag(kLandmarksTag).Set<std::vector<Landmark>>();
cc->Inputs().Tag(kLandmarksTag).Set<LandmarkList>();
}
if (cc->Inputs().HasTag(kNormLandmarksTag)) {
cc->Inputs().Tag(kNormLandmarksTag).Set<std::vector<NormalizedLandmark>>();
cc->Inputs().Tag(kNormLandmarksTag).Set<NormalizedLandmarkList>();
}
cc->Outputs().Tag(kRenderDataTag).Set<RenderData>();
return ::mediapipe::OkStatus();
@ -169,16 +170,17 @@ REGISTER_CALCULATOR(LandmarksToRenderDataCalculator);
float z_max = 0.f;
if (cc->Inputs().HasTag(kLandmarksTag)) {
const auto& landmarks =
cc->Inputs().Tag(kLandmarksTag).Get<std::vector<Landmark>>();
const LandmarkList& landmarks =
cc->Inputs().Tag(kLandmarksTag).Get<LandmarkList>();
RET_CHECK_EQ(options_.landmark_connections_size() % 2, 0)
<< "Number of entries in landmark connections must be a multiple of 2";
if (visualize_depth) {
GetMinMaxZ(landmarks, &z_min, &z_max);
GetMinMaxZ<LandmarkList, Landmark>(landmarks, &z_min, &z_max);
}
// Only change rendering if there are actually z values other than 0.
visualize_depth &= ((z_max - z_min) > 1e-3);
for (const auto& landmark : landmarks) {
for (int i = 0; i < landmarks.landmark_size(); ++i) {
const Landmark& landmark = landmarks.landmark(i);
auto* landmark_data_render =
AddPointRenderData(options_, render_data.get());
if (visualize_depth) {
@ -191,25 +193,27 @@ REGISTER_CALCULATOR(LandmarksToRenderDataCalculator);
landmark_data->set_y(landmark.y());
}
if (visualize_depth) {
AddConnectionsWithDepth(landmarks, /*normalized=*/false, z_min, z_max,
render_data.get());
AddConnectionsWithDepth<LandmarkList>(landmarks, /*normalized=*/false,
z_min, z_max, render_data.get());
} else {
AddConnections(landmarks, /*normalized=*/false, render_data.get());
AddConnections<LandmarkList>(landmarks, /*normalized=*/false,
render_data.get());
}
}
if (cc->Inputs().HasTag(kNormLandmarksTag)) {
const auto& landmarks = cc->Inputs()
.Tag(kNormLandmarksTag)
.Get<std::vector<NormalizedLandmark>>();
const NormalizedLandmarkList& landmarks =
cc->Inputs().Tag(kNormLandmarksTag).Get<NormalizedLandmarkList>();
RET_CHECK_EQ(options_.landmark_connections_size() % 2, 0)
<< "Number of entries in landmark connections must be a multiple of 2";
if (visualize_depth) {
GetMinMaxZ(landmarks, &z_min, &z_max);
GetMinMaxZ<NormalizedLandmarkList, NormalizedLandmark>(landmarks, &z_min,
&z_max);
}
// Only change rendering if there are actually z values other than 0.
visualize_depth &= ((z_max - z_min) > 1e-3);
for (const auto& landmark : landmarks) {
for (int i = 0; i < landmarks.landmark_size(); ++i) {
const NormalizedLandmark& landmark = landmarks.landmark(i);
auto* landmark_data_render =
AddPointRenderData(options_, render_data.get());
if (visualize_depth) {
@ -222,10 +226,11 @@ REGISTER_CALCULATOR(LandmarksToRenderDataCalculator);
landmark_data->set_y(landmark.y());
}
if (visualize_depth) {
AddConnectionsWithDepth(landmarks, /*normalized=*/true, z_min, z_max,
render_data.get());
AddConnectionsWithDepth<NormalizedLandmarkList>(
landmarks, /*normalized=*/true, z_min, z_max, render_data.get());
} else {
AddConnections(landmarks, /*normalized=*/true, render_data.get());
AddConnections<NormalizedLandmarkList>(landmarks, /*normalized=*/true,
render_data.get());
}
}
@ -235,13 +240,13 @@ REGISTER_CALCULATOR(LandmarksToRenderDataCalculator);
return ::mediapipe::OkStatus();
}
template <class LandmarkType>
template <class LandmarkListType>
void LandmarksToRenderDataCalculator::AddConnectionsWithDepth(
const std::vector<LandmarkType>& landmarks, bool normalized, float min_z,
const LandmarkListType& landmarks, bool normalized, float min_z,
float max_z, RenderData* render_data) {
for (int i = 0; i < options_.landmark_connections_size(); i += 2) {
const auto& ld0 = landmarks[options_.landmark_connections(i)];
const auto& ld1 = landmarks[options_.landmark_connections(i + 1)];
const auto& ld0 = landmarks.landmark(options_.landmark_connections(i));
const auto& ld1 = landmarks.landmark(options_.landmark_connections(i + 1));
const int gray_val1 =
255 - static_cast<int>(Remap(ld0.z(), min_z, max_z, 255));
const int gray_val2 =
@ -272,13 +277,13 @@ void LandmarksToRenderDataCalculator::AddConnectionToRenderData(
connection_annotation->set_thickness(options.thickness());
}
template <class LandmarkType>
template <class LandmarkListType>
void LandmarksToRenderDataCalculator::AddConnections(
const std::vector<LandmarkType>& landmarks, bool normalized,
const LandmarkListType& landmarks, bool normalized,
RenderData* render_data) {
for (int i = 0; i < options_.landmark_connections_size(); i += 2) {
const auto& ld0 = landmarks[options_.landmark_connections(i)];
const auto& ld1 = landmarks[options_.landmark_connections(i + 1)];
const auto& ld0 = landmarks.landmark(options_.landmark_connections(i));
const auto& ld1 = landmarks.landmark(options_.landmark_connections(i + 1));
AddConnectionToRenderData(ld0.x(), ld0.y(), ld1.x(), ld1.y(), options_,
normalized, render_data);
}

3
mediapipe/calculators/util/top_k_scores_calculator.cc
View File

@ -29,8 +29,7 @@
#include "mediapipe/framework/port/statusor.h"
#include "mediapipe/util/resource_util.h"
#if defined(MEDIAPIPE_LITE) || defined(__EMSCRIPTEN__) || \
defined(__ANDROID__) || (defined(__APPLE__) && !TARGET_OS_OSX)
#if defined(MEDIAPIPE_MOBILE)
#include "mediapipe/util/android/file/base/file.h"
#include "mediapipe/util/android/file/base/helpers.h"
#else

38
mediapipe/calculators/video/opencv_video_decoder_calculator.cc
View File

@ -12,6 +12,8 @@
// See the License for the specific language governing permissions and
// limitations under the License.
#include <stdlib.h>
#include "mediapipe/framework/calculator_framework.h"
#include "mediapipe/framework/formats/image_format.pb.h"
#include "mediapipe/framework/formats/image_frame.h"
@ -66,6 +68,20 @@ ImageFormat::Format GetImageFormat(int num_channels) {
// output_stream: "VIDEO:video_frames"
// output_stream: "VIDEO_PRESTREAM:video_header"
// }
//
// OpenCV's VideoCapture doesn't decode audio tracks. If the audio tracks need
// to be saved, specify an output side packet with tag "SAVED_AUDIO_PATH".
// The calculator will call FFmpeg binary to save audio tracks as an aac file.
//
// Example config:
// node {
// calculator: "OpenCvVideoDecoderCalculator"
// input_side_packet: "INPUT_FILE_PATH:input_file_path"
// output_side_packet: "SAVED_AUDIO_PATH:audio_path"
// output_stream: "VIDEO:video_frames"
// output_stream: "VIDEO_PRESTREAM:video_header"
// }
//
class OpenCvVideoDecoderCalculator : public CalculatorBase {
public:
static ::mediapipe::Status GetContract(CalculatorContract* cc) {
@ -74,6 +90,9 @@ class OpenCvVideoDecoderCalculator : public CalculatorBase {
if (cc->Outputs().HasTag("VIDEO_PRESTREAM")) {
cc->Outputs().Tag("VIDEO_PRESTREAM").Set<VideoHeader>();
}
if (cc->OutputSidePackets().HasTag("SAVED_AUDIO_PATH")) {
cc->OutputSidePackets().Tag("SAVED_AUDIO_PATH").Set<std::string>();
}
return ::mediapipe::OkStatus();
}
@ -127,6 +146,25 @@ class OpenCvVideoDecoderCalculator : public CalculatorBase {
}
// Rewind to the very first frame.
cap_->set(cv::CAP_PROP_POS_AVI_RATIO, 0);
if (cc->OutputSidePackets().HasTag("SAVED_AUDIO_PATH")) {
#ifdef HAVE_FFMPEG
std::string saved_audio_path = std::tmpnam(nullptr);
system(absl::StrCat("ffmpeg -nostats -loglevel 0 -i ", input_file_path,
" -vn -f adts ", saved_audio_path)
.c_str());
cc->OutputSidePackets()
.Tag("SAVED_AUDIO_PATH")
.Set(MakePacket<std::string>(saved_audio_path));
#else
return ::mediapipe::InvalidArgumentErrorBuilder(MEDIAPIPE_LOC)
<< "OpenCVVideoDecoderCalculator can't save the audio file "
"because FFmpeg is not installed. Please remove "
"output_side_packet: \"SAVED_AUDIO_PATH\" from the node "
"config.";
#endif
}
return ::mediapipe::OkStatus();
}

49
mediapipe/calculators/video/opencv_video_encoder_calculator.cc
View File

@ -12,6 +12,8 @@
// See the License for the specific language governing permissions and
// limitations under the License.
#include <stdlib.h>
#include <memory>
#include <string>
#include <vector>
@ -39,8 +41,7 @@ namespace mediapipe {
// packet. Currently, the calculator only supports one video stream (in
// mediapipe::ImageFrame).
//
// Example config to generate the output video file:
//
// Example config:
// node {
// calculator: "OpenCvVideoEncoderCalculator"
// input_stream: "VIDEO:video"
@ -53,6 +54,26 @@ namespace mediapipe {
// }
// }
// }
//
// OpenCV's VideoWriter doesn't encode audio. If an input side packet with tag
// "AUDIO_FILE_PATH" is specified, the calculator will call FFmpeg binary to
// attach the audio file to the video as the last step in Close().
//
// Example config:
// node {
// calculator: "OpenCvVideoEncoderCalculator"
// input_stream: "VIDEO:video"
// input_stream: "VIDEO_PRESTREAM:video_header"
// input_side_packet: "OUTPUT_FILE_PATH:output_file_path"
// input_side_packet: "AUDIO_FILE_PATH:audio_path"
// node_options {
// [type.googleapis.com/mediapipe.OpenCvVideoEncoderCalculatorOptions]: {
// codec: "avc1"
// video_format: "mp4"
// }
// }
// }
//
class OpenCvVideoEncoderCalculator : public CalculatorBase {
public:
static ::mediapipe::Status GetContract(CalculatorContract* cc);
@ -77,6 +98,9 @@ class OpenCvVideoEncoderCalculator : public CalculatorBase {
}
RET_CHECK(cc->InputSidePackets().HasTag("OUTPUT_FILE_PATH"));
cc->InputSidePackets().Tag("OUTPUT_FILE_PATH").Set<std::string>();
if (cc->InputSidePackets().HasTag("AUDIO_FILE_PATH")) {
cc->InputSidePackets().Tag("AUDIO_FILE_PATH").Set<std::string>();
}
return ::mediapipe::OkStatus();
}
@ -155,6 +179,27 @@ class OpenCvVideoEncoderCalculator : public CalculatorBase {
if (writer_ && writer_->isOpened()) {
writer_->release();
}
if (cc->InputSidePackets().HasTag("AUDIO_FILE_PATH")) {
#ifdef HAVE_FFMPEG
const std::string& audio_file_path =
cc->InputSidePackets().Tag("AUDIO_FILE_PATH").Get<std::string>();
// A temp output file is needed because FFmpeg can't do in-place editing.
const std::string temp_file_path = std::tmpnam(nullptr);
system(absl::StrCat("mv ", output_file_path_, " ", temp_file_path,
"&& ffmpeg -nostats -loglevel 0 -i ", temp_file_path,
" -i ", audio_file_path,
" -c copy -map 0:v:0 -map 1:a:0 ", output_file_path_,
"&& rm ", temp_file_path)
.c_str());
#else
return ::mediapipe::InvalidArgumentErrorBuilder(MEDIAPIPE_LOC)
<< "OpenCVVideoEncoderCalculator can't attach the audio tracks to "
"the video because FFmpeg is not installed. Please remove "
"input_side_packet: \"AUDIO_FILE_PATH\" from the node "
"config.";
#endif
}
return ::mediapipe::OkStatus();
}

6
mediapipe/docs/android_archive_library.md
View File

@ -92,7 +92,7 @@ project.
MediaPipe depends on OpenCV, you will need to copy the precompiled OpenCV so
files into app/src/main/jniLibs. You can download the official OpenCV
Android SDK from
[here](https://github.com/opencv/opencv/releases/download/4.1.0/opencv-4.1.0-android-sdk.zip)
[here](https://github.com/opencv/opencv/releases/download/3.4.3/opencv-3.4.3-android-sdk.zip)
and run:
```bash
@ -126,6 +126,6 @@ project.
```
6. Follow our Android app examples to use MediaPipe in Android Studio for your
use case. If you are looking for an example, a working face detection
use case. If you are looking for an example, a face detection
example can be found
[here](https://github.com/jiuqiant/mediapipe_aar_example).
[here](https://github.com/jiuqiant/mediapipe_face_detection_aar_example) and a multi-hand tracking example can be found [here](https://github.com/jiuqiant/mediapipe_multi_hands_tracking_aar_example).

17
mediapipe/docs/examples.md
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@ -157,3 +157,20 @@ how to use MediaPipe with a TFLite model for hair segmentation on desktop using
GPU with live video from a webcam.
* [Desktop GPU](./hair_segmentation_desktop.md)
## Google Coral (machine learning acceleration with Google EdgeTPU)
Below are code samples on how to run MediaPipe on Google Coral Dev Board.
### Object Detection on Coral
[Object Detection on Coral with Webcam](./object_detection_coral_devboard.md)
shows how to run quantized object detection TFlite model accelerated with
EdgeTPU on
[Google Coral Dev Board](https://coral.withgoogle.com/products/dev-board).
### Face Detection on Coral
[Face Detection on Coral with Webcam](./face_detection_coral_devboard.md) shows
how to use quantized face detection TFlite model accelerated with EdgeTPU on
[Google Coral Dev Board](https://coral.withgoogle.com/products/dev-board).

20
mediapipe/docs/face_detection_coral_devboard.md Normal file
View File

@ -0,0 +1,20 @@
## Face Detection on Coral with Webcam
MediaPipe is able to run cross platform across device types like desktop, mobile
and edge devices. Here is an example of running MediaPipe
[face detection pipeline](./face_detection_desktop.md) on edge device like
[Google Coral dev board](https://coral.withgoogle.com/products/dev-board) with
[Edge TPU](https://cloud.google.com/edge-tpu/). This MediaPipe Coral face
detection pipeline is running [coral specific quantized version](https://github.com/google/mediapipe/blob/master/mediapipe/examples/coral/models/face-detector-quantized_edgetpu.tflite)
of the [MediaPipe face detection TFLite model](https://github.com/google/mediapipe/blob/master/mediapipe/models/face_detection_front.tflite)
accelerated on Edge TPU.
### Cross compilation of MediaPipe Coral binaries in Docker
We recommend building the MediaPipe binaries not on the edge device due to
limited compute resulting in long build times. Instead, we will build MediaPipe
binaries using Docker containers on a more powerful host machine. For step by
step details of cross compiling and running MediaPipe binaries on Coral dev
board, please refer to [README.md in MediaPipe Coral example folder](https://github.com/google/mediapipe/blob/master/mediapipe/examples/coral/README.md).
![Face Detection running on Coral](images/face_detection_demo_coral.jpg)

28
mediapipe/docs/gpu.md
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@ -1,10 +1,10 @@
## Running on GPUs
- [Overview](#overview)
- [OpenGL Support](#graphconfig)
- [OpenGL Support](#opengl-support)
- [Life of a GPU calculator](#life-of-a-gpu-calculator)
- [GpuBuffer to ImageFrame converters](#gpubuffer-to-imageframe-converters)
- [Disable GPU support](#disable-gpu-support)
### Overview
MediaPipe supports calculator nodes for GPU compute and rendering, and allows combining multiple GPU nodes, as well as mixing them with CPU based calculator nodes. There exist several GPU APIs on mobile platforms (eg, OpenGL ES, Metal and Vulkan). MediaPipe does not attempt to offer a single cross-API GPU abstraction. Individual nodes can be written using different APIs, allowing them to take advantage of platform specific features when needed.
@ -23,6 +23,7 @@ Below are the design principles for GPU support in MediaPipe
* A calculator should be allowed maximum flexibility in using the GPU for all or part of its operation, combining it with the CPU if necessary.
### OpenGL support
MediaPipe supports OpenGL ES up to version 3.2 on Android and up to ES 3.0 on iOS. In addition, MediaPipe also supports Metal on iOS.
* MediaPipe allows graphs to run OpenGL in multiple GL contexts. For example, this can be very useful in graphs that combine a slower GPU inference path (eg, at 10 FPS) with a faster GPU rendering path (eg, at 30 FPS): since one GL context corresponds to one sequential command queue, using the same context for both tasks would reduce the rendering frame rate. One challenge MediaPipe's use of multiple contexts solves is the ability to communicate across them. An example scenario is one with an input video that is sent to both the rendering and inferences paths, and rendering needs to have access to the latest output from inference.
@ -128,3 +129,26 @@ The below diagram shows the data flow in a mobile application that captures vide
|:--:|
| *Video frames from the camera are fed into the graph as `GpuBuffer` packets. The input stream is accessed by two calculators in parallel. `GpuBufferToImageFrameCalculator` converts the buffer into an `ImageFrame`, which is then sent through a grayscale converter and a canny filter (both based on OpenCV and running on the CPU), whose output is then converted into a `GpuBuffer` again. A multi-input GPU calculator, GlOverlayCalculator, takes as input both the original `GpuBuffer` and the one coming out of the edge detector, and overlays them using a shader. The output is then sent back to the application using a callback calculator, and the application renders the image to the screen using OpenGL.* |
### Disable GPU Support
By default, building MediaPipe (with no special bazel flags) attempts to compile
and link against OpenGL/Metal libraries.
There are some command line build flags available to disable/enable GPU support
within the MediaPipe framework:
```
# To disable *all* gpu support
bazel build --define MEDIAPIPE_DISABLE_GPU=1 <my-target>
# to enable full GPU support (OpenGL ES 3.1+ & Metal)
bazel build --copt -DMESA_EGL_NO_X11_HEADERS <my-target>
# to enable only OpenGL ES 3.0 and below (no GLES 3.1+ features)
bazel build --copt -DMESA_EGL_NO_X11_HEADERS --copt -DMEDIAPIPE_DISABLE_GL_COMPUTE <my-target>
```
Note *MEDIAPIPE_DISABLE_GL_COMPUTE* is automatically defined on all Apple
systems (Apple doesn't support OpenGL ES 3.1+).
Note on iOS and Android, it is assumed that GPU support will be enabled.

2
mediapipe/docs/hello_world_android.md
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@ -629,7 +629,7 @@ to load both dependencies:
static {
// Load all native libraries needed by the app.
System.loadLibrary("mediapipe_jni");
System.loadLibrary("opencv_java4");
System.loadLibrary("opencv_java3");
}
```

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18
mediapipe/docs/install.md
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@ -245,19 +245,23 @@ To build and run iOS apps:
$ cd mediapipe
```
3. Install Bazel (0.24.1 and above required).
3. Install Bazel (version between 0.24.1 and 1.1.0).
Option 1. Use package manager tool to install the latest version of Bazel.
Option 1. Use package manager tool to install Bazel 1.1.0
```bash
$ brew install bazel
# Run 'bazel version' to check version of bazel installed
# If Bazel 1.1.0+ was installed.
$ brew uninstall bazel
# Install Bazel 1.1.0
$ brew install https://raw.githubusercontent.com/bazelbuild/homebrew-tap/f8a0fa981bcb1784a0d0823e14867b844e94fb3d/Formula/bazel.rb
$ brew link bazel
# Run 'bazel version' to check version of bazel
```
Option 2. Follow the official
[Bazel documentation](https://docs.bazel.build/versions/master/install-os-x.html#install-with-installer-mac-os-x)
to install any version of Bazel manually.
to install any version of Bazel manually. Note that MediaPipe doesn't
support Bazel 1.1.0+ on macOS yet.
4. Install OpenCV and FFmpeg.
@ -526,7 +530,7 @@ This will use a Docker image that will isolate mediapipe's installation from the
```bash
$ docker run -it --name mediapipe mediapipe:latest
root@bca08b91ff63:/mediapipe# bash ./setup_android_sdk_and_ndk
root@bca08b91ff63:/mediapipe# bash ./setup_android_sdk_and_ndk.sh
# Should print:
# Android NDK is now installed. Consider setting $ANDROID_NDK_HOME environment variable to be /root/Android/Sdk/ndk-bundle/android-ndk-r18b

17
mediapipe/docs/mediapipe_ios_setup.md
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@ -7,7 +7,8 @@
2. Install [Bazel](https://bazel.build/).
See their [instructions](https://docs.bazel.build/versions/master/install-os-x.html).
See their
[instructions](https://docs.bazel.build/versions/master/install-os-x.html).
We recommend using [Homebrew](https://brew.sh/):
```bash
@ -15,13 +16,23 @@
brew install bazelbuild/tap/bazel
```
3. Clone the MediaPipe repository.
3. Install python "future" and "six".
To make Mediapipe work with TensorFlow, please install the python "future"
library and the python "six" library:
```bash
pip install --user future six
```
4. Clone the MediaPipe repository.
```bash
git clone https://github.com/google/mediapipe.git
```
4. Symlink or copy your provisioning profile to `mediapipe/mediapipe/provisioning_profile.mobileprovision`.
5. Symlink or copy your provisioning profile to
`mediapipe/mediapipe/provisioning_profile.mobileprovision`.
```bash
cd mediapipe

2
mediapipe/docs/multi_hand_tracking_desktop.md
View File

@ -156,7 +156,7 @@ node {
output_stream: "multi_hand_rects"
node_options: {
[type.googleapis.com/mediapipe.AssociationCalculatorOptions] {
min_similarity_threshold: 0.1
min_similarity_threshold: 0.5
}
}
}

8
mediapipe/docs/multi_hand_tracking_mobile_gpu.md
View File

@ -219,7 +219,7 @@ node {
output_stream: "multi_hand_rects"
node_options: {
[type.googleapis.com/mediapipe.AssociationCalculatorOptions] {
min_similarity_threshold: 0.1
min_similarity_threshold: 0.5
}
}
}
@ -560,7 +560,7 @@ node {
# BATCH_END timestamp, outputs the vector of landmarks at the BATCH_END
# timestamp.
node {
calculator: "EndLoopNormalizedLandmarksVectorCalculator"
calculator: "EndLoopNormalizedLandmarkListVectorCalculator"
input_stream: "ITEM:single_hand_landmarks"
input_stream: "BATCH_END:single_hand_rect_timestamp"
output_stream: "ITERABLE:multi_hand_landmarks"
@ -580,7 +580,7 @@ node {
# hand. If the hand presence for hand #i is false, the set of landmarks
# corresponding to that hand are dropped from the vector.
node {
calculator: "FilterLandmarksCollectionCalculator"
calculator: "FilterLandmarkListCollectionCalculator"
input_stream: "ITERABLE:multi_hand_landmarks"
input_stream: "CONDITION:multi_hand_presence"
output_stream: "ITERABLE:filtered_multi_hand_landmarks"
@ -669,7 +669,7 @@ node {
# timestamp for downstream calculators to inform them that all elements in the
# vector have been processed.
node {
calculator: "BeginLoopNormalizedLandmarksVectorCalculator"
calculator: "BeginLoopNormalizedLandmarkListVectorCalculator"
input_stream: "ITERABLE:multi_hand_landmarks"
output_stream: "ITEM:single_hand_landmarks"
output_stream: "BATCH_END:landmark_timestamp"

20
mediapipe/docs/object_detection_coral_devboard.md Normal file
View File

@ -0,0 +1,20 @@
## Object Detection on Coral with Webcam
MediaPipe is able to run cross platform across device types like desktop, mobile
and edge devices. Here is an example of running MediaPipe
[object detection pipeline](./object_detection_desktop.md) on edge device like
[Google Coral dev board](https://coral.withgoogle.com/products/dev-board) with
[Edge TPU](https://cloud.google.com/edge-tpu/). This MediaPipe Coral object
detection pipeline is running [coral specific quantized version](https://github.com/google/mediapipe/blob/master/mediapipe/examples/coral/models/object-detector-quantized_edgetpu.tflite)
of the [MediaPipe object detection TFLite model](https://github.com/google/mediapipe/blob/master/mediapipe/models/object_detection_front.tflite)
accelerated on Edge TPU.
### Cross compilation of MediaPipe Coral binaries in Docker
We recommend building the MediaPipe binaries not on the edge device due to
limited compute resulting in long build times. Instead, we will build MediaPipe
binaries using Docker containers on a more powerful host machine. For step by
step details of cross compiling and running MediaPipe binaries on Coral dev
board, please refer to [README.md in MediaPipe Coral example folder](https://github.com/google/mediapipe/blob/master/mediapipe/examples/coral/README.md).
![Object Detection running on Coral](images/object_detection_demo_coral.jpg)

2
mediapipe/examples/android/src/java/com/google/mediapipe/apps/edgedetectiongpu/MainActivity.java
View File

@ -47,7 +47,7 @@ public class MainActivity extends AppCompatActivity {
static {
// Load all native libraries needed by the app.
System.loadLibrary("mediapipe_jni");
System.loadLibrary("opencv_java4");
System.loadLibrary("opencv_java3");
}
// {@link SurfaceTexture} where the camera-preview frames can be accessed.

2
mediapipe/examples/android/src/java/com/google/mediapipe/apps/facedetectioncpu/MainActivity.java
View File

@ -48,7 +48,7 @@ public class MainActivity extends AppCompatActivity {
static {
// Load all native libraries needed by the app.
System.loadLibrary("mediapipe_jni");
System.loadLibrary("opencv_java4");
System.loadLibrary("opencv_java3");
}
// {@link SurfaceTexture} where the camera-preview frames can be accessed.

2
mediapipe/examples/android/src/java/com/google/mediapipe/apps/facedetectiongpu/MainActivity.java
View File

@ -48,7 +48,7 @@ public class MainActivity extends AppCompatActivity {
static {
// Load all native libraries needed by the app.
System.loadLibrary("mediapipe_jni");
System.loadLibrary("opencv_java4");
System.loadLibrary("opencv_java3");
}
// {@link SurfaceTexture} where the camera-preview frames can be accessed.

2
mediapipe/examples/android/src/java/com/google/mediapipe/apps/hairsegmentationgpu/MainActivity.java
View File

@ -48,7 +48,7 @@ public class MainActivity extends AppCompatActivity {
static {
// Load all native libraries needed by the app.
System.loadLibrary("mediapipe_jni");
System.loadLibrary("opencv_java4");
System.loadLibrary("opencv_java3");
}
// {@link SurfaceTexture} where the camera-preview frames can be accessed.

2
mediapipe/examples/android/src/java/com/google/mediapipe/apps/handdetectiongpu/MainActivity.java
View File

@ -48,7 +48,7 @@ public class MainActivity extends AppCompatActivity {
static {
// Load all native libraries needed by the app.
System.loadLibrary("mediapipe_jni");
System.loadLibrary("opencv_java4");
System.loadLibrary("opencv_java3");
}
// {@link SurfaceTexture} where the camera-preview frames can be accessed.

1
mediapipe/examples/android/src/java/com/google/mediapipe/apps/handtrackinggpu/BUILD
View File

@ -75,6 +75,7 @@ android_library(
resource_files = glob(["res/**"]),
deps = [
":mediapipe_jni_lib",
"//mediapipe/framework/formats:landmark_java_proto_lite",
"//mediapipe/java/com/google/mediapipe/components:android_camerax_helper",
"//mediapipe/java/com/google/mediapipe/components:android_components",
"//mediapipe/java/com/google/mediapipe/framework:android_framework",

63
mediapipe/examples/android/src/java/com/google/mediapipe/apps/handtrackinggpu/MainActivity.java
View File

@ -17,18 +17,23 @@ package com.google.mediapipe.apps.handtrackinggpu;
import android.graphics.SurfaceTexture;
import android.os.Bundle;
import androidx.appcompat.app.AppCompatActivity;
import android.util.Log;
import android.util.Size;
import android.view.SurfaceHolder;
import android.view.SurfaceView;
import android.view.View;
import android.view.ViewGroup;
import com.google.mediapipe.formats.proto.LandmarkProto.NormalizedLandmark;
import com.google.mediapipe.formats.proto.LandmarkProto.NormalizedLandmarkList;
import com.google.mediapipe.components.CameraHelper;
import com.google.mediapipe.components.CameraXPreviewHelper;
import com.google.mediapipe.components.ExternalTextureConverter;
import com.google.mediapipe.components.FrameProcessor;
import com.google.mediapipe.components.PermissionHelper;
import com.google.mediapipe.framework.AndroidAssetUtil;
import com.google.mediapipe.framework.PacketGetter;
import com.google.mediapipe.glutil.EglManager;
import com.google.protobuf.InvalidProtocolBufferException;
/** Main activity of MediaPipe example apps. */
public class MainActivity extends AppCompatActivity {
@ -37,6 +42,8 @@ public class MainActivity extends AppCompatActivity {
private static final String BINARY_GRAPH_NAME = "handtrackinggpu.binarypb";
private static final String INPUT_VIDEO_STREAM_NAME = "input_video";
private static final String OUTPUT_VIDEO_STREAM_NAME = "output_video";
private static final String OUTPUT_HAND_PRESENCE_STREAM_NAME = "hand_presence";
private static final String OUTPUT_LANDMARKS_STREAM_NAME = "hand_landmarks";
private static final CameraHelper.CameraFacing CAMERA_FACING = CameraHelper.CameraFacing.FRONT;
// Flips the camera-preview frames vertically before sending them into FrameProcessor to be
@ -48,7 +55,7 @@ public class MainActivity extends AppCompatActivity {
static {
// Load all native libraries needed by the app.
System.loadLibrary("mediapipe_jni");
System.loadLibrary("opencv_java4");
System.loadLibrary("opencv_java3");
}
// {@link SurfaceTexture} where the camera-preview frames can be accessed.
@ -90,6 +97,41 @@ public class MainActivity extends AppCompatActivity {
OUTPUT_VIDEO_STREAM_NAME);
processor.getVideoSurfaceOutput().setFlipY(FLIP_FRAMES_VERTICALLY);
processor.addPacketCallback(
OUTPUT_HAND_PRESENCE_STREAM_NAME,
(packet) -> {
Boolean handPresence = PacketGetter.getBool(packet);
if (!handPresence) {
Log.d(
TAG,
"[TS:" + packet.getTimestamp() + "] Hand presence is false, no hands detected.");
}
});
processor.addPacketCallback(
OUTPUT_LANDMARKS_STREAM_NAME,
(packet) -> {
byte[] landmarksRaw = PacketGetter.getProtoBytes(packet);
try {
NormalizedLandmarkList landmarks = NormalizedLandmarkList.parseFrom(landmarksRaw);
if (landmarks == null) {
Log.d(TAG, "[TS:" + packet.getTimestamp() + "] No hand landmarks.");
return;
}
// Note: If hand_presence is false, these landmarks are useless.
Log.d(
TAG,
"[TS:"
+ packet.getTimestamp()
+ "] #Landmarks for hand: "
+ landmarks.getLandmarkCount());
Log.d(TAG, getLandmarksDebugString(landmarks));
} catch (InvalidProtocolBufferException e) {
Log.e(TAG, "Couldn't Exception received - " + e);
return;
}
});
PermissionHelper.checkAndRequestCameraPermissions(this);
}
@ -164,4 +206,23 @@ public class MainActivity extends AppCompatActivity {
});
cameraHelper.startCamera(this, CAMERA_FACING, /*surfaceTexture=*/ null);
}
private static String getLandmarksDebugString(NormalizedLandmarkList landmarks) {
int landmarkIndex = 0;
String landmarksString = "";
for (NormalizedLandmark landmark : landmarks.getLandmarkList()) {
landmarksString +=
"\t\tLandmark["
+ landmarkIndex
+ "]: ("
+ landmark.getX()
+ ", "
+ landmark.getY()
+ ", "
+ landmark.getZ()
+ ")\n";
++landmarkIndex;
}
return landmarksString;
}
}

1
mediapipe/examples/android/src/java/com/google/mediapipe/apps/multihandtrackinggpu/BUILD
View File

@ -75,6 +75,7 @@ android_library(
resource_files = glob(["res/**"]),
deps = [
":mediapipe_jni_lib",
"//mediapipe/framework/formats:landmark_java_proto_lite",
"//mediapipe/java/com/google/mediapipe/components:android_camerax_helper",
"//mediapipe/java/com/google/mediapipe/components:android_components",
"//mediapipe/java/com/google/mediapipe/framework:android_framework",

50
mediapipe/examples/android/src/java/com/google/mediapipe/apps/multihandtrackinggpu/MainActivity.java
View File

@ -17,18 +17,23 @@ package com.google.mediapipe.apps.multihandtrackinggpu;
import android.graphics.SurfaceTexture;
import android.os.Bundle;
import androidx.appcompat.app.AppCompatActivity;
import android.util.Log;
import android.util.Size;
import android.view.SurfaceHolder;
import android.view.SurfaceView;
import android.view.View;
import android.view.ViewGroup;
import com.google.mediapipe.formats.proto.LandmarkProto.NormalizedLandmark;
import com.google.mediapipe.formats.proto.LandmarkProto.NormalizedLandmarkList;
import com.google.mediapipe.components.CameraHelper;
import com.google.mediapipe.components.CameraXPreviewHelper;
import com.google.mediapipe.components.ExternalTextureConverter;
import com.google.mediapipe.components.FrameProcessor;
import com.google.mediapipe.components.PermissionHelper;
import com.google.mediapipe.framework.AndroidAssetUtil;
import com.google.mediapipe.framework.PacketGetter;
import com.google.mediapipe.glutil.EglManager;
import java.util.List;
/** Main activity of MediaPipe example apps. */
public class MainActivity extends AppCompatActivity {
@ -37,6 +42,7 @@ public class MainActivity extends AppCompatActivity {
private static final String BINARY_GRAPH_NAME = "multihandtrackinggpu.binarypb";
private static final String INPUT_VIDEO_STREAM_NAME = "input_video";
private static final String OUTPUT_VIDEO_STREAM_NAME = "output_video";
private static final String OUTPUT_LANDMARKS_STREAM_NAME = "multi_hand_landmarks";
private static final CameraHelper.CameraFacing CAMERA_FACING = CameraHelper.CameraFacing.FRONT;
// Flips the camera-preview frames vertically before sending them into FrameProcessor to be
@ -48,7 +54,7 @@ public class MainActivity extends AppCompatActivity {
static {
// Load all native libraries needed by the app.
System.loadLibrary("mediapipe_jni");
System.loadLibrary("opencv_java4");
System.loadLibrary("opencv_java3");
}
// {@link SurfaceTexture} where the camera-preview frames can be accessed.
@ -90,6 +96,20 @@ public class MainActivity extends AppCompatActivity {
OUTPUT_VIDEO_STREAM_NAME);
processor.getVideoSurfaceOutput().setFlipY(FLIP_FRAMES_VERTICALLY);
processor.addPacketCallback(
OUTPUT_LANDMARKS_STREAM_NAME,
(packet) -> {
Log.d(TAG, "Received multi-hand landmarks packet.");
List<NormalizedLandmarkList> multiHandLandmarks =
PacketGetter.getProtoVector(packet, NormalizedLandmarkList.parser());
Log.d(
TAG,
"[TS:"
+ packet.getTimestamp()
+ "] "
+ getMultiHandLandmarksDebugString(multiHandLandmarks));
});
PermissionHelper.checkAndRequestCameraPermissions(this);
}
@ -164,4 +184,32 @@ public class MainActivity extends AppCompatActivity {
});
cameraHelper.startCamera(this, CAMERA_FACING, /*surfaceTexture=*/ null);
}
private String getMultiHandLandmarksDebugString(List<NormalizedLandmarkList> multiHandLandmarks) {
if (multiHandLandmarks.isEmpty()) {
return "No hand landmarks";
}
String multiHandLandmarksStr = "Number of hands detected: " + multiHandLandmarks.size() + "\n";
int handIndex = 0;
for (NormalizedLandmarkList landmarks : multiHandLandmarks) {
multiHandLandmarksStr +=
"\t#Hand landmarks for hand[" + handIndex + "]: " + landmarks.getLandmarkCount() + "\n";
int landmarkIndex = 0;
for (NormalizedLandmark landmark : landmarks.getLandmarkList()) {
multiHandLandmarksStr +=
"\t\tLandmark ["
+ landmarkIndex
+ "]: ("
+ landmark.getX()
+ ", "
+ landmark.getY()
+ ", "
+ landmark.getZ()
+ ")\n";
++landmarkIndex;
}
++handIndex;
}
return multiHandLandmarksStr;
}
}

2
mediapipe/examples/android/src/java/com/google/mediapipe/apps/objectdetectioncpu/MainActivity.java
View File

@ -48,7 +48,7 @@ public class MainActivity extends AppCompatActivity {
static {
// Load all native libraries needed by the app.
System.loadLibrary("mediapipe_jni");
System.loadLibrary("opencv_java4");
System.loadLibrary("opencv_java3");
}
// {@link SurfaceTexture} where the camera-preview frames can be accessed.

2
mediapipe/examples/android/src/java/com/google/mediapipe/apps/objectdetectiongpu/MainActivity.java
View File

@ -48,7 +48,7 @@ public class MainActivity extends AppCompatActivity {
static {
// Load all native libraries needed by the app.
System.loadLibrary("mediapipe_jni");
System.loadLibrary("opencv_java4");
System.loadLibrary("opencv_java3");
}
// {@link SurfaceTexture} where the camera-preview frames can be accessed.

56
mediapipe/examples/coral/BUILD Normal file
View File

@ -0,0 +1,56 @@
# Copyright 2019 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.
licenses(["notice"]) # Apache 2.0
package(default_visibility = [
"//visibility:public",
])
# Graph Runner
cc_library(
name = "demo_run_graph_main",
srcs = ["demo_run_graph_main.cc"],
deps = [
"//mediapipe/framework:calculator_framework",
"//mediapipe/framework/formats:image_frame",
"//mediapipe/framework/formats:image_frame_opencv",
"//mediapipe/framework/port:commandlineflags",
"//mediapipe/framework/port:file_helpers",
"//mediapipe/framework/port:opencv_highgui",
"//mediapipe/framework/port:opencv_imgproc",
"//mediapipe/framework/port:opencv_video",
"//mediapipe/framework/port:parse_text_proto",
"//mediapipe/framework/port:status",
],
)
# Demos
cc_binary(
name = "object_detection_cpu",
deps = [
"//mediapipe/examples/coral:demo_run_graph_main",
"//mediapipe/graphs/object_detection:desktop_tflite_calculators",
],
)
cc_binary(
name = "face_detection_cpu",
deps = [
"//mediapipe/examples/coral:demo_run_graph_main",
"//mediapipe/graphs/face_detection:desktop_tflite_calculators",
],
)

87
mediapipe/examples/coral/Dockerfile Normal file
View File

@ -0,0 +1,87 @@
# Copyright 2019 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.
#==== ! Prerequisite ! ====
# $ sh mediapipe/examples/coral/setup.sh
#====
# for opencv 3.2 default
FROM ubuntu:18.04
MAINTAINER <mediapipe@google.com>
WORKDIR /mediapipe
ENV DEBIAN_FRONTEND=noninteractive
# Install MediaPipe & Coral deps
COPY update_sources.sh /
RUN /update_sources.sh
RUN dpkg --add-architecture armhf
RUN dpkg --add-architecture arm64
RUN apt-get update && apt-get install -y \
build-essential \
crossbuild-essential-arm64 \
libusb-1.0-0-dev \
libusb-1.0-0-dev:arm64 \
zlib1g-dev \
zlib1g-dev:arm64 \
pkg-config \
zip \
unzip \
curl \
wget \
git \
python \
python-pip \
python3-pip \
vim-common \
ca-certificates \
emacs \
software-properties-common && \
add-apt-repository -y ppa:openjdk-r/ppa && \
apt-get update && apt-get install -y openjdk-8-jdk
RUN pip install --upgrade setuptools
RUN pip install future
RUN pip3 install six
COPY . /mediapipe/
# Install bazel
ARG BAZEL_VERSION=0.29.1
RUN mkdir /bazel && \
wget --no-check-certificate -O /bazel/installer.sh "https://github.com/bazelbuild/bazel/releases/download/${BAZEL_VERSION}/bazel-${BAZEL_VERSION}-installer-linux-x86_64.sh" && \
wget --no-check-certificate -O /bazel/LICENSE.txt "https://raw.githubusercontent.com/bazelbuild/bazel/master/LICENSE" && \
chmod +x /bazel/installer.sh && \
/bazel/installer.sh && \
rm -f /bazel/installer.sh
# OpenCV (3.2 default in 18.04)
RUN apt-get update && apt-get install -y libopencv-dev
# Opencv libs copied from coral device into opencv32_arm64_libs
RUN cp opencv32_arm64_libs/* /usr/lib/aarch64-linux-gnu/.
# Edge tpu header and lib
RUN git clone https://github.com/google-coral/edgetpu.git /edgetpu
RUN cp /edgetpu/libedgetpu/direct/aarch64/libedgetpu.so.1.0 /usr/lib/aarch64-linux-gnu/libedgetpu.so
# See mediapipe/examples/coral/README.md to finish setup

137
mediapipe/examples/coral/README.md Normal file
View File

@ -0,0 +1,137 @@
# Coral Dev Board Setup (experimental)
**Dislaimer**: Running MediaPipe on Coral is experimental, and this process may not be exact and is subject to change. These instructions have only been tested on the coral dev board with OS version _mendel day_, and may vary for different devices and workstations.
This file describes how to prepare a Google Coral Dev Board and setup a linux Docker container for building MediaPipe applications that run on Edge TPU.
## Before creating the Docker
* (on host machine) run _setup.sh_ from MediaPipe root directory
sh mediapipe/examples/coral/setup.sh
* Setup the coral device via [here](https://coral.withgoogle.com/docs/dev-board/get-started/), and ensure the _mdt_ command works
* (on coral device) prepare MediaPipe
cd ~
sudo apt-get install git
git clone https://github.com/google/mediapipe.git
mkdir mediapipe/bazel-bin
* (on coral device) install opencv 3.2
sudo apt-get update && apt-get install -y libopencv-dev
* (on coral device) find all opencv libs
find /usr/lib/aarch64-linux-gnu/ -name 'libopencv*so'
* (on host machine) copy core opencv libs from coral device to a local folder inside MediaPipe checkout:
# in root level mediapipe folder #
mdt pull /usr/lib/aarch64-linux-gnu/libopencv_core.so opencv32_arm64_libs
mdt pull /usr/lib/aarch64-linux-gnu/libopencv_calib3d.so opencv32_arm64_libs
mdt pull /usr/lib/aarch64-linux-gnu/libopencv_features2d.so opencv32_arm64_libs
mdt pull /usr/lib/aarch64-linux-gnu/libopencv_highgui.so opencv32_arm64_libs
mdt pull /usr/lib/aarch64-linux-gnu/libopencv_imgcodecs.so opencv32_arm64_libs
mdt pull /usr/lib/aarch64-linux-gnu/libopencv_imgproc.so opencv32_arm64_libs
mdt pull /usr/lib/aarch64-linux-gnu/libopencv_video.so opencv32_arm64_libs
mdt pull /usr/lib/aarch64-linux-gnu/libopencv_videoio.so opencv32_arm64_libs
* (on host machine) Create and start the docker environment
# from mediapipe root level directory #
docker build -t coral .
docker run -it --name coral coral:latest
## Inside the Docker environment
* Update library paths in /mediapipe/third_party/opencv_linux.BUILD
(replace 'x86_64-linux-gnu' with 'aarch64-linux-gnu')
"lib/aarch64-linux-gnu/libopencv_core.so",
"lib/aarch64-linux-gnu/libopencv_calib3d.so",
"lib/aarch64-linux-gnu/libopencv_features2d.so",
"lib/aarch64-linux-gnu/libopencv_highgui.so",
"lib/aarch64-linux-gnu/libopencv_imgcodecs.so",
"lib/aarch64-linux-gnu/libopencv_imgproc.so",
"lib/aarch64-linux-gnu/libopencv_video.so",
"lib/aarch64-linux-gnu/libopencv_videoio.so",
* Attempt to build hello world (to download external deps)
bazel build -c opt --define MEDIAPIPE_DISABLE_GPU=1 mediapipe/examples/desktop/hello_world:hello_world
* Edit /mediapipe/bazel-mediapipe/external/com_github_glog_glog/src/signalhandler.cc
on line 78, replace
return (void*)context->PC_FROM_UCONTEXT;
with
return NULL;
* Edit /edgetpu/libedgetpu/BUILD
to add this build target
cc_library(
name = "lib",
srcs = [
"libedgetpu.so",
],
visibility = ["//visibility:public"],
)
* Edit *tflite_inference_calculator.cc* BUILD rules:
sed -i 's/\":tflite_inference_calculator_cc_proto\",/\":tflite_inference_calculator_cc_proto\",\n\t\"@edgetpu\/\/:header\",\n\t\"@libedgetpu\/\/:lib\",/g' mediapipe/calculators/tflite/BUILD
The above command should add
"@edgetpu//:header",
"@libedgetpu//:lib",
to the _deps_ of tflite_inference_calculator.cc
#### Now try cross-compiling for device
* Object detection demo
bazel build -c opt --crosstool_top=@crosstool//:toolchains --compiler=gcc --cpu=aarch64 --define MEDIAPIPE_DISABLE_GPU=1 --copt -DMEDIAPIPE_EDGE_TPU --copt=-flax-vector-conversions mediapipe/examples/coral:object_detection_cpu
Copy object_detection_cpu binary to the MediaPipe checkout on the coral device
# outside docker env, open new terminal on host machine #
docker ps
docker cp <container-id>:/mediapipe/bazel-bin/mediapipe/examples/coral/object_detection_cpu /tmp/.
mdt push /tmp/object_detection_cpu /home/mendel/mediapipe/bazel-bin/.
* Face detection demo
bazel build -c opt --crosstool_top=@crosstool//:toolchains --compiler=gcc --cpu=aarch64 --define MEDIAPIPE_DISABLE_GPU=1 --copt -DMEDIAPIPE_EDGE_TPU --copt=-flax-vector-conversions mediapipe/examples/coral:face_detection_cpu
Copy face_detection_cpu binary to the MediaPipe checkout on the coral device
# outside docker env, open new terminal on host machine #
docker ps
docker cp <container-id>:/mediapipe/bazel-bin/mediapipe/examples/coral/face_detection_cpu /tmp/.
mdt push /tmp/face_detection_cpu /home/mendel/mediapipe/bazel-bin/.
## On the coral device (with display)
# Object detection
cd ~/mediapipe
chmod +x bazel-bin/object_detection_cpu
export GLOG_logtostderr=1
bazel-bin/object_detection_cpu --calculator_graph_config_file=mediapipe/examples/coral/graphs/object_detection_desktop_live.pbtxt
# Face detection
cd ~/mediapipe
chmod +x bazel-bin/face_detection_cpu
export GLOG_logtostderr=1
bazel-bin/face_detection_cpu --calculator_graph_config_file=mediapipe/examples/coral/graphs/face_detection_desktop_live.pbtxt

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workspace(name = "mediapipe")
load("@bazel_tools//tools/build_defs/repo:http.bzl", "http_archive")
skylib_version = "0.8.0"
http_archive(
name = "bazel_skylib",
type = "tar.gz",
url = "https://github.com/bazelbuild/bazel-skylib/releases/download/{}/bazel-skylib.{}.tar.gz".format (skylib_version, skylib_version),
sha256 = "2ef429f5d7ce7111263289644d233707dba35e39696377ebab8b0bc701f7818e",
)
load("@bazel_skylib//lib:versions.bzl", "versions")
versions.check(minimum_bazel_version = "0.24.1")
# ABSL cpp library.
http_archive(
name = "com_google_absl",
# Head commit on 2019-04-12.
# TODO: Switch to the latest absl version when the problem gets
# fixed.
urls = [
"https://github.com/abseil/abseil-cpp/archive/a02f62f456f2c4a7ecf2be3104fe0c6e16fbad9a.tar.gz",
],
sha256 = "d437920d1434c766d22e85773b899c77c672b8b4865d5dc2cd61a29fdff3cf03",
strip_prefix = "abseil-cpp-a02f62f456f2c4a7ecf2be3104fe0c6e16fbad9a",
)
http_archive(
name = "rules_cc",
strip_prefix = "rules_cc-master",
urls = ["https://github.com/bazelbuild/rules_cc/archive/master.zip"],
)
# GoogleTest/GoogleMock framework. Used by most unit-tests.
http_archive(
name = "com_google_googletest",
urls = ["https://github.com/google/googletest/archive/master.zip"],
strip_prefix = "googletest-master",
)
# Google Benchmark library.
http_archive(
name = "com_google_benchmark",
urls = ["https://github.com/google/benchmark/archive/master.zip"],
strip_prefix = "benchmark-master",
build_file = "@//third_party:benchmark.BUILD",
)
# gflags needed by glog
http_archive(
name = "com_github_gflags_gflags",
sha256 = "6e16c8bc91b1310a44f3965e616383dbda48f83e8c1eaa2370a215057b00cabe",
strip_prefix = "gflags-77592648e3f3be87d6c7123eb81cbad75f9aef5a",
urls = [
"https://mirror.bazel.build/github.com/gflags/gflags/archive/77592648e3f3be87d6c7123eb81cbad75f9aef5a.tar.gz",
"https://github.com/gflags/gflags/archive/77592648e3f3be87d6c7123eb81cbad75f9aef5a.tar.gz",
],
)
# glog
http_archive(
name = "com_github_glog_glog",
url = "https://github.com/google/glog/archive/v0.3.5.zip",
sha256 = "267103f8a1e9578978aa1dc256001e6529ef593e5aea38193d31c2872ee025e8",
strip_prefix = "glog-0.3.5",
build_file = "@//third_party:glog.BUILD",
patches = [
"@//third_party:com_github_glog_glog_9779e5ea6ef59562b030248947f787d1256132ae.diff"
],
patch_args = [
"-p1",
],
)
# libyuv
http_archive(
name = "libyuv",
urls = ["https://chromium.googlesource.com/libyuv/libyuv/+archive/refs/heads/master.tar.gz"],
build_file = "@//third_party:libyuv.BUILD",
)
http_archive(
name = "com_google_protobuf_javalite",
sha256 = "79d102c61e2a479a0b7e5fc167bcfaa4832a0c6aad4a75fa7da0480564931bcc",
strip_prefix = "protobuf-384989534b2246d413dbcd750744faab2607b516",
urls = ["https://github.com/google/protobuf/archive/384989534b2246d413dbcd750744faab2607b516.zip"],
)
http_archive(
name = "com_google_audio_tools",
strip_prefix = "multichannel-audio-tools-master",
urls = ["https://github.com/google/multichannel-audio-tools/archive/master.zip"],
)
# Needed by TensorFlow
http_archive(
name = "io_bazel_rules_closure",
sha256 = "e0a111000aeed2051f29fcc7a3f83be3ad8c6c93c186e64beb1ad313f0c7f9f9",
strip_prefix = "rules_closure-cf1e44edb908e9616030cc83d085989b8e6cd6df",
urls = [
"http://mirror.tensorflow.org/github.com/bazelbuild/rules_closure/archive/cf1e44edb908e9616030cc83d085989b8e6cd6df.tar.gz",
"https://github.com/bazelbuild/rules_closure/archive/cf1e44edb908e9616030cc83d085989b8e6cd6df.tar.gz", # 2019-04-04
],
)
# 2019-11-12
_TENSORFLOW_GIT_COMMIT = "a5f9bcd64453ff3d1f64cb4da4786db3d2da7f82"
_TENSORFLOW_SHA256= "f2b6f2ab2ffe63e86eccd3ce4bea6b7197383d726638dfeeebcdc1e7de73f075"
http_archive(
name = "org_tensorflow",
urls = [
"https://mirror.bazel.build/github.com/tensorflow/tensorflow/archive/%s.tar.gz" % _TENSORFLOW_GIT_COMMIT,
"https://github.com/tensorflow/tensorflow/archive/%s.tar.gz" % _TENSORFLOW_GIT_COMMIT,
],
strip_prefix = "tensorflow-%s" % _TENSORFLOW_GIT_COMMIT,
sha256 = _TENSORFLOW_SHA256,
)
load("@org_tensorflow//tensorflow:workspace.bzl", "tf_workspace")
tf_workspace(tf_repo_name = "org_tensorflow")
# Please run
# $ sudo apt-get install libopencv-core-dev libopencv-highgui-dev \
# libopencv-imgproc-dev libopencv-video-dev
new_local_repository(
name = "linux_opencv",
build_file = "@//third_party:opencv_linux.BUILD",
path = "/usr",
)
new_local_repository(
name = "linux_ffmpeg",
build_file = "@//third_party:ffmpeg_linux.BUILD",
path = "/usr"
)
# Please run $ brew install opencv@3
new_local_repository(
name = "macos_opencv",
build_file = "@//third_party:opencv_macos.BUILD",
path = "/usr",
)
new_local_repository(
name = "macos_ffmpeg",
build_file = "@//third_party:ffmpeg_macos.BUILD",
path = "/usr",
)
http_archive(
name = "android_opencv",
sha256 = "056b849842e4fa8751d09edbb64530cfa7a63c84ccd232d0ace330e27ba55d0b",
build_file = "@//third_party:opencv_android.BUILD",
strip_prefix = "OpenCV-android-sdk",
type = "zip",
url = "https://github.com/opencv/opencv/releases/download/4.1.0/opencv-4.1.0-android-sdk.zip",
)
# After OpenCV 3.2.0, the pre-compiled opencv2.framework has google protobuf symbols, which will
# trigger duplicate symbol errors in the linking stage of building a mediapipe ios app.
# To get a higher version of OpenCV for iOS, opencv2.framework needs to be built from source with
# '-DBUILD_PROTOBUF=OFF -DBUILD_opencv_dnn=OFF'.
http_archive(
name = "ios_opencv",
sha256 = "7dd536d06f59e6e1156b546bd581523d8df92ce83440002885ec5abc06558de2",
build_file = "@//third_party:opencv_ios.BUILD",
type = "zip",
url = "https://github.com/opencv/opencv/releases/download/3.2.0/opencv-3.2.0-ios-framework.zip",
)
RULES_JVM_EXTERNAL_TAG = "2.2"
RULES_JVM_EXTERNAL_SHA = "f1203ce04e232ab6fdd81897cf0ff76f2c04c0741424d192f28e65ae752ce2d6"
http_archive(
name = "rules_jvm_external",
strip_prefix = "rules_jvm_external-%s" % RULES_JVM_EXTERNAL_TAG,
sha256 = RULES_JVM_EXTERNAL_SHA,
url = "https://github.com/bazelbuild/rules_jvm_external/archive/%s.zip" % RULES_JVM_EXTERNAL_TAG,
)
load("@rules_jvm_external//:defs.bzl", "maven_install")
maven_install(
artifacts = [
"androidx.annotation:annotation:aar:1.1.0",
"androidx.appcompat:appcompat:aar:1.1.0-rc01",
"androidx.constraintlayout:constraintlayout:aar:1.1.3",
"androidx.core:core:aar:1.1.0-rc03",
"androidx.legacy:legacy-support-v4:aar:1.0.0",
"androidx.recyclerview:recyclerview:aar:1.1.0-beta02",
"com.google.android.material:material:aar:1.0.0-rc01",
],
repositories = ["https://dl.google.com/dl/android/maven2"],
)
maven_server(
name = "google_server",
url = "https://dl.google.com/dl/android/maven2",
)
maven_jar(
name = "androidx_lifecycle",
artifact = "androidx.lifecycle:lifecycle-common:2.0.0",
sha1 = "e070ffae07452331bc5684734fce6831d531785c",
server = "google_server",
)
maven_jar(
name = "androidx_concurrent_futures",
artifact = "androidx.concurrent:concurrent-futures:1.0.0-alpha03",
sha1 = "b528df95c7e2fefa2210c0c742bf3e491c1818ae",
server = "google_server",
)
maven_jar(
name = "com_google_guava_android",
artifact = "com.google.guava:guava:27.0.1-android",
sha1 = "b7e1c37f66ef193796ccd7ea6e80c2b05426182d",
)
maven_jar(
name = "com_google_common_flogger",
artifact = "com.google.flogger:flogger:0.3.1",
sha1 = "585030fe1ec709760cbef997a459729fb965df0e",
)
maven_jar(
name = "com_google_common_flogger_system_backend",
artifact = "com.google.flogger:flogger-system-backend:0.3.1",
sha1 = "287b569d76abcd82f9de87fe41829fbc7ebd8ac9",
)
maven_jar(
name = "com_google_code_findbugs",
artifact = "com.google.code.findbugs:jsr305:3.0.2",
sha1 = "25ea2e8b0c338a877313bd4672d3fe056ea78f0d",
)
# You may run setup_android.sh to install Android SDK and NDK.
android_ndk_repository(
name = "androidndk",
)
android_sdk_repository(
name = "androidsdk",
)
# iOS basic build deps.
http_archive(
name = "build_bazel_rules_apple",
sha256 = "bdc8e66e70b8a75da23b79f1f8c6207356df07d041d96d2189add7ee0780cf4e",
strip_prefix = "rules_apple-b869b0d3868d78a1d4ffd866ccb304fb68aa12c3",
url = "https://github.com/bazelbuild/rules_apple/archive/b869b0d3868d78a1d4ffd866ccb304fb68aa12c3.tar.gz",
)
load(
"@build_bazel_rules_apple//apple:repositories.bzl",
"apple_rules_dependencies",
)
apple_rules_dependencies()
load(
"@build_bazel_rules_swift//swift:repositories.bzl",
"swift_rules_dependencies",
)
swift_rules_dependencies()
load(
"@build_bazel_apple_support//lib:repositories.bzl",
"apple_support_dependencies",
)
apple_support_dependencies()
# More iOS deps.
http_archive(
name = "google_toolbox_for_mac",
url = "https://github.com/google/google-toolbox-for-mac/archive/v2.2.1.zip",
sha256 = "e3ac053813c989a88703556df4dc4466e424e30d32108433ed6beaec76ba4fdc",
strip_prefix = "google-toolbox-for-mac-2.2.1",
build_file = "@//third_party:google_toolbox_for_mac.BUILD",
)
# Coral
#COMMIT=$(git ls-remote https://github.com/google-coral/crosstool master | awk '{print $1}')
#SHA256=$(curl -L "https://github.com/google-coral/crosstool/archive/${COMMIT}.tar.gz" | sha256sum | awk '{print $1}')
http_archive(
name = "coral_crosstool",
sha256 = "cb31b1417ccdcf7dd9fca5ec63e1571672372c30427730255997a547569d2feb",
strip_prefix = "crosstool-9e00d5be43bf001f883b5700f5d04882fea00229",
urls = [
"https://github.com/google-coral/crosstool/archive/9e00d5be43bf001f883b5700f5d04882fea00229.tar.gz",
],
)
load("@coral_crosstool//:configure.bzl", "cc_crosstool")
cc_crosstool(name = "crosstool")
# EdgeTPU
new_local_repository(
name = "edgetpu",
path = "/edgetpu/libedgetpu",
build_file = "/edgetpu/libedgetpu/BUILD"
)
new_local_repository(
name = "libedgetpu",
path = "/usr/lib/aarch64-linux-gnu",
build_file = "/edgetpu/libedgetpu/BUILD"
)

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// Copyright 2019 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.
//
// An example of sending OpenCV webcam frames into a MediaPipe graph.
#include "mediapipe/framework/calculator_framework.h"
#include "mediapipe/framework/formats/image_frame.h"
#include "mediapipe/framework/formats/image_frame_opencv.h"
#include "mediapipe/framework/port/commandlineflags.h"
#include "mediapipe/framework/port/file_helpers.h"
#include "mediapipe/framework/port/opencv_highgui_inc.h"
#include "mediapipe/framework/port/opencv_imgproc_inc.h"
#include "mediapipe/framework/port/opencv_video_inc.h"
#include "mediapipe/framework/port/parse_text_proto.h"
#include "mediapipe/framework/port/status.h"
constexpr char kInputStream[] = "input_video";
constexpr char kOutputStream[] = "output_video";
constexpr char kWindowName[] = "MediaPipe";
DEFINE_string(
calculator_graph_config_file, "",
"Name of file containing text format CalculatorGraphConfig proto.");
DEFINE_string(input_video_path, "",
"Full path of video to load. "
"If not provided, attempt to use a webcam.");
DEFINE_string(output_video_path, "",
"Full path of where to save result (.mp4 only). "
"If not provided, show result in a window.");
::mediapipe::Status RunMPPGraph() {
std::string calculator_graph_config_contents;
MP_RETURN_IF_ERROR(mediapipe::file::GetContents(
FLAGS_calculator_graph_config_file, &calculator_graph_config_contents));
LOG(INFO) << "Get calculator graph config contents: "
<< calculator_graph_config_contents;
mediapipe::CalculatorGraphConfig config =
mediapipe::ParseTextProtoOrDie<mediapipe::CalculatorGraphConfig>(
calculator_graph_config_contents);
LOG(INFO) << "Initialize the calculator graph.";
mediapipe::CalculatorGraph graph;
MP_RETURN_IF_ERROR(graph.Initialize(config));
LOG(INFO) << "Initialize the camera or load the video.";
cv::VideoCapture capture;
const bool load_video = !FLAGS_input_video_path.empty();
if (load_video) {
capture.open(FLAGS_input_video_path);
} else {
capture.open(0);
}
RET_CHECK(capture.isOpened());
cv::VideoWriter writer;
const bool save_video = !FLAGS_output_video_path.empty();
if (save_video) {
LOG(INFO) << "Prepare video writer.";
cv::Mat test_frame;
capture.read(test_frame); // Consume first frame.
capture.set(cv::CAP_PROP_POS_AVI_RATIO, 0); // Rewind to beginning.
writer.open(FLAGS_output_video_path,
mediapipe::fourcc('a', 'v', 'c', '1'), // .mp4
capture.get(cv::CAP_PROP_FPS), test_frame.size());
RET_CHECK(writer.isOpened());
} else {
cv::namedWindow(kWindowName, /*flags=WINDOW_AUTOSIZE*/ 1);
capture.set(cv::CAP_PROP_FRAME_WIDTH, 640);
capture.set(cv::CAP_PROP_FRAME_HEIGHT, 480);
capture.set(cv::CAP_PROP_AUTOFOCUS, 0);
capture.set(cv::CAP_PROP_FOCUS, 1);
capture.set(cv::CAP_PROP_FPS, 30);
}
LOG(INFO) << "Start running the calculator graph.";
ASSIGN_OR_RETURN(mediapipe::OutputStreamPoller poller,
graph.AddOutputStreamPoller(kOutputStream));
MP_RETURN_IF_ERROR(graph.StartRun({}));
LOG(INFO) << "Start grabbing and processing frames.";
size_t frame_timestamp = 0;
bool grab_frames = true;
while (grab_frames) {
// Capture opencv camera or video frame.
cv::Mat camera_frame_raw;
capture >> camera_frame_raw;
if (camera_frame_raw.empty()) break; // End of video.
cv::Mat camera_frame;
cv::cvtColor(camera_frame_raw, camera_frame, cv::COLOR_BGR2RGB);
if (!load_video) {
cv::flip(camera_frame, camera_frame, /*flipcode=HORIZONTAL*/ 1);
}
// Wrap Mat into an ImageFrame.
auto input_frame = absl::make_unique<mediapipe::ImageFrame>(
mediapipe::ImageFormat::SRGB, camera_frame.cols, camera_frame.rows,
mediapipe::ImageFrame::kDefaultAlignmentBoundary);
cv::Mat input_frame_mat = mediapipe::formats::MatView(input_frame.get());
camera_frame.copyTo(input_frame_mat);
// Send image packet into the graph.
MP_RETURN_IF_ERROR(graph.AddPacketToInputStream(
kInputStream, mediapipe::Adopt(input_frame.release())
.At(mediapipe::Timestamp(frame_timestamp++))));
// Get the graph result packet, or stop if that fails.
mediapipe::Packet packet;
if (!poller.Next(&packet)) break;
auto& output_frame = packet.Get<mediapipe::ImageFrame>();
// Convert back to opencv for display or saving.
cv::Mat output_frame_mat = mediapipe::formats::MatView(&output_frame);
cv::cvtColor(output_frame_mat, output_frame_mat, cv::COLOR_RGB2BGR);
if (save_video) {
writer.write(output_frame_mat);
} else {
cv::imshow(kWindowName, output_frame_mat);
// Press any key to exit.
const int pressed_key = cv::waitKey(5);
if (pressed_key >= 0 && pressed_key != 255) grab_frames = false;
}
}
LOG(INFO) << "Shutting down.";
if (writer.isOpened()) writer.release();
MP_RETURN_IF_ERROR(graph.CloseInputStream(kInputStream));
return graph.WaitUntilDone();
}
int main(int argc, char** argv) {
google::InitGoogleLogging(argv[0]);
gflags::ParseCommandLineFlags(&argc, &argv, true);
::mediapipe::Status run_status = RunMPPGraph();
if (!run_status.ok()) {
LOG(ERROR) << "Failed to run the graph: " << run_status.message();
} else {
LOG(INFO) << "Success!";
}
return 0;
}

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# MediaPipe graph that performs face detection with TensorFlow Lite on CPU.
# Used in the examples in
# mediapipe/examples/coral:face_detection_cpu.
# Images on GPU coming into and out of the graph.
input_stream: "input_video"
output_stream: "output_video"
# Throttles the images flowing downstream for flow control. It passes through
# the very first incoming image unaltered, and waits for
# TfLiteTensorsToDetectionsCalculator downstream in the graph to finish
# generating the corresponding detections before it passes through another
# image. All images that come in while waiting are dropped, limiting the number
# of in-flight images between this calculator and
# TfLiteTensorsToDetectionsCalculator to 1. This prevents the nodes in between
# from queuing up incoming images and data excessively, which leads to increased
# latency and memory usage, unwanted in real-time mobile applications. It also
# eliminates unnecessarily computation, e.g., a transformed image produced by
# ImageTransformationCalculator may get dropped downstream if the subsequent
# TfLiteConverterCalculator or TfLiteInferenceCalculator is still busy
# processing previous inputs.
node {
calculator: "FlowLimiterCalculator"
input_stream: "input_video"
input_stream: "FINISHED:detections"
input_stream_info: {
tag_index: "FINISHED"
back_edge: true
}
output_stream: "throttled_input_video"
}
# Transforms the input image on CPU to a 128x128 image. To scale the input
# image, the scale_mode option is set to FIT to preserve the aspect ratio,
# resulting in potential letterboxing in the transformed image.
node: {
calculator: "ImageTransformationCalculator"
input_stream: "IMAGE:throttled_input_video"
output_stream: "IMAGE:transformed_input_video_cpu"
output_stream: "LETTERBOX_PADDING:letterbox_padding"
options: {
[mediapipe.ImageTransformationCalculatorOptions.ext] {
output_width: 128
output_height: 128
scale_mode: FIT
}
}
}
# Converts the transformed input image on CPU into an image tensor stored as a
# TfLiteTensor.
node {
calculator: "TfLiteConverterCalculator"
input_stream: "IMAGE:transformed_input_video_cpu"
output_stream: "TENSORS:image_tensor"
options: {
[mediapipe.TfLiteConverterCalculatorOptions.ext] {
use_quantized_tensors: true
}
}
}
# Runs a TensorFlow Lite model on CPU that takes an image tensor and outputs a
# vector of tensors representing, for instance, detection boxes/keypoints and
# scores.
node {
calculator: "TfLiteInferenceCalculator"
input_stream: "TENSORS:image_tensor"
output_stream: "TENSORS:detection_tensors"
options: {
[mediapipe.TfLiteInferenceCalculatorOptions.ext] {
model_path: "mediapipe/examples/coral/models/face-detector-quantized_edgetpu.tflite"
}
}
}
# Generates a single side packet containing a vector of SSD anchors based on
# the specification in the options.
node {
calculator: "SsdAnchorsCalculator"
output_side_packet: "anchors"
options: {
[mediapipe.SsdAnchorsCalculatorOptions.ext] {
num_layers: 4
min_scale: 0.1484375
max_scale: 0.75
input_size_height: 128
input_size_width: 128
anchor_offset_x: 0.5
anchor_offset_y: 0.5
strides: 8
strides: 16
strides: 16
strides: 16
aspect_ratios: 1.0
fixed_anchor_size: true
}
}
}
# Decodes the detection tensors generated by the TensorFlow Lite model, based on
# the SSD anchors and the specification in the options, into a vector of
# detections. Each detection describes a detected object.
node {
calculator: "TfLiteTensorsToDetectionsCalculator"
input_stream: "TENSORS:detection_tensors"
input_side_packet: "ANCHORS:anchors"
output_stream: "DETECTIONS:detections"
options: {
[mediapipe.TfLiteTensorsToDetectionsCalculatorOptions.ext] {
num_classes: 1
num_boxes: 896
num_coords: 16
box_coord_offset: 0
keypoint_coord_offset: 4
num_keypoints: 6
num_values_per_keypoint: 2
sigmoid_score: true
score_clipping_thresh: 100.0
reverse_output_order: true
x_scale: 128.0
y_scale: 128.0
h_scale: 128.0
w_scale: 128.0
min_score_thresh: 0.75
}
}
}
# Performs non-max suppression to remove excessive detections.
node {
calculator: "NonMaxSuppressionCalculator"
input_stream: "detections"
output_stream: "filtered_detections"
options: {
[mediapipe.NonMaxSuppressionCalculatorOptions.ext] {
min_suppression_threshold: 0.3
overlap_type: INTERSECTION_OVER_UNION
algorithm: WEIGHTED
return_empty_detections: true
}
}
}
# Maps detection label IDs to the corresponding label text ("Face"). The label
# map is provided in the label_map_path option.
node {
calculator: "DetectionLabelIdToTextCalculator"
input_stream: "filtered_detections"
output_stream: "labeled_detections"
options: {
[mediapipe.DetectionLabelIdToTextCalculatorOptions.ext] {
label_map_path: "mediapipe/models/face_detection_front_labelmap.txt"
}
}
}
# Adjusts detection locations (already normalized to [0.f, 1.f]) on the
# letterboxed image (after image transformation with the FIT scale mode) to the
# corresponding locations on the same image with the letterbox removed (the
# input image to the graph before image transformation).
node {
calculator: "DetectionLetterboxRemovalCalculator"
input_stream: "DETECTIONS:labeled_detections"
input_stream: "LETTERBOX_PADDING:letterbox_padding"
output_stream: "DETECTIONS:output_detections"
}
# Converts the detections to drawing primitives for annotation overlay.
node {
calculator: "DetectionsToRenderDataCalculator"
input_stream: "DETECTIONS:output_detections"
output_stream: "RENDER_DATA:render_data"
options: {
[mediapipe.DetectionsToRenderDataCalculatorOptions.ext] {
thickness: 4.0
color { r: 255 g: 0 b: 0 }
}
}
}
# Draws annotations and overlays them on top of the input images.
node {
calculator: "AnnotationOverlayCalculator"
input_stream: "INPUT_FRAME:throttled_input_video"
input_stream: "render_data"
output_stream: "OUTPUT_FRAME:output_video"
}

179
mediapipe/examples/coral/graphs/object_detection_desktop_live.pbtxt Normal file
View File

@ -0,0 +1,179 @@
# MediaPipe graph that performs object detection with TensorFlow Lite on CPU.
# Used in the examples in
# mediapipie/examples/coral:object_detection_cpu.
# Images on CPU coming into and out of the graph.
input_stream: "input_video"
output_stream: "output_video"
# Throttles the images flowing downstream for flow control. It passes through
# the very first incoming image unaltered, and waits for
# TfLiteTensorsToDetectionsCalculator downstream in the graph to finish
# generating the corresponding detections before it passes through another
# image. All images that come in while waiting are dropped, limiting the number
# of in-flight images between this calculator and
# TfLiteTensorsToDetectionsCalculator to 1. This prevents the nodes in between
# from queuing up incoming images and data excessively, which leads to increased
# latency and memory usage, unwanted in real-time mobile applications. It also
# eliminates unnecessarily computation, e.g., a transformed image produced by
# ImageTransformationCalculator may get dropped downstream if the subsequent
# TfLiteConverterCalculator or TfLiteInferenceCalculator is still busy
# processing previous inputs.
node {
calculator: "FlowLimiterCalculator"
input_stream: "input_video"
input_stream: "FINISHED:detections"
input_stream_info: {
tag_index: "FINISHED"
back_edge: true
}
output_stream: "throttled_input_video"
}
# Transforms the input image on CPU to a 320x320 image. To scale the image, by
# default it uses the STRETCH scale mode that maps the entire input image to the
# entire transformed image. As a result, image aspect ratio may be changed and
# objects in the image may be deformed (stretched or squeezed), but the object
# detection model used in this graph is agnostic to that deformation.
node: {
calculator: "ImageTransformationCalculator"
input_stream: "IMAGE:throttled_input_video"
output_stream: "IMAGE:transformed_input_video"
options: {
[mediapipe.ImageTransformationCalculatorOptions.ext] {
output_width: 300
output_height: 300
}
}
}
# Converts the transformed input image on CPU into an image tensor stored as a
# TfLiteTensor.
node {
calculator: "TfLiteConverterCalculator"
input_stream: "IMAGE:transformed_input_video"
output_stream: "TENSORS:image_tensor"
options: {
[mediapipe.TfLiteConverterCalculatorOptions.ext] {
use_quantized_tensors: true
}
}
}
# Runs a TensorFlow Lite model on CPU that takes an image tensor and outputs a
# vector of tensors representing, for instance, detection boxes/keypoints and
# scores.
node {
calculator: "TfLiteInferenceCalculator"
input_stream: "TENSORS:image_tensor"
output_stream: "TENSORS:detection_tensors"
options: {
[mediapipe.TfLiteInferenceCalculatorOptions.ext] {
model_path: "mediapipe/examples/coral/models/object-detector-quantized_edgetpu.tflite"
}
}
}
# Generates a single side packet containing a vector of SSD anchors based on
# the specification in the options.
node {
calculator: "SsdAnchorsCalculator"
output_side_packet: "anchors"
options: {
[mediapipe.SsdAnchorsCalculatorOptions.ext] {
num_layers: 6
min_scale: 0.2
max_scale: 0.95
input_size_height: 300
input_size_width: 300
anchor_offset_x: 0.5
anchor_offset_y: 0.5
strides: 16
strides: 32
strides: 64
strides: 128
strides: 256
strides: 512
aspect_ratios: 1.0
aspect_ratios: 2.0
aspect_ratios: 0.5
aspect_ratios: 3.0
aspect_ratios: 0.3333
reduce_boxes_in_lowest_layer: true
}
}
}
# Decodes the detection tensors generated by the TensorFlow Lite model, based on
# the SSD anchors and the specification in the options, into a vector of
# detections. Each detection describes a detected object.
node {
calculator: "TfLiteTensorsToDetectionsCalculator"
input_stream: "TENSORS:detection_tensors"
input_side_packet: "ANCHORS:anchors"
output_stream: "DETECTIONS:detections"
options: {
[mediapipe.TfLiteTensorsToDetectionsCalculatorOptions.ext] {
num_classes: 91
num_boxes: 2034
num_coords: 4
ignore_classes: 0
sigmoid_score: true
apply_exponential_on_box_size: true
x_scale: 10.0
y_scale: 10.0
h_scale: 5.0
w_scale: 5.0
min_score_thresh: 0.6
}
}
}
# Performs non-max suppression to remove excessive detections.
node {
calculator: "NonMaxSuppressionCalculator"
input_stream: "detections"
output_stream: "filtered_detections"
options: {
[mediapipe.NonMaxSuppressionCalculatorOptions.ext] {
min_suppression_threshold: 0.4
max_num_detections: 3
overlap_type: INTERSECTION_OVER_UNION
return_empty_detections: true
}
}
}
# Maps detection label IDs to the corresponding label text. The label map is
# provided in the label_map_path option.
node {
calculator: "DetectionLabelIdToTextCalculator"
input_stream: "filtered_detections"
output_stream: "output_detections"
options: {
[mediapipe.DetectionLabelIdToTextCalculatorOptions.ext] {
label_map_path: "mediapipe/examples/coral/models/object_detection_labelmap.txt"
}
}
}
# Converts the detections to drawing primitives for annotation overlay.
node {
calculator: "DetectionsToRenderDataCalculator"
input_stream: "DETECTIONS:output_detections"
output_stream: "RENDER_DATA:render_data"
options: {
[mediapipe.DetectionsToRenderDataCalculatorOptions.ext] {
thickness: 4.0
color { r: 255 g: 0 b: 0 }
}
}
}
# Draws annotations and overlays them on top of the input images.
node {
calculator: "AnnotationOverlayCalculator"
input_stream: "INPUT_FRAME:throttled_input_video"
input_stream: "render_data"
output_stream: "OUTPUT_FRAME:output_video"
}

BIN
mediapipe/examples/coral/models/face-detector-quantized_edgetpu.tflite Normal file
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BIN
mediapipe/examples/coral/models/object-detector-quantized_edgetpu.tflite Normal file
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90
mediapipe/examples/coral/models/object_detection_labelmap.txt Normal file
View File

@ -0,0 +1,90 @@
person
bicycle
car
motorcycle
airplane
bus
train
truck
boat
traffic light
fire hydrant
???
stop sign
parking meter
bench
bird
cat
dog
horse
sheep
cow
elephant
bear
zebra
giraffe
???
backpack
umbrella
???
???
handbag
tie
suitcase
frisbee
skis
snowboard
sports ball
kite
baseball bat
baseball glove
skateboard
surfboard
tennis racket
bottle
???
wine glass
cup
fork
knife
spoon
bowl
banana
apple
sandwich
orange
broccoli
carrot
hot dog
pizza
donut
cake
chair
couch
potted plant
bed
???
dining table
???
???
toilet
???
tv
laptop
mouse
remote
keyboard
cell phone
microwave
oven
toaster
sink
refrigerator
???
book
clock
vase
scissors
teddy bear
hair drier
toothbrush

21
mediapipe/examples/coral/setup.sh Executable file
View File

@ -0,0 +1,21 @@
#!/bin/sh
set -e
set -v
echo 'Please run this from root level mediapipe directory! \n Ex:'
echo ' sh mediapipe/examples/coral/setup.sh '
sleep 3
mkdir opencv32_arm64_libs
cp mediapipe/examples/coral/update_sources.sh update_sources.sh
chmod +x update_sources.sh
mv Dockerfile Dockerfile.orig
cp mediapipe/examples/coral/Dockerfile Dockerfile
cp WORKSPACE WORKSPACE.orig
cp mediapipe/examples/coral/WORKSPACE WORKSPACE

11
mediapipe/examples/coral/update_sources.sh Executable file
View File

@ -0,0 +1,11 @@
#!/bin/bash
# To run in the Coral Docker environment.
. /etc/os-release
sed -i "s/deb\ /deb \[arch=amd64\]\ /g" /etc/apt/sources.list
echo "deb [arch=arm64,armhf] http://ports.ubuntu.com/ubuntu-ports ${UBUNTU_CODENAME} main universe" >> /etc/apt/sources.list
echo "deb [arch=arm64,armhf] http://ports.ubuntu.com/ubuntu-ports ${UBUNTU_CODENAME}-updates main universe" >> /etc/apt/sources.list
echo "deb [arch=arm64,armhf] http://ports.ubuntu.com/ubuntu-ports ${UBUNTU_CODENAME}-security main universe" >> /etc/apt/sources.list

73
mediapipe/examples/desktop/README.md
View File

@ -9,7 +9,9 @@ bazel build -c opt mediapipe/examples/desktop/hello_world:hello_world
and then run it using:
```
bazel-bin/mediapipe/examples/desktop/hello_world/hello_world --logtostderr
export GLOG_logtostderr=1
bazel-bin/mediapipe/examples/desktop/hello_world/hello_world
```
**TFlite Object Detection**
@ -23,10 +25,11 @@ bazel build -c opt mediapipe/examples/desktop/object_detection:object_detection_
and run it using:
```
export GLOG_logtostderr=1
bazel-bin/mediapipe/examples/desktop/object_detection/object_detection_tflite \
--calculator_graph_config_file=mediapipe/graphs/object_detection/object_detection_desktop_tflite_graph.pbtxt \
--input_side_packets=input_video_path=/path/to/input/file,output_video_path=/path/to/output/file \
--alsologtostderr
--input_side_packets=input_video_path=/path/to/input/file,output_video_path=/path/to/output/file
```
**TensorFlow Object Detection**
@ -34,6 +37,8 @@ bazel-bin/mediapipe/examples/desktop/object_detection/object_detection_tflite \
To build the object detection demo using a TensorFlow model on desktop, use:
```
export GLOG_logtostderr=1
bazel build -c opt mediapipe/examples/desktop/object_detection:object_detection_tensorflow \
--define MEDIAPIPE_DISABLE_GPU=1
```
@ -41,8 +46,68 @@ bazel build -c opt mediapipe/examples/desktop/object_detection:object_detection_
and run it using:
```
export GLOG_logtostderr=1
bazel-bin/mediapipe/examples/desktop/object_detection/object_detection_tensorflow \
--calculator_graph_config_file=mediapipe/graphs/object_detection/object_detection_desktop_tensorflow_graph.pbtxt \
--input_side_packets=input_video_path=/path/to/input/file,output_video_path=/path/to/output/file
--alsologtostderr
```
**TFlite Hand Detection**
To build the hand detection demo using a TFLite model on desktop, use:
```
bazel build -c opt mediapipe/examples/desktop/hand_tracking:hand_tracking_tflite --define MEDIAPIPE_DISABLE_GPU=1
```
and run it using:
```
export GLOG_logtostderr=1
bazel-bin/mediapipe/examples/desktop/hand_tracking/hand_tracking_tflite \
--calculator_graph_config_file=mediapipe/graphs/hand_tracking/hand_detection_desktop.pbtxt \
--input_side_packets=input_video_path=/path/to/input/file,output_video_path=/path/to/output/file
```
**TFlite Hand Tracking**
To build the hand tracking demo using a TFLite model on desktop, use:
```
bazel build -c opt mediapipe/examples/desktop/hand_tracking:hand_tracking_tflite --define MEDIAPIPE_DISABLE_GPU=1
```
and run it using:
```
export GLOG_logtostderr=1
bazel-bin/mediapipe/examples/desktop/hand_tracking/hand_tracking_tflite \
--calculator_graph_config_file=mediapipe/graphs/hand_tracking/hand_tracking_desktop.pbtxt \
--input_side_packets=input_video_path=/path/to/input/file,output_video_path=/path/to/output/file
```
**TFlite Multi-Hand Tracking**
To build the multi-hand tracking demo using a TFLite model on desktop, use:
```
bazel build -c opt mediapipe/examples/desktop/multi_hand_tracking:multi_hand_tracking_tflite --define MEDIAPIPE_DISABLE_GPU=1
```
and run it using:
```
export GLOG_logtostderr=1
bazel-bin/mediapipe/examples/desktop/multi_hand_tracking/multi_hand_tracking_tflite \
--calculator_graph_config_file=mediapipe/graphs/hand_tracking/multi_hand_tracking_desktop.pbtxt \
--input_side_packets=input_video_path=/path/to/input/file,output_video_path=/path/to/output/file
```
To change the number of hands to `x` in this application, change:
1. `min_size:x` in `CollectionHasMinSizeCalculatorOptions` in `mediapipe/graphs/hand_tracking/multi_hand_tracking_desktop.pbtxt`.
2. `max_vec_size:x` in `ClipVectorSizeCalculatorOptions` in `mediapipe/examples/dekstop/hand_tracking/subgraphs/multi_hand_detection_cpu.pbtxt`.

5
mediapipe/examples/desktop/demo_run_graph_main.cc
View File

@ -76,6 +76,11 @@ DEFINE_string(output_video_path, "",
RET_CHECK(writer.isOpened());
} else {
cv::namedWindow(kWindowName, /*flags=WINDOW_AUTOSIZE*/ 1);
#if (CV_MAJOR_VERSION >= 3) && (CV_MINOR_VERSION >= 2)
capture.set(cv::CAP_PROP_FRAME_WIDTH, 640);
capture.set(cv::CAP_PROP_FRAME_HEIGHT, 480);
capture.set(cv::CAP_PROP_FPS, 30);
#endif
}
LOG(INFO) << "Start running the calculator graph.";

5
mediapipe/examples/desktop/demo_run_graph_main_gpu.cc
View File

@ -86,6 +86,11 @@ DEFINE_string(output_video_path, "",
RET_CHECK(writer.isOpened());
} else {
cv::namedWindow(kWindowName, /*flags=WINDOW_AUTOSIZE*/ 1);
#if (CV_MAJOR_VERSION >= 3) && (CV_MINOR_VERSION >= 2)
capture.set(cv::CAP_PROP_FRAME_WIDTH, 640);
capture.set(cv::CAP_PROP_FRAME_HEIGHT, 480);
capture.set(cv::CAP_PROP_FPS, 30);
#endif
}
LOG(INFO) << "Start running the calculator graph.";

6
mediapipe/examples/desktop/media_sequence/kinetics_dataset.py
View File

@ -93,15 +93,15 @@ FILEPATTERN = "kinetics_700_%s_25fps_rgb_flow"
SPLITS = {
"train": {
"shards": 1000,
"examples": 541632
"examples": 541490
},
"validate": {
"shards": 100,
"examples": 34727
"examples": 34715
},
"test": {
"shards": 100,
"examples": 69347
"examples": 69321
},
"custom": {
"csv": None, # Add a CSV for your own data here.

9
mediapipe/examples/ios/handtrackinggpu/BUILD
View File

@ -12,15 +12,15 @@
# See the License for the specific language governing permissions and
# limitations under the License.
licenses(["notice"]) # Apache 2.0
MIN_IOS_VERSION = "10.0"
load(
"@build_bazel_rules_apple//apple:ios.bzl",
"ios_application",
)
licenses(["notice"]) # Apache 2.0
MIN_IOS_VERSION = "10.0"
# To use the 3D model instead of the default 2D model, add "--define 3D=true" to the
# bazel build command.
config_setting(
@ -90,6 +90,7 @@ objc_library(
"//mediapipe:ios_x86_64": [],
"//conditions:default": [
"//mediapipe/graphs/hand_tracking:mobile_calculators",
"//mediapipe/framework/formats:landmark_cc_proto",
],
}),
)

23
mediapipe/examples/ios/handtrackinggpu/ViewController.mm
View File

@ -18,10 +18,13 @@
#import "mediapipe/objc/MPPCameraInputSource.h"
#import "mediapipe/objc/MPPLayerRenderer.h"
#include "mediapipe/framework/formats/landmark.pb.h"
static NSString* const kGraphName = @"hand_tracking_mobile_gpu";
static const char* kInputStream = "input_video";
static const char* kOutputStream = "output_video";
static const char* kLandmarksOutputStream = "hand_landmarks";
static const char* kVideoQueueLabel = "com.google.mediapipe.example.videoQueue";
@interface ViewController () <MPPGraphDelegate, MPPInputSourceDelegate>
@ -80,6 +83,7 @@ static const char* kVideoQueueLabel = "com.google.mediapipe.example.videoQueue";
// Create MediaPipe graph with mediapipe::CalculatorGraphConfig proto object.
MPPGraph* newGraph = [[MPPGraph alloc] initWithGraphConfig:config];
[newGraph addFrameOutputStream:kOutputStream outputPacketType:MPPPacketTypePixelBuffer];
[newGraph addFrameOutputStream:kLandmarksOutputStream outputPacketType:MPPPacketTypeRaw];
return newGraph;
}
@ -160,6 +164,25 @@ static const char* kVideoQueueLabel = "com.google.mediapipe.example.videoQueue";
}
}
// Receives a raw packet from the MediaPipe graph. Invoked on a MediaPipe worker thread.
- (void)mediapipeGraph:(MPPGraph*)graph
didOutputPacket:(const ::mediapipe::Packet&)packet
fromStream:(const std::string&)streamName {
if (streamName == kLandmarksOutputStream) {
if (packet.IsEmpty()) {
NSLog(@"[TS:%lld] No hand landmarks", packet.Timestamp().Value());
return;
}
const auto& landmarks = packet.Get<::mediapipe::NormalizedLandmarkList>();
NSLog(@"[TS:%lld] Number of landmarks on hand: %d", packet.Timestamp().Value(),
landmarks.landmark_size());
for (int i = 0; i < landmarks.landmark_size(); ++i) {
NSLog(@"\tLandmark[%d]: (%f, %f, %f)", i, landmarks.landmark(i).x(),
landmarks.landmark(i).y(), landmarks.landmark(i).z());
}
}
}
#pragma mark - MPPInputSourceDelegate methods
// Must be invoked on _videoQueue.

9
mediapipe/examples/ios/multihandtrackinggpu/BUILD
View File

@ -12,15 +12,15 @@
# See the License for the specific language governing permissions and
# limitations under the License.
licenses(["notice"]) # Apache 2.0
MIN_IOS_VERSION = "10.0"
load(
"@build_bazel_rules_apple//apple:ios.bzl",
"ios_application",
)
licenses(["notice"]) # Apache 2.0
MIN_IOS_VERSION = "10.0"
# To use the 3D model instead of the default 2D model, add "--define 3D=true" to the
# bazel build command.
config_setting(
@ -90,6 +90,7 @@ objc_library(
"//mediapipe:ios_x86_64": [],
"//conditions:default": [
"//mediapipe/graphs/hand_tracking:multi_hand_mobile_calculators",
"//mediapipe/framework/formats:landmark_cc_proto",
],
}),
)

27
mediapipe/examples/ios/multihandtrackinggpu/ViewController.mm
View File

@ -18,10 +18,13 @@
#import "mediapipe/objc/MPPCameraInputSource.h"
#import "mediapipe/objc/MPPLayerRenderer.h"
#include "mediapipe/framework/formats/landmark.pb.h"
static NSString* const kGraphName = @"multi_hand_tracking_mobile_gpu";
static const char* kInputStream = "input_video";
static const char* kOutputStream = "output_video";
static const char* kLandmarksOutputStream = "multi_hand_landmarks";
static const char* kVideoQueueLabel = "com.google.mediapipe.example.videoQueue";
@interface ViewController () <MPPGraphDelegate, MPPInputSourceDelegate>
@ -80,6 +83,7 @@ static const char* kVideoQueueLabel = "com.google.mediapipe.example.videoQueue";
// Create MediaPipe graph with mediapipe::CalculatorGraphConfig proto object.
MPPGraph* newGraph = [[MPPGraph alloc] initWithGraphConfig:config];
[newGraph addFrameOutputStream:kOutputStream outputPacketType:MPPPacketTypePixelBuffer];
[newGraph addFrameOutputStream:kLandmarksOutputStream outputPacketType:MPPPacketTypeRaw];
return newGraph;
}
@ -160,6 +164,29 @@ static const char* kVideoQueueLabel = "com.google.mediapipe.example.videoQueue";
}
}
// Receives a raw packet from the MediaPipe graph. Invoked on a MediaPipe worker thread.
- (void)mediapipeGraph:(MPPGraph*)graph
didOutputPacket:(const ::mediapipe::Packet&)packet
fromStream:(const std::string&)streamName {
if (streamName == kLandmarksOutputStream) {
if (packet.IsEmpty()) {
NSLog(@"[TS:%lld] No hand landmarks", packet.Timestamp().Value());
return;
}
const auto& multi_hand_landmarks = packet.Get<std::vector<::mediapipe::NormalizedLandmarkList>>();
NSLog(@"[TS:%lld] Number of hand instances with landmarks: %lu", packet.Timestamp().Value(),
multi_hand_landmarks.size());
for (int hand_index = 0; hand_index < multi_hand_landmarks.size(); ++hand_index) {
const auto& landmarks = multi_hand_landmarks[hand_index];
NSLog(@"\tNumber of landmarks for hand[%d]: %d", hand_index, landmarks.landmark_size());
for (int i = 0; i < landmarks.landmark_size(); ++i) {
NSLog(@"\t\tLandmark[%d]: (%f, %f, %f)", i, landmarks.landmark(i).x(),
landmarks.landmark(i).y(), landmarks.landmark(i).z());
}
}
}
}
#pragma mark - MPPInputSourceDelegate methods
// Must be invoked on _videoQueue.

2
mediapipe/framework/BUILD
View File

@ -1524,7 +1524,9 @@ cc_test(
"//mediapipe/framework/stream_handler:fixed_size_input_stream_handler",
"//mediapipe/framework/stream_handler:immediate_input_stream_handler",
"//mediapipe/framework/stream_handler:mux_input_stream_handler",
"//mediapipe/framework/stream_handler:sync_set_input_stream_handler",
"//mediapipe/framework/tool:sink",
"@com_google_absl//absl/strings",
],
)

35
mediapipe/framework/calculator_graph.cc
View File

@ -1094,6 +1094,19 @@ bool CalculatorGraph::IsNodeThrottled(int node_id) {
return max_queue_size_ != -1 && !full_input_streams_[node_id].empty();
}
// Returns true if an input stream serves as a graph-output-stream.
bool IsGraphOutputStream(
InputStreamManager* stream,
const std::vector<std::shared_ptr<internal::GraphOutputStream>>&
graph_output_streams) {
for (auto& graph_output_stream : graph_output_streams) {
if (stream == graph_output_stream->input_stream()) {
return true;
}
}
return false;
}
bool CalculatorGraph::UnthrottleSources() {
// NOTE: We can be sure that this function will grow input streams enough
// to unthrottle at least one source node. The current stream queue sizes
@ -1105,25 +1118,17 @@ bool CalculatorGraph::UnthrottleSources() {
{
absl::MutexLock lock(&full_input_streams_mutex_);
for (absl::flat_hash_set<InputStreamManager*>& s : full_input_streams_) {
if (!s.empty()) {
full_streams.insert(s.begin(), s.end());
for (auto& stream : s) {
// The queue size of a graph output stream shouldn't change. Throttling
// should continue until the caller of the graph output stream consumes
// enough packets.
if (!IsGraphOutputStream(stream, graph_output_streams_)) {
full_streams.insert(stream);
}
}
}
}
for (InputStreamManager* stream : full_streams) {
// The queue size of a graph output stream shouldn't change. Throttling
// should continue until the caller of the graph output stream consumes
// enough packets.
bool is_graph_output_stream = false;
for (auto& graph_output_stream : graph_output_streams_) {
if (stream == graph_output_stream->input_stream()) {
is_graph_output_stream = true;
break;
}
}
if (is_graph_output_stream) {
continue;
}
if (Config().report_deadlock()) {
RecordError(::mediapipe::UnavailableError(absl::StrCat(
"Detected a deadlock due to input throttling for: \"", stream->Name(),

144
mediapipe/framework/calculator_graph_bounds_test.cc
View File

@ -12,6 +12,7 @@
// See the License for the specific language governing permissions and
// limitations under the License.
#include "absl/strings/str_replace.h"
#include "mediapipe/framework/calculator_context.h"
#include "mediapipe/framework/calculator_framework.h"
#include "mediapipe/framework/port/canonical_errors.h"
@ -642,6 +643,36 @@ REGISTER_CALCULATOR(OffsetBoundCalculator);
// A Calculator that produces a packet for each call to Process.
class BoundToPacketCalculator : public CalculatorBase {
public:
static ::mediapipe::Status GetContract(CalculatorContract* cc) {
for (int i = 0; i < cc->Inputs().NumEntries(); ++i) {
cc->Inputs().Index(i).SetAny();
}
for (int i = 0; i < cc->Outputs().NumEntries(); ++i) {
cc->Outputs().Index(i).Set<Timestamp>();
}
return ::mediapipe::OkStatus();
}
::mediapipe::Status Open(CalculatorContext* cc) final {
return ::mediapipe::OkStatus();
}
::mediapipe::Status Process(CalculatorContext* cc) final {
for (int i = 0; i < cc->Outputs().NumEntries(); ++i) {
Timestamp t = cc->Inputs().Index(i).Value().Timestamp();
cc->Outputs().Index(i).AddPacket(
mediapipe::MakePacket<Timestamp>(t).At(cc->InputTimestamp()));
}
return ::mediapipe::OkStatus();
}
};
REGISTER_CALCULATOR(BoundToPacketCalculator);
// A Calculator that produces packets at timestamps beyond the input timestamp.
class FuturePacketCalculator : public CalculatorBase {
static constexpr int64 kOutputFutureMicros = 3;
public:
static ::mediapipe::Status GetContract(CalculatorContract* cc) {
cc->Inputs().Index(0).Set<int>();
@ -654,11 +685,14 @@ class BoundToPacketCalculator : public CalculatorBase {
}
::mediapipe::Status Process(CalculatorContext* cc) final {
cc->Outputs().Index(0).AddPacket(Adopt(new int(33)));
const Packet& packet = cc->Inputs().Index(0).Value();
Timestamp timestamp =
Timestamp(packet.Timestamp().Value() + kOutputFutureMicros);
cc->Outputs().Index(0).AddPacket(packet.At(timestamp));
return ::mediapipe::OkStatus();
}
};
REGISTER_CALCULATOR(BoundToPacketCalculator);
REGISTER_CALCULATOR(FuturePacketCalculator);
// Verifies that SetOffset still propagates when Process is called and
// produces no output packets.
@ -964,5 +998,111 @@ TEST(CalculatorGraphBoundsTest, LastPacketCheck) {
MP_ASSERT_OK(graph.WaitUntilDone());
}
// Shows that bounds are indicated for input streams without input packets.
void TestBoundsForEmptyInputs(std::string input_stream_handler) {
// FuturePacketCalculator and OffsetBoundCalculator produce future ts bounds.
// BoundToPacketCalculator reports all of its bounds, including empty inputs.
std::string config_str = R"(
input_stream: 'input'
node {
calculator: 'FuturePacketCalculator'
input_stream: 'input'
output_stream: 'futures'
}
node {
calculator: 'OffsetBoundCalculator'
input_stream: 'futures'
output_stream: 'bounds'
}
node {
calculator: 'BoundToPacketCalculator'
input_stream: 'input'
input_stream: 'bounds'
output_stream: 'input_ts'
output_stream: 'bounds_ts'
input_stream_handler { $input_stream_handler }
}
)";
absl::StrReplaceAll({{"$input_stream_handler", input_stream_handler}},
&config_str);
CalculatorGraphConfig config =
::mediapipe::ParseTextProtoOrDie<CalculatorGraphConfig>(config_str);
CalculatorGraph graph;
std::vector<Packet> input_ts_packets;
std::vector<Packet> bounds_ts_packets;
MP_ASSERT_OK(graph.Initialize(config));
MP_ASSERT_OK(graph.ObserveOutputStream("input_ts", [&](const Packet& p) {
input_ts_packets.push_back(p);
return ::mediapipe::OkStatus();
}));
MP_ASSERT_OK(graph.ObserveOutputStream("bounds_ts", [&](const Packet& p) {
bounds_ts_packets.push_back(p);
return ::mediapipe::OkStatus();
}));
MP_ASSERT_OK(graph.StartRun({}));
MP_ASSERT_OK(graph.WaitUntilIdle());
// Add four packets into the graph, with timedtamps 0, 10, 20, 30.
constexpr int kNumInputs = 4;
for (int i = 0; i < kNumInputs; ++i) {
Packet p = MakePacket<int>(33).At(Timestamp(i * 10));
MP_ASSERT_OK(graph.AddPacketToInputStream("input", p));
MP_ASSERT_OK(graph.WaitUntilIdle());
}
// Packets arrive. The input packet timestamps are: 0, 10, 20, 30.
// The corresponding empty packet timestamps are: 3, 13, 23, 33.
MP_ASSERT_OK(graph.WaitUntilIdle());
EXPECT_EQ(input_ts_packets.size(), 4);
EXPECT_EQ(bounds_ts_packets.size(), 4);
// The timestamp bounds from OffsetBoundCalculator are: 3, 13, 23, 33.
// Because the process call waits for the input packets and not for
// the empty packets, the first empty packet timestamp can be
// either Timestamp::Unstarted() or Timestamp(3).
std::vector<Timestamp> expected = {Timestamp::Unstarted(), Timestamp(3),
Timestamp(13), Timestamp(23),
Timestamp(33)};
for (int i = 0; i < bounds_ts_packets.size(); ++i) {
Timestamp ts = bounds_ts_packets[i].Get<Timestamp>();
EXPECT_GE(ts, expected[i]);
EXPECT_LE(ts, expected[i + 1]);
}
// Shutdown the graph.
MP_ASSERT_OK(graph.CloseAllPacketSources());
MP_ASSERT_OK(graph.WaitUntilDone());
}
// Shows that bounds are indicated for input streams without input packets.
TEST(CalculatorGraphBoundsTest, BoundsForEmptyInputs_Immediate) {
TestBoundsForEmptyInputs(R"(
input_stream_handler: "ImmediateInputStreamHandler")");
}
// Shows that bounds are indicated for input streams without input packets.
TEST(CalculatorGraphBoundsTest, BoundsForEmptyInputs_Default) {
TestBoundsForEmptyInputs(R"(
input_stream_handler: "DefaultInputStreamHandler")");
}
// Shows that bounds are indicated for input streams without input packets.
TEST(CalculatorGraphBoundsTest, BoundsForEmptyInputs_SyncSet) {
TestBoundsForEmptyInputs(R"(
input_stream_handler: "SyncSetInputStreamHandler")");
}
// Shows that bounds are indicated for input streams without input packets.
TEST(CalculatorGraphBoundsTest, BoundsForEmptyInputs_SyncSets) {
TestBoundsForEmptyInputs(R"(
input_stream_handler: "SyncSetInputStreamHandler"
options {
[mediapipe.SyncSetInputStreamHandlerOptions.ext] {
sync_set { tag_index: ":0" }
}
}
)");
}
} // namespace
} // namespace mediapipe

60
mediapipe/framework/formats/BUILD
View File

@ -13,10 +13,6 @@
# limitations under the License.
#
licenses(["notice"]) # Apache 2.0
exports_files(["LICENSE"])
load("//mediapipe/framework/port:build_config.bzl", "mediapipe_cc_proto_library")
package(
@ -24,75 +20,74 @@ package(
features = ["-layering_check"],
)
licenses(["notice"]) # Apache 2.0
exports_files(["LICENSE"])
proto_library(
name = "detection_proto",
srcs = ["detection.proto"],
visibility = ["//mediapipe:__subpackages__"],
visibility = ["//visibility:public"],
deps = ["//mediapipe/framework/formats:location_data_proto"],
)
proto_library(
name = "classification_proto",
srcs = ["classification.proto"],
visibility = ["//mediapipe:__subpackages__"],
visibility = ["//visibility:public"],
)
proto_library(
name = "image_format_proto",
srcs = ["image_format.proto"],
visibility = ["//mediapipe:__subpackages__"],
visibility = ["//visibility:public"],
)
proto_library(
name = "matrix_data_proto",
srcs = ["matrix_data.proto"],
visibility = ["//mediapipe:__subpackages__"],
visibility = ["//visibility:public"],
)
proto_library(
name = "location_data_proto",
srcs = ["location_data.proto"],
visibility = ["//visibility:public"],
deps = ["//mediapipe/framework/formats/annotation:rasterization_proto"],
)
proto_library(
name = "time_series_header_proto",
srcs = ["time_series_header.proto"],
visibility = ["//mediapipe:__subpackages__"],
visibility = ["//visibility:public"],
)
mediapipe_cc_proto_library(
name = "detection_cc_proto",
srcs = ["detection.proto"],
cc_deps = [":location_data_cc_proto"],
visibility = ["//mediapipe:__subpackages__"],
visibility = ["//visibility:public"],
deps = [":detection_proto"],
)
mediapipe_cc_proto_library(
name = "classification_cc_proto",
srcs = ["classification.proto"],
visibility = ["//mediapipe:__subpackages__"],
visibility = ["//visibility:public"],
deps = [":classification_proto"],
)
mediapipe_cc_proto_library(
name = "image_format_cc_proto",
srcs = ["image_format.proto"],
visibility = [
"//mediapipe:__subpackages__",
"//mediapipe/java/com/google/mediapipe/framework:__subpackages__",
],
visibility = ["//visibility:public"],
deps = [":image_format_proto"],
)
mediapipe_cc_proto_library(
name = "matrix_data_cc_proto",
srcs = ["matrix_data.proto"],
visibility = [
"//mediapipe:__subpackages__",
"//mediapipe/java/com/google/mediapipe/framework:__subpackages__",
],
visibility = ["//visibility:public"],
deps = [":matrix_data_proto"],
)
@ -100,17 +95,14 @@ mediapipe_cc_proto_library(
name = "location_data_cc_proto",
srcs = ["location_data.proto"],
cc_deps = ["//mediapipe/framework/formats/annotation:rasterization_cc_proto"],
visibility = ["//mediapipe:__subpackages__"],
visibility = ["//visibility:public"],
deps = [":location_data_proto"],
)
mediapipe_cc_proto_library(
name = "time_series_header_cc_proto",
srcs = ["time_series_header.proto"],
visibility = [
"//mediapipe:__subpackages__",
"//mediapipe/java/com/google/mediapipe/framework:__subpackages__",
],
visibility = ["//visibility:public"],
deps = [":time_series_header_proto"],
)
@ -249,14 +241,14 @@ cc_test(
proto_library(
name = "rect_proto",
srcs = ["rect.proto"],
visibility = ["//mediapipe:__subpackages__"],
visibility = ["//visibility:public"],
)
mediapipe_cc_proto_library(
name = "rect_cc_proto",
srcs = ["rect.proto"],
visibility = [
"//mediapipe:__subpackages__",
"//visibility:public",
],
deps = [":rect_proto"],
)
@ -264,12 +256,26 @@ mediapipe_cc_proto_library(
proto_library(
name = "landmark_proto",
srcs = ["landmark.proto"],
visibility = ["//mediapipe:__subpackages__"],
visibility = ["//visibility:public"],
)
mediapipe_cc_proto_library(
name = "landmark_cc_proto",
srcs = ["landmark.proto"],
visibility = ["//visibility:public"],
deps = [":landmark_proto"],
)
java_lite_proto_library(
name = "landmark_java_proto_lite",
strict_deps = 0,
visibility = ["//mediapipe:__subpackages__"],
deps = [":landmark_proto"],
)
# Expose the proto source files for building mediapipe AAR.
filegroup(
name = "protos_src",
srcs = glob(["*.proto"]),
visibility = ["//mediapipe:__subpackages__"],
)

7
mediapipe/framework/formats/annotation/BUILD
View File

@ -25,26 +25,27 @@ package(default_visibility = ["//visibility:private"])
proto_library(
name = "locus_proto",
srcs = ["locus.proto"],
visibility = ["//visibility:public"],
deps = ["//mediapipe/framework/formats/annotation:rasterization_proto"],
)
proto_library(
name = "rasterization_proto",
srcs = ["rasterization.proto"],
visibility = ["//mediapipe:__subpackages__"],
visibility = ["//visibility:public"],
)
mediapipe_cc_proto_library(
name = "locus_cc_proto",
srcs = ["locus.proto"],
cc_deps = [":rasterization_cc_proto"],
visibility = ["//mediapipe:__subpackages__"],
visibility = ["//visibility:public"],
deps = [":locus_proto"],
)
mediapipe_cc_proto_library(
name = "rasterization_cc_proto",
srcs = ["rasterization.proto"],
visibility = ["//mediapipe:__subpackages__"],
visibility = ["//visibility:public"],
deps = [":rasterization_proto"],
)

2
mediapipe/framework/formats/annotation/rasterization.proto
View File

@ -27,7 +27,7 @@ message Rasterization {
required int32 right_x = 3;
}
// Intervals are always sorted by y-corrdinate.
// Intervals are always sorted by y-coordinate.
// Therefore, a region occupies a set of scanlines ranging
// from interval(0).y() to interval(interval_size() - 1)).y().
// Note: In video, at some scanlines no interval might be present.

8
mediapipe/framework/formats/landmark.proto
View File

@ -16,6 +16,9 @@ syntax = "proto2";
package mediapipe;
option java_package = "com.google.mediapipe.formats.proto";
option java_outer_classname = "LandmarkProto";
// A landmark that can have 1 to 3 dimensions. Use x for 1D points, (x, y) for
// 2D points and (x, y, z) for 3D points. For more dimensions, consider using
// matrix_data.proto.
@ -25,6 +28,11 @@ message Landmark {
optional float z = 3;
}
// Group of Landmark protos.
message LandmarkList {
repeated Landmark landmark = 1;
}
// A normalized version of above Landmark proto. All coordiates should be within
// [0, 1].
message NormalizedLandmark {

5
mediapipe/framework/formats/motion/BUILD
View File

@ -27,12 +27,13 @@ package(default_visibility = ["//visibility:private"])
proto_library(
name = "optical_flow_field_data_proto",
srcs = ["optical_flow_field_data.proto"],
visibility = ["//visibility:public"],
)
mediapipe_cc_proto_library(
name = "optical_flow_field_data_cc_proto",
srcs = ["optical_flow_field_data.proto"],
visibility = ["//mediapipe:__subpackages__"],
visibility = ["//visibility:public"],
deps = [":optical_flow_field_data_proto"],
)
@ -41,7 +42,7 @@ cc_library(
srcs = ["optical_flow_field.cc"],
hdrs = ["optical_flow_field.h"],
visibility = [
"//mediapipe:__subpackages__",
"//visibility:public",
],
deps = [
"//mediapipe/framework:type_map",

4
mediapipe/framework/formats/object_detection/BUILD
View File

@ -24,12 +24,12 @@ package(default_visibility = ["//visibility:private"])
proto_library(
name = "anchor_proto",
srcs = ["anchor.proto"],
visibility = ["//mediapipe:__subpackages__"],
visibility = ["//visibility:public"],
)
mediapipe_cc_proto_library(
name = "anchor_cc_proto",
srcs = ["anchor.proto"],
visibility = ["//mediapipe:__subpackages__"],
visibility = ["//visibility:public"],
deps = [":anchor_proto"],
)

9
mediapipe/framework/input_stream_manager.cc
View File

@ -229,6 +229,11 @@ Timestamp InputStreamManager::MinTimestampOrBound(bool* is_empty) const {
if (is_empty) {
*is_empty = queue_.empty();
}
return MinTimestampOrBoundHelper();
}
Timestamp InputStreamManager::MinTimestampOrBoundHelper() const
EXCLUSIVE_LOCKS_REQUIRED(stream_mutex_) {
return queue_.empty() ? next_timestamp_bound_ : queue_.front().Timestamp();
}
@ -271,7 +276,9 @@ Packet InputStreamManager::PopPacketAtTimestamp(Timestamp timestamp,
}
// Clear value_ if it doesn't have exactly the right timestamp.
if (current_timestamp != timestamp) {
packet = Packet();
// The timestamp bound reported when no packet is sent.
Timestamp bound = MinTimestampOrBoundHelper();
packet = Packet().At(bound.PreviousAllowedInStream());
++(*num_packets_dropped);
}

3
mediapipe/framework/input_stream_manager.h
View File

@ -189,6 +189,9 @@ class InputStreamManager {
// Returns true if the next timestamp bound reaches Timestamp::Done().
bool IsDone() const EXCLUSIVE_LOCKS_REQUIRED(stream_mutex_);
// Returns the smallest timestamp at which this stream might see an input.
Timestamp MinTimestampOrBoundHelper() const;
mutable absl::Mutex stream_mutex_;
std::deque<Packet> queue_ GUARDED_BY(stream_mutex_);
// The number of packets added to queue_. Used to verify a packet at

8
mediapipe/framework/packet.cc
View File

@ -107,6 +107,14 @@ const proto_ns::MessageLite& Packet::GetProtoMessageLite() const {
return *proto;
}
StatusOr<std::vector<const proto_ns::MessageLite*>>
Packet::GetVectorOfProtoMessageLitePtrs() {
if (holder_ == nullptr) {
return ::mediapipe::InternalError("Packet is empty.");
}
return holder_->GetVectorOfProtoMessageLite();
}
MEDIAPIPE_REGISTER_TYPE(::mediapipe::Packet, "::mediapipe::Packet", nullptr,
nullptr);
MEDIAPIPE_REGISTER_TYPE(::std::vector<::mediapipe::Packet>,

52
mediapipe/framework/packet.h
View File

@ -163,6 +163,13 @@ class Packet {
// object type is protocol buffer, crashes otherwise.
const proto_ns::MessageLite& GetProtoMessageLite() const;
// Returns a vector of pointers to MessageLite data, if the underlying
// object type is a vector of MessageLite data, returns an error otherwise.
// Note: This function is meant to be used internally within the MediaPipe
// framework only.
StatusOr<std::vector<const proto_ns::MessageLite*>>
GetVectorOfProtoMessageLitePtrs();
// Returns an error if the packet does not contain data of type T.
template <typename T>
::mediapipe::Status ValidateAsType() const;
@ -347,6 +354,12 @@ class HolderBase {
// underlying object is protocol buffer type, otherwise, nullptr is returned.
virtual const proto_ns::MessageLite* GetProtoMessageLite() = 0;
// Returns a vector<MessageLite*> for the data in the holder, if the
// underlying object is a vector of protocol buffer objects, otherwise,
// returns an error.
virtual StatusOr<std::vector<const proto_ns::MessageLite*>>
GetVectorOfProtoMessageLite() = 0;
private:
size_t type_id_;
};
@ -364,6 +377,37 @@ const proto_ns::MessageLite* ConvertToProtoMessageLite(const T* data,
return data;
}
// Helper structs for determining if a type is an std::vector<Proto>.
template <typename Type>
struct is_proto_vector : public std::false_type {};
template <typename ItemT, typename Allocator>
struct is_proto_vector<std::vector<ItemT, Allocator>>
: public std::is_base_of<proto_ns::MessageLite, ItemT>::type {};
// Helper function to create and return a vector of pointers to proto message
// elements of the vector passed into the function.
template <typename T>
StatusOr<std::vector<const proto_ns::MessageLite*>>
ConvertToVectorOfProtoMessageLitePtrs(const T* data,
/*is_proto_vector=*/std::false_type) {
return ::mediapipe::InvalidArgumentError(absl::StrCat(
"The Packet stores \"", typeid(T).name(), "\"",
"which is not convertible to vector<proto_ns::MessageLite*>."));
}
template <typename T>
StatusOr<std::vector<const proto_ns::MessageLite*>>
ConvertToVectorOfProtoMessageLitePtrs(const T* data,
/*is_proto_vector=*/std::true_type) {
std::vector<const proto_ns::MessageLite*> result;
for (auto it = data->begin(); it != data->end(); ++it) {
const proto_ns::MessageLite* element = &(*it);
result.push_back(element);
}
return result;
}
template <typename T>
class Holder : public HolderBase {
public:
@ -421,6 +465,14 @@ class Holder : public HolderBase {
ptr_, std::is_base_of<proto_ns::MessageLite, T>());
}
// Returns a vector<MessageLite*> for the data in the holder, if the
// underlying object is a vector of protocol buffer objects, otherwise,
// returns an error.
StatusOr<std::vector<const proto_ns::MessageLite*>>
GetVectorOfProtoMessageLite() override {
return ConvertToVectorOfProtoMessageLitePtrs(ptr_, is_proto_vector<T>());
}
private:
// Call delete[] if T is an array, delete otherwise.
template <typename U = T>

10
mediapipe/framework/port.h
View File

@ -37,12 +37,16 @@
#if !defined(MEDIAPIPE_IOS) && !TARGET_OS_OSX
#define MEDIAPIPE_IOS
#endif
#if !defined(MEDIAPIPE_OSX) && TARGET_OS_OSX
#define MEDIAPIPE_OSX
#endif
#endif
// These platforms do not support OpenGL ES Compute Shaders (v3.1 and up),
// but can still run OpenGL ES 3.0 and below.
#if !defined(MEDIAPIPE_DISABLE_GL_COMPUTE) && \
(defined(__APPLE__) || defined(__EMSCRIPTEN__))
// but may or may not still be able to run other OpenGL code.
#if !defined(MEDIAPIPE_DISABLE_GL_COMPUTE) && \
(defined(__APPLE__) || defined(__EMSCRIPTEN__) || \
defined(MEDIAPIPE_DISABLE_GPU))
#define MEDIAPIPE_DISABLE_GL_COMPUTE
#endif

2
mediapipe/framework/port/opencv_core_inc.h
View File

@ -20,6 +20,8 @@
#ifdef CV_VERSION_EPOCH // for OpenCV 2.x
#include <opencv2/core/core.hpp>
#else
#include <opencv2/cvconfig.h>
#include <opencv2/core.hpp>
#endif

6
mediapipe/framework/scheduler.cc
View File

@ -158,9 +158,11 @@ void Scheduler::HandleIdle() {
if (!active_sources_.empty() || throttled_graph_input_stream_count_ > 0) {
VLOG(2) << "HandleIdle: unthrottling";
state_mutex_.Unlock();
graph_->UnthrottleSources();
bool did_unthrottle = graph_->UnthrottleSources();
state_mutex_.Lock();
continue;
if (did_unthrottle) {
continue;
}
}
// Nothing left to do.

7
mediapipe/framework/stream_handler/immediate_input_stream_handler.cc
View File

@ -115,9 +115,10 @@ void ImmediateInputStreamHandler::FillInputSet(Timestamp input_timestamp,
AddPacketToShard(&input_set->Get(id), std::move(current_packet),
stream_is_done);
} else {
bool empty = false;
bool is_done = stream->MinTimestampOrBound(&empty) == Timestamp::Done();
AddPacketToShard(&input_set->Get(id), Packet(), is_done);
Timestamp bound = stream->MinTimestampOrBound(nullptr);
AddPacketToShard(&input_set->Get(id),
Packet().At(bound.PreviousAllowedInStream()),
bound == Timestamp::Done());
}
}
}

24
mediapipe/framework/stream_handler/sync_set_input_stream_handler.cc
View File

@ -56,9 +56,16 @@ class SyncSetInputStreamHandler : public InputStreamHandler {
NodeReadiness GetNodeReadiness(Timestamp* min_stream_timestamp) override;
// Only invoked when associated GetNodeReadiness() returned kReadyForProcess.
// Populates packets for the ready sync-set, and populates timestamp bounds
// for all sync-sets.
void FillInputSet(Timestamp input_timestamp,
InputStreamShardSet* input_set) override;
// Populates timestamp bounds for streams outside the ready sync-set.
void FillInputBounds(Timestamp input_timestamp,
InputStreamShardSet* input_set)
EXCLUSIVE_LOCKS_REQUIRED(mutex_);
private:
absl::Mutex mutex_;
// The ids of each set of inputs.
@ -181,6 +188,22 @@ NodeReadiness SyncSetInputStreamHandler::GetNodeReadiness(
return NodeReadiness::kNotReady;
}
void SyncSetInputStreamHandler::FillInputBounds(
Timestamp input_timestamp, InputStreamShardSet* input_set) {
for (int i = 0; i < sync_sets_.size(); ++i) {
if (i != ready_sync_set_index_) {
// Set the input streams for the not-ready sync sets.
for (CollectionItemId id : sync_sets_[i]) {
const auto stream = input_stream_managers_.Get(id);
Timestamp bound = stream->MinTimestampOrBound(nullptr);
AddPacketToShard(&input_set->Get(id),
Packet().At(bound.PreviousAllowedInStream()),
bound == Timestamp::Done());
}
}
}
}
void SyncSetInputStreamHandler::FillInputSet(Timestamp input_timestamp,
InputStreamShardSet* input_set) {
// Assume that all current packets are already cleared.
@ -202,6 +225,7 @@ void SyncSetInputStreamHandler::FillInputSet(Timestamp input_timestamp,
AddPacketToShard(&input_set->Get(id), std::move(current_packet),
stream_is_done);
}
FillInputBounds(input_timestamp, input_set);
ready_sync_set_index_ = -1;
ready_timestamp_ = Timestamp::Done();
}

20
mediapipe/framework/timestamp.cc
View File

@ -123,13 +123,23 @@ std::string TimestampDiff::DebugString() const {
Timestamp Timestamp::NextAllowedInStream() const {
CHECK(IsAllowedInStream()) << "Timestamp is: " << DebugString();
if (IsRangeValue() && *this != Max()) {
return *this + 1;
} else {
// Returning this value effectively means no futher timestamps may
// occur (however, the stream is not yet closed).
if (*this >= Max() || *this == PreStream()) {
// Indicates that no further timestamps may occur.
return OneOverPostStream();
} else if (*this < Min()) {
return Min();
}
return *this + 1;
}
Timestamp Timestamp::PreviousAllowedInStream() const {
if (*this <= Min() || *this == PostStream()) {
// Indicates that no previous timestamps may occur.
return Unstarted();
} else if (*this > Max()) {
return Max();
}
return *this - 1;
}
std::ostream& operator<<(std::ostream& os, Timestamp arg) {

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