اليوم ، تستخدم العديد من الخدمات نماذج الشبكات العصبية في عملها. ومع ذلك ، بسبب الأداء المنخفض لأجهزة العملاء ، في معظم الحالات ، يتم إجراء العمليات الحسابية على الخادم. ومع ذلك ، فإن أداء الهواتف الذكية ينمو كل عام ، والآن أصبح من الممكن إطلاق نماذج صغيرة على أجهزة العميل. السؤال الذي يطرح نفسه: كيف نفعل ذلك؟ بالإضافة إلى تشغيل النموذج ، تحتاج إلى معالجة البيانات مسبقًا ومعالجتها. بالإضافة إلى ذلك ، هناك نظامان أساسيان على الأقل يجب تنفيذ ذلك فيهما: Android و iOS. Mediapipe هو إطار لإطلاق خطوط الأنابيب (المعالجة المسبقة للبيانات ، والاستدلال من النموذج ، وكذلك المعالجة اللاحقة لنتائج النموذج) للتعلم الآلي ، والذي يسمح بحل المشاكل الموضحة أعلاه وتبسيط كتابة التعليمات البرمجية عبر الأنظمة الأساسية لتشغيل النماذج.

المحتوى

1. نظرة عامة على نظام بناء Bazel
2. إطار Mediapipe
   
   1. ما هو أنبوب الميديا. ما يتكون ولماذا هناك حاجة
   2. تطبيق HelloWorld على Mediapipe
   3. تشغيل نموذج باستخدام Mediapipe
3. إنشاء تطبيق باستخدام Mediapipe
4. استنتاج

نظرة عامة على نظام بناء Bazel

يستخدم Mediapipe Bazel للتجميع. كانت هذه هي المرة الأولى التي صادفت فيها نظام البناء هذا ، لذلك بالتوازي مع Mediapipe ، اكتشفته مع Bazel ، وجمع المعلومات من مصادر مختلفة والخطو في أشعل النار. لذلك ، قبل أن أخبر عن Mediapipe نفسها ، أود أن أقدم مقدمة صغيرة عن نظام تجميع Bazel ، والذي يستخدم لبناء Mediapipe والمشاريع القائمة عليه. Bazel هو نظام بناء من Google ، وهو نسخة منقحة من نظام Blaze الداخلي الخاص بهم ، تمت مشاركته. يسمح لك Bazel بدمج اللغات والأطر المختلفة في مشروع واحد مع أمر بسيط:

bazel build TARGET

, ? , Bazel-. WORKSPACE, . , . , A, B, WORKSPACE A, B. - WORKSPACE , Mediapipe. Bazel , , , , , . WORKSPACE:

#      
load("@bazel_tools//tools/build_defs/repo:http.bzl", "http_archive")

#        c++
http_archive(
    name = "rules_cc",
    strip_prefix = "rules_cc-master",
    urls = ["https://github.com/bazelbuild/rules_cc/archive/master.zip"],
)

#   googletest
http_archive(
    name = "com_google_googletest",
    strip_prefix = "googletest-master",
    urls = ["https://github.com/google/googletest/archive/master.zip"],
)

(bazel rule). — Starlark. Bazel , , . , http_archive , http_archive C++ googletest.

WORKSPACE Bazel BUILD. BUILD , , . BUILD C++ :

cc_binary(
    name="HelloWorld",
    srcs=["main.cpp"],
    copts=["-Wall", "-Wpedantic", "-Werror"]
)

, , WORKSPACE http_archive. Bazel- main.cpp.

• ./WORKSPACE
• ./Source/BUILD
• ./Source/main.cpp

bazel build //Source:HelloWorld

Source — , HelloWorld — .

cmake, ( ) ./bazel-bin.

, , . Bazel (libraries).

cc_library(
    name = "lib",
    hdrs = ["utils.h"],
    srcs = ["utils.cpp"],
    visibility = [
        "//visibility:public",
    ],
)

cc_binary(
    name="HelloWorld",
    srcs=["main.cpp"],
    deps=[":lib"],
    copts=["-Wall", "-Wpedantic", "-Werror"]
)

BUILD , . , //

@some_repository//Module:lib

Bazel , . http_archive, git_repository, local_repository .

Mediapipe

Mediapipe

Mediapipe — . , ( ):

1. (Calculators) — (). . C++ , CalculatorBase:
   
   • static Status GetContract(CalculatorContract*); — , , .
   • Status Open(CalculatorContext*); — . , , , .
   • Status Process(CalculatorContext*); — .
   • Status Close(CalculatorContext*); — .
2. (Streams) . . , (input) (output). , , , .
3. (Packet) — , . — , , , protobuf. timestamp — , . , , , , .

protobuf.

Mediapipe , . Linux, WSL, MacOS, Android, iOS. Mediapipe TensorFlow TFLite . , , .

, Mediapipe. , . Mediapipe, , - .

HelloWorld Mediapipe

, , N , .

.pbtxt , main.cpp.

├── hello-world
│   ├── BUILD
│   ├── graph.pbtxt
│   ├── main.cpp
│   ├── RepeatNTimesCalculator.cpp
│   └── RepeatNTimesCalculator.proto
└── WORKSPACE

:

input_stream: "in"
output_stream: "out"

node {
    calculator: "RepeatNTimesCalculator"
    input_stream: "in"
    output_stream: "OUTPUT_TAG:out"
    node_options: {
        [type.googleapis.com/mediapipe_demonstration.RepeatNTimesCalculatoOptions] {
            n: 3
        }
    }
}

RepeatNTimesCalculator . GetContract, , Open, , Process, .

class RepeatNTimesCalculator : public mediapipe::CalculatorBase {
public:
    static mediapipe::Status GetContract(mediapipe::CalculatorContract* cc) {
        //        ,  
        cc->Inputs().Get("", 0).Set<std::string>();
        //       OUTPUT_TAG,     
        cc->Outputs().Get("OUTPUT_TAG", 0).Set<std::string>();
        return mediapipe::OkStatus();
    }

    mediapipe::Status Open(mediapipe::CalculatorContext* cc) final {
        //   ,    
        const auto& options = cc->Options<mediapipe_demonstration::RepeatNTimesCalculatoOptions>();
        // n -      
        n_ = options.n();
        return mediapipe::OkStatus();
    }

    mediapipe::Status Process(mediapipe::CalculatorContext* cc) final {
        //     
        //           
        //       std::string
        auto txt = cc->Inputs().Index(0).Value().Get<std::string>();

        for (int i = 0; i < n_; ++i) {
            //      
            auto packet = mediapipe::MakePacket<std::string>(txt).At(cc->InputTimestamp() + i);
            //       OUTPUT_TAG   0
            cc->Outputs().Get("OUTPUT_TAG", 0).AddPacket(packet);
        }

        return mediapipe::OkStatus();
    }
private:
    int n_;
};
//    
REGISTER_CALCULATOR(RepeatNTimesCalculator);

.cpp , , REGISTER_CALCULATOR.

proto- . RepeatNTimesCalculatoOptions, , .

syntax = "proto2";
package mediapipe_demonstration;
import "mediapipe/framework/calculator_options.proto";
message RepeatNTimesCalculatoOptions {
  extend mediapipe.CalculatorOptions {
    optional RepeatNTimesCalculatoOptions ext = 350607623;
  }
  required int32 n = 2;
}

:

mediapipe::Status RunGraph() {
    //    
    std::ifstream file("./hello-world/graph.pbtxt");
    std::string graph_file_content;
    graph_file_content.assign(
        std::istreambuf_iterator<char>(file), 
        std::istreambuf_iterator<char>());
    mediapipe::CalculatorGraphConfig config = 
        mediapipe::ParseTextProtoOrDie<mediapipe::CalculatorGraphConfig>(graph_file_content);
    //  
    mediapipe::CalculatorGraph graph;
    MP_RETURN_IF_ERROR(graph.Initialize(config));
    //    
    ASSIGN_OR_RETURN(mediapipe::OutputStreamPoller poller, graph.AddOutputStreamPoller("out"));
    //  
    MP_RETURN_IF_ERROR(graph.StartRun({}));
    //        
    auto input_packet = mediapipe::MakePacket<std::string>("Hello!").At(mediapipe::Timestamp(0));
    MP_RETURN_IF_ERROR(graph.AddPacketToInputStream("in", input_packet));
    MP_RETURN_IF_ERROR(graph.CloseInputStream("in"));
    //    
    mediapipe::Packet packet;
    while (poller.Next(&packet)) {
        std::cout << packet.Get<std::string>() << std::endl;
    }
    return graph.WaitUntilDone();
}

, , BUILD , .

load("@mediapipe_repository//mediapipe/framework/port:build_config.bzl", "mediapipe_cc_proto_library")
#     
proto_library(
    name = "repeat_n_times_calculator_proto",
    srcs = ["RepeatNTimesCalculator.proto"],
    visibility = ["//visibility:public"],
    deps = [
        "@mediapipe_repository//mediapipe/framework:calculator_proto",
    ],
)
#     
mediapipe_cc_proto_library(
    name = "repeat_n_times_calculator_cc_proto",
    srcs = ["RepeatNTimesCalculator.proto"],
    cc_deps = [
        "@mediapipe_repository//mediapipe/framework:calculator_cc_proto",
    ],
    visibility = ["//visibility:public"],
    deps = [":repeat_n_times_calculator_proto"],
)
#   .  ,    
cc_library(
    name = "repeat_n_times_calculator",
    srcs = ["RepeatNTimesCalculator.cpp"],
    visibility = [
        "//visibility:public",
    ],
    deps = [
        ":repeat_n_times_calculator_cc_proto",
        "@mediapipe_repository//mediapipe/framework:calculator_framework",
        "@mediapipe_repository//mediapipe/framework/port:status",
    ],
    alwayslink = 1,
)
#    ,    .
cc_binary(
    name = "HelloMediapipe",
    srcs = ["main.cpp"],
    deps = [
        "repeat_n_times_calculator",
        "@mediapipe_repository//mediapipe/framework/port:logging",
        "@mediapipe_repository//mediapipe/framework/port:parse_text_proto",
        "@mediapipe_repository//mediapipe/framework/port:status",
    ],
)

:

$ bazel-2.0.0 build --define MEDIAPIPE_DISABLE_GPU=1 //hello-world:HelloMediapipe
...
INFO: Build completed successfully, 4 total actions
$ ./bazel-bin/hello-world/HelloMediapipe
Hello!
Hello!
Hello!

Mediapipe, ML .

Mediapipe

, Mediapipe tflite . . , ImageNet1k. , , . , .

├── inference
│   ├── android/src/main/java/com/com/mediapipe_demonstration/inference
│   │   ├── AndroidManifest.xml
│   │   ├── BUILD
│   │   ├── MainActivity.kt
│   │   └── res
│   │       └── ...
│   ├── BUILD
│   ├── desktop
│   │   ├── BUILD
│   │   └── main.cpp
│   ├── graph.pbtxt
│   ├── img.jpg
│   └── mobilenetv2_imagenet.tflite
└── WORKSPACE

:

input_stream: "in"
output_stream: "out"
node: {
  calculator: "ImageTransformationCalculator"
  input_stream: "IMAGE:in"
  output_stream: "IMAGE:transformed_input"
  node_options: {
    [type.googleapis.com/mediapipe.ImageTransformationCalculatorOptions] {
      output_width: 224
      output_height: 224
    }
  }
}
node {
  calculator: "TfLiteConverterCalculator"
  input_stream: "IMAGE:transformed_input"
  output_stream: "TENSORS:image_tensor"
  node_options: {
      [type.googleapis.com/mediapipe.TfLiteConverterCalculatorOptions] {
        zero_center: false
      }
  }
}
node {
  calculator: "TfLiteInferenceCalculator"
  input_stream: "TENSORS:image_tensor"
  output_stream: "TENSORS:prediction_tensor"
  node_options: {
    [type.googleapis.com/mediapipe.TfLiteInferenceCalculatorOptions] {
      model_path: "inference/mobilenetv2_imagenet.tflite"
    }
  }
}
node {
  calculator: "TfLiteTensorsToFloatsCalculator"
  input_stream: "TENSORS:prediction_tensor"
  output_stream: "FLOATS:out"
}

4 :

1. 224x224.
2. [-1, 1] ( zero_center: false, [0, 1]), ImageFrame ( Mediapipe, , ) TfLiteTensor ( std::vector<TfLiteTensor> 1). TfLiteTensor ( GetContract), .
3. .
4. TfLiteTensor std::vector<float>. .

, .

. . , , .

//  
auto img_mat = cv::imread("./inference/img.jpg");
//    
auto input_frame = std::make_unique<mediapipe::ImageFrame>(
    mediapipe::ImageFormat::SRGB, img_mat.cols, img_mat.rows,
    mediapipe::ImageFrame::kDefaultAlignmentBoundary);
cv::Mat input_frame_mat = mediapipe::formats::MatView(input_frame.get());
img_mat.copyTo(input_frame_mat);
auto frame = mediapipe::Adopt(input_frame.release()).At(mediapipe::Timestamp(0));
//    
MP_RETURN_IF_ERROR(graph.AddPacketToInputStream("in", frame));
MP_RETURN_IF_ERROR(graph.CloseInputStream("in"));
//       
mediapipe::Packet packet;
while (poller.Next(&packet)) {
    auto predictions = packet.Get<std::vector<float>>();
    int idx = std::max_element(predictions.begin(), predictions.end()) - predictions.begin();
    std::cout << idx << std::endl;
}

, :

$ bazel-2.0.0 build --define MEDIAPIPE_DISABLE_GPU=1 //inference/desktop:Inference
...
INFO: Build completed successfully, 3 total actions
$ ./bazel-bin/inference/desktop/Inference
INFO: Initialized TensorFlow Lite runtime.
151

151 ImageNet "Chihuahua".

. Activity, ( onCreate) , Mediapipe. protobuf-. , . onActivityResult, . Mediapipe - ( ).

class MainActivity : AppCompatActivity() {
    val PICK_IMAGE = 1
    var mpGraph: Graph? = null
    var timestamp = 0L
    override fun onCreate(savedInstanceState: Bundle?) {
        super.onCreate(savedInstanceState)
        this.setContentView(R.layout.activity_main)
        val outputTv = findViewById<TextView>(R.id.outputTv)
        val button = findViewById<Button>(R.id.selectButton)
        AndroidAssetUtil.initializeNativeAssetManager(this)
        //    
        //        
        val graph = Graph()
        assets.open("mobile_binary_graph.binarypb").use {
            val graphBytes = it.readBytes()
            graph.loadBinaryGraph(graphBytes)
        }
        //    
        graph.addPacketCallback("out") {
            val res = PacketGetter.getFloat32Vector(it)
            val label = res.indices.maxBy { i -> res[i] } ?: -1
            this@MainActivity.runOnUiThread {
                outputTv.text = label.toString()
            }
        }
        graph.startRunningGraph()
        //      
        button.setOnClickListener {
            val intent = Intent()
            intent.type = "image/*"
            intent.action = Intent.ACTION_GET_CONTENT
            startActivityForResult(Intent.createChooser(intent, "Select Picture"), PICK_IMAGE)
        }
        mpGraph = graph
    }
    override fun onActivityResult(requestCode: Int, resultCode: Int, data: Intent?) {
        if (requestCode == PICK_IMAGE) {
            //        
            val outputTv = findViewById<TextView>(R.id.outputTv)
            val imageView = findViewById<ImageView>(R.id.imageView)
            val uri = data?.data!!
            //    
            val graph = mpGraph!!
            val creator = AndroidPacketCreator(graph)
            val stream = contentResolver.openInputStream(uri)
            val bitmap = BitmapFactory.decodeStream(stream)
            imageView.setImageBitmap(bitmap)
            val packet = creator.createRgbImageFrame(bitmap)
            graph.addPacketToInputStream("in", packet, timestamp)
        }
    }
    companion object {
        init {
            //   mediapipe 
            System.loadLibrary("mediapipe_jni")
        }
    }
}

:

$ bazel-2.0.0 mobile-install --start_app -c opt --config=android_arm64 //inference/android/src/main/java/com/mediapipe_demonstration/inference:Inference

, :

, .

, github

Mediapipe

Mediapipe

. . , . , . , . :

• , GPU, ;
• .

, Mediapipe. , , , . . : , .

Mediapipe, . , ImageNet1k, Stanford Cars Dataset. . :

logit- NLL loss. "" mean pool.

Android , Mediapipe, Mediapipe , .

.
├── Android/src/main/java/com/kshmax/objectrecognition
│   └── objectrecognition
│       ├── AndroidManifest.xml
│       ├── BUILD
│       ├── MainActivity.kt
│       ├── Models.kt
│       ├── ObjectDetectionFrameProcessor.java
│       └── res
│           └── ...
├── Calculators
│   ├── BoundaryBoxCropCalculator.cpp
│   ├── BUILD
│   ├── ClickLocation.proto
│   ├── DetectionFilterCalculator.cpp
│   ├── DetectionFilterCalculator.proto
├── Graphs
│   ├── BUILD
│   └── ObjectRecognitionGraph.pbtxt
├── Models
│   ├── BUILD
│   └── car_vectorizer.tflite
├── Server
│   ├── labels.npy
│   ├── server.py
│   └── vectors.npy
└── WORKSPACE

Mediapipe :

, .

, . , , . : , . FlowLimiter . . AnnotationOverlay , . .

DetectionFilter, , . DetectionFilter, , , .

Process DetectionFilter :

mediapipe::Status DetectionFilterCalculator::Process(mediapipe::CalculatorContext *cc) {
    const auto& input_detections = cc->Inputs().Get("", 0).Get<std::vector<::mediapipe::Detection>>();
    std::vector<::mediapipe::Detection> output_detections;
    for (const auto& input_detection : input_detections) {
        bool next_detection = false;

        for (int pass_id : pass_ids_) {
            for (int label_id : input_detection.label_id()) {
                if (pass_id == label_id) {
                    output_detections.push_back(input_detection);
                    next_detection = true;
                    break;
                }
            }
            if (next_detection) {
                break;
            }
        }
    }
    auto out_packet = mediapipe::MakePacket<std::vector<mediapipe::Detection>>(output_detections).At(cc->InputTimestamp());
    cc->Outputs().Get("", 0).AddPacket(out_packet);

    return mediapipe::OkStatus();
}

DetectionFilterCalculator :

node {
    calculator: "DetectionFilterCalculator"
    input_stream: "filtered_detections"
    output_stream: "car_detections"
    node_options: {
        [type.googleapis.com/objectrecognition.DetectionFilterCalculatorOptions] {
            pass_id: 3
        }
    }
}

. , , .

ScreenTap, BoundaryBoxCrop. , ScreenTap FlowLimiter, , , . , , , , . FrameProcessor, ObjectDetectionFrameProcessor.

ScreenTap , , , " ". . , , , (, , [0, 1]).

BoundaryBoxCrop , . , DetectionFilter , FlowLimiter , GPU CPU. , , , .

mediapipe::Status BoundaryBoxCropCalculator::Process(mediapipe::CalculatorContext *cc) {
    auto& detections_packet = cc->Inputs().Get("DETECTION", 0);
    auto& frames_packet = cc->Inputs().Get("IMAGE", 0);
    auto& click_packet = cc->Inputs().Get("CLICK", 0);

    if (detections_packet.IsEmpty()
        || frames_packet.IsEmpty()
        || click_packet.IsEmpty()) {
        return mediapipe::OkStatus();
    }

    const std::vector<mediapipe::Detection>& detections =
            detections_packet.Get<std::vector<mediapipe::Detection>>();
    const mediapipe::ImageFrame& image_frame = frames_packet.Get<mediapipe::ImageFrame>();

    // Java    protobuf  ,     .
    auto click_location_str = click_packet.Get<std::string>();
    objectrecognition::ClickLocation click_location;
    click_location.ParseFromString(click_location_str);
    //   ,    
    if (click_location.x() == -1 || click_location.y() == -1) {
        return mediapipe::OkStatus();
    }

    //      
    absl::optional<mediapipe::Detection> detection = FindOverlappedDetection(click_location, detections);

    if (detection.has_value()) {
        //     ,     
        std::unique_ptr<mediapipe::ImageFrame> cropped_image = CropImage(image_frame, detection.value());
        cc->Outputs().Get("", 0).Add(cropped_image.release(), cc->InputTimestamp());
    }

    return mediapipe::OkStatus();
}

absl::optional<mediapipe::Detection> BoundaryBoxCropCalculator::FindOverlappedDetection(
        const objectrecognition::ClickLocation& click_location,
        const std::vector<mediapipe::Detection>& detections) {
    for (const auto& input_detection : detections) {
        const auto& b_box = input_detection.location_data().relative_bounding_box();

        if (b_box.xmin() < click_location.x() && click_location.x() < (b_box.xmin() + b_box.width())
            && b_box.ymin() < click_location.y() && click_location.y() < (b_box.ymin() + b_box.height())) {
            return input_detection;
        }
    }

    return absl::nullopt;
}

std::unique_ptr<mediapipe::ImageFrame> BoundaryBoxCropCalculator::CropImage(
        const mediapipe::ImageFrame& image_frame,
        const mediapipe::Detection& detection) {
    const uint8* pixel_data = image_frame.PixelData();
    const auto& b_box = detection.location_data().relative_bounding_box();

    int height = static_cast<int>(b_box.height() * static_cast<float>(image_frame.Height()));
    int width = static_cast<int>(b_box.width() * static_cast<float>(image_frame.Width()));
    int xmin = static_cast<int>(b_box.xmin() * static_cast<float>(image_frame.Width()));
    int ymin = static_cast<int>(b_box.ymin() * static_cast<float>(image_frame.Height()));

    if (xmin < 0) {
        width += xmin;
        xmin = 0;
    }
    if (ymin < 0) {
        height += ymin;
        ymin = 0;
    }
    if (width > image_frame.Width()) {
        width = image_frame.Width();
    }
    if (height > image_frame.Height()) {
        height = image_frame.Height();
    }

    std::vector<uint8_t> pixels;
    pixels.reserve(height * width * image_frame.NumberOfChannels());
    for (int y = ymin; y < ymin + height; ++y) {
        int row_offset = y * image_frame.WidthStep();
        for (int x = xmin; x < xmin + width; ++x) {
            for (int ch = 0; ch < image_frame.NumberOfChannels(); ++ch) {
                pixels.push_back(pixel_data[row_offset + x * image_frame.NumberOfChannels() + ch]);
            }
        }
    }

    std::unique_ptr<mediapipe::ImageFrame> cropped_image = std::make_unique<mediapipe::ImageFrame>();
    cropped_image->CopyPixelData(image_frame.Format(), width, height, pixels.data(),
            mediapipe::ImageFrame::kDefaultAlignmentBoundary);

    return cropped_image;
}

. TfLiteTensor .

(Java ) , HTTP . , "" . , ( ).

, — . :

. ( camry hummer), ford focus , .. -, (Hyundai SantaFe).

4 , , .

, , , , . , , .

, Mediapipe, , , , . real-time . UX, , . proof-of-concept, :

• CPU, , , GPU. GPU CPU GPU;
• , . ;
• ;
• ;
• . ANN (Approximate Nearest Neighbors).