[
  {
    "path": ".gitmodules",
    "content": "[submodule \"yolov5\"]\n\tpath = yolov5\n\turl = https://github.com/ultralytics/yolov5\n"
  },
  {
    "path": "LICENSE.md",
    "content": "                    GNU GENERAL PUBLIC LICENSE\n                       Version 3, 29 June 2007\n\n Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>\n Everyone is permitted to copy and distribute verbatim copies\n of this license document, but changing it is not allowed.\n\n                            Preamble\n\n  The GNU General Public License is a free, copyleft license for\nsoftware and other kinds of works.\n\n  The licenses for most software and other practical works are designed\nto take away your freedom to share and change the works.  By contrast,\nthe GNU General Public License is intended to guarantee your freedom to\nshare and change all versions of a program--to make sure it remains free\nsoftware for all its users.  We, the Free Software Foundation, use the\nGNU General Public License for most of our software; it applies also to\nany other work released this way by its authors.  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You may not convey a covered\nwork if you are a party to an arrangement with a third party that is\nin the business of distributing software, under which you make payment\nto the third party based on the extent of your activity of conveying\nthe work, and under which the third party grants, to any of the\nparties who would receive the covered work from you, a discriminatory\npatent license (a) in connection with copies of the covered work\nconveyed by you (or copies made from those copies), or (b) primarily\nfor and in connection with specific products or compilations that\ncontain the covered work, unless you entered into that arrangement,\nor that patent license was granted, prior to 28 March 2007.\n\n  Nothing in this License shall be construed as excluding or limiting\nany implied license or other defenses to infringement that may\notherwise be available to you under applicable patent law.\n\n  12. No Surrender of Others' Freedom.\n\n  If conditions are imposed on you (whether by court order, agreement or\notherwise) that contradict the conditions of this License, they do not\nexcuse you from the conditions of this License.  If you cannot convey a\ncovered work so as to satisfy simultaneously your obligations under this\nLicense and any other pertinent obligations, then as a consequence you may\nnot convey it at all.  For example, if you agree to terms that obligate you\nto collect a royalty for further conveying from those to whom you convey\nthe Program, the only way you could satisfy both those terms and this\nLicense would be to refrain entirely from conveying the Program.\n\n  13. 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If the\nProgram specifies that a certain numbered version of the GNU General\nPublic License \"or any later version\" applies to it, you have the\noption of following the terms and conditions either of that numbered\nversion or of any later version published by the Free Software\nFoundation.  If the Program does not specify a version number of the\nGNU General Public License, you may choose any version ever published\nby the Free Software Foundation.\n\n  If the Program specifies that a proxy can decide which future\nversions of the GNU General Public License can be used, that proxy's\npublic statement of acceptance of a version permanently authorizes you\nto choose that version for the Program.\n\n  Later license versions may give you additional or different\npermissions.  However, no additional obligations are imposed on any\nauthor or copyright holder as a result of your choosing to follow a\nlater version.\n\n  15. 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Interpretation of Sections 15 and 16.\n\n  If the disclaimer of warranty and limitation of liability provided\nabove cannot be given local legal effect according to their terms,\nreviewing courts shall apply local law that most closely approximates\nan absolute waiver of all civil liability in connection with the\nProgram, unless a warranty or assumption of liability accompanies a\ncopy of the Program in return for a fee.\n\n                     END OF TERMS AND CONDITIONS\n\n            How to Apply These Terms to Your New Programs\n\n  If you develop a new program, and you want it to be of the greatest\npossible use to the public, the best way to achieve this is to make it\nfree software which everyone can redistribute and change under these terms.\n\n  To do so, attach the following notices to the program.  It is safest\nto attach them to the start of each source file to most effectively\nstate the exclusion of warranty; and each file should have at least\nthe \"copyright\" line and a pointer to where the full notice is found.\n\n    <one line to give the program's name and a brief idea of what it does.>\n    Copyright (C) <year>  <name of author>\n\n    This program is free software: you can redistribute it and/or modify\n    it under the terms of the GNU General Public License as published by\n    the Free Software Foundation, either version 3 of the License, or\n    (at your option) any later version.\n\n    This program is distributed in the hope that it will be useful,\n    but WITHOUT ANY WARRANTY; without even the implied warranty of\n    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n    GNU General Public License for more details.\n\n    You should have received a copy of the GNU General Public License\n    along with this program.  If not, see <https://www.gnu.org/licenses/>.\n\nAlso add information on how to contact you by electronic and paper mail.\n\n  If the program does terminal interaction, make it output a short\nnotice like this when it starts in an interactive mode:\n\n    <program>  Copyright (C) <year>  <name of author>\n    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.\n    This is free software, and you are welcome to redistribute it\n    under certain conditions; type `show c' for details.\n\nThe hypothetical commands `show w' and `show c' should show the appropriate\nparts of the General Public License.  Of course, your program's commands\nmight be different; for a GUI interface, you would use an \"about box\".\n\n  You should also get your employer (if you work as a programmer) or school,\nif any, to sign a \"copyright disclaimer\" for the program, if necessary.\nFor more information on this, and how to apply and follow the GNU GPL, see\n<https://www.gnu.org/licenses/>.\n\n  The GNU General Public License does not permit incorporating your program\ninto proprietary programs.  If your program is a subroutine library, you\nmay consider it more useful to permit linking proprietary applications with\nthe library.  If this is what you want to do, use the GNU Lesser General\nPublic License instead of this License.  But first, please read\n<https://www.gnu.org/licenses/why-not-lgpl.html>.\n"
  },
  {
    "path": "README.md",
    "content": "# yolov5s_android:rocket: \n<div align=\"center\">\n<img src=\"https://github.com/lp6m/yolov5s_android/raw/media/android_app.gif\" width=30%>\n<img src=\"https://github.com/lp6m/yolov5s_android/raw/media/screenshot.jpg\" width=30%>\n</div>\n\nThe implementation of yolov5s on android for the [yolov5s export contest](https://github.com/ultralytics/yolov5/discussions/3213).    \nDownload the latest android apk from [release](https://github.com/lp6m/yolov5s_android/releases) and install your device.  \n\n**UPDATE:rocket: 2022/06/25** Added tutorial on how to integrate models trained with custom data. [Custom Model Intergration Tutorial](https://github.com/lp6m/yolov5s_android/issues/14)  \n\n## Environment\n- Host Ubuntu18.04\n- Docker \n    * Tensorflow 2.4.0\n    * PyTorch 1.7.0\n    * OpenVino 2021.3\n- Android App\n    * Android Studio 4.2.1\n    * minSdkVersion 28\n    * targetSdkVersion 29\n    * TfLite 2.4.0\n- Android Device\n    * Xiaomi Mi11 (Storage 128GB/ RAM8GB)\n    * OS MIUI 12.5.8  \n  \nWe use docker container for host evaluation and model conversion.\n```sh\ngit clone --recursive https://github.com/lp6m/yolov5s_android\ncd yolov5s_android\ndocker build ./ -f ./docker/Dockerfile  -t yolov5s_android\ndocker run -it --gpus all -v `pwd`:/workspace yolov5s_android bash\n```\n\n## Files\n- `./app`\n    * Android application.\n    * To build application by yourself, copy `./tflite_model/*.tflite` to `app/tflite_yolov5_test/app/src/main/assets/`, and build on Android Studio.\n    * The app can perform inference with various configurations of input size, inference accuracy, and model accuracy. \n    * For 'Open Directory Mode', save the detected bounding boxes results as a json file in coco format.\n    * Realtime deteciton from camera image (precision and input size is fixed to int8/320). Achieved FPS is about **15FPS** on Mi11.\n    * **NOTE** Please select image/directory as an absolute path from 'Device'. The app does not support select image/directory from 'Recent' in some devices.\n- `./benchmark` \n    * Benchmark script and results by [TFLite Model Benchmark Tool with C++ Binary](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/tools/benchmark#profiling-model-operators).\n- `./convert_model`\n    * Model conversion guide and model quantization script.\n- `./docker`\n    * Dockerfile for the evaluation and model conversion environment.\n- `./host`\n    * `detect.py`  : Run detection for image with TfLite model on host environment.\n    * `evaluate.py`: Run evaluation with coco validation dataset and inference results.\n- `./tflite_model`\n    * Converted TfLite Model.\n\n## Performance\n### Latency \nThese results are measured on `Xiaomi Mi11`.  \nPlease refer [`benchmark/README.md`](https://github.com/lp6m/yolov5s_android/tree/master/benchmark) about the detail of benchmark command.  \nThe latency does not contain the pre/post processing time and data transfer time.  \n#### float32 model  \n\n|       delegate        | 640x640 [ms] | 320x320 [ms] |\n| :-------------------- | -----------: | -----------: |\n| None (CPU)            |          249 |           61 |\n| NNAPI (qti-gpu, fp32) |          156 |          112 |\n| NNAPI (qti-gpu, fp16) |           92 |           79 |\n  \n#### int8 model\nWe tried to accelerate the inference process by using `NNAPI (qti-dsp)` and offload calculation to Hexagon DSP, but it doesn't work for now. Please see [here](https://github.com/lp6m/yolov5s_android/tree/dev/benchmark#nnapi-qti-dsp-not-working) in detail.\n\n|       delegate       | 640x640 [ms] | 320x320 [ms] |\n| :------------------- | -----------: | -----------: |\n| None (CPU)           |           95 |           23 |\n| NNAPI  (qti-default) |  Not working |  Not working |\n| NNAPI  (qti-dsp)     |  Not working |  Not working |\n\n## Accuracy\nPlease refer [host/README.md](https://github.com/lp6m/yolov5s_android/tree/master/host#example2) about the evaluation method.    \nWe set `conf_thresh=0.25` and `iou_thresh=0.45` for nms parameter.\n|      device, model, delegate      | 640x640 mAP | 320x320 mAP |\n| :-------------------------------- | ----------: | ----------: |\n| host GPU (Tflite + PyTorch, fp32) |        27.8 |        26.6 |\n| host CPU (Tflite + PyTorch, int8) |        26.6 |        25.5 |\n| NNAPI  (qti-gpu, fp16)            |        28.5 |        26.8 |\n| CPU    (int8)                     |        27.2 |        25.8 |\n\n\n## Model conversion\nThis project focuses on obtaining a tflite model by **model conversion from PyTorch original implementation, rather than doing own implementation in tflite**.  \nWe convert models in this way: `PyTorch -> ONNX -> OpenVino -> TfLite`.  \nTo convert the model from OpenVino to TfLite, we use [openvino2tensorflow](https://github.com/PINTO0309/openvino2tensorflow).\nPlease refer [convert_model/README.md](https://github.com/lp6m/yolov5s_android/tree/master/convert_model) about the model conversion.\n\n"
  },
  {
    "path": "app/README.md",
    "content": "# Android App\nThis applications uses [TFLite Android Support](https://www.tensorflow.org/lite/guide/android).  \n\n### TfliteRunner \n[`TfliteRunner.java`](https://github.com/lp6m/yolov5s_android/blob/master/app/tflite_yolov5_test/app/src/main/java/com/example/tflite_yolov5_test/TfliteRunner.java) is the main class for running TfLite model.  Apply a delegate according to the specified running mode.  \n\n### postprocess.cpp\n[`postprocess.cpp`](https://github.com/lp6m/yolov5s_android/blob/master/app/tflite_yolov5_test/app/src/main/cpp/postprocess.cpp) corresponds to the [detect layer module](https://github.com/ultralytics/yolov5/blob/master/models/yolo.py#L33) and `non_max_suppression` of original yolov5.  This C++ code is called by `TfliteRunner` via JNI Interface.  \n\n### Realtime inference\nFor realtime inference from camera image, We copy a lot of codes from [TensorFlow Lite Object Detection Android Demo](https://github.com/tensorflow/examples/tree/master/lite/examples/object_detection/android) to `src/main/java/com/example/tflite_yolov5_test/customview, camera`.  \nRealtime inference is not the main topic of the contest, so please forgive me for porting the code in a messy way!  \n  \n"
  },
  {
    "path": "app/tflite_yolov5_test/.gitignore",
    "content": "*.iml\n.gradle\n/local.properties\n/.idea/caches\n/.idea/libraries\n/.idea/modules.xml\n/.idea/workspace.xml\n/.idea/navEditor.xml\n/.idea/assetWizardSettings.xml\n.DS_Store\n/build\n/captures\n.externalNativeBuild\n.cxx\nlocal.properties\n\napp/src/main/assets/*.tflite\napp/release"
  },
  {
    "path": "app/tflite_yolov5_test/.idea/.gitignore",
    "content": "# Default ignored files\n/shelf/\n/workspace.xml\n"
  },
  {
    "path": "app/tflite_yolov5_test/.idea/compiler.xml",
    "content": "<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<project version=\"4\">\n  <component name=\"CompilerConfiguration\">\n    <bytecodeTargetLevel target=\"11\" />\n  </component>\n</project>"
  },
  {
    "path": "app/tflite_yolov5_test/.idea/gradle.xml",
    "content": "<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<project version=\"4\">\n  <component name=\"GradleMigrationSettings\" migrationVersion=\"1\" />\n  <component name=\"GradleSettings\">\n    <option name=\"linkedExternalProjectsSettings\">\n      <GradleProjectSettings>\n        <option name=\"testRunner\" value=\"PLATFORM\" />\n        <option name=\"distributionType\" value=\"DEFAULT_WRAPPED\" />\n        <option name=\"externalProjectPath\" value=\"$PROJECT_DIR$\" />\n        <option name=\"modules\">\n          <set>\n            <option value=\"$PROJECT_DIR$\" />\n            <option value=\"$PROJECT_DIR$/app\" />\n          </set>\n        </option>\n        <option name=\"resolveModulePerSourceSet\" value=\"false\" />\n      </GradleProjectSettings>\n    </option>\n  </component>\n</project>"
  },
  {
    "path": "app/tflite_yolov5_test/.idea/jarRepositories.xml",
    "content": "<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<project version=\"4\">\n  <component name=\"RemoteRepositoriesConfiguration\">\n    <remote-repository>\n      <option name=\"id\" value=\"central\" />\n      <option name=\"name\" value=\"Maven Central repository\" />\n      <option name=\"url\" value=\"https://repo1.maven.org/maven2\" />\n    </remote-repository>\n    <remote-repository>\n      <option name=\"id\" value=\"jboss.community\" />\n      <option name=\"name\" value=\"JBoss Community repository\" />\n      <option name=\"url\" value=\"https://repository.jboss.org/nexus/content/repositories/public/\" />\n    </remote-repository>\n    <remote-repository>\n      <option name=\"id\" value=\"MavenRepo\" />\n      <option name=\"name\" value=\"MavenRepo\" />\n      <option name=\"url\" value=\"https://repo.maven.apache.org/maven2/\" />\n    </remote-repository>\n    <remote-repository>\n      <option name=\"id\" value=\"BintrayJCenter\" />\n      <option name=\"name\" value=\"BintrayJCenter\" />\n      <option name=\"url\" value=\"https://jcenter.bintray.com/\" />\n    </remote-repository>\n    <remote-repository>\n      <option name=\"id\" value=\"Google\" />\n      <option name=\"name\" value=\"Google\" />\n      <option name=\"url\" value=\"https://dl.google.com/dl/android/maven2/\" />\n    </remote-repository>\n  </component>\n</project>"
  },
  {
    "path": "app/tflite_yolov5_test/.idea/misc.xml",
    "content": "<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<project version=\"4\">\n  <component name=\"ProjectRootManager\" version=\"2\" languageLevel=\"JDK_11\" default=\"true\" project-jdk-name=\"11\" project-jdk-type=\"JavaSDK\">\n    <output url=\"file://$PROJECT_DIR$/build/classes\" />\n  </component>\n  <component name=\"ProjectType\">\n    <option name=\"id\" value=\"Android\" />\n  </component>\n</project>"
  },
  {
    "path": "app/tflite_yolov5_test/.idea/runConfigurations.xml",
    "content": "<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<project version=\"4\">\n  <component name=\"RunConfigurationProducerService\">\n    <option name=\"ignoredProducers\">\n      <set>\n        <option value=\"com.android.tools.idea.compose.preview.runconfiguration.ComposePreviewRunConfigurationProducer\" />\n      </set>\n    </option>\n  </component>\n</project>"
  },
  {
    "path": "app/tflite_yolov5_test/.idea/vcs.xml",
    "content": "<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<project version=\"4\">\n  <component name=\"VcsDirectoryMappings\">\n    <mapping directory=\"$PROJECT_DIR$/../..\" vcs=\"Git\" />\n    <mapping directory=\"$PROJECT_DIR$\" vcs=\"Git\" />\n  </component>\n</project>"
  },
  {
    "path": "app/tflite_yolov5_test/app/.gitignore",
    "content": "/build"
  },
  {
    "path": "app/tflite_yolov5_test/app/CMakeLists.txt",
    "content": "cmake_minimum_required(VERSION 3.4.1)\n\nadd_library( # Sets the name of the library.\n             native-lib\n\n             # Sets the library as a shared library.\n             SHARED\n\n             # Provides a relative path to your source file(s).\n             src/main/cpp/postprocess.cpp )\n\n# Searches for a specified prebuilt library and stores the path as a\n# variable. Because CMake includes system libraries in the search path by\n# default, you only need to specify the name of the public NDK library\n# you want to add. CMake verifies that the library exists before\n# completing its build.\n\nfind_library( # Sets the name of the path variable.\n              log-lib\n\n              # Specifies the name of the NDK library that\n              # you want CMake to locate.\n              log )\n\n# Specifies libraries CMake should link to your target library. You\n# can link multiple libraries, such as libraries you define in this\n# build script, prebuilt third-party libraries, or system libraries.\n\ntarget_link_libraries( # Specifies the target library.\n                       native-lib\n\n                       # Links the target library to the log library\n                       # included in the NDK.\n                       ${log-lib} )\n"
  },
  {
    "path": "app/tflite_yolov5_test/app/build.gradle",
    "content": "plugins {\n    id 'com.android.application'\n}\n\nandroid {\n    compileSdkVersion 29\n    buildToolsVersion \"30.0.3\"\n\n    defaultConfig {\n        ndk {\n            abiFilters 'armeabi-v7a', 'arm64-v8a'\n        }\n        applicationId \"com.example.tflite_yolov5_test\"\n        minSdkVersion 28\n        targetSdkVersion 29\n        versionCode 1\n        versionName \"1.0\"\n\n        testInstrumentationRunner \"androidx.test.runner.AndroidJUnitRunner\"\n        externalNativeBuild {\n            cmake {\n                cppFlags \"-O3\"\n            }\n        }\n    }\n\n    buildTypes {\n        release {\n            minifyEnabled false\n            proguardFiles getDefaultProguardFile('proguard-android-optimize.txt'), 'proguard-rules.pro'\n        }\n    }\n    compileOptions {\n        sourceCompatibility JavaVersion.VERSION_1_8\n        targetCompatibility JavaVersion.VERSION_1_8\n    }\n    externalNativeBuild {\n        cmake {\n            path \"CMakeLists.txt\"\n        }\n    }\n    buildFeatures {\n        viewBinding true\n    }\n}\n\ndependencies {\n    implementation 'org.nanohttpd:nanohttpd:2.3.1'\n    implementation 'androidx.appcompat:appcompat:1.2.0'\n    implementation 'com.google.android.material:material:1.2.1'\n    implementation 'androidx.constraintlayout:constraintlayout:2.0.1'\n    implementation 'androidx.lifecycle:lifecycle-livedata-ktx:2.2.0'\n    implementation 'androidx.lifecycle:lifecycle-viewmodel-ktx:2.2.0'\n    implementation 'androidx.navigation:navigation-fragment:2.3.0'\n    implementation 'androidx.navigation:navigation-ui:2.3.0'\n\n    implementation 'org.tensorflow:tensorflow-lite:2.4.0'\n    //implementation 'org.tensorflow:tensorflow-lite:0.0.0-nightly-SNAPSHOT'\n    //implementation 'org.tensorflow:tensorflow-lite:0.0.0-nightly'\n    //implementation 'org.tensorflow:tensorflow-lite-select-tf-ops:0.0.0-nightly'\n    //implementation 'org.tensorflow:tensorflow-lite-gpu:0.0.0-nightly-SNAPSHOT'\n    //implementation 'org.tensorflow:tensorflow-lite-support:0.0.0-nightly-SNAPSHOT'\n\n    testImplementation 'junit:junit:4.+'\n    androidTestImplementation 'androidx.test.ext:junit:1.1.2'\n    androidTestImplementation 'androidx.test.espresso:espresso-core:3.3.0'\n}"
  },
  {
    "path": "app/tflite_yolov5_test/app/proguard-rules.pro",
    "content": "# Add project specific ProGuard rules here.\n# You can control the set of applied configuration files using the\n# proguardFiles setting in build.gradle.\n#\n# For more details, see\n#   http://developer.android.com/guide/developing/tools/proguard.html\n\n# If your project uses WebView with JS, uncomment the following\n# and specify the fully qualified class name to the JavaScript interface\n# class:\n#-keepclassmembers class fqcn.of.javascript.interface.for.webview {\n#   public *;\n#}\n\n# Uncomment this to preserve the line number information for\n# debugging stack traces.\n#-keepattributes SourceFile,LineNumberTable\n\n# If you keep the line number information, uncomment this to\n# hide the original source file name.\n#-renamesourcefileattribute SourceFile"
  },
  {
    "path": "app/tflite_yolov5_test/app/src/androidTest/java/com/example/tflite_yolov5_test/ExampleInstrumentedTest.java",
    "content": "package com.example.tflite_yolov5_test;\n\nimport android.content.Context;\n\nimport androidx.test.platform.app.InstrumentationRegistry;\nimport androidx.test.ext.junit.runners.AndroidJUnit4;\n\nimport org.junit.Test;\nimport org.junit.runner.RunWith;\n\nimport static org.junit.Assert.*;\n\n/**\n * Instrumented test, which will execute on an Android device.\n *\n * @see <a href=\"http://d.android.com/tools/testing\">Testing documentation</a>\n */\n@RunWith(AndroidJUnit4.class)\npublic class ExampleInstrumentedTest {\n    @Test\n    public void useAppContext() {\n        // Context of the app under test.\n        Context appContext = InstrumentationRegistry.getInstrumentation().getTargetContext();\n        assertEquals(\"com.example.tflite_yolov5_test\", appContext.getPackageName());\n    }\n}"
  },
  {
    "path": "app/tflite_yolov5_test/app/src/main/AndroidManifest.xml",
    "content": "<?xml version=\"1.0\" encoding=\"utf-8\"?>\n<manifest xmlns:android=\"http://schemas.android.com/apk/res/android\"\n    package=\"com.example.tflite_yolov5_test\">\n\n    <uses-permission android:name=\"android.permission.CAMERA\" />\n    <uses-permission android:name=\"android.permission.CAMERA\" />\n    <uses-permission android:name=\"android.permission.READ_EXTERNAL_STORAGE\" />\n    <uses-permission android:name=\"android.permission.WRITE_EXTERNAL_STORAGE\" />\n    <uses-permission android:name=\"android.permission.CAMERA\" />\n\n    <uses-feature android:name=\"android.hardware.camera\" />\n    <uses-feature android:name=\"android.hardware.camera.autofocus\" />\n\n    <application\n        android:allowBackup=\"true\"\n        android:icon=\"@mipmap/ic_launcher\"\n        android:label=\"@string/app_name\"\n        android:requestLegacyExternalStorage=\"true\"\n        android:roundIcon=\"@mipmap/ic_launcher_round\"\n        android:supportsRtl=\"true\"\n        android:theme=\"@style/Theme.Tflite_yolov5_test\">\n        <activity android:name=\".MainActivity\">\n            <intent-filter>\n                <action android:name=\"android.intent.action.MAIN\" />\n\n                <category android:name=\"android.intent.category.LAUNCHER\" />\n            </intent-filter>\n        </activity>\n        <activity android:name=\".camera.CameraActivity\" />\n        <activity android:name=\".camera.DetectorActivity\" />\n    </application>\n\n</manifest>"
  },
  {
    "path": "app/tflite_yolov5_test/app/src/main/cpp/nms.h",
    "content": "//https://github.com/martinkersner/non-maximum-suppression-cpp/blob/master/nms.cpp\n#include <vector>\n#include <numeric>\n#include <iostream>\n\nusing namespace std;\n\nstruct bbox{\n    float x1;\n    float y1;\n    float x2;\n    float y2;\n    float conf;\n    int class_idx;\n    bbox(float x1, float y1, float x2, float y2, float conf, int class_idx) :\n            x1(x1), y1(y1), x2(x2), y2(y2), conf(conf), class_idx(class_idx){\n    }\n};\n\n#define Point_XMIN 0\n#define Point_XMAX 1\n#define Point_YMIN 2\n#define Point_YMAX 3\n\nvector<float> GetPointFromRect(const vector<bbox> &rect,\n                               const int pos)\n{\n    vector<float> points;\n\n    for (const auto & p: rect) {\n        float point;\n        if (pos == Point_XMIN) point = p.x1;\n        else if (pos == Point_XMAX) point = p.x2;\n        else if (pos == Point_YMIN) point = p.y1;\n        else if (pos == Point_YMAX) point = p.y2;\n\n        points.push_back(point);\n    }\n\n    return points;\n}\n\nvector<float> ComputeArea(const vector<float> & x1,\n                          const vector<float> & y1,\n                          const vector<float> & x2,\n                          const vector<float> & y2)\n{\n    vector<float> area;\n    auto len = x1.size();\n\n    for (decltype(len) idx = 0; idx < len; ++idx) {\n        auto tmpArea = (x2[idx] - x1[idx] + 1) * (y2[idx] - y1[idx] + 1);\n        area.push_back(tmpArea);\n    }\n\n    return area;\n}\n\nvector<int> argsort_byscore(const vector<bbox> & v)\n{\n    // initialize original index locations\n    vector<int> idx(v.size());\n    std::iota(idx.begin(), idx.end(), 0);\n\n    // sort indexes based on comparing values in v\n    sort(idx.begin(), idx.end(),\n         [&v](int i1, int i2) {return v[i1].conf < v[i2].conf;});\n\n    return idx;\n}\n\nvector<float> Maximum(const float & num,\n                      const vector<float> & vec)\n{\n    auto maxVec = vec;\n    auto len = vec.size();\n\n    for (decltype(len) idx = 0; idx < len; ++idx)\n        if (vec[idx] < num)\n            maxVec[idx] = num;\n\n    return maxVec;\n}\n\nvector<float> Minimum(const float & num,\n                      const vector<float> & vec)\n{\n    auto minVec = vec;\n    auto len = vec.size();\n\n    for (decltype(len) idx = 0; idx < len; ++idx)\n        if (vec[idx] > num)\n            minVec[idx] = num;\n\n    return minVec;\n}\n\nvector<float> CopyByIndexes(const vector<float> & vec,\n                            const vector<int> & idxs)\n{\n    vector<float> resultVec;\n\n    for (const auto & idx : idxs)\n        resultVec.push_back(vec[idx]);\n\n    return resultVec;\n}\n\nvector<int> RemoveLast(const vector<int> & vec)\n{\n    auto resultVec = vec;\n    resultVec.erase(resultVec.end()-1);\n    return resultVec;\n}\n\nvector<float> Subtract(const vector<float> & vec1,\n                       const vector<float> & vec2)\n{\n    vector<float> result;\n    auto len = vec1.size();\n\n    for (decltype(len) idx = 0; idx < len; ++idx)\n        result.push_back(vec1[idx] - vec2[idx] + 1);\n\n    return result;\n}\n\nvector<float> Multiply(const vector<float> & vec1,\n                       const vector<float> & vec2)\n{\n    vector<float> resultVec;\n    auto len = vec1.size();\n\n    for (decltype(len) idx = 0; idx < len; ++idx)\n        resultVec.push_back(vec1[idx] * vec2[idx]);\n\n    return resultVec;\n}\n\nvector<float> Divide(const vector<float> & vec1,\n                     const vector<float> & vec2)\n{\n    vector<float> resultVec;\n    auto len = vec1.size();\n\n    for (decltype(len) idx = 0; idx < len; ++idx)\n        resultVec.push_back(vec1[idx] / vec2[idx]);\n\n    return resultVec;\n}\n\nvector<int> WhereLarger(const vector<float> & vec,\n                        const float & threshold)\n{\n    vector<int> resultVec;\n    auto len = vec.size();\n\n    for (decltype(len) idx = 0; idx < len; ++idx)\n        if (vec[idx] > threshold)\n            resultVec.push_back(idx);\n\n    return resultVec;\n}\n\nvector<int> RemoveByIndexes(const vector<int> & vec,\n                            const vector<int> & idxs)\n{\n    auto resultVec = vec;\n    auto offset = 0;\n\n    for (const auto & idx : idxs) {\n        resultVec.erase(resultVec.begin() + idx + offset);\n        offset -= 1;\n    }\n\n    return resultVec;\n}\n\n\ntemplate <typename T>\nvector<T> FilterVector(const vector<T> & vec,\n                       const vector<int> & idxs)\n{\n    vector<T> resultVec;\n\n    for (const auto & idx: idxs)\n        resultVec.push_back(vec[idx]);\n\n    return resultVec;\n}\n\n\nvector<bbox> nms(const vector<bbox> & candidates,\n                 const float &iou_threshold)\n{\n    if (candidates.empty()) return vector<bbox>();\n\n    // grab the coordinates of the bounding boxes\n    auto x1 = GetPointFromRect(candidates, Point_XMIN);\n    auto y1 = GetPointFromRect(candidates, Point_YMIN);\n    auto x2 = GetPointFromRect(candidates, Point_XMAX);\n    auto y2 = GetPointFromRect(candidates, Point_YMAX);\n\n    // compute the area of the bounding boxes and sort the bounding\n    // boxes by the bottom-right y-coordinate of the bounding box\n    auto area = ComputeArea(x1, y1, x2, y2);\n    auto idxs = argsort_byscore(candidates);\n\n    int last;\n    int i;\n    vector<int> pick;\n\n    // keep looping while some indexes still remain in the indexes list\n    while (idxs.size() > 0) {\n        // grab the last index in the indexes list and add the\n        // index value to the list of picked indexes\n        last = idxs.size() - 1;\n        i    = idxs[last];\n        pick.push_back(i);\n\n        // find the largest (x, y) coordinates for the start of\n        // the bounding box and the smallest (x, y) coordinates\n        // for the end of the bounding box\n        auto idxsWoLast = RemoveLast(idxs);\n\n        auto xx1 = Maximum(x1[i], CopyByIndexes(x1, idxsWoLast));\n        auto yy1 = Maximum(y1[i], CopyByIndexes(y1, idxsWoLast));\n        auto xx2 = Minimum(x2[i], CopyByIndexes(x2, idxsWoLast));\n        auto yy2 = Minimum(y2[i], CopyByIndexes(y2, idxsWoLast));\n\n        // compute the width and height of the bounding box\n        auto w = Maximum(0, Subtract(xx2, xx1));\n        auto h = Maximum(0, Subtract(yy2, yy1));\n\n        // compute the ratio of overlap\n        auto overlap = Divide(Multiply(w, h), CopyByIndexes(area, idxsWoLast));\n\n        // delete all indexes from the index list that have\n        auto deleteIdxs = WhereLarger(overlap, iou_threshold);\n        deleteIdxs.push_back(last);\n        idxs = RemoveByIndexes(idxs, deleteIdxs);\n    }\n\n    return FilterVector(candidates, pick);\n}\n\n"
  },
  {
    "path": "app/tflite_yolov5_test/app/src/main/cpp/postprocess.cpp",
    "content": "#include <string.h>\n#include <jni.h>\n#include <math.h>\n#include <iostream>\n#include \"nms.h\"\n\nusing namespace std;\n\nfloat sigmoid(float f){\n    return (float)(1.0f / (1.0f + exp(-f)));\n}\n\nfloat revsigmoid(float f){\n    const float eps = 1e-8;\n    return -1.0f * (float)log((1.0f / (f + eps)) - 1.0f);\n}\n\n#define CLASS_NUM 80\n#define max_wh 4096\nvoid detector(\n        vector<bbox>* bbox_candidates,\n        JNIEnv *env,\n        jobjectArray input,\n        const int gridnum,\n        const int strides,\n        const int anchorgrid[3][2],\n        const float conf_thresh){\n    float revsigmoid_conf = revsigmoid(conf_thresh);\n    //Warning: For now, we assume batch_size is always 1.\n    for(int bi = 0; bi < 1; bi++){\n        jobjectArray ptr_d0 = (jobjectArray)env->GetObjectArrayElement(input , bi);\n        for(int gy = 0; gy < gridnum; gy++){\n            jobjectArray ptr_d1 = (jobjectArray)env->GetObjectArrayElement(ptr_d0 ,gy);\n            for(int gx = 0; gx < gridnum; gx++){\n                jobjectArray ptr_d2 = (jobjectArray)env->GetObjectArrayElement(ptr_d1 ,gx);\n                auto elmptr = env->GetFloatArrayElements((jfloatArray)ptr_d2 , nullptr);\n                for(int ch = 0; ch < 3; ch++){\n                    int offset = 85 * ch;\n                    auto elmptr_ch = elmptr + offset;\n                    //don't apply sigmoid to all bbox candidates for efficiency\n                    float obj_conf_unsigmoid = elmptr_ch[4];\n                    //if (sigmoid(obj_conf_unsigmoid) < conf_thresh) continue;\n                    if (obj_conf_unsigmoid >= revsigmoid_conf) {\n                        //get maximum conf class\n                        float max_class_conf = elmptr_ch[5];\n                        int max_class_idx = 0;\n                        for(int class_idx = 1; class_idx < CLASS_NUM; class_idx++){\n                            float class_conf = elmptr_ch[class_idx + 5];\n                            if (class_conf > max_class_conf){\n                                max_class_conf = class_conf;\n                                max_class_idx = class_idx;\n                            }\n                        }\n                        // class conf filter\n                        float bbox_conf = sigmoid(max_class_conf) * sigmoid(obj_conf_unsigmoid);\n                        //if (bbox_conf < conf_thresh) continue;\n                        // xywh2xyxy\n                        // batched nms (by adding class * max_wh to coordinates,\n                        // we can get nms result for all classes by just one nms call)\n                        //grid[gridnum][gy][gx][0] = gx\n                        //grid[gridnum][gy][gx][1] = gy\n                        float cx = ((sigmoid(elmptr_ch[0]) * 2.0f) - 0.5f + (float)gx) * (float)strides;\n                        float cy = ((sigmoid(elmptr_ch[1]) * 2.0f) - 0.5f + (float)gy) * (float)strides;\n                        float w  = (sigmoid(elmptr_ch[2]) * sigmoid(elmptr_ch[2])) * 4.0f * (float)anchorgrid[ch][0];\n                        float h  = (sigmoid(elmptr_ch[3]) * sigmoid(elmptr_ch[3])) * 4.0f * (float)anchorgrid[ch][1];\n                        float x1 = cx - w / 2.0f + max_wh * max_class_idx;\n                        float y1 = cy - h / 2.0f + max_wh * max_class_idx;\n                        float x2 = cx + w / 2.0f + max_wh * max_class_idx;\n                        float y2 = cy + h / 2.0f + max_wh * max_class_idx;\n                        bbox box = bbox(x1, y1, x2, y2, bbox_conf, max_class_idx);\n                        bbox_candidates->push_back(box);\n                    }\n                }\n                env->ReleaseFloatArrayElements((jfloatArray)ptr_d2, elmptr, 0);\n                env->DeleteLocalRef(ptr_d2);\n            }\n            env->DeleteLocalRef(ptr_d1);\n        }\n        env->DeleteLocalRef(ptr_d0);\n    }\n    env->DeleteLocalRef(input);\n}\n\nextern \"C\" jobjectArray Java_com_example_tflite_1yolov5_1test_TfliteRunner_postprocess (\n        JNIEnv *env,\n        jobject /* this */,\n        jobjectArray input1,//80x80 or 40x40\n        jobjectArray input2, //40x40 or 20x20,\n        jobjectArray input3, //20x20 or 10x10\n        jint input_size,\n        jfloat conf_thresh,\n        jfloat iou_thresh){\n    //conf\n    const int anchorgrids[3][3][2]  = {\n        {{10, 13}, {16, 30}, {33, 23}}, //80\n        {{30, 61}, {62, 45}, {59, 119}}, //40\n        {{116, 90}, {156, 198}, {373, 326}} //20\n    };\n    const int strides[3] = {8, 16, 32};\n\n    vector<bbox> bbox_candidates; //TODO: reserve\n    //Detector\n    detector(&bbox_candidates, env, input1, input_size / 8, strides[0], anchorgrids[0], conf_thresh);\n    detector(&bbox_candidates, env, input2, input_size / 16, strides[1], anchorgrids[1], conf_thresh);\n    detector(&bbox_candidates, env, input3, input_size / 32, strides[2], anchorgrids[2], conf_thresh);\n    //non-max-suppression\n    vector<bbox> nms_results = nms(bbox_candidates, iou_thresh);\n\n    //return 2-dimension array [detected_box][6(x1, y1, x2, y2, conf, class)]\n    jobjectArray objArray;\n    jclass floatArray = env->FindClass(\"[F\");\n    if (floatArray == NULL) return NULL;\n    int size = nms_results.size();\n    objArray = env->NewObjectArray(size, floatArray, NULL);\n    if (objArray == NULL) return NULL;\n    for(int i = 0; i < nms_results.size(); i++){\n        int class_idx = nms_results[i].class_idx;\n        float x1 = nms_results[i].x1 - class_idx * max_wh;\n        float y1 = nms_results[i].y1 - class_idx * max_wh;\n        float x2 = nms_results[i].x2 - class_idx * max_wh;\n        float y2 = nms_results[i].y2 - class_idx * max_wh;\n        float conf = nms_results[i].conf;\n        float boxres[6] = {x1, y1, x2, y2, conf, (float)class_idx};\n        jfloatArray iarr = env->NewFloatArray((jsize)6);\n        if (iarr == NULL) return NULL;\n        env->SetFloatArrayRegion(iarr, 0, 6, boxres);\n        env->SetObjectArrayElement(objArray, i, iarr);\n        env->DeleteLocalRef(iarr);\n    }\n    return objArray;\n}"
  },
  {
    "path": "app/tflite_yolov5_test/app/src/main/java/com/example/tflite_yolov5_test/ImageProcess.java",
    "content": "package com.example.tflite_yolov5_test;\n\nimport android.graphics.Bitmap;\nimport android.graphics.Canvas;\nimport android.graphics.Color;\nimport android.graphics.Paint;\nimport android.graphics.RectF;\nimport android.text.TextUtils;\n\nimport com.example.tflite_yolov5_test.camera.env.BorderedText;\n\nimport java.util.List;\n\npublic class ImageProcess {\n    private static final int[] COLORS = {\n            Color.BLUE,\n            Color.RED,\n            Color.GREEN,\n            Color.YELLOW,\n            Color.CYAN,\n            Color.MAGENTA,\n            Color.WHITE,\n            Color.parseColor(\"#55FF55\"),\n            Color.parseColor(\"#FFA500\"),\n            Color.parseColor(\"#FF8888\"),\n            Color.parseColor(\"#AAAAFF\"),\n            Color.parseColor(\"#FFFFAA\"),\n            Color.parseColor(\"#55AAAA\"),\n            Color.parseColor(\"#AA33AA\"),\n            Color.parseColor(\"#0D0068\")\n    };\n    static public Bitmap drawBboxes(List<TfliteRunner.Recognition> bboxes, Bitmap bitmap, int inputSize) {\n        Bitmap mutableBitmap = bitmap.copy(Bitmap.Config.ARGB_8888, true);\n        bitmap.recycle();\n        final Canvas canvas = new Canvas(mutableBitmap);\n        final Paint paint = new Paint();\n        BorderedText borderedText = new BorderedText(25);\n        paint.setStyle(Paint.Style.STROKE);\n        paint.setStrokeWidth(3.0f);\n        for (TfliteRunner.Recognition bbox : bboxes) {\n            int color_idx = bbox.getClass_idx() % COLORS.length;\n            paint.setColor(COLORS[color_idx]);\n            RectF location = bbox.getLocation();\n            float left = location.left * bitmap.getWidth() / inputSize;\n            float right = location.right * bitmap.getWidth() / inputSize;\n            float top = location.top * bitmap.getHeight() / inputSize;\n            float bottom = location.bottom * bitmap.getHeight() / inputSize;\n            RectF drawBoxRect = new RectF(left, top, right, bottom);\n            canvas.drawRect(drawBoxRect, paint);\n            String labelString = String.format(\"%s %.2f\", bbox.getTitle(), (100 * bbox.getConfidence()));\n            borderedText.drawText(\n                    canvas, left - 10, top - 10, labelString, paint);\n        }\n\n        return mutableBitmap;\n    }\n}\n"
  },
  {
    "path": "app/tflite_yolov5_test/app/src/main/java/com/example/tflite_yolov5_test/MainActivity.java",
    "content": "package com.example.tflite_yolov5_test;\n\nimport android.content.Context;\nimport android.graphics.RectF;\nimport android.os.Bundle;\n\nimport com.example.tflite_yolov5_test.camera.DetectorActivity;\n\nimport androidx.appcompat.app.AppCompatActivity;\n\nimport android.Manifest;\nimport android.app.AlertDialog;\nimport android.content.Intent;\nimport android.content.pm.PackageManager;\nimport android.graphics.BitmapFactory;\nimport android.net.Uri;\nimport android.os.Handler;\nimport android.os.HandlerThread;\nimport android.provider.DocumentsContract;\nimport android.view.View;\nimport android.widget.Button;\nimport android.widget.ImageView;\nimport android.widget.ProgressBar;\nimport android.widget.RadioButton;\nimport android.widget.SeekBar;\nimport android.widget.TextView;\nimport android.widget.Toast;\n\nimport java.io.IOException;\n\nimport org.json.JSONArray;\n//import org.tensorflow.lite.gpu.GpuDelegate;\n\nimport java.io.*;\nimport java.util.ArrayList;\nimport java.util.Arrays;\nimport java.util.HashMap;\nimport java.util.List;\nimport java.util.Map;\n\nimport android.graphics.Bitmap;\n\nimport java.lang.Math;\npublic class MainActivity extends AppCompatActivity {\n\n    final int REQUEST_OPEN_FILE = 1;\n    final int REQUEST_OPEN_DIRECTORY = 9999;\n    //permission\n    private int inputSize = -1;\n    private File[] process_files = null;\n    private final int REQUEST_PERMISSION = 1000;\n    private final String[] PERMISSIONS = {\n            Manifest.permission.READ_EXTERNAL_STORAGE,\n            Manifest.permission.WRITE_EXTERNAL_STORAGE,\n    };\n    //background task\n    private boolean handler_stop_request;\n    private Handler handler;\n    private HandlerThread handlerThread;\n\n    private void checkPermission(){\n        if (!isGranted()){\n            requestPermissions(PERMISSIONS, REQUEST_PERMISSION);\n        }\n    }\n    @Override\n    public void onRequestPermissionsResult(int requestCode, String[] permissions, int[] grantResults) {\n        super.onRequestPermissionsResult(requestCode, permissions, grantResults);\n        Toast.makeText(this, \"onRequestPermissionResult\", Toast.LENGTH_LONG).show();\n        if (requestCode == REQUEST_PERMISSION){\n            boolean result = isGranted();\n            Toast.makeText(getApplicationContext(), result ? \"OK\" : \"NG\", Toast.LENGTH_SHORT).show();\n        }\n    }\n    private boolean isGranted(){\n        for (int i = 0; i < PERMISSIONS.length; i++){\n            if (checkSelfPermission(PERMISSIONS[i]) != PackageManager.PERMISSION_GRANTED) {\n                if (shouldShowRequestPermissionRationale(PERMISSIONS[i])) {\n                    Toast.makeText(this, \"permission is required\", Toast.LENGTH_LONG).show();\n                }\n                return false;\n            }\n        }\n        return true;\n    }\n\n    @Override\n    protected void onCreate(Bundle savedInstanceState) {\n        super.onCreate(savedInstanceState);\n\n        setContentView(R.layout.activity_main);\n        SeekBar conf_seekBar = (SeekBar)findViewById(R.id.conf_seekBar);\n        conf_seekBar.setOnSeekBarChangeListener(new SeekBar.OnSeekBarChangeListener(){\n            @Override\n            public void onProgressChanged(SeekBar seekBar, int progress, boolean fromUser) {\n                TextView conf_textView = (TextView)findViewById(R.id.conf_TextView);\n                conf_textView.setText(String.format(\"Confidence Threshold: %.2f\", (float)progress / 100));\n            }\n            @Override\n            public void onStopTrackingTouch(SeekBar seekBar) {\n            }\n            @Override\n            public void onStartTrackingTouch(SeekBar seekBar) {\n            }\n        });\n        conf_seekBar.setMax(100);\n        conf_seekBar.setProgress(25);//0.25\n        SeekBar iou_seekBar = (SeekBar)findViewById(R.id.iou_seekBar);\n        iou_seekBar.setOnSeekBarChangeListener(new SeekBar.OnSeekBarChangeListener(){\n            @Override\n            public void onProgressChanged(SeekBar seekBar, int progress, boolean fromUser) {\n                TextView iou_textView = (TextView)findViewById(R.id.iou_TextView);\n                iou_textView.setText(String.format(\"IoU Threshold: %.2f\", (float)progress / 100));\n            }\n            @Override\n            public void onStopTrackingTouch(SeekBar seekBar) {\n            }\n            @Override\n            public void onStartTrackingTouch(SeekBar seekBar) {\n            }\n        });\n        iou_seekBar.setMax(100);\n        iou_seekBar.setProgress(45);//0.45\n    }\n    public void OnOpenImageButtonClick(View view){\n        checkPermission();\n        Intent intent = new Intent(Intent.ACTION_GET_CONTENT);\n        intent.setType(\"image/*\");\n        intent.addCategory(Intent.CATEGORY_OPENABLE);\n        startActivityForResult(Intent.createChooser(intent, \"Open an image\"), REQUEST_OPEN_FILE);\n    }\n    public void OnOpenDirButtonClick(View view){\n        checkPermission();\n        Intent intent = new Intent(Intent.ACTION_OPEN_DOCUMENT_TREE);\n        intent.addCategory(Intent.CATEGORY_DEFAULT);\n        startActivityForResult(Intent.createChooser(intent, \"Open directory\"), REQUEST_OPEN_DIRECTORY);\n    }\n    public void setResultImage(Bitmap bitmap){\n        ImageView imageview = (ImageView)findViewById(R.id.resultImageView);\n        imageview.setImageBitmap(bitmap);\n    }\n    ArrayList<HashMap<String, Object>> bboxesToMap(File file, List<TfliteRunner.Recognition> bboxes, int orig_h, int orig_w){\n        ArrayList<HashMap<String, Object>> resList = new ArrayList<HashMap<String, Object>>();\n        String basename = file.getName();\n        basename = basename.substring(0, basename.lastIndexOf('.'));\n        Object image_id;\n        try{\n            image_id = Integer.parseInt(basename);\n        } catch (Exception e){\n            image_id = basename;\n        }\n        for(TfliteRunner.Recognition bbox : bboxes){\n            //clamp and scale to original image size\n            RectF location = bbox.getLocation();\n            float x1 = Math.min(Math.max(0, location.left), this.inputSize) * orig_w / (float)this.inputSize;\n            float y1 = Math.min(Math.max(0, location.top), this.inputSize) * orig_h / (float)this.inputSize;\n            float x2 = Math.min(Math.max(0, location.right), this.inputSize) * orig_w / (float)this.inputSize;\n            float y2 = Math.min(Math.max(0, location.bottom), this.inputSize) * orig_h / (float)this.inputSize;\n            float x = x1;\n            float y = y1;\n            float w = x2 - x1;\n            float h = y2 - y1;\n            float conf = bbox.getConfidence();\n            int class_idx = TfliteRunner.get_coco91_from_coco80(bbox.getClass_idx());\n            HashMap<String, Object> mapbox = new HashMap<>();\n            mapbox.put(\"image_id\", image_id);\n            mapbox.put(\"bbox\", new float[]{x, y, w, h});\n            mapbox.put(\"score\", conf);\n            mapbox.put(\"category_id\", class_idx);\n            resList.add(mapbox);\n        }\n        return resList;\n    }\n    private boolean isBackgroundTaskRunning() {\n        return this.handlerThread != null && this.handlerThread.isAlive();\n    }\n    public void OnInferenceTaskCompleted() {\n        Button button = (Button) findViewById(R.id.runInferenceButton);\n        button.setText(\"Run Inference\");\n        SeekBar conf_seekBar = (SeekBar) findViewById(R.id.conf_seekBar);\n        conf_seekBar.setEnabled(true);\n        SeekBar iou_seekBar = (SeekBar) findViewById(R.id.iou_seekBar);\n        iou_seekBar.setEnabled(true);\n    }\n    public void OnInferenceTaskStart(){\n        Button button = (Button)findViewById(R.id.runInferenceButton);\n        button.setText(\"Stop Inference\");\n        SeekBar conf_seekBar = (SeekBar) findViewById(R.id.conf_seekBar);\n        conf_seekBar.setEnabled(false);\n        SeekBar iou_seekBar = (SeekBar) findViewById(R.id.iou_seekBar);\n        iou_seekBar.setEnabled(false);\n    }\n    public float getConfThreshFromGUI(){ return ((float)((SeekBar)findViewById(R.id.conf_seekBar)).getProgress()) / 100.0f;}\n    public float getIoUThreshFromGUI(){ return ((float)((SeekBar)findViewById(R.id.iou_seekBar)).getProgress()) / 100.0f;}\n    public void OnRunInferenceButtonClick(View view){\n        TfliteRunner runner;\n        TfliteRunMode.Mode runmode = getRunModeFromGUI();\n        this.inputSize = getInputSizeFromGUI();\n        //validation\n        if (this.process_files == null || this.process_files.length == 0){\n            showErrorDialog(\"Please select image or directory.\");\n            return;\n        }\n        if (runmode == null) {\n            showErrorDialog(\"Please select valid configurations.\");\n            return;\n        }\n\n        //open model\n        try {\n            Context context = getApplicationContext();\n            runner = new TfliteRunner(context, runmode, this.inputSize, getConfThreshFromGUI(), getIoUThreshFromGUI());\n        } catch (Exception e) {\n            showErrorDialog(\"Model load failed: \" + e.getMessage());\n            return;\n        }\n        //check background task status\n        if(isBackgroundTaskRunning()){\n            //already inference is running, stop inference\n            this.handler_stop_request = true;\n            this.handlerThread.quitSafely();\n            try {\n                handlerThread.join();\n                handlerThread = null;\n                handler = null;\n            } catch (final InterruptedException e) {\n                addLog(e.getMessage() +  \"Exception!\");\n            }\n            OnInferenceTaskCompleted();\n            return;\n        } else {\n            //start inference task\n            this.handler_stop_request = false;\n            OnInferenceTaskStart();\n        }\n\n        //run inference in background\n        this.handlerThread = new HandlerThread(\"inference\");\n        this.handlerThread.start();\n        this.handler = new Handler(this.handlerThread.getLooper());\n        ProgressBar pbar = (ProgressBar)findViewById(R.id.progressBar);\n        File[] process_files = this.process_files;\n        pbar.setProgress(0);\n        ArrayList<HashMap<String, Object>> resList = new ArrayList<>();\n        runInBackground(\n                new Runnable() {\n                    @Override\n                    public void run() {\n                        try {\n                            for(int i = 0; i < process_files.length; i++){\n                                if (handler_stop_request) break;\n                                File file = process_files[i];\n                                InputStream is = new FileInputStream(file);\n                                Bitmap bitmap = BitmapFactory.decodeStream(is);\n                                Bitmap resized = TfliteRunner.getResizedImage(bitmap, inputSize);\n                                runner.setInput(resized);\n                                List<TfliteRunner.Recognition> bboxes = runner.runInference();\n                                Bitmap resBitmap = ImageProcess.drawBboxes(bboxes, bitmap, getInputSizeFromGUI());\n                                ArrayList<HashMap<String, Object>> bboxmaps = bboxesToMap(file, bboxes, bitmap.getHeight(), bitmap.getWidth());\n                                resList.addAll(bboxmaps);\n                                int ii = i;\n                                runOnUiThread(\n                                        new Runnable() {\n                                            @Override\n                                            public void run () {\n                                                pbar.setProgress(Math.min(100, (ii+1) * 100 / process_files.length));\n                                                setResultImage(resBitmap);\n                                            }\n                                        });\n                                bitmap.recycle();\n                            }\n                        } catch (Exception e) {\n                            runOnUiThread(\n                                    new Runnable() {\n                                        @Override\n                                        public void run() {\n                                            showErrorDialog(\"Inference failed : \" + e.getMessage()) ;\n                                        }\n                                    }\n                            );\n                        }\n                        //completed\n                        runOnUiThread(\n                                new Runnable() {\n                                    @Override\n                                    public void run() {\n                                        handler_stop_request = false;\n                                        OnInferenceTaskCompleted();\n                                        //output json if directory mode\n                                        if (process_files.length > 1) {\n                                            try {\n                                                String jsonpath = saveBboxesToJson(resList, process_files[0], \"result.json\");\n                                                showInfoDialog(\"result json is saved : \" + jsonpath);\n                                            } catch (Exception e){\n                                                showErrorDialog(\"json output failed : \" + e.getMessage());\n                                            }\n                                        }\n                                        addLog(runner.getLastElapsedTimeLog());\n                                    }\n                                }\n                        );\n                    }\n                }\n        );\n\n    }\n    String saveBboxesToJson(ArrayList<HashMap<String, Object>> resList, File file, String output_filename)\n            throws org.json.JSONException, IOException{\n//        HashMap<String, Object>[] resArr = (HashMap<String, Object>[])resList.toArray();\n        JSONArray jarr = new JSONArray(resList);\n        String jstr = jarr.toString();\n\n        String filepath = file.getParent() + \"/\" + output_filename;\n        FileOutputStream fileOutputStream = new FileOutputStream(filepath, false);\n        fileOutputStream.write(jstr.getBytes());\n        return filepath;\n    }\n    private void showErrorDialog(String text){ showDialog(\"Error\", text);}\n    private void showInfoDialog(String text){ showDialog(\"Info\", text);}\n    private void showDialog(String title, String text){\n        new AlertDialog.Builder(this)\n                .setTitle(title)\n                .setMessage(text)\n                .setPositiveButton(\"OK\" , null )\n                .create().show();\n    }\n    private void addLog(String logtxt){\n        TextView logtext = findViewById(R.id.logTextView);\n        logtext.setText(logtext.getText() + logtxt + \"\\n\");\n    }\n    private void setOneLineLog(String text){\n        TextView onelinetextview = findViewById(R.id.oneLineLabel);\n        onelinetextview.setText(text);\n    }\n    public void OnClearLogButton(View view) {\n        TextView logtext = findViewById(R.id.logTextView);\n        logtext.setText(\"\");\n    }\n    void setImageView(Bitmap bitmap){\n        ImageView imageview = (ImageView)findViewById(R.id.resultImageView);\n        imageview.setImageBitmap(bitmap);\n    }\n    public void OnOpenCameraButtonClick(View view){\n        if (isBackgroundTaskRunning()) {\n            showErrorDialog(\"Please stop inference task\");\n            return;\n        }\n        Intent intent = new Intent(MainActivity.this, DetectorActivity.class);\n        startActivity(intent);\n    }\n    @Override\n    protected void onActivityResult(int requestCode, int resultCode, Intent data) {\n        super.onActivityResult(requestCode, resultCode, data);\n        if (requestCode == REQUEST_OPEN_FILE) {\n            // one image file is selected\n            if (resultCode == RESULT_OK && data != null) {\n                Uri uri = data.getData();\n                if (uri != null) {\n                    String fullpath = PathUtils.getPath(getApplicationContext(), uri);\n                    this.process_files = new File[]{new File(fullpath)};\n                }\n            }\n        } else if (requestCode == REQUEST_OPEN_DIRECTORY) {\n            // image directory is selected\n            if (resultCode == RESULT_OK && data != null) {\n                Uri uri = data.getData();\n                if (uri != null) {\n                    Uri docUri = DocumentsContract.buildDocumentUriUsingTree(uri,\n                            DocumentsContract.getTreeDocumentId(uri));\n                    String fullpath = PathUtils.getPath(getApplicationContext(), docUri);\n                    File directory = new File(fullpath);\n                    this.process_files = directory.listFiles(new ImageFilenameFIlter());\n                }\n            }\n        }\n        if (this.process_files != null && this.process_files.length > 0){\n            setOneLineLog(String.valueOf(this.process_files.length) + \" images loaded.\");\n            try{\n                InputStream is = new FileInputStream(this.process_files[0]);\n                Bitmap bitmap = BitmapFactory.decodeStream(is);\n                setResultImage(bitmap);\n            } catch(Exception ex){\n                setOneLineLog(ex.getMessage());\n            }\n        }\n    }\n    class ImageFilenameFIlter implements FilenameFilter {\n        public boolean accept(File dir, String name) {\n            if (name.toLowerCase().matches(\".*\\\\.jpg$|.*\\\\.jpeg$|.*\\\\.png$|.*\\\\.bmp$\")) {\n                return true;\n            }\n            return false;\n        }\n    }\n    protected synchronized void runInBackground(final Runnable r) {\n        if (this.handler != null) {\n            this.handler.post(r);\n        }\n    }\n    private TfliteRunMode.Mode getRunModeFromGUI(){\n        boolean model_float = ((RadioButton)findViewById(R.id.radioButton_modelFloat)).isChecked();\n        boolean model_int8 = ((RadioButton)findViewById(R.id.radioButton_modelInt)).isChecked();\n        boolean precision_fp32 = ((RadioButton)findViewById(R.id.radioButton_runFP32)).isChecked();\n        boolean precision_fp16 = ((RadioButton)findViewById(R.id.radioButton_runFP16)).isChecked();\n        boolean precision_int8 = ((RadioButton)findViewById(R.id.radioButton_runInt8)).isChecked();\n        boolean delegate_none = ((RadioButton)findViewById(R.id.radioButton_delegateNone)).isChecked();\n        boolean delegate_nnapi = ((RadioButton)findViewById(R.id.radioButton_delegateNNAPI)).isChecked();\n        boolean[] gui_selected = {model_float, model_int8, precision_fp32, precision_fp16, precision_int8, delegate_none, delegate_nnapi};\n        final Map<TfliteRunMode.Mode, boolean[]> candidates = new HashMap<TfliteRunMode.Mode, boolean[]>(){{\n            put(TfliteRunMode.Mode.NONE_FP32,      new boolean[]{true, false, true, false, false, true, false});\n            put(TfliteRunMode.Mode.NONE_FP16,      new boolean[]{true, false, false, true, false, true, false});\n            put(TfliteRunMode.Mode.NNAPI_GPU_FP32, new boolean[]{true, false, true, false, false, false, true});\n            put(TfliteRunMode.Mode.NNAPI_GPU_FP16, new boolean[]{true, false, false, true, false, false, true});\n            put(TfliteRunMode.Mode.NONE_INT8,      new boolean[]{false, true, false, false, true, true, false});\n            put(TfliteRunMode.Mode.NNAPI_DSP_INT8, new boolean[]{false, true, false, false, true, false, true});\n        }};\n        for(Map.Entry<TfliteRunMode.Mode, boolean[]> entry : candidates.entrySet()){\n            if (Arrays.equals(gui_selected, entry.getValue())) return entry.getKey();\n        }\n        //not found\n        return null;\n    }\n    public int getInputSizeFromGUI(){\n        RadioButton input_640 = findViewById(R.id.radioButton_640);\n        if (input_640.isChecked()) return 640;\n        else return 320;\n    }\n    //Eliminate infeasible run configurations(model, precision)\n    public void onModelFloatClick(View view) {\n        RadioButton precision_int8 = findViewById(R.id.radioButton_runInt8);\n        if (precision_int8.isChecked()){\n            RadioButton precision_fp32 = findViewById(R.id.radioButton_runFP32);\n            precision_fp32.setChecked(true);\n        }\n    }\n    public void onModelIntClick(View view) {\n        RadioButton precision_fp32 = findViewById(R.id.radioButton_runFP32);\n        RadioButton precision_fp16 = findViewById(R.id.radioButton_runFP16);\n        if (precision_fp32.isChecked() || precision_fp16.isChecked()){\n            RadioButton precision_int8 = findViewById(R.id.radioButton_runInt8);\n            precision_int8.setChecked(true);\n        }\n    }\n    public void onPrecisionFPClick(View view){\n        RadioButton model_int = findViewById(R.id.radioButton_modelInt);\n        if (model_int.isChecked()) {\n            RadioButton model_fp = findViewById(R.id.radioButton_modelFloat);\n            model_fp.setChecked(true);\n        }\n    }\n    public void onPrecisionIntClick(View view){\n        RadioButton model_fp = findViewById(R.id.radioButton_modelFloat);\n        if (model_fp.isChecked()) {\n            RadioButton model_int = findViewById(R.id.radioButton_modelInt);\n            model_int.setChecked(true);\n        }\n    }\n}"
  },
  {
    "path": "app/tflite_yolov5_test/app/src/main/java/com/example/tflite_yolov5_test/PathUtils.java",
    "content": "package com.example.tflite_yolov5_test;\nimport android.content.ContentUris;\nimport android.content.Context;\nimport android.database.Cursor;\nimport android.graphics.Bitmap;\nimport android.graphics.Canvas;\nimport android.graphics.Color;\nimport android.graphics.Paint;\nimport android.graphics.RectF;\nimport android.net.Uri;\nimport android.os.Build;\nimport android.os.Environment;\nimport android.provider.DocumentsContract;\nimport android.provider.MediaStore;\n\nimport java.util.List;\n\npublic class PathUtils {\n\n    public static String getPath(final Context context, final Uri uri) {\n\n        // DocumentProvider\n        if (Build.VERSION.SDK_INT >= Build.VERSION_CODES.KITKAT && DocumentsContract.isDocumentUri(context, uri)) {\n\n            if (isExternalStorageDocument(uri)) {// ExternalStorageProvider\n                final String docId = DocumentsContract.getDocumentId(uri);\n                final String[] split = docId.split(\":\");\n                final String type = split[0];\n                String storageDefinition;\n\n\n                if(\"primary\".equalsIgnoreCase(type)){\n\n                    return Environment.getExternalStorageDirectory() + \"/\" + split[1];\n\n                } else {\n\n                    if(Environment.isExternalStorageRemovable()){\n                        storageDefinition = \"EXTERNAL_STORAGE\";\n\n                    } else{\n                        storageDefinition = \"SECONDARY_STORAGE\";\n                    }\n\n                    return System.getenv(storageDefinition) + \"/\" + split[1];\n                }\n\n            } else if (isDownloadsDocument(uri)) {// DownloadsProvider\n\n                final String id = DocumentsContract.getDocumentId(uri);\n                final Uri contentUri = ContentUris.withAppendedId(\n                        Uri.parse(\"content://downloads/public_downloads\"), Long.valueOf(id));\n\n                return getDataColumn(context, contentUri, null, null);\n\n            } else if (isMediaDocument(uri)) {// MediaProvider\n                final String docId = DocumentsContract.getDocumentId(uri);\n                final String[] split = docId.split(\":\");\n                final String type = split[0];\n\n                Uri contentUri = null;\n                if (\"image\".equals(type)) {\n                    contentUri = MediaStore.Images.Media.EXTERNAL_CONTENT_URI;\n                } else if (\"video\".equals(type)) {\n                    contentUri = MediaStore.Video.Media.EXTERNAL_CONTENT_URI;\n                } else if (\"audio\".equals(type)) {\n                    contentUri = MediaStore.Audio.Media.EXTERNAL_CONTENT_URI;\n                }\n\n                final String selection = \"_id=?\";\n                final String[] selectionArgs = new String[]{\n                        split[1]\n                };\n\n                return getDataColumn(context, contentUri, selection, selectionArgs);\n            }\n\n        } else if (\"content\".equalsIgnoreCase(uri.getScheme())) {// MediaStore (and general)\n\n            // Return the remote address\n            if (isGooglePhotosUri(uri))\n                return uri.getLastPathSegment();\n\n            return getDataColumn(context, uri, null, null);\n\n        } else if (\"file\".equalsIgnoreCase(uri.getScheme())) {// File\n            return uri.getPath();\n        }\n\n        return null;\n    }\n\n    public static String getDataColumn(Context context, Uri uri, String selection, String[] selectionArgs) {\n\n        Cursor cursor = null;\n        final String column = \"_data\";\n        final String[] projection = {\n                column\n        };\n\n        try {\n            cursor = context.getContentResolver().query(uri, projection, selection, selectionArgs, null);\n            if (cursor != null && cursor.moveToFirst()) {\n                final int column_index = cursor.getColumnIndexOrThrow(column);\n                return cursor.getString(column_index);\n            }\n        } finally {\n            if (cursor != null)\n                cursor.close();\n        }\n        return null;\n    }\n\n\n    public static boolean isExternalStorageDocument(Uri uri) {\n        return \"com.android.externalstorage.documents\".equals(uri.getAuthority());\n    }\n\n\n    public static boolean isDownloadsDocument(Uri uri) {\n        return \"com.android.providers.downloads.documents\".equals(uri.getAuthority());\n    }\n\n    public static boolean isMediaDocument(Uri uri) {\n        return \"com.android.providers.media.documents\".equals(uri.getAuthority());\n    }\n\n    public static boolean isGooglePhotosUri(Uri uri) {\n        return \"com.google.android.apps.photos.content\".equals(uri.getAuthority());\n    }\n\n}"
  },
  {
    "path": "app/tflite_yolov5_test/app/src/main/java/com/example/tflite_yolov5_test/TfliteRunMode.java",
    "content": "package com.example.tflite_yolov5_test;\n\npublic class TfliteRunMode {\n    public enum Mode{\n        NONE_FP32,\n        NONE_FP16,\n        NONE_INT8,\n        NNAPI_GPU_FP32,\n        NNAPI_GPU_FP16,\n        NNAPI_DSP_INT8\n    }\n    static public boolean isQuantizedMode(Mode mode){\n        return mode == Mode.NONE_INT8 || mode == Mode.NNAPI_DSP_INT8;\n    }\n}\n"
  },
  {
    "path": "app/tflite_yolov5_test/app/src/main/java/com/example/tflite_yolov5_test/TfliteRunner.java",
    "content": "package com.example.tflite_yolov5_test;\n\nimport android.content.Context;\nimport android.content.res.AssetFileDescriptor;\nimport android.content.res.AssetManager;\nimport android.graphics.Bitmap;\nimport android.graphics.BitmapFactory;\nimport android.graphics.RectF;\nimport android.widget.ImageView;\n\nimport org.tensorflow.lite.Interpreter;\n\nimport org.tensorflow.lite.nnapi.NnApiDelegate;\n\nimport java.io.File;\nimport java.io.FileInputStream;\nimport java.io.FileNotFoundException;\nimport java.io.IOException;\nimport java.io.InputStream;\nimport java.nio.ByteBuffer;\nimport java.nio.ByteOrder;\nimport java.nio.MappedByteBuffer;\nimport java.nio.channels.FileChannel;\nimport java.util.ArrayList;\nimport java.util.HashMap;\nimport java.util.List;\nimport java.util.Map;\nimport com.example.tflite_yolov5_test.TfliteRunMode.*;\n\npublic class TfliteRunner {\n    final int numBytesPerChannel_float = 4;\n    final int numBytesPerChannel_int = 1;\n    static {\n        System.loadLibrary(\"native-lib\");\n    }\n    public native float[][] postprocess(float[][][][] out1, float[][][][] out2, float[][][][] out3, int inputSize, float conf_thresh, float iou_thresh);\n    private Interpreter tfliteInterpreter;\n    Mode runmode;\n    int inputSize;\n    class InferenceRawResult{\n        public float[][][][] out1;\n        public float[][][][] out2;\n        public float[][][][] out3;\n\n        public InferenceRawResult(int inputSize){\n            this.out1 = new float[1][inputSize/8][inputSize/8][3*85];\n            this.out2 = new float[1][inputSize/16][inputSize/16][3*85];\n            this.out3 = new float[1][inputSize/32][inputSize/32][3*85];\n        }\n    }\n    Object[] inputArray;\n    Map<Integer, Object> outputMap;\n    InferenceRawResult rawres;\n    float conf_thresh;\n    float iou_thresh;\n\n    public TfliteRunner(Context context, Mode runmode, int inputSize, float conf_thresh, float iou_thresh) throws Exception{\n        this.runmode = runmode;\n        this.rawres = new InferenceRawResult(inputSize);\n        this.inputSize = inputSize;\n        this.conf_thresh = conf_thresh;\n        this.iou_thresh = iou_thresh;\n        loadModel(context, runmode, inputSize, 4);\n    }\n    private static MappedByteBuffer loadModelFile(AssetManager assets, String modelFilename)\n            throws IOException {\n        AssetFileDescriptor fileDescriptor = assets.openFd(modelFilename);\n        FileInputStream inputStream = new FileInputStream(fileDescriptor.getFileDescriptor());\n        FileChannel fileChannel = inputStream.getChannel();\n        long startOffset = fileDescriptor.getStartOffset();\n        long declaredLength = fileDescriptor.getDeclaredLength();\n        return fileChannel.map(FileChannel.MapMode.READ_ONLY, startOffset, declaredLength);\n    }\n    public void loadModel(Context context, Mode runmode, int inputSize, int num_threads) throws Exception{\n        Interpreter.Options options = new Interpreter.Options();\n        NnApiDelegate.Options nnapi_options = new NnApiDelegate.Options();\n        options.setNumThreads(num_threads);\n        nnapi_options.setExecutionPreference(1);//sustain-spped\n        switch (runmode){\n            case NONE_FP32:\n                options.setUseXNNPACK(true);\n                break;\n            case NONE_FP16:\n                //TODO:deprecated?\n                options.setAllowFp16PrecisionForFp32(true);\n                break;\n            case NNAPI_GPU_FP32:\n                nnapi_options.setAcceleratorName(\"qti-gpu\");\n                nnapi_options.setAllowFp16(false);\n                options.addDelegate(new NnApiDelegate(nnapi_options));\n                break;\n            case NNAPI_GPU_FP16:\n                nnapi_options.setAcceleratorName(\"qti-gpu\");\n                nnapi_options.setAllowFp16(true);\n                options.addDelegate(new NnApiDelegate(nnapi_options));\n                break;\n            case NONE_INT8:\n                options.setUseXNNPACK(true);\n                break;\n            case NNAPI_DSP_INT8:\n                nnapi_options.setAcceleratorName(\"qti-dsp\");\n                options.addDelegate(new NnApiDelegate(nnapi_options));\n                break;\n            default:\n                throw new RuntimeException(\"Unknown runmode!\");\n        }\n        boolean quantized_mode = TfliteRunMode.isQuantizedMode(runmode);\n        String precision_str = quantized_mode ? \"int8\" : \"fp32\";\n        String modelname = \"yolov5s_\" + precision_str + \"_\" + String.valueOf(inputSize) + \".tflite\";\n        MappedByteBuffer tflite_model_buf = TfliteRunner.loadModelFile(context.getAssets(), modelname);\n        this.tfliteInterpreter = new Interpreter(tflite_model_buf, options);\n    }\n    static public Bitmap getResizedImage(Bitmap bitmap, int inputSize) {\n        Bitmap resized = Bitmap.createScaledBitmap(bitmap, inputSize, inputSize, true);\n        return resized;\n    }\n    public void setInput(Bitmap resizedbitmap){\n        boolean quantized_mode = TfliteRunMode.isQuantizedMode(this.runmode);\n        int numBytesPerChannel = quantized_mode ? numBytesPerChannel_int : numBytesPerChannel_float;\n        ByteBuffer imgData = ByteBuffer.allocateDirect(1 * inputSize * inputSize * 3 * numBytesPerChannel);\n\n        int[] intValues = new int[inputSize * inputSize];\n        resizedbitmap.getPixels(intValues, 0, resizedbitmap.getWidth(), 0, 0, resizedbitmap.getWidth(), resizedbitmap.getHeight());\n\n        imgData.order(ByteOrder.nativeOrder());\n        imgData.rewind();\n        for (int i = 0; i < inputSize; ++i) {\n            for (int j = 0; j < inputSize; ++j) {\n                int pixelValue = intValues[i * inputSize + j];\n                if (quantized_mode) {\n                    // Quantized model\n                    imgData.put((byte) ((pixelValue >> 16) & 0xFF));\n                    imgData.put((byte) ((pixelValue >> 8) & 0xFF));\n                    imgData.put((byte) (pixelValue & 0xFF));\n                } else { // Float model\n                    float r = (((pixelValue >> 16) & 0xFF)) / 255.0f;\n                    float g = (((pixelValue >> 8) & 0xFF)) / 255.0f;\n                    float b = ((pixelValue & 0xFF)) / 255.0f;\n                    imgData.putFloat(r);\n                    imgData.putFloat(g);\n                    imgData.putFloat(b);\n                }\n            }\n        }\n        this.inputArray = new Object[]{imgData};\n        this.outputMap = new HashMap<>();\n        outputMap.put(0, this.rawres.out1);\n        outputMap.put(1, this.rawres.out2);\n        outputMap.put(2, this.rawres.out3);\n    }\n    private int inference_elapsed;\n    private int postprocess_elapsed;\n    public String getLastElapsedTimeLog() {\n        return String.format(\"inference: %dms postprocess: %dms\", this.inference_elapsed, this.postprocess_elapsed);\n    }\n    public List<Recognition> runInference(){\n        List<Recognition> bboxes = new ArrayList<>();\n        long start = System.currentTimeMillis();\n        this.tfliteInterpreter.runForMultipleInputsOutputs(inputArray, outputMap);\n        long end = System.currentTimeMillis();\n        this.inference_elapsed = (int)(end - start);\n\n        //float[bbox_num][6]\n        //                       (x1, y1, x2, y2, conf, class_idx)\n        float[][] bbox_arrs = postprocess(this.rawres.out1,\n                this.rawres.out2,\n                this.rawres.out3,\n                this.inputSize,\n                this.conf_thresh,\n                this.iou_thresh);\n        long end2 = System.currentTimeMillis();\n        this.postprocess_elapsed = (int)(end2 - end);\n        for(float[] bbox_arr: bbox_arrs){\n            bboxes.add(new Recognition(bbox_arr));\n        }\n        return bboxes;\n    }\n    static int[] coco80_to_91class_map = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 28, 31, 32, 33, 34,\n            35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63,\n            64, 65, 67, 70, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90};\n    static public int get_coco91_from_coco80(int idx){\n        //assume idx < 80\n        return coco80_to_91class_map[idx];\n    }\n    public void setConfThresh(float thresh){ this.conf_thresh = thresh;}\n    public void setIoUThresh(float thresh) {this.iou_thresh = thresh;}\n\n    //port from TfLite Object Detection example\n    /** An immutable result returned by a Detector describing what was recognized. */\n    public class Recognition {\n        private final String[] coco_class_names = new String[]{\"person\", \"bicycle\", \"car\", \"motorbike\", \"aeroplane\", \"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\", \"sofa\", \"pottedplant\", \"bed\", \"diningtable\", \"toilet\", \"tvmonitor\", \"laptop\", \"mouse\", \"remote\", \"keyboard\", \"cell phone\", \"microwave\", \"oven\", \"toaster\", \"sink\", \"refrigerator\", \"book\", \"clock\", \"vase\", \"scissors\", \"teddy bear\", \"hair drier\", \"toothbrush\"};\n        private final Integer class_idx;\n        /**\n         * A unique identifier for what has been recognized. Specific to the class, not the instance of\n         * the object.\n         */\n        //private final String id;\n\n        /** Display name for the recognition. */\n        private final String title;\n\n        /**\n         * A sortable score for how good the recognition is relative to others. Higher should be better.\n         */\n        private final Float confidence;\n\n        /** Optional location within the source image for the location of the recognized object. */\n        private RectF location;\n\n        public Recognition(\n                float[] bbox_array) {\n            float x1 = bbox_array[0];\n            float y1 = bbox_array[1];\n            float x2 = bbox_array[2];\n            float y2 = bbox_array[3];\n            //this.id = (int)bbox_array[5];\n            int class_id = (int)bbox_array[5];\n            this.class_idx = class_id;\n            this.title = coco_class_names[class_id];\n            this.confidence = bbox_array[4];\n            this.location = new RectF(x1, y1, x2, y2);\n        }\n        public Integer getClass_idx(){\n            return class_idx;\n        }\n        /*public String getId() {\n            return id;\n        }*/\n\n        public String getTitle() {\n            return title;\n        }\n\n        public Float getConfidence() {\n            return confidence;\n        }\n\n        public RectF getLocation() {\n            return new RectF(location);\n        }\n\n        public void setLocation(RectF location) {\n            this.location = location;\n        }\n\n        @Override\n        public String toString() {\n            String resultString = \"\";\n            /*if (id != null) {\n                resultString += \"[\" + id + \"] \";\n            }*/\n\n            if (title != null) {\n                resultString += title + \" \";\n            }\n\n            if (confidence != null) {\n                resultString += String.format(\"(%.1f%%) \", confidence * 100.0f);\n            }\n\n            if (location != null) {\n                resultString += location + \" \";\n            }\n\n            return resultString.trim();\n        }\n    }\n}\n"
  },
  {
    "path": "app/tflite_yolov5_test/app/src/main/java/com/example/tflite_yolov5_test/camera/CameraActivity.java",
    "content": "package com.example.tflite_yolov5_test.camera;\n\nimport androidx.appcompat.app.AppCompatActivity;\nimport androidx.appcompat.widget.SwitchCompat;\n\nimport android.Manifest;\nimport android.app.Fragment;\nimport android.content.Context;\nimport android.content.pm.PackageManager;\nimport android.hardware.Camera;\nimport android.hardware.camera2.CameraAccessException;\nimport android.hardware.camera2.CameraCharacteristics;\nimport android.hardware.camera2.CameraManager;\nimport android.hardware.camera2.params.StreamConfigurationMap;\nimport android.media.Image;\nimport android.media.ImageReader;\nimport android.os.Build;\nimport android.os.Bundle;\nimport android.os.Handler;\nimport android.os.HandlerThread;\nimport android.os.Trace;\nimport android.util.Size;\nimport android.view.Surface;\nimport android.view.View;\nimport android.widget.CompoundButton;\nimport android.widget.ImageView;\nimport android.widget.LinearLayout;\nimport android.widget.SeekBar;\nimport android.widget.TextView;\nimport android.widget.Toast;\n\nimport com.example.tflite_yolov5_test.R;\nimport com.google.android.material.bottomsheet.BottomSheetBehavior;\nimport com.example.tflite_yolov5_test.camera.env.ImageUtils;\nimport java.nio.ByteBuffer;\n\npublic abstract class CameraActivity extends AppCompatActivity\n        implements ImageReader.OnImageAvailableListener,\n        Camera.PreviewCallback,\n        CompoundButton.OnCheckedChangeListener,\n        View.OnClickListener {\n    private static final int PERMISSIONS_REQUEST = 1;\n\n    private static final String PERMISSION_CAMERA = Manifest.permission.CAMERA;\n    protected int previewWidth = 0;\n    protected int previewHeight = 0;\n    private boolean debug = false;\n    private Handler handler;\n    private HandlerThread handlerThread;\n    private boolean useCamera2API;\n    private boolean isProcessingFrame = false;\n    private byte[][] yuvBytes = new byte[3][];\n    private int[] rgbBytes = null;\n    private int yRowStride;\n    private Runnable postInferenceCallback;\n    private Runnable imageConverter;\n\n    private LinearLayout bottomSheetLayout;\n    private LinearLayout gestureLayout;\n    private BottomSheetBehavior<LinearLayout> sheetBehavior;\n\n    protected TextView frameValueTextView, cropValueTextView, inferenceTimeTextView;\n    protected ImageView bottomSheetArrowImageView;\n    private ImageView plusImageView, minusImageView;\n    private SwitchCompat apiSwitchCompat;\n    private TextView threadsTextView;\n    @Override\n    protected void onCreate(Bundle savedInstanceState) {\n        super.onCreate(savedInstanceState);\n        setContentView(R.layout.activity_camera);\n        if (hasPermission()) {\n            setFragment();\n        } else {\n            requestPermission();\n        }\n\n    }\n    private String chooseCamera() {\n        final CameraManager manager = (CameraManager) getSystemService(Context.CAMERA_SERVICE);\n        try {\n            for (final String cameraId : manager.getCameraIdList()) {\n                final CameraCharacteristics characteristics = manager.getCameraCharacteristics(cameraId);\n\n                // We don't use a front facing camera in this sample.\n                final Integer facing = characteristics.get(CameraCharacteristics.LENS_FACING);\n                if (facing != null && facing == CameraCharacteristics.LENS_FACING_FRONT) {\n                    continue;\n                }\n\n                final StreamConfigurationMap map =\n                        characteristics.get(CameraCharacteristics.SCALER_STREAM_CONFIGURATION_MAP);\n\n                if (map == null) {\n                    continue;\n                }\n\n                // Fallback to camera1 API for internal cameras that don't have full support.\n                // This should help with legacy situations where using the camera2 API causes\n                // distorted or otherwise broken previews.\n                useCamera2API =\n                        (facing == CameraCharacteristics.LENS_FACING_EXTERNAL)\n                                || isHardwareLevelSupported(\n                                characteristics, CameraCharacteristics.INFO_SUPPORTED_HARDWARE_LEVEL_FULL);\n                return cameraId;\n            }\n        } catch (CameraAccessException e) {\n        }\n\n        return null;\n    }\n    protected void fillBytes(final Image.Plane[] planes, final byte[][] yuvBytes) {\n        // Because of the variable row stride it's not possible to know in\n        // advance the actual necessary dimensions of the yuv planes.\n        for (int i = 0; i < planes.length; ++i) {\n            final ByteBuffer buffer = planes[i].getBuffer();\n            if (yuvBytes[i] == null) {\n                yuvBytes[i] = new byte[buffer.capacity()];\n            }\n            buffer.get(yuvBytes[i]);\n        }\n    }\n    protected int getScreenOrientation() {\n        switch (getWindowManager().getDefaultDisplay().getRotation()) {\n            case Surface.ROTATION_270:\n                return 270;\n            case Surface.ROTATION_180:\n                return 180;\n            case Surface.ROTATION_90:\n                return 90;\n            default:\n                return 0;\n        }\n    }\n    /** Callback for android.hardware.Camera API */\n    @Override\n    public void onPreviewFrame(final byte[] bytes, final Camera camera) {\n        if (isProcessingFrame) {\n            return;\n        }\n\n        try {\n            // Initialize the storage bitmaps once when the resolution is known.\n            if (rgbBytes == null) {\n                Camera.Size previewSize = camera.getParameters().getPreviewSize();\n                previewHeight = previewSize.height;\n                previewWidth = previewSize.width;\n                rgbBytes = new int[previewWidth * previewHeight];\n                onPreviewSizeChosen(new Size(previewSize.width, previewSize.height), 90);\n            }\n        } catch (final Exception e) {\n            return;\n        }\n    }\n    /** Callback for Camera2 API */\n    @Override\n    public void onImageAvailable(final ImageReader reader) {\n        // We need wait until we have some size from onPreviewSizeChosen\n        if (previewWidth == 0 || previewHeight == 0) {\n            return;\n        }\n        if (rgbBytes == null) {\n            rgbBytes = new int[previewWidth * previewHeight];\n        }\n        try {\n            final Image image = reader.acquireLatestImage();\n\n            if (image == null) {\n                return;\n            }\n\n            if (isProcessingFrame) {\n                image.close();\n                return;\n            }\n            isProcessingFrame = true;\n            Trace.beginSection(\"imageAvailable\");\n            final Image.Plane[] planes = image.getPlanes();\n            fillBytes(planes, yuvBytes);\n            yRowStride = planes[0].getRowStride();\n            final int uvRowStride = planes[1].getRowStride();\n            final int uvPixelStride = planes[1].getPixelStride();\n\n            imageConverter =\n                    new Runnable() {\n                        @Override\n                        public void run() {\n                            ImageUtils.convertYUV420ToARGB8888(\n                                    yuvBytes[0],\n                                    yuvBytes[1],\n                                    yuvBytes[2],\n                                    previewWidth,\n                                    previewHeight,\n                                    yRowStride,\n                                    uvRowStride,\n                                    uvPixelStride,\n                                    rgbBytes);\n                        }\n                    };\n\n            postInferenceCallback =\n                    new Runnable() {\n                        @Override\n                        public void run() {\n                            image.close();\n                            isProcessingFrame = false;\n                        }\n                    };\n\n            processImage();\n        } catch (final Exception e) {\n            Trace.endSection();\n            return;\n        }\n        Trace.endSection();\n    }\n\n    @Override\n    public void onCheckedChanged(CompoundButton buttonView, boolean isChecked) {\n    }\n    @Override\n    public void onClick(View v) {\n    }\n    @Override\n    public synchronized void onResume() {\n        super.onResume();\n\n        handlerThread = new HandlerThread(\"inference\");\n        handlerThread.start();\n        handler = new Handler(handlerThread.getLooper());\n    }\n\n    @Override\n    public synchronized void onPause() {\n        handlerThread.quitSafely();\n        try {\n            handlerThread.join();\n            handlerThread = null;\n            handler = null;\n        } catch (final InterruptedException e) {\n        }\n\n        super.onPause();\n    }\n    protected synchronized void runInBackground(final Runnable r) {\n        if (handler != null) {\n            handler.post(r);\n        }\n    }\n    protected int[] getRgbBytes() {\n        imageConverter.run();\n        return rgbBytes;\n    }\n    protected void readyForNextImage() {\n        if (postInferenceCallback != null) {\n            postInferenceCallback.run();\n        }\n    }\n    private boolean isHardwareLevelSupported(\n            CameraCharacteristics characteristics, int requiredLevel) {\n        int deviceLevel = characteristics.get(CameraCharacteristics.INFO_SUPPORTED_HARDWARE_LEVEL);\n        if (deviceLevel == CameraCharacteristics.INFO_SUPPORTED_HARDWARE_LEVEL_LEGACY) {\n            return requiredLevel == deviceLevel;\n        }\n        // deviceLevel is not LEGACY, can use numerical sort\n        return requiredLevel <= deviceLevel;\n    }\n    protected void setFragment() {\n        String cameraId = chooseCamera();\n\n        Fragment fragment;\n        if (useCamera2API) {\n            CameraConnectionFragment camera2Fragment =\n                    CameraConnectionFragment.newInstance(\n                            new CameraConnectionFragment.ConnectionCallback() {\n                                @Override\n                                public void onPreviewSizeChosen(final Size size, final int rotation) {\n                                    previewHeight = size.getHeight();\n                                    previewWidth = size.getWidth();\n                                    CameraActivity.this.onPreviewSizeChosen(size, rotation);\n                                }\n                            },\n                            this,\n                            getLayoutId(),\n                            getDesiredPreviewFrameSize());\n\n            camera2Fragment.setCamera(cameraId);\n            fragment = camera2Fragment;\n        } else {\n            fragment =\n                    new LegacyCameraConnectionFragment(this, getLayoutId(), getDesiredPreviewFrameSize());\n        }\n\n        getFragmentManager().beginTransaction().replace(R.id.container, fragment).commit();\n        int a = 0;\n        a = a + 1;\n    }\n    protected abstract void processImage();\n    protected abstract void onPreviewSizeChosen(final Size size, final int rotation);\n\n    protected abstract int getLayoutId();\n\n    protected abstract Size getDesiredPreviewFrameSize();\n\n    protected abstract void setNumThreads(int numThreads);\n\n    protected abstract void setUseNNAPI(boolean isChecked);\n\n    private static boolean allPermissionsGranted(final int[] grantResults) {\n        for (int result : grantResults) {\n            if (result != PackageManager.PERMISSION_GRANTED) {\n                return false;\n            }\n        }\n        return true;\n    }\n    private boolean hasPermission() {\n        if (Build.VERSION.SDK_INT >= Build.VERSION_CODES.M) {\n            return checkSelfPermission(PERMISSION_CAMERA) == PackageManager.PERMISSION_GRANTED;\n        } else {\n            return true;\n        }\n    }\n\n    private void requestPermission() {\n        if (Build.VERSION.SDK_INT >= Build.VERSION_CODES.M) {\n            if (shouldShowRequestPermissionRationale(PERMISSION_CAMERA)) {\n                Toast.makeText(\n                        CameraActivity.this,\n                        \"Camera permission is required for this demo\",\n                        Toast.LENGTH_LONG)\n                        .show();\n            }\n            requestPermissions(new String[] {PERMISSION_CAMERA}, PERMISSIONS_REQUEST);\n        }\n    }\n    @Override\n    public void onRequestPermissionsResult(\n            final int requestCode, final String[] permissions, final int[] grantResults) {\n        super.onRequestPermissionsResult(requestCode, permissions, grantResults);\n        if (requestCode == PERMISSIONS_REQUEST) {\n            if (allPermissionsGranted(grantResults)) {\n                setFragment();\n            } else {\n                requestPermission();\n            }\n        }\n    }\n\n}"
  },
  {
    "path": "app/tflite_yolov5_test/app/src/main/java/com/example/tflite_yolov5_test/camera/CameraConnectionFragment.java",
    "content": "package com.example.tflite_yolov5_test.camera;\n\nimport android.annotation.SuppressLint;\nimport android.app.Activity;\nimport android.app.AlertDialog;\nimport android.app.Dialog;\nimport android.app.DialogFragment;\nimport android.app.Fragment;\nimport android.content.Context;\nimport android.content.DialogInterface;\nimport android.content.res.Configuration;\nimport android.graphics.ImageFormat;\nimport android.graphics.Matrix;\nimport android.graphics.RectF;\nimport android.graphics.SurfaceTexture;\nimport android.hardware.camera2.CameraAccessException;\nimport android.hardware.camera2.CameraCaptureSession;\nimport android.hardware.camera2.CameraCharacteristics;\nimport android.hardware.camera2.CameraDevice;\nimport android.hardware.camera2.CameraManager;\nimport android.hardware.camera2.CaptureRequest;\nimport android.hardware.camera2.CaptureResult;\nimport android.hardware.camera2.TotalCaptureResult;\nimport android.hardware.camera2.params.StreamConfigurationMap;\nimport android.media.ImageReader;\nimport android.os.Bundle;\nimport android.os.Handler;\nimport android.os.HandlerThread;\nimport android.util.Size;\nimport android.util.SparseIntArray;\nimport android.view.LayoutInflater;\nimport android.view.Surface;\nimport android.view.TextureView;\nimport android.view.View;\nimport android.view.ViewGroup;\nimport android.widget.Toast;\n\nimport java.util.ArrayList;\nimport java.util.Arrays;\nimport java.util.Collections;\nimport java.util.Comparator;\nimport java.util.List;\nimport java.util.concurrent.Semaphore;\nimport java.util.concurrent.TimeUnit;\n\n\nimport com.example.tflite_yolov5_test.R;\nimport com.example.tflite_yolov5_test.customview.AutoFitTextureView;\n\n@SuppressLint(\"ValidFragment\")\npublic class CameraConnectionFragment extends Fragment {\n\n    /**\n     * The camera preview size will be chosen to be the smallest frame by pixel size capable of\n     * containing a DESIRED_SIZE x DESIRED_SIZE square.\n     */\n    private static final int MINIMUM_PREVIEW_SIZE = 320;\n\n    /** Conversion from screen rotation to JPEG orientation. */\n    private static final SparseIntArray ORIENTATIONS = new SparseIntArray();\n\n    private static final String FRAGMENT_DIALOG = \"dialog\";\n\n    static {\n        ORIENTATIONS.append(Surface.ROTATION_0, 90);\n        ORIENTATIONS.append(Surface.ROTATION_90, 0);\n        ORIENTATIONS.append(Surface.ROTATION_180, 270);\n        ORIENTATIONS.append(Surface.ROTATION_270, 180);\n    }\n\n    /** A {@link Semaphore} to prevent the app from exiting before closing the camera. */\n    private final Semaphore cameraOpenCloseLock = new Semaphore(1);\n    /** A {@link ImageReader.OnImageAvailableListener} to receive frames as they are available. */\n    private final ImageReader.OnImageAvailableListener imageListener;\n    /** The input size in pixels desired by TensorFlow (width and height of a square bitmap). */\n    private final Size inputSize;\n    /** The layout identifier to inflate for this Fragment. */\n    private final int layout;\n\n    private final ConnectionCallback cameraConnectionCallback;\n    private final CameraCaptureSession.CaptureCallback captureCallback =\n            new CameraCaptureSession.CaptureCallback() {\n                @Override\n                public void onCaptureProgressed(\n                        final CameraCaptureSession session,\n                        final CaptureRequest request,\n                        final CaptureResult partialResult) {}\n\n                @Override\n                public void onCaptureCompleted(\n                        final CameraCaptureSession session,\n                        final CaptureRequest request,\n                        final TotalCaptureResult result) {}\n            };\n    /** ID of the current {@link CameraDevice}. */\n    private String cameraId;\n    /** An {@link AutoFitTextureView} for camera preview. */\n    private AutoFitTextureView textureView;\n    /** A {@link CameraCaptureSession } for camera preview. */\n    private CameraCaptureSession captureSession;\n    /** A reference to the opened {@link CameraDevice}. */\n    private CameraDevice cameraDevice;\n    /** The rotation in degrees of the camera sensor from the display. */\n    private Integer sensorOrientation;\n    /** The {@link Size} of camera preview. */\n    private Size previewSize;\n    /** An additional thread for running tasks that shouldn't block the UI. */\n    private HandlerThread backgroundThread;\n    /** A {@link Handler} for running tasks in the background. */\n    private Handler backgroundHandler;\n    /** An {@link ImageReader} that handles preview frame capture. */\n    private ImageReader previewReader;\n    /** {@link CaptureRequest.Builder} for the camera preview */\n    private CaptureRequest.Builder previewRequestBuilder;\n    /** {@link CaptureRequest} generated by {@link #previewRequestBuilder} */\n    private CaptureRequest previewRequest;\n    /** {@link CameraDevice.StateCallback} is called when {@link CameraDevice} changes its state. */\n    private final CameraDevice.StateCallback stateCallback =\n            new CameraDevice.StateCallback() {\n                @Override\n                public void onOpened(final CameraDevice cd) {\n                    // This method is called when the camera is opened.  We start camera preview here.\n                    cameraOpenCloseLock.release();\n                    cameraDevice = cd;\n                    createCameraPreviewSession();\n                }\n\n                @Override\n                public void onDisconnected(final CameraDevice cd) {\n                    cameraOpenCloseLock.release();\n                    cd.close();\n                    cameraDevice = null;\n                }\n\n                @Override\n                public void onError(final CameraDevice cd, final int error) {\n                    cameraOpenCloseLock.release();\n                    cd.close();\n                    cameraDevice = null;\n                    final Activity activity = getActivity();\n                    if (null != activity) {\n                        activity.finish();\n                    }\n                }\n            };\n    /**\n     * {@link TextureView.SurfaceTextureListener} handles several lifecycle events on a {@link\n     * TextureView}.\n     */\n    private final TextureView.SurfaceTextureListener surfaceTextureListener =\n            new TextureView.SurfaceTextureListener() {\n                @Override\n                public void onSurfaceTextureAvailable(\n                        final SurfaceTexture texture, final int width, final int height) {\n                    openCamera(width, height);\n                }\n\n                @Override\n                public void onSurfaceTextureSizeChanged(\n                        final SurfaceTexture texture, final int width, final int height) {\n                    configureTransform(width, height);\n                }\n\n                @Override\n                public boolean onSurfaceTextureDestroyed(final SurfaceTexture texture) {\n                    return true;\n                }\n\n                @Override\n                public void onSurfaceTextureUpdated(final SurfaceTexture texture) {}\n            };\n\n    private CameraConnectionFragment(\n            final ConnectionCallback connectionCallback,\n            final ImageReader.OnImageAvailableListener imageListener,\n            final int layout,\n            final Size inputSize) {\n        this.cameraConnectionCallback = connectionCallback;\n        this.imageListener = imageListener;\n        this.layout = layout;\n        this.inputSize = inputSize;\n    }\n\n    /**\n     * Given {@code choices} of {@code Size}s supported by a camera, chooses the smallest one whose\n     * width and height are at least as large as the minimum of both, or an exact match if possible.\n     *\n     * @param choices The list of sizes that the camera supports for the intended output class\n     * @param width The minimum desired width\n     * @param height The minimum desired height\n     * @return The optimal {@code Size}, or an arbitrary one if none were big enough\n     */\n    protected static Size chooseOptimalSize(final Size[] choices, final int width, final int height) {\n        final int minSize = Math.max(Math.min(width, height), MINIMUM_PREVIEW_SIZE);\n        final Size desiredSize = new Size(width, height);\n\n        // Collect the supported resolutions that are at least as big as the preview Surface\n        boolean exactSizeFound = false;\n        final List<Size> bigEnough = new ArrayList<Size>();\n        final List<Size> tooSmall = new ArrayList<Size>();\n        for (final Size option : choices) {\n            if (option.equals(desiredSize)) {\n                // Set the size but don't return yet so that remaining sizes will still be logged.\n                exactSizeFound = true;\n            }\n\n            if (option.getHeight() >= minSize && option.getWidth() >= minSize) {\n                bigEnough.add(option);\n            } else {\n                tooSmall.add(option);\n            }\n        }\n\n\n        if (exactSizeFound) {\n            return desiredSize;\n        }\n\n        // Pick the smallest of those, assuming we found any\n        if (bigEnough.size() > 0) {\n            final Size chosenSize = Collections.min(bigEnough, new CompareSizesByArea());\n            return chosenSize;\n        } else {\n            return choices[0];\n        }\n    }\n\n    public static CameraConnectionFragment newInstance(\n            final ConnectionCallback callback,\n            final ImageReader.OnImageAvailableListener imageListener,\n            final int layout,\n            final Size inputSize) {\n        return new CameraConnectionFragment(callback, imageListener, layout, inputSize);\n    }\n\n    /**\n     * Shows a {@link Toast} on the UI thread.\n     *\n     * @param text The message to show\n     */\n    private void showToast(final String text) {\n        final Activity activity = getActivity();\n        if (activity != null) {\n            activity.runOnUiThread(\n                    new Runnable() {\n                        @Override\n                        public void run() {\n                            Toast.makeText(activity, text, Toast.LENGTH_SHORT).show();\n                        }\n                    });\n        }\n    }\n\n    @Override\n    public View onCreateView(\n            final LayoutInflater inflater, final ViewGroup container, final Bundle savedInstanceState) {\n        return inflater.inflate(layout, container, false);\n    }\n\n    @Override\n    public void onViewCreated(final View view, final Bundle savedInstanceState) {\n        textureView = (AutoFitTextureView) view.findViewById(R.id.texture);\n    }\n\n    @Override\n    public void onActivityCreated(final Bundle savedInstanceState) {\n        super.onActivityCreated(savedInstanceState);\n    }\n\n    @Override\n    public void onResume() {\n        super.onResume();\n        startBackgroundThread();\n\n        // When the screen is turned off and turned back on, the SurfaceTexture is already\n        // available, and \"onSurfaceTextureAvailable\" will not be called. In that case, we can open\n        // a camera and start preview from here (otherwise, we wait until the surface is ready in\n        // the SurfaceTextureListener).\n        if (textureView.isAvailable()) {\n            openCamera(textureView.getWidth(), textureView.getHeight());\n        } else {\n            textureView.setSurfaceTextureListener(surfaceTextureListener);\n        }\n    }\n\n    @Override\n    public void onPause() {\n        closeCamera();\n        stopBackgroundThread();\n        super.onPause();\n    }\n\n    public void setCamera(String cameraId) {\n        this.cameraId = cameraId;\n    }\n\n    /** Sets up member variables related to camera. */\n    private void setUpCameraOutputs() {\n        final Activity activity = getActivity();\n        final CameraManager manager = (CameraManager) activity.getSystemService(Context.CAMERA_SERVICE);\n        try {\n            final CameraCharacteristics characteristics = manager.getCameraCharacteristics(cameraId);\n\n            final StreamConfigurationMap map =\n                    characteristics.get(CameraCharacteristics.SCALER_STREAM_CONFIGURATION_MAP);\n\n            sensorOrientation = characteristics.get(CameraCharacteristics.SENSOR_ORIENTATION);\n\n            // Danger, W.R.! Attempting to use too large a preview size could  exceed the camera\n            // bus' bandwidth limitation, resulting in gorgeous previews but the storage of\n            // garbage capture data.\n            previewSize =\n                    chooseOptimalSize(\n                            map.getOutputSizes(SurfaceTexture.class),\n                            inputSize.getWidth(),\n                            inputSize.getHeight());\n\n            // We fit the aspect ratio of TextureView to the size of preview we picked.\n            final int orientation = getResources().getConfiguration().orientation;\n            if (orientation == Configuration.ORIENTATION_LANDSCAPE) {\n                textureView.setAspectRatio(previewSize.getWidth(), previewSize.getHeight());\n            } else {\n                textureView.setAspectRatio(previewSize.getHeight(), previewSize.getWidth());\n            }\n        } catch (final CameraAccessException e) {\n        } catch (final NullPointerException e) {\n            // Currently an NPE is thrown when the Camera2API is used but not supported on the\n            // device this code runs.\n//            ErrorDialog.newInstance(getString(R.string.tfe_od_camera_error))\n//                    .show(getChildFragmentManager(), FRAGMENT_DIALOG);\n//            throw new IllegalStateException(getString(R.string.tfe_od_camera_error));\n        }\n\n        cameraConnectionCallback.onPreviewSizeChosen(previewSize, sensorOrientation);\n    }\n\n    /** Opens the camera specified by {@link CameraConnectionFragment#cameraId}. */\n    @SuppressLint(\"MissingPermission\")\n    private void openCamera(final int width, final int height) {\n        setUpCameraOutputs();\n        configureTransform(width, height);\n        final Activity activity = getActivity();\n        final CameraManager manager = (CameraManager) activity.getSystemService(Context.CAMERA_SERVICE);\n        try {\n            if (!cameraOpenCloseLock.tryAcquire(2500, TimeUnit.MILLISECONDS)) {\n                throw new RuntimeException(\"Time out waiting to lock camera opening.\");\n            }\n            manager.openCamera(cameraId, stateCallback, backgroundHandler);\n        } catch (final CameraAccessException e) {\n        } catch (final InterruptedException e) {\n            throw new RuntimeException(\"Interrupted while trying to lock camera opening.\", e);\n        }\n    }\n\n    /** Closes the current {@link CameraDevice}. */\n    private void closeCamera() {\n        try {\n            cameraOpenCloseLock.acquire();\n            if (null != captureSession) {\n                captureSession.close();\n                captureSession = null;\n            }\n            if (null != cameraDevice) {\n                cameraDevice.close();\n                cameraDevice = null;\n            }\n            if (null != previewReader) {\n                previewReader.close();\n                previewReader = null;\n            }\n        } catch (final InterruptedException e) {\n            throw new RuntimeException(\"Interrupted while trying to lock camera closing.\", e);\n        } finally {\n            cameraOpenCloseLock.release();\n        }\n    }\n\n    /** Starts a background thread and its {@link Handler}. */\n    private void startBackgroundThread() {\n        backgroundThread = new HandlerThread(\"ImageListener\");\n        backgroundThread.start();\n        backgroundHandler = new Handler(backgroundThread.getLooper());\n    }\n\n    /** Stops the background thread and its {@link Handler}. */\n    private void stopBackgroundThread() {\n        backgroundThread.quitSafely();\n        try {\n            backgroundThread.join();\n            backgroundThread = null;\n            backgroundHandler = null;\n        } catch (final InterruptedException e) {\n        }\n    }\n\n    /** Creates a new {@link CameraCaptureSession} for camera preview. */\n    private void createCameraPreviewSession() {\n        try {\n            final SurfaceTexture texture = textureView.getSurfaceTexture();\n            assert texture != null;\n\n            // We configure the size of default buffer to be the size of camera preview we want.\n            texture.setDefaultBufferSize(previewSize.getWidth(), previewSize.getHeight());\n\n            // This is the output Surface we need to start preview.\n            final Surface surface = new Surface(texture);\n\n            // We set up a CaptureRequest.Builder with the output Surface.\n            previewRequestBuilder = cameraDevice.createCaptureRequest(CameraDevice.TEMPLATE_PREVIEW);\n            previewRequestBuilder.addTarget(surface);\n\n\n            // Create the reader for the preview frames.\n            previewReader =\n                    ImageReader.newInstance(\n                            previewSize.getWidth(), previewSize.getHeight(), ImageFormat.YUV_420_888, 2);\n\n            previewReader.setOnImageAvailableListener(imageListener, backgroundHandler);\n            previewRequestBuilder.addTarget(previewReader.getSurface());\n\n            // Here, we create a CameraCaptureSession for camera preview.\n            cameraDevice.createCaptureSession(\n                    Arrays.asList(surface, previewReader.getSurface()),\n                    new CameraCaptureSession.StateCallback() {\n\n                        @Override\n                        public void onConfigured(final CameraCaptureSession cameraCaptureSession) {\n                            // The camera is already closed\n                            if (null == cameraDevice) {\n                                return;\n                            }\n\n                            // When the session is ready, we start displaying the preview.\n                            captureSession = cameraCaptureSession;\n                            try {\n                                // Auto focus should be continuous for camera preview.\n                                previewRequestBuilder.set(\n                                        CaptureRequest.CONTROL_AF_MODE,\n                                        CaptureRequest.CONTROL_AF_MODE_CONTINUOUS_PICTURE);\n                                // Flash is automatically enabled when necessary.\n                                previewRequestBuilder.set(\n                                        CaptureRequest.CONTROL_AE_MODE, CaptureRequest.CONTROL_AE_MODE_ON_AUTO_FLASH);\n\n                                // Finally, we start displaying the camera preview.\n                                previewRequest = previewRequestBuilder.build();\n                                captureSession.setRepeatingRequest(\n                                        previewRequest, captureCallback, backgroundHandler);\n                            } catch (final CameraAccessException e) {\n                            }\n                        }\n\n                        @Override\n                        public void onConfigureFailed(final CameraCaptureSession cameraCaptureSession) {\n                            showToast(\"Failed\");\n                        }\n                    },\n                    null);\n        } catch (final CameraAccessException e) {\n        }\n    }\n\n    /**\n     * Configures the necessary {@link Matrix} transformation to `mTextureView`. This method should be\n     * called after the camera preview size is determined in setUpCameraOutputs and also the size of\n     * `mTextureView` is fixed.\n     *\n     * @param viewWidth The width of `mTextureView`\n     * @param viewHeight The height of `mTextureView`\n     */\n    private void configureTransform(final int viewWidth, final int viewHeight) {\n        final Activity activity = getActivity();\n        if (null == textureView || null == previewSize || null == activity) {\n            return;\n        }\n        final int rotation = activity.getWindowManager().getDefaultDisplay().getRotation();\n        final Matrix matrix = new Matrix();\n        final RectF viewRect = new RectF(0, 0, viewWidth, viewHeight);\n        final RectF bufferRect = new RectF(0, 0, previewSize.getHeight(), previewSize.getWidth());\n        final float centerX = viewRect.centerX();\n        final float centerY = viewRect.centerY();\n        if (Surface.ROTATION_90 == rotation || Surface.ROTATION_270 == rotation) {\n            bufferRect.offset(centerX - bufferRect.centerX(), centerY - bufferRect.centerY());\n            matrix.setRectToRect(viewRect, bufferRect, Matrix.ScaleToFit.FILL);\n            final float scale =\n                    Math.max(\n                            (float) viewHeight / previewSize.getHeight(),\n                            (float) viewWidth / previewSize.getWidth());\n            matrix.postScale(scale, scale, centerX, centerY);\n            matrix.postRotate(90 * (rotation - 2), centerX, centerY);\n        } else if (Surface.ROTATION_180 == rotation) {\n            matrix.postRotate(180, centerX, centerY);\n        }\n        textureView.setTransform(matrix);\n    }\n\n    /**\n     * Callback for Activities to use to initialize their data once the selected preview size is\n     * known.\n     */\n    public interface ConnectionCallback {\n        void onPreviewSizeChosen(Size size, int cameraRotation);\n    }\n\n    /** Compares two {@code Size}s based on their areas. */\n    static class CompareSizesByArea implements Comparator<Size> {\n        @Override\n        public int compare(final Size lhs, final Size rhs) {\n            // We cast here to ensure the multiplications won't overflow\n            return Long.signum(\n                    (long) lhs.getWidth() * lhs.getHeight() - (long) rhs.getWidth() * rhs.getHeight());\n        }\n    }\n\n    /** Shows an error message dialog. */\n    public static class ErrorDialog extends DialogFragment {\n        private static final String ARG_MESSAGE = \"message\";\n\n        public static ErrorDialog newInstance(final String message) {\n            final ErrorDialog dialog = new ErrorDialog();\n            final Bundle args = new Bundle();\n            args.putString(ARG_MESSAGE, message);\n            dialog.setArguments(args);\n            return dialog;\n        }\n\n        @Override\n        public Dialog onCreateDialog(final Bundle savedInstanceState) {\n            final Activity activity = getActivity();\n            return new AlertDialog.Builder(activity)\n                    .setMessage(getArguments().getString(ARG_MESSAGE))\n                    .setPositiveButton(\n                            android.R.string.ok,\n                            new DialogInterface.OnClickListener() {\n                                @Override\n                                public void onClick(final DialogInterface dialogInterface, final int i) {\n                                    activity.finish();\n                                }\n                            })\n                    .create();\n        }\n    }\n}\n"
  },
  {
    "path": "app/tflite_yolov5_test/app/src/main/java/com/example/tflite_yolov5_test/camera/DetectorActivity.java",
    "content": "package com.example.tflite_yolov5_test.camera;\n\nimport android.graphics.Bitmap;\nimport android.graphics.Canvas;\nimport android.graphics.Color;\nimport android.graphics.Matrix;\nimport android.graphics.Paint;\nimport android.graphics.RectF;\nimport android.graphics.Typeface;\nimport android.media.ImageReader;\nimport android.os.Bundle;\nimport android.os.SystemClock;\nimport android.util.Log;\nimport android.util.Size;\nimport android.util.TypedValue;\nimport android.widget.SeekBar;\nimport android.widget.TextView;\nimport android.widget.Toast;\n\nimport com.example.tflite_yolov5_test.R;\nimport com.example.tflite_yolov5_test.camera.env.BorderedText;\nimport com.example.tflite_yolov5_test.camera.env.ImageUtils;\nimport com.example.tflite_yolov5_test.camera.tracker.MultiBoxTracker;\nimport com.example.tflite_yolov5_test.customview.OverlayView;\nimport com.example.tflite_yolov5_test.TfliteRunner;\nimport com.example.tflite_yolov5_test.TfliteRunMode;\nimport java.io.IOException;\nimport java.util.ArrayList;\nimport java.util.List;\n\npublic class DetectorActivity extends CameraActivity implements ImageReader.OnImageAvailableListener {\n\n\n    private static final int TF_OD_API_INPUT_SIZE = 320;\n    private static final boolean TF_OD_API_IS_QUANTIZED = true;\n    private static final String TF_OD_API_MODEL_FILE = \"detect.tflite\";\n    private static final String TF_OD_API_LABELS_FILE = \"labelmap.txt\";\n    private static final TfliteRunMode.Mode MODE = TfliteRunMode.Mode.NONE_INT8;\n    // Minimum detection confidence to track a detection.\n    private static final float MINIMUM_CONFIDENCE_TF_OD_API = 0.5f;\n    private static final boolean MAINTAIN_ASPECT = false;\n    private static final Size DESIRED_PREVIEW_SIZE = new Size(640, 480);\n    private static final boolean SAVE_PREVIEW_BITMAP = false;\n    private static final float TEXT_SIZE_DIP = 10;\n    OverlayView trackingOverlay;\n    private Integer sensorOrientation;\n\n    private TfliteRunner detector;\n\n    private long lastProcessingTimeMs = 0;\n    private Bitmap rgbFrameBitmap = null;\n    private Bitmap croppedBitmap = null;\n    private Bitmap cropCopyBitmap = null;\n\n    private boolean computingDetection = false;\n\n    private long timestamp = 0;\n\n    private Matrix frameToCropTransform;\n    private Matrix cropToFrameTransform;\n\n    private MultiBoxTracker tracker;\n\n    private BorderedText borderedText;\n\n    protected Size getDesiredPreviewFrameSize() {\n        return DESIRED_PREVIEW_SIZE;\n    }\n\n    protected int getLayoutId() {\n        return R.layout.tfe_od_camera_connection_fragment_tracking;\n    }\n\n\n    public float getConfThreshFromGUI(){ return ((float)((SeekBar)findViewById(R.id.conf_seekBar2)).getProgress()) / 100.0f;}\n    public float getIoUThreshFromGUI(){ return ((float)((SeekBar)findViewById(R.id.iou_seekBar2)).getProgress()) / 100.0f;}\n    @Override\n    protected void onCreate(Bundle savedInstanceState) {\n        super.onCreate(savedInstanceState);\n        SeekBar conf_seekBar = (SeekBar)findViewById(R.id.conf_seekBar2);\n        conf_seekBar.setOnSeekBarChangeListener(new SeekBar.OnSeekBarChangeListener(){\n            @Override\n            public void onProgressChanged(SeekBar seekBar, int progress, boolean fromUser) {\n                TextView conf_textView = (TextView)findViewById(R.id.conf_TextView2);\n                float thresh = (float)progress / 100.0f;\n                conf_textView.setText(String.format(\"Confidence Threshold: %.2f\", thresh));\n                if (detector != null) detector.setConfThresh(thresh);\n            }\n            @Override\n            public void onStopTrackingTouch(SeekBar seekBar) {\n            }\n            @Override\n            public void onStartTrackingTouch(SeekBar seekBar) {\n            }\n        });\n        conf_seekBar.setMax(100);\n        conf_seekBar.setProgress(25);//0.25\n        SeekBar iou_seekBar = (SeekBar)findViewById(R.id.iou_seekBar2);\n        iou_seekBar.setOnSeekBarChangeListener(new SeekBar.OnSeekBarChangeListener(){\n            @Override\n            public void onProgressChanged(SeekBar seekBar, int progress, boolean fromUser) {\n                TextView iou_textView = (TextView)findViewById(R.id.iou_TextView2);\n                float thresh = (float)progress / 100.0f;\n                iou_textView.setText(String.format(\"IoU Threshold: %.2f\", thresh));\n                if (detector != null) detector.setIoUThresh(thresh);\n            }\n            @Override\n            public void onStopTrackingTouch(SeekBar seekBar) {\n            }\n            @Override\n            public void onStartTrackingTouch(SeekBar seekBar) {\n            }\n        });\n        iou_seekBar.setMax(100);\n        iou_seekBar.setProgress(45);//0.45\n    }\n\n    @Override\n    protected void setUseNNAPI(final boolean isChecked) {\n\n    }\n\n    @Override\n    protected void setNumThreads(final int numThreads) {\n        //runInBackground(() -> detector.setNumThreads(numThreads));\n    }\n    @Override\n    public void onPreviewSizeChosen(final Size size, final int rotation) {\n        final float textSizePx =\n                TypedValue.applyDimension(\n                        TypedValue.COMPLEX_UNIT_DIP, TEXT_SIZE_DIP, getResources().getDisplayMetrics());\n        borderedText = new BorderedText(textSizePx);\n        borderedText.setTypeface(Typeface.MONOSPACE);\n\n        tracker = new MultiBoxTracker(this);\n\n        int cropSize = TF_OD_API_INPUT_SIZE;\n\n        try {\n            detector = new TfliteRunner(this, MODE, TF_OD_API_INPUT_SIZE, 0.25f, 0.45f);\n            cropSize = TF_OD_API_INPUT_SIZE;\n        } catch (final Exception e) {\n            e.printStackTrace();\n            Toast toast =\n                    Toast.makeText(\n                            getApplicationContext(), \"Detector could not be initialized\", Toast.LENGTH_SHORT);\n            toast.show();\n            finish();\n        }\n\n        previewWidth = size.getWidth();\n        previewHeight = size.getHeight();\n        int a = getScreenOrientation();\n        sensorOrientation = rotation - getScreenOrientation();\n        rgbFrameBitmap = Bitmap.createBitmap(previewWidth, previewHeight, Bitmap.Config.ARGB_8888);\n        croppedBitmap = Bitmap.createBitmap(cropSize, cropSize, Bitmap.Config.ARGB_8888);\n\n        frameToCropTransform =\n                ImageUtils.getTransformationMatrix(\n                        previewWidth, previewHeight,\n                        cropSize, cropSize,\n                        sensorOrientation, MAINTAIN_ASPECT);\n\n        cropToFrameTransform = new Matrix();\n        frameToCropTransform.invert(cropToFrameTransform);\n\n        trackingOverlay = (OverlayView) findViewById(R.id.tracking_overlay);\n        trackingOverlay.addCallback(\n                new OverlayView.DrawCallback() {\n                    @Override\n                    public void drawCallback(final Canvas canvas) {\n                        tracker.draw(canvas);\n                    }\n                });\n\n        tracker.setFrameConfiguration(getDesiredPreviewFrameSize(), TF_OD_API_INPUT_SIZE, sensorOrientation);\n    }\n\n    @Override\n    protected void processImage() {\n        trackingOverlay.postInvalidate();\n\n        // No mutex needed as this method is not reentrant.\n        if (computingDetection) {\n            readyForNextImage();\n            return;\n        }\n        computingDetection = true;\n\n        rgbFrameBitmap.setPixels(getRgbBytes(), 0, previewWidth, 0, 0, previewWidth, previewHeight);\n\n        readyForNextImage();\n\n        final Canvas canvas = new Canvas(croppedBitmap);\n        canvas.drawBitmap(rgbFrameBitmap, frameToCropTransform, null);\n\n        runInBackground(\n                new Runnable() {\n                    @Override\n                    public void run() {\n                        final long nowTime = SystemClock.uptimeMillis();\n                        float fps = (float)1000 / (float)(nowTime - lastProcessingTimeMs);\n                        lastProcessingTimeMs = nowTime;\n\n\n                        //ImageUtils.saveBitmap(croppedBitmap);\n                        detector.setInput(croppedBitmap);\n                        final List<TfliteRunner.Recognition> results = detector.runInference();\n\n                        cropCopyBitmap = Bitmap.createBitmap(croppedBitmap);\n                        final Canvas canvas = new Canvas(cropCopyBitmap);\n                        final Paint paint = new Paint();\n                        paint.setColor(Color.RED);\n                        paint.setStyle(Paint.Style.STROKE);\n                        paint.setStrokeWidth(2.0f);\n\n                        for (final TfliteRunner.Recognition result : results) {\n                            final RectF location = result.getLocation();\n                            //canvas.drawRect(location, paint);\n                        }\n\n                        tracker.trackResults(results);\n                        trackingOverlay.postInvalidate();\n\n                        computingDetection = false;\n\n                        runOnUiThread(\n                                new Runnable() {\n                                    @Override\n                                    public void run() {\n                                        TextView fpsTextView = (TextView)findViewById(R.id.textViewFPS);\n                                        String fpsText = String.format(\"FPS: %.2f\", fps);\n                                        fpsTextView.setText(fpsText);\n                                        TextView latencyTextView = (TextView)findViewById(R.id.textViewLatency);\n                                        latencyTextView.setText(detector.getLastElapsedTimeLog());\n                                    }\n                                });\n                    }\n                });\n    }\n\n}\n"
  },
  {
    "path": "app/tflite_yolov5_test/app/src/main/java/com/example/tflite_yolov5_test/camera/LegacyCameraConnectionFragment.java",
    "content": "package com.example.tflite_yolov5_test.camera;\n\nimport android.annotation.SuppressLint;\nimport android.app.Fragment;\nimport android.graphics.SurfaceTexture;\nimport android.hardware.Camera;\nimport android.os.Bundle;\nimport android.os.Handler;\nimport android.os.HandlerThread;\nimport android.util.Size;\nimport android.util.SparseIntArray;\nimport android.view.LayoutInflater;\nimport android.view.Surface;\nimport android.view.TextureView;\nimport android.view.View;\nimport android.view.ViewGroup;\n\nimport java.io.IOException;\nimport java.util.List;\n\n\nimport com.example.tflite_yolov5_test.R;\nimport com.example.tflite_yolov5_test.customview.AutoFitTextureView;\nimport com.example.tflite_yolov5_test.camera.env.ImageUtils;\n\npublic class LegacyCameraConnectionFragment extends Fragment {\n    /** Conversion from screen rotation to JPEG orientation. */\n    private static final SparseIntArray ORIENTATIONS = new SparseIntArray();\n\n    static {\n        ORIENTATIONS.append(Surface.ROTATION_0, 90);\n        ORIENTATIONS.append(Surface.ROTATION_90, 0);\n        ORIENTATIONS.append(Surface.ROTATION_180, 270);\n        ORIENTATIONS.append(Surface.ROTATION_270, 180);\n    }\n\n    private Camera camera;\n    private Camera.PreviewCallback imageListener;\n    private Size desiredSize;\n    /** The layout identifier to inflate for this Fragment. */\n    private int layout;\n    /** An {@link AutoFitTextureView} for camera preview. */\n    private AutoFitTextureView textureView;\n    private SurfaceTexture availableSurfaceTexture = null;\n\n    /**\n     * {@link TextureView.SurfaceTextureListener} handles several lifecycle events on a {@link\n     * TextureView}.\n     */\n    private final TextureView.SurfaceTextureListener surfaceTextureListener =\n            new TextureView.SurfaceTextureListener() {\n                @Override\n                public void onSurfaceTextureAvailable(\n                        final SurfaceTexture texture, final int width, final int height) {\n                    availableSurfaceTexture = texture;\n                    startCamera();\n                }\n\n                @Override\n                public void onSurfaceTextureSizeChanged(\n                        final SurfaceTexture texture, final int width, final int height) {}\n\n                @Override\n                public boolean onSurfaceTextureDestroyed(final SurfaceTexture texture) {\n                    return true;\n                }\n\n                @Override\n                public void onSurfaceTextureUpdated(final SurfaceTexture texture) {}\n            };\n    /** An additional thread for running tasks that shouldn't block the UI. */\n    private HandlerThread backgroundThread;\n\n    @SuppressLint(\"ValidFragment\")\n    public LegacyCameraConnectionFragment(\n            final Camera.PreviewCallback imageListener, final int layout, final Size desiredSize) {\n        this.imageListener = imageListener;\n        this.layout = layout;\n        this.desiredSize = desiredSize;\n    }\n\n    @Override\n    public View onCreateView(\n            final LayoutInflater inflater, final ViewGroup container, final Bundle savedInstanceState) {\n        return inflater.inflate(layout, container, false);\n    }\n\n    @Override\n    public void onViewCreated(final View view, final Bundle savedInstanceState) {\n        textureView = (AutoFitTextureView) view.findViewById(R.id.texture);\n    }\n\n    @Override\n    public void onActivityCreated(final Bundle savedInstanceState) {\n        super.onActivityCreated(savedInstanceState);\n    }\n\n    @Override\n    public void onResume() {\n        super.onResume();\n        startBackgroundThread();\n        // When the screen is turned off and turned back on, the SurfaceTexture is already\n        // available, and \"onSurfaceTextureAvailable\" will not be called. In that case, we can open\n        // a camera and start preview from here (otherwise, we wait until the surface is ready in\n        // the SurfaceTextureListener).\n\n        if (textureView.isAvailable()) {\n            startCamera();\n        } else {\n            textureView.setSurfaceTextureListener(surfaceTextureListener);\n        }\n    }\n\n    @Override\n    public void onPause() {\n        stopCamera();\n        stopBackgroundThread();\n        super.onPause();\n    }\n\n    /** Starts a background thread and its {@link Handler}. */\n    private void startBackgroundThread() {\n        backgroundThread = new HandlerThread(\"CameraBackground\");\n        backgroundThread.start();\n    }\n\n    /** Stops the background thread and its {@link Handler}. */\n    private void stopBackgroundThread() {\n        backgroundThread.quitSafely();\n        try {\n            backgroundThread.join();\n            backgroundThread = null;\n        } catch (final InterruptedException e) {\n        }\n    }\n\n    private void startCamera() {\n        int index = getCameraId();\n        camera = Camera.open(index);\n\n        try {\n            Camera.Parameters parameters = camera.getParameters();\n            List<String> focusModes = parameters.getSupportedFocusModes();\n            if (focusModes != null\n                    && focusModes.contains(Camera.Parameters.FOCUS_MODE_CONTINUOUS_PICTURE)) {\n                parameters.setFocusMode(Camera.Parameters.FOCUS_MODE_CONTINUOUS_PICTURE);\n            }\n            List<Camera.Size> cameraSizes = parameters.getSupportedPreviewSizes();\n            Size[] sizes = new Size[cameraSizes.size()];\n            int i = 0;\n            for (Camera.Size size : cameraSizes) {\n                sizes[i++] = new Size(size.width, size.height);\n            }\n            Size previewSize =\n                    CameraConnectionFragment.chooseOptimalSize(\n                            sizes, desiredSize.getWidth(), desiredSize.getHeight());\n            parameters.setPreviewSize(previewSize.getWidth(), previewSize.getHeight());\n            camera.setDisplayOrientation(90);\n            camera.setParameters(parameters);\n            camera.setPreviewTexture(availableSurfaceTexture);\n        } catch (IOException exception) {\n            camera.release();\n        }\n\n        camera.setPreviewCallbackWithBuffer(imageListener);\n        Camera.Size s = camera.getParameters().getPreviewSize();\n        camera.addCallbackBuffer(new byte[ImageUtils.getYUVByteSize(s.height, s.width)]);\n\n        textureView.setAspectRatio(s.height, s.width);\n\n        camera.startPreview();\n    }\n\n    protected void stopCamera() {\n        if (camera != null) {\n            camera.stopPreview();\n            camera.setPreviewCallback(null);\n            camera.release();\n            camera = null;\n        }\n    }\n\n    private int getCameraId() {\n        Camera.CameraInfo ci = new Camera.CameraInfo();\n        for (int i = 0; i < Camera.getNumberOfCameras(); i++) {\n            Camera.getCameraInfo(i, ci);\n            if (ci.facing == Camera.CameraInfo.CAMERA_FACING_BACK) return i;\n        }\n        return -1; // No camera found\n    }\n}\n"
  },
  {
    "path": "app/tflite_yolov5_test/app/src/main/java/com/example/tflite_yolov5_test/camera/env/BorderedText.java",
    "content": "/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.\n\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n    http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing, software\ndistributed under the License is distributed on an \"AS IS\" BASIS,\nWITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\nSee the License for the specific language governing permissions and\nlimitations under the License.\n==============================================================================*/\n\npackage com.example.tflite_yolov5_test.camera.env;\n\nimport android.graphics.Canvas;\nimport android.graphics.Color;\nimport android.graphics.Paint;\nimport android.graphics.Paint.Align;\nimport android.graphics.Paint.Style;\nimport android.graphics.Rect;\nimport android.graphics.Typeface;\nimport java.util.Vector;\n\n/** A class that encapsulates the tedious bits of rendering legible, bordered text onto a canvas. */\npublic class BorderedText {\n  private final Paint interiorPaint;\n  private final Paint exteriorPaint;\n\n  private final float textSize;\n\n  /**\n   * Creates a left-aligned bordered text object with a white interior, and a black exterior with\n   * the specified text size.\n   *\n   * @param textSize text size in pixels\n   */\n  public BorderedText(final float textSize) {\n    this(Color.WHITE, Color.BLACK, textSize);\n  }\n\n  /**\n   * Create a bordered text object with the specified interior and exterior colors, text size and\n   * alignment.\n   *\n   * @param interiorColor the interior text color\n   * @param exteriorColor the exterior text color\n   * @param textSize text size in pixels\n   */\n  public BorderedText(final int interiorColor, final int exteriorColor, final float textSize) {\n    interiorPaint = new Paint();\n    interiorPaint.setTextSize(textSize);\n    interiorPaint.setColor(interiorColor);\n    interiorPaint.setStyle(Style.FILL);\n    interiorPaint.setAntiAlias(false);\n    interiorPaint.setAlpha(255);\n\n    exteriorPaint = new Paint();\n    exteriorPaint.setTextSize(textSize);\n    exteriorPaint.setColor(exteriorColor);\n    exteriorPaint.setStyle(Style.FILL_AND_STROKE);\n    exteriorPaint.setStrokeWidth(textSize / 8);\n    exteriorPaint.setAntiAlias(false);\n    exteriorPaint.setAlpha(255);\n\n    this.textSize = textSize;\n  }\n\n  public void setTypeface(Typeface typeface) {\n    interiorPaint.setTypeface(typeface);\n    exteriorPaint.setTypeface(typeface);\n  }\n\n  public void drawText(final Canvas canvas, final float posX, final float posY, final String text) {\n    canvas.drawText(text, posX, posY, exteriorPaint);\n    canvas.drawText(text, posX, posY, interiorPaint);\n  }\n\n  public void drawText(\n      final Canvas canvas, final float posX, final float posY, final String text, Paint bgPaint) {\n\n    float width = exteriorPaint.measureText(text);\n    float textSize = exteriorPaint.getTextSize();\n    Paint paint = new Paint(bgPaint);\n    paint.setStyle(Paint.Style.FILL);\n    paint.setAlpha(160);\n    canvas.drawRect(posX, (posY + (int) (textSize)), (posX + (int) (width)), posY, paint);\n\n    canvas.drawText(text, posX, (posY + textSize), interiorPaint);\n  }\n\n  public void drawLines(Canvas canvas, final float posX, final float posY, Vector<String> lines) {\n    int lineNum = 0;\n    for (final String line : lines) {\n      drawText(canvas, posX, posY - getTextSize() * (lines.size() - lineNum - 1), line);\n      ++lineNum;\n    }\n  }\n\n  public void setInteriorColor(final int color) {\n    interiorPaint.setColor(color);\n  }\n\n  public void setExteriorColor(final int color) {\n    exteriorPaint.setColor(color);\n  }\n\n  public float getTextSize() {\n    return textSize;\n  }\n\n  public void setAlpha(final int alpha) {\n    interiorPaint.setAlpha(alpha);\n    exteriorPaint.setAlpha(alpha);\n  }\n\n  public void getTextBounds(\n      final String line, final int index, final int count, final Rect lineBounds) {\n    interiorPaint.getTextBounds(line, index, count, lineBounds);\n  }\n\n  public void setTextAlign(final Align align) {\n    interiorPaint.setTextAlign(align);\n    exteriorPaint.setTextAlign(align);\n  }\n}\n"
  },
  {
    "path": "app/tflite_yolov5_test/app/src/main/java/com/example/tflite_yolov5_test/camera/env/ImageUtils.java",
    "content": "package com.example.tflite_yolov5_test.camera.env;\nimport android.graphics.Bitmap;\nimport android.graphics.Matrix;\nimport android.os.Environment;\n\nimport java.io.File;\nimport java.io.FileOutputStream;\n\nimport android.graphics.Bitmap;\nimport android.graphics.Matrix;\nimport android.os.Environment;\nimport java.io.File;\nimport java.io.FileOutputStream;\n\n/** Utility class for manipulating images. */\npublic class ImageUtils {\n  // This value is 2 ^ 18 - 1, and is used to clamp the RGB values before their ranges\n  // are normalized to eight bits.\n  static final int kMaxChannelValue = 262143;\n\n\n  /**\n   * Utility method to compute the allocated size in bytes of a YUV420SP image of the given\n   * dimensions.\n   */\n  public static int getYUVByteSize(final int width, final int height) {\n    // The luminance plane requires 1 byte per pixel.\n    final int ySize = width * height;\n\n    // The UV plane works on 2x2 blocks, so dimensions with odd size must be rounded up.\n    // Each 2x2 block takes 2 bytes to encode, one each for U and V.\n    final int uvSize = ((width + 1) / 2) * ((height + 1) / 2) * 2;\n\n    return ySize + uvSize;\n  }\n\n  /**\n   * Saves a Bitmap object to disk for analysis.\n   *\n   * @param bitmap The bitmap to save.\n   */\n  public static void saveBitmap(final Bitmap bitmap) {\n    saveBitmap(bitmap, \"preview.png\");\n  }\n\n  /**\n   * Saves a Bitmap object to disk for analysis.\n   *\n   * @param bitmap The bitmap to save.\n   * @param filename The location to save the bitmap to.\n   */\n  public static void saveBitmap(final Bitmap bitmap, final String filename) {\n    final String root =\n            Environment.getExternalStorageDirectory().getAbsolutePath() + File.separator + \"tensorflow\";\n    final File myDir = new File(root);\n\n    if (!myDir.mkdirs()) {\n    }\n\n    final String fname = filename;\n    final File file = new File(myDir, fname);\n    if (file.exists()) {\n      file.delete();\n    }\n    try {\n      final FileOutputStream out = new FileOutputStream(file);\n      bitmap.compress(Bitmap.CompressFormat.PNG, 99, out);\n      out.flush();\n      out.close();\n    } catch (final Exception e) {\n    }\n  }\n\n  public static void convertYUV420SPToARGB8888(byte[] input, int width, int height, int[] output) {\n    final int frameSize = width * height;\n    for (int j = 0, yp = 0; j < height; j++) {\n      int uvp = frameSize + (j >> 1) * width;\n      int u = 0;\n      int v = 0;\n\n      for (int i = 0; i < width; i++, yp++) {\n        int y = 0xff & input[yp];\n        if ((i & 1) == 0) {\n          v = 0xff & input[uvp++];\n          u = 0xff & input[uvp++];\n        }\n\n        output[yp] = YUV2RGB(y, u, v);\n      }\n    }\n  }\n\n  private static int YUV2RGB(int y, int u, int v) {\n    // Adjust and check YUV values\n    y = (y - 16) < 0 ? 0 : (y - 16);\n    u -= 128;\n    v -= 128;\n\n    // This is the floating point equivalent. We do the conversion in integer\n    // because some Android devices do not have floating point in hardware.\n    // nR = (int)(1.164 * nY + 2.018 * nU);\n    // nG = (int)(1.164 * nY - 0.813 * nV - 0.391 * nU);\n    // nB = (int)(1.164 * nY + 1.596 * nV);\n    int y1192 = 1192 * y;\n    int r = (y1192 + 1634 * v);\n    int g = (y1192 - 833 * v - 400 * u);\n    int b = (y1192 + 2066 * u);\n\n    // Clipping RGB values to be inside boundaries [ 0 , kMaxChannelValue ]\n    r = r > kMaxChannelValue ? kMaxChannelValue : (r < 0 ? 0 : r);\n    g = g > kMaxChannelValue ? kMaxChannelValue : (g < 0 ? 0 : g);\n    b = b > kMaxChannelValue ? kMaxChannelValue : (b < 0 ? 0 : b);\n\n    return 0xff000000 | ((r << 6) & 0xff0000) | ((g >> 2) & 0xff00) | ((b >> 10) & 0xff);\n  }\n\n  public static void convertYUV420ToARGB8888(\n          byte[] yData,\n          byte[] uData,\n          byte[] vData,\n          int width,\n          int height,\n          int yRowStride,\n          int uvRowStride,\n          int uvPixelStride,\n          int[] out) {\n    int yp = 0;\n    for (int j = 0; j < height; j++) {\n      int pY = yRowStride * j;\n      int pUV = uvRowStride * (j >> 1);\n\n      for (int i = 0; i < width; i++) {\n        int uv_offset = pUV + (i >> 1) * uvPixelStride;\n\n        out[yp++] = YUV2RGB(0xff & yData[pY + i], 0xff & uData[uv_offset], 0xff & vData[uv_offset]);\n      }\n    }\n  }\n\n  /**\n   * Returns a transformation matrix from one reference frame into another. Handles cropping (if\n   * maintaining aspect ratio is desired) and rotation.\n   *\n   * @param srcWidth Width of source frame.\n   * @param srcHeight Height of source frame.\n   * @param dstWidth Width of destination frame.\n   * @param dstHeight Height of destination frame.\n   * @param applyRotation Amount of rotation to apply from one frame to another. Must be a multiple\n   *     of 90.\n   * @param maintainAspectRatio If true, will ensure that scaling in x and y remains constant,\n   *     cropping the image if necessary.\n   * @return The transformation fulfilling the desired requirements.\n   */\n  public static Matrix getTransformationMatrix(\n          final int srcWidth,\n          final int srcHeight,\n          final int dstWidth,\n          final int dstHeight,\n          final int applyRotation,\n          final boolean maintainAspectRatio) {\n    final Matrix matrix = new Matrix();\n\n    if (applyRotation != 0) {\n      if (applyRotation % 90 != 0) {\n      }\n\n      // Translate so center of image is at origin.\n      matrix.postTranslate(-srcWidth / 2.0f, -srcHeight / 2.0f);\n\n      // Rotate around origin.\n      matrix.postRotate(applyRotation);\n    }\n\n    // Account for the already applied rotation, if any, and then determine how\n    // much scaling is needed for each axis.\n    final boolean transpose = (Math.abs(applyRotation) + 90) % 180 == 0;\n\n    final int inWidth = transpose ? srcHeight : srcWidth;\n    final int inHeight = transpose ? srcWidth : srcHeight;\n\n    // Apply scaling if necessary.\n    if (inWidth != dstWidth || inHeight != dstHeight) {\n      final float scaleFactorX = dstWidth / (float) inWidth;\n      final float scaleFactorY = dstHeight / (float) inHeight;\n\n      if (maintainAspectRatio) {\n        // Scale by minimum factor so that dst is filled completely while\n        // maintaining the aspect ratio. Some image may fall off the edge.\n        final float scaleFactor = Math.max(scaleFactorX, scaleFactorY);\n        matrix.postScale(scaleFactor, scaleFactor);\n      } else {\n        // Scale exactly to fill dst from src.\n        matrix.postScale(scaleFactorX, scaleFactorY);\n      }\n    }\n\n    if (applyRotation != 0) {\n      // Translate back from origin centered reference to destination frame.\n      matrix.postTranslate(dstWidth / 2.0f, dstHeight / 2.0f);\n    }\n\n    return matrix;\n  }\n}\n"
  },
  {
    "path": "app/tflite_yolov5_test/app/src/main/java/com/example/tflite_yolov5_test/camera/tracker/MultiBoxTracker.java",
    "content": "package com.example.tflite_yolov5_test.camera.tracker;\n\nimport android.content.Context;\nimport android.graphics.Canvas;\nimport android.graphics.Color;\nimport android.graphics.Matrix;\nimport android.graphics.Paint;\nimport android.graphics.RectF;\nimport android.text.TextUtils;\nimport android.util.Log;\nimport android.util.Pair;\nimport android.util.Size;\nimport android.util.TypedValue;\n\nimport java.util.LinkedList;\nimport java.util.List;\nimport java.util.Queue;\n\nimport android.content.Context;\nimport android.graphics.Canvas;\nimport android.graphics.Color;\nimport android.graphics.Matrix;\nimport android.graphics.Paint;\nimport android.graphics.Paint.Cap;\nimport android.graphics.Paint.Join;\nimport android.graphics.Paint.Style;\nimport android.graphics.RectF;\nimport android.text.TextUtils;\nimport android.util.Pair;\nimport android.util.TypedValue;\nimport java.util.LinkedList;\nimport java.util.List;\nimport java.util.Queue;\nimport com.example.tflite_yolov5_test.camera.env.BorderedText;\nimport com.example.tflite_yolov5_test.camera.env.ImageUtils;\nimport com.example.tflite_yolov5_test.TfliteRunner.Recognition;\n\n/** A tracker that handles non-max suppression and matches existing objects to new detections. */\npublic class MultiBoxTracker {\n    private static final float TEXT_SIZE_DIP = 18;\n    private static final float MIN_SIZE = 16.0f;\n    private static final int[] COLORS = {\n            Color.BLUE,\n            Color.RED,\n            Color.GREEN,\n            Color.YELLOW,\n            Color.CYAN,\n            Color.MAGENTA,\n            Color.WHITE,\n            Color.parseColor(\"#55FF55\"),\n            Color.parseColor(\"#FFA500\"),\n            Color.parseColor(\"#FF8888\"),\n            Color.parseColor(\"#AAAAFF\"),\n            Color.parseColor(\"#FFFFAA\"),\n            Color.parseColor(\"#55AAAA\"),\n            Color.parseColor(\"#AA33AA\"),\n            Color.parseColor(\"#0D0068\")\n    };\n    final List<Pair<Float, RectF>> screenRects = new LinkedList<Pair<Float, RectF>>();\n    private final Queue<Integer> availableColors = new LinkedList<Integer>();\n    private final List<TrackedRecognition> trackedObjects = new LinkedList<TrackedRecognition>();\n    private final Paint boxPaint = new Paint();\n    private final float textSizePx;\n    private final BorderedText borderedText;\n    private Matrix frameToCanvasMatrix;\n    private int detectorInputSize;\n    private int frameWidth;\n    private int frameHeight;\n    private int sensorOrientation;\n\n    public MultiBoxTracker(final Context context) {\n        for (final int color : COLORS) {\n            availableColors.add(color);\n        }\n\n        boxPaint.setColor(Color.RED);\n        boxPaint.setStyle(Paint.Style.STROKE);\n        boxPaint.setStrokeWidth(10.0f);\n        boxPaint.setStrokeCap(Paint.Cap.ROUND);\n        boxPaint.setStrokeJoin(Paint.Join.ROUND);\n        boxPaint.setStrokeMiter(100);\n\n        textSizePx =\n                TypedValue.applyDimension(\n                        TypedValue.COMPLEX_UNIT_DIP, TEXT_SIZE_DIP, context.getResources().getDisplayMetrics());\n        borderedText = new BorderedText(textSizePx);\n    }\n\n    public synchronized void setFrameConfiguration(\n            final Size frameSize, final int detectorInputSize, final int sensorOrientation) {\n        this.detectorInputSize = detectorInputSize;\n        //reverse\n        frameWidth = frameSize.getHeight();\n        frameHeight = frameSize.getWidth();\n        this.sensorOrientation = sensorOrientation;\n    }\n\n    public synchronized void drawDebug(final Canvas canvas) {\n        final Paint textPaint = new Paint();\n        textPaint.setColor(Color.WHITE);\n        textPaint.setTextSize(60.0f);\n\n        final Paint boxPaint = new Paint();\n        boxPaint.setColor(Color.RED);\n        boxPaint.setAlpha(200);\n        boxPaint.setStyle(Paint.Style.STROKE);\n\n        for (final Pair<Float, RectF> detection : screenRects) {\n            final RectF rect = detection.second;\n            canvas.drawRect(rect, boxPaint);\n            canvas.drawText(\"\" + detection.first, rect.left, rect.top, textPaint);\n            borderedText.drawText(canvas, rect.centerX(), rect.centerY(), \"\" + detection.first);\n        }\n    }\n\n    public synchronized void trackResults(final List<Recognition> results) {\n        processResults(results);\n    }\n\n    private Matrix getFrameToCanvasMatrix() {\n        return frameToCanvasMatrix;\n    }\n\n    public synchronized void draw(final Canvas canvas) {\n        //WARNING(from lp6m): I don't understand about sensorOrientation, so fix to 0 for now...\n\n        for (final TrackedRecognition recognition : trackedObjects) {\n            float frameToCanvasScale = Math.min((float)canvas.getHeight() / frameHeight, (float)canvas.getWidth() / frameWidth);\n            float scale_width = frameToCanvasScale * ((float)frameWidth / detectorInputSize);\n            float scale_height = frameToCanvasScale * ((float)frameHeight / detectorInputSize);\n            float x1 = recognition.location.left * scale_width;\n            float y1 = recognition.location.top * scale_height;\n            float x2 = recognition.location.right * scale_width;\n            float y2 = recognition.location.bottom * scale_height;\n\n            final RectF trackedPos = new RectF(x1, y1, x2, y2);\n            boxPaint.setColor(recognition.color);\n\n            float cornerSize = Math.min(trackedPos.width(), trackedPos.height()) / 8.0f;\n            canvas.drawRoundRect(trackedPos, cornerSize, cornerSize, boxPaint);\n\n            final String labelString =\n                    !TextUtils.isEmpty(recognition.title)\n                            ? String.format(\"%s %.2f\", recognition.title, (100 * recognition.detectionConfidence))\n                            : String.format(\"%.2f\", (100 * recognition.detectionConfidence));\n            //            borderedText.drawText(canvas, trackedPos.left + cornerSize, trackedPos.top,\n            // labelString);\n            borderedText.drawText(\n                    canvas, trackedPos.left + cornerSize, trackedPos.top, labelString + \"%\", boxPaint);\n        }\n    }\n\n    private void processResults(final List<Recognition> results) {\n        final List<Pair<Float, Recognition>> rectsToTrack = new LinkedList<Pair<Float, Recognition>>();\n\n        screenRects.clear();\n        final Matrix rgbFrameToScreen = new Matrix(getFrameToCanvasMatrix());\n\n        for (final Recognition result : results) {\n            if (result.getLocation() == null) {\n                continue;\n            }\n            final RectF detectionFrameRect = new RectF(result.getLocation());\n\n            final RectF detectionScreenRect = new RectF();\n            rgbFrameToScreen.mapRect(detectionScreenRect, detectionFrameRect);\n            screenRects.add(new Pair<Float, RectF>(result.getConfidence(), detectionScreenRect));\n\n            if (detectionFrameRect.width() < MIN_SIZE || detectionFrameRect.height() < MIN_SIZE) {\n                continue;\n            }\n\n            rectsToTrack.add(new Pair<Float, Recognition>(result.getConfidence(), result));\n        }\n\n        trackedObjects.clear();\n        if (rectsToTrack.isEmpty()) {\n            return;\n        }\n\n        for (final Pair<Float, Recognition> potential : rectsToTrack) {\n            final TrackedRecognition trackedRecognition = new TrackedRecognition();\n            trackedRecognition.detectionConfidence = potential.first;\n            trackedRecognition.location = new RectF(potential.second.getLocation());\n            trackedRecognition.title = potential.second.getTitle();\n            trackedRecognition.color = COLORS[trackedObjects.size()];\n            trackedObjects.add(trackedRecognition);\n\n            if (trackedObjects.size() >= COLORS.length) {\n                break;\n            }\n        }\n    }\n\n    private static class TrackedRecognition {\n        RectF location;\n        float detectionConfidence;\n        int color;\n        String title;\n    }\n}\n"
  },
  {
    "path": "app/tflite_yolov5_test/app/src/main/java/com/example/tflite_yolov5_test/customview/AutoFitTextureView.java",
    "content": "package com.example.tflite_yolov5_test.customview;\n\nimport android.content.Context;\nimport android.util.AttributeSet;\nimport android.view.TextureView;\n\n\n/** A {@link TextureView} that can be adjusted to a specified aspect ratio. */\npublic class AutoFitTextureView extends TextureView {\n    private int ratioWidth = 0;\n    private int ratioHeight = 0;\n\n    public AutoFitTextureView(final Context context) {\n        this(context, null);\n    }\n\n    public AutoFitTextureView(final Context context, final AttributeSet attrs) {\n        this(context, attrs, 0);\n    }\n\n    public AutoFitTextureView(final Context context, final AttributeSet attrs, final int defStyle) {\n        super(context, attrs, defStyle);\n    }\n\n    /**\n     * Sets the aspect ratio for this view. The size of the view will be measured based on the ratio\n     * calculated from the parameters. Note that the actual sizes of parameters don't matter, that is,\n     * calling setAspectRatio(2, 3) and setAspectRatio(4, 6) make the same result.\n     *\n     * @param width Relative horizontal size\n     * @param height Relative vertical size\n     */\n    public void setAspectRatio(final int width, final int height) {\n        if (width < 0 || height < 0) {\n            throw new IllegalArgumentException(\"Size cannot be negative.\");\n        }\n        ratioWidth = width;\n        ratioHeight = height;\n        requestLayout();\n    }\n\n    @Override\n    protected void onMeasure(final int widthMeasureSpec, final int heightMeasureSpec) {\n        super.onMeasure(widthMeasureSpec, heightMeasureSpec);\n        final int width = MeasureSpec.getSize(widthMeasureSpec);\n        final int height = MeasureSpec.getSize(heightMeasureSpec);\n        if (0 == ratioWidth || 0 == ratioHeight) {\n            setMeasuredDimension(width, height);\n        } else {\n            if (width < height * ratioWidth / ratioHeight) {\n                setMeasuredDimension(width, width * ratioHeight / ratioWidth);\n            } else {\n                setMeasuredDimension(height * ratioWidth / ratioHeight, height);\n            }\n        }\n    }\n}\n\n"
  },
  {
    "path": "app/tflite_yolov5_test/app/src/main/java/com/example/tflite_yolov5_test/customview/OverlayView.java",
    "content": "package com.example.tflite_yolov5_test.customview;\n\nimport android.content.Context;\nimport android.graphics.Canvas;\nimport android.util.AttributeSet;\nimport android.view.View;\n\nimport java.util.LinkedList;\nimport java.util.List;\n\nimport android.content.Context;\nimport android.graphics.Canvas;\nimport android.util.AttributeSet;\nimport android.view.View;\nimport java.util.LinkedList;\nimport java.util.List;\n\n/** A simple View providing a render callback to other classes. */\npublic class OverlayView extends View {\n    private final List<DrawCallback> callbacks = new LinkedList<DrawCallback>();\n\n    public OverlayView(final Context context, final AttributeSet attrs) {\n        super(context, attrs);\n    }\n\n    public void addCallback(final DrawCallback callback) {\n        callbacks.add(callback);\n    }\n\n    @Override\n    public synchronized void draw(final Canvas canvas) {\n        for (final DrawCallback callback : callbacks) {\n            callback.drawCallback(canvas);\n        }\n    }\n\n    /** Interface defining the callback for client classes. */\n    public interface DrawCallback {\n        public void drawCallback(final Canvas canvas);\n    }\n}\n"
  },
  {
    "path": "app/tflite_yolov5_test/app/src/main/res/drawable/ic_dashboard_black_24dp.xml",
    "content": "<vector xmlns:android=\"http://schemas.android.com/apk/res/android\"\n    android:width=\"24dp\"\n    android:height=\"24dp\"\n    android:viewportWidth=\"24.0\"\n    android:viewportHeight=\"24.0\">\n    <path\n        android:fillColor=\"#FF000000\"\n        android:pathData=\"M3,13h8L11,3L3,3v10zM3,21h8v-6L3,15v6zM13,21h8L21,11h-8v10zM13,3v6h8L21,3h-8z\" />\n</vector>\n"
  },
  {
    "path": "app/tflite_yolov5_test/app/src/main/res/drawable/ic_home_black_24dp.xml",
    "content": "<vector xmlns:android=\"http://schemas.android.com/apk/res/android\"\n    android:width=\"24dp\"\n    android:height=\"24dp\"\n    android:viewportWidth=\"24.0\"\n    android:viewportHeight=\"24.0\">\n    <path\n        android:fillColor=\"#FF000000\"\n        android:pathData=\"M10,20v-6h4v6h5v-8h3L12,3 2,12h3v8z\" />\n</vector>\n"
  },
  {
    "path": "app/tflite_yolov5_test/app/src/main/res/drawable/ic_launcher_background.xml",
    "content": "<?xml version=\"1.0\" encoding=\"utf-8\"?>\n<vector xmlns:android=\"http://schemas.android.com/apk/res/android\"\n    android:width=\"108dp\"\n    android:height=\"108dp\"\n    android:viewportWidth=\"108\"\n    android:viewportHeight=\"108\">\n    <path\n        android:fillColor=\"#3DDC84\"\n        android:pathData=\"M0,0h108v108h-108z\" />\n    <path\n        android:fillColor=\"#00000000\"\n        android:pathData=\"M9,0L9,108\"\n        android:strokeWidth=\"0.8\"\n        android:strokeColor=\"#33FFFFFF\" />\n    <path\n        android:fillColor=\"#00000000\"\n        android:pathData=\"M19,0L19,108\"\n        android:strokeWidth=\"0.8\"\n        android:strokeColor=\"#33FFFFFF\" />\n    <path\n        android:fillColor=\"#00000000\"\n        android:pathData=\"M29,0L29,108\"\n        android:strokeWidth=\"0.8\"\n        android:strokeColor=\"#33FFFFFF\" />\n    <path\n        android:fillColor=\"#00000000\"\n        android:pathData=\"M39,0L39,108\"\n        android:strokeWidth=\"0.8\"\n        android:strokeColor=\"#33FFFFFF\" />\n    <path\n        android:fillColor=\"#00000000\"\n        android:pathData=\"M49,0L49,108\"\n        android:strokeWidth=\"0.8\"\n        android:strokeColor=\"#33FFFFFF\" />\n    <path\n        android:fillColor=\"#00000000\"\n        android:pathData=\"M59,0L59,108\"\n        android:strokeWidth=\"0.8\"\n        android:strokeColor=\"#33FFFFFF\" />\n    <path\n        android:fillColor=\"#00000000\"\n        android:pathData=\"M69,0L69,108\"\n        android:strokeWidth=\"0.8\"\n        android:strokeColor=\"#33FFFFFF\" />\n    <path\n        android:fillColor=\"#00000000\"\n        android:pathData=\"M79,0L79,108\"\n        android:strokeWidth=\"0.8\"\n        android:strokeColor=\"#33FFFFFF\" />\n    <path\n        android:fillColor=\"#00000000\"\n        android:pathData=\"M89,0L89,108\"\n        android:strokeWidth=\"0.8\"\n        android:strokeColor=\"#33FFFFFF\" />\n    <path\n        android:fillColor=\"#00000000\"\n        android:pathData=\"M99,0L99,108\"\n        android:strokeWidth=\"0.8\"\n        android:strokeColor=\"#33FFFFFF\" />\n    <path\n        android:fillColor=\"#00000000\"\n        android:pathData=\"M0,9L108,9\"\n        android:strokeWidth=\"0.8\"\n        android:strokeColor=\"#33FFFFFF\" />\n    <path\n        android:fillColor=\"#00000000\"\n        android:pathData=\"M0,19L108,19\"\n        android:strokeWidth=\"0.8\"\n        android:strokeColor=\"#33FFFFFF\" />\n    <path\n        android:fillColor=\"#00000000\"\n        android:pathData=\"M0,29L108,29\"\n        android:strokeWidth=\"0.8\"\n        android:strokeColor=\"#33FFFFFF\" />\n    <path\n        android:fillColor=\"#00000000\"\n        android:pathData=\"M0,39L108,39\"\n        android:strokeWidth=\"0.8\"\n        android:strokeColor=\"#33FFFFFF\" />\n    <path\n        android:fillColor=\"#00000000\"\n        android:pathData=\"M0,49L108,49\"\n        android:strokeWidth=\"0.8\"\n        android:strokeColor=\"#33FFFFFF\" />\n    <path\n        android:fillColor=\"#00000000\"\n        android:pathData=\"M0,59L108,59\"\n        android:strokeWidth=\"0.8\"\n        android:strokeColor=\"#33FFFFFF\" />\n    <path\n        android:fillColor=\"#00000000\"\n        android:pathData=\"M0,69L108,69\"\n        android:strokeWidth=\"0.8\"\n        android:strokeColor=\"#33FFFFFF\" />\n    <path\n        android:fillColor=\"#00000000\"\n        android:pathData=\"M0,79L108,79\"\n        android:strokeWidth=\"0.8\"\n        android:strokeColor=\"#33FFFFFF\" />\n    <path\n        android:fillColor=\"#00000000\"\n        android:pathData=\"M0,89L108,89\"\n        android:strokeWidth=\"0.8\"\n        android:strokeColor=\"#33FFFFFF\" />\n    <path\n        android:fillColor=\"#00000000\"\n        android:pathData=\"M0,99L108,99\"\n        android:strokeWidth=\"0.8\"\n        android:strokeColor=\"#33FFFFFF\" />\n    <path\n        android:fillColor=\"#00000000\"\n        android:pathData=\"M19,29L89,29\"\n        android:strokeWidth=\"0.8\"\n        android:strokeColor=\"#33FFFFFF\" />\n    <path\n        android:fillColor=\"#00000000\"\n        android:pathData=\"M19,39L89,39\"\n        android:strokeWidth=\"0.8\"\n        android:strokeColor=\"#33FFFFFF\" />\n    <path\n        android:fillColor=\"#00000000\"\n        android:pathData=\"M19,49L89,49\"\n        android:strokeWidth=\"0.8\"\n        android:strokeColor=\"#33FFFFFF\" />\n    <path\n        android:fillColor=\"#00000000\"\n        android:pathData=\"M19,59L89,59\"\n        android:strokeWidth=\"0.8\"\n        android:strokeColor=\"#33FFFFFF\" />\n    <path\n        android:fillColor=\"#00000000\"\n        android:pathData=\"M19,69L89,69\"\n        android:strokeWidth=\"0.8\"\n        android:strokeColor=\"#33FFFFFF\" />\n    <path\n        android:fillColor=\"#00000000\"\n        android:pathData=\"M19,79L89,79\"\n        android:strokeWidth=\"0.8\"\n        android:strokeColor=\"#33FFFFFF\" />\n    <path\n        android:fillColor=\"#00000000\"\n        android:pathData=\"M29,19L29,89\"\n        android:strokeWidth=\"0.8\"\n        android:strokeColor=\"#33FFFFFF\" />\n    <path\n        android:fillColor=\"#00000000\"\n        android:pathData=\"M39,19L39,89\"\n        android:strokeWidth=\"0.8\"\n        android:strokeColor=\"#33FFFFFF\" />\n    <path\n        android:fillColor=\"#00000000\"\n        android:pathData=\"M49,19L49,89\"\n        android:strokeWidth=\"0.8\"\n        android:strokeColor=\"#33FFFFFF\" />\n    <path\n        android:fillColor=\"#00000000\"\n        android:pathData=\"M59,19L59,89\"\n        android:strokeWidth=\"0.8\"\n        android:strokeColor=\"#33FFFFFF\" />\n    <path\n        android:fillColor=\"#00000000\"\n        android:pathData=\"M69,19L69,89\"\n        android:strokeWidth=\"0.8\"\n        android:strokeColor=\"#33FFFFFF\" />\n    <path\n        android:fillColor=\"#00000000\"\n        android:pathData=\"M79,19L79,89\"\n        android:strokeWidth=\"0.8\"\n        android:strokeColor=\"#33FFFFFF\" />\n</vector>\n"
  },
  {
    "path": "app/tflite_yolov5_test/app/src/main/res/drawable/ic_notifications_black_24dp.xml",
    "content": "<vector xmlns:android=\"http://schemas.android.com/apk/res/android\"\n    android:width=\"24dp\"\n    android:height=\"24dp\"\n    android:viewportWidth=\"24.0\"\n    android:viewportHeight=\"24.0\">\n    <path\n        android:fillColor=\"#FF000000\"\n        android:pathData=\"M12,22c1.1,0 2,-0.9 2,-2h-4c0,1.1 0.89,2 2,2zM18,16v-5c0,-3.07 -1.64,-5.64 -4.5,-6.32L13.5,4c0,-0.83 -0.67,-1.5 -1.5,-1.5s-1.5,0.67 -1.5,1.5v0.68C7.63,5.36 6,7.92 6,11v5l-2,2v1h16v-1l-2,-2z\" />\n</vector>\n"
  },
  {
    "path": "app/tflite_yolov5_test/app/src/main/res/drawable-v24/ic_launcher_foreground.xml",
    "content": "<vector xmlns:android=\"http://schemas.android.com/apk/res/android\"\n    xmlns:aapt=\"http://schemas.android.com/aapt\"\n    android:width=\"108dp\"\n    android:height=\"108dp\"\n    android:viewportWidth=\"108\"\n    android:viewportHeight=\"108\">\n    <path android:pathData=\"M31,63.928c0,0 6.4,-11 12.1,-13.1c7.2,-2.6 26,-1.4 26,-1.4l38.1,38.1L107,108.928l-32,-1L31,63.928z\">\n        <aapt:attr name=\"android:fillColor\">\n            <gradient\n                android:endX=\"85.84757\"\n                android:endY=\"92.4963\"\n                android:startX=\"42.9492\"\n                android:startY=\"49.59793\"\n                android:type=\"linear\">\n                <item\n                    android:color=\"#44000000\"\n                    android:offset=\"0.0\" />\n                <item\n                    android:color=\"#00000000\"\n                    android:offset=\"1.0\" />\n            </gradient>\n        </aapt:attr>\n    </path>\n    <path\n        android:fillColor=\"#FFFFFF\"\n        android:fillType=\"nonZero\"\n        android:pathData=\"M65.3,45.828l3.8,-6.6c0.2,-0.4 0.1,-0.9 -0.3,-1.1c-0.4,-0.2 -0.9,-0.1 -1.1,0.3l-3.9,6.7c-6.3,-2.8 -13.4,-2.8 -19.7,0l-3.9,-6.7c-0.2,-0.4 -0.7,-0.5 -1.1,-0.3C38.8,38.328 38.7,38.828 38.9,39.228l3.8,6.6C36.2,49.428 31.7,56.028 31,63.928h46C76.3,56.028 71.8,49.428 65.3,45.828zM43.4,57.328c-0.8,0 -1.5,-0.5 -1.8,-1.2c-0.3,-0.7 -0.1,-1.5 0.4,-2.1c0.5,-0.5 1.4,-0.7 2.1,-0.4c0.7,0.3 1.2,1 1.2,1.8C45.3,56.528 44.5,57.328 43.4,57.328L43.4,57.328zM64.6,57.328c-0.8,0 -1.5,-0.5 -1.8,-1.2s-0.1,-1.5 0.4,-2.1c0.5,-0.5 1.4,-0.7 2.1,-0.4c0.7,0.3 1.2,1 1.2,1.8C66.5,56.528 65.6,57.328 64.6,57.328L64.6,57.328z\"\n        android:strokeWidth=\"1\"\n        android:strokeColor=\"#00000000\" />\n</vector>"
  },
  {
    "path": "app/tflite_yolov5_test/app/src/main/res/layout/activity_camera.xml",
    "content": "<?xml version=\"1.0\" encoding=\"utf-8\"?>\n\n<androidx.coordinatorlayout.widget.CoordinatorLayout xmlns:android=\"http://schemas.android.com/apk/res/android\"\n    xmlns:app=\"http://schemas.android.com/apk/res-auto\"\n    xmlns:tools=\"http://schemas.android.com/tools\"\n    android:layout_width=\"match_parent\"\n    android:layout_height=\"match_parent\"\n    android:background=\"#00000000\">\n\n    <LinearLayout\n        android:layout_width=\"match_parent\"\n        android:layout_height=\"match_parent\"\n        android:orientation=\"vertical\">\n\n        <FrameLayout\n            android:id=\"@+id/container\"\n            android:layout_width=\"match_parent\"\n            android:layout_height=\"600dp\"\n            tools:context=\".camera.CameraActivity\">\n\n        </FrameLayout>\n\n        <TextView\n            android:id=\"@+id/textView\"\n            android:layout_width=\"wrap_content\"\n            android:layout_height=\"wrap_content\"\n            android:layout_alignParentBottom=\"true\"\n            android:text=\"int8 320x320\" />\n\n        <TextView\n            android:id=\"@+id/textViewFPS\"\n            android:layout_width=\"match_parent\"\n            android:layout_height=\"wrap_content\"\n            android:layout_alignParentBottom=\"true\"\n            android:text=\"FPS:\" />\n\n        <TextView\n            android:id=\"@+id/textViewLatency\"\n            android:layout_width=\"match_parent\"\n            android:layout_height=\"wrap_content\"\n            android:layout_alignParentBottom=\"true\"\n            android:text=\"Inference: xxms Postprocess: yyms\" />\n\n        <TextView\n            android:id=\"@+id/conf_TextView2\"\n            android:layout_width=\"match_parent\"\n            android:layout_height=\"wrap_content\"\n            android:layout_alignParentBottom=\"true\"\n            android:text=\"Confidence Threshold\" />\n\n        <SeekBar\n            android:id=\"@+id/conf_seekBar2\"\n            android:layout_width=\"match_parent\"\n            android:layout_height=\"wrap_content\" />\n\n        <TextView\n            android:id=\"@+id/iou_TextView2\"\n            android:layout_width=\"match_parent\"\n            android:layout_height=\"wrap_content\"\n            android:layout_alignParentBottom=\"true\"\n            android:text=\"IoU Threshold\" />\n\n        <SeekBar\n            android:id=\"@+id/iou_seekBar2\"\n            android:layout_width=\"match_parent\"\n            android:layout_height=\"wrap_content\" />\n\n    </LinearLayout>\n\n</androidx.coordinatorlayout.widget.CoordinatorLayout>\n"
  },
  {
    "path": "app/tflite_yolov5_test/app/src/main/res/layout/activity_main.xml",
    "content": "<?xml version=\"1.0\" encoding=\"utf-8\"?>\n<androidx.constraintlayout.widget.ConstraintLayout xmlns:android=\"http://schemas.android.com/apk/res/android\"\n    xmlns:app=\"http://schemas.android.com/apk/res-auto\"\n    xmlns:tools=\"http://schemas.android.com/tools\"\n    android:layout_width=\"match_parent\"\n    android:layout_height=\"match_parent\"\n    tools:context=\".MainActivity\">\n    <ScrollView\n        android:layout_width=\"match_parent\"\n        android:layout_height=\"match_parent\"\n        android:layout_marginBottom=\"60dp\"\n        app:layout_constraintBottom_toBottomOf=\"parent\">\n\n        <LinearLayout\n            android:layout_width=\"match_parent\"\n            android:layout_height=\"wrap_content\"\n            android:orientation=\"vertical\">\n\n            <TextView\n                android:id=\"@+id/select_model_label\"\n                android:layout_width=\"match_parent\"\n                android:layout_height=\"wrap_content\"\n                android:ems=\"10\"\n                android:text=\"Select Model/Delegate\"\n                android:textAppearance=\"@style/TextAppearance.AppCompat.Large\"\n                android:textColor=\"#000000\" />\n\n            <LinearLayout\n                android:layout_width=\"match_parent\"\n                android:layout_height=\"wrap_content\"\n                android:orientation=\"vertical\">\n\n                <LinearLayout\n                    android:layout_width=\"match_parent\"\n                    android:layout_height=\"match_parent\"\n                    android:layout_weight=\"1\"\n                    android:orientation=\"horizontal\">\n\n                    <RadioGroup\n                        android:layout_width=\"match_parent\"\n                        android:layout_height=\"match_parent\"\n                        android:layout_weight=\"1\"\n                        android:orientation=\"horizontal\">\n\n                        <TextView\n                            android:id=\"@+id/textView4\"\n                            android:layout_width=\"59dp\"\n                            android:layout_height=\"wrap_content\"\n                            android:layout_weight=\"1\"\n                            android:text=\"Input Size\" />\n\n                        <RadioButton\n                            android:id=\"@+id/radioButton_640\"\n                            android:layout_width=\"wrap_content\"\n                            android:layout_height=\"23dp\"\n                            android:layout_weight=\"1\"\n                            android:checked=\"true\"\n                            android:text=\"640\"\n                            android:textAppearance=\"@style/TextAppearance.AppCompat.Body1\"\n                            android:textSize=\"16sp\"\n                            android:typeface=\"normal\" />\n\n                        <RadioButton\n                            android:id=\"@+id/radioButton_320\"\n                            android:layout_width=\"wrap_content\"\n                            android:layout_height=\"23dp\"\n                            android:layout_weight=\"1\"\n                            android:text=\"320\"\n                            android:textAppearance=\"@style/TextAppearance.AppCompat.Body1\"\n                            android:textSize=\"16sp\"\n                            android:typeface=\"normal\" />\n                    </RadioGroup>\n\n                </LinearLayout>\n\n                <LinearLayout\n                    android:layout_width=\"match_parent\"\n                    android:layout_height=\"match_parent\"\n                    android:layout_weight=\"1\"\n                    android:orientation=\"horizontal\">\n\n                    <RadioGroup\n                        android:layout_width=\"match_parent\"\n                        android:layout_height=\"match_parent\"\n                        android:layout_weight=\"1\"\n                        android:orientation=\"horizontal\">\n\n                        <TextView\n                            android:id=\"@+id/textView7\"\n                            android:layout_width=\"59dp\"\n                            android:layout_height=\"wrap_content\"\n                            android:layout_weight=\"1\"\n                            android:text=\"Model\" />\n\n                        <RadioButton\n                            android:id=\"@+id/radioButton_modelFloat\"\n                            android:layout_width=\"wrap_content\"\n                            android:layout_height=\"23dp\"\n                            android:layout_weight=\"1\"\n                            android:checked=\"true\"\n                            android:text=\"float\"\n                            android:onClick=\"onModelFloatClick\"\n                            android:textAppearance=\"@style/TextAppearance.AppCompat.Body1\"\n                            android:textSize=\"16sp\"\n                            android:typeface=\"normal\" />\n\n                        <RadioButton\n                            android:id=\"@+id/radioButton_modelInt\"\n                            android:layout_width=\"wrap_content\"\n                            android:layout_height=\"23dp\"\n                            android:layout_weight=\"1\"\n                            android:text=\"int8\"\n                            android:onClick=\"onModelIntClick\"\n                            android:textAppearance=\"@style/TextAppearance.AppCompat.Body1\"\n                            android:textSize=\"16sp\"\n                            android:typeface=\"normal\" />\n                    </RadioGroup>\n\n                </LinearLayout>\n\n                <LinearLayout\n                    android:layout_width=\"match_parent\"\n                    android:layout_height=\"match_parent\"\n                    android:layout_weight=\"1\"\n                    android:orientation=\"horizontal\">\n\n                    <RadioGroup\n                        android:layout_width=\"match_parent\"\n                        android:layout_height=\"match_parent\"\n                        android:layout_weight=\"1\"\n                        android:orientation=\"horizontal\">\n\n                        <TextView\n                            android:id=\"@+id/textView5\"\n                            android:layout_width=\"104dp\"\n                            android:layout_height=\"wrap_content\"\n                            android:layout_weight=\"1\"\n                            android:text=\"Inference Precision\" />\n\n                        <RadioButton\n                            android:id=\"@+id/radioButton_runFP32\"\n                            android:layout_width=\"wrap_content\"\n                            android:layout_height=\"23dp\"\n                            android:layout_weight=\"1\"\n                            android:checked=\"true\"\n                            android:text=\"fp32\"\n                            android:onClick=\"onPrecisionFPClick\"\n                            android:textAppearance=\"@style/TextAppearance.AppCompat.Body1\"\n                            android:textSize=\"16sp\"\n                            android:typeface=\"normal\" />\n\n                        <RadioButton\n                            android:id=\"@+id/radioButton_runFP16\"\n                            android:layout_width=\"wrap_content\"\n                            android:layout_height=\"23dp\"\n                            android:layout_weight=\"1\"\n                            android:text=\"fp16\"\n                            android:onClick=\"onPrecisionFPClick\"\n                            android:textAppearance=\"@style/TextAppearance.AppCompat.Body1\"\n                            android:textSize=\"16sp\"\n                            android:typeface=\"normal\" />\n\n                        <RadioButton\n                            android:id=\"@+id/radioButton_runInt8\"\n                            android:layout_width=\"wrap_content\"\n                            android:layout_height=\"23dp\"\n                            android:layout_weight=\"1\"\n                            android:text=\"int8\"\n                            android:onClick=\"onPrecisionIntClick\"\n                            android:textAppearance=\"@style/TextAppearance.AppCompat.Body1\"\n                            android:textSize=\"16sp\"\n                            android:typeface=\"normal\" />\n                    </RadioGroup>\n\n                </LinearLayout>\n\n                <LinearLayout\n                    android:layout_width=\"match_parent\"\n                    android:layout_height=\"match_parent\"\n                    android:layout_weight=\"1\"\n                    android:orientation=\"horizontal\">\n\n                    <RadioGroup\n                        android:layout_width=\"match_parent\"\n                        android:layout_height=\"match_parent\"\n                        android:layout_weight=\"1\"\n                        android:orientation=\"horizontal\">\n\n                        <TextView\n                            android:id=\"@+id/textView6\"\n                            android:layout_width=\"72dp\"\n                            android:layout_height=\"wrap_content\"\n                            android:layout_weight=\"1\"\n                            android:text=\"Delegate\" />\n\n                        <RadioButton\n                            android:id=\"@+id/radioButton_delegateNone\"\n                            android:layout_width=\"wrap_content\"\n                            android:layout_height=\"23dp\"\n                            android:layout_weight=\"1\"\n                            android:checked=\"true\"\n                            android:text=\"None\"\n                            android:textAppearance=\"@style/TextAppearance.AppCompat.Body1\"\n                            android:textSize=\"16sp\"\n                            android:typeface=\"normal\" />\n\n                        <RadioButton\n                            android:id=\"@+id/radioButton_delegateNNAPI\"\n                            android:layout_width=\"wrap_content\"\n                            android:layout_height=\"23dp\"\n                            android:layout_weight=\"1\"\n                            android:text=\"NNAPI\"\n                            android:textAppearance=\"@style/TextAppearance.AppCompat.Body1\"\n                            android:textSize=\"16sp\"\n                            android:typeface=\"normal\" />\n                    </RadioGroup>\n\n                </LinearLayout>\n\n            </LinearLayout>\n\n            <Button\n                android:id=\"@+id/openCameraButton\"\n                android:layout_width=\"match_parent\"\n                android:layout_height=\"wrap_content\"\n                android:onClick=\"OnOpenCameraButtonClick\"\n                android:text=\"Open Camera\" />\n\n            <Button\n                android:id=\"@+id/openImageButton\"\n                android:layout_width=\"match_parent\"\n                android:layout_height=\"wrap_content\"\n                android:onClick=\"OnOpenImageButtonClick\"\n                android:text=\"Open Image\" />\n\n            <Button\n                android:id=\"@+id/openDirButton\"\n                android:layout_width=\"match_parent\"\n                android:layout_height=\"wrap_content\"\n                android:onClick=\"OnOpenDirButtonClick\"\n                android:text=\"Open Directory\" />\n\n            <Button\n                android:id=\"@+id/runInferenceButton\"\n                android:layout_width=\"match_parent\"\n                android:layout_height=\"wrap_content\"\n                android:onClick=\"OnRunInferenceButtonClick\"\n                android:text=\"Run inference\" />\n\n            <TextView\n                android:id=\"@+id/conf_TextView\"\n                android:layout_width=\"match_parent\"\n                android:layout_height=\"wrap_content\"\n                android:layout_weight=\"1\"\n                android:text=\"Confidence Threshold\" />\n\n            <SeekBar\n                android:id=\"@+id/conf_seekBar\"\n                android:layout_width=\"match_parent\"\n                android:layout_height=\"wrap_content\"\n                android:max=\"1\" />\n\n            <TextView\n                android:id=\"@+id/iou_TextView\"\n                android:layout_width=\"match_parent\"\n                android:layout_height=\"wrap_content\"\n                android:layout_weight=\"1\"\n                android:text=\"IoU Threshold\" />\n\n            <SeekBar\n                android:id=\"@+id/iou_seekBar\"\n                android:layout_width=\"match_parent\"\n                android:layout_height=\"wrap_content\" />\n\n            <TextView\n                android:id=\"@+id/oneLineLabel\"\n                android:layout_width=\"match_parent\"\n                android:layout_height=\"wrap_content\"\n                android:ems=\"10\"\n                android:textAppearance=\"@style/TextAppearance.AppCompat.Medium\"\n                android:textColor=\"#000000\" />\n\n            <ProgressBar\n                android:id=\"@+id/progressBar\"\n                style=\"?android:attr/progressBarStyleHorizontal\"\n                android:layout_width=\"match_parent\"\n                android:layout_height=\"wrap_content\" />\n\n            <ImageView\n                android:id=\"@+id/resultImageView\"\n                android:layout_width=\"match_parent\"\n                android:layout_height=\"310dp\"\n                tools:src=\"@tools:sample/avatars\" />\n\n            <TextView\n                android:id=\"@+id/loglabel\"\n                android:layout_width=\"match_parent\"\n                android:layout_height=\"wrap_content\"\n                android:ems=\"10\"\n                android:textAppearance=\"@style/TextAppearance.AppCompat.Large\"\n                android:text=\"Log\"\n                android:textColor=\"#000000\" />\n\n            <TextView\n                android:id=\"@+id/logTextView\"\n                android:layout_width=\"match_parent\"\n                android:layout_height=\"match_parent\"\n                android:inputType=\"textMultiLine\"\n                android:scrollbars=\"vertical\" />\n\n            <Button\n                android:id=\"@+id/clearLogButton\"\n                android:layout_width=\"match_parent\"\n                android:layout_height=\"wrap_content\"\n                android:onClick=\"OnClearLogButton\"\n                android:text=\"clear log\" />\n\n        </LinearLayout>\n    </ScrollView>\n</androidx.constraintlayout.widget.ConstraintLayout>"
  },
  {
    "path": "app/tflite_yolov5_test/app/src/main/res/layout/tfe_od_camera_connection_fragment_tracking.xml",
    "content": "<?xml version=\"1.0\" encoding=\"utf-8\"?><!--\n Copyright 2019 The TensorFlow Authors. All Rights Reserved.\n\n Licensed under the Apache License, Version 2.0 (the \"License\");\n you may not use this file except in compliance with the License.\n You may obtain a copy of the License at\n\n     http://www.apache.org/licenses/LICENSE-2.0\n\n Unless required by applicable law or agreed to in writing, software\n distributed under the License is distributed on an \"AS IS\" BASIS,\n WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n See the License for the specific language governing permissions and\n limitations under the License.\n-->\n<FrameLayout xmlns:android=\"http://schemas.android.com/apk/res/android\"\n    android:layout_width=\"match_parent\"\n    android:layout_height=\"match_parent\">\n\n    <com.example.tflite_yolov5_test.customview.AutoFitTextureView\n        android:id=\"@+id/texture\"\n        android:layout_width=\"wrap_content\"\n        android:layout_height=\"wrap_content\" />\n\n    <com.example.tflite_yolov5_test.customview.OverlayView\n        android:id=\"@+id/tracking_overlay\"\n        android:layout_width=\"match_parent\"\n        android:layout_height=\"match_parent\" />\n\n</FrameLayout>\n"
  },
  {
    "path": "app/tflite_yolov5_test/app/src/main/res/menu/bottom_nav_menu.xml",
    "content": "<?xml version=\"1.0\" encoding=\"utf-8\"?>\n<menu xmlns:android=\"http://schemas.android.com/apk/res/android\">\n\n    <item\n        android:id=\"@+id/navigation_home\"\n        android:icon=\"@drawable/ic_home_black_24dp\"\n        android:title=\"@string/title_home\" />\n\n    <item\n        android:id=\"@+id/navigation_dashboard\"\n        android:icon=\"@drawable/ic_dashboard_black_24dp\"\n        android:title=\"@string/title_dashboard\" />\n\n    <item\n        android:id=\"@+id/navigation_notifications\"\n        android:icon=\"@drawable/ic_notifications_black_24dp\"\n        android:title=\"@string/title_notifications\" />\n\n</menu>"
  },
  {
    "path": "app/tflite_yolov5_test/app/src/main/res/mipmap-anydpi-v26/ic_launcher.xml",
    "content": "<?xml version=\"1.0\" encoding=\"utf-8\"?>\n<adaptive-icon xmlns:android=\"http://schemas.android.com/apk/res/android\">\n    <background android:drawable=\"@drawable/ic_launcher_background\" />\n    <foreground android:drawable=\"@drawable/ic_launcher_foreground\" />\n</adaptive-icon>"
  },
  {
    "path": "app/tflite_yolov5_test/app/src/main/res/mipmap-anydpi-v26/ic_launcher_round.xml",
    "content": "<?xml version=\"1.0\" encoding=\"utf-8\"?>\n<adaptive-icon xmlns:android=\"http://schemas.android.com/apk/res/android\">\n    <background android:drawable=\"@drawable/ic_launcher_background\" />\n    <foreground android:drawable=\"@drawable/ic_launcher_foreground\" />\n</adaptive-icon>"
  },
  {
    "path": "app/tflite_yolov5_test/app/src/main/res/navigation/mobile_navigation.xml",
    "content": "<?xml version=\"1.0\" encoding=\"utf-8\"?>\n<navigation xmlns:android=\"http://schemas.android.com/apk/res/android\"\n    xmlns:app=\"http://schemas.android.com/apk/res-auto\"\n    xmlns:tools=\"http://schemas.android.com/tools\"\n    android:id=\"@+id/mobile_navigation\"\n    app:startDestination=\"@+id/navigation_home\">\n\n    <fragment\n        android:id=\"@+id/navigation_home\"\n        android:name=\"com.example.tflite_yolov5_test.ui.home.HomeFragment\"\n        android:label=\"@string/title_home\"\n        tools:layout=\"@layout/fragment_home\" />\n\n    <fragment\n        android:id=\"@+id/navigation_dashboard\"\n        android:name=\"com.example.tflite_yolov5_test.ui.dashboard.DashboardFragment\"\n        android:label=\"@string/title_dashboard\"\n        tools:layout=\"@layout/fragment_dashboard\" />\n\n    <fragment\n        android:id=\"@+id/navigation_notifications\"\n        android:name=\"com.example.tflite_yolov5_test.ui.notifications.NotificationsFragment\"\n        android:label=\"@string/title_notifications\"\n        tools:layout=\"@layout/fragment_notifications\" />\n</navigation>"
  },
  {
    "path": "app/tflite_yolov5_test/app/src/main/res/values/colors.xml",
    "content": "<?xml version=\"1.0\" encoding=\"utf-8\"?>\n<resources>\n    <color name=\"purple_200\">#FFBB86FC</color>\n    <color name=\"purple_500\">#FF6200EE</color>\n    <color name=\"purple_700\">#FF3700B3</color>\n    <color name=\"teal_200\">#FF03DAC5</color>\n    <color name=\"teal_700\">#FF018786</color>\n    <color name=\"black\">#FF000000</color>\n    <color name=\"white\">#FFFFFFFF</color>\n</resources>"
  },
  {
    "path": "app/tflite_yolov5_test/app/src/main/res/values/dimens.xml",
    "content": "<resources>\n    <!-- Default screen margins, per the Android Design guidelines. -->\n    <dimen name=\"activity_horizontal_margin\">16dp</dimen>\n    <dimen name=\"activity_vertical_margin\">16dp</dimen>\n</resources>"
  },
  {
    "path": "app/tflite_yolov5_test/app/src/main/res/values/strings.xml",
    "content": "<resources>\n    <string name=\"app_name\">tflite_yolov5_test</string>\n    <string name=\"title_activity_main2\">MainActivity</string>\n    <string name=\"title_home\">Home</string>\n    <string name=\"title_dashboard\">Dashboard</string>\n    <string name=\"title_notifications\">Notifications</string>\n</resources>"
  },
  {
    "path": "app/tflite_yolov5_test/app/src/main/res/values/themes.xml",
    "content": "<resources xmlns:tools=\"http://schemas.android.com/tools\">\n    <!-- Base application theme. -->\n    <style name=\"Theme.Tflite_yolov5_test\" parent=\"Theme.MaterialComponents.DayNight.NoActionBar\">\n        <!-- Primary brand color. -->\n        <item name=\"colorPrimary\">@color/purple_500</item>\n        <item name=\"colorPrimaryVariant\">@color/purple_700</item>\n        <item name=\"colorOnPrimary\">@color/white</item>\n        <!-- Secondary brand color. -->\n        <item name=\"colorSecondary\">@color/teal_200</item>\n        <item name=\"colorSecondaryVariant\">@color/teal_700</item>\n        <item name=\"colorOnSecondary\">@color/black</item>\n        <!-- Status bar color. -->\n        <item name=\"android:statusBarColor\" tools:targetApi=\"l\">?attr/colorPrimaryVariant</item>\n        <!-- Customize your theme here. -->\n    </style>\n</resources>"
  },
  {
    "path": "app/tflite_yolov5_test/app/src/main/res/values-night/themes.xml",
    "content": "<resources xmlns:tools=\"http://schemas.android.com/tools\">\n    <!-- Base application theme. -->\n    <style name=\"Theme.Tflite_yolov5_test\" parent=\"Theme.MaterialComponents.DayNight.DarkActionBar\">\n        <!-- Primary brand color. -->\n        <item name=\"colorPrimary\">@color/purple_200</item>\n        <item name=\"colorPrimaryVariant\">@color/purple_700</item>\n        <item name=\"colorOnPrimary\">@color/black</item>\n        <!-- Secondary brand color. -->\n        <item name=\"colorSecondary\">@color/teal_200</item>\n        <item name=\"colorSecondaryVariant\">@color/teal_200</item>\n        <item name=\"colorOnSecondary\">@color/black</item>\n        <!-- Status bar color. -->\n        <item name=\"android:statusBarColor\" tools:targetApi=\"l\">?attr/colorPrimaryVariant</item>\n        <!-- Customize your theme here. -->\n    </style>\n</resources>"
  },
  {
    "path": "app/tflite_yolov5_test/app/src/test/java/com/example/tflite_yolov5_test/ExampleUnitTest.java",
    "content": "package com.example.tflite_yolov5_test;\n\nimport org.junit.Test;\n\nimport static org.junit.Assert.*;\n\n/**\n * Example local unit test, which will execute on the development machine (host).\n *\n * @see <a href=\"http://d.android.com/tools/testing\">Testing documentation</a>\n */\npublic class ExampleUnitTest {\n    @Test\n    public void addition_isCorrect() {\n        assertEquals(4, 2 + 2);\n    }\n}"
  },
  {
    "path": "app/tflite_yolov5_test/build.gradle",
    "content": "// Top-level build file where you can add configuration options common to all sub-projects/modules.\nbuildscript {\n    repositories {\n        google()\n        mavenCentral()\n    }\n    dependencies {\n        classpath \"com.android.tools.build:gradle:4.2.1\"\n\n        // NOTE: Do not place your application dependencies here; they belong\n        // in the individual module build.gradle files\n    }\n}\n\nallprojects {\n    repositories {\n        google()\n        mavenCentral()\n        jcenter() // Warning: this repository is going to shut down soon\n    }\n}\n\ntask clean(type: Delete) {\n    delete rootProject.buildDir\n}"
  },
  {
    "path": "app/tflite_yolov5_test/gradle/wrapper/gradle-wrapper.properties",
    "content": "#Wed Jun 23 22:23:21 JST 2021\ndistributionBase=GRADLE_USER_HOME\ndistributionUrl=https\\://services.gradle.org/distributions/gradle-6.7.1-bin.zip\ndistributionPath=wrapper/dists\nzipStorePath=wrapper/dists\nzipStoreBase=GRADLE_USER_HOME\n"
  },
  {
    "path": "app/tflite_yolov5_test/gradle.properties",
    "content": "# Project-wide Gradle settings.\n# IDE (e.g. Android Studio) users:\n# Gradle settings configured through the IDE *will override*\n# any settings specified in this file.\n# For more details on how to configure your build environment visit\n# http://www.gradle.org/docs/current/userguide/build_environment.html\n# Specifies the JVM arguments used for the daemon process.\n# The setting is particularly useful for tweaking memory settings.\norg.gradle.jvmargs=-Xmx2048m -Dfile.encoding=UTF-8\n# When configured, Gradle will run in incubating parallel mode.\n# This option should only be used with decoupled projects. More details, visit\n# http://www.gradle.org/docs/current/userguide/multi_project_builds.html#sec:decoupled_projects\n# org.gradle.parallel=true\n# AndroidX package structure to make it clearer which packages are bundled with the\n# Android operating system, and which are packaged with your app\"s APK\n# https://developer.android.com/topic/libraries/support-library/androidx-rn\nandroid.useAndroidX=true"
  },
  {
    "path": "app/tflite_yolov5_test/gradlew",
    "content": "#!/usr/bin/env sh\n\n##############################################################################\n##\n##  Gradle start up script for UN*X\n##\n##############################################################################\n\n# Attempt to set APP_HOME\n# Resolve links: $0 may be a link\nPRG=\"$0\"\n# Need this for relative symlinks.\nwhile [ -h \"$PRG\" ] ; do\n    ls=`ls -ld \"$PRG\"`\n    link=`expr \"$ls\" : '.*-> \\(.*\\)$'`\n    if expr \"$link\" : '/.*' > /dev/null; then\n        PRG=\"$link\"\n    else\n        PRG=`dirname \"$PRG\"`\"/$link\"\n    fi\ndone\nSAVED=\"`pwd`\"\ncd \"`dirname \\\"$PRG\\\"`/\" >/dev/null\nAPP_HOME=\"`pwd -P`\"\ncd \"$SAVED\" >/dev/null\n\nAPP_NAME=\"Gradle\"\nAPP_BASE_NAME=`basename \"$0\"`\n\n# Add default JVM options here. You can also use JAVA_OPTS and GRADLE_OPTS to pass JVM options to this script.\nDEFAULT_JVM_OPTS=\"\"\n\n# Use the maximum available, or set MAX_FD != -1 to use that value.\nMAX_FD=\"maximum\"\n\nwarn () {\n    echo \"$*\"\n}\n\ndie () {\n    echo\n    echo \"$*\"\n    echo\n    exit 1\n}\n\n# OS specific support (must be 'true' or 'false').\ncygwin=false\nmsys=false\ndarwin=false\nnonstop=false\ncase \"`uname`\" in\n  CYGWIN* )\n    cygwin=true\n    ;;\n  Darwin* )\n    darwin=true\n    ;;\n  MINGW* )\n    msys=true\n    ;;\n  NONSTOP* )\n    nonstop=true\n    ;;\nesac\n\nCLASSPATH=$APP_HOME/gradle/wrapper/gradle-wrapper.jar\n\n# Determine the Java command to use to start the JVM.\nif [ -n \"$JAVA_HOME\" ] ; then\n    if [ -x \"$JAVA_HOME/jre/sh/java\" ] ; then\n        # IBM's JDK on AIX uses strange locations for the executables\n        JAVACMD=\"$JAVA_HOME/jre/sh/java\"\n    else\n        JAVACMD=\"$JAVA_HOME/bin/java\"\n    fi\n    if [ ! -x \"$JAVACMD\" ] ; then\n        die \"ERROR: JAVA_HOME is set to an invalid directory: $JAVA_HOME\n\nPlease set the JAVA_HOME variable in your environment to match the\nlocation of your Java installation.\"\n    fi\nelse\n    JAVACMD=\"java\"\n    which java >/dev/null 2>&1 || die \"ERROR: JAVA_HOME is not set and no 'java' command could be found in your PATH.\n\nPlease set the JAVA_HOME variable in your environment to match the\nlocation of your Java installation.\"\nfi\n\n# Increase the maximum file descriptors if we can.\nif [ \"$cygwin\" = \"false\" -a \"$darwin\" = \"false\" -a \"$nonstop\" = \"false\" ] ; then\n    MAX_FD_LIMIT=`ulimit -H -n`\n    if [ $? -eq 0 ] ; then\n        if [ \"$MAX_FD\" = \"maximum\" -o \"$MAX_FD\" = \"max\" ] ; then\n            MAX_FD=\"$MAX_FD_LIMIT\"\n        fi\n        ulimit -n $MAX_FD\n        if [ $? -ne 0 ] ; then\n            warn \"Could not set maximum file descriptor limit: $MAX_FD\"\n        fi\n    else\n        warn \"Could not query maximum file descriptor limit: $MAX_FD_LIMIT\"\n    fi\nfi\n\n# For Darwin, add options to specify how the application appears in the dock\nif $darwin; then\n    GRADLE_OPTS=\"$GRADLE_OPTS \\\"-Xdock:name=$APP_NAME\\\" \\\"-Xdock:icon=$APP_HOME/media/gradle.icns\\\"\"\nfi\n\n# For Cygwin, switch paths to Windows format before running java\nif $cygwin ; then\n    APP_HOME=`cygpath --path --mixed \"$APP_HOME\"`\n    CLASSPATH=`cygpath --path --mixed \"$CLASSPATH\"`\n    JAVACMD=`cygpath --unix \"$JAVACMD\"`\n\n    # We build the pattern for arguments to be converted via cygpath\n    ROOTDIRSRAW=`find -L / -maxdepth 1 -mindepth 1 -type d 2>/dev/null`\n    SEP=\"\"\n    for dir in $ROOTDIRSRAW ; do\n        ROOTDIRS=\"$ROOTDIRS$SEP$dir\"\n        SEP=\"|\"\n    done\n    OURCYGPATTERN=\"(^($ROOTDIRS))\"\n    # Add a user-defined pattern to the cygpath arguments\n    if [ \"$GRADLE_CYGPATTERN\" != \"\" ] ; then\n        OURCYGPATTERN=\"$OURCYGPATTERN|($GRADLE_CYGPATTERN)\"\n    fi\n    # Now convert the arguments - kludge to limit ourselves to /bin/sh\n    i=0\n    for arg in \"$@\" ; do\n        CHECK=`echo \"$arg\"|egrep -c \"$OURCYGPATTERN\" -`\n        CHECK2=`echo \"$arg\"|egrep -c \"^-\"`                                 ### Determine if an option\n\n        if [ $CHECK -ne 0 ] && [ $CHECK2 -eq 0 ] ; then                    ### Added a condition\n            eval `echo args$i`=`cygpath --path --ignore --mixed \"$arg\"`\n        else\n            eval `echo args$i`=\"\\\"$arg\\\"\"\n        fi\n        i=$((i+1))\n    done\n    case $i in\n        (0) set -- ;;\n        (1) set -- \"$args0\" ;;\n        (2) set -- \"$args0\" \"$args1\" ;;\n        (3) set -- \"$args0\" \"$args1\" \"$args2\" ;;\n        (4) set -- \"$args0\" \"$args1\" \"$args2\" \"$args3\" ;;\n        (5) set -- \"$args0\" \"$args1\" \"$args2\" \"$args3\" \"$args4\" ;;\n        (6) set -- \"$args0\" \"$args1\" \"$args2\" \"$args3\" \"$args4\" \"$args5\" ;;\n        (7) set -- \"$args0\" \"$args1\" \"$args2\" \"$args3\" \"$args4\" \"$args5\" \"$args6\" ;;\n        (8) set -- \"$args0\" \"$args1\" \"$args2\" \"$args3\" \"$args4\" \"$args5\" \"$args6\" \"$args7\" ;;\n        (9) set -- \"$args0\" \"$args1\" \"$args2\" \"$args3\" \"$args4\" \"$args5\" \"$args6\" \"$args7\" \"$args8\" ;;\n    esac\nfi\n\n# Escape application args\nsave () {\n    for i do printf %s\\\\n \"$i\" | sed \"s/'/'\\\\\\\\''/g;1s/^/'/;\\$s/\\$/' \\\\\\\\/\" ; done\n    echo \" \"\n}\nAPP_ARGS=$(save \"$@\")\n\n# Collect all arguments for the java command, following the shell quoting and substitution rules\neval set -- $DEFAULT_JVM_OPTS $JAVA_OPTS $GRADLE_OPTS \"\\\"-Dorg.gradle.appname=$APP_BASE_NAME\\\"\" -classpath \"\\\"$CLASSPATH\\\"\" org.gradle.wrapper.GradleWrapperMain \"$APP_ARGS\"\n\n# by default we should be in the correct project dir, but when run from Finder on Mac, the cwd is wrong\nif [ \"$(uname)\" = \"Darwin\" ] && [ \"$HOME\" = \"$PWD\" ]; then\n  cd \"$(dirname \"$0\")\"\nfi\n\nexec \"$JAVACMD\" \"$@\"\n"
  },
  {
    "path": "app/tflite_yolov5_test/gradlew.bat",
    "content": "@if \"%DEBUG%\" == \"\" @echo off\r\n@rem ##########################################################################\r\n@rem\r\n@rem  Gradle startup script for Windows\r\n@rem\r\n@rem ##########################################################################\r\n\r\n@rem Set local scope for the variables with windows NT shell\r\nif \"%OS%\"==\"Windows_NT\" setlocal\r\n\r\nset DIRNAME=%~dp0\r\nif \"%DIRNAME%\" == \"\" set DIRNAME=.\r\nset APP_BASE_NAME=%~n0\r\nset APP_HOME=%DIRNAME%\r\n\r\n@rem Add default JVM options here. You can also use JAVA_OPTS and GRADLE_OPTS to pass JVM options to this script.\r\nset DEFAULT_JVM_OPTS=\r\n\r\n@rem Find java.exe\r\nif defined JAVA_HOME goto findJavaFromJavaHome\r\n\r\nset JAVA_EXE=java.exe\r\n%JAVA_EXE% -version >NUL 2>&1\r\nif \"%ERRORLEVEL%\" == \"0\" goto init\r\n\r\necho.\r\necho ERROR: JAVA_HOME is not set and no 'java' command could be found in your PATH.\r\necho.\r\necho Please set the JAVA_HOME variable in your environment to match the\r\necho location of your Java installation.\r\n\r\ngoto fail\r\n\r\n:findJavaFromJavaHome\r\nset JAVA_HOME=%JAVA_HOME:\"=%\r\nset JAVA_EXE=%JAVA_HOME%/bin/java.exe\r\n\r\nif exist \"%JAVA_EXE%\" goto init\r\n\r\necho.\r\necho ERROR: JAVA_HOME is set to an invalid directory: %JAVA_HOME%\r\necho.\r\necho Please set the JAVA_HOME variable in your environment to match the\r\necho location of your Java installation.\r\n\r\ngoto fail\r\n\r\n:init\r\n@rem Get command-line arguments, handling Windows variants\r\n\r\nif not \"%OS%\" == \"Windows_NT\" goto win9xME_args\r\n\r\n:win9xME_args\r\n@rem Slurp the command line arguments.\r\nset CMD_LINE_ARGS=\r\nset _SKIP=2\r\n\r\n:win9xME_args_slurp\r\nif \"x%~1\" == \"x\" goto execute\r\n\r\nset CMD_LINE_ARGS=%*\r\n\r\n:execute\r\n@rem Setup the command line\r\n\r\nset CLASSPATH=%APP_HOME%\\gradle\\wrapper\\gradle-wrapper.jar\r\n\r\n@rem Execute Gradle\r\n\"%JAVA_EXE%\" %DEFAULT_JVM_OPTS% %JAVA_OPTS% %GRADLE_OPTS% \"-Dorg.gradle.appname=%APP_BASE_NAME%\" -classpath \"%CLASSPATH%\" org.gradle.wrapper.GradleWrapperMain %CMD_LINE_ARGS%\r\n\r\n:end\r\n@rem End local scope for the variables with windows NT shell\r\nif \"%ERRORLEVEL%\"==\"0\" goto mainEnd\r\n\r\n:fail\r\nrem Set variable GRADLE_EXIT_CONSOLE if you need the _script_ return code instead of\r\nrem the _cmd.exe /c_ return code!\r\nif  not \"\" == \"%GRADLE_EXIT_CONSOLE%\" exit 1\r\nexit /b 1\r\n\r\n:mainEnd\r\nif \"%OS%\"==\"Windows_NT\" endlocal\r\n\r\n:omega\r\n"
  },
  {
    "path": "app/tflite_yolov5_test/settings.gradle",
    "content": "rootProject.name = \"tflite_yolov5_test\"\ninclude ':app'\n"
  },
  {
    "path": "benchmark/README.md",
    "content": "These results are measured by [TFLite Model Benchmark Tool with C++ Binary](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/tools/benchmark#profiling-model-operators) on `Xiaomi Mi11`.  \nWe build the benchmark tool on Tensorflow v2.4.0.\n```sh\n# before build benchmark tool, you have to install AndroidStudio.\ngit clone https://github.com/tensorflow/tensorflow\ncd tensorflow\ngit checkout refs/tags/v2.4.0\nexport ANDROID_NDK_HOME=\"/home/lp6m/Android/Sdk/ndk/21.4.7075529\"\nexport ANDROID_NDK_API_LEVEL=\"21\"\nexport ANDROID_BUILD_TOOLS_VERSION=\"30.0.3\"\nexport ANDROID_SDK_API_LEVEL=\"29\"\nexport ANDROID_SDK_HOME=\"/home/lp6m/Android/Sdk\"\nbazel build -c opt \\\n  --config=android_arm64 \\\n  tensorflow/lite/tools/benchmark:benchmark_model\n# copy benchmark tool to Android device\nadb push bazel-bin/tensorflow/lite/tools/benchmark/benchmark_model /data/local/tmp\n```\n\n## FP32 model\n### None(CPU)\n```sh\nadb shell /data/local/tmp/benchmark_model \\\n  --graph=/data/local/tmp/yolov5s_fp32_640.tflite \\\n  --num_threads=4 \n```\n```\ncount=50 first=249042 curr=250889 min=247067 max=254474 avg=249684 std=1442\nInference timings in us: Init: 16242, First inference: 360262, Warmup (avg): 304586, Inference (avg): 249684\nNote: as the benchmark tool itself affects memory footprint, the following is only APPROXIMATE to the actual memory footprint of the model at runtime. Take the information at your discretion.\nPeak memory footprint (MB): init=4.92969 overall=138.809\n```\n### NNAPI qti-gpu fp32\n```sh\nadb shell /data/local/tmp/benchmark_model \\\n  --graph=/data/local/tmp/yolov5s_fp32_640.tflite \\\n  --num_threads=4 \\\n  --use_nnapi=true \\\n  --nnapi_execution_preference=sustained_speed \\\n  --nnapi_accelerator_name=qti-gpu\n```\n```\ncount=50 first=157195 curr=159511 min=149738 max=161935 avg=156669 std=2352\nInference timings in us: Init: 404292, First inference: 188086, Warmup (avg): 175313, Inference (avg): 156669\nNote: as the benchmark tool itself affects memory footprint, the following is only APPROXIMATE to the actual memory footprint of the model at runtime. Take the information at your discretion.\nPeak memory footprint (MB): init=6.80469 overall=61.0078\n\n```\n### NNAPI qti-gpu fp16\n```sh\nadb shell /data/local/tmp/benchmark_model \\\n  --graph=/data/local/tmp/yolov5s_fp32_640.tflite \\\n  --num_threads=4 \\\n  --use_nnapi=true \\\n  --nnapi_execution_preference=sustained_speed \\\n  --nnapi_accelerator_name=qti-gpu \\\n  --nnapi_allow_fp16=true\n```\n```\ncount=50 first=96719 curr=90158 min=89745 max=96719 avg=92571.2 std=1638\nInference timings in us: Init: 365972, First inference: 120877, Warmup (avg): 108605, Inference (avg): 92571.2\nNote: as the benchmark tool itself affects memory footprint, the following is only APPROXIMATE to the actual memory footprint of the model at runtime. Take the information at your discretion.\nPeak memory footprint (MB): init=6.90625 overall=61.0547\n```\n\n### NNAPI qti-gpu Profile\nYou can profile and check calculation latencies for each operations in inference.\n```sh\nadb shell taskset f0 /data/local/tmp/benchmark_model \\\n  --graph=/data/local/tmp/yolov5s_fp32_640.tflite \\\n  --num_threads=4 \\\n  --use_nnapi=true \\\n  --nnapi_execution_preference=sustained_speed \\\n  --nnapi_accelerator_name=qti-gpu \\\n  --nnapi_allow_fp16=true \\\n  --enable_op_profiling=true\n```\n<details><summary>Profile Output:</summary><div>\n\n```\nProfiling Info for Benchmark Initialization:\n============================== Run Order ==============================\n\t             [node type]\t          [start]\t  [first]\t [avg ms]\t     [%]\t  [cdf%]\t  [mem KB]\t[times called]\t[Name]\n\t ModifyGraphWithDelegate\t            0.000\t  413.346\t  413.346\t 56.670%\t 56.670%\t  1748.000\t        1\tModifyGraphWithDelegate/0\n\t         AllocateTensors\t          255.360\t  316.039\t  158.023\t 43.330%\t100.000%\t     0.000\t        2\tAllocateTensors/0\n\n============================== Top by Computation Time ==============================\n\t             [node type]\t          [start]\t  [first]\t [avg ms]\t     [%]\t  [cdf%]\t  [mem KB]\t[times called]\t[Name]\n\t ModifyGraphWithDelegate\t            0.000\t  413.346\t  413.346\t 56.670%\t 56.670%\t  1748.000\t        1\tModifyGraphWithDelegate/0\n\t         AllocateTensors\t          255.360\t  316.039\t  158.023\t 43.330%\t100.000%\t     0.000\t        2\tAllocateTensors/0\n\nNumber of nodes executed: 2\n============================== Summary by node type ==============================\n\t             [Node type]\t  [count]\t  [avg ms]\t    [avg %]\t    [cdf %]\t  [mem KB]\t[times called]\n\t ModifyGraphWithDelegate\t        1\t   413.346\t    56.670%\t    56.670%\t  1748.000\t        1\n\t         AllocateTensors\t        1\t   316.045\t    43.330%\t   100.000%\t     0.000\t        2\n\nTimings (microseconds): count=1 curr=729391\nMemory (bytes): count=0\n2 nodes observed\n\n\n\nOperator-wise Profiling Info for Regular Benchmark Runs:\n============================== Run Order ==============================\n\t             [node type]\t          [start]\t  [first]\t [avg ms]\t     [%]\t  [cdf%]\t  [mem KB]\t[times called]\t[Name]\n\t           STRIDED_SLICE\t            0.021\t    2.167\t    2.118\t  1.335%\t  1.335%\t     0.000\t        1\t[model/tf.strided_slice/StridedSlice]:0\n\t           STRIDED_SLICE\t            2.141\t    1.032\t    1.020\t  0.643%\t  1.978%\t     0.000\t        1\t[model/tf.strided_slice_1/StridedSlice]:1\n\t           STRIDED_SLICE\t            3.162\t    2.058\t    2.118\t  1.334%\t  3.312%\t     0.000\t        1\t[model/tf.strided_slice_2/StridedSlice3]:2\n\t           STRIDED_SLICE\t            5.281\t    1.097\t    1.030\t  0.649%\t  3.961%\t     0.000\t        1\t[model/tf.strided_slice_3/StridedSlice1]:3\n\t           STRIDED_SLICE\t            6.311\t    0.984\t    1.040\t  0.655%\t  4.616%\t     0.000\t        1\t[model/tf.strided_slice_5/StridedSlice]:4\n\t           STRIDED_SLICE\t            7.352\t    0.996\t    1.024\t  0.645%\t  5.261%\t     0.000\t        1\t[model/tf.strided_slice_7/StridedSlice3]:5\n\t     TfLiteNnapiDelegate\t            8.377\t   78.701\t   79.010\t 49.781%\t 55.042%\t     0.000\t        1\t[model/tf.nn.silu_16/mul, model/tf.nn.silu_26/mul, model/tf.nn.silu_35/mul]:231\n\t RESIZE_NEAREST_NEIGHBOR\t           87.387\t    0.254\t    0.253\t  0.160%\t 55.201%\t     0.000\t        1\t[model/lambda/resize/ResizeNearestNeighbor]:143\n\t     TfLiteNnapiDelegate\t           87.642\t   15.910\t   14.418\t  9.084%\t 64.285%\t     0.000\t        1\t[model/tf.nn.silu_41/mul]:232\n\t RESIZE_NEAREST_NEIGHBOR\t          102.060\t    0.614\t    0.627\t  0.395%\t 64.680%\t     0.000\t        1\t[model/lambda_1/resize/ResizeNearestNeighbor]:165\n\t     TfLiteNnapiDelegate\t          102.688\t   60.220\t   56.058\t 35.320%\t100.000%\t     0.000\t        1\t[Identity, Identity_1, Identity_2]:233\n\n============================== Top by Computation Time ==============================\n\t             [node type]\t          [start]\t  [first]\t [avg ms]\t     [%]\t  [cdf%]\t  [mem KB]\t[times called]\t[Name]\n\t     TfLiteNnapiDelegate\t            8.377\t   78.701\t   79.010\t 49.781%\t 49.781%\t     0.000\t        1\t[model/tf.nn.silu_16/mul, model/tf.nn.silu_26/mul, model/tf.nn.silu_35/mul]:231\n\t     TfLiteNnapiDelegate\t          102.688\t   60.220\t   56.058\t 35.320%\t 85.101%\t     0.000\t        1\t[Identity, Identity_1, Identity_2]:233\n\t     TfLiteNnapiDelegate\t           87.642\t   15.910\t   14.418\t  9.084%\t 94.185%\t     0.000\t        1\t[model/tf.nn.silu_41/mul]:232\n\t           STRIDED_SLICE\t            0.021\t    2.167\t    2.118\t  1.335%\t 95.519%\t     0.000\t        1\t[model/tf.strided_slice/StridedSlice]:0\n\t           STRIDED_SLICE\t            3.162\t    2.058\t    2.118\t  1.334%\t 96.854%\t     0.000\t        1\t[model/tf.strided_slice_2/StridedSlice3]:2\n\t           STRIDED_SLICE\t            6.311\t    0.984\t    1.040\t  0.655%\t 97.509%\t     0.000\t        1\t[model/tf.strided_slice_5/StridedSlice]:4\n\t           STRIDED_SLICE\t            5.281\t    1.097\t    1.030\t  0.649%\t 98.158%\t     0.000\t        1\t[model/tf.strided_slice_3/StridedSlice1]:3\n\t           STRIDED_SLICE\t            7.352\t    0.996\t    1.024\t  0.645%\t 98.803%\t     0.000\t        1\t[model/tf.strided_slice_7/StridedSlice3]:5\n\t           STRIDED_SLICE\t            2.141\t    1.032\t    1.020\t  0.643%\t 99.445%\t     0.000\t        1\t[model/tf.strided_slice_1/StridedSlice]:1\n\t RESIZE_NEAREST_NEIGHBOR\t          102.060\t    0.614\t    0.627\t  0.395%\t 99.840%\t     0.000\t        1\t[model/lambda_1/resize/ResizeNearestNeighbor]:165\n\nNumber of nodes executed: 11\n============================== Summary by node type ==============================\n\t             [Node type]\t  [count]\t  [avg ms]\t    [avg %]\t    [cdf %]\t  [mem KB]\t[times called]\n\t     TfLiteNnapiDelegate\t        3\t   149.483\t    94.187%\t    94.187%\t     0.000\t        3\n\t           STRIDED_SLICE\t        6\t     8.347\t     5.259%\t    99.446%\t     0.000\t        6\n\t RESIZE_NEAREST_NEIGHBOR\t        2\t     0.879\t     0.554%\t   100.000%\t     0.000\t        2\n\nTimings (microseconds): count=50 first=164033 curr=158560 min=146535 max=166638 avg=158715 std=2931\nMemory (bytes): count=0\n11 nodes observed\n```\n\n</div></details>\n\n## int8 model\n\n### None(CPU)\nWhether to enable `--use_xnnpack` did not affect performance.  \n```sh\nadb shell /data/local/tmp/benchmark_model \\\n  --graph=/data/local/tmp/yolov5s_int8_640.tflite \\\n  --num_threads=4 \n```\n```\ncount=50 first=94518 curr=95117 min=93066 max=99490 avg=95788.3 std=1284\nInference timings in us: Init: 1779, First inference: 152949, Warmup (avg): 110690, Inference (avg): 95788.3\nNote: as the benchmark tool itself affects memory footprint, the following is only APPROXIMATE to the actual memory footprint of the model at runtime. Take the information at your discretion.\nPeak memory footprint (MB): init=5.15625 overall=36.9883\n```\n\n### NNAPI qti-default (Not working)\n```sh\nadb shell /data/local/tmp/benchmark_model \\\n  --graph=/data/local/tmp/yolov5s_int8_640.tflite \\\n  --num_threads=4 \\\n  --use_nnapi=true \\\n  --nnapi_execution_preference=sustained_speed \\\n  --nnapi_accelerator_name=qti-default \n```\n```\nSTARTING!\nLog parameter values verbosely: [0]\nNum threads: [4]\nGraph: [/data/local/tmp/yolov5s_int8_640.tflite]\n#threads used for CPU inference: [4]\nUse NNAPI: [1]\nNNAPI execution preference: [sustained_speed]\nNNAPI accelerator name: [qti-default]\nNNAPI accelerators available: [qti-default,qti-dsp,qti-gpu,nnapi-reference]\nLoaded model /data/local/tmp/yolov5s_int8_640.tflite\nINFO: Initialized TensorFlow Lite runtime.\nINFO: Created TensorFlow Lite delegate for NNAPI.\nERROR: NN API returned error ANEURALNETWORKS_OP_FAILED at line 3779 while completing NNAPI compilation.\n\nERROR: Node number 248 (TfLiteNnapiDelegate) failed to prepare.\n\nERROR: Restored original execution plan after delegate application failure.\nFailed to apply NNAPI delegate.\nBenchmarking failed.\n```\n### NNAPI qti-dsp (Not working)\n\n```\nadb shell /data/local/tmp/benchmark_model \\\n  --graph=/data/local/tmp/yolov5s_int8_640.tflite \\\n  --num_threads=4 \\\n  --use_nnapi=true \\\n  --nnapi_execution_preference=sustained_speed \\\n  --nnapi_accelerator_name=qti-dsp \n```\n```\nSTARTING!\nLog parameter values verbosely: [0]\nNum threads: [4]\nGraph: [/data/local/tmp/yolov5s_int8_640.tflite]\n#threads used for CPU inference: [4]\nUse NNAPI: [1]\nNNAPI execution preference: [sustained_speed]\nNNAPI accelerator name: [qti-dsp]\nNNAPI accelerators available: [qti-default,qti-dsp,qti-gpu,nnapi-reference]\nLoaded model /data/local/tmp/yolov5s_int8_640.tflite\nINFO: Initialized TensorFlow Lite runtime.\nINFO: Created TensorFlow Lite delegate for NNAPI.\nNNAPI delegate created.\nERROR: NN API returned error ANEURALNETWORKS_BAD_DATA at line 1068 while adding operation.\n\nERROR: Restored original execution plan after delegate application failure.\nFailed to apply NNAPI delegate.\nBenchmarking failed.\n```\n\n\nMore detail error message is obtained by `adb logcat`. The error is as the following:\n\n<details><summary>adb logcat output</summary><div>\n\n```\n08-17 21:47:49.138 29423 29423 I ExecutionBuilder: add()\n08-17 21:47:49.162 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(DEQUANTIZE) = 0 (qti-dsp)\n08-17 21:47:49.162 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(QUANTIZE) = 0 (qti-dsp)\n08-17 21:47:49.162 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(STRIDED_SLICE) = 0 (qti-dsp)\n08-17 21:47:49.162 29423 29423 I chatty  : uid=2000(shell) /data/local/tmp/benchmark_model identical 4 lines\n08-17 21:47:49.162 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(STRIDED_SLICE) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(CONCATENATION) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(PAD) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(CONV_2D) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(LOGISTIC) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(MUL) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(PAD) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(CONV_2D) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(LOGISTIC) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(MUL) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(CONV_2D) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(LOGISTIC) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(MUL) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(DEQUANTIZE) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(QUANTIZE) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(CONV_2D) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(LOGISTIC) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(MUL) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(CONV_2D) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(LOGISTIC) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(MUL) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(PAD) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(CONV_2D) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(LOGISTIC) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(MUL) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(ADD) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(DEQUANTIZE) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(QUANTIZE) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(CONCATENATION) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(CONV_2D) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(LOGISTIC) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(MUL) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(PAD) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(CONV_2D) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(LOGISTIC) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(MUL) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(CONV_2D) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(LOGISTIC) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(MUL) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(CONV_2D) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(LOGISTIC) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(MUL) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(PAD) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(CONV_2D) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(LOGISTIC) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(MUL) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(ADD) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(CONV_2D) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(LOGISTIC) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(MUL) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(PAD) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(CONV_2D) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(LOGISTIC) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(MUL) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(ADD) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(CONV_2D) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(LOGISTIC) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(MUL) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(PAD) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(CONV_2D) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(LOGISTIC) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(MUL) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(ADD) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(CONV_2D) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(LOGISTIC) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(MUL) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(DEQUANTIZE) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(QUANTIZE) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(CONCATENATION) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(CONV_2D) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(LOGISTIC) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(MUL) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(PAD) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(CONV_2D) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(LOGISTIC) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(MUL) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(CONV_2D) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(LOGISTIC) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(MUL) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(DEQUANTIZE) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(QUANTIZE) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(CONV_2D) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(LOGISTIC) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(MUL) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(CONV_2D) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(LOGISTIC) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(MUL) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(PAD) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(CONV_2D) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(LOGISTIC) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(MUL) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(ADD) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(CONV_2D) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(LOGISTIC) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(MUL) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(PAD) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(CONV_2D) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(LOGISTIC) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(MUL) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(ADD) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(CONV_2D) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(LOGISTIC) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(MUL) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(PAD) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(CONV_2D) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(LOGISTIC) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(MUL) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(ADD) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(CONCATENATION) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(CONV_2D) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(LOGISTIC) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(MUL) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(PAD) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(CONV_2D) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(LOGISTIC) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(MUL) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(CONV_2D) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(LOGISTIC) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(MUL) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(MAX_POOL_2D) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I chatty  : uid=2000(shell) /data/local/tmp/benchmark_model identical 1 line\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(MAX_POOL_2D) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(CONCATENATION) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(CONV_2D) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(LOGISTIC) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(MUL) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(CONV_2D) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(LOGISTIC) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(MUL) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(CONV_2D) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(LOGISTIC) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(MUL) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(CONV_2D) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(LOGISTIC) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(MUL) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(PAD) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(CONV_2D) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(LOGISTIC) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(MUL) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(DEQUANTIZE) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(QUANTIZE) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(CONCATENATION) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(CONV_2D) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(LOGISTIC) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(MUL) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(CONV_2D) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(LOGISTIC) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(MUL) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(DEQUANTIZE) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(QUANTIZE) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(RESIZE_NEAREST_NEIGHBOR) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(DEQUANTIZE) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(QUANTIZE) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(CONCATENATION) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(CONV_2D) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(LOGISTIC) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(MUL) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(DEQUANTIZE) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(QUANTIZE) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(CONV_2D) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(LOGISTIC) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(MUL) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(CONV_2D) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(LOGISTIC) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(MUL) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(PAD) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(CONV_2D) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(LOGISTIC) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(MUL) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(CONCATENATION) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(CONV_2D) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(LOGISTIC) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(MUL) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(CONV_2D) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(LOGISTIC) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(MUL) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(DEQUANTIZE) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(QUANTIZE) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(RESIZE_NEAREST_NEIGHBOR) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(DEQUANTIZE) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(QUANTIZE) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(CONCATENATION) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(CONV_2D) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(LOGISTIC) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(MUL) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(DEQUANTIZE) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(QUANTIZE) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(CONV_2D) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(LOGISTIC) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(MUL) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(CONV_2D) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(LOGISTIC) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(MUL) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(PAD) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(CONV_2D) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(LOGISTIC) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(MUL) = 0 (qti-dsp)\n08-17 21:47:49.163 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(CONCATENATION) = 0 (qti-dsp)\n08-17 21:47:49.164 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(CONV_2D) = 0 (qti-dsp)\n08-17 21:47:49.164 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(LOGISTIC) = 0 (qti-dsp)\n08-17 21:47:49.164 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(MUL) = 0 (qti-dsp)\n08-17 21:47:49.164 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(PAD) = 0 (qti-dsp)\n08-17 21:47:49.164 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(CONV_2D) = 0 (qti-dsp)\n08-17 21:47:49.164 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(LOGISTIC) = 0 (qti-dsp)\n08-17 21:47:49.164 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(MUL) = 0 (qti-dsp)\n08-17 21:47:49.164 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(CONCATENATION) = 0 (qti-dsp)\n08-17 21:47:49.164 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(CONV_2D) = 0 (qti-dsp)\n08-17 21:47:49.164 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(LOGISTIC) = 0 (qti-dsp)\n08-17 21:47:49.164 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(MUL) = 0 (qti-dsp)\n08-17 21:47:49.164 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(DEQUANTIZE) = 0 (qti-dsp)\n08-17 21:47:49.164 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(QUANTIZE) = 0 (qti-dsp)\n08-17 21:47:49.164 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(CONV_2D) = 0 (qti-dsp)\n08-17 21:47:49.164 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(LOGISTIC) = 0 (qti-dsp)\n08-17 21:47:49.164 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(MUL) = 0 (qti-dsp)\n08-17 21:47:49.164 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(CONV_2D) = 0 (qti-dsp)\n08-17 21:47:49.164 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(LOGISTIC) = 0 (qti-dsp)\n08-17 21:47:49.164 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(MUL) = 0 (qti-dsp)\n08-17 21:47:49.164 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(PAD) = 0 (qti-dsp)\n08-17 21:47:49.164 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(CONV_2D) = 0 (qti-dsp)\n08-17 21:47:49.164 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(LOGISTIC) = 0 (qti-dsp)\n08-17 21:47:49.164 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(MUL) = 0 (qti-dsp)\n08-17 21:47:49.164 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(CONCATENATION) = 0 (qti-dsp)\n08-17 21:47:49.164 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(CONV_2D) = 0 (qti-dsp)\n08-17 21:47:49.164 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(LOGISTIC) = 0 (qti-dsp)\n08-17 21:47:49.164 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(MUL) = 0 (qti-dsp)\n08-17 21:47:49.164 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(PAD) = 0 (qti-dsp)\n08-17 21:47:49.164 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(CONV_2D) = 0 (qti-dsp)\n08-17 21:47:49.164 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(LOGISTIC) = 0 (qti-dsp)\n08-17 21:47:49.164 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(MUL) = 0 (qti-dsp)\n08-17 21:47:49.164 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(CONCATENATION) = 0 (qti-dsp)\n08-17 21:47:49.164 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(CONV_2D) = 0 (qti-dsp)\n08-17 21:47:49.164 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(LOGISTIC) = 0 (qti-dsp)\n08-17 21:47:49.164 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(MUL) = 0 (qti-dsp)\n08-17 21:47:49.164 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(DEQUANTIZE) = 0 (qti-dsp)\n08-17 21:47:49.164 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(QUANTIZE) = 0 (qti-dsp)\n08-17 21:47:49.164 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(CONV_2D) = 0 (qti-dsp)\n08-17 21:47:49.164 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(LOGISTIC) = 0 (qti-dsp)\n08-17 21:47:49.164 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(MUL) = 0 (qti-dsp)\n08-17 21:47:49.164 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(CONV_2D) = 0 (qti-dsp)\n08-17 21:47:49.164 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(LOGISTIC) = 0 (qti-dsp)\n08-17 21:47:49.164 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(MUL) = 0 (qti-dsp)\n08-17 21:47:49.164 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(PAD) = 0 (qti-dsp)\n08-17 21:47:49.164 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(CONV_2D) = 0 (qti-dsp)\n08-17 21:47:49.164 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(LOGISTIC) = 0 (qti-dsp)\n08-17 21:47:49.164 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(MUL) = 0 (qti-dsp)\n08-17 21:47:49.164 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(CONCATENATION) = 0 (qti-dsp)\n08-17 21:47:49.164 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(CONV_2D) = 0 (qti-dsp)\n08-17 21:47:49.164 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(LOGISTIC) = 0 (qti-dsp)\n08-17 21:47:49.164 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(MUL) = 0 (qti-dsp)\n08-17 21:47:49.164 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(CONV_2D) = 0 (qti-dsp)\n08-17 21:47:49.164 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(DEQUANTIZE) = 0 (qti-dsp)\n08-17 21:47:49.164 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(CONV_2D) = 0 (qti-dsp)\n08-17 21:47:49.164 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(DEQUANTIZE) = 0 (qti-dsp)\n08-17 21:47:49.164 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(CONV_2D) = 0 (qti-dsp)\n08-17 21:47:49.164 29423 29423 I ExecutionPlan: ModelBuilder::findBestDeviceForEachOperation(DEQUANTIZE) = 0 (qti-dsp)\n08-17 21:47:49.164 29423 29423 I ExecutionPlan: ModelBuilder::partitionTheWork: only one best device: 0 = qti-dsp\n08-17 21:47:49.164 29423 29423 I ExecutionPlan: ExecutionPlan::SimpleBody::finish, compilation\n08-17 21:47:49.164 29423 29423 I ExecutionBuilder: add()\n08-17 21:47:49.164 29423 29423 I ExecutionBuilder: It's new\n08-17 21:47:49.164 29423 29423 I ExecutionBuilder: add()\n08-17 21:47:49.164 29423 29423 I chatty  : uid=2000(shell) /data/local/tmp/benchmark_model identical 42 lines\n08-17 21:47:49.164 29423 29423 I ExecutionBuilder: add()\n08-17 21:47:49.214   928 16565 E QnnDsp  : /prj/qct/webtech_yyz5/pcgbait/projects/qnn_qnn-0.7.8/QNN/DSP/HTP/src/hexagon/include/nn_axis.h:13:Axis value 8 is out of range. Must be in the range -4 < axis < 4\n08-17 21:47:49.214   928 16565 E QnnDsp  : \n08-17 21:47:49.214   928 16565 I chatty  : uid=1000(system) HwBinder:928_1 identical 4 lines\n08-17 21:47:49.214   928 16565 E QnnDsp  : /prj/qct/webtech_yyz5/pcgbait/projects/qnn_qnn-0.7.8/QNN/DSP/HTP/src/hexagon/include/nn_axis.h:13:Axis value 8 is out of range. Must be in the range -4 < axis < 4\n08-17 21:47:49.214   928 16565 E QnnDsp  : \n08-17 21:47:49.335   575   575 E SELinux : avc:  denied  { find } for pid=5278 uid=10208 name=tethering scontext=u:r:vendor_systemhelper_app:s0:c512,c768 tcontext=u:object_r:tethering_service:s0 tclass=service_manager permissive=0\n08-17 21:47:49.737   575   575 I chatty  : uid=1000(system) /system/bin/servicemanager identical 2 lines\n08-17 21:47:49.939   575   575 E SELinux : avc:  denied  { find } for pid=5278 uid=10208 name=tethering scontext=u:r:vendor_systemhelper_app:s0:c512,c768 tcontext=u:object_r:tethering_service:s0 tclass=service_manager permissive=0\n08-17 21:47:50.009  1712  2451 I MiuiNetworkPolicy: bandwidth: 0 KB/s, Max bandwidth: 2068 KB/s\n08-17 21:47:50.142   575   575 E SELinux : avc:  denied  { find } for pid=5278 uid=10208 name=tethering scontext=u:r:vendor_systemhelper_app:s0:c512,c768 tcontext=u:object_r:tethering_service:s0 tclass=service_manager permissive=0\n08-17 21:47:50.179 29423 29423 I ExecutionPlan: ModelBuilder::partitionTheWork: source model: \n08-17 21:47:50.179 29423 29423 I ExecutionBuilder: add()\n08-17 21:47:50.179 29423 29423 I ExecutionBuilder: It's new\n08-17 21:47:50.179 29423 29423 I ExecutionBuilder: add()\n08-17 21:47:50.180 29423 29423 I chatty  : uid=2000(shell) /data/local/tmp/benchmark_model identical 129 lines\n08-17 21:47:50.180 29423 29423 I ExecutionBuilder: add()\n08-17 21:47:50.180 29423 29423 I Utils   : V1_3::Model start\n08-17 21:47:50.186 29423 29423 I Utils   : main.operands[769]{{.type = TENSOR_QUANT8_ASYMM, .dimensions = [4]{1, 320, 320, 3}, .numberOfConsumers = 1, .scale = 0.003922, .zeroPoint = 0, .lifetime = SUBGRAPH_INPUT, .location = {.poolIndex = 0, .offset = 0, .length = 0}, .extraParams = {.none = {}}}, {.type = TENSOR_FLOAT32, .dimensions = [4]{1, 320, 320, 3}, .numberOfConsumers = 1, .scale = 0.000000, .zeroPoint = 0, .lifetime = TEMPORARY_VARIABLE, .location = {.poolIndex = 0, .offset = 0, .length = 0}, .extraParams = {.none = {}}}, {.type = TENSOR_QUANT8_ASYMM_SIGNED, .dimensions = [4]{1, 320, 320, 3}, .numberOfConsumers = 2, .scale = 0.003922, .zeroPoint = -128, .lifetime = TEMPORARY_VARIABLE, .location = {.poolIndex = 0, .offset = 0, .length = 0}, .extraParams = {.none = {}}}, {.type = TENSOR_INT32, .dimensions = [1]{4}, .numberOfConsumers = 3, .scale = 0.000000, .zeroPoint = 0, .lifetime = CONSTANT_REFERENCE, .location = {.poolIndex = 0, .offset = 44960, .length = 16}, .extraParams = {.none = {}}}, {.type = TENSOR_INT32, .dimensions = [1]{4}, .numberOfConsumers = 2, .scale = 0.000000, .zeroPoint = 0, .lifetime = CONSTANT_REFERENCE, .location = {.poolIndex = 0, .offset = 45016, .length = 16}, .extraParams = {.none = {}}}, {.type = TENSOR_INT32, .dimensions = [1]{4}, .numberOfConsumers = 2, .scale = 0.000000, .zeroPoint = 0, .lifetime = CONSTANT_REFERENCE, .location = {.poolIndex = 0, .offset = 44988, .length = 16}, .extraParams = {.none = {}}}, {.type = INT32, .dimensions = [0]{}, .numberOfConsumers = 1, .scale = 0.000000, .zeroPoint = 0, .lifetime = CONSTANT_COPY, .location = {.poolIndex = 0, .offset = 0, .length = 4}, .extraParams = {.none = {}}}, {.type = INT32, .dimensions = [0]{}, .numberOfConsumers = 1, .scale = 0.000000, .zeroPoint = 0, .lifetime = CONSTANT_COPY, .location = {.poolIndex = 0, .offset = 4, .length = 4}, .extraParams = {.none = {}}}, {.type = INT32, .dimensions = [0]{}, .numberOfConsumers = 1, .scale = 0.000000, .zeroPoint = 0, .lifetime = CONSTANT_COPY, .location = {.poolIndex = 0, .offset = 8, .length = 4}, .extraParams = {.none = {}}}, {.type = TENSOR_QUANT8_ASYMM_SIGNED, .dimensions = [4]{1, 160, 320, 3}, .numberOfConsumers = 2, .scale = 0.003922, .zeroPoint = -128, .lifetime = TEMPORARY_VARIABLE, .location = {.poolIndex = 0, .offset = 0, .length = 0}, .extraParams = {.none = {}}}, {.type = TENSOR_INT32, .dimensions = [1]{4}, .numberOfConsumers = 4, .scale = 0.000000, .zeroPoint = 0, .lifetime = CONSTANT_REFERENCE, .location = {.poolIndex = 0, .offset = 44904, .length = 16}, .extraParams = {.none = {}}}, {.type = TENSOR_INT32, .dimensions = [1]{4}, .numberOfConsumers = 4, .scale = 0.000000, .zeroPoint = 0, .lifetime = CONSTANT_REFERENCE, .location = {.poolIndex = 0, .offset = 44876, .length = 16}, .extraParams = {.none = {}}}, {.type = INT32, .dimensions = [0]{}, .numberOfConsumers = 1, .scale = 0.000000, .zeroPoint = 0, .lifetime = CONSTANT_COPY, .location = {.poolIndex = 0, .offset = 12, .length = 4}, .extraParams = {.none = {}}}, {.type = INT32, .dimensions = [0]{}, .numberOfConsumers = 1, .scale = 0.000000, .zeroPoint = 0, .lifetime = CONSTANT_COPY, .location = {.poolIndex = 0, .offset = 16, .length = 4}, .extraParams = {.none = {}}}, {.type = INT32, .dimensions = [0]{}, .numberOfConsumers = 1, .scale = 0.000000, .zeroPoint = 0, .lifetime = CONSTANT_COPY, .location = {.poolIndex = 0, .offset = 20, .length = 4}, .extraParams = {.none = {}}}, {.type = TENSOR_QUANT8_ASYMM_SIGNED, .dimensions = [4]{1, 160, 160, 3}, .numberOfConsumers = 1, .scale = 0.003922, .zeroPoint = -128, .lifetime = TEMPORARY_VARIABLE, .location = {.poolIndex = 0, .offset = 0, .length = 0}, .extraParams = {.none = {}}}, {.type = TENSOR_INT32, .dimensions = [1]{4}, .numberOfConsumers = 1, .scale = 0.000000, .zeroPoint = 0, .lifetime = CONSTANT_REFERENCE, .location = {.poolIndex = 0, .offset = 45044, .length = 16}, .extraParams = {.none = {}}}, {.type = INT32, .dimensions = [0]{}, .numberOfConsumers = 1, .scale = 0.000000, .zeroPoint = 0, .lifetime = CONSTANT_COPY, .location = {.poolIndex = 0, .offset = 24, .length = 4}, .extraParam\n08-17 21:47:50.187 29423 29423 I Utils   : main.operations[262]{{.type = DEQUANTIZE, .inputs = [1]{0}, .outputs = [1]{1}}, {.type = QUANTIZE, .inputs = [1]{1}, .outputs = [1]{2}}, {.type = STRIDED_SLICE, .inputs = [7]{2, 16, 4, 5, 17, 18, 19}, .outputs = [1]{20}}, {.type = STRIDED_SLICE, .inputs = [7]{20, 25, 10, 11, 30, 31, 32}, .outputs = [1]{33}}, {.type = STRIDED_SLICE, .inputs = [7]{20, 3, 10, 11, 21, 22, 23}, .outputs = [1]{24}}, {.type = STRIDED_SLICE, .inputs = [7]{2, 3, 4, 5, 6, 7, 8}, .outputs = [1]{9}}, {.type = STRIDED_SLICE, .inputs = [7]{9, 25, 10, 11, 26, 27, 28}, .outputs = [1]{29}}, {.type = STRIDED_SLICE, .inputs = [7]{9, 3, 10, 11, 12, 13, 14}, .outputs = [1]{15}}, {.type = CONCATENATION, .inputs = [5]{15, 24, 29, 33, 34}, .outputs = [1]{35}}, {.type = PAD, .inputs = [2]{35, 36}, .outputs = [1]{37}}, {.type = CONV_2D, .inputs = [7]{37, 38, 39, 40, 41, 42, 43}, .outputs = [1]{44}}, {.type = LOGISTIC, .inputs = [1]{44}, .outputs = [1]{45}}, {.type = MUL, .inputs = [3]{44, 45, 46}, .outputs = [1]{47}}, {.type = PAD, .inputs = [2]{47, 36}, .outputs = [1]{48}}, {.type = CONV_2D, .inputs = [7]{48, 49, 50, 51, 52, 53, 54}, .outputs = [1]{55}}, {.type = LOGISTIC, .inputs = [1]{55}, .outputs = [1]{56}}, {.type = MUL, .inputs = [3]{55, 56, 57}, .outputs = [1]{58}}, {.type = CONV_2D, .inputs = [7]{58, 79, 80, 81, 82, 83, 84}, .outputs = [1]{85}}, {.type = LOGISTIC, .inputs = [1]{85}, .outputs = [1]{86}}, {.type = MUL, .inputs = [3]{85, 86, 87}, .outputs = [1]{88}}, {.type = DEQUANTIZE, .inputs = [1]{88}, .outputs = [1]{102}}, {.type = QUANTIZE, .inputs = [1]{102}, .outputs = [1]{103}}, {.type = CONV_2D, .inputs = [7]{58, 59, 60, 61, 62, 63, 64}, .outputs = [1]{65}}, {.type = LOGISTIC, .inputs = [1]{65}, .outputs = [1]{66}}, {.type = MUL, .inputs = [3]{65, 66, 67}, .outputs = [1]{68}}, {.type = CONV_2D, .inputs = [7]{68, 69, 70, 71, 72, 73, 74}, .outputs = [1]{75}}, {.type = LOGISTIC, .inputs = [1]{75}, .outputs = [1]{76}}, {.type = MUL, .inputs = [3]{75, 76, 77}, .outputs = [1]{78}}, {.type = PAD, .inputs = [2]{78, 36}, .outputs = [1]{89}}, {.type = CONV_2D, .inputs = [7]{89, 90, 91, 92, 93, 94, 95}, .outputs = [1]{96}}, {.type = LOGISTIC, .inputs = [1]{96}, .outputs = [1]{97}}, {.type = MUL, .inputs = [3]{96, 97, 98}, .outputs = [1]{99}}, {.type = ADD, .inputs = [3]{68, 99, 100}, .outputs = [1]{101}}, {.type = DEQUANTIZE, .inputs = [1]{101}, .outputs = [1]{104}}, {.type = QUANTIZE, .inputs = [1]{104}, .outputs = [1]{105}}, {.type = CONCATENATION, .inputs = [3]{105, 103, 106}, .outputs = [1]{107}}, {.type = CONV_2D, .inputs = [7]{107, 108, 109, 110, 111, 112, 113}, .outputs = [1]{114}}, {.type = LOGISTIC, .inputs = [1]{114}, .outputs = [1]{115}}, {.type = MUL, .inputs = [3]{114, 115, 116}, .outputs = [1]{117}}, {.type = PAD, .inputs = [2]{117, 36}, .outputs = [1]{118}}, {.type = CONV_2D, .inputs = [7]{118, 119, 120, 121, 122, 123, 124}, .outputs = [1]{125}}, {.type = LOGISTIC, .inputs = [1]{125}, .outputs = [1]{126}}, {.type = MUL, .inputs = [3]{125, 126, 127}, .outputs = [1]{128}}, {.type = CONV_2D, .inputs = [7]{128, 139, 140, 141, 142, 143, 144}, .outputs = [1]{145}}, {.type = LOGISTIC, .inputs = [1]{145}, .outputs = [1]{146}}, {.type = MUL, .inputs = [3]{145, 146, 147}, .outputs = [1]{148}}, {.type = CONV_2D, .inputs = [7]{148, 149, 150, 151, 152, 153, 154}, .outputs = [1]{155}}, {.type = LOGISTIC, .inputs = [1]{155}, .outputs = [1]{156}}, {.type = MUL, .inputs = [3]{155, 156, 157}, .outputs = [1]{158}}, {.type = PAD, .inputs = [2]{158, 36}, .outputs = [1]{159}}, {.type = CONV_2D, .inputs = [7]{159, 160, 161, 162, 163, 164, 165}, .outputs = [1]{166}}, {.type = LOGISTIC, .inputs = [1]{166}, .outputs = [1]{167}}, {.type = MUL, .inputs = [3]{166, 167, 168}, .outputs = [1]{169}}, {.type = ADD, .inputs = [3]{148, 169, 170}, .outputs = [1]{171}}, {.type = CONV_2D, .inputs = [7]{171, 172, 173, 174, 175, 176, 177}, .outputs = [1]{178}}, {.type = LOGISTIC, .inputs = [1]{178}, .outputs = [1]{179}}, {.type = MUL, .inputs = [3]{178, 179, 180}, .outputs = [1]{181}}, {.type = PAD, .inputs = [2]{181, 36}, .outputs = [1]{182}}, {.t\n08-17 21:47:50.187 29423 29423 I Utils   : main.inputIndexes[1]{0}\n08-17 21:47:50.187 29423 29423 I Utils   : main.outputIndexes[3]{766, 767, 768}\n08-17 21:47:50.187 29423 29423 I Utils   : operandValues size 1480\n08-17 21:47:50.187 29423 29423 I Utils   : pools\n08-17 21:47:50.187 29423 29423 I Utils   : relaxComputationFloat32toFloat16 0\n08-17 21:47:50.187 29423 29423 I Utils   : extensionNameToPrefix[0]{}\n08-17 21:47:50.187 29423 29423 I ExecutionPlan: SIMPLE for qti-dsp\n08-17 21:47:50.198 29423 29423 I ExecutionBuilder: ExecutionBuilder::ExecutionBuilder with 1 inputs and 3 outputs\n08-17 21:47:50.198 29423 29423 I ExecutionBuilder: add()\n08-17 21:47:50.198 29423 29423 I ExecutionBuilder: It's new\n08-17 21:47:50.198 29423 29423 I ExecutionBuilder: add()\n08-17 21:47:50.198 29423 29423 I ExecutionBuilder: It's new\n08-17 21:47:50.198 29423 29423 I ExecutionBuilder: add()\n08-17 21:47:50.198 29423 29423 I ExecutionBuilder: add()\n08-17 21:47:50.198 29423 29423 I ExecutionBuilder: ExecutionBuilder::compute (synchronous API)\n08-17 21:47:50.198 29423 29423 I ExecutionBuilder: ExecutionBuilder::compute (from plan, iteratively)\n08-17 21:47:50.198 29423 29423 I ExecutionBuilder: looking for next StepExecutor\n08-17 21:47:50.198 29423 29423 I ExecutionPlan: ExecutionPlan::next(): mNextStepIndex = 0\n08-17 21:47:50.199 29423 29423 I ExecutionBuilder: StepExecutor::StepExecutor with 1 inputs and 3 outputs\n08-17 21:47:50.199 29423 29423 I ExecutionBuilder: input[0] = MEMORY(pool=0, off=0)\n08-17 21:47:50.199 29423 29423 I ExecutionBuilder: output[0] = MEMORY(pool=1, off=0)\n08-17 21:47:50.199 29423 29423 I ExecutionBuilder: output[1] = MEMORY(pool=1, off=1632000)\n08-17 21:47:50.199 29423 29423 I ExecutionBuilder: output[2] = MEMORY(pool=1, off=2040000)\n08-17 21:47:50.199 29423 29423 I VersionedInterfaces: Before executeSynchronously() \n08-17 21:47:50.312   928 29327 E android.hardware.neuralnetworks@1.3-service-qti: vendor/qcom/proprietary/commonsys-intf/adsprpc/src/fastrpc_apps_user.c:1076: Error 0xffffffff: remote_handle_invoke failed for handle 0x3, method 4 on domain 3 (sc 0x4020200) (errno Operation not permitted)\n08-17 21:47:50.313   928  1099 E android.hardware.neuralnetworks@1.3-service-qti: vendor/qcom/proprietary/commonsys-intf/adsprpc/src/fastrpc_apps_user.c:1096: Error 0xffffffff: remote_handle64_invoke failed for handle 0x7a207410, method 4 on domain 3 (sc 0x4030300) (errno Operation not permitted)\n08-17 21:47:50.313   928  1099 E HtpError: bool qti::nnhal::htp::HtpModel::execute(qti::nnhal::IGraph *, qti::nnhal::ExecutionPerformance, v2_0::IExecuteParameters *) in file vendor/qcom/proprietary/ml/nnhal/qti-htp/src/HtpModel.cpp on line 554 : HTP execution with err 5032\n08-17 21:47:50.313   928  1099 E android.hardware.neuralnetworks@1.3-service: NnHalExecutorHost::asyncExecute accelerator execute() failed\n08-17 21:47:50.313   928 29327 E android.hardware.neuralnetworks@1.3-service-qti: vendor/qcom/proprietary/commonsys-intf/adsprpc/src/fastrpc_apps_user.c:1076: Error 0x27: remote_handle_invoke failed for handle 0x3, method 4 on domain 3 (sc 0x4020200) (errno Success)\n08-17 21:47:50.313   928 29327 E android.hardware.neuralnetworks@1.3-service-qti: vendor/qcom/proprietary/commonsys-intf/adsprpc/src/listener_android.c:143:Error 0x27: listener response with result 0x0 for ctx 0xd147, handle 0x7e5ede48, sc 0xffffffff failed\n08-17 21:47:50.314   928 29327 E android.hardware.neuralnetworks@1.3-service-qti: vendor/qcom/proprietary/commonsys-intf/adsprpc/src/listener_android.c:229:Error 0x27: listener thread exited (errno Success)\n08-17 21:47:50.315 29423 29423 I Manager : **Execution failed**\n08-17 21:47:50.315 29423 29423 E tflite  : NN API returned error ANEURALNETWORKS_OP_FAILED at line 4113 while running computation.\n08-17 21:47:50.315 29423 29423 E tflite  : Node number 248 (TfLiteNnapiDelegate) failed to invoke.\n08-17 21:47:50.315 29423 29423 I ExecutionBuilder: ExecutionBuilder::ExecutionBuilder with 1 inputs and 3 outputs\n08-17 21:47:50.315 29423 29423 I ExecutionBuilder: add()\n08-17 21:47:50.315 29423 29423 I ExecutionBuilder: It's new\n08-17 21:47:50.315 29423 29423 I ExecutionBuilder: add()\n08-17 21:47:50.315 29423 29423 I ExecutionBuilder: It's new\n08-17 21:47:50.315 29423 29423 I ExecutionBuilder: add()\n08-17 21:47:50.315 29423 29423 I ExecutionBuilder: add()\n08-17 21:47:50.316 29423 29423 I ExecutionBuilder: ExecutionBuilder::compute (synchronous API)\n08-17 21:47:50.316 29423 29423 I ExecutionBuilder: ExecutionBuilder::compute (from plan, iteratively)\n08-17 21:47:50.316 29423 29423 I ExecutionBuilder: looking for next StepExecutor\n08-17 21:47:50.316 29423 29423 I ExecutionPlan: ExecutionPlan::next(): mNextStepIndex = 0\n08-17 21:47:50.316 29423 29423 I ExecutionBuilder: StepExecutor::StepExecutor with 1 inputs and 3 outputs\n08-17 21:47:50.316 29423 29423 I ExecutionBuilder: input[0] = MEMORY(pool=0, off=0)\n08-17 21:47:50.316 29423 29423 I ExecutionBuilder: output[0] = MEMORY(pool=1, off=0)\n08-17 21:47:50.316 29423 29423 I ExecutionBuilder: output[1] = MEMORY(pool=1, off=1632000)\n08-17 21:47:50.316 29423 29423 I ExecutionBuilder: output[2] = MEMORY(pool=1, off=2040000)\n08-17 21:47:50.316 29423 29423 I VersionedInterfaces: Before executeSynchronously() \n08-17 21:47:50.316   928 29327 E android.hardware.neuralnetworks@1.3-service-qti: vendor/qcom/proprietary/commonsys-intf/adsprpc/src/fastrpc_apps_user.c:1096: Error 0x27: remote_handle64_invoke failed for handle 0x6bcc19f0, method 3 on domain 3 (sc 0x3000000) (errno Success)\n08-17 21:47:50.318   928  1099 E android.hardware.neuralnetworks@1.3-service-qti: vendor/qcom/proprietary/commonsys-intf/adsprpc/src/fastrpc_apps_user.c:1096: Error 0x27: remote_handle64_invoke failed for handle 0x7a207410, method 4 on domain 3 (sc 0x4030300) (errno Success)\n08-17 21:47:50.319   928  1099 E QnnDsp  :  <E> CDSP crashed\n08-17 21:47:50.319   928  1099 E HtpError: bool qti::nnhal::htp::HtpModel::execute(qti::nnhal::IGraph *, qti::nnhal::ExecutionPerformance, v2_0::IExecuteParameters *) in file vendor/qcom/proprietary/ml/nnhal/qti-htp/src/HtpModel.cpp on line 554 : HTP execution with err 5032\n08-17 21:47:50.319   928  1099 E android.hardware.neuralnetworks@1.3-service: NnHalExecutorHost::asyncExecute accelerator execute() failed\n08-17 21:47:50.323 29423 29423 I Manager : **Execution failed**\n08-17 21:47:50.323 29423 29423 E tflite  : NN API returned error ANEURALNETWORKS_OP_FAILED at line 4113 while running computation.\n08-17 21:47:50.323 29423 29423 E tflite  : Node number 248 (TfLiteNnapiDelegate) failed to invoke.\n```\n\n</div></details>"
  },
  {
    "path": "convert_model/README.md",
    "content": "# Model Conversion (PyTorch -> ONNX -> OpenVino -> Tensorflow -> TfLite)\n## Why we convert the model via OpenVino format?\nAs you know, PyTorch have the `NCHW` layout, and Tensorfloww have `NHWC` layout.  \n[`onnx-tf`](https://github.com/onnx/onnx-tensorflow) supports model conversion from onnx to tensorflow, but the converted model includes a lot of `Transpose` layer because of the layout difference.  \nBy using OpenVino's excellent model optimizer and [`openvino2tensorflow`](https://github.com/PINTO0309/openvino2tensorflow), we can obtain a model without unnecessary transpose layers.  \nFor more information, please refer this article by the developer of `openvino2tensorflow` : [Converting PyTorch, ONNX, Caffe, and OpenVINO (NCHW) models to Tensorflow / TensorflowLite (NHWC) in a snap](https://qiita.com/PINTO/items/ed06e03eb5c007c2e102)\n  \n## Docker build\n```sh\ngit clone --recursive https://github.com/lp6m/yolov5s_android\ncd yolov5s_android\ndocker build ./ -f ./docker/Dockerfile  -t yolov5s_android\ndocker run -it --gpus all -v `pwd`:/workspace yolov5s_android bash\n```\nThe following process is performed in docker container.  \n\n## PyTorch -> ONNX\nDownload the pytorch pretrained weights and export to ONNX format.  \n```sh\ncd yolov5\n./data/scripts/download_weights.sh #modify 'python' to 'python3' if needed\npython3 export.py --weights ./yolov5s.pt --img-size 640 640 --simplify\n```\n\n## ONNX -> OpenVino\n```sh\npython3 /opt/intel/openvino_2021.3.394/deployment_tools/model_optimizer/mo.py \\\n --input_model yolov5s.onnx \\\n --input_shape [1,3,640,640] \\\n --output_dir ./openvino \\\n --data_type FP32 \\\n --output Conv_245,Conv_325,Conv_405\n```\nYou will get `yolov5s.bin  yolov5s.mapping  yolov5s.xml` as OpenVino model.  \nIf you use the other verion yolov5, you have to check the output layer IDs in netron.  \nThe output layers are three most bottom Convolution layers. \n```sh\nnetron yolov5s.onnx\n```\n<img src=\"https://github.com/lp6m/yolov5s_android/raw/media/onnx_output_layers.png\" width=50%> \n  \nIn this model, the output layer IDs are `Conv_245,Conv_325,Conv_405`.  \n**We convert the ONNX model without detect head layers.**\n### Why we exclude detect head layers?\nNNAPI does not support some layers included in detect head layers.  \nFor example, The number of dimension supported by [ANEURALNETWORKS_MUL](https://developer.android.com/ndk/reference/group/neural-networks#group___neural_networks_1ggaabbe492c60331b13038e39d4207940e0ab34ca99890c827b536ce66256a803d7a) operator for multiply layer is up to 4.  \nThe input of multiply layer in detect head layers has 5 dimension, so NNAPI delegate cannot load the model.  \nWe tried to include detect head layers into tflite [in other method](https://github.com/lp6m/yolov5s_android/issues/2), but not successful yet.\n  \nFor the inference, the calculation of detect head layers are implemented outside of the tflite model.  \nFor Android, the detect head layer is [implemented in C++ and executed on the CPU through JNI](https://github.com/lp6m/yolov5s_android/blob/host/app/tflite_yolov5_test/app/src/main/cpp/postprocess.cpp).  \nFor host evaluation, we use [PyTorch model](https://github.com/lp6m/yolov5s_android/blob/host/host/detector_head.py) ported from original yolov5 repository.\n\n\n## OpenVino -> TfLite\nConvert OpenVino model to Tensorflow and TfLite by using `openvino2tensorflow`.\n```sh\nsource /opt/intel/openvino_2021/bin/setupvars.sh \nexport PYTHONPATH=/opt/intel/openvino_2021/python/python3.6/:$PYTHONPATH\nopenvino2tensorflow \\\n--model_path ./openvino/yolov5s.xml \\\n--model_output_path tflite \\\n--output_pb \\\n--output_saved_model \\\n--output_no_quant_float32_tflite \n```\nYou will get `model_float32.pb, model_float32.tflite`.  \n\n### Quantize model\nLoad the tensorflow frozen graph model (pb) obtained by the previous step, and quantize the model.  \nThe precision of input layer is `uint8`. The precision of the output layer is `float32` for commonalize the postprocess implemented in C++(JNI). \nFor calibration process in quantization, you have to prepare coco dataset in tfds format.  \n```sh\ncd ../convert_model\nusage: quantize.py [-h] [--input_size INPUT_SIZE] [--pb_path PB_PATH]\n                   [--output_path OUTPUT_PATH] [--calib_num CALIB_NUM]\n                   [--tfds_root TFDS_ROOT] [--download_tfds]\n\noptional arguments:\n  -h, --help            show this help message and exit\n  --input_size INPUT_SIZE\n  --pb_path PB_PATH\n  --output_path OUTPUT_PATH\n  --calib_num CALIB_NUM\n                        number of images for calibration.\n  --tfds_root TFDS_ROOT\n  --download_tfds       download tfds. it takes a lot of time.\n```\n```sh\npython3 quantize.py --input_size 640 --pb_path /workspace/yolov5/tflite/model_float32.pb \\\n--output_path /workspace/yolov5/tflite/model_quantized.tflite\n--calib_num 100\n```\n"
  },
  {
    "path": "convert_model/quantize.py",
    "content": "import argparse\nimport sys\nimport os\n\nimport numpy as np\nimport tensorflow as tf\nimport tensorflow_datasets as tfds\n\ndef quantize_model(INPUT_SIZE, pb_path, output_path, calib_num, tfds_root, download_flag):\n    raw_test_data = tfds.load(name='coco/2017',\n                                    with_info=False,\n                                    split='validation',\n                                    data_dir=tfds_root,\n                                    download=download_flag)\n    input_shapes = [(3, INPUT_SIZE, INPUT_SIZE)]\n    def representative_dataset_gen():\n        for i, data in enumerate(raw_test_data.take(calib_num)):\n            print('calibrating...', i)\n            image = data['image'].numpy()\n            images = []\n            for shape in input_shapes:\n                data = tf.image.resize(image, (shape[1], shape[2]))\n                tmp_image = data / 255.\n                tmp_image = tmp_image[np.newaxis,:,:,:]\n                images.append(tmp_image)\n            yield images\n    \n    input_arrays = ['inputs']\n    output_arrays = ['Identity', 'Identity_1', 'Identity_2']\n    converter = tf.compat.v1.lite.TFLiteConverter.from_frozen_graph(pb_path, input_arrays, output_arrays)\n    converter.experimental_new_quantizer = False\n    converter.optimizations = [tf.lite.Optimize.DEFAULT]\n    converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]\n    converter.allow_custom_ops = False\n    converter.inference_input_type = tf.uint8\n    # To commonalize postprocess, output_type is float32\n    converter.inference_output_type = tf.float32\n    converter.representative_dataset = representative_dataset_gen\n    tflite_model = converter.convert()\n    with open(output_path, 'wb') as w:\n        w.write(tflite_model)\n    print('Quantization Completed!', output_path)\n\nif __name__ == '__main__':\n    import argparse\n    parser = argparse.ArgumentParser()\n    parser.add_argument('--input_size', type=int, default=640)\n    parser.add_argument('--pb_path', default=\"/workspace/yolov5/tflite/model_float32.pb\")\n    parser.add_argument('--output_path', default='/workspace/yolov5/tflite/model_quantized.tflite')\n    parser.add_argument('--calib_num', type=int, default=100, help='number of images for calibration.')\n    parser.add_argument('--tfds_root', default='/workspace/TFDS/')\n    parser.add_argument('--download_tfds', action='store_true', help='download tfds. it takes a lot of time.')\n    args = parser.parse_args()\n    quantize_model(args.input_size, args.pb_path, args.output_path, args.calib_num, args.tfds_root, args.download_tfds)\n\n\n"
  },
  {
    "path": "docker/Dockerfile",
    "content": "FROM nvidia/cuda:10.2-cudnn8-devel-ubuntu18.04\n\nENV DEBIAN_FRONTEND noninteractive\n\nRUN apt-get update --fix-missing\nRUN apt-get install -y python3 python3-pip\nRUN pip3 install --upgrade pip\nRUN pip3 install tensorflow-gpu==2.4.0\nRUN pip3 install torch==1.7.0\nRUN pip3 install tensorflow-datasets\n\n# install openvino\nENV http_proxy $HTTP_PROXY\nENV https_proxy $HTTP_PROXY\nARG DOWNLOAD_LINK=https://registrationcenter-download.intel.com/akdlm/irc_nas/17662/l_openvino_toolkit_p_2021.3.394.tgz\nARG INSTALL_DIR=/opt/intel/computer_vision_sdk\nARG TEMP_DIR=/tmp/openvino_installer\nRUN apt-get update && apt-get install -y --no-install-recommends \\\n    wget \\\n    cpio \\\n    sudo \\\n    lsb-release && \\\n    rm -rf /var/lib/apt/lists/*\nRUN mkdir -p $TEMP_DIR && cd $TEMP_DIR && \\\n    wget -c $DOWNLOAD_LINK && \\\n    tar xf l_openvino_toolkit*.tgz && \\\n    cd l_openvino_toolkit* && \\\n    sed -i 's/decline/accept/g' silent.cfg && \\\n    ./install.sh -s silent.cfg && \\\n    rm -rf $TEMP_DIR\nRUN pip3 install networkx\nRUN pip3 install defusedxml\n\n# install requirements for yolov5\nCOPY yolov5/requirements.txt /yolov5/requirements.txt\nRUN cd /yolov5 && pip3 install -r requirements.txt\nRUN apt-get update --fix-missing\nRUN apt-get install -y libgl1 libgtk-3-0 libgtk-3-dev\nRUN pip3 install pycocotools\n\n# install requirements for model conversion\nRUN pip3 install netron\nRUN pip3 install onnx\nRUN pip3 install onnx-simplifier\nRUN pip3 install tensorflow-datasets\nRUN pip3 install openvino2tensorflow==1.17.2\nRUN pip3 install gdown\n\n# Add a user that UID:GID will be updated by vscode\nARG USERNAME=developer\nARG GROUPNAME=developer\nARG UID=1000\nARG GID=1000\nARG PASSWORD=developer\nRUN groupadd -g $GID $GROUPNAME && \\\n    useradd -m -s /bin/bash -u $UID -g $GID -G sudo $USERNAME && \\\n    echo $USERNAME:$PASSWORD | chpasswd && \\\n    echo \"$USERNAME   ALL=(ALL) NOPASSWD:ALL\" >> /etc/sudoers\nUSER $USERNAME\nENV HOME /home/developer\n"
  },
  {
    "path": "host/README.md",
    "content": "## Detect\nRun detection for image.  \n```sh\nusage: detect.py [-h] [--image IMAGE] [-m MODEL_PATH]\n                 [--output_path OUTPUT_PATH] [--conf_thres CONF_THRES]\n                 [--iou_thres IOU_THRES]\n\noptional arguments:\n  -h, --help            show this help message and exit\n  --image IMAGE\n  -m MODEL_PATH, --model_path MODEL_PATH\n  --output_path OUTPUT_PATH\n  --conf_thres CONF_THRES\n  --iou_thres IOU_THRES\n```\n### Example\n```sh\npython3 detect.py --image dog.jpg\nbicycle [tensor(108.2864), tensor(134.7546), tensor(487.6782), tensor(595.6481)]\n(108, 134) (179, 116)\ntruck [tensor(389.3984), tensor(83.5512), tensor(575.9429), tensor(190.0590)]\n(389, 83) (443, 65)\ndog [tensor(113.6239), tensor(236.7202), tensor(261.3951), tensor(604.4341)]\n(113, 236) (152, 218)\n```\nResults:  \n![result.jpg](https://github.com/lp6m/yolov5s_android/raw/media/host/result.jpg)\n\n## Evaluate\nYou can perform inference and evaluation for COCO dataset by using pycocotools.  \nThe inference results are saved to `--output_json_path`.\n```sh\nusage: evaluate.py [-h] [-m MODEL_PATH]\n                   [--path_to_annotation PATH_TO_ANNOTATION]\n                   [--coco_root COCO_ROOT] [--mode {run,loadjson}]\n                   [--load_json_path LOAD_JSON_PATH]\n                   [--output_json_path OUTPUT_JSON_PATH]\n                   [--run_image_num RUN_IMAGE_NUM] [--conf_thres CONF_THRES]\n                   [--iou_thres IOU_THRES]\n\noptional arguments:\n  -h, --help            show this help message and exit\n  -m MODEL_PATH, --model_path MODEL_PATH\n  --path_to_annotation PATH_TO_ANNOTATION\n  --coco_root COCO_ROOT\n  --mode {run,loadjson}\n                        'run': evaluate inference results by running tflite\n                        model. 'loadjson': load inference results from json\n  --load_json_path LOAD_JSON_PATH\n                        inference results of coco format json for 'loadjson'\n                        mode.\n  --output_json_path OUTPUT_JSON_PATH\n                        save inference results for 'run' mode\n  --run_image_num RUN_IMAGE_NUM\n                        if specified, use first 'run_image_num' images for\n                        evaluation\n  --conf_thres CONF_THRES\n  --iou_thres IOU_THRES\n```\n\n### Example1\n```\npython3 evaluate.py --mode run  --model_path /workspace/yolov5/tflite/model_float32.tflite --output_json_path coco_5000.json\nLoading and preparing results...\nDONE (t=0.25s)\ncreating index...\nindex created!\nRunning per image evaluation...\nEvaluate annotation type *bbox*\nDONE (t=11.08s).\nAccumulating evaluation results...\nDONE (t=1.64s).\n Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.278\n Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.409\n Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.306\n Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.137\n Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.335\n Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.348\n Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.230\n Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.330\n Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.335\n Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.159\n Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.393\n Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.424\n```\n  \n### Example2\nYou can load and evaluate json files in cocodt format.  \nThis is how to evaluate the inference results by Android app:  \n1. Install the app, and copy validation images like `val2017.zip` to the android device.\n1. Launch the app, open the home tab.\n1. Tap the `Open Directory` Button, and tap `Run Inference` Button.\n1. Inference results for each image is previewed to the app.\n1. After the inference process is completed, `result.json` file is saved to the selected image directory.\n1. Copy `results.json` from the android device, and run the following command.\n```\npython3 evaluate.py --mode loadjson --load_json_path result.json\n```\n`results/android_result.json` in this repository is evaluated on the app at `NNAPI fp16` mode.  \nResults:\n```\nAverage Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.285\nAverage Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.422\nAverage Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.311\nAverage Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.144\nAverage Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.336\nAverage Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.363\nAverage Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.244\nAverage Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.344\nAverage Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.348\nAverage Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.171\nAverage Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.401\nAverage Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.458\n```\n\n\n"
  },
  {
    "path": "host/cococlass.txt",
    "content": "person\nbicycle\ncar\nmotorbike\naeroplane\nbus\ntrain\ntruck\nboat\ntraffic light\nfire hydrant\nstop sign\nparking meter\nbench\nbird\ncat\ndog\nhorse\nsheep\ncow\nelephant\nbear\nzebra\ngiraffe\nbackpack\numbrella\nhandbag\ntie\nsuitcase\nfrisbee\nskis\nsnowboard\nsports ball\nkite\nbaseball bat\nbaseball glove\nskateboard\nsurfboard\ntennis racket\nbottle\nwine glass\ncup\nfork\nknife\nspoon\nbowl\nbanana\napple\nsandwich\norange\nbroccoli\ncarrot\nhot dog\npizza\ndonut\ncake\nchair\nsofa\npottedplant\nbed\ndiningtable\ntoilet\ntvmonitor\nlaptop\nmouse\nremote\nkeyboard\ncell phone\nmicrowave\noven\ntoaster\nsink\nrefrigerator\nbook\nclock\nvase\nscissors\nteddy bear\nhair drier\ntoothbrush"
  },
  {
    "path": "host/detect.py",
    "content": "from PIL import Image\nimport cv2\n\nfrom runner import TfLiteRunner\nfrom postprocess import plot_and_save \n\n\nif __name__ == '__main__':\n    import argparse\n    parser = argparse.ArgumentParser()\n    parser.add_argument('--image', default=\"dog.jpg\")\n    parser.add_argument('-m', '--model_path', default=\"/workspace/yolov5/tflite/model_float32.tflite\")\n    parser.add_argument('--input_size', type=int, default=640)\n    parser.add_argument('--output_path', default='result.jpg')\n    parser.add_argument('--conf_thres', type=float, default=0.25)\n    parser.add_argument('--iou_thres', type=float, default=0.45)\n    parser.add_argument('--quantize_mode', action='store_true')\n    args = parser.parse_args()\n\n    runner = TfLiteRunner(args.model_path, args.input_size, args.conf_thres, args.iou_thres, args.quantize_mode)\n    img = Image.open(args.image)\n    bboxres = runner.detect(img, from_pil_img=True)\n    img_cv = cv2.imread(args.image)\n    img_cv = cv2.resize(img_cv, (args.input_size, args.input_size))\n    plot_and_save(bboxres, img_cv, args.output_path)\n"
  },
  {
    "path": "host/detector_head.py",
    "content": "import torch\nimport torch.nn as nn\nimport numpy as np\n\nclass Detect(nn.Module):\n    def __init__(self, nc=80, INPUT_SIZE=640):\n        super(Detect, self).__init__()\n        self.nc = nc\n        self.no = nc + 5\n        self.nl = 3 #number of outputs per anchor\n        self.na = 3\n        self.grid = [Detect._make_grid(n, n) for n in [80, 40, 20]]\n        self.stride = [8, 16, 32]\n        anchorgrid = [\n            [10, 13, 16, 30, 33, 23], #80\n            [30, 61, 62, 45, 59, 119], #40\n            [116, 90, 156, 198, 373, 326] #20\n        ]\n        self.anchor_grid = [\n            torch.from_numpy(np.reshape(np.array(ag, dtype='float32'), (1, 3, 1, 1, 2)))\n            for ag in anchorgrid\n        ]\n    \n    def forward(self, x):\n        z = []\n        for i in range(self.nl):\n            # bs, 20, 20, 255\n            bs, ny, nx, _ = x[i].shape\n            # bs, 20, 20, 3, 85 -> bs, 3, 20, 20, 85\n            x[i] = x[i].reshape(bs, ny, nx, 3, 85).permute(0, 3, 1, 2, 4).contiguous()\n            if self.grid[i].shape[2:4] != x[i].shape[2:4]:\n                self.grid[i] = self._make_grid(nx, ny).to(x[i].device)\n\n            y = x[i].sigmoid()\n            y[..., 0:2] = (y[..., 0:2] * 2. - 0.5 + self.grid[i].to(x[i].device)) * self.stride[i]  # xy\n            y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i]  # wh\n            z.append(y.view(bs, -1, self.no))\n        return torch.cat(z, 1)\n    \n    @staticmethod\n    def _make_grid(nx=20, ny=20):\n        yv, xv = torch.meshgrid([torch.arange(ny), torch.arange(nx)])\n        return torch.stack((xv, yv), 2).view((1, 1, ny, nx, 2)).float()"
  },
  {
    "path": "host/evaluate.py",
    "content": "import torch\n\nfrom metric import CocoMetric\n\nfrom runner import TfLiteRunner\n\nimport torch.utils.data\nimport torchvision.transforms\nimport torchvision.datasets\n\ndef evaluate_coco_dataset(\n    model_path,\n    input_size,\n    coco_root,\n    path_to_annotation,\n    conf_thres,\n    iou_thres,\n    result_output_path=None,\n    validation_num=-1,\n    quantize_mode=False\n    ):\n    transform = torchvision.transforms.Compose([\n        torchvision.transforms.ToTensor()\n    ])\n    valdata_set = torchvision.datasets.CocoDetection(root=coco_root,\n                                        annFile=path_to_annotation,\n                                        transform=transform)\n    data_loader = torch.utils.data.DataLoader(valdata_set)\n    runner = TfLiteRunner(model_path, input_size, conf_thres, iou_thres, quantize_mode)\n    metric = CocoMetric(path_to_annotation, input_size)\n    cnt = 0\n    for data, target in data_loader:\n        print(cnt)\n        if validation_num >= 0 and cnt >= validation_num:\n            break\n        _, _, im_h, im_w = data.shape\n        if len(target) <= 0:\n            continue\n        bboxes = runner.detect(data)\n        image_id = int(target[0]['image_id'])\n        metric.add_bboxes(bboxes, (im_h, im_w), image_id)\n        cnt = cnt + 1\n    metric.summarize(result_output_path)\n\nif __name__ == '__main__':\n    import argparse\n    parser = argparse.ArgumentParser()\n    parser.add_argument('-m', '--model_path', default=\"/workspace/yolov5/tflite/model_float32.tflite\")\n    parser.add_argument('--input_size', type=int, default=640)\n    parser.add_argument('--path_to_annotation', default='/workspace/coco/instances_val2017.json')\n    parser.add_argument('--coco_root', default='/workspace/coco/val2017')\n    parser.add_argument('--mode', choices=['run', 'loadjson'], default='run', help=\"'run': evaluate inference results by running tflite model. 'loadjson': load inference results from json\")\n    parser.add_argument('--load_json_path', default=None, help=\"inference results of coco format json for 'loadjson' mode.\")\n    parser.add_argument('--output_json_path', default=None, help=\"save inference results for 'run' mode\")\n    parser.add_argument('--run_image_num', default=-1, type=int, help=\"if specified, use first 'run_image_num' images for evaluation\")\n    parser.add_argument('--conf_thres', type=float, default=0.25)\n    parser.add_argument('--iou_thres', type=float, default=0.45)\n    parser.add_argument('--quantize_mode', action='store_true')\n    args = parser.parse_args()\n\n    if args.mode == 'run':\n        evaluate_coco_dataset(args.model_path,\n                              args.input_size,\n                              args.coco_root,\n                              args.path_to_annotation,\n                              args.conf_thres,\n                              args.iou_thres,\n                              args.output_json_path,\n                              args.run_image_num,\n                              args.quantize_mode)\n    elif args.mode == 'loadjson':\n        assert(args.load_json_path is not None)\n        metric = CocoMetric(args.path_to_annotation)\n        metric.load_results_from_json(args.load_json_path)\n        metric.summarize()\n"
  },
  {
    "path": "host/metric.py",
    "content": "import json\n\nfrom pycocotools.coco import COCO\nfrom pycocotools.cocoeval import COCOeval\n\n\nclass CocoMetric():\n    @staticmethod\n    def coco80_to_coco91_class():  # converts 80-index (val2014) to 91-index (paper)\n    # https://tech.amikelive.com/node-718/what-object-categories-labels-are-in-coco-dataset/\n        x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 28, 31, 32, 33, 34,\n            35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63,\n            64, 65, 67, 70, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90]\n        return x\n\n    def __init__(self, path_to_annotation , INPUT_SIZE=640):\n        \"\"\"\n\n        Parameters\n        ----------\n        path_to_annotation : [str]\n            path to COCO Val2017 ground truth json: instances_val2017.json\n        \"\"\"\n        self.cocoGt = COCO(path_to_annotation)\n        self.results = []\n        self.imgIds = set()\n        self.INPUT_SIZE = INPUT_SIZE\n\n    def load_results_from_json(self, json_path):\n        \"\"\"\n        load detection results from COCO detection format json file.\n        \"\"\"\n        assert(len(self.results) == 0 and len(self.imgIds) == 0)\n        self.results = json.load(open(json_path, 'r'))\n        for result in self.results:\n            self.imgIds.add(result['image_id'])\n\n        print(f'loaded json contains inference result for {len(self.imgIds)}.')\n\n    def add_bboxes(self, bboxes, im_shape, image_id):\n        # add bboxes for one input image\n        # for now, we assume batch_size is 1\n        # TODO: support multi-batch size\n        bboxes = bboxes[0] \n        im_h, im_w = im_shape\n        for bbox in bboxes:\n            box_dict = {}\n            x1, y1, x2, y2, score, class_idx = bbox.numpy()\n            x1 = min(max(x1, 0), self.INPUT_SIZE)\n            y1 = min(max(y1, 0), self.INPUT_SIZE)\n            x2 = min(max(x2, 0), self.INPUT_SIZE)\n            y2 = min(max(y2, 0), self.INPUT_SIZE)\n            x = float(x1) * im_w / self.INPUT_SIZE\n            y = float(y1) * im_h / self.INPUT_SIZE\n            w = float(x2 - x1) * im_w / self.INPUT_SIZE\n            h = float(y2 - y1) * im_h / self.INPUT_SIZE\n            box_dict['image_id'] = image_id\n            box_dict['bbox'] = [x, y, w, h]\n            box_dict['category_id'] = CocoMetric.coco80_to_coco91_class()[int(class_idx)]\n            box_dict['score'] = float(score)\n            self.results.append(box_dict)\n        self.imgIds.add(image_id)\n\n    def summarize(self, output_json_path = None):\n        import tempfile\n        with tempfile.TemporaryDirectory() as tmp_dir:\n            if output_json_path is None:\n                output_json_path = tmp_dir + '/' + 'result.json'\n            json.dump(self.results, open(output_json_path, 'w'))\n            cocoDt = self.cocoGt.loadRes(output_json_path)\n            self.cocoEval = COCOeval(self.cocoGt, cocoDt, 'bbox')\n            if self.imgIds is not None:\n                self.cocoEval.params.imgIds = list(self.imgIds)\n            # calculate only 80 class, not 90 class \n            self.cocoEval.params.catIds = list(set(CocoMetric.coco80_to_coco91_class()))\n            self.cocoEval.evaluate()\n            self.cocoEval.accumulate()\n            self.cocoEval.summarize()\n"
  },
  {
    "path": "host/postprocess.py",
    "content": "import numpy as np\nimport torch\nimport torchvision\n\ndef xywh2xyxy(x):\n    # Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right\n    y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)\n    y[:, 0] = x[:, 0] - x[:, 2] / 2  # top left x\n    y[:, 1] = x[:, 1] - x[:, 3] / 2  # top left y\n    y[:, 2] = x[:, 0] + x[:, 2] / 2  # bottom right x\n    y[:, 3] = x[:, 1] + x[:, 3] / 2  # bottom right y\n    return y\n\n\ndef box_iou(box1, box2):\n    # https://github.com/pytorch/vision/blob/master/torchvision/ops/boxes.py\n    \"\"\"\n    Return intersection-over-union (Jaccard index) of boxes.\n    Both sets of boxes are expected to be in (x1, y1, x2, y2) format.\n    Arguments:\n        box1 (Tensor[N, 4])\n        box2 (Tensor[M, 4])\n    Returns:\n        iou (Tensor[N, M]): the NxM matrix containing the pairwise\n            IoU values for every element in boxes1 and boxes2\n    \"\"\"\n\n    def box_area(box):\n        # box = 4xn\n        return (box[2] - box[0]) * (box[3] - box[1])\n\n    area1 = box_area(box1.T)\n    area2 = box_area(box2.T)\n\n    # inter(N,M) = (rb(N,M,2) - lt(N,M,2)).clamp(0).prod(2)\n    inter = (torch.min(box1[:, None, 2:], box2[:, 2:]) -\n             torch.max(box1[:, None, :2], box2[:, :2])).clamp(0).prod(2)\n    # iou = inter / (area1 + area2 - inter)\n    return inter / (area1[:, None] + area2 - inter)\n\n\ndef non_max_suppression(prediction, conf_thres=0.25, iou_thres=0.45, classes=None,\n                        agnostic=False, multi_label=False, labels=(), max_det=300):\n    \"\"\"Runs Non-Maximum Suppression (NMS) on inference results\n    Returns:\n         list of detections, on (n,6) tensor per image [xyxy, conf, cls]\n    \"\"\"\n    nc = prediction.shape[2] - 5  # number of classes\n    xc = prediction[..., 4] > conf_thres  # candidates\n    # Checks\n    assert 0 <= conf_thres <= 1, f'Invalid Confidence threshold {conf_thres}, valid values are between 0.0 and 1.0'\n    assert 0 <= iou_thres <= 1, f'Invalid IoU {iou_thres}, valid values are between 0.0 and 1.0'\n\n    # Settings\n    # (pixels) minimum and maximum box width and height\n    min_wh, max_wh = 2, 4096\n    max_nms = 30000  # maximum number of boxes into torchvision.ops.nms()\n    time_limit = 10.0  # seconds to quit after\n    redundant = True  # require redundant detections\n    multi_label &= nc > 1  # multiple labels per box (adds 0.5ms/img)\n    merge = False  # use merge-NMS\n\n    output = [torch.zeros((0, 6), device=prediction.device)\n              ] * prediction.shape[0]\n    for xi, x in enumerate(prediction):  # image index, image inference\n        # Apply constraints\n        x = x[xc[xi]]  # confidence\n        \n        # If none remain process next image\n        if not x.shape[0]:\n            continue\n\n        # Compute conf\n        x[:, 5:] *= x[:, 4:5]  # conf = obj_conf * cls_conf\n\n        # Box (center x, center y, width, height) to (x1, y1, x2, y2)\n        box = xywh2xyxy(x[:, :4])\n\n        # Detections matrix nx6 (xyxy, conf, cls)\n        if multi_label:\n            i, j = (x[:, 5:] > conf_thres).nonzero(as_tuple=False).T\n            x = torch.cat((box[i], x[i, j + 5, None], j[:, None].float()), 1)\n        else:  # best class only\n            conf, j = x[:, 5:].max(1, keepdim=True)\n            x = torch.cat((box, conf, j.float()), 1)[\n                conf.view(-1) > conf_thres]\n\n        # Filter by class\n        if classes is not None:\n            x = x[(x[:, 5:6] == torch.tensor(classes, device=x.device)).any(1)]\n\n        # Check shape\n        n = x.shape[0]  # number of boxes\n        if not n:  # no boxes\n            continue\n        elif n > max_nms:  # excess boxes\n            # sort by confidence\n            x = x[x[:, 4].argsort(descending=True)[:max_nms]]\n\n        # Batched NMS\n        c = x[:, 5:6] * (0 if agnostic else max_wh)  # classes\n        # boxes (offset by class), scores\n        boxes, scores = x[:, :4] + c, x[:, 4]\n        i = torchvision.ops.nms(boxes, scores, iou_thres)  # NMS\n        if i.shape[0] > max_det:  # limit detections\n            i = i[:max_det]\n        if merge and (1 < n < 3E3):  # Merge NMS (boxes merged using weighted mean)\n            # update boxes as boxes(i,4) = weights(i,n) * boxes(n,4)\n            iou = box_iou(boxes[i], boxes) > iou_thres  # iou matrix\n            weights = iou * scores[None]  # box weights\n            x[i, :4] = torch.mm(weights, x[:, :4]).float(\n            ) / weights.sum(1, keepdim=True)  # merged boxes\n            if redundant:\n                i = i[iou.sum(1) > 1]  # require redundancy\n\n        output[xi] = x[i]\n\n    return output\n\n\nimport cv2\ndef plot_one_box(x, im, color=(128, 128, 128), label=None, line_thickness=3):\n    # Plots one bounding box on image 'im' using OpenCV\n    assert im.data.contiguous, 'Image not contiguous. Apply np.ascontiguousarray(im) to plot_on_box() input image.'\n    tl = line_thickness or round(0.002 * (im.shape[0] + im.shape[1]) / 2) + 1  # line/font thickness\n    c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3]))\n    cv2.rectangle(im, c1, c2, color, thickness=tl, lineType=cv2.LINE_AA)\n    if label:\n        tf = max(tl - 1, 1)  # font thickness\n        t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0]\n        c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3\n        cv2.rectangle(im, c1, c2, color, -1, cv2.LINE_AA)  # filled\n        print(c1, c2)\n        cv2.putText(im, label, (c1[0], c1[1] - 2), 0, tl / 3, [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA)\n\ndef clip_coords(boxes, shape):\n    # Clip bounding xyxy bounding boxes to image shape (height, width)\n    if isinstance(boxes, torch.Tensor):  # faster individually\n        boxes[:, 0].clamp_(0, shape[1])  # x1\n        boxes[:, 1].clamp_(0, shape[0])  # y1\n        boxes[:, 2].clamp_(0, shape[1])  # x2\n        boxes[:, 3].clamp_(0, shape[0])  # y2\n    else:  # np.array (faster grouped)\n        boxes[:, [0, 2]] = boxes[:, [0, 2]].clip(0, shape[1])  # x1, x2\n        boxes[:, [1, 3]] = boxes[:, [1, 3]].clip(0, shape[0])  # y1, y2\n\ndef scale_coords(img1_shape, coords, img0_shape, ratio_pad=None):\n    # Rescale coords (xyxy) from img1_shape to img0_shape\n    if ratio_pad is None:  # calculate from img0_shape\n        gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1])  # gain  = old / new\n        pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2  # wh padding\n    else:\n        gain = ratio_pad[0][0]\n        pad = ratio_pad[1]\n\n    coords[:, [0, 2]] -= pad[0]  # x padding\n    coords[:, [1, 3]] -= pad[1]  # y padding\n    coords[:, :4] /= gain\n    clip_coords(coords, img0_shape)\n    return coords\n\n\nclass Colors:\n    # Ultralytics color palette https://ultralytics.com/\n    def __init__(self):\n        # hex = matplotlib.colors.TABLEAU_COLORS.values()\n        hex = ('FF3838', 'FF9D97', 'FF701F', 'FFB21D', 'CFD231', '48F90A', '92CC17', '3DDB86', '1A9334', '00D4BB',\n               '2C99A8', '00C2FF', '344593', '6473FF', '0018EC', '8438FF', '520085', 'CB38FF', 'FF95C8', 'FF37C7')\n        self.palette = [self.hex2rgb('#' + c) for c in hex]\n        self.n = len(self.palette)\n\n    def __call__(self, i, bgr=False):\n        c = self.palette[int(i) % self.n]\n        return (c[2], c[1], c[0]) if bgr else c\n\n    @staticmethod\n    def hex2rgb(h):  # rgb order (PIL)\n        return tuple(int(h[1 + i:1 + i + 2], 16) for i in (0, 2, 4))\n\ncolors = Colors()\n\ndef plot_and_save(bboxres, input_img, path):\n    names = open('./cococlass.txt').readlines()\n    for i, det in enumerate(bboxres):  # detections per image\n\n        gn = torch.tensor(input_img.shape)[[1, 0, 1, 0]]  # normalization gain whwh\n        if len(det):\n            # Rescale boxes from img_size to im0 size\n            # det[:, :4] = scale_coords(input_img.shape[1:], det[:, :4], input_img.shape).round()\n            # det[:, :4] = scale_coords(input_img.shape[2:], det[:, :4], input_img.shape).round()\n\n            # Print results\n            for c in det[:, -1].unique():\n                n = (det[:, -1] == c).sum()  # detections per class\n\n            # Write results\n            for *xyxy, conf, cls in reversed(det):\n                c = int(cls)  # integer class\n                # label = None if hide_labels else (names[c] if hide_conf else f'{names[c]} {conf:.2f}')\n                label = names[c].strip()\n                print(label, xyxy)\n                plot_one_box(xyxy, input_img, label=label, color=colors(c, True), line_thickness=2)\n                # plot_one_box(xyxy, im0, label=label, color=colors(c, True), line_thickness=line_thickness)\n                # save_one_box(xyxy, imc, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True)\n    cv2.imwrite(path, input_img)"
  },
  {
    "path": "host/results/android_result.json",
    "content": 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  },
  {
    "path": "host/runner.py",
    "content": "import cv2\nimport numpy as np\nimport tensorflow as tf\nimport torch\nimport torchvision\n\nfrom postprocess import non_max_suppression\nfrom detector_head import Detect\nfrom PIL import Image\n\nclass TfLiteRunner():\n    def __init__(self, model_path, INPUT_SIZE=640, conf_thres=0.25, iou_thres=0.45, quantize_mode=False, is_output_quantized=False):\n        self.INPUT_SIZE = INPUT_SIZE\n        self.interpreter = tf.lite.Interpreter(model_path, num_threads=8)\n        self.interpreter.allocate_tensors()\n\n        self.input_details = self.interpreter.get_input_details()\n        self.output_tensor_names = [\n            'Identity',\n            'Identity_1',\n            'Identity_2'\n        ]\n        # reorder output details because sometimes output_details() order is unintended order.\n        output_details = self.interpreter.get_output_details()\n        self.output_details = [\n            next(filter(lambda detail: detail['name'] == name, output_details), None)\n            for name in self.output_tensor_names\n        ]\n        assert (None not in self.output_details), \"model does not contain specified 'output_tensor_names'  \"\n\n        self.input_shape = self.input_details[0]['shape']\n        self.detector = Detect(nc=80, INPUT_SIZE=INPUT_SIZE)\n\n        self.conf_thres = conf_thres\n        self.iou_thres = iou_thres\n        self.quantize_mode = quantize_mode\n        self.is_output_quantized = is_output_quantized\n\n    def __run_tflite(self, input_data):\n        assert(isinstance(input_data, np.ndarray))\n        assert(input_data.shape == (1, self.INPUT_SIZE, self.INPUT_SIZE, 3))\n        \n        input_dtype = np.uint8 if self.quantize_mode else np.float32 \n        self.interpreter.set_tensor(self.input_details[0]['index'], input_data.astype(input_dtype))\n        self.interpreter.invoke()\n\n        output_datas = []\n        for output_detail in self.output_details:\n            output_data = self.interpreter.get_tensor(output_detail['index'])\n            if self.is_output_quantized:\n                # dequantize\n                scale, zero_point = output_detail['quantization']\n                print(f'scale = {scale}, zero_point = {zero_point}')\n                output_data = output_data.astype(np.float32)\n                output_data = (output_data - zero_point) * scale\n            output_datas.append(output_data)\n        return output_datas\n\n    def __preprocess(self, input_data, from_pil_img):\n        if from_pil_img:\n            # PIL image\n            input_data = input_data.resize((self.INPUT_SIZE, self.INPUT_SIZE))\n            input_data = np.array(input_data)\n            input_data = input_data[np.newaxis, ...]\n            if not self.quantize_mode:\n                preprocessed = input_data / 255.0\n            else:\n                scale, zero_point = self.input_details[0]['quantization']\n                # scale is always 1/255, and zero_point is always 0\n                print(f'scale = {scale}, zero_point = {zero_point}')\n                input_data = input_data / 255.0\n                input_data = input_data / scale + zero_point\n                preprocessed = input_data.astype(np.uint8)\n        else:\n            # torchvision dataset\n            input_data = torchvision.transforms.functional.resize(input_data, (self.INPUT_SIZE, self.INPUT_SIZE))\n            preprocessed = np.transpose(input_data.numpy(), (0, 2, 3, 1))\n            if self.quantize_mode:\n                preprocessed = np.clip(preprocessed * 255, 0, 255)\n                preprocessed = preprocessed.astype(np.uint8)\n        return preprocessed\n\n    def detect(self, input_data, from_pil_img=False):\n        preprocessed = self.__preprocess(input_data, from_pil_img)\n        tflite_out = self.__run_tflite(preprocessed)\n        tflite_out = [torch.from_numpy(x) for x in tflite_out]\n        detector_out = self.detector(tflite_out)\n        bboxres = non_max_suppression(detector_out, conf_thres=self.conf_thres, iou_thres=self.iou_thres)\n        return bboxres"
  }
]