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devzwydevzwy
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重构库，升级到1.3.0
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Lines changed: 1028 additions & 883 deletions

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‎.idea/codeStyles/Project.xml‎

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‎.idea/gradle.xml‎

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nsfw/.gitignore renamed to OpenNSFW/.gitignore

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nsfw/build.gradle renamed to OpenNSFW/build.gradle

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@@ -3,39 +3,36 @@ apply plugin: 'kotlin-android'
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apply plugin: 'kotlin-android-extensions'
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android {
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compileSdkVersion 28
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compileSdkVersion 29
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buildToolsVersion "29.0.3"
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defaultConfig {
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minSdkVersion 19
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targetSdkVersion 28
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versionCode 6
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versionName "1.2.9"
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minSdkVersion 16
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targetSdkVersion 29
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versionCode 1
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versionName "1.0"
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testInstrumentationRunner "android.support.test.runner.AndroidJUnitRunner"
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consumerProguardFiles 'consumer-rules.pro'
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}
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buildTypes {
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debug {
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minifyEnabled false //开启混淆®
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release {
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minifyEnabled false
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proguardFiles getDefaultProguardFile('proguard-android-optimize.txt'), 'proguard-rules.pro'
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}
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}
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kotlin {
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experimental {
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coroutines 'enable'
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}
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}
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}
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dependencies {
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implementation fileTree(dir: 'libs', include: ['*.jar'])
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implementation "org.jetbrains.kotlin:kotlin-stdlib-jdk7:$kotlin_version"
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//noinspection GradleCompatible
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implementation 'com.android.support:appcompat-v7:28.0.0'
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testImplementation 'junit:junit:4.12'
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testImplementation 'junit:junit:4.13'
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androidTestImplementation 'com.android.support.test:runner:1.0.2'
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androidTestImplementation 'com.android.support.test.espresso:espresso-core:3.0.2'
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implementation 'org.tensorflow:tensorflow-lite:1.14.0'
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implementation 'org.tensorflow:tensorflow-lite-gpu:1.14.0'
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implementation "org.jetbrains.kotlin:kotlin-stdlib-jdk7:$kotlin_version"
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implementation 'org.tensorflow:tensorflow-lite:2.1.0'
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implementation 'org.tensorflow:tensorflow-lite-gpu:2.1.0'
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}
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repositories {
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mavenCentral()
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}

‎OpenNSFW/consumer-rules.pro‎

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nsfw/proguard-rules.pro renamed to OpenNSFW/proguard-rules.pro

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# If you keep the line number information, uncomment this to
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# hide the original source file name.
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#-renamesourcefileattribute SourceFile
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-keep class com.zwy.nsfw.api.**{*;}

‎OpenNSFW/src/androidTest/java/com/zwy/opennsfw/ExampleInstrumentedTest.kt‎

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package com.zwy.opennsfw
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import android.support.test.InstrumentationRegistry
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import android.support.test.runner.AndroidJUnit4
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import org.junit.Assert.assertEquals
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import org.junit.Test
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import org.junit.runner.RunWith
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/**
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* Instrumented test, which will execute on an Android device.
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*
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* See [testing documentation](http://d.android.com/tools/testing).
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*/
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@RunWith(AndroidJUnit4::class)
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class ExampleInstrumentedTest {
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@Test
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fun useAppContext() {
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// Context of the app under test.
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val appContext = InstrumentationRegistry.getInstrumentation().targetContext
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assertEquals("com.zwy.opennsfw.test", appContext.packageName)
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}
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}

‎OpenNSFW/src/main/AndroidManifest.xml‎

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<manifest
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package="com.zwy.opennsfw"/>

nsfw/src/main/assets/nsfw.tflite renamed to OpenNSFW/src/main/assets/nsfw.tflite

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‎OpenNSFW/src/main/java/com/zwy/opennsfw/Classifier.kt‎

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//package com.zwy.opennsfw
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//
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//import android.content.Context
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//import android.content.res.AssetManager
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//import android.graphics.Bitmap
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//import android.graphics.Color
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//import android.os.SystemClock
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//import android.util.Log
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//import org.tensorflow.lite.Interpreter
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//import org.tensorflow.lite.gpu.GpuDelegate
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//import java.io.FileInputStream
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//import java.lang.Math.max
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//import java.nio.ByteBuffer
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//import java.nio.ByteOrder
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//import java.nio.MappedByteBuffer
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//import java.nio.channels.FileChannel
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//
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//class Classifier
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//private constructor(assetManager: AssetManager, isGPU: Boolean?, numThreads: Int) {
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//
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// /**
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// * 数据宽高
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// */
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// private val INPUT_WIDTH = 224
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//
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// /**
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// * 数据宽高
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// */
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// private val INPUT_HEIGHT = 224
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//
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// /**
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// * 通道
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// */
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// private val BYTES_PER_CHANNEL_NUM = 4
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//
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// /**
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// * Resize后的数据源
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// */
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// private val intValues = IntArray(INPUT_WIDTH * INPUT_HEIGHT)
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//
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// /**
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// * 载入模型的客户端
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// */
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// private var tfliteModel: MappedByteBuffer? = null
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//
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// /**
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// * GPU代理
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// */
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// private var gpuDelegate: GpuDelegate? = null
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//
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// /**
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// * Tensorflow Lite
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// */
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// private var tflite: Interpreter? = null
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//
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// /**
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// * 喂入模型的最终数据源
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// */
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// private val imgData: ByteBuffer?
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//
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//
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// init {
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// tfliteModel = loadModelFile(assetManager)
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// val tfliteOptions = Interpreter.Options()
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// if (isGPU == true) {
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// gpuDelegate = GpuDelegate()
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// tfliteOptions.addDelegate(gpuDelegate)
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// }
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// tfliteOptions.setNumThreads(numThreads)
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// tflite = Interpreter(tfliteModel!!, tfliteOptions)
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//
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// val tensor = tflite!!.getInputTensor(tflite!!.getInputIndex("input"))
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// val stringBuilder = (" \n"
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// + "dataType : " +
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// tensor.dataType() +
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// "\n" +
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// "numBytes : " +
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// tensor.numBytes() +
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// "\n" +
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// "numDimensions : " +
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// tensor.numDimensions() +
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// "\n" +
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// "numElements : " +
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// tensor.numElements() +
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// "\n" +
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// "shape : " +
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// tensor.shape().size)
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// Log.d(TAG, stringBuilder)
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//
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// imgData = ByteBuffer.allocateDirect(
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// DIM_BATCH_SIZE
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// * INPUT_WIDTH
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// * INPUT_HEIGHT
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// * DIM_PIXEL_SIZE
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// * BYTES_PER_CHANNEL_NUM
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// )
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//
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// imgData!!.order(ByteOrder.LITTLE_ENDIAN)
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// Log.d(TAG, "Tensorflow Lite Image Classifier Initialization Success.")
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// }
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//
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// /**
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// * Memory-map the model file in Assets.
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// */
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// private fun loadModelFile(assetManager: AssetManager): MappedByteBuffer {
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// val context:Context
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// val fileDescriptor = assetManager.openFd("nsfw.tflite")
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// val inputStream = FileInputStream(fileDescriptor.fileDescriptor)
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// val fileChannel = inputStream.channel
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// val startOffset = fileDescriptor.startOffset
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// val declaredLength = fileDescriptor.declaredLength
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// return fileChannel.map(FileChannel.MapMode.READ_ONLY, startOffset, declaredLength)
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// }
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//
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//
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// /**
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// * Writes Image data into a `ByteBuffer`.
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// */
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// private fun convertBitmapToByteBuffer(bitmap_: Bitmap) {
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// if (imgData == null || bitmap_ == null) {
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// return
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// }
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// imgData.rewind()
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// val W = bitmap_.width
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// val H = bitmap_.height
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//
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// val w_off = max((W - INPUT_WIDTH) / 2, 0)
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// val h_off = max((H - INPUT_HEIGHT) / 2, 0)
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//
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// //把每个像素的颜色值转为int 存入intValues
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// bitmap_.getPixels(intValues, 0, INPUT_WIDTH, h_off, w_off, INPUT_WIDTH, INPUT_HEIGHT)
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// // Convert the image to floating point.
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// val startTime = SystemClock.uptimeMillis()
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// for (color in intValues) {
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// val r1 = Color.red(color)
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// val g1 = Color.green(color)
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// val b1 = Color.blue(color)
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//
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// val rr1 = r1 - 123
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// val gg1 = g1 - 117
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// val bb1 = b1 - 104
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//
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// imgData.putFloat(bb1.toFloat())
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// imgData.putFloat(gg1.toFloat())
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// imgData.putFloat(rr1.toFloat())
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// }
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// val endTime = SystemClock.uptimeMillis()
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// Log.d(TAG, "Timecost to put values into ByteBuffer: " + (endTime - startTime) + "ms")
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// }
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//
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// fun run(bitmap: Bitmap): NsfwBean {
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//
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// val bitmap_256 = Bitmap.createScaledBitmap(bitmap, 256, 256, true)
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//
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// //Writes image data into byteBuffer
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// convertBitmapToByteBuffer(bitmap_256)
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//
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// val startTime = SystemClock.uptimeMillis()
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// // out
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// val outArray = Array(1) { FloatArray(2) }
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//
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// tflite!!.run(imgData, outArray)
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//
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// val endTime = SystemClock.uptimeMillis()
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//
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// Log.d(TAG, "SFW score :" + outArray[0][0] + ",NSFW score :" + outArray[0][1])
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// Log.d(TAG, "Timecost to run model inference: " + (endTime - startTime) + "ms")
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// return NsfwBean(outArray[0][0], outArray[0][1])
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// }
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//
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// /**
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// * Closes the interpreter and model to release resources.
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// */
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// fun close() {
175+
// if (tflite != null) {
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// tflite!!.close()
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// tflite = null
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// Log.d(TAG, "Tensorflow Lite Image Classifier close.")
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// }
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// if (gpuDelegate != null) {
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// gpuDelegate!!.close()
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// Log.d(TAG, "Tensorflow Lite Image gpuDelegate close.")
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// gpuDelegate = null
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// }
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// tfliteModel = null
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// Log.d(TAG, "Tensorflow Lite destroyed.")
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// }
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//
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// companion object {
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//
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// val TAG = "open_nsfw_android"
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// /**
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// * Dimensions of inputs.
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// */
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// private val DIM_BATCH_SIZE = 1
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//
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// private val DIM_PIXEL_SIZE = 3
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//
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// fun create(assetManager: AssetManager, isAddGpuDelegate: Boolean?, numThreads: Int): Classifier {
200+
// return Classifier(assetManager, isAddGpuDelegate!!, numThreads)
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// }
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//
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// }
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//
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//}

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