【Android,Kotlin,TFLite】移动设备集成深度学习轻模型TFlite(图像分类篇)

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【Android,Kotlin,TFLite】移动设备集成深度学习轻模型TFlite(图像分类篇)

深度学习.Tensorflow.TFLite.移动设备集成深度学习轻模型TFlite.图像分类篇

Why i create it?

为了创建一个易用且易于集成的TFlite加载库, 所以TFLiteLoader应运而生

• 开源Github项目地址 TFLiteLoader

集成 ImageClassifier

依赖

allprojects {repositories {...maven { url '' }maven { url '/' }//for scank}
}

dependencies {implementation 'com.github.mozhimen.TFLiteLoader:imageclassifier:1.0.6'
}

配置模块的build.gradle

android {...buildFeatures {dataBinding true}aaptOptions {noCompress "tflite","pb"}
}

接入

1. 全局声明

private lateinit var _tFLiteImageClassifier: TFLiteImageClassifier

2. 在onCreate中进行初始化

_tFLiteImageClassifier = TFLiteImageClassifier.create("health_model.tflite", resultSize = 3)

3. 分类图片

val objList = _tFLiteImageClassifier.classify([你的bitmap], 0)

• 对返回数据的处理示例, 可以pull代码参考demo

val objList = _tFLiteImageClassifier.classify(rotateBitmap, 0)
Log.d(TAG, "analyze: $objList")
runOnUiThread {if (objList.isEmpty()) return@runOnUiThreadobjList.forEachIndexed { index, _ ->_stringBuilder.append("${objList[index].title}: ${objList[index].confidence}").append(" ")}vb.imageClassifierRes.text = _stringBuilder.toString()_stringBuilder.clear()
}

对于返回值的说明

• List{Recognition}

data class Recognition(/*** 已识别事物的唯一标识符。特定于类，而不是实例* A unique identifier for what has been recognized. Specific to the class, not the instance of* the object.*/val id: String?,/*** 显示名称以进行识别* Display name for the recognition.*/val title: String?,/*** 这是一个相对于其他识别程度的可分类分数。越高越好* A sortable score for how good the recognition is relative to others. Higher should be better.*/val confidence: Float?,/*** 源图像中可选的位置，用于识别对象的位置, 图像分类中不返回obj的位置* Optional location within the source image for the location of the recognized object.*/private var location: RectF?
) {fun getLocation(): RectF {return RectF(location)}fun setLocation(location: RectF?) {this.location = location}override fun toString(): String {var resultString = ""if (id != null) {resultString += "[$id] "}if (title != null) {resultString += "$title "}if (confidence != null) {resultString += String.format("(%.1f%%) ", confidence * 100.0f)}if (location != null) {resultString += location.toString() + " "}return resultString.trim { it <= ' ' }}
}

完整demo代码

@PermissionKAnnor(permissions = [Manifest.permission.CAMERA])
class ImageClassifierActivity : BaseKActivity<ActivityImageClassifierBinding, BaseKViewModel>(R.layout.activity_image_classifier) {private lateinit var _tFLiteImageClassifier: TFLiteImageClassifier
//    private lateinit var _tFLiteLabelImageClassifier: TFLiteLabelImageClassifier
//    private lateinit var _tFImageClassifier: TFImageClassifieroverride fun initData(savedInstanceState: Bundle?) {PermissionK.initPermissions(this) {if (it) {initView(savedInstanceState)} else {PermissionK.applySetting(this)}}}override fun initView(savedInstanceState: Bundle?) {initLiteLoader()initCamera()}private fun initLiteLoader() {_tFLiteImageClassifier = TFLiteImageClassifier.create("health_model.tflite", resultSize = 3)
//        _tFLiteLabelImageClassifier = TFLiteLabelImageClassifier.create("?", "labels.txt", modelType = ModelType.QUANTIZED_EFFICIENTNET)
//        _tFImageClassifier = TFImageClassifier.create("output_graph.pb", "output_labels.txt", "input", 299, "output", 128f, 128f, 0.1f, 1)}private fun initCamera() {vb.imageClassifierPreview.initCamera(this, CameraSelector.DEFAULT_BACK_CAMERA)vb.imageClassifierPreview.setImageAnalyzer(_frameAnalyzer)vb.imageClassifierPreview.startCamera()}private val _frameAnalyzer: ImageAnalysis.Analyzer by lazy {object : ImageAnalysis.Analyzer {private val _reentrantLock = ReentrantLock()private val _stringBuilder = StringBuilder()@SuppressLint("UnsafeOptInUsageError", "SetTextI18n")override fun analyze(image: ImageProxy) {try {_reentrantLock.lock()val bitmap: Bitmap = if (image.format == ImageFormat.YUV_420_888) {ImageConverter.yuv2Bitmap(image)!!} else {ImageConverter.jpeg2Bitmap(image)}val rotateBitmap = UtilKBitmap.rotateBitmap(bitmap, 90)val objList = _tFLiteImageClassifier.classify(rotateBitmap, 0)Log.d(TAG, "analyze: $objList")runOnUiThread {if (objList.isEmpty()) return@runOnUiThreadobjList.forEachIndexed { index, _ ->_stringBuilder.append("${objList[index].title}: ${objList[index].confidence}").append(" ")}vb.imageClassifierRes.text = _stringBuilder.toString()_stringBuilder.clear()}} finally {_reentrantLock.unlock()}image.close()}}}
}

关于这里的框架代码, 可以参考我另一个开源框架库: SwiftKit ,不过因为还未完成, 没有完整的wiki, 过段时间推出

• 本示例代码所持引用:

implementation 'com.github.mozhimen.SwiftKit:basick:1.1.1'
implementation('com.github.mozhimen.SwiftKit:abilityk:1.1.1') {exclude group: 'com.mozhimen.abilityk.scank'exclude group: 'com.huawei.hms'
}
implementation 'com.github.mozhimen.SwiftKit:componentk:1.1.1'

综上所述: 集成是不是很简单, 那赶快试试吧

本文标签： Android kotlin TFLite移动设备集成深度学习轻模型TFlite(图像分类篇)