[Android, kotlin, tflite] mobile device integration depth learning light model tflite (image classification)

Why i create it?

To create an easy to use and easy to integrate TFlite Load the library , therefore TFLiteLoader emerge as the times require

• Open source Github Project address TFLiteLoader

Integrate ImageClassifier

rely on

allprojects {
	repositories {
		...
		maven { url 'https://jitpack.io' }
	}
}

dependencies {
	implementation 'com.github.mozhimen.TFLiteLoader:imageclassifier:1.0.2'
}

Access

1. Global declarations

private lateinit var _tFLiteImageClassifier: TFLiteImageClassifier

2. stay onCreate Initialize in

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

3. Sort of pictures

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

• An example of processing the returned data , Sure pull Code reference demo

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

Description of the return value

• List{Recognition}

data class Recognition(
    /**
     *  Unique identifier of the identified thing . Class specific , Not an instance 
     * A unique identifier for what has been recognized. Specific to the class, not the instance of
     * the object.
     */
    val id: String?,
    /**
     *  Display the name for identification 
     * Display name for the recognition.
     */
    val title: String?,
    /**
     *  This is a classifiable score relative to other recognition levels . The higher, the better 
     * A sortable score for how good the recognition is relative to others. Higher should be better.
     */
    val confidence: Float?,
    /**
     *  Optional location in the source image , Used to identify the position of the object ,  Image classification does not return obj The location of 
     * 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 <= ' ' }
    }
}

complete demo Code

@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: TFImageClassifier

    override 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: $objList")
                    runOnUiThread {
                        if (objList.isEmpty()) [email protected]
                        objList.forEachIndexed { index, _ ->
                            _stringBuilder.append("${objList[index].title}: ${objList[index].confidence}").append(" ")
                        }
                        vb.imageClassifierRes.text = _stringBuilder.toString()
                        _stringBuilder.clear()
                    }
                } finally {
                    _reentrantLock.unlock()
                }

                image.close()
            }
        }
    }
}

About the framework code here , You can refer to my other open source framework library : SwiftKit , But because it hasn't been finished yet , There is no complete wiki, It will be launched later

• This sample code holds references :

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'

in summary : Integration is not very simple , Then try it quickly