Usage

You can use Tengine Kit to detect faces in images and video.

You can first give the repository a star, your star is the driving force of our efforts, this SDK is definitely your star.

Before you begin

first download tengine-kit-sdk1.0.0.aar

Then build.gradle in Main Module add aar dependency

    dependencies {
        ...
        implementation files('path/tengine-kit-sdk1.0.0.aar')
        ...
    }

Face Detecter

1.Configure the face detector

Before you apply face detection to an image, you can change any of the face detector’s default settings with a FaceConfig object. You can change the following settings:

Settings
detect	Whether or not to detect faces
landmark2d	Whether to attempt to identify facial "landmarks": eyes, ears, nose,mouth, and so on.
video	Whether or not use camera mode, image process is specially optimized in camera mode

Images

    val config = FaceConfig().apply {
        detect = true
        landmark2d = true
        video = false
    }

Video

    val config = FaceConfig().apply {
        detect = true
        landmark2d = true
        video = true
    }

2.Prepare the input image

You can change the input image settings with a ImageConfig object. You can change the following settings:

Settings
data	Set image data byte array of image raw data
degree	Set rotate degree need if in camera mode, need to rotate the right angle to detect the face
height	Set bitmap height or preview height.
width	Set bitmap width or preview width.
format	Set image format, support RGB format and NV21 format current now.

Images

    val byte = ImageUtils.bitmap2RGB(bitmap)
    val imageConfig = ImageConfig().apply {
        data = byte
        degree = 0
        mirror = false
        height = bitmapHeight
        width = bitmapWidth
        format = ImageConfig.FaceImageFormat.RGB
    }

Video

    val imageConfig = ImageConfig().apply {
        data = mNV21Bytes
        degree = rotateDegree
        mirror = true
        height = previewHeight
        width = previewWidth
        format = ImageConfig.FaceImageFormat.YUV
    }

3.Use TengineKitSdk to predect

    val faces = TengineKitSdk.getInstance().detectFace(imageConfig, config)

4.Get information about detected faces

Each Face object represents a face that was detected in the image. For each face, you can get its bounding coordinates in the input image, as well as any other information you configured the face detector to find. For example:

    if (faces.isNotEmpty()) {
        val faceRects = arrayOfNulls<Rect>(faces.size)
        val faceLandmarks: MutableList<List<TenginekitPoint>> = ArrayList<List<TenginekitPoint>>()
        for ((i, face) in faces.withIndex()) {
    			val faceLandmarkList = mutableListOf<TenginekitPoint>()
    	       for (j in 0..211) {
        			faceLandmarkList.add(j, TenginekitPoint(face.landmark[j * 2] * width, face.landmark[j * 2 + 1] * height))
    			}
		val rect = Rect(
		    (face.x1 * width).toInt(),
		    (face.y1 * height).toInt(),
		    (face.x2 * width).toInt(),
		    (face.y2 * height).toInt()
		)
		faceLandmarks.add(i, faceLandmarkList)
		faceRects[i] = rect
        }
    }

Human Seg

human seg only support portrait segmentation current now

val byte = ImageUtils.bitmap2RGB(bitmap)
val config = SegConfig()
val imageConfig = ImageConfig().apply {
		data = byte
		degree = 0
		mirror = false
		height = it.height
		width = it.width
		format = ImageConfig.FaceImageFormat.RGB
}
val bitmapMask = TengineKitSdk.getInstance().segHuman(imageConfig, config)
imageSegMask.setImageBitmap(bitmapMask)