API¶

The located Function under com.tenginekit.

1. Init Context¶

	val sdkConfig = SdkConfig()
	TengineKitSdk.getInstance().initSdk(path, config, context)

SdkConfig¶

backend: predict backend, cpu default now

2. FaceDetect¶

init¶

	TengineKitSdk.getInstance().initFaceDetect()

predict¶

We merge all the functions into one interface

	val byte = ImageUtils.bitmap2RGB(bitmap)
	val faceConfig = FaceConfig().apply {
			detect = true
			landmark2d = true
			video = false
	}
	val imageConfig = ImageConfig().apply {
			data = byte
			degree = 0
			mirror = false
			height = bitmapHeight
			width = bitmapWidth
			format = ImageConfig.FaceImageFormat.RGB
	}
	val faces = TengineKitSdk.getInstance().detectFace(faceConfig, config)

release¶

	TengineKitSdk.getInstance().releaseFaceDetect()

3. InsightFace¶

init¶

	TengineKitSdk.getInstance().initInsightFace()

predict¶

We merge all the functions into one interface

   val byte = ImageUtils.bitmap2RGB(bitmap)
   val config = InsightFaceConfig().apply {
   	scrfd = true
   	recognition = true
   	registered = false
   	video = false
   }
   val imageConfig = ImageConfig().apply {
   	data = byte
   	degree = 0
   	mirror = false
   	height = it.height
   	width = it.width
   	format = ImageConfig.FaceImageFormat.RGB
   }
   val faces = TengineKitSdk.getInstance().detectInsightFace(imageConfig, config)

release¶

	TengineKitSdk.getInstance().releaseInsightFace()

4. SegBody¶

init¶

	TengineKitSdk.getInstance().initSegBody()

Predict¶

directly return a mask, the mask is a android bitmap, the mask’s width is 398, the height is 224; the mask’s format is ARGB_8888

	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)

release¶

	TengineKitSdk.getInstance().releaseSegBody()

5. BodyDetect¶

init¶

	TengineKitSdk.getInstance().initBodyDetect()

predict¶

We merge all the functions into one interface

   val data = ImageUtils.bitmap2RGB(bitmap)
   val imageConfig = ImageConfig().apply {
   	this.data = data
   	this.format = ImageConfig.FaceImageFormat.RGB
   	this.height = it.height
   	this.width = it.width
   	this.mirror = false
   	this.degree = 0
   }
   val bodyConfig = BodyConfig()
   val bodyS = TengineKitSdk.getInstance().bodyDetect(imageConfig, bodyConfig)

release¶

	 TengineKitSdk.getInstance().releaseBodyDetect()

6.Release Context¶

	TengineKitSdk.getInstance().release()

DataStruct¶

Config¶

ImageConfig¶

data(byte[]): set image data byte array of image raw data
degree(int): set rotate degree need if in camera mode, need to rotate the right angle to detect the face
width(int): set bitmap width or preview width
height(int): set bitmap height or preview height
format(enum ImageConfig.FaceImageFormat): set image format, support RGB format and NV21 format current now

FaceConfig¶

detect(boolean): set true if need detect face rect
landmark2d(boolean): set true if need landmark2d except detect face rect
video(boolean): set true if in camera mode

InsightFaceConfig¶

scrfd(boolean): set true if need do scrfd
recognition(boolean): set true if need do arcface
registered(boolean): set true if already registered face

SegConfig¶

default portrait segmentation config current

BodyConfig¶

landmark(boolean): set true if need body landmark

Info¶

Face¶

all detect values are normalized from 0 to 1

x1: face rect left
y1: face rect top
x2: face rect right
y2: face rect bottom
landmark: if not null landmark contain 212 face key points
headX: Human face pitch direction corner
headY: Human face yaw direction corner
headZ: Human face roll direction corner
leftEyeClose: Left eye closure confidence 0~1
rightEyeClose: Right eye closure confidence 0~1
mouthClose: Mouth closure confidence 0~1
mouthBigOpen: Open mouth Big confidence 0~1

InsightFace¶

x1: detect body rect left
y1: detect body rect top
x2: detect body rect right
y2: detect body rect bottom
landmark: if not null landmark contain 5 face key points
confidence: face detect confidence
feature: if not null feature contain 512 face feature points

Body¶

x1: detect body rect left
y1: detect body rect top
x2: detect body rect right
y2: detect body rect bottom
landmark: if not null landmark contain 16 body key points