ML Kit 提供兩個針對姿勢偵測所最佳化的 SDK。
SDK 名稱 | pose-detection | pose-detection-accurate |
---|---|---|
導入作業 | 在建構期間,程式碼和資產會以靜態方式連結至您的應用程式。 | 在建構期間,程式碼和資產會以靜態方式連結至您的應用程式。 |
對應用程式大小的影響 (包括程式碼和素材資源) | 約 10.1 MB | 約 13.3 MB |
成效 | Pixel 3 XL:約 30 FPS | Pixel 3XL:使用 CPU 約 23FPS,使用 GPU 可達約 30 FPS |
立即試用
- 使用範例應用程式試試 請查看此 API 的使用範例。
事前準備
- 在專案層級的
build.gradle
檔案中,請務必在buildscript
和allprojects
區段中納入 Google 的 Maven 存放區。 將 ML Kit Android 程式庫的依附元件新增至模組的應用程式層級的 Gradle 檔案,通常為
app/build.gradle
:dependencies { // If you want to use the base sdk implementation 'com.google.mlkit:pose-detection:18.0.0-beta5' // If you want to use the accurate sdk implementation 'com.google.mlkit:pose-detection-accurate:18.0.0-beta5' }
1. 建立「PoseDetector
」的執行個體
PoseDetector
種付款方式
如要偵測圖片中的姿勢,請先建立 PoseDetector
的執行個體,並
指定偵測工具設定
偵測模式
PoseDetector
會在兩種偵測模式下運作。請務必選擇符合的選項
所需用途
STREAM_MODE
(預設)- 姿勢偵測器會先偵測到 然後執行姿勢偵測在後續影格中 除非人員符合,否則系統不會執行人偵測步驟 模糊錯誤,或系統不再以高可信度偵測到錯誤。姿勢偵測器會 嘗試追蹤最有名的使用者,並逐一傳回他們的姿勢 推論這麼做可減少延遲時間和順暢偵測。以下模式使用時機: 或需要偵測影片串流中的姿勢。
SINGLE_IMAGE_MODE
- 姿勢偵測器會偵測人然後跑步姿勢 偵測。每張圖片都會執行人為偵測步驟,因此延遲 值較高,且無人追蹤。使用姿勢時使用這個模式 偵測靜態圖像或不需要追蹤的位置
硬體設定
PoseDetector
支援多種硬體設定,可進行最佳化調整
成效:
CPU
:僅使用 CPU 執行偵測工具CPU_GPU
:同時使用 CPU 和 GPU 執行偵測工具
建構偵測工具選項時,您可以使用 API
setPreferredHardwareConfigs
:控制硬體選取。根據預設
所有硬體設定都會設為首選
ML Kit 會採用每項設定的可用性、穩定性、正確性和延遲時間
並從首選設定中挑選最適合的設定如果沒有
適用偏好的設定,系統會自動使用 CPU
設定
並設為備用委刊項ML Kit 會依據
不會造成阻力
使用者初次執行偵測工具時,會使用 CPU
。在所有
準備作業完成後,下列執行作業將使用最合適的設定。
setPreferredHardwareConfigs
的使用範例:
- 為了讓 ML Kit 挑選最佳設定,請勿呼叫這個 API。
- 如果不想啟用任何加速功能,請僅傳入
CPU
。 - 如果您想使用 GPU 卸載 CPU (即使 GPU 速度可能較慢),請改為
在
CPU_GPU
之內。
指定姿勢偵測器選項:
Kotlin
// Base pose detector with streaming frames, when depending on the pose-detection sdk val options = PoseDetectorOptions.Builder() .setDetectorMode(PoseDetectorOptions.STREAM_MODE) .build() // Accurate pose detector on static images, when depending on the pose-detection-accurate sdk val options = AccuratePoseDetectorOptions.Builder() .setDetectorMode(AccuratePoseDetectorOptions.SINGLE_IMAGE_MODE) .build()
Java
// Base pose detector with streaming frames, when depending on the pose-detection sdk PoseDetectorOptions options = new PoseDetectorOptions.Builder() .setDetectorMode(PoseDetectorOptions.STREAM_MODE) .build(); // Accurate pose detector on static images, when depending on the pose-detection-accurate sdk AccuratePoseDetectorOptions options = new AccuratePoseDetectorOptions.Builder() .setDetectorMode(AccuratePoseDetectorOptions.SINGLE_IMAGE_MODE) .build();
最後,建立 PoseDetector
的執行個體。傳送您指定的選項:
Kotlin
val poseDetector = PoseDetection.getClient(options)
Java
PoseDetector poseDetector = PoseDetection.getClient(options);
2. 準備輸入圖片
如要偵測圖片中的姿勢,請建立 InputImage
物件
從 Bitmap
、media.Image
、ByteBuffer
、位元組陣列或
裝置。然後,將 InputImage
物件傳遞至
PoseDetector
。
如要使用姿勢偵測功能,圖片尺寸應至少為 480x360 像素。如要即時偵測姿勢,請擷取影格 達到這個最低解析度將有助於縮短延遲時間
您可以建立InputImage
不同來源的 ANR 物件,說明如下。
使用 media.Image
如要建立InputImage
物件,例如從 media.Image
物件擷取圖片
裝置的相機,請傳遞 media.Image
物件和映像檔的
旋轉為 InputImage.fromMediaImage()
。
如果您使用
CameraX 程式庫、OnImageCapturedListener
和
ImageAnalysis.Analyzer
類別會計算旋轉值
不必確保憑證管理是否適當
因為 Google Cloud 會為您管理安全性
Kotlin
private class YourImageAnalyzer : ImageAnalysis.Analyzer { override fun analyze(imageProxy: ImageProxy) { val mediaImage = imageProxy.image if (mediaImage != null) { val image = InputImage.fromMediaImage(mediaImage, imageProxy.imageInfo.rotationDegrees) // Pass image to an ML Kit Vision API // ... } } }
Java
private class YourAnalyzer implements ImageAnalysis.Analyzer { @Override public void analyze(ImageProxy imageProxy) { Image mediaImage = imageProxy.getImage(); if (mediaImage != null) { InputImage image = InputImage.fromMediaImage(mediaImage, imageProxy.getImageInfo().getRotationDegrees()); // Pass image to an ML Kit Vision API // ... } } }
如果您沒有使用相機程式庫提供圖片的旋轉角度, 可根據裝置的旋轉角度和相機方向來計算 感應器:
Kotlin
private val ORIENTATIONS = SparseIntArray() init { ORIENTATIONS.append(Surface.ROTATION_0, 0) ORIENTATIONS.append(Surface.ROTATION_90, 90) ORIENTATIONS.append(Surface.ROTATION_180, 180) ORIENTATIONS.append(Surface.ROTATION_270, 270) } /** * Get the angle by which an image must be rotated given the device's current * orientation. */ @RequiresApi(api = Build.VERSION_CODES.LOLLIPOP) @Throws(CameraAccessException::class) private fun getRotationCompensation(cameraId: String, activity: Activity, isFrontFacing: Boolean): Int { // Get the device's current rotation relative to its "native" orientation. // Then, from the ORIENTATIONS table, look up the angle the image must be // rotated to compensate for the device's rotation. val deviceRotation = activity.windowManager.defaultDisplay.rotation var rotationCompensation = ORIENTATIONS.get(deviceRotation) // Get the device's sensor orientation. val cameraManager = activity.getSystemService(CAMERA_SERVICE) as CameraManager val sensorOrientation = cameraManager .getCameraCharacteristics(cameraId) .get(CameraCharacteristics.SENSOR_ORIENTATION)!! if (isFrontFacing) { rotationCompensation = (sensorOrientation + rotationCompensation) % 360 } else { // back-facing rotationCompensation = (sensorOrientation - rotationCompensation + 360) % 360 } return rotationCompensation }
Java
private static final SparseIntArray ORIENTATIONS = new SparseIntArray(); static { ORIENTATIONS.append(Surface.ROTATION_0, 0); ORIENTATIONS.append(Surface.ROTATION_90, 90); ORIENTATIONS.append(Surface.ROTATION_180, 180); ORIENTATIONS.append(Surface.ROTATION_270, 270); } /** * Get the angle by which an image must be rotated given the device's current * orientation. */ @RequiresApi(api = Build.VERSION_CODES.LOLLIPOP) private int getRotationCompensation(String cameraId, Activity activity, boolean isFrontFacing) throws CameraAccessException { // Get the device's current rotation relative to its "native" orientation. // Then, from the ORIENTATIONS table, look up the angle the image must be // rotated to compensate for the device's rotation. int deviceRotation = activity.getWindowManager().getDefaultDisplay().getRotation(); int rotationCompensation = ORIENTATIONS.get(deviceRotation); // Get the device's sensor orientation. CameraManager cameraManager = (CameraManager) activity.getSystemService(CAMERA_SERVICE); int sensorOrientation = cameraManager .getCameraCharacteristics(cameraId) .get(CameraCharacteristics.SENSOR_ORIENTATION); if (isFrontFacing) { rotationCompensation = (sensorOrientation + rotationCompensation) % 360; } else { // back-facing rotationCompensation = (sensorOrientation - rotationCompensation + 360) % 360; } return rotationCompensation; }
然後,請傳遞 media.Image
物件和
將度數值旋轉為 InputImage.fromMediaImage()
:
Kotlin
val image = InputImage.fromMediaImage(mediaImage, rotation)
Java
InputImage image = InputImage.fromMediaImage(mediaImage, rotation);
使用檔案 URI
如要建立InputImage
物件,將應用程式結構定義與檔案 URI 傳遞至
InputImage.fromFilePath()
。如果您要
使用 ACTION_GET_CONTENT
意圖提示使用者選取
取自圖片庫應用程式中的圖片。
Kotlin
val image: InputImage try { image = InputImage.fromFilePath(context, uri) } catch (e: IOException) { e.printStackTrace() }
Java
InputImage image; try { image = InputImage.fromFilePath(context, uri); } catch (IOException e) { e.printStackTrace(); }
使用 ByteBuffer
或 ByteArray
如要建立InputImage
ByteBuffer
或 ByteArray
的物件,請先計算圖片
與先前 media.Image
輸入中所述的旋轉角度相同。
接著,使用緩衝區或陣列建立 InputImage
物件,以及
高度、寬度、顏色編碼格式以及旋轉角度:
Kotlin
val image = InputImage.fromByteBuffer( byteBuffer, /* image width */ 480, /* image height */ 360, rotationDegrees, InputImage.IMAGE_FORMAT_NV21 // or IMAGE_FORMAT_YV12 ) // Or: val image = InputImage.fromByteArray( byteArray, /* image width */ 480, /* image height */ 360, rotationDegrees, InputImage.IMAGE_FORMAT_NV21 // or IMAGE_FORMAT_YV12 )
Java
InputImage image = InputImage.fromByteBuffer(byteBuffer, /* image width */ 480, /* image height */ 360, rotationDegrees, InputImage.IMAGE_FORMAT_NV21 // or IMAGE_FORMAT_YV12 ); // Or: InputImage image = InputImage.fromByteArray( byteArray, /* image width */480, /* image height */360, rotation, InputImage.IMAGE_FORMAT_NV21 // or IMAGE_FORMAT_YV12 );
使用 Bitmap
如要建立InputImage
物件中,Bitmap
物件,請做出以下宣告:
Kotlin
val image = InputImage.fromBitmap(bitmap, 0)
Java
InputImage image = InputImage.fromBitmap(bitmap, rotationDegree);
圖像以 Bitmap
物件和旋轉角度表示。
3. 處理圖片
將準備好的 InputImage
物件傳遞至 PoseDetector
的 process
方法。
Kotlin
Task<Pose> result = poseDetector.process(image) .addOnSuccessListener { results -> // Task completed successfully // ... } .addOnFailureListener { e -> // Task failed with an exception // ... }
Java
Task<Pose> result = poseDetector.process(image) .addOnSuccessListener( new OnSuccessListener<Pose>() { @Override public void onSuccess(Pose pose) { // Task completed successfully // ... } }) .addOnFailureListener( new OnFailureListener() { @Override public void onFailure(@NonNull Exception e) { // Task failed with an exception // ... } });
4. 取得偵測到的姿勢相關資訊
如果系統在圖片中偵測到人物,姿勢偵測 API 會傳回 Pose
具有 33 PoseLandmark
秒的物件。
如果人物出現在圖像中,模型會指派該人物 外框 外面缺少的地標座標,會將其低 InFrameConfidence 值。
如果在 Pose
畫面中未偵測到任何人
物件不包含 PoseLandmark
。
Kotlin
// Get all PoseLandmarks. If no person was detected, the list will be empty val allPoseLandmarks = pose.getAllPoseLandmarks() // Or get specific PoseLandmarks individually. These will all be null if no person // was detected val leftShoulder = pose.getPoseLandmark(PoseLandmark.LEFT_SHOULDER) val rightShoulder = pose.getPoseLandmark(PoseLandmark.RIGHT_SHOULDER) val leftElbow = pose.getPoseLandmark(PoseLandmark.LEFT_ELBOW) val rightElbow = pose.getPoseLandmark(PoseLandmark.RIGHT_ELBOW) val leftWrist = pose.getPoseLandmark(PoseLandmark.LEFT_WRIST) val rightWrist = pose.getPoseLandmark(PoseLandmark.RIGHT_WRIST) val leftHip = pose.getPoseLandmark(PoseLandmark.LEFT_HIP) val rightHip = pose.getPoseLandmark(PoseLandmark.RIGHT_HIP) val leftKnee = pose.getPoseLandmark(PoseLandmark.LEFT_KNEE) val rightKnee = pose.getPoseLandmark(PoseLandmark.RIGHT_KNEE) val leftAnkle = pose.getPoseLandmark(PoseLandmark.LEFT_ANKLE) val rightAnkle = pose.getPoseLandmark(PoseLandmark.RIGHT_ANKLE) val leftPinky = pose.getPoseLandmark(PoseLandmark.LEFT_PINKY) val rightPinky = pose.getPoseLandmark(PoseLandmark.RIGHT_PINKY) val leftIndex = pose.getPoseLandmark(PoseLandmark.LEFT_INDEX) val rightIndex = pose.getPoseLandmark(PoseLandmark.RIGHT_INDEX) val leftThumb = pose.getPoseLandmark(PoseLandmark.LEFT_THUMB) val rightThumb = pose.getPoseLandmark(PoseLandmark.RIGHT_THUMB) val leftHeel = pose.getPoseLandmark(PoseLandmark.LEFT_HEEL) val rightHeel = pose.getPoseLandmark(PoseLandmark.RIGHT_HEEL) val leftFootIndex = pose.getPoseLandmark(PoseLandmark.LEFT_FOOT_INDEX) val rightFootIndex = pose.getPoseLandmark(PoseLandmark.RIGHT_FOOT_INDEX) val nose = pose.getPoseLandmark(PoseLandmark.NOSE) val leftEyeInner = pose.getPoseLandmark(PoseLandmark.LEFT_EYE_INNER) val leftEye = pose.getPoseLandmark(PoseLandmark.LEFT_EYE) val leftEyeOuter = pose.getPoseLandmark(PoseLandmark.LEFT_EYE_OUTER) val rightEyeInner = pose.getPoseLandmark(PoseLandmark.RIGHT_EYE_INNER) val rightEye = pose.getPoseLandmark(PoseLandmark.RIGHT_EYE) val rightEyeOuter = pose.getPoseLandmark(PoseLandmark.RIGHT_EYE_OUTER) val leftEar = pose.getPoseLandmark(PoseLandmark.LEFT_EAR) val rightEar = pose.getPoseLandmark(PoseLandmark.RIGHT_EAR) val leftMouth = pose.getPoseLandmark(PoseLandmark.LEFT_MOUTH) val rightMouth = pose.getPoseLandmark(PoseLandmark.RIGHT_MOUTH)
Java
// Get all PoseLandmarks. If no person was detected, the list will be empty List<PoseLandmark> allPoseLandmarks = pose.getAllPoseLandmarks(); // Or get specific PoseLandmarks individually. These will all be null if no person // was detected PoseLandmark leftShoulder = pose.getPoseLandmark(PoseLandmark.LEFT_SHOULDER); PoseLandmark rightShoulder = pose.getPoseLandmark(PoseLandmark.RIGHT_SHOULDER); PoseLandmark leftElbow = pose.getPoseLandmark(PoseLandmark.LEFT_ELBOW); PoseLandmark rightElbow = pose.getPoseLandmark(PoseLandmark.RIGHT_ELBOW); PoseLandmark leftWrist = pose.getPoseLandmark(PoseLandmark.LEFT_WRIST); PoseLandmark rightWrist = pose.getPoseLandmark(PoseLandmark.RIGHT_WRIST); PoseLandmark leftHip = pose.getPoseLandmark(PoseLandmark.LEFT_HIP); PoseLandmark rightHip = pose.getPoseLandmark(PoseLandmark.RIGHT_HIP); PoseLandmark leftKnee = pose.getPoseLandmark(PoseLandmark.LEFT_KNEE); PoseLandmark rightKnee = pose.getPoseLandmark(PoseLandmark.RIGHT_KNEE); PoseLandmark leftAnkle = pose.getPoseLandmark(PoseLandmark.LEFT_ANKLE); PoseLandmark rightAnkle = pose.getPoseLandmark(PoseLandmark.RIGHT_ANKLE); PoseLandmark leftPinky = pose.getPoseLandmark(PoseLandmark.LEFT_PINKY); PoseLandmark rightPinky = pose.getPoseLandmark(PoseLandmark.RIGHT_PINKY); PoseLandmark leftIndex = pose.getPoseLandmark(PoseLandmark.LEFT_INDEX); PoseLandmark rightIndex = pose.getPoseLandmark(PoseLandmark.RIGHT_INDEX); PoseLandmark leftThumb = pose.getPoseLandmark(PoseLandmark.LEFT_THUMB); PoseLandmark rightThumb = pose.getPoseLandmark(PoseLandmark.RIGHT_THUMB); PoseLandmark leftHeel = pose.getPoseLandmark(PoseLandmark.LEFT_HEEL); PoseLandmark rightHeel = pose.getPoseLandmark(PoseLandmark.RIGHT_HEEL); PoseLandmark leftFootIndex = pose.getPoseLandmark(PoseLandmark.LEFT_FOOT_INDEX); PoseLandmark rightFootIndex = pose.getPoseLandmark(PoseLandmark.RIGHT_FOOT_INDEX); PoseLandmark nose = pose.getPoseLandmark(PoseLandmark.NOSE); PoseLandmark leftEyeInner = pose.getPoseLandmark(PoseLandmark.LEFT_EYE_INNER); PoseLandmark leftEye = pose.getPoseLandmark(PoseLandmark.LEFT_EYE); PoseLandmark leftEyeOuter = pose.getPoseLandmark(PoseLandmark.LEFT_EYE_OUTER); PoseLandmark rightEyeInner = pose.getPoseLandmark(PoseLandmark.RIGHT_EYE_INNER); PoseLandmark rightEye = pose.getPoseLandmark(PoseLandmark.RIGHT_EYE); PoseLandmark rightEyeOuter = pose.getPoseLandmark(PoseLandmark.RIGHT_EYE_OUTER); PoseLandmark leftEar = pose.getPoseLandmark(PoseLandmark.LEFT_EAR); PoseLandmark rightEar = pose.getPoseLandmark(PoseLandmark.RIGHT_EAR); PoseLandmark leftMouth = pose.getPoseLandmark(PoseLandmark.LEFT_MOUTH); PoseLandmark rightMouth = pose.getPoseLandmark(PoseLandmark.RIGHT_MOUTH);
提升成效的訣竅
結果的品質取決於輸入圖片的品質:
- 為了讓 ML Kit 準確偵測姿勢,圖像中的人物 足夠的像素資料來呈現出來為獲得最佳成效,主旨應該 至少 256x256 像素
- 在即時應用程式中偵測到姿勢時,建議您也考量 輸入圖片的整體尺寸可處理較小的圖片 因此,為了縮短延遲時間,擷取解析度較低的圖片 務必遵循上述解決要求,並確保拍攝主體 盡量增加圖片張量
- 圖像對焦品質不佳也可能會影響準確度。如果沒有可接受的結果 請要求使用者重新擷取圖片
如要在即時應用程式中使用姿勢偵測功能,請遵循下列準則,以達到最佳影格速率:
- 使用基本姿勢偵測 SDK 和
STREAM_MODE
。 - 建議以較低的解析度拍攝圖片。不過,也請留意這個 API 的圖片尺寸規定。
- 如果您使用
Camera
或camera2
API、 限制對偵測工具的呼叫如果影片有新影片 影格掉落時,表示影格是否可用。詳情請參閱VisionProcessorBase
類別的範例。 - 如果您是使用
CameraX
API, 請務必將背壓策略設為預設值ImageAnalysis.STRATEGY_KEEP_ONLY_LATEST
。 這麼做可保證系統一次只會傳送一張圖片進行分析。如果圖片較多 會在分析器忙碌時產生,這些作業會自動遭到捨棄,不會排入佇列 廣告放送。以呼叫方式關閉要分析的圖片後 ImageProxy.close(),最新一張圖片才會放送。 - 如果使用偵測工具的輸出內容將圖像重疊
先從 ML Kit 取得結果,然後算繪圖片
並疊加單一步驟這會轉譯至顯示介面
每個輸入影格只能建立一次詳情請參閱
CameraSourcePreview
和 如需範例,請前往快速入門導覽課程範例應用程式中的GraphicOverlay
類別。 - 如果你使用 Camera2 API,
ImageFormat.YUV_420_888
格式。如果使用舊版 Camera API,請以ImageFormat.NV21
格式。
後續步驟
- 想瞭解如何使用姿勢地標來分類姿勢,請參閱姿勢分類提示。