你可以使用 ML Kit 偵測圖片和影片中的臉孔。
事前準備
- 如果還沒試過 將 Firebase 新增至您的 Android 專案。
- 將 ML Kit Android 程式庫的依附元件新增至模組
(應用程式層級) Gradle 檔案 (通常是
app/build.gradle
):apply plugin: 'com.android.application' apply plugin: 'com.google.gms.google-services' dependencies { // ... implementation 'com.google.firebase:firebase-ml-vision:24.0.3' // If you want to detect face contours (landmark detection and classification // don't require this additional model): implementation 'com.google.firebase:firebase-ml-vision-face-model:20.0.1' }
-
選用 (建議選用):將應用程式設為自動下載
將應用程式從 Play 商店安裝到裝置上
方法是在應用程式的
AndroidManifest.xml
檔案:<application ...> ... <meta-data android:name="com.google.firebase.ml.vision.DEPENDENCIES" android:value="face" /> <!-- To use multiple models: android:value="face,model2,model3" --> </application>
如果您未啟用安裝期間模型下載功能,模型就會 。您在 下載完成不會產生任何結果。
輸入圖片規範
為了讓 ML Kit 準確偵測臉孔,輸入圖片必須包含臉孔 以充足的像素資料表示基本上, 至少需要 100x100 像素如要偵測 臉部輪廓線,則 ML Kit 需要較高的解析度輸入: 至少應為 200 x 200 像素。
如果您在即時應用程式中偵測臉孔,您可能還需要 將輸入圖片的整體尺寸納入考量較小的圖片 加快處理速度,因此為了縮短延遲時間,擷取解析度較低的圖片 (請謹記上述準確率規定),並確保 拍攝主體的臉孔會盡量佔滿圖片。另請參閱 即時效能改善秘訣。
圖片焦點不佳可能會降低準確性。如果沒有可接受的結果 請試著要求使用者重新擷取圖片
臉部與相機相對的方向也會影響臉部表情 ML Kit 偵測到的特徵詳情請見 臉部偵測 概念。
1. 設定臉部偵測工具
為圖像套用臉部偵測功能之前,如果想變更 臉部偵測器的預設設定,請用FirebaseVisionFaceDetectorOptions
物件。
您可以變更下列設定:
設定 | |
---|---|
效能模式 |
FAST (預設)
|ACCURATE
改善偵測臉孔的速度或精確度。 |
偵測地標 |
NO_LANDMARKS (預設)
|ALL_LANDMARKS
是否嘗試辨識臉部「地標」:眼睛、耳朵、鼻子、 臉頰、嘴巴等 |
偵測輪廓線 |
NO_CONTOURS (預設)
|ALL_CONTOURS
是否偵測臉部特徵的輪廓。輪廓線是 只會偵測到圖片中最醒目的臉孔。 |
將臉孔分類 |
NO_CLASSIFICATIONS (預設)
|ALL_CLASSIFICATIONS
是否將臉孔分類 (例如「微笑」)、 和「睜開雙眼」 |
臉孔最小尺寸 |
float (預設:0.1f )
待偵測臉孔的最小尺寸 (相對於圖片)。 |
啟用臉部追蹤功能 |
false (預設) |true
是否要指派臉孔 ID,以用於追蹤 圖像中的人物臉孔。 請注意,啟用輪廓偵測功能後,只有一張臉孔 因此臉部追蹤功能無法產生實用的結果。為此 原因及加快偵測速度,請勿同時啟用兩個輪廓線 偵測及臉部追蹤 |
例如:
Java
// High-accuracy landmark detection and face classification FirebaseVisionFaceDetectorOptions highAccuracyOpts = new FirebaseVisionFaceDetectorOptions.Builder() .setPerformanceMode(FirebaseVisionFaceDetectorOptions.ACCURATE) .setLandmarkMode(FirebaseVisionFaceDetectorOptions.ALL_LANDMARKS) .setClassificationMode(FirebaseVisionFaceDetectorOptions.ALL_CLASSIFICATIONS) .build(); // Real-time contour detection of multiple faces FirebaseVisionFaceDetectorOptions realTimeOpts = new FirebaseVisionFaceDetectorOptions.Builder() .setContourMode(FirebaseVisionFaceDetectorOptions.ALL_CONTOURS) .build();
Kotlin+KTX
// High-accuracy landmark detection and face classification val highAccuracyOpts = FirebaseVisionFaceDetectorOptions.Builder() .setPerformanceMode(FirebaseVisionFaceDetectorOptions.ACCURATE) .setLandmarkMode(FirebaseVisionFaceDetectorOptions.ALL_LANDMARKS) .setClassificationMode(FirebaseVisionFaceDetectorOptions.ALL_CLASSIFICATIONS) .build() // Real-time contour detection of multiple faces val realTimeOpts = FirebaseVisionFaceDetectorOptions.Builder() .setContourMode(FirebaseVisionFaceDetectorOptions.ALL_CONTOURS) .build()
2. 執行臉部偵測工具
如要偵測圖片中的臉孔,請建立FirebaseVisionImage
物件
從 Bitmap
、media.Image
、ByteBuffer
、位元組陣列或
裝置。然後,將 FirebaseVisionImage
物件傳遞至
FirebaseVisionFaceDetector
的 detectInImage
方法。
如要使用臉部辨識功能,圖片尺寸應至少為 480x360 像素。如果您可以即時辨識臉孔,就必須擷取影格 達到這個最低解析度將有助於縮短延遲時間
透過所需位置建立
FirebaseVisionImage
物件。 圖片。-
要使用
FirebaseVisionImage
物件media.Image
物件,例如從 裝置的相機,請傳遞media.Image
物件和圖片的 旋轉至FirebaseVisionImage.fromMediaImage()
。如果您使用 CameraX 程式庫、
OnImageCapturedListener
和ImageAnalysis.Analyzer
類別會計算旋轉值 因此只需將旋轉模型 轉換為 ML Kit 的 呼叫前ROTATION_
常數FirebaseVisionImage.fromMediaImage()
:Java
private class YourAnalyzer implements ImageAnalysis.Analyzer { private int degreesToFirebaseRotation(int degrees) { switch (degrees) { case 0: return FirebaseVisionImageMetadata.ROTATION_0; case 90: return FirebaseVisionImageMetadata.ROTATION_90; case 180: return FirebaseVisionImageMetadata.ROTATION_180; case 270: return FirebaseVisionImageMetadata.ROTATION_270; default: throw new IllegalArgumentException( "Rotation must be 0, 90, 180, or 270."); } } @Override public void analyze(ImageProxy imageProxy, int degrees) { if (imageProxy == null || imageProxy.getImage() == null) { return; } Image mediaImage = imageProxy.getImage(); int rotation = degreesToFirebaseRotation(degrees); FirebaseVisionImage image = FirebaseVisionImage.fromMediaImage(mediaImage, rotation); // Pass image to an ML Kit Vision API // ... } }
Kotlin+KTX
private class YourImageAnalyzer : ImageAnalysis.Analyzer { private fun degreesToFirebaseRotation(degrees: Int): Int = when(degrees) { 0 -> FirebaseVisionImageMetadata.ROTATION_0 90 -> FirebaseVisionImageMetadata.ROTATION_90 180 -> FirebaseVisionImageMetadata.ROTATION_180 270 -> FirebaseVisionImageMetadata.ROTATION_270 else -> throw Exception("Rotation must be 0, 90, 180, or 270.") } override fun analyze(imageProxy: ImageProxy?, degrees: Int) { val mediaImage = imageProxy?.image val imageRotation = degreesToFirebaseRotation(degrees) if (mediaImage != null) { val image = FirebaseVisionImage.fromMediaImage(mediaImage, imageRotation) // Pass image to an ML Kit Vision API // ... } } }
如果您沒有使用相機程式庫來提供圖像旋轉角度, 可根據裝置旋轉角度和相機方向計算 感應器:
Java
private static final SparseIntArray ORIENTATIONS = new SparseIntArray(); static { ORIENTATIONS.append(Surface.ROTATION_0, 90); ORIENTATIONS.append(Surface.ROTATION_90, 0); ORIENTATIONS.append(Surface.ROTATION_180, 270); ORIENTATIONS.append(Surface.ROTATION_270, 180); } /** * 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, Context context) 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); // On most devices, the sensor orientation is 90 degrees, but for some // devices it is 270 degrees. For devices with a sensor orientation of // 270, rotate the image an additional 180 ((270 + 270) % 360) degrees. CameraManager cameraManager = (CameraManager) context.getSystemService(CAMERA_SERVICE); int sensorOrientation = cameraManager .getCameraCharacteristics(cameraId) .get(CameraCharacteristics.SENSOR_ORIENTATION); rotationCompensation = (rotationCompensation + sensorOrientation + 270) % 360; // Return the corresponding FirebaseVisionImageMetadata rotation value. int result; switch (rotationCompensation) { case 0: result = FirebaseVisionImageMetadata.ROTATION_0; break; case 90: result = FirebaseVisionImageMetadata.ROTATION_90; break; case 180: result = FirebaseVisionImageMetadata.ROTATION_180; break; case 270: result = FirebaseVisionImageMetadata.ROTATION_270; break; default: result = FirebaseVisionImageMetadata.ROTATION_0; Log.e(TAG, "Bad rotation value: " + rotationCompensation); } return result; }
Kotlin+KTX
private val ORIENTATIONS = SparseIntArray() init { ORIENTATIONS.append(Surface.ROTATION_0, 90) ORIENTATIONS.append(Surface.ROTATION_90, 0) ORIENTATIONS.append(Surface.ROTATION_180, 270) ORIENTATIONS.append(Surface.ROTATION_270, 180) } /** * 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, context: Context): 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) // On most devices, the sensor orientation is 90 degrees, but for some // devices it is 270 degrees. For devices with a sensor orientation of // 270, rotate the image an additional 180 ((270 + 270) % 360) degrees. val cameraManager = context.getSystemService(CAMERA_SERVICE) as CameraManager val sensorOrientation = cameraManager .getCameraCharacteristics(cameraId) .get(CameraCharacteristics.SENSOR_ORIENTATION)!! rotationCompensation = (rotationCompensation + sensorOrientation + 270) % 360 // Return the corresponding FirebaseVisionImageMetadata rotation value. val result: Int when (rotationCompensation) { 0 -> result = FirebaseVisionImageMetadata.ROTATION_0 90 -> result = FirebaseVisionImageMetadata.ROTATION_90 180 -> result = FirebaseVisionImageMetadata.ROTATION_180 270 -> result = FirebaseVisionImageMetadata.ROTATION_270 else -> { result = FirebaseVisionImageMetadata.ROTATION_0 Log.e(TAG, "Bad rotation value: $rotationCompensation") } } return result }
然後,請傳遞
media.Image
物件和 將旋轉值轉換為FirebaseVisionImage.fromMediaImage()
:Java
FirebaseVisionImage image = FirebaseVisionImage.fromMediaImage(mediaImage, rotation);
Kotlin+KTX
val image = FirebaseVisionImage.fromMediaImage(mediaImage, rotation)
- 如要從檔案 URI 建立
FirebaseVisionImage
物件,請傳遞 應用程式環境和檔案 URIFirebaseVisionImage.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。Java
FirebaseVisionImage image; try { image = FirebaseVisionImage.fromFilePath(context, uri); } catch (IOException e) { e.printStackTrace(); }
Kotlin+KTX
val image: FirebaseVisionImage try { image = FirebaseVisionImage.fromFilePath(context, uri) } catch (e: IOException) { e.printStackTrace() }
- 要使用
FirebaseVisionImage
物件ByteBuffer
或位元組陣列,請先計算圖片 旋轉 (方法如上所述)media.Image
輸入欄位。接著建立
FirebaseVisionImageMetadata
物件 包含圖片的高度、寬度、色彩編碼格式 和輪替金鑰Java
FirebaseVisionImageMetadata metadata = new FirebaseVisionImageMetadata.Builder() .setWidth(480) // 480x360 is typically sufficient for .setHeight(360) // image recognition .setFormat(FirebaseVisionImageMetadata.IMAGE_FORMAT_NV21) .setRotation(rotation) .build();
Kotlin+KTX
val metadata = FirebaseVisionImageMetadata.Builder() .setWidth(480) // 480x360 is typically sufficient for .setHeight(360) // image recognition .setFormat(FirebaseVisionImageMetadata.IMAGE_FORMAT_NV21) .setRotation(rotation) .build()
使用緩衝區或陣列和中繼資料物件
FirebaseVisionImage
物件:Java
FirebaseVisionImage image = FirebaseVisionImage.fromByteBuffer(buffer, metadata); // Or: FirebaseVisionImage image = FirebaseVisionImage.fromByteArray(byteArray, metadata);
Kotlin+KTX
val image = FirebaseVisionImage.fromByteBuffer(buffer, metadata) // Or: val image = FirebaseVisionImage.fromByteArray(byteArray, metadata)
- 要使用
FirebaseVisionImage
物件Bitmap
物件:Java
FirebaseVisionImage image = FirebaseVisionImage.fromBitmap(bitmap);
Kotlin+KTX
val image = FirebaseVisionImage.fromBitmap(bitmap)
Bitmap
物件代表的圖片必須 保持直立,不用另外旋轉。
-
取得
FirebaseVisionFaceDetector
的執行個體:Java
FirebaseVisionFaceDetector detector = FirebaseVision.getInstance() .getVisionFaceDetector(options);
Kotlin+KTX
val detector = FirebaseVision.getInstance() .getVisionFaceDetector(options)
最後,將圖片傳遞至
detectInImage
方法:Java
Task<List<FirebaseVisionFace>> result = detector.detectInImage(image) .addOnSuccessListener( new OnSuccessListener<List<FirebaseVisionFace>>() { @Override public void onSuccess(List<FirebaseVisionFace> faces) { // Task completed successfully // ... } }) .addOnFailureListener( new OnFailureListener() { @Override public void onFailure(@NonNull Exception e) { // Task failed with an exception // ... } });
Kotlin+KTX
val result = detector.detectInImage(image) .addOnSuccessListener { faces -> // Task completed successfully // ... } .addOnFailureListener { e -> // Task failed with an exception // ... }
3. 取得系統偵測到的臉孔資訊
如果臉部辨識作業成功,系統會顯示FirebaseVisionFace
物件將傳遞至成功
接聽程式。每個 FirebaseVisionFace
物件都代表偵測到的臉孔
在圖片中定義文字您可以在輸入中取得每個臉孔的定界座標
以及你設定臉部偵測器
發現。例如:
Java
for (FirebaseVisionFace face : faces) { Rect bounds = face.getBoundingBox(); float rotY = face.getHeadEulerAngleY(); // Head is rotated to the right rotY degrees float rotZ = face.getHeadEulerAngleZ(); // Head is tilted sideways rotZ degrees // If landmark detection was enabled (mouth, ears, eyes, cheeks, and // nose available): FirebaseVisionFaceLandmark leftEar = face.getLandmark(FirebaseVisionFaceLandmark.LEFT_EAR); if (leftEar != null) { FirebaseVisionPoint leftEarPos = leftEar.getPosition(); } // If contour detection was enabled: List<FirebaseVisionPoint> leftEyeContour = face.getContour(FirebaseVisionFaceContour.LEFT_EYE).getPoints(); List<FirebaseVisionPoint> upperLipBottomContour = face.getContour(FirebaseVisionFaceContour.UPPER_LIP_BOTTOM).getPoints(); // If classification was enabled: if (face.getSmilingProbability() != FirebaseVisionFace.UNCOMPUTED_PROBABILITY) { float smileProb = face.getSmilingProbability(); } if (face.getRightEyeOpenProbability() != FirebaseVisionFace.UNCOMPUTED_PROBABILITY) { float rightEyeOpenProb = face.getRightEyeOpenProbability(); } // If face tracking was enabled: if (face.getTrackingId() != FirebaseVisionFace.INVALID_ID) { int id = face.getTrackingId(); } }
Kotlin+KTX
for (face in faces) { val bounds = face.boundingBox val rotY = face.headEulerAngleY // Head is rotated to the right rotY degrees val rotZ = face.headEulerAngleZ // Head is tilted sideways rotZ degrees // If landmark detection was enabled (mouth, ears, eyes, cheeks, and // nose available): val leftEar = face.getLandmark(FirebaseVisionFaceLandmark.LEFT_EAR) leftEar?.let { val leftEarPos = leftEar.position } // If contour detection was enabled: val leftEyeContour = face.getContour(FirebaseVisionFaceContour.LEFT_EYE).points val upperLipBottomContour = face.getContour(FirebaseVisionFaceContour.UPPER_LIP_BOTTOM).points // If classification was enabled: if (face.smilingProbability != FirebaseVisionFace.UNCOMPUTED_PROBABILITY) { val smileProb = face.smilingProbability } if (face.rightEyeOpenProbability != FirebaseVisionFace.UNCOMPUTED_PROBABILITY) { val rightEyeOpenProb = face.rightEyeOpenProbability } // If face tracking was enabled: if (face.trackingId != FirebaseVisionFace.INVALID_ID) { val id = face.trackingId } }
臉部輪廓範例
啟用臉部輪廓偵測功能後,畫面上會列出 偵測到的臉部特徵這些點代表 而不是每個特徵的分數查看臉孔 偵測概念總覽,進一步瞭解輪廓如何 。
下圖說明這些點如何對應到表面 (按一下 可放大的圖片):
即時臉部偵測
如要在即時應用程式中使用臉部偵測功能,請按照下列步驟操作: 實現最佳影格速率:
設定臉部偵測工具, 臉部輪廓偵測或分類及地標偵測,但兩者只能擇一:
輪廓偵測
地標偵測
分類
地標偵測與分類
模型偵測和地標偵測
模型偵測與分類
輪廓偵測、地標偵測與分類啟用
FAST
模式 (預設為啟用)。建議以較低的解析度拍攝圖片。請特別注意 這個 API 的圖片尺寸規定
- 限制對偵測工具的呼叫。如果新的影片影格 因此請在偵測器執行時捨棄影格。
- 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 並疊加單一步驟這麼一來,您的應用程式就會算繪到顯示途徑 每個輸入影格只能建立一次
-
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。