您可以使用 ML Kit 為圖片中辨識出的物件加上標籤, 例如裝置端模型或雲端模式詳情請參閱 總覽,以瞭解 。
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' implementation 'com.google.firebase:firebase-ml-vision-image-label-model:20.0.1' }
-
選用步驟,但建議使用:如果您使用裝置端 API,請設定
應用程式,在應用程式完成更新後,自動將機器學習模型下載至裝置
安裝
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="label" /> <!-- To use multiple models: android:value="label,model2,model3" --> </application>
如果您未啟用安裝期間模型下載功能,模型就會 。您提出的要求 無法完成下載。 -
如要使用雲端模型,且尚未啟用 為專案設定雲端式 API,請立即採用以下做法:
- 開啟 ML Kit Firebase 控制台的 API 頁面。
-
如果您尚未將專案升級至 Blaze 定價方案,請按一下 如要這麼做,請升級。(只有在您的 專案並未採用 Blaze 方案)。
只有 Blaze 層級的專案可以使用以雲端為基礎的 API。
- 如果尚未啟用雲端式 API,請按一下「Enable Cloud-based API」(啟用雲端式 API) API
如果只想使用裝置端模型,可以略過這個步驟。
您現在可以使用裝置上的模型或 雲端式模型
1. 準備輸入圖片
使用圖片建立FirebaseVisionImage
物件。
當您使用 Bitmap
或
camera2 API 是 JPEG 格式的 media.Image
,建議在
-
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。
2. 設定並執行映像檔標籤工具
如要為圖片中的物件加上標籤,請將FirebaseVisionImage
物件傳遞至
FirebaseVisionImageLabeler
的 processImage
方法。
首先,請取得
FirebaseVisionImageLabeler
。如要使用裝置端圖片標籤工具:
Java
FirebaseVisionImageLabeler labeler = FirebaseVision.getInstance() .getOnDeviceImageLabeler(); // Or, to set the minimum confidence required: // FirebaseVisionOnDeviceImageLabelerOptions options = // new FirebaseVisionOnDeviceImageLabelerOptions.Builder() // .setConfidenceThreshold(0.7f) // .build(); // FirebaseVisionImageLabeler labeler = FirebaseVision.getInstance() // .getOnDeviceImageLabeler(options);
Kotlin+KTX
val labeler = FirebaseVision.getInstance().getOnDeviceImageLabeler() // Or, to set the minimum confidence required: // val options = FirebaseVisionOnDeviceImageLabelerOptions.Builder() // .setConfidenceThreshold(0.7f) // .build() // val labeler = FirebaseVision.getInstance().getOnDeviceImageLabeler(options)
如何使用雲端映像檔標籤工具:
Java
FirebaseVisionImageLabeler labeler = FirebaseVision.getInstance() .getCloudImageLabeler(); // Or, to set the minimum confidence required: // FirebaseVisionCloudImageLabelerOptions options = // new FirebaseVisionCloudImageLabelerOptions.Builder() // .setConfidenceThreshold(0.7f) // .build(); // FirebaseVisionImageLabeler labeler = FirebaseVision.getInstance() // .getCloudImageLabeler(options);
Kotlin+KTX
val labeler = FirebaseVision.getInstance().getCloudImageLabeler() // Or, to set the minimum confidence required: // val options = FirebaseVisionCloudImageLabelerOptions.Builder() // .setConfidenceThreshold(0.7f) // .build() // val labeler = FirebaseVision.getInstance().getCloudImageLabeler(options)
接著,將圖片傳遞至
processImage()
方法:Java
labeler.processImage(image) .addOnSuccessListener(new OnSuccessListener<List<FirebaseVisionImageLabel>>() { @Override public void onSuccess(List<FirebaseVisionImageLabel> labels) { // Task completed successfully // ... } }) .addOnFailureListener(new OnFailureListener() { @Override public void onFailure(@NonNull Exception e) { // Task failed with an exception // ... } });
Kotlin+KTX
labeler.processImage(image) .addOnSuccessListener { labels -> // Task completed successfully // ... } .addOnFailureListener { e -> // Task failed with an exception // ... }
3. 取得加上標籤的物件相關資訊
如果圖片標籤作業成功,系統會顯示FirebaseVisionImageLabel
物件會傳遞到
成功事件監聽器每個 FirebaseVisionImageLabel
物件都代表一個
預先在圖片中加上標籤的文字您可以看到每個標籤的文字
說明、其
知識圖譜實體 ID
以及比對結果的可信度分數。例如:
Java
for (FirebaseVisionImageLabel label: labels) {
String text = label.getText();
String entityId = label.getEntityId();
float confidence = label.getConfidence();
}
Kotlin+KTX
for (label in labels) {
val text = label.text
val entityId = label.entityId
val confidence = label.confidence
}
即時效能改善訣竅
如要在即時應用程式中為圖片加上標籤,請按照下列步驟操作: 實現最佳影格速率:
- 限制對圖片標籤人員的呼叫。如果新的影片影格 請在圖片標籤工具執行期間捨棄頁框。
- 如果您使用圖片標籤人員的輸出內容,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。
後續步驟
- 部署至使用 Cloud API 的正式版應用程式之前,您應先完成 防範及減少 未經授權 API 存取的影響