在 Android 上使用 ML Kit 為圖片加上標籤

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

  1. 如果還沒試過 將 Firebase 新增至您的 Android 專案
  2. 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'
    }
  3. 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>
    敬上 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。
  4. 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:

    1. 開啟 ML Kit Firebase 控制台的 API 頁面
    2. 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。

    3. 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
    ,瞭解如何調查及移除這項存取權。

    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、OnImageCapturedListenerImageAnalysis.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 URI FirebaseVisionImage.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 FirebaseVisionImageLabelerprocessImage 方法。

  1. 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)
    

  2. 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