计数使用TensorFlowlite Android检测到的对象数

Question

计数使用tensorflowlite android检测到的对象数。这是tflite给出结果的代码：

public class TensorFlowImageClassifier implements Classifier {

    private static final int MAX_RESULTS = 3;

    private static final int BATCH_SIZE = 1;

    private static final int PIXEL_SIZE = 3;

    private static final float THRESHOLD = 0.1f;

    private Interpreter interpreter;

    private int inputSize;

    private List<String> labelList;

    @Override
    public List<Recognition> recognizeImage(Bitmap bitmap) {
        ByteBuffer byteBuffer = convertBitmapToByteBuffer(bitmap);
        byte[][] result = new byte[1][labelList.size()];
        interpreter.run(byteBuffer, result);
        return getSortedResult(result);
    }

    private ByteBuffer convertBitmapToByteBuffer(Bitmap bitmap) {
        ByteBuffer byteBuffer = ByteBuffer.allocateDirect(BATCH_SIZE * inputSize * inputSize * PIXEL_SIZE);
        byteBuffer.order(ByteOrder.nativeOrder());
        int[] intValues = new int[inputSize * inputSize];
        bitmap.getPixels(intValues, 0, bitmap.getWidth(), 0, 0, bitmap.getWidth(), bitmap.getHeight());
        int pixel = 0;
        for (int i = 0; i < inputSize; ++i) {
            for (int j = 0; j < inputSize; ++j) {
                final int val = intValues[pixel++];
                byteBuffer.put((byte) ((val >> 16) & 0xFF));
                byteBuffer.put((byte) ((val >> 8) & 0xFF));
                byteBuffer.put((byte) (val & 0xFF));
            }
        }
        return byteBuffer;
    }

    private List<Recognition> getSortedResult(byte[][] labelProbArray) {

        PriorityQueue<Recognition> pq =
                new PriorityQueue<>(
                        MAX_RESULTS,
                        new Comparator<Recognition>() {
                            @Override
                            public int compare(Recognition lhs, Recognition rhs) {
                                return Float.compare(rhs.getConfidence(), lhs.getConfidence());
                            }
                        });

        for (int i = 0; i < labelList.size(); ++i) {
            float confidence = (labelProbArray[0][i] & 0xff) / 255.0f;
            if (confidence > THRESHOLD) {
                pq.add(new Recognition("" + i,
                        labelList.size() > i ? labelList.get(i) : "unknown",
                        confidence));
            }
        }

        final ArrayList<Recognition> recognitions = new ArrayList<>();
        int recognitionsSize = Math.min(pq.size(), MAX_RESULTS);
        for (int i = 0; i < recognitionsSize; ++i) {
            recognitions.add(pq.poll());
        }

        return recognitions;
    }
}