多类别图像分类中处理未知或无关输入的策略

private void classifyImage(Bitmap image) {
    // 假设我们有一个预训练的二分类模型来判断图像中是否存在水果
    // isFruitPresent() 方法需要您自行实现，它会返回一个布尔值
    // 或者返回一个置信度，您再根据阈值判断
    boolean fruitDetected = isFruitPresent(image); 

    if (fruitDetected) {
        try {
            // 如果检测到水果，则进行多类别分类
            FruitDisease model = FruitDisease.newInstance(getApplicationContext());

            // 图像预处理 (与原代码相同)
            TensorBuffer inputFeature = TensorBuffer.createFixedSize(new int[]{1, 224, 224, 3}, DataType.FLOAT32);
            ByteBuffer byteBuffer = ByteBuffer.allocateDirect(4 * imageSize * imageSize * 3);
            byteBuffer.order(ByteOrder.nativeOrder());

            int[] intValue = new int[imageSize * imageSize];
            image.getPixels(intValue, 0, image.getWidth(), 0, 0, image.getWidth(), image.getHeight());

            int pixel = 0;
            for (int i = 0; i < imageSize; i++) {
                for (int j = 0; j < imageSize; j++) {
                    int val = intValue[pixel++];
                    byteBuffer.putFloat(((val >> 16) & 0xFF) * (1.f / 255.f));
                    byteBuffer.putFloat(((val >> 8) & 0xFF) * (1.f / 255.f));
                    byteBuffer.putFloat((val & 0xFF) * (1.f / 255.f));
                }
            }
            inputFeature.loadBuffer(byteBuffer);

            // 执行模型推理
            FruitDisease.Outputs outputs = model.process(inputFeature);
            TensorBuffer outputFeature0 = outputs.getOutputFeature0AsTensorBuffer();

            float[] confidences = outputFeature0.getFloatArray();
            int maxPos = 0;
            float maxConfidence = 0;
            for (int i = 0; i < confidences.length; i++) {
                if (confidences[i] > maxConfidence) {
                    maxConfidence = confidences[i];
                    maxPos = i;
                }
            }

            String[] classes = {"Watermelon Healthy", "Watermelon Blossom End Rot", "Watermelon Anthracnose",
                    "Mango Healthy", "Mango Bacterial Canker", "Mango Anthracnose",
                    "Orange Scab", "Orange Healthy",
                    "Orange Bacterial Citrus Canker", "Banana Healthy", "Banana Crown Rot",
                    "Banana Anthracnose", "Apple Scab", "Apple Healthy", "Apple Black Rot Canker"};

            // 显示识别结果和置信度
            result.setText(classes[maxPos]);
            StringBuilder s = new StringBuilder();
            for (int i = 0; i < classes.length; i++) {
                s.append(String.format("%s: %.1f%%\n", classes[i], confidences[i] * 100));
            }
            confidence.setText(s.toString());
            confidence.setVisibility(View.VISIBLE);

            // 释放模型资源
            model.close();

        } catch (IOException e) {
            // 处理错误
            result.setText("模型加载失败或图像处理错误。");
            confidence.setVisibility(View.GONE);
            e.printStackTrace();
        }
    } else {
        // 如果未检测到水果，显示相应的消息
        result.setText("未检测到植物。请上传一张包含水果的图片。");
        confidence.setVisibility(View.GONE); // 隐藏置信度信息
    }
}

// 这是一个概念性的函数，需要根据您的二分类模型进行具体实现
private boolean isFruitPresent(Bitmap image) {
    // 这里将集成您的二分类模型推理逻辑
    // 例如，使用另一个 TensorFlow Lite 模型来判断图像是否包含水果
    // 假设该模型返回一个置信度分数，我们设置一个阈值
    // float fruitPresenceConfidence = runBinaryClassifier(image);
    // return fruitPresenceConfidence > 0.7; // 假设阈值为0.7

    // 暂时返回true，以便编译，实际项目中需要替换为真实逻辑
    return true; 
}