是否有任何与 NumPy 等效的 Java？

Question

我试图从 tflite 模型中获得预测，以获取眼睛区域地标。 我使用 python 脚本成功地从 tflite 模型中获取了值，但我不知道如何使用 MLKit firebase 获取这些值。 Python 脚本使用 numpy 库将图像转换为数组。 我想知道这里有什么问题？ 有什么方法可以在 java 代码中使用 numpy 函数？ 如果没有，java中的numpy库是什么等价物？

Python 脚本

data = np.asarray( img, dtype="float32" ) 

# Inference on input data normalized to [0, 1]
inputImg = np.expand_dims(data,0).astype(np.float32)
input_details = interpreter.get_input_details()
interpreter.set_tensor(input_details[0]['index'], inputImg)

interpreter.invoke()

output_details = interpreter.get_output_details()
output_data = interpreter.get_tensor(output_details[0]['index'])
print(output_data)

Java 代码 (Android) 的问题在于这些功能：

private float[][][][] bitmapToInputArray() {
        // [START mlkit_bitmap_input]
        Bitmap bitmap= getYourInputImage();
        bitmap = Bitmap.createScaledBitmap(bitmap, 112, 112, true);

        int batchNum = 0;
        float[][][][] input = new float[1][112][112][3];
        for (int x = 0; x < 112; x++) {
            for (int y = 0; y < 112; y++) {
                int pixel = bitmap.getPixel(x, y);
                // Normalize channel values to [-1.0, 1.0]. This requirement varies by
                // model. For example, some models might require values to be normalized
                // to the range [0.0, 1.0] instead.
                input[batchNum][x][y][0] = (Color.red(pixel) - 127) / 128.0f;
                input[batchNum][x][y][1] = (Color.green(pixel) - 127) / 128.0f;
                input[batchNum][x][y][2] = (Color.blue(pixel) - 127) / 128.0f;
                Log.i("Input","input"+input[batchNum][x][y]);

            }
        }
        // [END mlkit_bitmap_input]

      return input;
    }

 private void useInferenceResult(float[] probabilities) throws IOException {
        String[] result=new String[80];
        String x="";
        String y="";
        ArrayList<Point> listpoint= new ArrayList<Point>();

        double viewWidth = canvas.getWidth();
        double viewHeight = canvas.getHeight();
        double imageWidth = mutableBitmap.getWidth();
        double imageHeight = mutableBitmap.getHeight();
        Log.i("viewWidth","viewwidth "+viewWidth);
        Log.i("viewHeight","viewheight "+viewHeight);
        Log.i("imagewidth","imagewidth "+imageWidth);
        Log.i("imaageHeigh","imageheigh "+imageHeight);
        double scale = Math.min(viewWidth / imageWidth, viewHeight / imageHeight);
        Log.i("Scale","Scale"+scale);
        try {
            for (int i = 0; i < probabilities.length; i++) {

                Log.i("MLKit", String.format("%1.8f", probabilities[i]));
                float i1 = probabilities[i];
                Log.i("floaaat", "" + i1);
            }
        }

• python 脚本的结果：

[0.33135968 0.19592011 0.34212315 0.17297666 0.36624995 0.16413747 0.3894139 0.17440952 0.39828074 0.1978043 0.3891497 0.22268474 0.36345637 0.22974193 0.3401759 0.2193309 0.30167252 0.20411113 0.3167112 0.19134495 0.33793524 0.18388326 0.3642417 0.18049955 0.3903508 0.18533507 0.40906873 0.1957745 0.42142123 0.21091096 0.40550107 0.21829814 0.38345626 0.22071144 0.35900232 0.22142673 0.3363348 0.21877256 0.3161971 0.2133534 0.62843406 0.21482795 0.6389724 0.1914106 0.6628249 0.1835615 0.6858679 0.19583184 0.6946868 0.22111627 0.6840309 0.24444285 0.66027373 0.25241333 0.6351568 0.24192403 0.60499936 0.22642238 0.6210091 0.21289764 0.6423563 0.2042976 0.6685919 0.20277795 0.69201195 0.20948553 0.70882106 0.22015369 0.71931773 0.23518339 0.7076659 0.24166131 0.69054717 0.24350837 0.6694564 0.24258481 0.64537776 0.23927754 0.62199306 0.23511863]

• Android java 代码的结果：

2019-08-17 14:47:50.617 21349-21349/com.example.irisdetection I/MLKit: 0,23961355 2019-08-17 14:47:50.620 21349-21349/com.example.irisdetection I/MLKit: 0,25104424 2019-08-17 14:47:50.621 21349-21349/com.example.irisdetection I/MLKit: 0,28179651 2019-08-17 14:47:50.622 21349-21349/com.example.irisdetection I/MLKit: 0,31467810 2019-08-17 14:47:50.623 21349-21349/com.example.irisdetection I/MLKit: 0,33257431 2019-08-17 14:47:50.624 21349-21349/com.example.irisdetection I/MLKit: 0,32645294 2019-08-17 14:47:50.625 21349-21349/com.example.irisdetection I/MLKit: 0,29138848 2019-08-17 14:47:50.626 21349-21349/com.example.irisdetection I/MLKit: 0,25581932 2019-08-17 14:47:50.627 21349-21349/com.example.irisdetection I/MLKit: 0,19593856 2019-08-17 14:47:50.628 21349-21349/com.example.irisdetection I/MLKit: 0,21698779 2019-08-17 14:47:50.631 21349-21349/com.example.irisdetection I/MLKit: 0,24266151 2019-08-17 14:47:50.632 21349-21349/com.example.irisdetection I/MLKit: 0,27562365 2019-08-17 14:47:50.633 21349-21349/com.example.irisdetection I/MLKit: 0,30823168 2019-08-17 14:47:50.635 21349-21349/com.example.irisdetection I/MLKit: 0,33465266 2019-08-17 14:47:50.636 21349-21349/com.example.irisdetection I/MLKit: 0,35355449 2019-08-17 14:47:50.637 21349-21349/com.example.irisdetection I/MLKit: 0,34009647 2019-08-17 14:47:50.638 21349-21349/com.example.irisdetection I/MLKit: 0,31358159 2019-08-17 14:47:50.640 21349-21349/com.example.irisdetection I/MLKit: 0,28156102 2019-08-17 14:47:50.642 21349-21349/com.example.irisdetection I/MLKit: 0,25063315 2019-08-17 14:47:50.643 21349-21349/com.example.irisdetection I/MLKit: 0,21878451 2019-08-17 14:47:50.644 21349-21349/com.example.irisdetection I/MLKit: 0,69623101 2019-08-17 14:47:50.646 21349-21349/com.example.irisdetection I/MLKit: 0,70167470 2019-08-17 14:47:50.646 21349-21349/com.example.irisdetection I/MLKit: 0,73317540 2019-08-17 14:47:50.648 21349-21349/com.example.irisdetection I/MLKit: 0,76974392 2019-08-17 14:47:50.649 21349-21349/com.example.irisdetection I/MLKit: 0,79195201 2019-08-17 14:47:50.651 21349-21349/com.example.irisdetection I/MLKit: 0,78359401 2019-08-17 14:47:50.652 21349-21349/com.example.irisdetection I/MLKit: 0,75674009 2019-08-17 14:47:50.653 21349-21349/com.example.irisdetection I/MLKit: 0,71786618 2019-08-17 14:47:50.654 21349-21349/com.example.irisdetection I/MLKit: 0,66782737 2019-08-17 14:47:50.655 21349-21349/com.example.irisdetection I/MLKit: 0,68930006 2019-08-17 14:47:50.656 21349-21349/com.example.irisdetection I/MLKit: 0,71668541 2019-08-17 14:47:50.657 21349-21349/com.example.irisdetection I/MLKit: 0,75279719 2019-08-17 14:47:50.658 21349-21349/com.example.irisdetection I/MLKit: 0,78872705 2019-08-17 14:47:50.659 21349-21349/com.example.irisdetection I/MLKit: 0,81867975 2019-08-17 14:47:50.661 21349-21349/com.example.irisdetection I/MLKit: 0,83806717 2019-08-17 14:47:50.662 21349-21349/com.example.irisdetection I/MLKit: 0,82371044 2019-08-17 14:47:50.664 21349-21349/com.example.irisdetection I/MLKit: 0,79749656 2019-08-17 14:47:50.665 21349-21349/com.example.irisdetection I/MLKit: 0,76317006 2019-08-17 14:47:50.666 21349-21349/com.example.irisdetection I/MLKit: 0,72700304 2019-08-17 14:47:50.667 21349-21349/com.example.irisdetection I/MLKit: 0,69159627

Answer 1

对我来说，将颜色顺序从 RGB 更改为 BRG 很有帮助。如果您使用 cv2.imread 获取图像，那么它将是 BGR 和 8 位调色板（像素为 256 色）。所以你做的几乎是对的，只需改变顺序