I’m trying to upscale image in flutter. But i’m getting unexpected result.
I’m totally newbie on working with inferencing model.
Orginal Image:
enter image description here

Output Image:
enter image description here

LOG: I/flutter ( 592): Image converted to normalized Float32List
I/flutter ( 592): Image normalized successfully.
I/flutter ( 592): Input tensor created successfully.
I/flutter ( 592): Height 2880 Width 1640

Orginal Image: Height 720 Width 210

Here is the code i tried:

  Future<void> inference() async {
    if (selectedImage == null) {
      debugPrint('No image selected');
      return;
    }

    var preprocessedImage =
    NormalizeImage.imageToNormalizedFloat32List(selectedImage!);

    final shape = [1, 3, selectedImage!.height, selectedImage!.width];

    debugPrint('Image normalized successfully.');

    final inputOrt =
    OrtValueTensor.createTensorWithDataList(preprocessedImage, shape);

    final inputs = {'input': inputOrt};

    debugPrint('Input tensor created successfully.');

    final runOptions = OrtRunOptions();
    final outputs = await ortSession.runAsync(runOptions, inputs);

    inputOrt.release();
    runOptions.release();


    outputs?.forEach((element) {
      final outputValue = element?.value;
      if (outputValue is List<List<List<List<double>>>>) {
        img.Image generatedImage = generateImageFromOutput(outputValue);
        List<int> pngBytes = img.encodePng(generatedImage);
        img.Image decodedImage = img.decodeImage(Uint8List.fromList(pngBytes))!;

        showDialog(
          context: context,
          builder: (BuildContext context) {
            return Dialog(
              child: SizedBox(
                width: 200,
                height: 200,
                child: Image.memory(Uint8List.fromList(img.encodePng(decodedImage))),
              ),
            );
          },
        );
      } else {
        debugPrint("Output is of unknown type");
      }
      element?.release();
    });
  }
  img.Image generateImageFromOutput(List<List<List<List<double>>>> output) {
    final width = output[0][0].length;
    final height = output[0][0][0].length;

    debugPrint("Height $height Width $width");

    Float32List float32Data = flattenList(output);
    var imgData = NormalizeImage.denormalizedFloat32ListToImage(float32Data, width, height);
    return imgData;
  }

  Float32List flattenList(List<List<List<List<double>>>> nestedList) {
    List<double> flattened = [];
    for (var fourDimList in nestedList) {
      for (var threeDimList in fourDimList) {
        for (var twoDimList in threeDimList) {
          for (var value in twoDimList) {
            flattened.add(value);
          }
        }
      }
    }
    return Float32List.fromList(flattened);
  }

Normalize Class

class NormalizeImage {
  static Float32List imageToNormalizedFloat32List(Image image) {
    final int height = image.height;
    final int width = image.width;

    Float32List float32Image = Float32List(3 * height * width);

    for (int i = 0; i < height; i++) {
      for (int j = 0; j < width; j++) {
        final int pixelIndex = (i * width + j) * 3;
        final int pixel = image.getPixel(j, i);

        float32Image[pixelIndex] = getRed(pixel) / 255.0;
        float32Image[pixelIndex + 1] = getGreen(pixel) / 255.0;
        float32Image[pixelIndex + 2] = getBlue(pixel) / 255.0;
      }
    }

    print("Image converted to normalized Float32List");
    return float32Image;
  }

  static Image denormalizedFloat32ListToImage(Float32List float32Image, int width, int height) {
    final imgData = Image(width, height);
    for (int i = 0; i < height; i++) {
      for (int j = 0; j < width; j++) {
        final int pixelIndex = (i * width + j) * 3;
        final int r = (float32Image[pixelIndex] * 255).toInt().clamp(0, 255);
        final int g = (float32Image[pixelIndex + 1] * 255).toInt().clamp(0, 255);
        final int b = (float32Image[pixelIndex + 2] * 255).toInt().clamp(0, 255);
        final int color = getColor(r, g, b);
        imgData.setPixel(j, i, color);
      }
    }
    return imgData;
  }
}