Inferenced output image is weired result, can someone help me?

[md-rifatkhan]

I'm trying in inference upscaling model but its not working. Someone help me.

Orginal Image: https://github.com/gtbluesky/onnxruntime_flutter/assets/102645154/62188e6d-457f-4783-9b83-7d409cc16eb8

Output Image: https://github.com/gtbluesky/onnxruntime_flutter/assets/102645154/4de92532-d225-4f08-b6d6-b99ad9854135

Some Details:

Model input shape: ['batch_size', 3, 'width', 'height']
Model output shape: ['batch_size', 3, 'width', 'height']

flutter: Is normalized: true
flutter: Image normalized successfully.
flutter: Input tensor created successfully.
flutter: Width: 1080, Height: 1396, Channel: 3

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

    if (selectedImage != null) {
      Float32List? floatData;
      try {
        final normalizedImage = selectedImage!.convert(format: img.Format.float32, numChannels: 3,);
        final pixelData = normalizedImage.buffer.asFloat32List();
        floatData = pixelData;
        debugPrint("Is normalized: ${isNormalized(pixelData)}");
      } catch (e) {
        debugPrint("Error during normalization: $e");
      }

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

      debugPrint('Image normalized successfully.');

      final inputOrt =
          OrtValueTensor.createTensorWithDataList(floatData!, shape);

      final inputs = {'input': inputOrt};

      debugPrint('Input tensor created successfully.');

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

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

      List c = outputs?[0]?.value as List;
      if (c is List<List<List<List<double>>>>) {
        img.Image generatedImage = generateImageFromOutput(c);
        if(!context.mounted) return;
        showDialog(
          context: context,
          builder: (BuildContext context) {
            return Dialog(
              child: SizedBox(
                width: generatedImage.width.toDouble(),
                height: generatedImage.height.toDouble(),
                child: Image.memory(
                  Uint8List.fromList(img.encodePng(generatedImage)),
                  fit: BoxFit.contain,
                ),
              ),
            );
          },
        );
      } else {
        debugPrint("Output is of unknown type");
      }
      outputs?.forEach((element) {
        element?.release();
      });
    }
  }

  img.Image generateImageFromOutput(
      List<List<List<List<double>>>> outputValue) {
    int width = outputValue[0][0].length;
    int height = outputValue[0][0][0].length;
    int channel = outputValue[0].length;

    print("Width: $width, Height: $height, Channel: $channel");

    // Create the image
    img.Image generatedImage = img.Image(width: width, height: height);

    // Set pixel values
    for (int y = 0; y < height; y++) {
      for (int x = 0; x < width; x++) {
        // Extract RGB values from the output tensor data
        int r = (outputValue[0][0][x][y] * 255).toInt().clamp(0, 255);
        int g = (outputValue[0][1][x][y] * 255).toInt().clamp(0, 255);
        int b = (outputValue[0][2][x][y] * 255).toInt().clamp(0, 255);
        // Set pixel value in the generated image
        generatedImage.setPixelRgb(x, y, r, g, b);
      }
    }
    return generatedImage;
  }

  bool isNormalized(Float32List data) {
    for (var pixelValue in data) {
      if (pixelValue < 0 || pixelValue > 1) {
        return false;
      }
    }
    return true;
  }

[md-rifatkhan]