python results different from tensorflowsharp

I use tensorflowsharp to locate items on a conveyor belt - it works 😃 but the results i see on a python (anaconda) are much better than the tensorflowharp

python image is

when i run tensorflowsharp with my inference graph i get

To get here i changed the tnsorflosharp object detection sample like this

_catalogPath = "C:/Users/user 67/Downloads/TensorFlowSharp-master/Examples/ExampleObjectDetection/bin/Debug/demo/inferencegraph/labelmap.pbtxt";
         if (_catalogPath == null) {
  _catalogPath = DownloadDefaultTexts (_currentDir);
 }
_modelPath = "C:/Users/user 67/Downloads/TensorFlowSharp-master/Examples/ExampleObjectDetection/bin/Debug/demo/inferencegraph/frozen_inference_graph.pb";
         if (_modelPath == null) {
  _modelPath = DownloadDefaultModel (_currentDir);
 }

the .pb file is too large to be here i will try to upload it later

all of this in a slightly nicer form in a link link

in the tensorflosharp console differ in that in the original i get some lines of warnings (5-6) while if i try to load my file i get this tensorflosharp results.txt tensorflowsharp does not say if it is an error or just reading of .pb file contents

windows 10 .net 4.7.1

[SignalRT]

Thank you for your answer. I arrive to the same solution redoing the code following the python version:

private static TFGraph ConstructGraphToNormalizeImage( out TFOutput input, out TFOutput output, TFDataType destinationDataType = TFDataType.Float) { var graph = new TFGraph();

        input = graph.Placeholder(TFDataType.String);

        output = graph.Cast(
                    graph.ExpandDims(
                            input: graph.Cast(graph.DecodeJpeg(contents: input, channels: 3), DstT: TFDataType.Float),
                            dim: graph.Const(0, "make_batch")
                   )
                   , destinationDataType
                );

        return graph;
    }

The output from the Object Detection demo it’s the same that in the Python version.

[erlonted]

Before the execution the image is resized based in the model training, you need to change it based in your model before execute take a look when converting an image to tensor see my code const int W = 300; const int H = 300; const float Mean = 0; const float Scale = 1;

  graph = new TFGraph();
  input = graph.Placeholder(TFDataType.String);

  if (resizeTensor)
  {
    output = graph.Cast(graph.Div(
      x: graph.Sub(
        x: graph.ResizeBilinear(
          images: graph.ExpandDims(
            input: graph.Cast(
              graph.DecodeJpeg(contents: input, channels: 3), DstT: TFDataType.Float),
            dim: graph.Const(0, "make_batch")),
          size: graph.Const(new int[] { W, H }, "size")),
        y: graph.Const(Mean, "mean")),
      y: graph.Const(Scale, "scale")), destinationDataType);
  }
  else
  {
    output = graph.Cast(
     graph.ExpandDims(
     input: graph.Cast(
       graph.DecodeJpeg(contents: input, channels: 3), DstT: TFDataType.Float),
     dim: graph.Const(0, "make_batch")), destinationDataType);

  }