before candybox editor update
This commit is contained in:
meelstorm
107
Plugins/CandyboxPlugin/Detectors/BlisterDetector.cs
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107
Plugins/CandyboxPlugin/Detectors/BlisterDetector.cs
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using System.Reflection;
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using Microsoft.ML.OnnxRuntime;
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using Microsoft.ML.OnnxRuntime.Tensors;
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using OpenCvSharp;
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namespace CandyboxPlugin.Detectors
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{
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public class BlisterDetector
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{
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private readonly InferenceSession _AIsession;
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private readonly string _inputName;
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private readonly int[] _dimensions;
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static bool _initialized = false;
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private const string MODEL_NAME = "model_transparent_blister_resnet_navy.onnx";
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public BlisterDetector()
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{
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var opts = new SessionOptions();
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// get this dll path
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var path=Assembly.GetCallingAssembly().Location;
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var dir=System.IO.Path.GetDirectoryName(path);
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var modelPath = Path.Combine(dir,"models\\" + MODEL_NAME);
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_AIsession = new InferenceSession(modelPath, opts);
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_inputName = _AIsession.InputMetadata.First().Key;
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_dimensions = _AIsession.InputMetadata.First().Value.Dimensions;
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// inputs
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foreach (var input in _AIsession.InputMetadata)
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{
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Console.WriteLine($"Name: {input.Key}");
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Console.WriteLine($" Type: {input.Value.ElementType}");
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Console.WriteLine($" Shape: [{string.Join(", ", input.Value.Dimensions)}]");
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}
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// outputs
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foreach (var output in _AIsession.OutputMetadata)
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{
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Console.WriteLine($"Name: {output.Key}");
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Console.WriteLine($" Type: {output.Value.ElementType}");
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Console.WriteLine($" Shape: [{string.Join(", ", output.Value.Dimensions)}]");
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}
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}
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public Mat Eval(Mat bmpTest)
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{
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var currentImage = bmpTest;
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var rightColor = currentImage.CvtColor(ColorConversionCodes.BGR2RGB);
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// convert to float32
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var floatImage = new Mat();
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rightColor.ConvertTo(floatImage, MatType.CV_32FC3, 1.0 / 255);
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// to array
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var width = _dimensions[1];
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var height = _dimensions[2];
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// check if need resize
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if (floatImage.Width != width || floatImage.Height != height)
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Cv2.Resize(floatImage, floatImage, new OpenCvSharp.Size(width, height));
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float[] data = new float[3 * width * height];
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int idx = 0;
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var rows = floatImage.Rows;
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var cols = floatImage.Cols;
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for (int y = 0; y < rows; y++)
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{
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for (int x = 0; x < cols; x++)
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{
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Vec3f pixel = floatImage.At<Vec3f>(y, x);
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for (int c = 0; c < 3; c++) // channels
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{
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data[idx++] = pixel[c]; // width*height*channel
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}
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}
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}
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var inputTensor = new DenseTensor<float>(
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data,
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new int[] { 1, floatImage.Width, floatImage.Height, 3 }
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);
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var inputs = new List<NamedOnnxValue> {
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NamedOnnxValue.CreateFromTensor("input", inputTensor)
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};
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var sw = System.Diagnostics.Stopwatch.StartNew();
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using var results = _AIsession.Run(inputs);
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sw.Stop();
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Console.WriteLine($"Inference time: {sw.ElapsedMilliseconds} ms");
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var output = results.First().AsEnumerable<float>().ToArray();
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// convert output to Mat and normalize to 0-255
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var outputMat = new Mat(new OpenCvSharp.Size(width, height), MatType.CV_32FC1);
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outputMat.SetArray<float>(output);
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outputMat.ConvertTo(outputMat, MatType.CV_8UC1, 255);
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// resize back to original size
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Cv2.Resize(outputMat, outputMat, new OpenCvSharp.Size(currentImage.Width, currentImage.Height));
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return outputMat;
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}
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}
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}
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94
Plugins/CandyboxPlugin/Detectors/CandyDetector.cs
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94
Plugins/CandyboxPlugin/Detectors/CandyDetector.cs
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@@ -0,0 +1,94 @@
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using System.Reflection;
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using Microsoft.ML.OnnxRuntime;
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using Microsoft.ML.OnnxRuntime.Tensors;
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using OpenCvSharp;
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namespace CandyboxPlugin.Detectors
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{
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public class CandyDetector
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{
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private const string MODEL_NAME = "model_transparent_candy_resnet.onnx";
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private readonly InferenceSession _AIsession;
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private readonly string _inputName;
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private readonly int[] _dimensions;
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public CandyDetector()
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{
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var opts = new SessionOptions();
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// get this dll path
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var path = Assembly.GetCallingAssembly().Location;
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var dir = System.IO.Path.GetDirectoryName(path);
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var modelPath = Path.Combine(dir, "models\\" + MODEL_NAME);
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_AIsession = new InferenceSession(modelPath, opts);
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_inputName = _AIsession.InputMetadata.First().Key;
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_dimensions = _AIsession.InputMetadata.First().Value.Dimensions;
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foreach (var input in _AIsession.InputMetadata)
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{
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Console.WriteLine($"Name: {input.Key}");
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Console.WriteLine($" Type: {input.Value.ElementType}");
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Console.WriteLine($" Shape: [{string.Join(", ", input.Value.Dimensions)}]");
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}
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}
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public Mat Eval(Mat bmpTest)
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{
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var currentImage = bmpTest;
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var rightColor = currentImage.CvtColor(ColorConversionCodes.BGR2RGB);
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// convert to float32
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var floatImage = new Mat();
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rightColor.ConvertTo(floatImage, MatType.CV_32FC3, 1.0 / 255);
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// to array
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var width = _dimensions[1];
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var height = _dimensions[2];
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// check if need resize
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if (floatImage.Width != width || floatImage.Height != height)
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Cv2.Resize(floatImage, floatImage, new OpenCvSharp.Size(width, height));
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float[] data = new float[3 * width * height];
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int idx = 0;
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var rows = floatImage.Rows;
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var cols = floatImage.Cols;
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for (int y = 0; y < rows; y++)
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{
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for (int x = 0; x < cols; x++)
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{
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Vec3f pixel = floatImage.At<Vec3f>(y, x);
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for (int c = 0; c < 3; c++) // channels
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{
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data[idx++] = pixel[c]; // width*height*channel
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}
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}
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}
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var inputTensor = new DenseTensor<float>(
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data,
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new int[] { 1, floatImage.Width, floatImage.Height, 3 }
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);
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var inputs = new List<NamedOnnxValue> {
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NamedOnnxValue.CreateFromTensor("input", inputTensor)
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};
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var sw = System.Diagnostics.Stopwatch.StartNew();
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using var results = _AIsession.Run(inputs);
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sw.Stop();
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Console.WriteLine($"Inference time: {sw.ElapsedMilliseconds} ms");
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var output = results.First().AsEnumerable<float>().ToArray();
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// convert output to Mat and normalize to 0-255
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var outputMat = new Mat(new OpenCvSharp.Size(width, height), MatType.CV_32FC1);
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outputMat.SetArray<float>(output);
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outputMat.ConvertTo(outputMat, MatType.CV_8UC1, 255);
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// resize back to original size
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Cv2.Resize(outputMat, outputMat, new OpenCvSharp.Size(currentImage.Width, currentImage.Height));
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return outputMat;
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}
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}
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}
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