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| <Project Sdk="Microsoft.NET.Sdk"> | ||
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| <PropertyGroup> | ||
| <OutputType>Exe</OutputType> | ||
| <TargetFramework>net6.0</TargetFramework> | ||
| <ImplicitUsings>enable</ImplicitUsings> | ||
| <Nullable>enable</Nullable> | ||
| </PropertyGroup> | ||
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| <ItemGroup> | ||
| <PackageReference Include="NumSharp" Version="0.30.0" /> | ||
| <PackageReference Include="OpenCvSharp4" Version="4.9.0.20240103" /> | ||
| <PackageReference Include="SciSharp.TensorFlow.Redist" Version="2.16.0" /> | ||
| <PackageReference Include="SciSharp.TensorFlowHub" Version="0.1.5" /> | ||
| <PackageReference Include="TensorFlow.NET" Version="0.150.0" /> | ||
| </ItemGroup> | ||
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| </Project> |
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| //using System; | ||
| //using System.IO; | ||
| //using System.Linq; | ||
| //using NumSharp; | ||
| //using OpenCvSharp; | ||
| //using Tensorflow; | ||
| //using Tensorflow.Hub; | ||
| //using static Tensorflow.Binding; | ||
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| //class Program | ||
| //{ | ||
| // static void Main(string[] args) | ||
| // { | ||
| // string modelPath = "ssd_mobilenet_v2"; | ||
| // string imagePath = "input_image.jpg"; | ||
| // string outputImagePath = "output_image.jpg"; | ||
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| // // Load the model from TensorFlow Hub | ||
| // var model = LoadModelFromHub("https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2"); | ||
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| // // Preprocess the input image | ||
| // var inputTensor = PreprocessImage(imagePath); | ||
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| // // Run the model | ||
| // var outputs = model.Call(new Tensor[] { inputTensor }); | ||
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| // // Extract detection results | ||
| // var detectionBoxes = outputs[0]["detection_boxes"].numpy(); | ||
| // var detectionScores = outputs[0]["detection_scores"].numpy(); | ||
| // var detectionClasses = outputs[0]["detection_classes"].numpy(); | ||
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| // // Highlight detected objects | ||
| // HighlightObjects(imagePath, detectionBoxes, detectionScores, detectionClasses, outputImagePath); | ||
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| // Console.WriteLine($"Output image saved to {outputImagePath}"); | ||
| // } | ||
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| // static SavedModel LoadModelFromHub(string url) | ||
| // { | ||
| // var modulePath = Path.Combine(Path.GetTempPath(), "hub_module"); | ||
| // if (!Directory.Exists(modulePath)) | ||
| // { | ||
| // Directory.CreateDirectory(modulePath); | ||
| // Console.WriteLine($"Downloading model from {url} to {modulePath}"); | ||
| // // Manually download the model using HttpClient if tfhub.Module.download is unavailable | ||
| // using (var client = new System.Net.Http.HttpClient()) | ||
| // { | ||
| // var data = client.GetByteArrayAsync(url).Result; | ||
| // File.WriteAllBytes(modulePath, data); | ||
| // } | ||
| // } | ||
| // return SavedModel.Load(modulePath); | ||
| // } | ||
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| // static Tensor PreprocessImage(string imagePath) | ||
| // { | ||
| // var img = Cv2.ImRead(imagePath); | ||
| // Cv2.Resize(img, img, new Size(300, 300)); | ||
| // img.ConvertTo(img, MatType.CV_32F); | ||
| // img = img / 255.0f; // Normalize to [0,1] | ||
| // var imgArray = np.array(img.Reshape(new int[] { 1, 300, 300, 3 })); | ||
| // return ops.convert_to_tensor(imgArray); | ||
| // } | ||
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| // static void HighlightObjects(string imagePath, NDArray detectionBoxes, NDArray detectionScores, NDArray detectionClasses, string outputImagePath, float threshold = 0.5f) | ||
| // { | ||
| // var img = Cv2.ImRead(imagePath); | ||
| // int height = img.Height; | ||
| // int width = img.Width; | ||
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| // for (int i = 0; i < detectionScores.size; i++) | ||
| // { | ||
| // float score = detectionScores[i].ToSingle(); | ||
| // if (score < threshold) continue; | ||
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| // int classId = detectionClasses[i].ToInt32(); | ||
| // if (classId != 1 && classId != 43 && classId != 37) continue; // Filter only relevant classes | ||
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| // float[] box = detectionBoxes[i].ToArray<float>(); | ||
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| // int yMin = (int)(box[0] * height); | ||
| // int xMin = (int)(box[1] * width); | ||
| // int yMax = (int)(box[2] * height); | ||
| // int xMax = (int)(box[3] * width); | ||
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| // Cv2.Rectangle(img, new Point(xMin, yMin), new Point(xMax, yMax), Scalar.Green, 2); | ||
| // string label = GetLabel(classId, score); | ||
| // Cv2.PutText(img, label, new Point(xMin, yMin - 10), HersheyFonts.HersheySimplex, 0.5, Scalar.Green, 2); | ||
| // } | ||
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| // Cv2.ImWrite(outputImagePath, img); | ||
| // } | ||
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| // static string GetLabel(int classId, float score) | ||
| // { | ||
| // return classId switch | ||
| // { | ||
| // 1 => $"Person: {score:P2}", | ||
| // 43 => $"Knife: {score:P2}", | ||
| // 37 => $"Gun: {score:P2}", | ||
| // _ => $"Unknown: {score:P2}" | ||
| // }; | ||
| // } | ||
| //} |
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