private async Task InitLicensePlateDetector(Dnn.Backend preferredBackend, Dnn.Target preferredTarget, System.Net.DownloadProgressChangedEventHandler onDownloadProgressChanged = null)
{
if (_vehicleLicensePlateDetectionModel == null)
{
FileDownloadManager manager = new FileDownloadManager();
manager.AddFile(
"https://storage.openvinotoolkit.org/repositories/open_model_zoo/2021.2/models_bin/3/vehicle-license-plate-detection-barrier-0106/FP32/vehicle-license-plate-detection-barrier-0106.xml",
_modelFolderName);
manager.AddFile(
"https://storage.openvinotoolkit.org/repositories/open_model_zoo/2021.2/models_bin/3/vehicle-license-plate-detection-barrier-0106/FP32/vehicle-license-plate-detection-barrier-0106.bin",
_modelFolderName);
manager.OnDownloadProgressChanged += onDownloadProgressChanged;
await manager.Download();
_vehicleLicensePlateDetectionModel =
new DetectionModel(manager.Files[1].LocalFile, manager.Files[0].LocalFile);
_vehicleLicensePlateDetectionModel.SetInputSize(new Size(300, 300));
_vehicleLicensePlateDetectionModel.SetInputMean(new MCvScalar());
_vehicleLicensePlateDetectionModel.SetInputScale(1.0);
_vehicleLicensePlateDetectionModel.SetInputSwapRB(false);
_vehicleLicensePlateDetectionModel.SetInputCrop(false);
_vehicleLicensePlateDetectionModel.SetPreferableBackend(preferredBackend);
_vehicleLicensePlateDetectionModel.SetPreferableTarget(preferredTarget);
}
}
public void Run_WithValidData_ExpectSuccess()
{
// Arrange:
var inputData = new RadarRangeEquationInputData()
{
TransmitFrequency = 10e9,
TransmitterPeakPower = 8000.0,
TransmitPulseDuration = 1e-6,
TransmitAntennaGain_dB = 34.0,
ReceiveAntennaGain_dB = 34.0,
TargetRadarCrossSection = 5.0,
TargetRange = 100e3,
SystemLosses_dB = 6.0,
SystemNoiseFactor_dB = 3.0,
NumberOfPulses = 1024,
};
var detectionModel = new DetectionModel()
{
InputData = inputData,
TargetStartRange = 1000,
TargetEndRange = 200000,
TargetRangeStep = 1000
};
// Act:
detectionModel.Run();
//detectionModel.Results.WriteToCSVFile(@"c:\temp\DetectionModelResults.csv");
// Assert:
Assert.Inconclusive();
}
/// <summary>
/// Release the memory associated with this Yolo detector.
/// </summary>
protected override void DisposeObject()
{
if (_yoloDetectionModel != null)
{
_yoloDetectionModel.Dispose();
_yoloDetectionModel = null;
}
}
/// <summary>
/// Clear and reset the model. Required Init function to be called again before calling ProcessAndRender.
/// </summary>
public void Clear()
{
if (_yoloDetectionModel != null)
{
_yoloDetectionModel.Dispose();
_yoloDetectionModel = null;
}
}
protected override void DisposeObject()
{
if (_yoloDetectionModel != null)
{
_yoloDetectionModel.Dispose();
_yoloDetectionModel = null;
}
//throw new NotImplementedException();
}
/// <summary>
/// Builds a DetectionModel based on the model config.
/// </summary>
/// <param name="model_config">A model.proto object containing the config for the desired DetectionModel.</param>
/// <param name="is_training">True if this model is being built for training purposes.</param>
/// <param name="add_summaries">Whether to add tensorflow summaries in the model graph.</param>
/// <returns>DetectionModel based on the config.</returns>
public FasterRCNNMetaArch build(DetectionModel model_config, bool is_training, bool add_summaries = true)
{
var meta_architecture = model_config.ModelCase;
if (meta_architecture == ModelOneofCase.Ssd)
{
throw new NotImplementedException("");
}
else if (meta_architecture == ModelOneofCase.FasterRcnn)
{
return(_build_faster_rcnn_model(model_config.FasterRcnn, is_training, add_summaries));
}
throw new ValueError($"Unknown meta architecture: {meta_architecture}");
}
public static ImageResizer get_image_resizer_config(DetectionModel model_config)
{
var meta_architecture = model_config.ModelCase;
if (meta_architecture == DetectionModel.ModelOneofCase.FasterRcnn)
{
return(model_config.FasterRcnn.ImageResizer);
}
else if (meta_architecture == DetectionModel.ModelOneofCase.Ssd)
{
return(model_config.Ssd.ImageResizer);
}
throw new Exception($"Unknown model type: {meta_architecture}");
}
public async Task GetDetectionModelShouldBeSuccessful()
{
ClarifaiResponse <IModel <Detection> > response =
await Client.GetModel <Detection>(Client.PublicModels.LogoModel.ModelID)
.ExecuteAsync();
AssertResponseSuccess(response);
Assert.AreEqual(10000, response.Status.StatusCode);
Assert.AreEqual(HttpStatusCode.OK, response.HttpCode);
Assert.NotNull(response.RawBody);
DetectionModel logoModel = (DetectionModel)response.Get();
Assert.NotNull(logoModel.ModelID);
Assert.NotNull(logoModel.OutputInfo.Concepts);
}
public static int get_number_of_classes(DetectionModel model_config)
{
var meta_architecture = model_config.ModelCase;
if (meta_architecture == DetectionModel.ModelOneofCase.FasterRcnn)
{
return(model_config.FasterRcnn.NumClasses);
}
if (meta_architecture == DetectionModel.ModelOneofCase.Ssd)
{
return(model_config.Ssd.NumClasses);
}
throw new Exception("Expected the model to be one of 'faster_rcnn' or 'ssd'.");
}
public async Task Init(System.Net.DownloadProgressChangedEventHandler onDownloadProgressChanged = null)
#endif
{
if (_faceDetectionModel == null)
{
FileDownloadManager manager = new FileDownloadManager();
manager.AddFile(
"https://github.com/opencv/opencv_3rdparty/raw/dnn_samples_face_detector_20170830/res10_300x300_ssd_iter_140000.caffemodel",
_modelFolderName,
"2A56A11A57A4A295956B0660B4A3D76BBDCA2206C4961CEA8EFE7D95C7CB2F2D");
manager.AddFile(
"https://raw.githubusercontent.com/opencv/opencv/4.0.1/samples/dnn/face_detector/deploy.prototxt",
_modelFolderName,
"F62621CAC923D6F37BD669298C428BB7EE72233B5F8C3389BB893E35EBBCF795");
if (onDownloadProgressChanged != null)
{
manager.OnDownloadProgressChanged += onDownloadProgressChanged;
}
#if UNITY_EDITOR || UNITY_IOS || UNITY_ANDROID || UNITY_STANDALONE || UNITY_WEBGL
yield return(manager.Download());
#else
await manager.Download();
#endif
if (manager.AllFilesDownloaded)
{
_faceDetectionModel = new DetectionModel(manager.Files[0].LocalFile, manager.Files[1].LocalFile);
_faceDetectionModel.SetInputMean(new MCvScalar(104, 177, 123));
_faceDetectionModel.SetInputSize(new Size(300, 300));
_faceDetectionModel.SetInputSwapRB(false);
_faceDetectionModel.SetInputScale(1.0);
_faceDetectionModel.SetInputCrop(false);
#if !(UNITY_EDITOR || UNITY_IOS || UNITY_ANDROID || UNITY_STANDALONE || UNITY_WEBGL)
if (Emgu.CV.Cuda.CudaInvoke.HasCuda)
{
_faceDetectionModel.SetPreferableBackend(Emgu.CV.Dnn.Backend.Cuda);
_faceDetectionModel.SetPreferableTarget(Emgu.CV.Dnn.Target.Cuda);
}
#endif
}
}
}
/// <summary>
/// Release the memory associated with this vehicle license plate detector
/// </summary>
protected override void DisposeObject()
{
if (_vehicleLicensePlateDetectionModel != null)
{
_vehicleLicensePlateDetectionModel.Dispose();
_vehicleLicensePlateDetectionModel = null;
}
if (_vehicleAttrRecognizer != null)
{
_vehicleAttrRecognizer.Dispose();
_vehicleAttrRecognizer = null;
}
if (_ocr != null)
{
_ocr.Dispose();
_ocr = null;
}
}
/// <summary>
/// Clear and reset the model. Required Init function to be called again before calling ProcessAndRender.
/// </summary>
public void Clear()
{
if (_vehicleLicensePlateDetectionModel != null)
{
_vehicleLicensePlateDetectionModel.Dispose();
_vehicleLicensePlateDetectionModel = null;
}
if (_vehicleAttrRecognizerModel != null)
{
_vehicleAttrRecognizerModel.Dispose();
_vehicleAttrRecognizerModel = null;
}
if (_ocr != null)
{
_ocr.Dispose();
_ocr = null;
}
}
public Func <DatasetV1Adapter> create_train_input_fn(TrainConfig train_config, InputReader train_input_config, DetectionModel model_config)
{
Func <DatasetV1Adapter> _train_input_fn = () =>
train_input(train_config, train_input_config, model_config);
return(_train_input_fn);
}
/// <summary>
/// Download and initialize the yolo model
/// </summary>
/// <param name="version">The model version</param>
/// <param name="onDownloadProgressChanged">Call back method during download</param>
/// <returns>Asyn task</returns>
public async Task Init(
YoloVersion version = YoloVersion.YoloV3,
System.Net.DownloadProgressChangedEventHandler onDownloadProgressChanged = null)
{
if (_yoloDetectionModel == null)
{
FileDownloadManager manager = new FileDownloadManager();
if (version == YoloVersion.YoloV3Spp)
{
manager.AddFile(
"https://pjreddie.com/media/files/yolov3-spp.weights",
_modelFolderName,
"87A1E8C85C763316F34E428F2295E1DB9ED4ABCEC59DD9544F8052F50DE327B4");
manager.AddFile(
"https://github.com/pjreddie/darknet/raw/master/cfg/yolov3-spp.cfg",
_modelFolderName,
"7A4EC2D7427340FB12059F2B0EF76D6FCFCAC132CC287CBBF0BE5E3ABAA856FD");
}
else if (version == YoloVersion.YoloV3)
{
manager.AddFile(
"https://pjreddie.com/media/files/yolov3.weights",
_modelFolderName,
"523E4E69E1D015393A1B0A441CEF1D9C7659E3EB2D7E15F793F060A21B32F297");
manager.AddFile(
"https://github.com/pjreddie/darknet/raw/master/cfg/yolov3.cfg",
_modelFolderName,
"22489EA38575DFA36C67A90048E8759576416A79D32DC11E15D2217777B9A953");
}
else if (version == YoloVersion.YoloV3Tiny)
{
manager.AddFile(
"https://pjreddie.com/media/files/yolov3-tiny.weights",
_modelFolderName,
"DCCEA06F59B781EC1234DDF8D1E94B9519A97F4245748A7D4DB75D5B7080A42C");
manager.AddFile(
"https://github.com/pjreddie/darknet/raw/master/cfg/yolov3-tiny.cfg",
_modelFolderName,
"84EB7A675EF87C906019FF5A6E0EFFE275D175ADB75100DCB47F0727917DC2C7");
}
manager.AddFile("https://github.com/pjreddie/darknet/raw/master/data/coco.names",
_modelFolderName,
"634A1132EB33F8091D60F2C346ABABE8B905AE08387037AED883953B7329AF84");
if (onDownloadProgressChanged != null)
{
manager.OnDownloadProgressChanged += onDownloadProgressChanged;
}
await manager.Download();
if (manager.AllFilesDownloaded)
{
_yoloDetectionModel = new DetectionModel(manager.Files[0].LocalFile, manager.Files[1].LocalFile);
_yoloDetectionModel.SetInputSize(new Size(416, 416));
_yoloDetectionModel.SetInputScale(0.00392);
_yoloDetectionModel.SetInputMean(new MCvScalar());
_labels = File.ReadAllLines(manager.Files[2].LocalFile);
}
}
}
public ActionResult AddDetection([FromBody] DetectionModel detection)
{
_Apprepo.Add(detection);
_Apprepo.SaveAll();
return(Ok(detection));
}
public ActionResult UpdateDetection([FromBody] DetectionModel detection)
{
_Apprepo.Update(detection);
_Apprepo.SaveAll();
return(Ok(detection));
}
public async Task Init(
YoloVersion version = YoloVersion.YoloV4,
System.Net.DownloadProgressChangedEventHandler onDownloadProgressChanged = null)
#endif
{
if (_yoloDetectionModel == null)
{
FileDownloadManager manager = new FileDownloadManager();
bool version3 = false;
bool version4 = false;
if (version == YoloVersion.YoloV3Spp)
{
manager.AddFile(
"https://pjreddie.com/media/files/yolov3-spp.weights",
_modelFolderName,
"87A1E8C85C763316F34E428F2295E1DB9ED4ABCEC59DD9544F8052F50DE327B4");
manager.AddFile(
"https://github.com/pjreddie/darknet/raw/master/cfg/yolov3-spp.cfg",
_modelFolderName,
"7A4EC2D7427340FB12059F2B0EF76D6FCFCAC132CC287CBBF0BE5E3ABAA856FD");
version3 = true;
}
else if (version == YoloVersion.YoloV3)
{
manager.AddFile(
"https://pjreddie.com/media/files/yolov3.weights",
_modelFolderName,
"523E4E69E1D015393A1B0A441CEF1D9C7659E3EB2D7E15F793F060A21B32F297");
manager.AddFile(
"https://github.com/pjreddie/darknet/raw/master/cfg/yolov3.cfg",
_modelFolderName,
"22489EA38575DFA36C67A90048E8759576416A79D32DC11E15D2217777B9A953");
version3 = true;
}
else if (version == YoloVersion.YoloV3Tiny)
{
manager.AddFile(
"https://pjreddie.com/media/files/yolov3-tiny.weights",
_modelFolderName,
"DCCEA06F59B781EC1234DDF8D1E94B9519A97F4245748A7D4DB75D5B7080A42C");
manager.AddFile(
"https://github.com/pjreddie/darknet/raw/master/cfg/yolov3-tiny.cfg",
_modelFolderName,
"84EB7A675EF87C906019FF5A6E0EFFE275D175ADB75100DCB47F0727917DC2C7");
version3 = true;
}
else if (version == YoloVersion.YoloV4)
{
manager.AddFile(
"https://github.com/aj-ames/YOLOv4-OpenCV-CUDA-DNN/raw/dd58dba457fff98e483e0f67113e0be1f17f2120/models/yolov4.weights",
_modelFolderName,
"8463FDE6EE7130A947A73104CE73C6FA88618A9D9ECD4A65D0B38F07E17EC4E4");
manager.AddFile(
"https://github.com/aj-ames/YOLOv4-OpenCV-CUDA-DNN/raw/dd58dba457fff98e483e0f67113e0be1f17f2120/models/yolov4.cfg",
_modelFolderName,
"A15524EC710005ADD4EB672140CF15CBFE46DEA0561F1AEA90CB1140B466073E");
version4 = true;
}
else if (version == YoloVersion.YoloV4Tiny)
{
manager.AddFile(
"https://github.com/aj-ames/YOLOv4-OpenCV-CUDA-DNN/raw/dd58dba457fff98e483e0f67113e0be1f17f2120/models/yolov4-tiny.weights",
_modelFolderName,
"CF9FBFD0F6D4869B35762F56100F50ED05268084078805F0E7989EFE5BB8CA87");
manager.AddFile(
"https://github.com/aj-ames/YOLOv4-OpenCV-CUDA-DNN/raw/dd58dba457fff98e483e0f67113e0be1f17f2120/models/yolov4-tiny.cfg",
_modelFolderName,
"6CBF5ECE15235F66112E0BEDEBB324F37199B31AEE385B7E18F0BBFB536B258E");
version4 = true;
}
if (version3)
{
manager.AddFile("https://github.com/pjreddie/darknet/raw/master/data/coco.names",
_modelFolderName,
"634A1132EB33F8091D60F2C346ABABE8B905AE08387037AED883953B7329AF84");
}
if (version4)
{
manager.AddFile("https://github.com/aj-ames/YOLOv4-OpenCV-CUDA-DNN/raw/dd58dba457fff98e483e0f67113e0be1f17f2120/models/coco.names",
_modelFolderName,
"634A1132EB33F8091D60F2C346ABABE8B905AE08387037AED883953B7329AF84");
}
if (onDownloadProgressChanged != null)
{
manager.OnDownloadProgressChanged += onDownloadProgressChanged;
}
#if UNITY_EDITOR || UNITY_IOS || UNITY_ANDROID || UNITY_STANDALONE || UNITY_WEBGL
yield return(manager.Download());
#else
await manager.Download();
#endif
if (manager.AllFilesDownloaded)
{
_yoloDetectionModel = new DetectionModel(manager.Files[0].LocalFile, manager.Files[1].LocalFile);
_yoloDetectionModel.SetInputSize(new Size(416, 416));
_yoloDetectionModel.SetInputScale(0.00392);
_yoloDetectionModel.SetInputMean(new MCvScalar());
_labels = File.ReadAllLines(manager.Files[2].LocalFile);
}
}
}
/// <summary>
/// Returns `features` and `labels` tensor dictionaries for training.
/// </summary>
/// <param name="train_config"></param>
/// <param name="train_input_config"></param>
/// <param name="model_config"></param>
/// <returns></returns>
public DatasetV1Adapter train_input(TrainConfig train_config, InputReader train_input_config, DetectionModel model_config)
{
var arch = modelBuilder.build(model_config, true, true);
Func <Tensor, (Tensor, Tensor)> model_preprocess_fn = arch.preprocess;
Func <Dictionary <string, Tensor>, (Dictionary <string, Tensor>, Dictionary <string, Tensor>)> transform_and_pad_input_data_fn = (tensor_dict) =>
{
return(_get_features_dict(tensor_dict), _get_labels_dict(tensor_dict));
};
var dataset = datasetBuilder.build(train_input_config);
return(dataset);
}
