#if !UNITY_WSA_10_0 using OpenCVForUnity.CoreModule; using OpenCVForUnity.UtilsModule; using System; using System.Collections.Generic; using System.Runtime.InteropServices; namespace OpenCVForUnity.DnnModule { // C++: class DetectionModel /** * This class represents high-level API for object detection networks. * * DetectionModel allows to set params for preprocessing input image. * DetectionModel creates net from file with trained weights and config, * sets preprocessing input, runs forward pass and return result detections. * For DetectionModel SSD, Faster R-CNN, YOLO topologies are supported. */ public class DetectionModel : Model { protected override void Dispose(bool disposing) { try { if (disposing) { } if (IsEnabledDispose) { if (nativeObj != IntPtr.Zero) dnn_DetectionModel_delete(nativeObj); nativeObj = IntPtr.Zero; } } finally { base.Dispose(disposing); } } protected internal DetectionModel(IntPtr addr) : base(addr) { } // internal usage only public static new DetectionModel __fromPtr__(IntPtr addr) { return new DetectionModel(addr); } // // C++: cv::dnn::DetectionModel::DetectionModel(String model, String config = "") // /** * Create detection model from network represented in one of the supported formats. * An order of {code model} and {code config} arguments does not matter. * param model Binary file contains trained weights. * param config Text file contains network configuration. */ public DetectionModel(string model, string config) : base(DisposableObject.ThrowIfNullIntPtr(dnn_DetectionModel_DetectionModel_10(model, config))) { } /** * Create detection model from network represented in one of the supported formats. * An order of {code model} and {code config} arguments does not matter. * param model Binary file contains trained weights. */ public DetectionModel(string model) : base(DisposableObject.ThrowIfNullIntPtr(dnn_DetectionModel_DetectionModel_11(model))) { } // // C++: cv::dnn::DetectionModel::DetectionModel(Net network) // /** * Create model from deep learning network. * param network Net object. */ public DetectionModel(Net network) : base(DisposableObject.ThrowIfNullIntPtr(dnn_DetectionModel_DetectionModel_12(network.nativeObj))) { } // // C++: DetectionModel cv::dnn::DetectionModel::setNmsAcrossClasses(bool value) // /** * nmsAcrossClasses defaults to false, * such that when non max suppression is used during the detect() function, it will do so per-class. * This function allows you to toggle this behaviour. * param value The new value for nmsAcrossClasses * return automatically generated */ public DetectionModel setNmsAcrossClasses(bool value) { ThrowIfDisposed(); return new DetectionModel(DisposableObject.ThrowIfNullIntPtr(dnn_DetectionModel_setNmsAcrossClasses_10(nativeObj, value))); } // // C++: bool cv::dnn::DetectionModel::getNmsAcrossClasses() // /** * Getter for nmsAcrossClasses. This variable defaults to false, * such that when non max suppression is used during the detect() function, it will do so only per-class * return automatically generated */ public bool getNmsAcrossClasses() { ThrowIfDisposed(); return dnn_DetectionModel_getNmsAcrossClasses_10(nativeObj); } // // C++: void cv::dnn::DetectionModel::detect(Mat frame, vector_int& classIds, vector_float& confidences, vector_Rect& boxes, float confThreshold = 0.5f, float nmsThreshold = 0.0f) // /** * Given the {code input} frame, create input blob, run net and return result detections. * param classIds Class indexes in result detection. * param confidences A set of corresponding confidences. * param boxes A set of bounding boxes. * param confThreshold A threshold used to filter boxes by confidences. * param nmsThreshold A threshold used in non maximum suppression. * param frame automatically generated */ public void detect(Mat frame, MatOfInt classIds, MatOfFloat confidences, MatOfRect boxes, float confThreshold, float nmsThreshold) { ThrowIfDisposed(); if (frame != null) frame.ThrowIfDisposed(); if (classIds != null) classIds.ThrowIfDisposed(); if (confidences != null) confidences.ThrowIfDisposed(); if (boxes != null) boxes.ThrowIfDisposed(); Mat classIds_mat = classIds; Mat confidences_mat = confidences; Mat boxes_mat = boxes; dnn_DetectionModel_detect_10(nativeObj, frame.nativeObj, classIds_mat.nativeObj, confidences_mat.nativeObj, boxes_mat.nativeObj, confThreshold, nmsThreshold); } /** * Given the {code input} frame, create input blob, run net and return result detections. * param classIds Class indexes in result detection. * param confidences A set of corresponding confidences. * param boxes A set of bounding boxes. * param confThreshold A threshold used to filter boxes by confidences. * param frame automatically generated */ public void detect(Mat frame, MatOfInt classIds, MatOfFloat confidences, MatOfRect boxes, float confThreshold) { ThrowIfDisposed(); if (frame != null) frame.ThrowIfDisposed(); if (classIds != null) classIds.ThrowIfDisposed(); if (confidences != null) confidences.ThrowIfDisposed(); if (boxes != null) boxes.ThrowIfDisposed(); Mat classIds_mat = classIds; Mat confidences_mat = confidences; Mat boxes_mat = boxes; dnn_DetectionModel_detect_11(nativeObj, frame.nativeObj, classIds_mat.nativeObj, confidences_mat.nativeObj, boxes_mat.nativeObj, confThreshold); } /** * Given the {code input} frame, create input blob, run net and return result detections. * param classIds Class indexes in result detection. * param confidences A set of corresponding confidences. * param boxes A set of bounding boxes. * param frame automatically generated */ public void detect(Mat frame, MatOfInt classIds, MatOfFloat confidences, MatOfRect boxes) { ThrowIfDisposed(); if (frame != null) frame.ThrowIfDisposed(); if (classIds != null) classIds.ThrowIfDisposed(); if (confidences != null) confidences.ThrowIfDisposed(); if (boxes != null) boxes.ThrowIfDisposed(); Mat classIds_mat = classIds; Mat confidences_mat = confidences; Mat boxes_mat = boxes; dnn_DetectionModel_detect_12(nativeObj, frame.nativeObj, classIds_mat.nativeObj, confidences_mat.nativeObj, boxes_mat.nativeObj); } #if (UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR const string LIBNAME = "__Internal"; #else const string LIBNAME = "opencvforunity"; #endif // C++: cv::dnn::DetectionModel::DetectionModel(String model, String config = "") [DllImport(LIBNAME)] private static extern IntPtr dnn_DetectionModel_DetectionModel_10(string model, string config); [DllImport(LIBNAME)] private static extern IntPtr dnn_DetectionModel_DetectionModel_11(string model); // C++: cv::dnn::DetectionModel::DetectionModel(Net network) [DllImport(LIBNAME)] private static extern IntPtr dnn_DetectionModel_DetectionModel_12(IntPtr network_nativeObj); // C++: DetectionModel cv::dnn::DetectionModel::setNmsAcrossClasses(bool value) [DllImport(LIBNAME)] private static extern IntPtr dnn_DetectionModel_setNmsAcrossClasses_10(IntPtr nativeObj, [MarshalAs(UnmanagedType.U1)] bool value); // C++: bool cv::dnn::DetectionModel::getNmsAcrossClasses() [DllImport(LIBNAME)] [return: MarshalAs(UnmanagedType.U1)] private static extern bool dnn_DetectionModel_getNmsAcrossClasses_10(IntPtr nativeObj); // C++: void cv::dnn::DetectionModel::detect(Mat frame, vector_int& classIds, vector_float& confidences, vector_Rect& boxes, float confThreshold = 0.5f, float nmsThreshold = 0.0f) [DllImport(LIBNAME)] private static extern void dnn_DetectionModel_detect_10(IntPtr nativeObj, IntPtr frame_nativeObj, IntPtr classIds_mat_nativeObj, IntPtr confidences_mat_nativeObj, IntPtr boxes_mat_nativeObj, float confThreshold, float nmsThreshold); [DllImport(LIBNAME)] private static extern void dnn_DetectionModel_detect_11(IntPtr nativeObj, IntPtr frame_nativeObj, IntPtr classIds_mat_nativeObj, IntPtr confidences_mat_nativeObj, IntPtr boxes_mat_nativeObj, float confThreshold); [DllImport(LIBNAME)] private static extern void dnn_DetectionModel_detect_12(IntPtr nativeObj, IntPtr frame_nativeObj, IntPtr classIds_mat_nativeObj, IntPtr confidences_mat_nativeObj, IntPtr boxes_mat_nativeObj); // native support for java finalize() [DllImport(LIBNAME)] private static extern void dnn_DetectionModel_delete(IntPtr nativeObj); } } #endif