add dnn

lib/src/dnn/dnn.dart

@@ -0,0 +1,78 @@
+library cv;
+
+import 'dart:ffi' as ffi;
+
+import 'package:ffi/ffi.dart';
+
+import '../core/contours.dart';
+import '../core/point.dart';
+import '../core/rect.dart';
+import '../constants.g.dart';
+import '../core/extensions.dart';
+import '../core/base.dart';
+import '../core/core.dart';
+import '../core/scalar.dart';
+
+import '../core/mat.dart';
+import '../opencv.g.dart' as cvg;
+
+final _bindings = cvg.CvNative(loadNativeLibrary());
+
+// Net allows you to create and manipulate comprehensive artificial neural networks.
+//
+// For further details, please see:
+// https://docs.opencv.org/master/db/d30/classcv_1_1dnn_1_1Net.html
+class Net implements ffi.Finalizable {
+  Net._(this.ptr) {
+    finalizer.attach(this, ptr);
+  }
+
+  /// Empty returns true if there are no layers in the network.
+  ///
+  /// For further details, please see:
+  /// https://docs.opencv.org/master/db/d30/classcv_1_1dnn_1_1Net.html#a6a5778787d5b8770deab5eda6968e66c
+  bool get isEmpty => _bindings.Net_Empty(ptr);
+
+  // SetInput sets the new value for the layer output blob.
+//
+// For further details, please see:
+// https://docs.opencv.org/trunk/db/d30/classcv_1_1dnn_1_1Net.html#a672a08ae76444d75d05d7bfea3e4a328
+  void setInput(InputArray blob, {String name = "", double scalefactor = 1.0, Scalar? mean}) {
+    mean ??= Scalar.default_();
+    using((arena) {
+      final cname = name.toNativeUtf8(allocator: arena);
+      _bindings.Net_SetInput(ptr, blob.ptr, cname.cast());
+    });
+  }
+
+  cvg.Net ptr;
+  static final finalizer = ffi.NativeFinalizer(_bindings.addresses.Net_Close);
+}
+
+// DNN_BACKEND_DEFAULT equals to OPENCV_DNN_BACKEND_DEFAULT, which can be defined using CMake or a configuration parameter
+const int DNN_BACKEND_DEFAULT = 0;
+const int DNN_BACKEND_HALIDE = 1;
+// Intel OpenVINO computational backend
+const int DNN_BACKEND_INFERENCE_ENGINE = 2;
+const int DNN_BACKEND_OPENCV = 3;
+const int DNN_BACKEND_VKCOM = 4;
+const int DNN_BACKEND_CUDA = 5;
+const int DNN_BACKEND_WEBNN = 6;
+const int DNN_BACKEND_TIMVX = 7;
+const int DNN_BACKEND_CANN = 8;
+
+const int DNN_TARGET_CPU = 0;
+const int DNN_TARGET_OPENCL = 1;
+const int DNN_TARGET_OPENCL_FP16 = 2;
+const int DNN_TARGET_MYRIAD = 3;
+const int DNN_TARGET_VULKAN = 4;
+
+/// FPGA device with CPU fallbacks using Inference Engine's Heterogeneous plugin.
+const int DNN_TARGET_FPGA = 5;
+const int DNN_TARGET_CUDA = 6;
+const int DNN_TARGET_CUDA_FP16 = 7;
+const int DNN_TARGET_HDDL = 8;
+const int DNN_TARGET_NPU = 9;
+
+/// Only the ARM platform is supported. Low precision computing, accelerate model inference.
+const int DNN_TARGET_CPU_FP16 = 10;