本文主要是介绍微信二维码检测的C# 实现——opencvsharp Dnn Caffe推理部署，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！
早些时候微信二维码开源在opencv， 找码快解码强，最近我研究DataMartix解码库libdmtx的时候，发现它解码还行，找码有点慢，心想何不让深度学习助它一臂之力？于有了这个；
internal class SSDDetector{private static float CLIP(float x, float x1, float x2) => Math.Max(x1, Math.Min(x, x2));public SSDDetector(string proto_path, string model_path){net_ = CvDnn.ReadNetFromCaffe(proto_path, model_path);}private Net net_;unsafe public List<Mat> Forward(Mat img, int target_width, int target_height){int img_w = img.Cols;int img_h = img.Rows;using Mat input = new();Cv2.Resize(img, input, new Size(target_width, target_height), 0, 0, InterpolationFlags.Cubic);using var blob = CvDnn.BlobFromImage(input, 1.0 / 255, new Size(input.Cols, input.Rows), new Scalar(0f, 0f, 0f),false, false);net_.SetInput(blob, "data");using var prob = net_.Forward("detection_output");List<Mat> point_list = new();// the shape is (1,1,100,7)=>(batch,channel,count,dim)for (int row = 0; row < prob.Size(2); row++){float* prob_score = (float*)prob.Ptr(0, 0, row).ToPointer();// prob_score[0] is not used.// prob_score[1]==1 stands for qrcodeif (prob_score[1] == 1 && prob_score[2] > 1E-5){// add a safe score threshold due to https://github.com/opencv/opencv_contrib/issues/2877// prob_score[2] is the probability of the qrcode, which is not used.var point = new Mat(4, 2, MatType.CV_32FC1);float x0 = CLIP(prob_score[3] * img_w, 0.0f, img_w - 1.0f);float y0 = CLIP(prob_score[4] * img_h, 0.0f, img_h - 1.0f);float x1 = CLIP(prob_score[5] * img_w, 0.0f, img_w - 1.0f);float y1 = CLIP(prob_score[6] * img_h, 0.0f, img_h - 1.0f);point.At<float>(0, 0) = x0;point.At<float>(0, 1) = y0;point.At<float>(1, 0) = x1;point.At<float>(1, 1) = y0;point.At<float>(2, 0) = x1;point.At<float>(2, 1) = y1;point.At<float>(3, 0) = x0;point.At<float>(3, 1) = y1;point_list.Add(point);}}net_.Dispose();return point_list;}} private static void Main(string[] args){Mat src = Cv2.ImRead("dm.bmp");int img_w = src.Cols;int img_h = src.Rows;// hard code input sizeint minInputSize = 1600;float resizeRatio = (float)Math.Sqrt(img_w * img_h * 1.0 / (minInputSize * minInputSize));int detect_width = (int)(img_w / resizeRatio);int detect_height = (int)(img_h / resizeRatio);var key = Cv2.WaitKey(1);int fconut = 0;Cv2.NamedWindow("img", WindowFlags.FreeRatio);int windowH = 1200 * img_h / img_w;Cv2.ResizeWindow("img", new(1200, windowH));Cv2.MoveWindow("img", 200, 20);while (key != 113) // q 退出{fconut++; Scalar scalar = Scalar.RandomColor();int thickness = 2;using Mat img = src.Clone();using Mat gray = src.CvtColor(ColorConversionCodes.BGR2GRAY);SSDDetector SSDD = new("detect.prototxt", "detect.caffemodel");var pointslist = SSDD.Forward(gray, detect_width, detect_height);foreach (var points in pointslist){img.Line((int)points.At<float>(0, 0), (int)points.At<float>(0, 1),(int)points.At<float>(1, 0), (int)points.At<float>(1, 1),scalar, thickness);img.Line((int)points.At<float>(1, 0), (int)points.At<float>(1, 1),(int)points.At<float>(2, 0), (int)points.At<float>(2, 1),scalar, thickness);img.Line((int)points.At<float>(2, 0), (int)points.At<float>(2, 1),(int)points.At<float>(3, 0), (int)points.At<float>(3, 1),scalar, thickness);img.Line((int)points.At<float>(3, 0), (int)points.At<float>(3, 1),(int)points.At<float>(0, 0), (int)points.At<float>(0, 1),scalar, thickness);}img.PutText(fconut.ToString(), new(20, 20), HersheyFonts.HersheyDuplex, 1, Scalar.Red);img.PutText("q : quit", new(20, 60), HersheyFonts.HersheyDuplex, 1, Scalar.Red);Cv2.ImShow("img", img);key = Cv2.WaitKey();}}
原连接：
https://github.com/opencv/opencv_contrib/blob/master/modules/wechat_qrcode/src/detector/ssd_detector.cpp
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