C#调用tensorflow模型EAST识别文字

主要利用了Emgucv这个opencv的C#库，但是由于Emgucv里的NMS只能计算Rect不能计算RotateRect所以识别水平文字还行，倾斜的就不行了，代码如下：（写的不规范什么的勿喷....如果有什么问题请告诉我，互相提高嘛）

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using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using System.Threading.Tasks;
using System.IO;
using System.Drawing;
using Emgu;
using Emgu.CV;
using Emgu.Util;
using Emgu.CV.Structure;
using Emgu.CV.Dnn;
using Emgu.CV.Util;

namespace emgucv_east
{
    class Program
    {
        static void Main(string[] args)
        {
            
            Double confThreshold = 0.5;
            Double nmsThreshold = 0.4;
            int inpWidth = 320;
            int inpHeight = 320;
            String model = "frozen_east_text_detection.pb";

            Net net = DnnInvoke.ReadNet(model);
            CvInvoke.NamedWindow("EAST: An Efficient and Accurate Scene Text Detector",Emgu.CV.CvEnum.NamedWindowType.AutoSize);

            VectorOfMat outs = new VectorOfMat();
            string[] outNames = new string[2];
            outNames[0] = "feature_fusion/Conv_7/Sigmoid";
            outNames[1] = "feature_fusion/concat_3";

            Mat frame, blob;
            frame = CvInvoke.Imread("timg (2).jpg");
            CvInvoke.Imshow("frame", frame);

            if (frame.IsEmpty)
            {
                Console.WriteLine("no image");
            }
            else
            {
                blob = DnnInvoke.BlobFromImage(frame, 1, new System.Drawing.Size(inpWidth, inpHeight),new MCvScalar(123.68, 116.78, 103.94),true,false);
                net.SetInput(blob);
                net.Forward(outs, outNames);
                Mat scores = outs[0];
                Mat geometry = outs[1];

                //List<RotatedRect> detections = new List<RotatedRect>();
                
                VectorOfRect boxes = new VectorOfRect();
                VectorOfFloat confidences= new VectorOfFloat();

                Decode dct = new Decode();

                dct.Decode_box(scores, geometry, confThreshold, boxes, confidences);

                VectorOfInt indices = new VectorOfInt();
                DnnInvoke.NMSBoxes(boxes,confidences,(float)confThreshold,(float)nmsThreshold,indices);

                PointF ratio = new PointF((float)((float)frame.Cols/(float)inpWidth), (float)((float)frame.Rows/ (float)inpHeight));
                for (int i = 0; i < indices.Size; i++)
                {
                    Rectangle box = boxes[indices[i]];

                    var vertices_x = box.X * ratio.X;
                    var vertices_y = box.Y * ratio.Y;
                    var vertices_width = box.Width * ratio.X;
                    var vertices_height = box.Height * ratio.Y;

                    var p_x = vertices_x - 0.5 * vertices_width;
                    var p_y = vertices_y - 0.5 * vertices_height;

                    Rectangle box_in = new Rectangle(new Point((int)(p_x), (int)(p_y)),new Size((int)vertices_width,(int)vertices_height));

                    CvInvoke.Rectangle(frame, box_in, new MCvScalar(255,255,0),4);
                    
                }

                CvInvoke.Imshow("result", frame);
            }

            CvInvoke.WaitKey();
        }

        //decode
        


    }

    public class Decode
    {
        public void Decode_box(Mat scores, Mat geometry, double scoreThresh, VectorOfRect detections, VectorOfFloat confidences)
        {
            if (detections.Size>0)
            {
                detections.Clear();
            }

            //CV_Assert(scores.dims == 4); CV_Assert(geometry.dims == 4); CV_Assert(scores.size[0] == 1);
            //CV_Assert(geometry.size[0] == 1); CV_Assert(scores.size[1] == 1); CV_Assert(geometry.size[1] == 5);
            //CV_Assert(scores.size[2] == geometry.size[2]); CV_Assert(scores.size[3] == geometry.size[3]);

            CvException.Equals(scores.Dims, 4);
            CvException.Equals(geometry.Dims, 4);

            var s =scores.SizeOfDimension;
            var sdata = scores.GetData();
            //var x = sdata.GetValue(0,0,0,0);
            var gdata = geometry.GetData();
            //var y = sdata.GetValue(0, 0, 0, 0);

            List<RotatedRect> t_detections = new List<RotatedRect>();
            List<float> t_confidences = new List<float>();
            List<Rectangle> r_detections = new List<Rectangle>();

            for (int y = 0; y < s[2]; ++y)
            {
                for (int x = 0; x < s[3]; x++)
                {
                    float score = (float)sdata.GetValue(0, 0, y, x);
                    if (score<scoreThresh)
                    {
                        continue;
                    }

                    float offsetX = x * 4.0f, offsetY = y * 4.0f;
                    float angle = (float)gdata.GetValue(0, 4, y, x);
                    float cosA = (float)Math.Cos(angle);
                    float sinA = (float)Math.Sin(angle);
                    float h = (float)gdata.GetValue(0, 0, y, x)+ (float)gdata.GetValue(0, 2, y, x);
                    float w = (float)gdata.GetValue(0, 1, y, x) + (float)gdata.GetValue(0, 3, y, x);

                    PointF offset = new PointF(offsetX + cosA * (float)gdata.GetValue(0, 1, y, x) + sinA * (float)gdata.GetValue(0, 2, y, x),
                                    offsetY - sinA * (float)gdata.GetValue(0, 1, y, x) + cosA * (float)gdata.GetValue(0, 2, y, x));

                    PointF p1 = new PointF();
                    p1.X = -sinA * h + offset.X;
                    p1.Y = -cosA * h + offset.Y;

                    PointF p3 = new PointF();
                    p3.X = -cosA * w + offset.X;
                    p3.Y = sinA * w + offset.Y;

                    PointF center = new PointF();
                    center.X = (float)0.5*(p1.X+p3.X);
                    center.Y = (float)0.5 * (p1.Y + p3.Y);

                    SizeF size = new SizeF(w,h);
                    float box_angle = -angle * 180.0f / (float)Math.PI;

                    RotatedRect r = new RotatedRect(center,size,box_angle);
                    Point i_center = new Point((int)(center.X),(int)(center.Y));
                    Size i_size = new Size((int)w,(int)h);
                    
                    Rectangle r_d = new Rectangle(i_center, i_size);

                    r_detections.Add(r_d);
                    t_detections.Add(r);
                    t_confidences.Add(score);

                }
            }

            Rectangle[] k_detections = r_detections.ToArray();
            detections.Push(k_detections);

            float[] k_confidences = t_confidences.ToArray();
            confidences.Push(k_confidences);

        }
    }

}

思路和C++版本一样，只是有些数据类型什么的比较不一样，稍微麻烦点，最后识别效果

NMS的问题只能自己手写了。。。。