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C# CvDnn परिभ्रमणं कार्यान्वितुं CoupledTPS परिनियोजयतिचित्रशुद्धिकरणम्

सामग्रीसूची

दृष्टान्तरूपेण दर्शयतु

परिणाम

आदर्शसूचना

प्रकल्प

कोड

अवाहरन

---

दृष्टान्तरूपेण दर्शयतु

TPAMI2024 - घूर्णनसुधारार्थं तथा ततः परं अर्धनिरीक्षितयुग्मितपतली-प्लेटस्प्लाइनमाडलम्

गिथुबपत्रसङ्केतः:https://github.com/nie-lang/युग्मितटीपीएस

संहिता कार्यान्वयनसन्दर्भः : १.https://github.com/hpc203/युग्मितटीपीएस-opencv-dnn

परिणाम

आदर्शसूचना

विशेषता_निष्कर्षक.onnx

आदर्श गुण
-------------------------
---------------------------------------------------------------

निवेशाः
-------------------------
नाम:निवेशः
टेन्सर:प्लव[१, ३, ३८४, ५१२] ।
---------------------------------------------------------------

आउटपुट्
-------------------------
नाम:विशेषता
tensor:प्लव[१, २५६, २४, ३२] ।
---------------------------------------------------------------

regressnet.onnx

आदर्श गुण
-------------------------
---------------------------------------------------------------

निवेशाः
-------------------------
नाम:विशेषता
tensor:प्लव[१, २५६, २४, ३२] ।
---------------------------------------------------------------

आउटपुट्
-------------------------
नाम:मेष_गति
tensor:प्लवम्[१, ७, ९, २] ।
---------------------------------------------------------------

प्रकल्प

कोड

रूप1.cs

OpenCvSharp इत्यस्य उपयोगेन;
System इत्यस्य उपयोगेन;
System.Drawing इत्यस्य उपयोगेन;
System.Drawing.Imaging इत्यस्य उपयोगेन;
System.Windows.Forms इत्यस्य उपयोगेन;

नामस्थान Onnx_Demo
{
public partial class Form1 : रूप
    {
public Form1() 1.1.
        {
घटक आरंभ ();
        }

string fileFilter = "*.*|*.bmp;*.jpg;*.jpeg;*.tiff;*.tiff;*.png";
स्ट्रिंग इमेज_पथ = "";
DateTime dt1 = तिथिसमय.अधुना;
DateTime dt2 = तिथिसमय.अधुना;
चटप्रतिमा;

युग्मितTPS_RotationNet घूर्णनजाल;
int इटर_संख्या = 3;

private void button1_Click (वस्तु प्रेषक, EventArgs ई)
        {
OpenFileDialog ofd = new ओपनफाइलसंवाद ();
ofd.InitialDirectory =अनुप्रयोग.प्रारंभमार्ग+"\test_img\";
ofd.Filter = सञ्चिकाफ़िल्टर;
            
if (ofd.ShowDialog () != संवादपरिणाम.ठीक) return;
pictureBox1.Image = शून्य;
चित्र_मार्ग = ofd.FileName;
pictureBox1.Image = नया बिटमैप (छवि_मार्ग);
textBox1.Text = "";
छवि = नया मैट (छवि_मार्ग);
pictureBox2.Image = शून्य;
        }

private void button2_Click (वस्तु प्रेषक, EventArgs ई)
        {
यदि (प्रतिबिम्ब_मार्ग == "")
            {
निर्वतनम्;
            }
button2.Enabled = मिथ्या;
pictureBox2.Image = शून्य;
textBox1.Text = "";
अनुप्रयोग.DoEvents ();
//चित्रं पठन्तु
छवि = नया मैट (छवि_मार्ग);
dt1 = तिथिसमय।अधुना;
मैट परिणाम_छवि = rotationNet.detect (छवि, iter_num);
dt2 = तिथिसमय।अधुना;
Cv2.CvtColor (परिणाम_छवि, परिणाम_छवि, ColorConversionCodes.BGR2RGB);
pictureBox2.Image = नया बिटमैप (परिणाम_छवि.ToMemoryStream ());
textBox1.Text = "अनुमान समय उपभोग:" + (dt2 - dt1).TotalMilliseconds + "ms";
button2.Enabled = सत्यम्;
        }

private void Form1_Load (वस्तु प्रेषक, EventArgs ई)
        {
rotationNet = new CoupledTPS_RotationNet ("मॉडल / फीचर_निष्कर्षक.onnx", "मॉडल / regressnet.onnx");
image_path = "परीक्षण_प्रतिबिम्ब/00150_-8.4.jpg";
pictureBox1.Image = नया बिटमैप (छवि_मार्ग);
छवि = नया मैट (छवि_मार्ग);
        }

private void pictureBox1_DoubleClick (वस्तु प्रेषक, EventArgs ई)
        {
Common.ShowNormalImg (चित्रबॉक्स1.छवि);
        }

private void pictureBox2_DoubleClick (वस्तु प्रेषक, EventArgs ई)
        {
Common.ShowNormalImg (चित्रबॉक्स2.छवि);
        }

SaveFileDialog sdf = new सञ्चिकासंवाद सहेजें ();
private void button3_Click (वस्तु प्रेषक, EventArgs ई)
        {
if (pictureBox2.Image == शून्य)
            {
निर्वतनम्;
            }
बिटमैप आउटपुट = नया बिटमैप (pictureBox2.Image);
sdf.Title = "बचतु";
sdf.Filter = "प्रतिमा (*.jpg)|*.jpg|प्रतिमा (*.png)|*.png|प्रतिमा (*.bmp)|*.bmp|प्रतिमा (*.emf)|*.emf|प्रतिमा।" (*.exif)|*.exif|प्रतिमा (*.gif)|*.gif|प्रतिमा (*.ico)|*.ico|प्रतिमा (*.tiff)|*.tiff|प्रतिमा (*.wmf)| *.wmf";
if (sdf.ShowDialog () == संवादपरिणाम.ठीक)
            {
स्विच (sdf.FilterIndex) 1.1.
                {
प्रकरणम् १: १.
                        {
output.Save (sdf.FileName, छविस्वरूप.Jpeg);
भङ्गः;
                        }
प्रकरणम् २: १.
                        {
output.Save (sdf.FileName, छविस्वरूप.Png);
भङ्गः;
                        }
प्रकरणम् ३: १.
                        {
output.Save (sdf.FileName, छविस्वरूप.Bmp);
भङ्गः;
                        }
प्रकरणम् ४: १.
                        {
output.Save (sdf.FileName, छविस्वरूप.Emf);
भङ्गः;
                        }
प्रकरणम् ५: १.
                        {
output.Save (sdf.FileName, चित्रस्वरूप.Exif);
भङ्गः;
                        }
प्रकरणम् ६: १.
                        {
output.Save (sdf.FileName, चित्रस्वरूप.Gif);
भङ्गः;
                        }
प्रकरणम् ७: १.
                        {
output.Save (sdf.FileName, चित्रस्वरूप.चिह्न);
भङ्गः;
                        }

प्रकरणम् ८: १.
                        {
output.Save (sdf.FileName, छविस्वरूप.Tiff);
भङ्गः;
                        }
प्रकरणम् ९: १.
                        {
output.Save (sdf.FileName, छविस्वरूप.Wmf);
भङ्गः;
                        }
                }
MessageBox.Show("सफलतया रक्षतु, स्थानम्: " + sdf.FileName);
            }
        }
    }
}

using OpenCvSharp;
using System;
using System.Drawing;
using System.Drawing.Imaging;
using System.Windows.Forms;
 
namespace Onnx_Demo
{
    public partial class Form1 : Form
    {
        public Form1()
        {
            InitializeComponent();
        }
 
        string fileFilter = "*.*|*.bmp;*.jpg;*.jpeg;*.tiff;*.tiff;*.png";
        string image_path = "";
        DateTime dt1 = DateTime.Now;
        DateTime dt2 = DateTime.Now;
        Mat image;
 
        CoupledTPS_RotationNet rotationNet;
        int iter_num = 3;
 
        private void button1_Click(object sender, EventArgs e)
        {
            OpenFileDialog ofd = new OpenFileDialog();
            ofd.InitialDirectory =Application.StartupPath+"\test_img\";
            ofd.Filter = fileFilter;
            
            if (ofd.ShowDialog() != DialogResult.OK) return;
            pictureBox1.Image = null;
            image_path = ofd.FileName;
            pictureBox1.Image = new Bitmap(image_path);
            textBox1.Text = "";
            image = new Mat(image_path);
            pictureBox2.Image = null;
        }
 
        private void button2_Click(object sender, EventArgs e)
        {
            if (image_path == "")
            {
                return;
            }
            button2.Enabled = false;
            pictureBox2.Image = null;
            textBox1.Text = "";
            Application.DoEvents();
            //读图片
            image = new Mat(image_path);
            dt1 = DateTime.Now;
            Mat result_image = rotationNet.detect(image, iter_num);
            dt2 = DateTime.Now;
            Cv2.CvtColor(result_image, result_image, ColorConversionCodes.BGR2RGB);
            pictureBox2.Image = new Bitmap(result_image.ToMemoryStream());
            textBox1.Text = "推理耗时:" + (dt2 - dt1).TotalMilliseconds + "ms";
            button2.Enabled = true;
        }
 
        private void Form1_Load(object sender, EventArgs e)
        {
            rotationNet = new CoupledTPS_RotationNet("model/feature_extractor.onnx", "model/regressnet.onnx");
            image_path = "test_img/00150_-8.4.jpg";
            pictureBox1.Image = new Bitmap(image_path);
            image = new Mat(image_path);
        }
 
        private void pictureBox1_DoubleClick(object sender, EventArgs e)
        {
            Common.ShowNormalImg(pictureBox1.Image);
        }
 
        private void pictureBox2_DoubleClick(object sender, EventArgs e)
        {
            Common.ShowNormalImg(pictureBox2.Image);
        }
 
        SaveFileDialog sdf = new SaveFileDialog();
        private void button3_Click(object sender, EventArgs e)
        {
            if (pictureBox2.Image == null)
            {
                return;
            }
            Bitmap output = new Bitmap(pictureBox2.Image);
            sdf.Title = "保存";
            sdf.Filter = "Images (*.jpg)|*.jpg|Images (*.png)|*.png|Images (*.bmp)|*.bmp|Images (*.emf)|*.emf|Images (*.exif)|*.exif|Images (*.gif)|*.gif|Images (*.ico)|*.ico|Images (*.tiff)|*.tiff|Images (*.wmf)|*.wmf";
            if (sdf.ShowDialog() == DialogResult.OK)
            {
                switch (sdf.FilterIndex)
                {
                    case 1:
                        {
                            output.Save(sdf.FileName, ImageFormat.Jpeg);
                            break;
                        }
                    case 2:
                        {
                            output.Save(sdf.FileName, ImageFormat.Png);
                            break;
                        }
                    case 3:
                        {
                            output.Save(sdf.FileName, ImageFormat.Bmp);
                            break;
                        }
                    case 4:
                        {
                            output.Save(sdf.FileName, ImageFormat.Emf);
                            break;
                        }
                    case 5:
                        {
                            output.Save(sdf.FileName, ImageFormat.Exif);
                            break;
                        }
                    case 6:
                        {
                            output.Save(sdf.FileName, ImageFormat.Gif);
                            break;
                        }
                    case 7:
                        {
                            output.Save(sdf.FileName, ImageFormat.Icon);
                            break;
                        }
 
                    case 8:
                        {
                            output.Save(sdf.FileName, ImageFormat.Tiff);
                            break;
                        }
                    case 9:
                        {
                            output.Save(sdf.FileName, ImageFormat.Wmf);
                            break;
                        }
                }
                MessageBox.Show("保存成功，位置：" + sdf.FileName);
            }
        }
    }
}

युग्मितTPS_RotationNet.cs

using OpenCvSharp;
using OpenCvSharp.Dnn;
using System.Collections.Generic;
using System.Linq;
 
namespace Onnx_Demo
{
    public class CoupledTPS_RotationNet
    {
 
        int input_height = 384;
        int input_width = 512;
        int grid_h = 6;
        int grid_w = 8;
        Mat grid = new Mat();
        Mat W_inv = new Mat();
 
        Net feature_extractor;
        Net regressNet;
 
        public CoupledTPS_RotationNet(string modelpatha, string modelpathb)
        {
            feature_extractor = CvDnn.ReadNet(modelpatha);
            regressNet = CvDnn.ReadNet(modelpathb);
            tps2flow.get_norm_rigid_mesh_inv_grid(ref grid, ref W_inv, input_height, input_width, grid_h, grid_w);
        }
 
        unsafe public Mat detect(Mat srcimg, int iter_num)
        {
            Mat img = new Mat();
            Cv2.Resize(srcimg, img, new Size(input_width, input_height));
            img.ConvertTo(img, MatType.CV_32FC3, 1.0 / 127.5d, -1.0d);
 
            Mat input_tensor = CvDnn.BlobFromImage(img);
 
            feature_extractor.SetInput(input_tensor);
 
            Mat[] feature_oris = new Mat[1] { new Mat() };
            string[] outBlobNames = feature_extractor.GetUnconnectedOutLayersNames().ToArray();
            feature_extractor.Forward(feature_oris, outBlobNames);
            Mat feature = feature_oris[0].Clone();
 
            int[] shape = { 1, 2, input_height, input_width };
            Mat flow = Mat.Zeros(MatType.CV_32FC1, shape);
 
            List<Mat> flow_list = new List<Mat>();
            for (int i = 0; i < iter_num; i++)
            {
                regressNet.SetInput(feature);
                Mat[] mesh_motions = new Mat[1] { new Mat() };
                regressNet.Forward(mesh_motions, regressNet.GetUnconnectedOutLayersNames().ToArray());
 
                float* offset = (float*)mesh_motions[0].Data;
 
                Mat tp = new Mat();
 
                tps2flow.get_ori_rigid_mesh_tp(ref tp, offset, input_height, input_width, grid_h, grid_w);
 
                Mat T = W_inv * tp;   //_solve_system
                T = T.T();    //舍弃batchsize
 
                Mat T_g = T * grid;
 
                Mat delta_flow = new Mat();
 
                tps2flow._transform(T_g, grid, input_height, input_width, ref delta_flow);
 
                if (i == 0)
                {
                    flow += delta_flow;
 
                }
                else
                {
                    Mat warped_flow = new Mat();
                    grid_sample.warp_with_flow(flow, delta_flow, ref warped_flow);
 
                    flow = delta_flow + warped_flow;
                }
                flow_list.Add(flow.Clone());
 
                if (i < (iter_num - 1))
                {
                    int fea_h = feature.Size(2);
                    int fea_w = feature.Size(3);
                    float scale_h = (float)fea_h / flow.Size(2);
                    float scale_w = (float)fea_w / flow.Size(3);
 
                    Mat down_flow = new Mat();
                    upsample.UpSamplingBilinear(flow, ref down_flow, fea_h, fea_w, true, scale_h, scale_w);
 
                    for (int h = 0; h < fea_h; h++)
                    {
                        for (int w = 0; w < fea_w; w++)
                        {
                            float* p_w = (float*)down_flow.Ptr(0, 0, h);
                            float temp_w = p_w[w];
                            temp_w = temp_w * scale_w;
                            p_w[w] = temp_w;
 
                            float* p_h = (float*)down_flow.Ptr(0, 1, h);
                            float temp_h = p_h[w];
                            temp_h = temp_h * scale_h;
                            p_h[w] = temp_h;
 
                        }
                    }
                    feature.Release();
                    feature = new Mat();
                    grid_sample.warp_with_flow(feature_oris[0], down_flow, ref feature);
                }
            }
            Mat correction_final = new Mat();
 
            grid_sample.warp_with_flow(input_tensor, flow_list[iter_num - 1], ref correction_final);
 
            Mat correction_img = grid_sample.convert4dtoimage(correction_final);
 
            return correction_img;
 
        }
    }
}

अवाहरन

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