2024-07-12
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C# CvDnn déploie CoupledTPS pour implémenter la rotationcorrection d'images
Table des matières
TPAMI2024 - Modèle de spline à plaque mince couplé semi-supervisé pour la correction de rotation et au-delà
GitHubadresse:https://github.com/nie-lang/CoupledTPS
Référence d'implémentation du code :https://github.com/hpc203/CoupledTPS-opencv-dnn
extracteur_de_fonctionnalités.onnx
Propriétés du modèle
-------------------------
---------------------------------------------------------------
Contributions
-------------------------
nom : entrée
tenseur:Flotteur[1, 3, 384, 512]
---------------------------------------------------------------
Les sorties
-------------------------
nom : fonctionnalité
tenseur : Float[1, 256, 24, 32]
---------------------------------------------------------------
régressnet.onnx
Propriétés du modèle
-------------------------
---------------------------------------------------------------
Contributions
-------------------------
nom : fonctionnalité
tenseur : Float[1, 256, 24, 32]
---------------------------------------------------------------
Les sorties
-------------------------
nom : mesh_motion
tenseur : Float[1, 7, 9, 2]
---------------------------------------------------------------
Formulaire1.cs
en utilisant OpenCvSharp;
en utilisant le système;
en utilisant System.Drawing;
en utilisant System.Drawing.Imaging;
en utilisant System.Windows.Forms;
espace de noms Onnx_Demo
{
classe partielle publique Form1 : Formulaire
{
formulaire public1()
{
InitialiserComposant();
}
chaîne fileFilter = "*.*|*.bmp;*.jpg;*.jpeg;*.tiff;*.tiff;*.png";
chaîne image_path = "";
DateTime dt1 = DateTime.Maintenant;
DateTime dt2 = DateTime.Maintenant;
Image du tapis;
CoupledTPS_RotationNet rotationNet ;
int iter_num = 3;
bouton privé void1_Click(objet expéditeur, EventArgs e)
{
OpenFileDialog ofd = nouveau OpenFileDialog();
ofd.InitialDirectory =Application.StartupPath+"\test_img\";
ofd.Filter = fichierFilter;
si (ofd.ShowDialog() != DialogResult.OK) renvoie ;
imageBox1.Image = null;
image_path = ofd.FileName;
pictureBox1.Image = nouveau Bitmap(image_path);
textBox1.Text = "";
image = nouveau Mat(image_path);
imageBox2.Image = null;
}
bouton privé void2_Click(objet expéditeur, EventArgs e)
{
si (image_path == "")
{
retour;
}
bouton2.Activé = faux;
imageBox2.Image = null;
textBox1.Text = "";
Application.DoEvents();
//Lire les images
image = nouveau Mat(image_path);
dt1 = DateHeure.Maintenant;
Mat result_image = rotationNet.detect(image, iter_num);
dt2 = DateHeure.Maintenant;
Cv2.CvtColor(image_résultat, image_résultat, ColorConversionCodes.BGR2RGB);
pictureBox2.Image = nouveau Bitmap(result_image.ToMemoryStream());
textBox1.Text = "Consommation du temps d'inférence :" + (dt2 - dt1).TotalMilliseconds + "ms" ;
bouton2.Activé = vrai;
}
privé void Form1_Load(expéditeur d'objet, EventArgs e)
{
rotationNet = nouveau CoupledTPS_RotationNet("model/feature_extractor.onnx", "model/regressnet.onnx");
image_path = "test_img/00150_-8.4.jpg";
pictureBox1.Image = nouveau Bitmap(image_path);
image = nouveau Mat(image_path);
}
privé void pictureBox1_DoubleClick(expéditeur d'objet, EventArgs e)
{
Commun.ShowNormalImg(pictureBox1.Image);
}
privé void pictureBox2_DoubleClick(expéditeur d'objet, EventArgs e)
{
Commun.ShowNormalImg(pictureBox2.Image);
}
EnregistrerFileDialog sdf = new EnregistrerFileDialog();
bouton privé void3_Click(objet expéditeur, EventArgs e)
{
si (pictureBox2.Image == null)
{
retour;
}
Sortie bitmap = nouveau Bitmap(pictureBox2.Image);
sdf.Titre = "Enregistrer" ;
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";
si (sdf.ShowDialog() == DialogResult.OK)
{
commutateur (sdf.FilterIndex)
{
cas 1:
{
sortie.Enregistrer(sdf.NomFichier, ImageFormat.Jpeg);
casser;
}
cas 2:
{
sortie.Enregistrer(sdf.NomFichier, ImageFormat.Png);
casser;
}
cas 3:
{
sortie.Enregistrer(sdf.NomFichier, ImageFormat.Bmp);
casser;
}
cas 4:
{
sortie.Enregistrer(sdf.NomFichier, ImageFormat.Emf);
casser;
}
cas 5:
{
sortie.Enregistrer(sdf.NomFichier, ImageFormat.Exif);
casser;
}
cas 6:
{
sortie.Enregistrer(sdf.NomFichier, ImageFormat.Gif);
casser;
}
cas 7:
{
sortie.Enregistrer(sdf.NomFichier, ImageFormat.Icon);
casser;
}
cas 8:
{
sortie.Enregistrer(sdf.NomFichier, ImageFormat.Tiff);
casser;
}
cas 9:
{
sortie.Enregistrer(sdf.NomFichier, ImageFormat.Wmf);
casser;
}
}
MessageBox.Show("Enregistrement réussi, emplacement : " + 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);
- }
- }
- }
- }
CoupledTPS_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;
-
- }
- }
- }