C# OpenCvSharp DNN 部署yolov3目标检测

目录

效果

yolov3.cfg

项目

代码

下载

C# OpenCvSharp DNN 部署yolov3目标检测

效果

yolov3.cfg

  1. [net]
  2. # Testing
  3. #batch=1
  4. #subdivisions=1
  5. # Training
  6. batch=16
  7. subdivisions=1
  8. width=416
  9. height=416
  10. channels=3
  11. momentum=0.9
  12. decay=0.0005
  13. angle=0
  14. saturation = 1.5
  15. exposure = 1.5
  16. hue=.1
  17.  
  18. learning_rate=0.001
  19. burn_in=1000
  20. max_batches = 500200
  21. policy=steps
  22. steps=400000,450000
  23. scales=.1,.1
  24.  
  25. [convolutional]
  26. batch_normalize=1
  27. filters=32
  28. size=3
  29. stride=1
  30. pad=1
  31. activation=leaky
  32.  
  33. # Downsample
  34.  
  35. [convolutional]
  36. batch_normalize=1
  37. filters=64
  38. size=3
  39. stride=2
  40. pad=1
  41. activation=leaky
  42.  
  43. [convolutional]
  44. batch_normalize=1
  45. filters=32
  46. size=1
  47. stride=1
  48. pad=1
  49. activation=leaky
  50.  
  51. [convolutional]
  52. batch_normalize=1
  53. filters=64
  54. size=3
  55. stride=1
  56. pad=1
  57. activation=leaky
  58.  
  59. [shortcut]
  60. from=-3
  61. activation=linear
  62.  
  63. # Downsample
  64.  
  65. [convolutional]
  66. batch_normalize=1
  67. filters=128
  68. size=3
  69. stride=2
  70. pad=1
  71. activation=leaky
  72.  
  73. [convolutional]
  74. batch_normalize=1
  75. filters=64
  76. size=1
  77. stride=1
  78. pad=1
  79. activation=leaky
  80.  
  81. [convolutional]
  82. batch_normalize=1
  83. filters=128
  84. size=3
  85. stride=1
  86. pad=1
  87. activation=leaky
  88.  
  89. [shortcut]
  90. from=-3
  91. activation=linear
  92.  
  93. [convolutional]
  94. batch_normalize=1
  95. filters=64
  96. size=1
  97. stride=1
  98. pad=1
  99. activation=leaky
  100.  
  101. [convolutional]
  102. batch_normalize=1
  103. filters=128
  104. size=3
  105. stride=1
  106. pad=1
  107. activation=leaky
  108.  
  109. [shortcut]
  110. from=-3
  111. activation=linear
  112.  
  113. # Downsample
  114.  
  115. [convolutional]
  116. batch_normalize=1
  117. filters=256
  118. size=3
  119. stride=2
  120. pad=1
  121. activation=leaky
  122.  
  123. [convolutional]
  124. batch_normalize=1
  125. filters=128
  126. size=1
  127. stride=1
  128. pad=1
  129. activation=leaky
  130.  
  131. [convolutional]
  132. batch_normalize=1
  133. filters=256
  134. size=3
  135. stride=1
  136. pad=1
  137. activation=leaky
  138.  
  139. [shortcut]
  140. from=-3
  141. activation=linear
  142.  
  143. [convolutional]
  144. batch_normalize=1
  145. filters=128
  146. size=1
  147. stride=1
  148. pad=1
  149. activation=leaky
  150.  
  151. [convolutional]
  152. batch_normalize=1
  153. filters=256
  154. size=3
  155. stride=1
  156. pad=1
  157. activation=leaky
  158.  
  159. [shortcut]
  160. from=-3
  161. activation=linear
  162.  
  163. [convolutional]
  164. batch_normalize=1
  165. filters=128
  166. size=1
  167. stride=1
  168. pad=1
  169. activation=leaky
  170.  
  171. [convolutional]
  172. batch_normalize=1
  173. filters=256
  174. size=3
  175. stride=1
  176. pad=1
  177. activation=leaky
  178.  
  179. [shortcut]
  180. from=-3
  181. activation=linear
  182.  
  183. [convolutional]
  184. batch_normalize=1
  185. filters=128
  186. size=1
  187. stride=1
  188. pad=1
  189. activation=leaky
  190.  
  191. [convolutional]
  192. batch_normalize=1
  193. filters=256
  194. size=3
  195. stride=1
  196. pad=1
  197. activation=leaky
  198.  
  199. [shortcut]
  200. from=-3
  201. activation=linear
  202.  
  203.  
  204. [convolutional]
  205. batch_normalize=1
  206. filters=128
  207. size=1
  208. stride=1
  209. pad=1
  210. activation=leaky
  211.  
  212. [convolutional]
  213. batch_normalize=1
  214. filters=256
  215. size=3
  216. stride=1
  217. pad=1
  218. activation=leaky
  219.  
  220. [shortcut]
  221. from=-3
  222. activation=linear
  223.  
  224. [convolutional]
  225. batch_normalize=1
  226. filters=128
  227. size=1
  228. stride=1
  229. pad=1
  230. activation=leaky
  231.  
  232. [convolutional]
  233. batch_normalize=1
  234. filters=256
  235. size=3
  236. stride=1
  237. pad=1
  238. activation=leaky
  239.  
  240. [shortcut]
  241. from=-3
  242. activation=linear
  243.  
  244. [convolutional]
  245. batch_normalize=1
  246. filters=128
  247. size=1
  248. stride=1
  249. pad=1
  250. activation=leaky
  251.  
  252. [convolutional]
  253. batch_normalize=1
  254. filters=256
  255. size=3
  256. stride=1
  257. pad=1
  258. activation=leaky
  259.  
  260. [shortcut]
  261. from=-3
  262. activation=linear
  263.  
  264. [convolutional]
  265. batch_normalize=1
  266. filters=128
  267. size=1
  268. stride=1
  269. pad=1
  270. activation=leaky
  271.  
  272. [convolutional]
  273. batch_normalize=1
  274. filters=256
  275. size=3
  276. stride=1
  277. pad=1
  278. activation=leaky
  279.  
  280. [shortcut]
  281. from=-3
  282. activation=linear
  283.  
  284. # Downsample
  285.  
  286. [convolutional]
  287. batch_normalize=1
  288. filters=512
  289. size=3
  290. stride=2
  291. pad=1
  292. activation=leaky
  293.  
  294. [convolutional]
  295. batch_normalize=1
  296. filters=256
  297. size=1
  298. stride=1
  299. pad=1
  300. activation=leaky
  301.  
  302. [convolutional]
  303. batch_normalize=1
  304. filters=512
  305. size=3
  306. stride=1
  307. pad=1
  308. activation=leaky
  309.  
  310. [shortcut]
  311. from=-3
  312. activation=linear
  313.  
  314.  
  315. [convolutional]
  316. batch_normalize=1
  317. filters=256
  318. size=1
  319. stride=1
  320. pad=1
  321. activation=leaky
  322.  
  323. [convolutional]
  324. batch_normalize=1
  325. filters=512
  326. size=3
  327. stride=1
  328. pad=1
  329. activation=leaky
  330.  
  331. [shortcut]
  332. from=-3
  333. activation=linear
  334.  
  335.  
  336. [convolutional]
  337. batch_normalize=1
  338. filters=256
  339. size=1
  340. stride=1
  341. pad=1
  342. activation=leaky
  343.  
  344. [convolutional]
  345. batch_normalize=1
  346. filters=512
  347. size=3
  348. stride=1
  349. pad=1
  350. activation=leaky
  351.  
  352. [shortcut]
  353. from=-3
  354. activation=linear
  355.  
  356.  
  357. [convolutional]
  358. batch_normalize=1
  359. filters=256
  360. size=1
  361. stride=1
  362. pad=1
  363. activation=leaky
  364.  
  365. [convolutional]
  366. batch_normalize=1
  367. filters=512
  368. size=3
  369. stride=1
  370. pad=1
  371. activation=leaky
  372.  
  373. [shortcut]
  374. from=-3
  375. activation=linear
  376.  
  377. [convolutional]
  378. batch_normalize=1
  379. filters=256
  380. size=1
  381. stride=1
  382. pad=1
  383. activation=leaky
  384.  
  385. [convolutional]
  386. batch_normalize=1
  387. filters=512
  388. size=3
  389. stride=1
  390. pad=1
  391. activation=leaky
  392.  
  393. [shortcut]
  394. from=-3
  395. activation=linear
  396.  
  397.  
  398. [convolutional]
  399. batch_normalize=1
  400. filters=256
  401. size=1
  402. stride=1
  403. pad=1
  404. activation=leaky
  405.  
  406. [convolutional]
  407. batch_normalize=1
  408. filters=512
  409. size=3
  410. stride=1
  411. pad=1
  412. activation=leaky
  413.  
  414. [shortcut]
  415. from=-3
  416. activation=linear
  417.  
  418.  
  419. [convolutional]
  420. batch_normalize=1
  421. filters=256
  422. size=1
  423. stride=1
  424. pad=1
  425. activation=leaky
  426.  
  427. [convolutional]
  428. batch_normalize=1
  429. filters=512
  430. size=3
  431. stride=1
  432. pad=1
  433. activation=leaky
  434.  
  435. [shortcut]
  436. from=-3
  437. activation=linear
  438.  
  439. [convolutional]
  440. batch_normalize=1
  441. filters=256
  442. size=1
  443. stride=1
  444. pad=1
  445. activation=leaky
  446.  
  447. [convolutional]
  448. batch_normalize=1
  449. filters=512
  450. size=3
  451. stride=1
  452. pad=1
  453. activation=leaky
  454.  
  455. [shortcut]
  456. from=-3
  457. activation=linear
  458.  
  459. # Downsample
  460.  
  461. [convolutional]
  462. batch_normalize=1
  463. filters=1024
  464. size=3
  465. stride=2
  466. pad=1
  467. activation=leaky
  468.  
  469. [convolutional]
  470. batch_normalize=1
  471. filters=512
  472. size=1
  473. stride=1
  474. pad=1
  475. activation=leaky
  476.  
  477. [convolutional]
  478. batch_normalize=1
  479. filters=1024
  480. size=3
  481. stride=1
  482. pad=1
  483. activation=leaky
  484.  
  485. [shortcut]
  486. from=-3
  487. activation=linear
  488.  
  489. [convolutional]
  490. batch_normalize=1
  491. filters=512
  492. size=1
  493. stride=1
  494. pad=1
  495. activation=leaky
  496.  
  497. [convolutional]
  498. batch_normalize=1
  499. filters=1024
  500. size=3
  501. stride=1
  502. pad=1
  503. activation=leaky
  504.  
  505. [shortcut]
  506. from=-3
  507. activation=linear
  508.  
  509. [convolutional]
  510. batch_normalize=1
  511. filters=512
  512. size=1
  513. stride=1
  514. pad=1
  515. activation=leaky
  516.  
  517. [convolutional]
  518. batch_normalize=1
  519. filters=1024
  520. size=3
  521. stride=1
  522. pad=1
  523. activation=leaky
  524.  
  525. [shortcut]
  526. from=-3
  527. activation=linear
  528.  
  529. [convolutional]
  530. batch_normalize=1
  531. filters=512
  532. size=1
  533. stride=1
  534. pad=1
  535. activation=leaky
  536.  
  537. [convolutional]
  538. batch_normalize=1
  539. filters=1024
  540. size=3
  541. stride=1
  542. pad=1
  543. activation=leaky
  544.  
  545. [shortcut]
  546. from=-3
  547. activation=linear
  548.  
  549. ######################
  550.  
  551. [convolutional]
  552. batch_normalize=1
  553. filters=512
  554. size=1
  555. stride=1
  556. pad=1
  557. activation=leaky
  558.  
  559. [convolutional]
  560. batch_normalize=1
  561. size=3
  562. stride=1
  563. pad=1
  564. filters=1024
  565. activation=leaky
  566.  
  567. [convolutional]
  568. batch_normalize=1
  569. filters=512
  570. size=1
  571. stride=1
  572. pad=1
  573. activation=leaky
  574.  
  575. [convolutional]
  576. batch_normalize=1
  577. size=3
  578. stride=1
  579. pad=1
  580. filters=1024
  581. activation=leaky
  582.  
  583. [convolutional]
  584. batch_normalize=1
  585. filters=512
  586. size=1
  587. stride=1
  588. pad=1
  589. activation=leaky
  590.  
  591. [convolutional]
  592. batch_normalize=1
  593. size=3
  594. stride=1
  595. pad=1
  596. filters=1024
  597. activation=leaky
  598.  
  599. [convolutional]
  600. size=1
  601. stride=1
  602. pad=1
  603. filters=255
  604. activation=linear
  605.  
  606.  
  607. [yolo]
  608. mask = 6,7,8
  609. anchors = 10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
  610. classes=80
  611. num=9
  612. jitter=.3
  613. ignore_thresh = .7
  614. truth_thresh = 1
  615. random=1
  616.  
  617.  
  618. [route]
  619. layers = -4
  620.  
  621. [convolutional]
  622. batch_normalize=1
  623. filters=256
  624. size=1
  625. stride=1
  626. pad=1
  627. activation=leaky
  628.  
  629. [upsample]
  630. stride=2
  631.  
  632. [route]
  633. layers = -1, 61
  634.  
  635.  
  636.  
  637. [convolutional]
  638. batch_normalize=1
  639. filters=256
  640. size=1
  641. stride=1
  642. pad=1
  643. activation=leaky
  644.  
  645. [convolutional]
  646. batch_normalize=1
  647. size=3
  648. stride=1
  649. pad=1
  650. filters=512
  651. activation=leaky
  652.  
  653. [convolutional]
  654. batch_normalize=1
  655. filters=256
  656. size=1
  657. stride=1
  658. pad=1
  659. activation=leaky
  660.  
  661. [convolutional]
  662. batch_normalize=1
  663. size=3
  664. stride=1
  665. pad=1
  666. filters=512
  667. activation=leaky
  668.  
  669. [convolutional]
  670. batch_normalize=1
  671. filters=256
  672. size=1
  673. stride=1
  674. pad=1
  675. activation=leaky
  676.  
  677. [convolutional]
  678. batch_normalize=1
  679. size=3
  680. stride=1
  681. pad=1
  682. filters=512
  683. activation=leaky
  684.  
  685. [convolutional]
  686. size=1
  687. stride=1
  688. pad=1
  689. filters=255
  690. activation=linear
  691.  
  692.  
  693. [yolo]
  694. mask = 3,4,5
  695. anchors = 10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
  696. classes=80
  697. num=9
  698. jitter=.3
  699. ignore_thresh = .7
  700. truth_thresh = 1
  701. random=1
  702.  
  703.  
  704.  
  705. [route]
  706. layers = -4
  707.  
  708. [convolutional]
  709. batch_normalize=1
  710. filters=128
  711. size=1
  712. stride=1
  713. pad=1
  714. activation=leaky
  715.  
  716. [upsample]
  717. stride=2
  718.  
  719. [route]
  720. layers = -1, 36
  721.  
  722.  
  723.  
  724. [convolutional]
  725. batch_normalize=1
  726. filters=128
  727. size=1
  728. stride=1
  729. pad=1
  730. activation=leaky
  731.  
  732. [convolutional]
  733. batch_normalize=1
  734. size=3
  735. stride=1
  736. pad=1
  737. filters=256
  738. activation=leaky
  739.  
  740. [convolutional]
  741. batch_normalize=1
  742. filters=128
  743. size=1
  744. stride=1
  745. pad=1
  746. activation=leaky
  747.  
  748. [convolutional]
  749. batch_normalize=1
  750. size=3
  751. stride=1
  752. pad=1
  753. filters=256
  754. activation=leaky
  755.  
  756. [convolutional]
  757. batch_normalize=1
  758. filters=128
  759. size=1
  760. stride=1
  761. pad=1
  762. activation=leaky
  763.  
  764. [convolutional]
  765. batch_normalize=1
  766. size=3
  767. stride=1
  768. pad=1
  769. filters=256
  770. activation=leaky
  771.  
  772. [convolutional]
  773. size=1
  774. stride=1
  775. pad=1
  776. filters=255
  777. activation=linear
  778.  
  779.  
  780. [yolo]
  781. mask = 0,1,2
  782. anchors = 10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
  783. classes=80
  784. num=9
  785. jitter=.3
  786. ignore_thresh = .7
  787. truth_thresh = 1
  788. random=1

项目

代码

using OpenCvSharp;
using OpenCvSharp.Dnn;
using System;
using System.Collections.Generic;
using System.Drawing;
using System.IO;
using System.Linq;
using System.Windows.Forms;

namespace OpenCvSharp_DNN_Demo
{
    public partial class frmMain : Form
    {
        public frmMain()
        {
            InitializeComponent();
        }

        string fileFilter = "*.*|*.bmp;*.jpg;*.jpeg;*.tiff;*.tiff;*.png";
        string image_path = "";

        DateTime dt1 = DateTime.Now;
        DateTime dt2 = DateTime.Now;

        float confThreshold;
        float nmsThreshold;

        int inpHeight;
        int inpWidth;

        List class_names;
        int num_class;

        Net opencv_net;
        Mat BN_image;

        Mat image;
        Mat result_image;

        private void button1_Click(object sender, EventArgs e)
        {
            OpenFileDialog ofd = new OpenFileDialog();
            ofd.Filter = fileFilter;
            if (ofd.ShowDialog() != DialogResult.OK) return;

            pictureBox1.Image = null;
            pictureBox2.Image = null;
            textBox1.Text = "";

            image_path = ofd.FileName;
            pictureBox1.Image = new Bitmap(image_path);
            image = new Mat(image_path);
        }

        private void Form1_Load(object sender, EventArgs e)
        {
            confThreshold = 0.5f;
            nmsThreshold = 0.4f;

            inpHeight = 416;
            inpWidth = 416;

            opencv_net = CvDnn.ReadNetFromDarknet("model/yolov3.cfg", "model/yolov3.weights");

            class_names = new List();
            StreamReader sr = new StreamReader("model/coco.names");
            string line;
            while ((line = sr.ReadLine()) != null)
            {
                class_names.Add(line);
            }
            num_class = class_names.Count();

            image_path = "test_img/dog.jpg";
            pictureBox1.Image = new Bitmap(image_path);

        }

        private unsafe void button2_Click(object sender, EventArgs e)
        {
            if (image_path == "")
            {
                return;
            }
            textBox1.Text = "检测中，请稍等……";
            pictureBox2.Image = null;
            Application.DoEvents();

            image = new Mat(image_path);

            BN_image = CvDnn.BlobFromImage(image, 1 / 255.0, new OpenCvSharp.Size(inpWidth, inpHeight), new Scalar(0, 0, 0), true, false);

            //配置图片输入数据
            opencv_net.SetInput(BN_image);

            //模型推理，读取推理结果
            var outNames = opencv_net.GetUnconnectedOutLayersNames();
            var outs = outNames.Select(_ => new Mat()).ToArray();

            dt1 = DateTime.Now;

            opencv_net.Forward(outs, outNames);

            dt2 = DateTime.Now;

            List classIds = new List();
            List confidences = new List();
            List boxes = new List();

            for (int i = 0; i < outs.Length; ++i)
            {
                float* data = (float*)outs[i].Data;
                for (int j = 0; j < outs[i].Rows; ++j, data += outs[i].Cols)
                {
                    Mat scores = outs[i].Row(j).ColRange(5, outs[i].Cols);

                    double minVal, max_class_socre;
                    OpenCvSharp.Point minLoc, classIdPoint;
                    // Get the value and location of the maximum score
                    Cv2.MinMaxLoc(scores, out minVal, out max_class_socre, out minLoc, out classIdPoint);

                    if (max_class_socre > confThreshold)
                    {
                        int centerX = (int)(data[0] * image.Cols);
                        int centerY = (int)(data[1] * image.Rows);
                        int width = (int)(data[2] * image.Cols);
                        int height = (int)(data[3] * image.Rows);
                        int left = centerX - width / 2;
                        int top = centerY - height / 2;

                        classIds.Add(classIdPoint.X);
                        confidences.Add((float)max_class_socre);
                        boxes.Add(new Rect(left, top, width, height));
                    }
                }
            }

            int[] indices;
            CvDnn.NMSBoxes(boxes, confidences, confThreshold, nmsThreshold, out indices);

            result_image = image.Clone();

            for (int i = 0; i < indices.Length; ++i)
            {
                int idx = indices[i];
                Rect box = boxes[idx];
                Cv2.Rectangle(result_image, new OpenCvSharp.Point(box.X, box.Y), new OpenCvSharp.Point(box.X + box.Width, box.Y + box.Height), new Scalar(0, 0, 255), 2);
                string label = class_names[classIds[idx]] + ":" + confidences[idx].ToString("0.00");
                Cv2.PutText(result_image, label, new OpenCvSharp.Point(box.X, box.Y - 5), HersheyFonts.HersheySimplex, 1, new Scalar(0, 0, 255), 2);
            }

            pictureBox2.Image = new Bitmap(result_image.ToMemoryStream());
            textBox1.Text = "推理耗时:" + (dt2 - dt1).TotalMilliseconds + "ms";

        }

        private void pictureBox2_DoubleClick(object sender, EventArgs e)
        {
            Common.ShowNormalImg(pictureBox2.Image);
        }

        private void pictureBox1_DoubleClick(object sender, EventArgs e)
        {
            Common.ShowNormalImg(pictureBox1.Image);
        }
    }
}

  1. using OpenCvSharp;
  2. using OpenCvSharp.Dnn;
  3. using System;
  4. using System.Collections.Generic;
  5. using System.Drawing;
  6. using System.IO;
  7. using System.Linq;
  8. using System.Windows.Forms;
  9.  
  10. namespace OpenCvSharp_DNN_Demo
  11. {
  12.     public partial class frmMain : Form
  13.     {
  14.         public frmMain()
  15.         {
  16.             InitializeComponent();
  17.         }
  18.  
  19.         string fileFilter = "*.*|*.bmp;*.jpg;*.jpeg;*.tiff;*.tiff;*.png";
  20.         string image_path = "";
  21.  
  22.         DateTime dt1 = DateTime.Now;
  23.         DateTime dt2 = DateTime.Now;
  24.  
  25.         float confThreshold;
  26.         float nmsThreshold;
  27.  
  28.         int inpHeight;
  29.         int inpWidth;
  30.  
  31.         List<string> class_names;
  32.         int num_class;
  33.  
  34.         Net opencv_net;
  35.         Mat BN_image;
  36.  
  37.         Mat image;
  38.         Mat result_image;
  39.  
  40.         private void button1_Click(object sender, EventArgs e)
  41.         {
  42.             OpenFileDialog ofd = new OpenFileDialog();
  43.             ofd.Filter = fileFilter;
  44.             if (ofd.ShowDialog() != DialogResult.OK) return;
  45.  
  46.             pictureBox1.Image = null;
  47.             pictureBox2.Image = null;
  48.             textBox1.Text = "";
  49.  
  50.             image_path = ofd.FileName;
  51.             pictureBox1.Image = new Bitmap(image_path);
  52.             image = new Mat(image_path);
  53.         }
  54.  
  55.         private void Form1_Load(object sender, EventArgs e)
  56.         {
  57.             confThreshold = 0.5f;
  58.             nmsThreshold = 0.4f;
  59.  
  60.             inpHeight = 416;
  61.             inpWidth = 416;
  62.  
  63.             opencv_net = CvDnn.ReadNetFromDarknet("model/yolov3.cfg", "model/yolov3.weights");
  64.  
  65.             class_names = new List<string>();
  66.             StreamReader sr = new StreamReader("model/coco.names");
  67.             string line;
  68.             while ((line = sr.ReadLine()) != null)
  69.             {
  70.                 class_names.Add(line);
  71.             }
  72.             num_class = class_names.Count();
  73.  
  74.             image_path = "test_img/dog.jpg";
  75.             pictureBox1.Image = new Bitmap(image_path);
  76.  
  77.         }
  78.  
  79.         private unsafe void button2_Click(object sender, EventArgs e)
  80.         {
  81.             if (image_path == "")
  82.             {
  83.                 return;
  84.             }
  85.             textBox1.Text = "检测中，请稍等……";
  86.             pictureBox2.Image = null;
  87.             Application.DoEvents();
  88.  
  89.             image = new Mat(image_path);
  90.  
  91.             BN_image = CvDnn.BlobFromImage(image, 1 / 255.0, new OpenCvSharp.Size(inpWidth, inpHeight), new Scalar(0, 0, 0), true, false);
  92.  
  93.             //配置图片输入数据
  94.             opencv_net.SetInput(BN_image);
  95.  
  96.             //模型推理，读取推理结果
  97.             var outNames = opencv_net.GetUnconnectedOutLayersNames();
  98.             var outs = outNames.Select(_ => new Mat()).ToArray();
  99.  
  100.             dt1 = DateTime.Now;
  101.  
  102.             opencv_net.Forward(outs, outNames);
  103.  
  104.             dt2 = DateTime.Now;
  105.  
  106.             List<int> classIds = new List<int>();
  107.             List<float> confidences = new List<float>();
  108.             List boxes = new List();
  109.  
  110.             for (int i = 0; i < outs.Length; ++i)
  111.             {
  112.                 float* data = (float*)outs[i].Data;
  113.                 for (int j = 0; j < outs[i].Rows; ++j, data += outs[i].Cols)
  114.                 {
  115.                     Mat scores = outs[i].Row(j).ColRange(5, outs[i].Cols);
  116.  
  117.                     double minVal, max_class_socre;
  118.                     OpenCvSharp.Point minLoc, classIdPoint;
  119.                     // Get the value and location of the maximum score
  120.                     Cv2.MinMaxLoc(scores, out minVal, out max_class_socre, out minLoc, out classIdPoint);
  121.  
  122.                     if (max_class_socre > confThreshold)
  123.                     {
  124.                         int centerX = (int)(data[0] * image.Cols);
  125.                         int centerY = (int)(data[1] * image.Rows);
  126.                         int width = (int)(data[2] * image.Cols);
  127.                         int height = (int)(data[3] * image.Rows);
  128.                         int left = centerX - width / 2;
  129.                         int top = centerY - height / 2;
  130.  
  131.                         classIds.Add(classIdPoint.X);
  132.                         confidences.Add((float)max_class_socre);
  133.                         boxes.Add(new Rect(left, top, width, height));
  134.                     }
  135.                 }
  136.             }
  137.  
  138.             int[] indices;
  139.             CvDnn.NMSBoxes(boxes, confidences, confThreshold, nmsThreshold, out indices);
  140.  
  141.             result_image = image.Clone();
  142.  
  143.             for (int i = 0; i < indices.Length; ++i)
  144.             {
  145.                 int idx = indices[i];
  146.                 Rect box = boxes[idx];
  147.                 Cv2.Rectangle(result_image, new OpenCvSharp.Point(box.X, box.Y), new OpenCvSharp.Point(box.X + box.Width, box.Y + box.Height), new Scalar(0, 0, 255), 2);
  148.                 string label = class_names[classIds[idx]] + ":" + confidences[idx].ToString("0.00");
  149.                 Cv2.PutText(result_image, label, new OpenCvSharp.Point(box.X, box.Y - 5), HersheyFonts.HersheySimplex, 1, new Scalar(0, 0, 255), 2);
  150.             }
  151.  
  152.             pictureBox2.Image = new Bitmap(result_image.ToMemoryStream());
  153.             textBox1.Text = "推理耗时:" + (dt2 - dt1).TotalMilliseconds + "ms";
  154.  
  155.         }
  156.  
  157.         private void pictureBox2_DoubleClick(object sender, EventArgs e)
  158.         {
  159.             Common.ShowNormalImg(pictureBox2.Image);
  160.         }
  161.  
  162.         private void pictureBox1_DoubleClick(object sender, EventArgs e)
  163.         {
  164.             Common.ShowNormalImg(pictureBox1.Image);
  165.         }
  166.     }
  167. }

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