Image Classification In ASP.NET Core Using ML.NET

• Open Visual Studio>New Projects>ASP.NET Core
• Set Project name as ImageClassification.Web
• Click Create button>MVC Web Application
• A new Project will be added to your solution.
• Open Nuget Manager Console and write Install-Package Microsoft.ML
• Right-click on the project and Add refrence>Select ImageClassification.Score>Press Ok
• Download assets from here and include in project
• Here is how your project will look,
  
  
  
  
  
• Now we need to consume the model. Create a new Controller or Open HomeController and add the following snippet.
  1. using System;
  2. using System.Collections.Generic;
  3. using System.Diagnostics;
  4. using System.Linq;
  5. using System.Threading.Tasks;
  6. using Microsoft.AspNetCore.Mvc;
  7. using ImageClassification.Web.Models;
  8. using System.IO;
  9. using ImageClassification.ModelScorer;
  10. using Microsoft.AspNetCore.Http;
  11. using ImageClassification.ImageDataStructures;
  13. namespace ImageClassification.Web.Controllers
  14. {
  15. public class HomeController : Controller
  16. {
  17. public IActionResult Index()
  18. {
  19. return View();
  20. }
  21. [HttpPost]
  22. public async Task<IActionResult> Index(IFormFile image)
  23. {
  24. string assetsRelativePath = @"../../../assets";
  25. string assetsPath = GetAbsolutePath(assetsRelativePath);
  27. var tagsTsv = Path.Combine(assetsPath, "inputs", "images", "tags.tsv");
  28. var imagesFolder = Path.Combine(assetsPath, "inputs", "images");
  29. //Path of Tensor Flow pre trained model
      
  30. var inceptionPb = Path.Combine(assetsPath, "inputs", "inception", "tensorflow_inception_graph.pb");
  31. var labelsTxt = Path.Combine(assetsPath, "inputs", "inception", "imagenet_comp_graph_label_strings.txt");
  33. try
  34. {
  35. var filePath = Path.GetTempFileName();
  36. //Save image to temp
  37. using(var ms=new FileStream(filePath,FileMode.Create))
  38. {
  39. await image.CopyToAsync(ms);
  40. }
  41. //Create Tensor Flow model for image classification and prediction using ML.NET API
      
  42. var modelScorer = new TFModelScorer(tagsTsv, imagesFolder, inceptionPb, labelsTxt);
  43. //Check image prediction
  44. var prediction=(ImageNetDataProbability)modelScorer.Score(new ImageNetData() { ImagePath=filePath,Label=""});
  45. //Store results to ViewBag to show in View
  46. ViewBag.PredictedLabel = prediction.PredictedLabel;
  47. ViewBag.Probability = prediction.Probability;
  48. }
  49. catch
  50. {
  51. return View();
  52. }
  53. return View();
  54. }
  55. public static string GetAbsolutePath(string relativePath)
  56. {
  57. FileInfo _dataRoot = new FileInfo(typeof(Program).Assembly.Location);
  58. string assemblyFolderPath = _dataRoot.Directory.FullName;
  59. string fullPath = Path.Combine(assemblyFolderPath, relativePath);
  60. return fullPath;
  61. }
  62. }
  63. }
• Now we need to create an index view which will be used to upload image to server and show image classification or prediction result from server. Please add following snippet to your view.
  1. @{
  2. ViewData["Title"] = "Home Page";
  3. }
  4. <h2>Image Classification in ASP.NET Core Using ML.NET with TensorFlow</h2>
  5. <hr />
  6. <div class="row">
  7. <div class="col-md-6">
  8. <form method="post" enctype="multipart/form-data">
  9. <input type="file" name="image" />
  10. <button class="btn btn-sm btn-primary" type="submit">Classify</button>
  11. </form>
  12. </div>
  13. <div class="col-md-6">
  14. @if (ViewBag.Probability != null)
  15. {
  16. <h3>Prediction</h3>
  17. <h6>Predicted Label: @ViewBag.PredictedLabel</h6>
  18. <h6>Probability: @ViewBag.Probability</h6>
  19. }
  21. </div>
  22. </div>
• Now try to build and run the ImageClassification.Web application

Demo

Conclusion