void run()
{
var caffeModelFilePath = VGG16.download_model_if_needed();
var N = 4;
var images = new float[150 * 150 * 3 * N];
for (int i = 0; i < N; i++)
{
var image = compute_image(caffeModelFilePath, i);
Array.Copy(image, 0, images, i * image.Length, image.Length);
}
var app = new System.Windows.Application();
var window = new PlotWindowBitMap("Filters", images, 150, 150, 3);
app.Run(window);
}
void run()
{
Console.Title = "Ch_05_Class_Activation_Heatmaps";
var text = System.IO.File.ReadAllText("imagenet_class_index.json");
var imagenetInfo = Newtonsoft.Json.JsonConvert.DeserializeObject <Dictionary <int, List <string> > >(text);
var imagePath = System.IO.Path.Combine(System.IO.Directory.GetCurrentDirectory(), imageName);
var pathToVGG16model = VGG16.download_model_if_needed();
var image = new float[224 * 224 * 3];
CPPUtil.load_image(imagePath, image);
int num_classes = 1000;
var predictions = new float[num_classes];
CPPUtil.evaluate_vgg16(pathToVGG16model, imagePath, predictions, num_classes);
var indices = Enumerable.Range(0, num_classes).ToArray <int>();
var floatComparer = Comparer <float> .Default;
Array.Sort(indices, (a, b) => floatComparer.Compare(predictions[b], predictions[a]));
Console.WriteLine("Predictions:");
for (int i = 0; i < 3; i++)
{
var imagenetClass = imagenetInfo[indices[i]];
var imagenetClassName = imagenetClass[1];
var predicted_score = predictions[indices[i]];
Console.WriteLine($"\t({imagenetClassName} -> {predicted_score:f3})");
}
var imageWithHeatMap = new float[image.Length];
CPPUtil.visualize_heatmap(pathToVGG16model, imagePath, "conv5_3", 386, imageWithHeatMap);
var app = new System.Windows.Application();
var window = new PlotWindowBitMap("Original Image", image, 224, 224, 3);
window.Show();
var windowHeat = new PlotWindowBitMap("Class Activation Heatmap [386]", imageWithHeatMap, 224, 224, 3);
windowHeat.Show();
app.Run();
}
