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Program.cs
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133 lines (104 loc) · 4.8 KB
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using System.Drawing;
using System.Drawing.Drawing2D;
using ObjectDetection.YoloParser;
using ObjectDetection.DataStructures;
using ObjectDetection;
using Microsoft.ML;
var assetsRelativePath = @"../../../assets";
string assetsPath = GetAbsolutePath(assetsRelativePath);
var modelFilePath = Path.Combine(assetsPath, "Model", "TinyYolo2_model.onnx");
var imagesFolder = Path.Combine(assetsPath, "images");
var outputFolder = Path.Combine(assetsPath, "images", "output");
// Initialize MLContext
MLContext mlContext = new MLContext();
try
{
// Load Data
ImageNetData[] images = ImageNetData.ReadFromFile(imagesFolder).ToArray();
IDataView imageDataView = mlContext.Data.LoadFromEnumerable(images);
// Create instance of model scorer
var modelScorer = new OnnxModelScorer(imagesFolder, modelFilePath, mlContext);
// Use model to score data
IEnumerable<float[]> probabilities = modelScorer.Score(imageDataView);
// Post-process model output
YoloOutputParser parser = new YoloOutputParser();
var boundingBoxes =
probabilities
.Select(probability => parser.ParseOutputs(probability))
.Select(boxes => parser.FilterBoundingBoxes(boxes, 5, .5F))
.ToArray();
// Draw bounding boxes for detected objects in each of the images
for (var i = 0; i < images.Length; i++)
{
string imageFileName = images[i].Label;
IList<YoloBoundingBox> detectedObjects = boundingBoxes[i];
DrawBoundingBox(imagesFolder, outputFolder, imageFileName, detectedObjects);
LogDetectedObjects(imageFileName, detectedObjects);
}
}
catch (Exception ex)
{
Console.WriteLine(ex.ToString());
}
Console.WriteLine("========= End of Process..Hit any Key ========");
string GetAbsolutePath(string relativePath)
{
FileInfo _dataRoot = new FileInfo(typeof(Program).Assembly.Location);
string assemblyFolderPath = _dataRoot.Directory.FullName;
string fullPath = Path.Combine(assemblyFolderPath, relativePath);
return fullPath;
}
void DrawBoundingBox(string inputImageLocation, string outputImageLocation, string imageName, IList<YoloBoundingBox> filteredBoundingBoxes)
{
Image image = Image.FromFile(Path.Combine(inputImageLocation, imageName));
var originalImageHeight = image.Height;
var originalImageWidth = image.Width;
foreach (var box in filteredBoundingBoxes)
{
// Get Bounding Box Dimensions
var x = (uint)Math.Max(box.Dimensions.X, 0);
var y = (uint)Math.Max(box.Dimensions.Y, 0);
var width = (uint)Math.Min(originalImageWidth - x, box.Dimensions.Width);
var height = (uint)Math.Min(originalImageHeight - y, box.Dimensions.Height);
// Resize To Image
x = (uint)originalImageWidth * x / OnnxModelScorer.ImageNetSettings.imageWidth;
y = (uint)originalImageHeight * y / OnnxModelScorer.ImageNetSettings.imageHeight;
width = (uint)originalImageWidth * width / OnnxModelScorer.ImageNetSettings.imageWidth;
height = (uint)originalImageHeight * height / OnnxModelScorer.ImageNetSettings.imageHeight;
// Bounding Box Text
string text = $"{box.Label} ({(box.Confidence * 100).ToString("0")}%)";
using (Graphics thumbnailGraphic = Graphics.FromImage(image))
{
thumbnailGraphic.CompositingQuality = CompositingQuality.HighQuality;
thumbnailGraphic.SmoothingMode = SmoothingMode.HighQuality;
thumbnailGraphic.InterpolationMode = InterpolationMode.HighQualityBicubic;
// Define Text Options
Font drawFont = new Font("Arial", 12, FontStyle.Bold);
SizeF size = thumbnailGraphic.MeasureString(text, drawFont);
SolidBrush fontBrush = new SolidBrush(Color.Black);
Point atPoint = new Point((int)x, (int)y - (int)size.Height - 1);
// Define BoundingBox options
Pen pen = new Pen(box.BoxColor, 3.2f);
SolidBrush colorBrush = new SolidBrush(box.BoxColor);
// Draw text on image
thumbnailGraphic.FillRectangle(colorBrush, (int)x, (int)(y - size.Height - 1), (int)size.Width, (int)size.Height);
thumbnailGraphic.DrawString(text, drawFont, fontBrush, atPoint);
// Draw bounding box on image
thumbnailGraphic.DrawRectangle(pen, x, y, width, height);
}
}
if (!Directory.Exists(outputImageLocation))
{
Directory.CreateDirectory(outputImageLocation);
}
image.Save(Path.Combine(outputImageLocation, imageName));
}
void LogDetectedObjects(string imageName, IList<YoloBoundingBox> boundingBoxes)
{
Console.WriteLine($".....The objects in the image {imageName} are detected as below....");
foreach (var box in boundingBoxes)
{
Console.WriteLine($"{box.Label} and its Confidence score: {box.Confidence}");
}
Console.WriteLine("");
}