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Update samples to .NET 6 (dotnet#932)
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* Update bike rental sample to .NET 6

* Update object detection sample to .NET 6
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@jwood803
jwood803 authored Dec 20, 2021
1 parent 89dbd6f commit fa027d3
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3 changes: 2 additions & 1 deletion ...tarted/DeepLearning_ObjectDetection_Onnx/ObjectDetectionConsoleApp/ObjectDetection.csproj
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<PropertyGroup>
<OutputType>Exe</OutputType>
<TargetFramework>net5</TargetFramework>
<TargetFramework>net6.0</TargetFramework>
<ImplicitUsings>enable</ImplicitUsings>
</PropertyGroup>

<ItemGroup>
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242 changes: 113 additions & 129 deletions ...rp/getting-started/DeepLearning_ObjectDetection_Onnx/ObjectDetectionConsoleApp/Program.cs
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@@ -1,148 +1,132 @@
using System;
using System.IO;
using System.Collections.Generic;
using System.Drawing;
using System.Drawing;
using System.Drawing.Drawing2D;
using System.Linq;
using Microsoft.ML;
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");

namespace ObjectDetection
// Initialize MLContext
MLContext mlContext = new MLContext();

try
{
class Program
// Load Data
IEnumerable<ImageNetData> images = ImageNetData.ReadFromFile(imagesFolder);
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));

// Draw bounding boxes for detected objects in each of the images
for (var i = 0; i < images.Count(); i++)
{
public static void Main()
{
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
IEnumerable<ImageNetData> images = ImageNetData.ReadFromFile(imagesFolder);
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));

// Draw bounding boxes for detected objects in each of the images
for (var i = 0; i < images.Count(); i++)
{
string imageFileName = images.ElementAt(i).Label;
IList<YoloBoundingBox> detectedObjects = boundingBoxes.ElementAt(i);

DrawBoundingBox(imagesFolder, outputFolder, imageFileName, detectedObjects);

LogDetectedObjects(imageFileName, detectedObjects);
}
}
catch (Exception ex)
{
Console.WriteLine(ex.ToString());
}

Console.WriteLine("========= End of Process..Hit any Key ========");
Console.ReadLine();
}
string imageFileName = images.ElementAt(i).Label;
IList<YoloBoundingBox> detectedObjects = boundingBoxes.ElementAt(i);

public static string GetAbsolutePath(string relativePath)
{
FileInfo _dataRoot = new FileInfo(typeof(Program).Assembly.Location);
string assemblyFolderPath = _dataRoot.Directory.FullName;
DrawBoundingBox(imagesFolder, outputFolder, imageFileName, detectedObjects);

string fullPath = Path.Combine(assemblyFolderPath, relativePath);
LogDetectedObjects(imageFileName, detectedObjects);
}
}
catch (Exception ex)
{
Console.WriteLine(ex.ToString());
}

return fullPath;
}
Console.WriteLine("========= End of Process..Hit any Key ========");

private static 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));
}
string GetAbsolutePath(string relativePath)
{
FileInfo _dataRoot = new FileInfo(typeof(Program).Assembly.Location);
string assemblyFolderPath = _dataRoot.Directory.FullName;

private static void LogDetectedObjects(string imageName, IList<YoloBoundingBox> boundingBoxes)
{
Console.WriteLine($".....The objects in the image {imageName} are detected as below....");
string fullPath = Path.Combine(assemblyFolderPath, relativePath);

foreach (var box in boundingBoxes)
{
Console.WriteLine($"{box.Label} and its Confidence score: {box.Confidence}");
}
return fullPath;
}

Console.WriteLine("");
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("");
}
3 changes: 2 additions & 1 deletion ...-started/Forecasting_BikeSharingDemand/BikeDemandForecasting/BikeDemandForecasting.csproj
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Expand Up @@ -2,7 +2,8 @@

<PropertyGroup>
<OutputType>Exe</OutputType>
<TargetFramework>net5</TargetFramework>
<TargetFramework>net6.0</TargetFramework>
<ImplicitUsings>enable</ImplicitUsings>
</PropertyGroup>

<ItemGroup>
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Binary file modified .../getting-started/Forecasting_BikeSharingDemand/BikeDemandForecasting/Data/DailyDemand.mdf
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