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C# samples: Faster R-CNN (#4733)
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* C# sample: Faster R-CNN

* Add link to new sample in samples README

* Remove duplicate image
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@marcusturewicz
marcusturewicz authored Aug 14, 2020
1 parent de26852 commit ce65275
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87 changes: 87 additions & 0 deletions csharp/sample/Microsoft.ML.OnnxRuntime.FasterRcnnSample/LabelMap.cs
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namespace Microsoft.ML.OnnxRuntime.FasterRcnnSample
{
public class LabelMap
{
public static readonly string[] Labels = new[] {"__background",
"person",
"bicycle",
"car",
"motorcycle",
"airplane",
"bus",
"train",
"truck",
"boat",
"traffic light",
"fire hydrant",
"stop sign",
"parking meter",
"bench",
"bird",
"cat",
"dog",
"horse",
"sheep",
"cow",
"elephant",
"bear",
"zebra",
"giraffe",
"backpack",
"umbrella",
"handbag",
"tie",
"suitcase",
"frisbee",
"skis",
"snowboard",
"sports ball",
"kite",
"baseball bat",
"baseball glove",
"skateboard",
"surfboard",
"tennis racket",
"bottle",
"wine glass",
"cup",
"fork",
"knife",
"spoon",
"bowl",
"banana",
"apple",
"sandwich",
"orange",
"broccoli",
"carrot",
"hot dog",
"pizza",
"donut",
"cake",
"chair",
"couch",
"potted plant",
"bed",
"dining table",
"toilet",
"tv",
"laptop",
"mouse",
"remote",
"keyboard",
"cell phone",
"microwave",
"oven",
"toaster",
"sink",
"refrigerator",
"book",
"clock",
"vase",
"scissors",
"teddy bear",
"hair drier",
"toothbrush"};
}
}
15 changes: 15 additions & 0 deletions ...icrosoft.ML.OnnxRuntime.FasterRcnnSample/Microsoft.ML.OnnxRuntime.FasterRcnnSample.csproj
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<Project Sdk="Microsoft.NET.Sdk">

<PropertyGroup>
<OutputType>Exe</OutputType>
<TargetFramework>netcoreapp3.1</TargetFramework>
<LangVersion>8.0</LangVersion>
</PropertyGroup>

<ItemGroup>
<PackageReference Include="Microsoft.ML.OnnxRuntime" Version="1.4.0" />
<PackageReference Include="Sixlabors.ImageSharp" Version="1.0.0" />
<PackageReference Include="SixLabors.ImageSharp.Drawing" Version="1.0.0-beta0010" />
</ItemGroup>

</Project>
26 changes: 26 additions & 0 deletions csharp/sample/Microsoft.ML.OnnxRuntime.FasterRcnnSample/Prediction.cs
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namespace Microsoft.ML.OnnxRuntime.FasterRcnnSample
{
public class Prediction
{
public Box Box { get; set; }
public string Label { get; set; }
public float Confidence { get; set; }
}

public class Box
{
public float Xmin { get; set; }
public float Ymin { get; set; }
public float Xmax { get; set; }
public float Ymax { get; set; }

public Box(float xmin, float ymin, float xmax, float ymax)
{
Xmin = xmin;
Ymin = ymin;
Xmax = xmax;
Ymax = ymax;

}
}
}
107 changes: 107 additions & 0 deletions csharp/sample/Microsoft.ML.OnnxRuntime.FasterRcnnSample/Program.cs
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using System;
using System.Collections.Generic;
using System.IO;
using System.Linq;
using Microsoft.ML.OnnxRuntime.Tensors;
using SixLabors.ImageSharp;
using SixLabors.ImageSharp.Formats;
using SixLabors.ImageSharp.PixelFormats;
using SixLabors.ImageSharp.Processing;
using SixLabors.ImageSharp.Drawing.Processing;
using SixLabors.Fonts;

namespace Microsoft.ML.OnnxRuntime.FasterRcnnSample
{
class Program
{
public static void Main(string[] args)
{
// Read paths
string modelFilePath = args[0];
string imageFilePath = args[1];
string outImageFilePath = args[2];

// Read image
using Image<Rgb24> image = Image.Load<Rgb24>(imageFilePath, out IImageFormat format);

// Resize image
float ratio = 800f / Math.Min(image.Width, image.Height);
using Stream imageStream = new MemoryStream();
image.Mutate(x => x.Resize((int)(ratio * image.Width), (int)(ratio * image.Height)));
image.Save(imageStream, format);

// Preprocess image
var paddedHeight = (int)(Math.Ceiling(image.Height / 32f) * 32f);
var paddedWidth = (int)(Math.Ceiling(image.Width / 32f) * 32f);
Tensor<float> input = new DenseTensor<float>(new[] { 3, paddedHeight, paddedWidth });
var mean = new[] { 102.9801f, 115.9465f, 122.7717f };
for (int y = paddedHeight - image.Height; y < image.Height; y++)
{
Span<Rgb24> pixelSpan = image.GetPixelRowSpan(y);
for (int x = paddedWidth - image.Width; x < image.Width; x++)
{
input[0, y, x] = pixelSpan[x].B - mean[0];
input[1, y, x] = pixelSpan[x].G - mean[1];
input[2, y, x] = pixelSpan[x].R - mean[2];
}
}

// Setup inputs and outputs
var inputs = new List<NamedOnnxValue>
{
NamedOnnxValue.CreateFromTensor("image", input)
};

// Run inference
using var session = new InferenceSession(modelFilePath);
using IDisposableReadOnlyCollection<DisposableNamedOnnxValue> results = session.Run(inputs);

// Postprocess to get predictions
var resultsArray = results.ToArray();
float[] boxes = resultsArray[0].AsEnumerable<float>().ToArray();
long[] labels = resultsArray[1].AsEnumerable<long>().ToArray();
float[] confidences = resultsArray[2].AsEnumerable<float>().ToArray();
var predictions = new List<Prediction>();
var minConfidence = 0.7f;
for (int i = 0; i < boxes.Length - 4; i += 4)
{
var index = i / 4;
if (confidences[index] >= minConfidence)
{
predictions.Add(new Prediction
{
Box = new Box(boxes[i], boxes[i + 1], boxes[i + 2], boxes[i + 3]),
Label = LabelMap.Labels[labels[index]],
Confidence = confidences[index]
});
}
}

// Put boxes, labels and confidence on image and save for viewing
using var outputImage = File.OpenWrite(outImageFilePath);
Font font = SystemFonts.CreateFont("Arial", 16);
foreach (var p in predictions)
{
image.Mutate(x =>
{
x.DrawLines(Color.Red, 2f, new PointF[] {

new PointF(p.Box.Xmin, p.Box.Ymin),
new PointF(p.Box.Xmax, p.Box.Ymin),

new PointF(p.Box.Xmax, p.Box.Ymin),
new PointF(p.Box.Xmax, p.Box.Ymax),

new PointF(p.Box.Xmax, p.Box.Ymax),
new PointF(p.Box.Xmin, p.Box.Ymax),

new PointF(p.Box.Xmin, p.Box.Ymax),
new PointF(p.Box.Xmin, p.Box.Ymin)
});
x.DrawText($"{p.Label}, {p.Confidence:0.00}", font, Color.White, new PointF(p.Box.Xmin, p.Box.Ymin));
});
}
image.Save(outputImage, format);
}
}
}
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