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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.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);
// Resize image
float ratio = 800f / Math.Min(image.Width, image.Height);
image.Mutate(x => x.Resize((int)(ratio * image.Width), (int)(ratio * image.Height)));
// 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 };
image.ProcessPixelRows(accessor =>
{
for (int y = paddedHeight - accessor.Height; y < accessor.Height; y++)
{
Span<Rgb24> pixelSpan = accessor.GetRowSpan(y);
for (int x = paddedWidth - accessor.Width; x < accessor.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.SaveAsJpeg(outputImage);
}
}
}
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