From ce65275edf8540248ba2b721ae2c7351c3284edd Mon Sep 17 00:00:00 2001
From: Marcus Turewicz <24448509+marcusturewicz@users.noreply.github.com>
Date: Fri, 14 Aug 2020 10:05:01 +1000
Subject: [PATCH] C# samples: Faster R-CNN (#4733)
* C# sample: Faster R-CNN
* Add link to new sample in samples README
* Remove duplicate image
---
.../LabelMap.cs | 87 +++++++++
...oft.ML.OnnxRuntime.FasterRcnnSample.csproj | 15 ++
.../Prediction.cs | 26 +++
.../Program.cs | 107 +++++++++++
.../README.md | 172 ++++++++++++++++++
.../demo.jpg | Bin 0 -> 171271 bytes
.../out.jpg | Bin 0 -> 315789 bytes
samples/README.md | 1 +
8 files changed, 408 insertions(+)
create mode 100644 csharp/sample/Microsoft.ML.OnnxRuntime.FasterRcnnSample/LabelMap.cs
create mode 100644 csharp/sample/Microsoft.ML.OnnxRuntime.FasterRcnnSample/Microsoft.ML.OnnxRuntime.FasterRcnnSample.csproj
create mode 100644 csharp/sample/Microsoft.ML.OnnxRuntime.FasterRcnnSample/Prediction.cs
create mode 100644 csharp/sample/Microsoft.ML.OnnxRuntime.FasterRcnnSample/Program.cs
create mode 100644 csharp/sample/Microsoft.ML.OnnxRuntime.FasterRcnnSample/README.md
create mode 100644 csharp/sample/Microsoft.ML.OnnxRuntime.FasterRcnnSample/demo.jpg
create mode 100644 csharp/sample/Microsoft.ML.OnnxRuntime.FasterRcnnSample/out.jpg
diff --git a/csharp/sample/Microsoft.ML.OnnxRuntime.FasterRcnnSample/LabelMap.cs b/csharp/sample/Microsoft.ML.OnnxRuntime.FasterRcnnSample/LabelMap.cs
new file mode 100644
index 0000000000..3dccd927b7
--- /dev/null
+++ b/csharp/sample/Microsoft.ML.OnnxRuntime.FasterRcnnSample/LabelMap.cs
@@ -0,0 +1,87 @@
+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"};
+ }
+}
\ No newline at end of file
diff --git a/csharp/sample/Microsoft.ML.OnnxRuntime.FasterRcnnSample/Microsoft.ML.OnnxRuntime.FasterRcnnSample.csproj b/csharp/sample/Microsoft.ML.OnnxRuntime.FasterRcnnSample/Microsoft.ML.OnnxRuntime.FasterRcnnSample.csproj
new file mode 100644
index 0000000000..757ca6ab5a
--- /dev/null
+++ b/csharp/sample/Microsoft.ML.OnnxRuntime.FasterRcnnSample/Microsoft.ML.OnnxRuntime.FasterRcnnSample.csproj
@@ -0,0 +1,15 @@
+
+
+
+ Exe
+ netcoreapp3.1
+ 8.0
+
+
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/csharp/sample/Microsoft.ML.OnnxRuntime.FasterRcnnSample/Prediction.cs b/csharp/sample/Microsoft.ML.OnnxRuntime.FasterRcnnSample/Prediction.cs
new file mode 100644
index 0000000000..4857f2d83f
--- /dev/null
+++ b/csharp/sample/Microsoft.ML.OnnxRuntime.FasterRcnnSample/Prediction.cs
@@ -0,0 +1,26 @@
+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;
+
+ }
+ }
+}
\ No newline at end of file
diff --git a/csharp/sample/Microsoft.ML.OnnxRuntime.FasterRcnnSample/Program.cs b/csharp/sample/Microsoft.ML.OnnxRuntime.FasterRcnnSample/Program.cs
new file mode 100644
index 0000000000..88edca5c77
--- /dev/null
+++ b/csharp/sample/Microsoft.ML.OnnxRuntime.FasterRcnnSample/Program.cs
@@ -0,0 +1,107 @@
+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 image = Image.Load(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 input = new DenseTensor(new[] { 3, paddedHeight, paddedWidth });
+ var mean = new[] { 102.9801f, 115.9465f, 122.7717f };
+ for (int y = paddedHeight - image.Height; y < image.Height; y++)
+ {
+ Span 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.CreateFromTensor("image", input)
+ };
+
+ // Run inference
+ using var session = new InferenceSession(modelFilePath);
+ using IDisposableReadOnlyCollection results = session.Run(inputs);
+
+ // Postprocess to get predictions
+ var resultsArray = results.ToArray();
+ float[] boxes = resultsArray[0].AsEnumerable().ToArray();
+ long[] labels = resultsArray[1].AsEnumerable().ToArray();
+ float[] confidences = resultsArray[2].AsEnumerable().ToArray();
+ var predictions = new List();
+ 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);
+ }
+ }
+}
\ No newline at end of file
diff --git a/csharp/sample/Microsoft.ML.OnnxRuntime.FasterRcnnSample/README.md b/csharp/sample/Microsoft.ML.OnnxRuntime.FasterRcnnSample/README.md
new file mode 100644
index 0000000000..5b042da0c6
--- /dev/null
+++ b/csharp/sample/Microsoft.ML.OnnxRuntime.FasterRcnnSample/README.md
@@ -0,0 +1,172 @@
+# C# Sample: Faster R-CNN
+
+The sample walks through how to run a pretrained Faster R-CNN object detection ONNX model using the ONNX Runtime C# API.
+
+The source code for this sample is available [here](Program.cs).
+
+## Prerequisites
+
+To run this sample, you'll need the following things:
+
+1. Install [.NET Core 3.1](https://dotnet.microsoft.com/download/dotnet-core/3.1) or higher for you OS (Mac, Windows or Linux).
+2. Download the [Faster R-CNN](https://github.com/onnx/models/blob/master/vision/object_detection_segmentation/faster-rcnn/model/FasterRCNN-10.onnx) ONNX model to your local system.
+3. Download [this demo image](demo.jpg) to test the model. You can also use any image you like.
+
+## Getting Started
+
+Now we have everything set up, we can start adding code to run the model on the image. We'll do this in the main method of the program for simplicity.
+
+### Read paths
+
+Firstly, let's read the path to the model, path to the image we want to test, and path to the output image:
+
+```cs
+string modelFilePath = args[0];
+string imageFilePath = args[1];
+string outImageFilePath = args[2];
+```
+
+### Read image
+
+Next, we will read the image in using the cross-platform image library [ImageSharp](https://www.nuget.org/packages/SixLabors.ImageSharp):
+
+```cs
+using Image image = Image.Load(imageFilePath, out IImageFormat format);
+```
+
+Note, we're specifically reading the `Rgb24` type so we can efficiently preprocess the image in a later step.
+
+### Resize image
+
+Next, we will resize the image to the appropriate size that the model is expecting; it is recommended to resize the image such that both height and width are within the range of [800, 1333].
+
+```cs
+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
+
+Next, we will preprocess the image according to the [requirements of the model](https://github.com/onnx/models/tree/master/vision/object_detection_segmentation/faster-rcnn#preprocessing-steps):
+
+```cs
+var paddedHeight = (int)(Math.Ceiling(image.Height / 32f) * 32f);
+var paddedWidth = (int)(Math.Ceiling(image.Width / 32f) * 32f);
+Tensor input = new DenseTensor(new[] { 3, paddedHeight, paddedWidth });
+var mean = new[] { 102.9801f, 115.9465f, 122.7717f };
+for (int y = paddedHeight - image.Height; y < image.Height; y++)
+{
+ Span 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];
+ }
+}
+```
+
+Here, we're creating a Tensor of the required size `(channels, paddedHeight, paddedWidth)`, accessing the pixel values, preprocessing them and finally assigning them to the tensor at the appropriate indicies.
+
+### Setup inputs
+
+Next, we will create the inputs to the model:
+
+```cs
+var inputs = new List
+{
+ NamedOnnxValue.CreateFromTensor("image", input)
+};
+```
+
+To check the input node names for an ONNX model, you can use [Netron](https://github.com/lutzroeder/netron) to visualise the model and see input/output names. In this case, this model has `image` as the input node name.
+
+### Run inference
+
+Next, we will create an inference session and run the input through it:
+
+```cs
+using var session = new InferenceSession(modelFilePath);
+using IDisposableReadOnlyCollection results = session.Run(inputs);
+```
+
+### Postprocess output
+
+Next, we will need to postprocess the output to get boxes and associated label and confidence scores for each box:
+
+```cs
+var resultsArray = results.ToArray();
+float[] boxes = resultsArray[0].AsEnumerable().ToArray();
+long[] labels = resultsArray[1].AsEnumerable().ToArray();
+float[] confidences = resultsArray[2].AsEnumerable().ToArray();
+var predictions = new List();
+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]
+ });
+ }
+}
+```
+
+Note, we're only taking boxes that have a confidence above 0.7 to remove false positives.
+
+### View prediction
+
+Next, we'll draw the boxes and associated labels and confidence scores on the image to see how the model went:
+
+```cs
+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);
+```
+
+For each box prediction, we're using ImageSharp to draw red lines to create the boxes, and drawing the label and confidence text.
+
+## Running the program
+
+Now the program is created, we can run it will the following command:
+
+```
+dotnet run [path-to-model] [path-to-image] [path-to-output-image]
+```
+
+e.g. running:
+
+```
+dotnet run ~/Downloads/FasterRCNN-10.onnx ~/Downloads/demo.jpg ~/Downloads/out.jpg
+```
+
+detects the following objects in the image:
+
+
\ No newline at end of file
diff --git a/csharp/sample/Microsoft.ML.OnnxRuntime.FasterRcnnSample/demo.jpg b/csharp/sample/Microsoft.ML.OnnxRuntime.FasterRcnnSample/demo.jpg
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