Random wanderings through Microsoft Azure esp. PaaS plumbing, the IoT bits, AI on Micro controllers, AI on Edge Devices, .NET nanoFramework, .NET Core on *nix and ML.NET+ONNX
public class Function1
{
private readonly ILogger<Function1> _logger;
private readonly List<string> _labels;
private readonly InferenceSession _session;
public Function1(ILogger<Function1> logger)
{
_logger = logger;
_labels = File.ReadAllLines(Path.Combine(AppContext.BaseDirectory, "labels.txt")).ToList();
_session = new InferenceSession(Path.Combine(AppContext.BaseDirectory, "FasterRCNN-10.onnx"));
}
[Function("ObjectDetectionFunction")]
public async Task<IActionResult> Run([HttpTrigger(AuthorizationLevel.Function, "post", Route = null)] HttpRequest req, ExecutionContext context)
{
if (!req.ContentType.StartsWith("image/"))
return new BadRequestObjectResult("Content-Type must be an image.");
using var ms = new MemoryStream();
await req.Body.CopyToAsync(ms);
ms.Position = 0;
using var image = Image.Load<Rgb24>(ms);
var inputTensor = PreprocessImage(image);
var inputs = new List<NamedOnnxValue>
{
NamedOnnxValue.CreateFromTensor("image", inputTensor)
};
using IDisposableReadOnlyCollection<DisposableNamedOnnxValue> results = _session.Run(inputs);
var output = results.ToDictionary(x => x.Name, x => x.Value);
var boxes = (DenseTensor<float>)output["6379"];
var labels = (DenseTensor<long>)output["6381"];
var scores = (DenseTensor<float>)output["6383"];
var detections = new List<object>();
for (int i = 0; i < scores.Length; i++)
{
if (scores[i] > 0.5)
{
detections.Add(new
{
label = _labels[(int)labels[i]],
score = scores[i],
box = new
{
x1 = boxes[i, 0],
y1 = boxes[i, 1],
x2 = boxes[i, 2],
y2 = boxes[i, 3]
}
});
}
}
return new OkObjectResult(detections);
}
private static DenseTensor<float> PreprocessImage(Image<Rgb24> image)
{
// Step 1: Resize so that min(H, W) = 800, max(H, W) <= 1333, keeping aspect ratio
int origWidth = image.Width;
int origHeight = image.Height;
int minSize = 800;
int maxSize = 1333;
float scale = Math.Min((float)minSize / Math.Min(origWidth, origHeight),
(float)maxSize / Math.Max(origWidth, origHeight));
int resizedWidth = (int)Math.Round(origWidth * scale);
int resizedHeight = (int)Math.Round(origHeight * scale);
image.Mutate(x => x.Resize(resizedWidth, resizedHeight));
// Step 2: Pad so that both dimensions are divisible by 32
int padWidth = ((resizedWidth + 31) / 32) * 32;
int padHeight = ((resizedHeight + 31) / 32) * 32;
var paddedImage = new Image<Rgb24>(padWidth, padHeight);
paddedImage.Mutate(ctx => ctx.DrawImage(image, new Point(0, 0), 1f));
// Step 3: Convert to BGR and normalize
float[] mean = { 102.9801f, 115.9465f, 122.7717f };
var tensor = new DenseTensor<float>(new[] { 3, padHeight, padWidth });
for (int y = 0; y < padHeight; y++)
{
for (int x = 0; x < padWidth; x++)
{
Rgb24 pixel = default;
if (x < resizedWidth && y < resizedHeight)
pixel = paddedImage[x, y];
tensor[0, y, x] = pixel.B - mean[0];
tensor[1, y, x] = pixel.G - mean[1];
tensor[2, y, x] = pixel.R - mean[2];
}
}
paddedImage.Dispose();
return tensor;
}
}
For my initial testing in the Azure Functions emulator using Fiddler Classic I manually generated 10 requests, then replayed them sequentially, and then finally concurrently.
The results for the manual, then sequential results were fairly consistent but the 10 concurrent requests each to took more than 10x longer. In addition, the CPU was at 100% usage while the concurrently executed functions were running.
The next couple of posts will compare and look at options for improving the “performance” (scalability, execution duration, latency, jitter, billing etc.) of the Github Copilot generated code.
While testing the FasterRCNNObjectDetectionHttpTrigger function with Telerik Fiddler Classic and my “standard” test image I noticed the response bodies were different sizes.
Initially the application plan was an S1 SKU (1 vCPU 1.75G RAM)
I used Netron to inspect the model properties to get the correct names for the output tensors
I had a couple of attempts at resizing the image to see what impact this had on the accuracy of the confidence and minimum bounding rectangles.
resize the image such that both height and width are within the range of [800, 1333], and then pad the image with zeros such that both height and width are divisible by 32.
modify the code to resize the image such that both height and width are within the range of [800, 1333], and then pad the image with zeros such that both height and width are divisible by 32 and the aspect ratio is not changed.
The final version of the image processing code scaled then right padded the image to keep the aspect ratio and MBR coordinates correct.
As a final test I deployed the code to Azure and the first time I ran the function it failed because the labels file couldn’t be found because Unix file paths are case sensitive (labels.txt vs. Labels.txt).
The inferencing time was a bit longer than I expected.
// please write an httpTrigger azure function that uses Faster RCNN and ONNX to detect the object in an image uploaded in the body of an HTTP Post
// manually added the ML.Net ONNX NuGet + using directives
// manually added the ImageSharp NuGet + using directives
// Used Copilot to add Microsoft.ML.OnnxRuntime.Tensors using directive
// Manually added ONNX FIle + labels file sorted out paths
// Used Netron to fixup output tensor names
// Change DenseTensor to BGR (based on https://github.com/onnx/models/tree/main/validated/vision/object_detection_segmentation/faster-rcnn#preprocessing-steps)
// Normalise colour values with mean = [102.9801, 115.9465, 122.7717]
// resize the image such that both height and width are within the range of [800, 1333], and then pad the image with zeros such that both height and width are divisible by 32.
// modify the code to resize the image such that both height and width are within the range of [800, 1333], and then pad the image with zeros such that both height and width are divisible by 32 and the aspect ratio is not changed.
using Microsoft.AspNetCore.Http;
using Microsoft.AspNetCore.Mvc;
using Microsoft.Azure.Functions.Worker;
using Microsoft.Extensions.Logging;
using Microsoft.ML.OnnxRuntime;
using Microsoft.ML.OnnxRuntime.Tensors;
using SixLabors.ImageSharp; // Couldn't get inteliisense after adding NuGet package
using SixLabors.ImageSharp.PixelFormats; // Couldn't get inteliisense after adding NuGet package
using SixLabors.ImageSharp.Processing; // Couldn't get inteliisense after adding NuGet package
namespace FasterRCNNObjectDetectionHttpTriggerGithubCopilot
{
public class Function1
{
private readonly ILogger<Function1> _logger;
private readonly InferenceSession _session;
private readonly List<string> _labels;
public Function1(ILogger<Function1> logger)
{
_logger = logger;
_session = new InferenceSession("FasterRCNN-10.onnx");
_labels = File.ReadAllLines("labels.txt").ToList();
}
[Function("ObjectDetectionFunction")]
public async Task<IActionResult> Run([HttpTrigger(AuthorizationLevel.Function, "post", Route = null)] HttpRequest req)
{
if (!req.ContentType.StartsWith("image/"))
return new BadRequestObjectResult("Content-Type must be an image.");
using var ms = new MemoryStream();
await req.Body.CopyToAsync(ms);
ms.Position = 0;
using var image = Image.Load<Rgb24>(ms);
var inputTensor = PreprocessImage(image);
var inputs = new List<NamedOnnxValue>
{
NamedOnnxValue.CreateFromTensor("image", inputTensor)
};
using IDisposableReadOnlyCollection<DisposableNamedOnnxValue> results = _session.Run(inputs);
var output = results.ToDictionary(x => x.Name, x => x.Value);
var boxes = (DenseTensor<float>)output["6379"];
var labels = (DenseTensor<long>)output["6381"];
var scores = (DenseTensor<float>)output["6383"];
var detections = new List<object>();
for (int i = 0; i < scores.Length; i++)
{
if (scores[i] > 0.5)
{
detections.Add(new
{
label = _labels[(int)labels[i]],
score = scores[i],
box = new
{
x1 = boxes[i, 0],
y1 = boxes[i, 1],
x2 = boxes[i, 2],
y2 = boxes[i, 3]
}
});
}
}
return new OkObjectResult(detections);
}
private static DenseTensor<float> PreprocessImage( Image<Rgb24> image)
{
// Step 1: Resize so that min(H, W) = 800, max(H, W) <= 1333, keeping aspect ratio
int origWidth = image.Width;
int origHeight = image.Height;
int minSize = 800;
int maxSize = 1333;
float scale = Math.Min((float)minSize / Math.Min(origWidth, origHeight),
(float)maxSize / Math.Max(origWidth, origHeight));
/*
float scale = 1.0f;
// If either dimension is less than 800, scale up so the smaller is 800
if (origWidth < minSize || origHeight < minSize)
{
scale = Math.Max((float)minSize / origWidth, (float)minSize / origHeight);
}
// If either dimension is greater than 1333, scale down so the larger is 1333
if (origWidth * scale > maxSize || origHeight * scale > maxSize)
{
scale = Math.Min((float)maxSize / origWidth, (float)maxSize / origHeight);
}
*/
int resizedWidth = (int)Math.Round(origWidth * scale);
int resizedHeight = (int)Math.Round(origHeight * scale);
image.Mutate(x => x.Resize(resizedWidth, resizedHeight));
// Step 2: Pad so that both dimensions are divisible by 32
int padWidth = ((resizedWidth + 31) / 32) * 32;
int padHeight = ((resizedHeight + 31) / 32) * 32;
var paddedImage = new Image<Rgb24>(padWidth, padHeight);
paddedImage.Mutate(ctx => ctx.DrawImage(image, new Point(0, 0), 1f));
// Step 3: Convert to BGR and normalize
float[] mean = { 102.9801f, 115.9465f, 122.7717f };
var tensor = new DenseTensor<float>(new[] { 3, padHeight, padWidth });
for (int y = 0; y < padHeight; y++)
{
for (int x = 0; x < padWidth; x++)
{
Rgb24 pixel = default;
if (x < resizedWidth && y < resizedHeight)
pixel = paddedImage[x, y];
tensor[0, y, x] = pixel.B - mean[0];
tensor[1, y, x] = pixel.G - mean[1];
tensor[2, y, x] = pixel.R - mean[2];
}
}
paddedImage.Dispose();
return tensor;
}
}
}
It took roughly an hour to “vibe code” the function, but it would have taken much longer for someone not familiar with the problem domain.
Summary
The Github Copilot generated code was okay but would be fragile, performance would suck and not scale terribly well.
The Copilot generated code in this post is not suitable for production
After some investigation I think the scaling of the image used for inferencing (based on the requirements on the model), then the scaling of the minimum bounding rectangles isn’t quite right.
public static class Function1
{
private static readonly InferenceSession session = new InferenceSession("resnet50.onnx");
[FunctionName("ImageClassification")]
public static IActionResult Run(
[HttpTrigger(AuthorizationLevel.Function, "post")] HttpRequest req,
ILogger log)
{
log.LogInformation("Processing image classification request...");
try
{
using var ms = new MemoryStream();
req.Body.CopyTo(ms);
using var image = Image.FromStream(ms);
var inputTensor = PreprocessImage(image);
var inputName = session.InputMetadata.Keys.First();
var outputName = session.OutputMetadata.Keys.First();
var result = session.Run(new Dictionary<string, NamedOnnxValue>
{
{ inputName, NamedOnnxValue.CreateFromTensor(inputName, inputTensor) }
});
var predictions = result.First().AsTensor<float>().ToArray();
return new JsonResult(new { predictions });
}
catch (Exception ex)
{
log.LogError($"Error: {ex.Message}");
return new BadRequestObjectResult("Invalid image or request.");
}
}
...
}
public static class Function1
{
private static readonly InferenceSession session = new InferenceSession("resnet50.onnx");
[Function("ImageClassification")]
public static IActionResult Run(
[HttpTrigger(AuthorizationLevel.Function, "post")] HttpRequest req,
ILogger log)
{
log.LogInformation("Processing image classification request...");
try
{
using var ms = new MemoryStream();
req.Body.CopyTo(ms);
using var image = Image.FromStream(ms);
var inputTensor = PreprocessImage(image);
var inputName = session.InputMetadata.Keys.First();
var outputName = session.OutputMetadata.Keys.First();
var inputList = new List<NamedOnnxValue>
{
NamedOnnxValue.CreateFromTensor(inputName, inputTensor)
};
var result = session.Run(inputList);
var predictions = result.First().AsTensor<float>().ToArray();
return new JsonResult(new { predictions });
}
catch (Exception ex)
{
log.LogError($"Error: {ex.Message}");
return new BadRequestObjectResult("Invalid image or request.");
}
}
The image needs to be preprocessed before fed to the network. The first step is to extract a 224x224 crop from the center of the image. For this, the image is first scaled to a minimum size of 256x256, while keeping aspect ratio. That is, the shortest side of the image is resized to 256 and the other side is scaled accordingly to maintain the original aspect ratio. After that, the image is normalized with mean = 255*[0.485, 0.456, 0.406] and std = 255*[0.229, 0.224, 0.225]. Last step is to transpose it from HWC to CHW layout.
Postprocessing
The post-processing involves calculating the softmax probability scores for each class. You can also sort them to report the most probable classes. Check imagenet_postprocess.py for code.
// Compute exponentials for all scores
var expScores = predictions.Select(MathF.Exp).ToArray();
// Compute sum of exponentials
float sumExpScores = expScores.Sum();
// Normalize scores into probabilities
var softmaxResults = expScores.Select(score => score / sumExpScores).ToArray();
// Get top 10 predictions (label ID and confidence)
var top10 = softmaxResults
.Select((confidence, labelId) => new { labelId, confidence, label = labelId < labels.Count ? labels[labelId] : $"Unknown-{labelId}" })
.OrderByDescending(p => p.confidence)
.Take(10)
.ToList();
public static class Function1
{
private static readonly ILogger logger;
private static readonly InferenceSession session = new InferenceSession("resnet50-v2-7.onnx");
private static readonly List<string> labels = LoadLabels("labels.txt");
...
[Function("ImageClassification")]
public static async Task<IActionResult> Run(
[HttpTrigger(AuthorizationLevel.Function, "post")] HttpRequest req)
{
logger.LogInformation("Processing image classification request...");
try
{
...
// Get top 10 predictions (label ID and confidence)
var top10 = softmaxResults
.Select((confidence, labelId) => new { labelId, confidence, label = labelId < labels.Count ? labels[labelId] : $"Unknown-{labelId}" })
.OrderByDescending(p => p.confidence)
.Take(10)
.ToList();
return new JsonResult(new { predictions = top10 });
}
catch (Exception ex)
{
logger.LogError($"Error: {ex.Message}");
return new BadRequestObjectResult("Invalid image or request.");
}
}
...
private static List<string> LoadLabels(string filePath)
{
try
{
return File.ReadAllLines(filePath).ToList();
}
catch (Exception ex)
{
logger.LogError($"Error loading labels file: {ex.Message}");
return new List<string>(); // Return empty list if file fails to load
}
}
}
Summary
The Github Copilot generated code was okay but would be fragile and not scale terribly well. The confidence values changing very slightly when the code was updated for Sixlabors ImageSharp was disconcerting, but not surprising.
The Copilot generated code in this post is not suitable for production
Input
All pre-trained models expect input images normalized in the same way, i.e. mini-batches
of 3-channel RGB images of shape (N x 3 x H x W), where N is the batch size, and H and
W are expected to be at least 224. The inference was done using jpeg image.
Preprocessing
The image needs to be preprocessed before fed to the network. The first step is to
extract a 224x224 crop from the center of the image. For this, the image is first scaled
to a minimum size of 256x256, while keeping aspect ratio. That is, the shortest side
of the image is resized to 256 and the other side is scaled accordingly to maintain
the original aspect ratio.
After that, the image is normalized with mean = 255*[0.485, 0.456, 0.406] and std
= 255*[0.229, 0.224, 0.225]. Last step is to transpose it from HWC to CHW layout.
static void Main()
{
string modelPath = "resnet50-v2-7.onnx"; // Path to your ONNX model
string imagePath = "pizza.jpg"; // Path to the input image
using var session = new InferenceSession(modelPath);
var inputTensor = LoadAndPreprocessImage(imagePath);
var inputs = new List<NamedOnnxValue>
{
NamedOnnxValue.CreateFromTensor("input", inputTensor)
};
using var results = session.Run(inputs);
var output = results.First().AsTensor<float>().ToArray();
Console.WriteLine("Predicted class index: " + Array.IndexOf(output, output.Max()));
}
static DenseTensor<float> LoadAndPreprocessImage(string imagePath)
{
using Bitmap bitmap = new Bitmap(imagePath);
int width = 224, height = 224; // ResNet50 expects 224x224 input
using Bitmap resized = new Bitmap(bitmap, new Size(width, height));
var tensor = new DenseTensor<float>(new[] { 1, 3, width, height });
for (int y = 0; y < height; y++)
{
for (int x = 0; x < width; x++)
{
Color pixel = resized.GetPixel(x, y);
tensor[0, 0, y, x] = pixel.R / 255f; // Normalize
tensor[0, 1, y, x] = pixel.G / 255f;
tensor[0, 2, y, x] = pixel.B / 255f;
}
}
return tensor;
}
Input
All pre-trained models expect input images normalized in the same way, i.e. mini-batches
of 3-channel RGB images of shape (N x 3 x H x W), where N is the batch size, and H and
W are expected to be at least 224. The inference was done using jpeg image.
Preprocessing
The image needs to be preprocessed before fed to the network. The first step is to
extract a 224x224 crop from the center of the image. For this, the image is first scaled
to a minimum size of 256x256, while keeping aspect ratio. That is, the shortest side
of the image is resized to 256 and the other side is scaled accordingly to maintain
the original aspect ratio.
After that, the image is normalized with mean = 255*[0.485, 0.456, 0.406] and std
= 255*[0.229, 0.224, 0.225]. Last step is to transpose it from HWC to CHW layout.
The Copilot generated code compiled and ran but the confidence values still didn’t look right, and the results tensor contained 1000 confidences values.
static void Main()
{
string modelPath = "resnet50-v2-7.onnx"; // Updated model path
string imagePath = "pizza.jpg"; // Updated image path
using var session = new InferenceSession(modelPath);
var inputTensor = LoadAndPreprocessImage(imagePath);
var inputs = new List<NamedOnnxValue>
{
NamedOnnxValue.CreateFromTensor("data", inputTensor) // Using "data" as the input tensor name
};
using var results = session.Run(inputs);
var output = results.First().AsTensor<float>().ToArray();
Console.WriteLine("Predicted class index: " + Array.IndexOf(output, output.Max()));
}
static DenseTensor<float> LoadAndPreprocessImage(string imagePath)
{
using Bitmap bitmap = new Bitmap(imagePath);
int width = 224, height = 224; // ResNet50 expects 224x224 input
using Bitmap resized = new Bitmap(bitmap, new Size(width, height));
var tensor = new DenseTensor<float>(new[] { 1, 3, width, height });
// ImageNet mean & standard deviation values
float[] mean = { 0.485f, 0.456f, 0.406f };
float[] stdev = { 0.229f, 0.224f, 0.225f };
for (int y = 0; y < height; y++)
{
for (int x = 0; x < width; x++)
{
Color pixel = resized.GetPixel(x, y);
// Normalize using mean and standard deviation
tensor[0, 0, y, x] = (pixel.R / 255f - mean[0]) / stdev[0]; // Red channel
tensor[0, 1, y, x] = (pixel.G / 255f - mean[1]) / stdev[1]; // Green channel
tensor[0, 2, y, x] = (pixel.B / 255f - mean[2]) / stdev[2]; // Blue channel
}
}
return tensor;
}
Output
The model outputs image scores for each of the 1000 classes of ImageNet.
Postprocessing
The post-processing involves calculating the softmax probability scores for each
class. You can also sort them to report the most probable classes. Check
imagenet_postprocess.py for code.
The Copilot generated code wouldn’t compile due to a syntax error.
static void Main()
{
string modelPath = "resnet50-v2-7.onnx"; // Updated model path
string imagePath = "pizza.jpg"; // Updated image path
using var session = new InferenceSession(modelPath);
var inputTensor = LoadAndPreprocessImage(imagePath);
var inputs = new List<NamedOnnxValue>
{
NamedOnnxValue.CreateFromTensor("data", inputTensor) // Using "data" as the input tensor name
};
using var results = session.Run(inputs);
var output = results.First().AsTensor<float>().ToArray();
// Calculate softmax
var probabilities = Softmax(output);
// Get the class index with the highest probability
int predictedClass = Array.IndexOf(probabilities, probabilities.Max());
Console.WriteLine($"Predicted class index: {predictedClass}");
Console.WriteLine($"Probabilities: {string.Join(", ", probabilities.Select(p => p.ToString("F4")))}");
}
...
static float[] Softmax(float[] logits)
{
// Compute softmax
var expScores = logits.Select(Math.Exp).ToArray();
double sumExpScores = expScores.Sum();
return expScores.Select(score => (float)(score / sumExpScores)).ToArray();
}
Copilot was adamant that the generated code was correct.
After trying different Copilot prompts the code had to be manually fixed, before it would compile
The Copilot generated code ran and the results for the top 10 confidence values looked reasonable
static void Main()
{
string modelPath = "resnet50-v2-7.onnx"; // Updated model path
string imagePath = "pizza.jpg"; // Updated image path
string labelsPath = "labels.txt"; // Path to labels file
using var session = new InferenceSession(modelPath);
var inputTensor = LoadAndPreprocessImage(imagePath);
var inputs = new List<NamedOnnxValue>
{
NamedOnnxValue.CreateFromTensor("data", inputTensor) // Using "data" as the input tensor name
};
using var results = session.Run(inputs);
var output = results.First().AsTensor<float>().ToArray();
// Calculate softmax
var probabilities = Softmax(output);
// Load labels
var labels = File.ReadAllLines(labelsPath);
// Find Top 10 labels and their confidence scores
var top10 = probabilities
.Select((prob, index) => new { Label = labels[index], Confidence = prob })
.OrderByDescending(item => item.Confidence)
.Take(10);
Console.WriteLine("Top 10 Predictions:");
foreach (var item in top10)
{
Console.WriteLine($"{item.Label}: {item.Confidence:F4}");
}
}
...
static float[] Softmax(float[] logits)
{
// Compute softmax
float maxVal = logits.Max();
var expScores = logits.Select(v => (float)Math.Exp(v - maxVal)).ToArray();
double sumExpScores = expScores.Sum();
return expScores.Select(score => (float)(score / sumExpScores)).ToArray();
}
using Microsoft.AspNetCore.Http;
using Microsoft.AspNetCore.Mvc;
using Microsoft.Azure.Functions.Worker;
using Microsoft.Extensions.Logging;
namespace YoloSharpxxxxxHttpTriggerFunction
{
public class Function1
{
private readonly ILogger<Function1> _logger;
public Function1(ILogger<Function1> logger)
{
_logger = logger;
}
[Function("Function1")]
public IActionResult Run([HttpTrigger(AuthorizationLevel.Anonymous, "get", "post")] HttpRequest req)
{
_logger.LogInformation("C# HTTP trigger function processed a request.");
return new OkObjectResult("Welcome to Azure Functions!");
}
}
}
[FunctionName("DetectObjects")]
public static async Task<IActionResult> Run(
[HttpTrigger(AuthorizationLevel.Function, "post", Route = "detect")] HttpRequest req,
ILogger log)
{
log.LogInformation("DetectObjects function received a request for object detection.");
...
}
Every so often, when uploading more than one image at a time there was a “System.IO.InvalidData Exception: The stream exceeded the data limit 16384” error. In previous examples I had tried the batch processing of multiple images but had hit memory issues, so putting the image to be in the message body rather than Multipart Form Data for future development seems like a reasonable approach.
Image in the body of the request
Object Classification
I started with an object classification function because in previous examples Github Copilot was better at unpacking the results.
please write an httpTrigger azure function that uses YoloSharp and a Yolo image classification onnx model Image classification not object detection The image is in the form data The multipart/form-data check can be removed The YoloPredictor should be released after use Many image files could be uploaded in one request Only one image file can be uploaded in one request
[Function("YoloImageClassificationFunction")]
public async Task<IActionResult> Run([HttpTrigger(AuthorizationLevel.Function, "post", Route = null)] HttpRequest req)
{
_logger.LogInformation("C# HTTP trigger function processed a request.");
var form = await req.ReadFormAsync();
var files = form.Files;
if (files.Count == 0)
{
return new BadRequestObjectResult("No images uploaded.");
}
if (files.Count > 1)
{
return new BadRequestObjectResult("Only one image file can be uploaded in one request.");
}
var file = files[0];
if (file.Length > 0)
{
using (var memoryStream = new MemoryStream())
{
await file.CopyToAsync(memoryStream);
var imageBytes = memoryStream.ToArray();
using (var yoloModel = new YoloPredictor("yolov8s-cls.onnx"))
{
var classifications = yoloModel.Classify(imageBytes);
return new OkObjectResult(new { file.FileName, classifications });
}
}
}
return new BadRequestObjectResult("Invalid image file.");
}
Image in the form data of the request
When I compared the results with previous applications, they were the same. I should have added code which discarded any result with less that a specified confidence to reduce the size of the results.
Object Detection
please write an azure function with an http trigger that uses yolosharp and an onnx file to detect objects in an image The image is not in the body it is in the form Image.Load is not used yolo.Detect can process an image file stream The YoloPredictor should be released after use Many image files could be uploaded in one request Only one image per request Add multipart/form-data content type check Add check that there are headers — Didn’t get this right Add check that req.Headers is not null Add check that request has ContentType and it is multipart/form-data
[Function("ObjectDetectionFunction")]
public async Task<IActionResult> Run( [HttpTrigger(AuthorizationLevel.Function, "post", Route = null)] HttpRequest req)
{
_logger.LogInformation("C# HTTP trigger function processed a request.");
// Intellisense fix
if (req.Headers == null || !req.Headers.TryGetValue("Content-Type", out Microsoft.Extensions.Primitives.StringValues value) || !value.ToString().Contains("multipart/form-data"))
{
return new BadRequestObjectResult("The request must have a Content-Type header with multipart/form-data.");
}
// Read the images from the form data
var form = await req.ReadFormAsync();
var files = form.Files;
if (files.Count == 0)
{
return new BadRequestObjectResult("No image files uploaded.");
}
if (files.Count > 1)
{
return new BadRequestObjectResult("Only one image file is allowed per request.");
}
var file = files[0];
if (file.Length == 0)
{
return new BadRequestObjectResult("The uploaded image file is empty.");
}
// Load the YOLOv8 model
using (var yolo = new YoloPredictor("yolov8s.onnx"))
{
// Perform object detection
using (var stream = file.OpenReadStream())
{
var items = yolo.Detect(stream);
var result = new { FileName = file.FileName, Detections = items };
// Return the detection results
return new OkObjectResult(result);
}
}
}
After a couple of manual fixes the function ran in the desktop emulator
When I compared the results with previous applications, they were the same.
Pose Estimation
I forgot the ILogger initialisation so had to do it at the end
please write an azure function with an http trigger that uses yolosharp to estimate the pose of humans in an uploaded image. Yolo v8 pose estimation model and yolosharp library Make into azure function The image files are in the form of the request Modify the code so more than one image per request can be processed Initialise ILogger in the constructor
//[FunctionName("PoseEstimation")]
[Function("PoseEstimation")]
public async Task<IActionResult> Run([HttpTrigger(AuthorizationLevel.Function, "post", Route = null)] HttpRequest req)
{
_log.LogInformation("Pose estimation function processed a request.");
if (!req.HasFormContentType || !req.Form.Files.Any())
{
return new BadRequestObjectResult("Please upload image files.");
}
var results = new List<object>();
foreach (var file in req.Form.Files)
{
using var memoryStream = new MemoryStream();
await file.CopyToAsync(memoryStream);
memoryStream.Position = 0;
using var image = Image.Load<Rgba32>(memoryStream);
// Initialize the YOLO model
//using var predictor = new YoloPredictor("path/to/model.onnx");
using var predictor = new YoloPredictor("yolov8s-pose.onnx");
// Perform pose estimation
var result = await predictor.PoseAsync(image);
// Format the results
//var poses = result.Poses.Select(pose => new
var poses = result.Select(pose => new
{
//Keypoints = pose.Keypoints.Select(k => new { k.X, k.Y }),
Keypoints = pose.Select(k => new { k.Point.X, k.Point.Y }),
Confidence = pose.Confidence
});
results.Add(new
{
Image = file.FileName,
Poses = poses
});
}
return new OkObjectResult(new { results });
}
After a couple of manual fixes including changing the way the results were generated the function ran in the desktop emulator.
Summary
The generated code worked but required manual fixes and was pretty ugly
The Github Copilot generated code in this post is not suitable for production
To better understand how Copilot and Github Copilot generated code was impacted by the wording and order of prompts I re-played one of my previous sessions.
The generated code looked “reasonable” and vaguely familiar.
static void Main(string[] args)
{
// Paths to the ONNX model and the image file (adjust these paths as needed)
string modelPath = @"..\\..\\..\\..\\Models\\FasterRCNN-10.onnx";
string imagePath = "sports.jpg";
// Create the OnnxInference session
using var session = new InferenceSession(modelPath);
// Load the image from disk using ImageSharp
using var image = Image.Load<Rgb24>(imagePath);
// Resize the image to fit within the range and adjust dimensions to be divisible by 32
ResizeImage(image);
// Extract tensor data from the image (with shape [3, height, width])
var inputTensor = ExtractTensorFromImage(image);
// Create NamedOnnxValue input (ensure that the input name "image" matches your model's input)
var inputs = new List<NamedOnnxValue>
{
NamedOnnxValue.CreateFromTensor("image", inputTensor)
};
// Run the model inference
using IDisposableReadOnlyCollection<DisposableNamedOnnxValue> results = session.Run(inputs);
// Process and display the outputs (bounding boxes, labels, and confidences)
ProcessOutput(results);
Console.WriteLine("Press Enter to exit");
Console.ReadLine();
}
/// <summary>
/// Resizes the input image such that both width and height are within the range [800, 1333]
/// and ensures the dimensions are divisible by 32.
/// </summary>
private static void ResizeImage(Image<Rgb24> image)
{
const int minSize = 800;
const int maxSize = 1333;
int originalWidth = image.Width;
int originalHeight = image.Height;
// Determine the scaling factor so that the smallest side is at least minSize and the largest does not exceed maxSize.
float scale = Math.Min((float)maxSize / Math.Max(originalWidth, originalHeight),
(float)minSize / Math.Min(originalWidth, originalHeight));
// Compute the new dimensions based on the scale
int newWidth = (int)(originalWidth * scale);
int newHeight = (int)(originalHeight * scale);
// Adjust dimensions to be divisible by 32
newWidth = (newWidth / 32) * 32;
newHeight = (newHeight / 32) * 32;
image.Mutate(x => x.Resize(newWidth, newHeight));
}
/// <summary>
/// Converts the resized image into a DenseTensor<float> with shape [3, height, width].
/// The image is processed to subtract the Faster‑RCNN channel means (B, G, R order).
/// </summary>
private static DenseTensor<float> ExtractTensorFromImage(Image<Rgb24> image)
{
int width = image.Width;
int height = image.Height;
// Create a tensor with shape [channels, height, width]
var tensor = new DenseTensor<float>(new[] { 3, height, width });
// Faster‑RCNN channel means (order: blue, green, red)
float[] mean = { 102.9801f, 115.9465f, 122.7717f };
// Process each pixel row; ImageSharp provides efficient pixel row access.
image.ProcessPixelRows(accessor =>
{
for (int y = 0; y < height; y++)
{
var pixelRow = accessor.GetRowSpan(y);
for (int x = 0; x < width; x++)
{
// Subtract the channel mean value (ensuring B, G, R order)
tensor[0, y, x] = pixelRow[x].B - mean[0];
tensor[1, y, x] = pixelRow[x].G - mean[1];
tensor[2, y, x] = pixelRow[x].R - mean[2];
}
}
});
return tensor;
}
/// <summary>
/// Processes the model output, extracting bounding boxes, labels, and confidences.
/// Only detections with confidence scores above a defined threshold are printed.
/// </summary>
private static void ProcessOutput(IDisposableReadOnlyCollection<DisposableNamedOnnxValue> output)
{
// Note: The output node names ("6379", "6381", "6383") are model-specific.
// They correspond, respectively, to boxes, labels, and confidence scores.
var boxesTensor = output.First(x => x.Name == "6379").AsTensor<float>();
var labelsTensor = output.First(x => x.Name == "6381").AsTensor<long>();
var confidencesTensor = output.First(x => x.Name == "6383").AsTensor<float>();
float[] boxes = boxesTensor.ToArray();
long[] labels = labelsTensor.ToArray();
float[] confidences = confidencesTensor.ToArray();
const float minConfidence = 0.7f;
// Each bounding box is represented by 4 values: x1, y1, x2, y2.
for (int i = 0; i < boxes.Length; i += 4)
{
int detectionIndex = i / 4;
if (confidences[detectionIndex] >= minConfidence)
{
long label = labels[detectionIndex];
float confidence = confidences[detectionIndex];
float x1 = boxes[i];
float y1 = boxes[i + 1];
float x2 = boxes[i + 2];
float y2 = boxes[i + 3];
Console.WriteLine($"Label: {label}, Confidence: {confidence}, Bounding Box: [{x1}, {y1}, {x2}, {y2}]");
}
}
}
The Copilot generated code had the names of the output tensors (6379,6381, 6383), the mean calculation and the order of the colours (B,G,R) correct. The name of the image file and the path to the model file in The Explanation and Additional information looked a lot like mine.
static void Main(string[] args)
{
// Path to the ONNX model and input image
string modelPath = "FasterRCNN-10.onnx";
string imagePath = "sports.jpg";
// Load the image
Bitmap image = new Bitmap(imagePath);
var inputTensor = PreprocessImage(image);
// Load the ONNX model
using var session = new InferenceSession(modelPath);
// Create input data
var inputName = session.InputMetadata.Keys.First();
var inputs = new List<NamedOnnxValue>
{
NamedOnnxValue.CreateFromTensor(inputName, inputTensor)
};
//// Run inference
using IDisposableReadOnlyCollection<DisposableNamedOnnxValue> results = session.Run(inputs);
// Process output
foreach (var result in results)
{
Console.WriteLine($"{result.Name}: {string.Join(", ", result.AsEnumerable<float>())}");
}
}
static DenseTensor<float> PreprocessImage(Bitmap image)
{
// Resize image to model's expected input size (e.g., 224x224)
int targetWidth = 224;
int targetHeight = 224;
Bitmap resizedImage = new Bitmap(image, new Size(targetWidth, targetHeight));
// Convert image to float array and normalize
var input = new DenseTensor<float>(new[] { 1, 3, targetHeight, targetWidth });
for (int y = 0; y < targetHeight; y++)
{
for (int x = 0; x < targetWidth; x++)
{
Color pixel = resizedImage.GetPixel(x, y);
input[0, 0, y, x] = pixel.R / 255f;
input[0, 1, y, x] = pixel.G / 255f;
input[0, 2, y, x] = pixel.B / 255f;
}
}
return input;
}
I fixed the paths to the ONNX model and image file, so the application ran but there was an error.
The dimensions of the input tensor were wrong based on the Faster R-CNN documentation because the declaration had an extra dimension.
Change dimensions of tensor by removing batch from declaration
static DenseTensor<float> PreprocessImage(Bitmap image)
{
// Resize image to model's expected input size (e.g., 224x224)
int targetWidth = 224;
int targetHeight = 224;
Bitmap resizedImage = new Bitmap(image, new Size(targetWidth, targetHeight));
// Convert image to float array and normalize
var input = new DenseTensor<float>(new[] { 3, targetHeight, targetWidth });
for (int y = 0; y < targetHeight; y++)
{
for (int x = 0; x < targetWidth; x++)
{
Color pixel = resizedImage.GetPixel(x, y);
input[0, y, x] = pixel.R / 255f;
input[1, y, x] = pixel.G / 255f;
input[2, y, x] = pixel.B / 255f;
}
}
return input;
}
GitHub Copilot fixed the tensor declaration by removing the batch id, then the application ran but there was an error.
resize the image such that both height and width are within the range of [800, 1333], such that both height and width are divisible by 32
static Bitmap ResizeImageForModel(Bitmap image)
{
// Define the target range and divisibility
const int minSize = 800;
const int maxSize = 1333;
const int divisor = 32;
// Get original dimensions
int originalWidth = image.Width;
int originalHeight = image.Height;
// Calculate scale factor to fit within the range while maintaining aspect ratio
float scale = Math.Min((float)maxSize / Math.Max(originalWidth, originalHeight),
(float)minSize / Math.Min(originalWidth, originalHeight));
// Calculate new dimensions
int newWidth = (int)(originalWidth * scale);
int newHeight = (int)(originalHeight * scale);
// Ensure dimensions are divisible by 32
newWidth = (newWidth / divisor) * divisor;
newHeight = (newHeight / divisor) * divisor;
// Resize the image
return new Bitmap(image, new Size(newWidth, newHeight));
}
GitHub Copilot fixed the image resizing, so the application ran but there was still an error.
I also manually added the using directive for System.Drawing.Drawing2D
static void ProcessOutput(IDisposableReadOnlyCollection<DisposableNamedOnnxValue> results, Bitmap image)
{
// Extract output tensors
var boxes = results.First(r => r.Name == "boxes").AsEnumerable<float>().ToArray();
var labels = results.First(r => r.Name == "labels").AsEnumerable<long>().ToArray();
var scores = results.First(r => r.Name == "scores").AsEnumerable<float>().ToArray();
using Graphics graphics = Graphics.FromImage(image);
graphics.SmoothingMode = SmoothingMode.AntiAlias;
for (int i = 0; i < labels.Length; i++)
{
if (scores[i] < 0.5) continue; // Filter low-confidence detections
// Extract bounding box coordinates
float x1 = boxes[i * 4];
float y1 = boxes[i * 4 + 1];
float x2 = boxes[i * 4 + 2];
float y2 = boxes[i * 4 + 3];
// Draw bounding box
RectangleF rect = new RectangleF(x1, y1, x2 - x1, y2 - y1);
graphics.DrawRectangle(Pens.Red, rect.X, rect.Y, rect.Width, rect.Height);
// Display label and confidence
string label = $"Label: {labels[i]}, Confidence: {scores[i]:0.00}";
graphics.DrawString(label, new Font("Arial", 12), Brushes.Yellow, new PointF(x1, y1 - 20));
}
// Save the image with annotations
image.Save("output.jpg");
Console.WriteLine("Output image saved as 'output.jpg'.");
}
The application ran but there was an error because the output tensor names were wrong.
I used Netron to determine the correct output tensor names.
It was quicker to manually fix the output tensor names
static void ProcessOutput(IDisposableReadOnlyCollection<DisposableNamedOnnxValue> results, Bitmap image)
{
// Extract output tensors
var boxes = results.First(r => r.Name == "6379").AsEnumerable<float>().ToArray();
var labels = results.First(r => r.Name == "6381").AsEnumerable<long>().ToArray();
var scores = results.First(r => r.Name == "6383").AsEnumerable<float>().ToArray();
using Graphics graphics = Graphics.FromImage(image);
graphics.SmoothingMode = SmoothingMode.AntiAlias;
for (int i = 0; i < labels.Length; i++)
{
if (scores[i] < 0.5) continue; // Filter low-confidence detections
// Extract bounding box coordinates
float x1 = boxes[i * 4];
float y1 = boxes[i * 4 + 1];
float x2 = boxes[i * 4 + 2];
float y2 = boxes[i * 4 + 3];
// Draw bounding box
RectangleF rect = new RectangleF(x1, y1, x2 - x1, y2 - y1);
graphics.DrawRectangle(Pens.Red, rect.X, rect.Y, rect.Width, rect.Height);
// Display label and confidence
string label = $"Label: {labels[i]}, Confidence: {scores[i]:0.00}";
graphics.DrawString(label, new Font("Arial", 12), Brushes.Yellow, new PointF(x1, y1 - 20));
}
// Save the image with annotations
image.Save("output.jpg");
Console.WriteLine("Output image saved as 'output.jpg'.");
}
The application ran but the results were bad, so I checked format of the input tensor and figured out the mean adjustment was missing.
Apply mean to each channel
I used GitHub Copilot to add code for the mean adjustment for each pixel
static DenseTensor<float> PreprocessImage(Bitmap image)
{
// Resize image to model's expected input size
Bitmap resizedImage = ResizeImageForModel(image);
// Apply FasterRCNN mean values to each channel
float[] mean = { 102.9801f, 115.9465f, 122.7717f };
// Convert image to float array and normalize
var input = new DenseTensor<float>(new[] { 3, resizedImage.Height, resizedImage.Width });
for (int y = 0; y < resizedImage.Height; y++)
{
for (int x = 0; x < resizedImage.Width; x++)
{
Color pixel = resizedImage.GetPixel(x, y);
input[0, y, x] = (pixel.R - mean[0]) / 255f;
input[1, y, x] = (pixel.G - mean[1]) / 255f;
input[2, y, x] = (pixel.B - mean[2]) / 255f;
}
}
return input;
}
The application ran but the results were still bad, so I checked format of the input tensor and figured out the mean adjustment was wrong. It was quicker to manually fix up the mean calculation.
static DenseTensor<float> PreprocessImage(Bitmap image)
{
// Resize image to model's expected input size
Bitmap resizedImage = ResizeImageForModel(image);
// Apply FasterRCNN mean values to each channel
float[] mean = { 102.9801f, 115.9465f, 122.7717f };
// Convert image to float array and normalize
var input = new DenseTensor<float>(new[] { 3, resizedImage.Height, resizedImage.Width });
for (int y = 0; y < resizedImage.Height; y++)
{
for (int x = 0; x < resizedImage.Width; x++)
{
Color pixel = resizedImage.GetPixel(x, y);
input[0, y, x] = pixel.R - mean[0];
input[1, y, x] = pixel.G - mean[1];
input[2, y, x] = pixel.B - mean[2];
}
}
return input;
}
The application ran but the results were still bad, so I checked format of the input tensor and figured out the input tensor was BGR rather than RGB.
Change to B,G,R
static DenseTensor<float> PreprocessImage(Bitmap image)
{
// Resize image to model's expected input size
Bitmap resizedImage = ResizeImageForModel(image);
// Apply FasterRCNN mean values to each channel
float[] mean = { 102.9801f, 115.9465f, 122.7717f };
// Convert image to float array and normalize
var input = new DenseTensor<float>(new[] { 3, resizedImage.Height, resizedImage.Width });
for (int y = 0; y < resizedImage.Height; y++)
{
for (int x = 0; x < resizedImage.Width; x++)
{
Color pixel = resizedImage.GetPixel(x, y);
input[0, y, x] = pixel.B - mean[0] ;
input[1, y, x] = pixel.G - mean[1] ;
input[2, y, x] = pixel.R - mean[2] ;
}
}
return input;
}
Finally, the application minimum bounding rectangles (MBRs), labels and confidences looked correct.
The additional code for marking up the image in the ProcessOutput was unexpected and I wonder if there wasn’t a Sixlabors.ImageSharp example for “inspiration”
The Copilot generated code in this post is not suitable for production
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