Hi !
This is a special post. It’s the 1st one I write completely in my MacBook, so I’m sure that Javier (@jsuarezruiz), Yeray (@JosueYeray), Braulio (@braulio_sl), Luis, Sara, Roberto and other mac users will be proud of me 😀
So, I build and run my Custom Vision Marvel project in Docker for Mac. Smooth build and also a fast one!
docker build -t elbruno/cvmarvel:3.0 .
Then get the image id and run the image
Final step is to play around with curl in bash to post the image (the file name with @ prefix took me some bing searches). Iron Fist detected !
curl -X POST http://127.0.0.1:8080/image -F imageData=@img1.jpg
Ok, the environment is working, so it’s time to create a .NetCore Console App to test this using amazing C# code. I have all my code in Azure Dev Ops, so I sync my repo and added a new project in my current solution
Some C# lines in my console app and I was able to analyze a local picture using the Custom Vision Model in a container
The source code is very simple
| using System; | |
| using System.IO; | |
| using System.Net.Http; | |
| using System.Net.Http.Headers; | |
| using System.Threading.Tasks; | |
| using Newtonsoft.Json; | |
| using Newtonsoft.Json.Linq; | |
| namespace CustomVisionMarvelConsoleDocker01 | |
| { | |
| static class Program | |
| { | |
| static void Main() | |
| { | |
| MakePredictionRequest("IMG01.jpg").Wait(); | |
| Console.ReadLine(); | |
| } | |
| static async Task MakePredictionRequest(string imageFilePath) | |
| { | |
| var client = new HttpClient(); | |
| var url = "http://127.0.0.1:8080/image"; | |
| var byteData = GetImageAsByteArray(imageFilePath); | |
| using (var content = new ByteArrayContent(byteData)) | |
| { | |
| content.Headers.ContentType = new MediaTypeHeaderValue("application/octet-stream"); | |
| var response = await client.PostAsync(url, content); | |
| var jsonResponse = await response.Content.ReadAsStringAsync(); | |
| var prettyJson = JToken.Parse(jsonResponse).ToString(Formatting.Indented); | |
| Console.WriteLine(prettyJson); | |
| } | |
| } | |
| static byte[] GetImageAsByteArray(string imageFilePath) | |
| { | |
| var fileStream = new FileStream(imageFilePath, FileMode.Open, FileAccess.Read); | |
| var binaryReader = new BinaryReader(fileStream); | |
| return binaryReader.ReadBytes((int)fileStream.Length); | |
| } | |
| } | |
| } |
Happy coding!
Greetings @ Toronto
El Bruno
References
My Posts
- Object recognition with Custom Vision and ONNX in Windows applications using WinML (1)
- Object recognition with Custom Vision and ONNX in Windows applications using WinML (2)
- Object recognition with Custom Vision and ONNX in Windows applications using Windows ML, drawing frames (3)
- Object recognition with Custom Vision and ONNX in Windows applications using Windows ML, calculate FPS (4)
- Can’t install Docker on Windows 10 Home, need Pro or Enterprise (5)
- Running a Custom Vision project in a local Docker Container (6)
Windows 10 and YOLOV2 for Object Detection Series
- Introduction to YoloV2 for object detection
- Create a basic Windows10 App and use YoloV2 in the camera for object detection
- Transform YoloV2 output analysis to C# classes and display them in frames
- Resize YoloV2 output to support multiple formats and process and display frames per second
- How to convert Tiny-YoloV3 model in CoreML format to ONNX and use it in a Windows 10 App
- Updated demo using Tiny YOLO V2 1.2, Windows 10 and YOLOV2 for Object Detection Series
- Alternatives to Yolo for object detection in ONNX format
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