C# Inference Client Example Project

This chapter provides a detailed explanation of the C# Inference Client code sample included in the DaoAI World SDK.

Import Libraries

In the C# example, we use the following libraries:

using System;
using System.IO;
using DaoAI.InferenceClient;

Read Images

The model inference function of the C# Inference Client requires the image to be represented as a base64-encoded string. You can use the Convert.ToBase64String() method for conversion.

First, define the file path. The format should be a .txt file containing the base64-encoded image, and then read it.

string fileName = "C:/Users/daoai/Documents/DWSDK_Demo/data/image.bmp";

string base64Image = Convert.ToBase64String(File.ReadAllBytes(fileName));

Load the Deep Learning Model

The deep learning models exported by DaoAI World are usually in .dwm format. You need to create a DaoAI.InferenceClient.KeypointDetection object and use the constructor to load the model.

// model path in server file system
string filemodel = "../../../../../../data/KeypointDetection.dwm";

DaoAI.InferenceClient.KeypointDetection model = new DaoAI.InferenceClient.KeypointDetection(filePath_model, DaoAI.InferenceClient.DeviceType.GPU);

Note that each detection task has a corresponding object:

// Instance Segmentation
DaoAI.InferenceClient.InstanceSegmentation model(model_path);

// Keypoint Detection
DaoAI.InferenceClient.KeypointDetection model(model_path);

// Classification
DaoAI.InferenceClient.Classification model(model_path);

// Object Detection
DaoAI.InferenceClient.ObjectDetection model(model_path);

// Unsupervised Defect Detection
DaoAI.InferenceClient.UnsupervisedDefectSegmentation model(model_path);

// Supervised Defect Detection
DaoAI.InferenceClient.SupervisedDefectSegmentation model(model_path);

// OCR
DaoAI.InferenceClient.OCR model(model_path);

// Positioning (Only supported in industrial version)
DaoAI.InferenceClient.Positioning model(model_path);

// Presence Checking (Only supported in industrial version)
DaoAI.InferenceClient.PresenceChecking model(model_path);

If you attempt to load the wrong model type, an error will be thrown, indicating which model type should be used.

Run Inference Using the Model

// get inference
DaoAI.InferenceClient.KeypointDetection prediction = model.inference(base64Image);

Note that each detection task returns a corresponding result object:

// Instance Segmentation
DaoAI.InferenceClient.InstanceSegmentationResult prediction = model.inference(base64Image);

// Keypoint Detection
DaoAI.InferenceClient.KeypointDetectionResult prediction = model.inference(base64Image);

// Classification
DaoAI.InferenceClient.ClassificationResult prediction = model.inference(base64Image);

// Object Detection
DaoAI.InferenceClient.ObjectDetectionResult prediction = model.inference(base64Image);

// Anomaly Detection
DaoAI.InferenceClient.AnomalyDetectionResult prediction = model.inference(base64Image);

// Semantic Segmentation
DaoAI.InferenceClient.SemanticSegmentationResult prediction = model.inference(base64Image);

// OCR
DaoAI.InferenceClient.OCRResult prediction = model.inference(base64Image);

// Positioning (Only supported in industrial version)
DaoAI.InferenceClient.PositioningResult prediction = model.inference(base64Image);

// Presence Checking (Only supported in industrial version)
DaoAI.InferenceClient.PresenceCheckingResult prediction = model.inference(base64Image);

Sample Output

Below is a sample output from the keypoint detection model inference.

This output shows the number of detections, class labels, confidence scores, bounding boxes, keypoints, and polygon masks.

for (int i = 0; i < result.NumDetections; i++)
{
    Console.WriteLine($"Object {i + 1}");
    Console.WriteLine($"Class: {result.ClassLabels[i]}");
    Console.WriteLine($"Bounding box: {result.Boxes[i].X1} {result.Boxes[i].Y1} {result.Boxes[i].X2} {result.Boxes[i].Y2}");
    Console.WriteLine($"Confidence: {result.Confidences[i]}");
    Console.WriteLine("Keypoints:");

    foreach (var keypoint in result.Keypoints[i])
    {
        Console.WriteLine($"{keypoint.X} {keypoint.Y} {keypoint.Confidence}");
    }
    Console.WriteLine();
}