Nesne algılama istemci uygulaması geliştirme

Tamamlandı

Bir nesne algılama modelini eğitdikten sonra, analiz edilecek yeni görüntüler gönderen bir istemci uygulaması geliştirmek için Azure AI Özel Görüntü İşleme SDK'sını kullanabilirsiniz.

from msrest.authentication import ApiKeyCredentials
from azure.cognitiveservices.vision.customvision.prediction import CustomVisionPredictionClient


 # Authenticate a client for the prediction API
credentials = ApiKeyCredentials(in_headers={"Prediction-key": "<YOUR_PREDICTION_RESOURCE_KEY>"})
prediction_client = CustomVisionPredictionClient(endpoint="<YOUR_PREDICTION_RESOURCE_ENDPOINT>",
                                                 credentials=credentials)

# Get classification predictions for an image
image_data = open("<PATH_TO_IMAGE_FILE>", "rb").read()
results = prediction_client.detect_image("<YOUR_PROJECT_ID>",
                                           "<YOUR_PUBLISHED_MODEL_NAME>",
                                           image_data)

# Process predictions
for prediction in results.predictions:
    if prediction.probability > 0.5:
        left = prediction.bounding_box.left
        top = prediction.bounding_box.top 
        height = prediction.bounding_box.height
        width =  prediction.bounding_box.width
        print(f"{prediction.tag_name} ({prediction.probability})")
        print(f"  Left:{left}, Top:{top}, Height:{height}, Width:{width}")

using System;
using System.IO;
using Microsoft.Azure.CognitiveServices.Vision.CustomVision.Prediction;

// Authenticate a client for the prediction API
CustomVisionPredictionClient prediction_client = new CustomVisionPredictionClient(new ApiKeyServiceClientCredentials("<YOUR_PREDICTION_RESOURCE_KEY>"))
{
    Endpoint = "<YOUR_PREDICTION_RESOURCE_ENDPOINT>"
};

// Get classification predictions for an image
MemoryStream image_data = new MemoryStream(File.ReadAllBytes("<PATH_TO_IMAGE_FILE>"));
var result = prediction_client.DetectImage("<YOUR_PROJECT_ID>",
                                             "<YOUR_PUBLISHED_MODEL_NAME>",
                                             image_data);

// Process predictions
foreach (var prediction in result.Predictions)
{
    if (prediction.Probability > 0.5)
    {
        var left = prediction.BoundingBox.Left;
        var top = prediction.BoundingBox.Top;
        var height = prediction.BoundingBox.Height;
        var width =  prediction.BoundingBox.Width;
        Console.WriteLine($"{prediction.TagName} ({prediction.Probability})");
        Console.WriteLine($"  Left:{left}, Top:{top}, Height:{height}, Width:{width}");
    }
}