该代码通过训练一个分类器来识别手写数字,并在测试数据上进行评估,最后使用该分类器来识别一个数字验证码。注意:在运行此代码之前,需要安装OpenCV和ML.NET库。
using System;
using System.IO;
using System.Linq;
using Microsoft.ML;
using Microsoft.ML.Data;
using OpenCvSharp;
using OpenCvSharp.Extensions;
namespace DigitRecognition
{
class Program
{
static void Main(string[] args)
{
// Load training data
var mlContext = new MLContext(seed: 0);
var data = mlContext.Data.LoadFromTextFile<Digit>(@"./data/digits.csv", separatorChar: ',');
var trainTestSplit = mlContext.Data.TrainTestSplit(data, testFraction: 0.2);
var trainData = trainTestSplit.TrainSet;
var testData = trainTestSplit.TestSet;
// Define pipeline
var pipeline = mlContext.Transforms.Conversion.MapValueToKey("Label")
.Append(mlContext.Transforms.Concatenate("Features", "PixelValues"))
.Append(mlContext.Transforms.NormalizeMinMax("Features"))
.Append(mlContext.Transforms.Conversion.MapKeyToValue("Label"))
.Append(mlContext.Transforms.Conversion.MapKeyToValue("PredictedLabel"))
.Append(mlContext.Transforms.Conversion.ConvertToSingle("Score"))
.Append(mlContext.Transforms.SelectColumns("Label", "PredictedLabel", "Score"));
// Train model
var estimator = mlContext.MulticlassClassification.Trainers.SdcaNonCalibrated()
.Append(mlContext.Transforms.Conversion.MapKeyToValue("PredictedLabel"));
var model = pipeline.Append(estimator).Fit(trainData);
// Evaluate model
var predictions = model.Transform(testData);
var metrics = mlContext.MulticlassClassification.Evaluate(predictions);
Console.WriteLine($"Micro-accuracy: {metrics.MacroAccuracy}");
Console.WriteLine($"Macro-accuracy: {metrics.MicroAccuracy}");
Console.WriteLine($"Log-loss: {metrics.LogLoss}");
// Load test image
var image = Cv2.ImRead(@"./data/test_image.png", ImreadModes.GrayScale);
// Threshold image
var thresholded = new Mat();
Cv2.Threshold(image, thresholded, 127, 255, ThresholdTypes.BinaryInv);
// Find contours
var contours = Cv2.FindContours(thresholded, RetrievalModes.External, ContourApproximationModes.ApproxSimple);
// Sort contours from left to right
var sortedContours = contours.OrderBy(c => Cv2.BoundingRect(c).X).ToList();
// Create prediction engine
var predictionEngine = mlContext.Model.CreatePredictionEngine<Digit, DigitPrediction>(model);
// Recognize digits
foreach (var contour in sortedContours)
{
// Extract digit image from contour
var boundingRect = Cv2.BoundingRect(contour);
var digitImage = image.SubMat(boundingRect);
// Resize digit image to 28x28 pixels
var resizedImage = new Mat();
Cv2.Resize(digitImage, resizedImage, new Size(28, 28));
// Convert digit image to pixel values
var pixelValues = resizedImage.Reshape(1, 1).GetArray<byte>(0);
// Create digit object and make prediction
var digit = new Digit { PixelValues = pixelValues };
var prediction = predictionEngine.Predict(d
igit);
// Print prediction
Console.WriteLine($"Predicted digit: {prediction.PredictedLabel}");
}
}
}
// Define data classes
class Digit
{
[LoadColumn(0)]
public float Label { get; set; }
[LoadColumn(1, 784)]
[VectorType(784)]
public float[] PixelValues { get; set; }
}
class DigitPrediction
{
[ColumnName("PredictedLabel")]
public float PredictedLabel { get; set; }
[ColumnName("Score")]
public float[] Score { get; set; }
}
该代码读取一个手写数字数据集(`digits.csv`),并使用ML.NET中的`SdcaNonCalibrated`分类器进行训练。训练后,代码将读取一个数字验证码图像(`test_image.png`),将其阈值化并找到包含每个数字的轮廓。然后,对于每个数字,代码将提取其图像并将其调整为28x28像素的大小。最后,代码将使用训练的分类器进行预测并输出识别出的数字。
请注意,此代码仅是示例,并且可能需要进行修改才能适应您的数据集和需求。