C# Emgucv 神经网络算法实现图像分类 例程

以下是一个基本的使用Emgu CV实现图像分类的例程，包括数据的预处理、神经网络的构建、训练和测试：

using Emgu.CV;
using Emgu.CV.ML;
using Emgu.CV.Structure;
using System;
using System.Collections.Generic;
using System.IO;
using System.Linq;

namespace ImageClassification
{
    class Program
    {
        static void Main(string[] args)
        {
            // 设置数据集的路径
            string dataPath = @"C:\data\images";

            // 读取数据集
            List<Image<Gray, byte>> images = LoadImages(dataPath);

            // 划分训练集和测试集
            List<Image<Gray, byte>> trainImages = images.Take(500).ToList();
            List<Image<Gray, byte>> testImages = images.Skip(500).ToList();

            // 准备训练和测试数据
            Matrix<float> trainData = PrepareData(trainImages);
            Matrix<float> trainLabels = PrepareLabels(trainImages);
            Matrix<float> testData = PrepareData(testImages);
            Matrix<float> testLabels = PrepareLabels(testImages);

            // 创建神经网络
            ANN_MLP neuralNetwork = new ANN_MLP();
            neuralNetwork.SetLayerSizes(new int[] { trainData.Rows, 100, 50, 10 });
            neuralNetwork.TermCriteria = new MCvTermCriteria(100, 0.01);
            neuralNetwork.SetActivationFunction(ANN_MLP.AnnMlpActivationFunction.SigmoidSym, 0, 0);
            neuralNetwork.SetTrainMethod(ANN_MLP.AnnMlpTrainMethod.Backprop, 0.1, 0.1);

            // 训练神经网络
            neuralNetwork.Train(trainData, trainLabels);

            // 测试神经网络
            float[] expectedLabels = new float[testLabels.Rows];
            float[] predictedLabels = new float[testLabels.Rows];
            for (int i = 0; i < testLabels.Rows; i++)
            {
                float[] testDataRow = testData.GetRow(i);
                float[] testLabelRow = testLabels.GetRow(i);
                neuralNetwork.Predict(testDataRow, predictedLabels);
                expectedLabels[i] = Array.IndexOf(testLabelRow, 1);
            }
            int correctPredictions = expectedLabels.Zip(predictedLabels, (e, p) => e == p ? 1 : 0).Sum();
            float accuracy = (float)correctPredictions / testLabels.Rows;
            Console.WriteLine($"Test accuracy: {accuracy}");
        }

        // 加载图像
        static List<Image<Gray, byte>> LoadImages(string dataPath)
        {
            List<Image<Gray, byte>> images = new List<Image<Gray, byte>>();
            foreach (string imagePath in Directory.GetFiles(dataPath))
            {
                Image<Gray, byte> image = new Image<Gray, byte>(imagePath);
                images.Add(image);
            }
            return images;
        }

        // 准备数据
        static Matrix<float> PrepareData(List<Image<Gray, byte>> images)
        {
            int numImages = images.Count;
            int numPixels = images[0].Width * images[0].Height;
            Matrix<float> data = new Matrix<float>(numImages, numPixels);
            for (int i = 0; i < numImages; i++)
            {
                Image<Gray, byte> image = images[i];
                float[] pixels = image.Data.SelectMany(row => row).Select(x => (float)x / 255f).ToArray();
                data.SetRow(i, pixels);
            }
            return data;
        }

        // 准备标签
        static Matrix<float> PrepareLabels(List<Image<Gray, byte>> images)
        {
            int numImages = images.Count;
            int numClasses = 10; // 假设有10个类别
            Matrix<float> labels = new Matrix<float>(numImages, numClasses);
            for (int i = 0; i < numImages; i++)
            {
                int label = i % numClasses;
                labels[i, label] = 1f;
            }
            return labels;
        }
    }
}

这个例程实现了一个简单的多层感知器神经网络，用于对图像进行分类。它假设数据集中有10个类别，每个类别有50张图像。它将前500张图像用于训练，后50张用于测试。在训练神经网络时，它使用反向传播算法和Sigmoid激活函数，并设置了一些超参数。在测试时，它计算了分类的准确度。