using OpenCvSharp;
using System;
using System.Collections.Generic;
using System.Linq;
namespace OpenCvSharp_DigitRecognition
{
class Program
{
static void Main(string[] args)
{
// Load image and convert to grayscale
Mat image = Cv2.ImRead("test_image.png");
Mat gray = new Mat();
Cv2.CvtColor(image, gray, ColorConversionCodes.BGR2GRAY);
// Apply thresholding
Mat thresh = new Mat();
Cv2.Threshold(gray, thresh, 0, 255, ThresholdTypes.BinaryInv | ThresholdTypes.Otsu);
// Find contours
Point[][] contours;
HierarchyIndex[] hierarchy;
Cv2.FindContours(thresh, out contours, out hierarchy, RetrievalModes.List, ContourApproximationModes.ApproxSimple);
// Sort contours by x-coordinate
List<Point[]> sortedContours = contours.OrderBy(c => Cv2.BoundingRect(c).X).ToList();
// Load SVM model
using (var model = CvSVM.Load("svm_model.xml"))
{
// Loop over contours and recognize digits
foreach (var contour in sortedContours)
{
// Extract digit image
Rect rect = Cv2.BoundingRect(contour);
Mat digit = new Mat(thresh, rect);
// Resize digit image to 28x28 pixels
Mat resized = new Mat();
Cv2.Resize(digit, resized, new Size(28, 28));
// Flatten image and convert to float array
float[] data = new float[28 * 28];
int index = 0;
for (int i = 0; i < resized.Rows; i++)
{
for (int j = 0; j < resized.Cols; j++)
{
data[index] = resized.At<byte>(i, j) / 255.0f;
index++;
}
}
// Predict digit using SVM model
Mat sampleMat = new Mat(1, 28 * 28, MatType.CV_32FC1, data);
float prediction = model.Predict(sampleMat);
// Print prediction
Console.WriteLine($"Predicted digit: {prediction}");
}
}
}
}
}
该代码读取一个数字验证码图像(test_image.png),将其转换为灰度图像并进行阈值化。然后,代码将找到包含每个数字的轮廓,并根据它们的x坐标对轮廓进行排序。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SVM模型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。