opencvsharp物料分拣示例代码

以下是使用OpenCvSharp进行物料分拣的示例代码：

using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using System.Threading.Tasks;
using OpenCvSharp;

namespace MaterialSorting
{
    class Program
    {
        static void Main(string[] args)
        {
            // Load the image
            Mat src = Cv2.ImRead("input.jpg");

            // Convert to grayscale
            Mat gray = new Mat();
            Cv2.CvtColor(src, gray, ColorConversionCodes.BGR2GRAY);

            // Threshold the image
            Mat thresh = new Mat();
            Cv2.Threshold(gray, thresh, 0, 255, ThresholdTypes.Binary | ThresholdTypes.Otsu);

            // Find contours
            Point[][] contours;
            HierarchyIndex[] hierarchy;
            Cv2.FindContours(thresh, out contours, out hierarchy, RetrievalModes.Tree, ContourApproximationModes.ApproxSimple);

            // Loop through each contour
            for (int i = 0; i < contours.Length; i++)
            {
                // Get bounding rectangle
                Rect rect = Cv2.BoundingRect(contours[i]);

                // Draw rectangle on the original image
                Cv2.Rectangle(src, rect, new Scalar(0, 255, 0), 2);

                // Calculate aspect ratio
                double aspectRatio = (double)rect.Width / rect.Height;

                // Determine if it is a square or a rectangle
                if (aspectRatio >= 0.9 && aspectRatio <= 1.1)
                {
                    // Square
                    Cv2.PutText(src, "Square", new Point(rect.X, rect.Y - 10), HersheyFonts.HersheyPlain, 1, new Scalar(0, 255, 0), 2);
                }
                else
                {
                    // Rectangle
                    Cv2.PutText(src, "Rectangle", new Point(rect.X, rect.Y - 10), HersheyFonts.HersheyPlain, 1, new Scalar(0, 255, 0), 2);
                }
            }

            // Save the output image
            Cv2.ImWrite("output.jpg", src);
        }
    }
}

该代码首先加载输入图像，然后将其转换为灰度图像并进行阈值处理。接下来，使用FindContours方法查找轮廓，并通过循环遍历每个轮廓来找到包围矩形和它的宽高比，以确定它是正方形还是矩形。最后，将包围矩形和文本绘制在原始图像上，并将结果保存为输出图像。