464 lines
19 KiB
C#
464 lines
19 KiB
C#
using OpenCvSharp;
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using System;
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using System.Collections.Generic;
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using System.Linq;
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using System.Text;
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using System.Threading.Tasks;
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namespace GeBoShi.SysCtrl
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{
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public static class OpencvUtils
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{
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public static int image_width = 2048;
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public static int image_height = 2048;
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#region 图像预处理
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public static Mat Resize(Mat mat, int width, int height, out int xw)
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{
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OpenCvSharp.Size dsize = new OpenCvSharp.Size(width, height);
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Mat mat2 = new Mat();
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//Cv2.Resize(mat, mat2, dsize);
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ResizeUniform(mat, dsize, out mat2, out xw);
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return mat2;
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}
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/// <summary>
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/// 等比例缩放
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/// </summary>
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/// <param name="src"></param>
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/// <param name="dst_size"></param>
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/// <param name="dst"></param>
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/// <returns></returns>
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public static int ResizeUniform(Mat src, Size dst_size, out Mat dst, out int xw)
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{
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xw = 0;
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int w = src.Cols;
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int h = src.Rows;
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int dst_w = dst_size.Width;
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int dst_h = dst_size.Height;
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//std::cout << "src: (" << h << ", " << w << ")" << std::endl;
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dst = new Mat(dst_h, dst_w, MatType.CV_8UC3, new Scalar(114, 114, 114));
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float[] ratio = new float[2];
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float ratio_src = w * 1.0f / h;
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float ratio_dst = dst_w * 1.0f / dst_h;
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int tmp_w = 0;
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int tmp_h = 0;
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if (ratio_src > ratio_dst)
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{
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tmp_w = dst_w;
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tmp_h = (int)(dst_w * 1.0f / w) * h;
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ratio[0] = (float)w / (float)tmp_w;
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ratio[1] = (float)h / (float)tmp_h;
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}
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else if (ratio_src < ratio_dst)
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{
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tmp_h = dst_h;
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tmp_w = (int)((dst_h * 1.0f / h) * w);
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ratio[0] = (float)w / (float)tmp_w;
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ratio[1] = (float)h / (float)tmp_h;
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}
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else
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{
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Cv2.Resize(src, dst, dst_size);
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ratio[0] = (float)w / (float)tmp_w;
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ratio[1] = (float)h / (float)tmp_h;
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return 0;
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}
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//std::cout << "tmp: (" << tmp_h << ", " << tmp_w << ")" << std::endl;
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Mat tmp = new Mat();
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Cv2.Resize(src, tmp, new Size(tmp_w, tmp_h));
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unsafe
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{
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if (tmp_w != dst_w)
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{ //高对齐，宽没对齐
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int index_w = (int)((dst_w - tmp_w) / 2.0);
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xw = index_w;
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//std::cout << "index_w: " << index_w << std::endl;
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for (int i = 0; i < dst_h; i++)
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{
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Buffer.MemoryCopy(IntPtr.Add(tmp.Data, i * tmp_w * 3).ToPointer(), IntPtr.Add(dst.Data, i * dst_w * 3 + index_w * 3).ToPointer(), tmp_w * 3, tmp_w * 3);
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}
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}
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else if (tmp_h != dst_h)
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{ //宽对齐， 高没有对齐
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int index_h = (int)((dst_h - tmp_h) / 2.0);
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//std::cout << "index_h: " << index_h << std::endl;
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Buffer.MemoryCopy(tmp.Data.ToPointer(), IntPtr.Add(dst.Data, index_h * dst_w * 3).ToPointer(), tmp_w * tmp_h * 3, tmp_w * tmp_h * 3);
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}
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else
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{
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}
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}
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return 0;
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}
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public static Mat ResizeMat(Mat mat, int width, int height)
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{
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OpenCvSharp.Size dsize = new OpenCvSharp.Size(width, height);
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Mat mat2 = new Mat();
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Cv2.Resize(mat, mat2, dsize);
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return mat2;
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}
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/// <summary>
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/// 计算合理宽幅
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/// </summary>
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/// <param name="sumWidth">多个相机图像总宽(外部去除重合部分)</param>
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/// <returns></returns>
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public static int GetWidthForResize(int sumWidth)
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{
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//保证计算8x2 16个小图
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int count = (int)Math.Round(sumWidth * 1.0f / image_width, 0);
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count = 8;
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return count * image_width;
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//int count = sumWidth / image_width;
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////int remainder = sumWidth % image_width;
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//if (count % 2 == 0)
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// return count * image_width;
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//else
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// return count * image_width+ image_width;
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}
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/// <summary>
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/// 裁切指定区域
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/// </summary>
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/// <param name="mat"></param>
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/// <param name="x"></param>
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/// <param name="y"></param>
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/// <param name="width"></param>
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/// <param name="height"></param>
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/// <returns></returns>
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public static Mat CutImage(Mat mat, int x, int y, int width, int height)
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{
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Rect roi = new Rect(x, y, width, height);
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return new Mat(mat, roi).Clone();
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}
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#endregion
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#region 裁边
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/// <summary>
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/// 裁边
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/// </summary>
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/// <param name="mat_rgb"></param>
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/// <param name="isLeft"></param>
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/// <param name="marginHoleWidth"></param>
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/// <param name="marginWidth"></param>
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/// <returns></returns>
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public static Mat getMaxInsetRect2(Mat mat_rgb, bool isLeft, int marginHoleWidth, out int marginWidth)
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{
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int bian = 3500;
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Rect Roi;
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if (!isLeft)
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Roi = new Rect(mat_rgb.Width - bian, 0, bian, mat_rgb.Height);
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else
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Roi = new Rect(0, 0, bian, mat_rgb.Height);
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int type = isLeft ? 1 : 0;
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int len = EdgeClipping2(mat_rgb, type, Roi, isLeft);
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#if false
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//Mat mat_rgb = new Mat("E:\\CPL\\测试代码\\边缘检测\\test\\test\\test\\img\\19.bmp");
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Mat image_gray = new Mat();
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Cv2.CvtColor(mat_rgb, image_gray, ColorConversionCodes.BGR2GRAY);
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//cvtColor(image_RGB, image, COLOR_RGB2GRAY);
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int height = image_gray.Rows;
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int width = image_gray.Cols;
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// 算法定义：取均分5段图片的五条横线，经过一系列处理之后，二值化，找到沿边位置，然后取均值作为直边，在缩进一段有针眼的位置
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// 定义每段的行数
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int num_rows = 5;
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int segment_height = height / num_rows - 1;
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// 定义空数组保存结果
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int[] total = new int[num_rows];
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// 平均截取5行数据并处理图像
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for (int i = 0; i < num_rows; i++)
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{
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// 截取当前行的图像
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int start_row = i * segment_height;
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Rect roi = new Rect(0, start_row, width, 1);
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Mat current_segment = image_gray.Clone(roi);
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// 对当前行的图像进行平滑处理
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Mat smoothed_image = new Mat();
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Cv2.GaussianBlur(current_segment, smoothed_image, new Size(5, 1), 0);
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// 计算当前行的灰度直方图
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Mat absolute_histo = new Mat();
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Cv2.CalcHist(new Mat[] { smoothed_image }, new int[] { 0 }, new Mat(), absolute_histo, 1, new int[] { 256 }, new Rangef[] { new Rangef(0, 256) });
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Cv2.GaussianBlur(current_segment, smoothed_image, new Size(19, 1), 0);
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// 对图片进行分割i+1
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//double otsu_threshold;
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//threshold(smoothed_image, smoothed_image, 0, 255, THRESH_BINARY + THRESH_OTSU, &otsu_threshold);
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Cv2.Threshold(smoothed_image, smoothed_image, 0, 255, ThresholdTypes.Binary | ThresholdTypes.Otsu);
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// 使用形态学操作进行孔洞填充
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Mat kernel = Cv2.GetStructuringElement(MorphShapes.Rect, new Size(25, 1));
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Mat filled_image = new Mat();
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Cv2.MorphologyEx(smoothed_image, filled_image, MorphTypes.Close, kernel);
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// 取较长的一个值作为皮革的宽度
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int num_255 = Cv2.CountNonZero(filled_image);
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int length_t = (num_255 > width / 2) ? num_255 : width - num_255;
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total[i] = (length_t);
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API.OutputDebugString($"getMaxInsetRect2: 【{i + 1}】{length_t}={num_255}|{width}");
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}
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// 取平均值作为宽度
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int length = (int)total.Average();
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marginWidth = width-length;
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#endif
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int length = (len > mat_rgb.Width / 2) ? len : mat_rgb.Width - len;
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marginWidth = mat_rgb.Width - length;
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// 判断数据是否异常，判断当前线段的宽度是否大于设定像素的偏差
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//int abnormal_pxl = 200;
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//for (int i = 0; i < num_rows; i++)
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//{
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// if (Math.Abs(total[i] - length) > abnormal_pxl)
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// throw new Exception("数据异常,当段图片的宽度有问题!");
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//}
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//右侧相机，拍摄产品，边缘位于右侧判断，缩进100像素，去点针眼
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//Cv2.Line(mat_rgb, new Point(length - 100, 0), new Point(length - 100, height), new Scalar(255, 0, 0), 20);
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////左侧相机，拍摄产品，边缘位于左侧判断，缩进100像素，去点针眼
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//Cv2.Line(mat_rgb, new Point(width - length + 100, 0), new Point(width - length + 100, height), new Scalar(0, 255, 0), 20);
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//int decWidth = width - length + marginHoleWidth;
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//if (isLeft)
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// return cutImage(mat_rgb, decWidth, 0, width- decWidth, height);
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//else
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// return cutImage(mat_rgb, 0, 0, width - decWidth, height);
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//API.OutputDebugString($"getMaxInsetRect2:margin={marginWidth}，length={length}({marginHoleWidth}),isLeft={isLeft},mat_rgb={mat_rgb.Width}*{mat_rgb.Height},w={length - marginHoleWidth},h={mat_rgb.Height}");
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if (isLeft)
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return CutImage(mat_rgb, mat_rgb.Width - length + marginHoleWidth, 0, length - marginHoleWidth, mat_rgb.Height);
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else
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return CutImage(mat_rgb, 0, 0, length - marginHoleWidth, mat_rgb.Height);
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}
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/// <summary>
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/// 寻边算法
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/// </summary>
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/// <param name="image"></param>
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/// <param name="FindType"></param>
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/// <param name="Roi"></param>
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/// <param name="IsLeft"></param>
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/// <returns></returns>
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public static int EdgeClipping2(Mat image, int FindType, Rect Roi, bool IsLeft)
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{
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DateTimeOffset startTime = DateTimeOffset.Now;
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Mat mat_rgb = image.Clone(Roi);
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int height = mat_rgb.Rows;
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int width = mat_rgb.Cols;
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int sf = 10; //缩放比例
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int pix = 5; //获取均值区域长宽像素
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int pointNum = 15; //获取找遍点数
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int offsetGray = 5; //二值化偏差
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//按比例缩放
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int sf_height = height / sf;
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int sf_width = width / sf;
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Cv2.Resize(mat_rgb, mat_rgb, new Size(sf_width, sf_height), 0, 0, InterpolationFlags.Linear);
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Mat himg = new Mat();
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himg = mat_rgb.Clone();
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DateTimeOffset endTime = DateTimeOffset.Now;
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//Console.WriteLine("图片缩小(ms): " + (endTime - startTime).TotalMilliseconds.ToString("0.000"));
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startTime = DateTimeOffset.Now;
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//滤过去除多余噪声
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//Cv2.EdgePreservingFilter(himg, himg, EdgePreservingMethods.NormconvFilter);
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//Cv2.PyrMeanShiftFiltering(himg, himg, 1, 2, 1);
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Cv2.PyrMeanShiftFiltering(himg, himg, 10, 17, 2);
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//himg.ImWrite("himg.jpg");
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endTime = DateTimeOffset.Now;
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//Console.WriteLine("滤过去除多余噪声(ms): " + (endTime - startTime).TotalMilliseconds.ToString("0.000"));
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startTime = DateTimeOffset.Now;
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//转灰度图
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Mat image_gray = new Mat();
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Cv2.CvtColor(himg, image_gray, ColorConversionCodes.BGR2GRAY);
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//image_gray.ImWrite("image_gray.jpg");
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Mat image_Canny = new Mat();
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Cv2.Canny(image_gray, image_Canny, 32, 64);
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//image_Canny.ImWrite("image_Canny.jpg");
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//二值化
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Mat image_Otsu = new Mat();
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int hDis = sf_height / (pointNum + 2); //去除边缘两点
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#if false //二值算法
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List<double> LeftAvg = new List<double>();
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List<double> RightAvg = new List<double>();
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//double thb = Cv2.Threshold(image_gray, image_Otsu, 0, 255, ThresholdTypes.Binary | ThresholdTypes.Otsu);
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#region 多点获取二值化均值
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for (int i = 0; i < pointNum; i++)
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{
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Rect roiLeft = new Rect(0, hDis + hDis * i, pix, pix);
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Mat current_segmentL = image_gray.Clone(roiLeft);
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//Scalar ttr = current_segmentL.Mean();
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LeftAvg.Add(current_segmentL.Mean().Val0);
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Rect roiRight = new Rect(sf_width - pix, hDis + hDis * i, pix, pix);
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Mat current_segmentR = image_gray.Clone(roiRight);
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RightAvg.Add(current_segmentR.Mean().Val0);
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}
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double thres = 0;
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if (IsLeft)
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{
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if (LeftAvg.Average() > RightAvg.Average())
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thres = RightAvg.Max() + offsetGray;
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else
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thres = RightAvg.Min() - offsetGray;
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}
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else
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{
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if (LeftAvg.Average() > RightAvg.Average())
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thres = LeftAvg.Min() - offsetGray;
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else
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thres = LeftAvg.Max() + offsetGray;
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}
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//double thres = (RightAvg.Average() + )/2;
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#endregion
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#endif
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#if false
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double min, max;
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image_gray.MinMaxLoc(out min, out max);
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double thres = (min + max) / 2;
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#endif
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#if false //二值化图片
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//Cv2.Threshold(image_gray, image_Otsu, 0, 255, ThresholdTypes.Otsu);
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double thb = Cv2.Threshold(image_gray, image_Otsu, thres, 255, ThresholdTypes.Binary);
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image_Otsu.ImWrite("Otsu1.jpg");
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Cv2.MedianBlur(image_Otsu, image_Otsu, 21);
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image_Otsu.ImWrite("Otsu2.jpg");
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endTime = DateTimeOffset.Now;
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Console.WriteLine("灰度图二值化(ms): " + (endTime - startTime).TotalMilliseconds.ToString("0.000"));
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startTime = DateTimeOffset.Now;
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#else
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image_Otsu = image_Canny;
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#endif
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// 定义空数组保存结果
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int[] total = new int[pointNum];
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List<int> total_t = new List<int>();
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bool isLeft = FindType == 0 ? true : false;
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// 平均截取pointNum行数据并处理图像
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for (int i = 0; i < pointNum; i++)
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{
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// 截取当前行的图像
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Rect roi = new Rect(0, hDis + hDis * i, sf_width, 1);
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Mat current_segment = image_Otsu.Clone(roi);
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#if false
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#region 预处理
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// 对当前行的图像进行平滑处理
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Mat smoothed_image2 = new Mat();
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Cv2.GaussianBlur(current_segment, smoothed_image2, new Size(5, 1), 0);
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// 计算当前行的灰度直方图
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Mat absolute_histo2 = new Mat();
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Cv2.CalcHist(new Mat[] { smoothed_image2 }, new int[] { 0 }, new Mat(), absolute_histo2, 1, new int[] { 256 }, new Rangef[] { new Rangef(0, 256) });
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Cv2.GaussianBlur(current_segment, smoothed_image2, new Size(9, 1), 0);
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// 对图片进行分割
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//double otsu_threshold;
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//threshold(smoothed_image, smoothed_image, 0, 255, THRESH_BINARY + THRESH_OTSU, &otsu_threshold);
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double otsu_threshold2 = Cv2.Threshold(smoothed_image2, smoothed_image2, 0, 255, ThresholdTypes.Binary | ThresholdTypes.Otsu);
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// 使用形态学操作进行孔洞填充
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Mat kernel3 = Cv2.GetStructuringElement(MorphShapes.Rect, new Size(5, 1));
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Mat filled_image3 = new Mat();
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Cv2.MorphologyEx(smoothed_image2, filled_image3, MorphTypes.Close, kernel3);
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#endregion
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#else
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//Mat filled_image3 = current_segment.Clone();
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Mat filled_image3 = current_segment;
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#endif
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#if true
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//从左到右判断边和从右到左判断边
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int numX = 0;
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byte tempVal = 0;
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if (isLeft)
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{
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tempVal = filled_image3.At<byte>(0, 0);
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for (int j = 0; j < filled_image3.Cols; j++)
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{
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if (filled_image3.At<byte>(0, j) != tempVal)
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{
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numX = j;
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break;
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}
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}
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}
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else
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{
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tempVal = filled_image3.At<byte>(0, filled_image3.Cols - 1);
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for (int j = filled_image3.Cols - 1; j >= 0; j--)
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{
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if (filled_image3.At<byte>(0, j) != tempVal)
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{
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numX = j;
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break;
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}
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}
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}
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#else
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int numX = Cv2.CountNonZero(filled_image3);
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#endif
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//int length_t = (numX > (sf_width / 2)) ? numX :sf_width - numX;
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int length_t = numX;
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total[i] = (length_t);
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if (length_t > 0)
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total_t.Add(length_t);
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}
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// 取平均值作为宽度
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int length = 0;
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if(total_t.Count> 0)
|
||
length = (int)total_t.Average();
|
||
|
||
endTime = DateTimeOffset.Now;
|
||
//Console.WriteLine("计算边(ms): " + (endTime - startTime).TotalMilliseconds.ToString("0.000"));
|
||
|
||
// 判断数据是否异常，判断当前线段的宽度是否大于设定像素的偏差
|
||
//int abnormal_pxl = 100 / 4;
|
||
//for (int i = 0; i < pointNum; i++)
|
||
//{
|
||
// if (Math.Abs(total[i] - length) > abnormal_pxl)
|
||
// Console.WriteLine("数据异常!");
|
||
// //出现数据异常，当段图片的宽度有问题
|
||
//}
|
||
|
||
//乘上换算系数还原
|
||
length = length * sf + Roi.X;
|
||
return length;
|
||
}
|
||
#endregion
|
||
|
||
#region 合并
|
||
/// <summary>
|
||
/// 合并MAT（宽高必需一致）
|
||
/// </summary>
|
||
/// <param name="mats"></param>
|
||
/// <param name="isHorizontal"></param>
|
||
/// <returns></returns>
|
||
public static Mat MergeImage_sameSize(Mat[] mats, bool isHorizontal = true)
|
||
{
|
||
Mat matOut = new Mat();
|
||
if (isHorizontal)
|
||
Cv2.HConcat(mats, matOut);//横向拼接
|
||
else
|
||
Cv2.VConcat(mats, matOut);//纵向拼接
|
||
return matOut;
|
||
}
|
||
#endregion
|
||
}
|
||
}
|