OpenCvSharp 3 入门
3.34/5 (13投票s)
OpenCvSharp 3 快速入门的示例代码。
引言
我对图像处理是新手，但对编程并不陌生。我上传了这个项目作为演示代码资源，供其他希望快速入门并感受 OpenCvSharp3 编码 API 的新手使用。这是一篇介绍如何将 C++ 代码转换为 OpenSharp3 代码的文章。您可以尝试代码参数，并查阅 C++ 文档以了解 API 的操作。
我考虑了 C# 包装类 emgu 和 OpenCV.NET... 但我觉得 OpenCvSharp3 是最简单且支持最好的平台。
演示代码被分成了（独立的区域），使得理解代码的意图非常简单。我不得不编译 https://github.com/shimat/opencvsharp/releases 的示例代码至少几次才能获得干净的构建。
背景
OpenVC 是一个流行的 C++ 计算机视觉库。它处理图像像素以找到感兴趣的特征。然而，C++ 是一个非托管代码平台，与 C# 相比有点笨拙。因此，我放弃了 C++ 编程，转而使用 C#。
Using the Code
要运行演示，请创建一个新的控制台应用程序，并将图像和程序文件复制到其中。包含其中一个程序文件（program、program2 或 program3），并从项目构建中排除其他程序文件。然后取消注释（如果适用）其中一个代码区域，并编译和执行该命令选择。根据需要更改 API 参数，然后重新编译并运行。请注意，您需要使用 Visual Studio nuget 管理器插件工具来安装 OpenCvSharp3 库。基本包将包含 OpenCv C++ DLL。
/*
* More Samples are available at: https://github.com/shimat/opencvsharp/releases
* Class NameSpace listing is at: http://shimat.github.io/opencvsharp/
*/
using System;
using System.Collections.Generic;
using System.Windows.Forms;
using OpenCvSharp;
using OpenCvSharp.ML;
class Program
{
static void Main()
{
#region Canny Edge Detection
//Mat source = new Mat("../../Images/lena.jpg", ImreadModes.Color);
//Mat grayFiltered = new Mat();
//Mat ClearEdge = new Mat();
//Mat filtered = new Mat();
//Mat d = createADiamond();
//Mat x = createAXShape();
//Mat Canny = new Mat();
//Cv2.Canny(source, Canny, 32, 192);
//binImage(source, out filtered);
//// A discussion on erode and dilate is at:
//// https://docs.opencv.ac.cn/2.4/doc/tutorials/imgproc/erosion_dilatation/erosion_dilatation.html
//Cv2.Erode(filtered, ClearEdge, x);
//Cv2.Dilate(ClearEdge, ClearEdge, x);
//Cv2.Erode(ClearEdge, ClearEdge, d);
//Cv2.Dilate(ClearEdge, ClearEdge, d);
//grayFiltered = Mat.FromImageData(filtered.ToBytes(), ImreadModes.GrayScale);
//new Window("source image", source);
//new Window("CannyEdge image", Canny);
//new Window("filtered image", filtered);
//new Window("SharpEdge image", ClearEdge);
//new Window("grayFiltered image", grayFiltered);
//Cv2.WaitKey();
#endregion
#region Sobel Edge Detection
//Mat dst = new Mat();
//Mat grad_x = new Mat(), grad_y = new Mat();
//Mat abs_grad_x = new Mat(), abs_grad_y = new Mat();
//Mat src_gray = new Mat("../../Images/lena.jpg", ImreadModes.GrayScale);
//// try to reduce image noises.
//Cv2.GaussianBlur(src_gray, src_gray, new Size(3, 3), 1.5);
//// Gradient X, ddepth is set it to CV_16S to avoid overflow
//Cv2.Sobel(src_gray, grad_x, MatType.CV_16U, 1, 0, 3);
//Cv2.ConvertScaleAbs(grad_x, abs_grad_x);
//// Gradient Y
//Cv2.Sobel(src_gray, grad_y, MatType.CV_16U, 0, 1, 3);
//Cv2.ConvertScaleAbs(grad_y, abs_grad_y);
//// Total Gradient (approximate)
//Cv2.AddWeighted(abs_grad_x, 0.5, abs_grad_y, 0.5, 0, dst);
//Cv2.ImShow("Sobel Edge", dst);
//Cv2.WaitKey(0);
#endregion
#region Camcorder Capture
//VideoCapture capture;
//Mat frame;
//capture = new VideoCapture();
//frame = new Mat();
//capture.Open(0);
//// Read the video stream
//{
// Cv2.NamedWindow("Video", WindowMode.AutoSize);
// while (true)
// {
// if (capture.Read(frame))
// {
// Cv2.ImShow("Video", frame);
// // press a key to end execution
// int c = Cv2.WaitKey(10);
// if (c != -1) { break; } // Assuming image has focus
// }
// }
//}
#endregion
#region Assigning pixel values
//Mat src = new Mat("../../Images/lena.jpg", ImreadModes.Color);
//// place a green box at the upper left hand corner
//for (int i = 10; i < 99; i++)
// for (int j = 10; j < 99; j++)
// src.Set<Vec3b>(i, j, new Vec3b(0, 255, 0));
//using (new Window("dot image", src2))
//{
// MessageBox.Show("Depth: " + src.Depth());
// MessageBox.Show("Channels: " + src.Channels());
// Cv2.WaitKey();
//}
#endregion
#region Erode/Dilate Morphing
//Mat src, dst;
//src = Cv2.ImRead("../../Images/lena.jpg", ImreadModes.Color);
//dst = new Mat();
// Create a structuring element
//int erosion_size = 6;
//Mat element = Cv2.GetStructuringElement(MorphShapes.Cross,
// new Size(2 * erosion_size + 1, 2 * erosion_size + 1),
// new Point(erosion_size, erosion_size));
// Apply erosion "OR" dilation on the image
//Cv2.Erode(src, dst, element);
//Cv2.Dilate(src, dst, element);
//using (new Window("Display window", src))
//using (new Window("Result window", dst))
// Cv2.WaitKey(0);
#endregion
#region draw a line
//// Create black empty images
//Mat image = Mat.Zeros(400, 400, MatType.CV_8UC3);
//// Draw a line
//Cv2.Line(image, new Point(15, 20), new Point(375, 375),
// new Scalar(255, 128, 0), 2);
//using (new Window("Image", image))
// Cv2.WaitKey(0);
#endregion
#region draw a circle
//// Create black empty images
//Mat image = Mat.Zeros(400, 400, MatType.CV_8UC3);
//// Draw a line
//Cv2.Circle(image, new Point(200, 200), 100, new Scalar(255, 128, 0), 2);
//using (new Window("Image", image))
// Cv2.WaitKey(0);
#endregion
#region draw a Polygon
//Mat src = new Mat(new Size(400, 400), MatType.CV_8U, new Scalar(1));
//src.SetTo(Scalar.Black);
//List<List<Point>> ListOfListOfPoint = new List<List<Point>>();
//List<Point> points = new List<Point>();
//points.Add(new Point(100, 100));
//points.Add(new Point(100, 300));
//points.Add(new Point(300, 300));
//points.Add(new Point(300, 100));
//ListOfListOfPoint.Add(points);
//src.FillPoly(ListOfListOfPoint, Scalar.White);
//Cv2.ImShow("Square", src);
//Cv2.WaitKey();;
#endregion
#region draw a text string
//// Create black empty images
//Mat src = Mat.Zeros( 400, 400, MatType.CV_8UC3 );
//Cv2.PutText(src, "Hi all...", new Point(50,100),
// HersheyFonts.HersheySimplex, 1, new Scalar(0,200,200), 4);
//using (new Window("Image", src))
// Cv2.WaitKey(0);
#endregion
#region weighed filter
//Mat src = new Mat("../../Images/lena.jpg", ImreadModes.GrayScale);
//Mat kernel = new Mat(3, 3, MatType.CV_32F, new Scalar(0));
//Mat dst = new Mat();
//kernel.Set<float>(1, 1, 5.0f);
//kernel.Set<float>(0, 1, -1.0f);
//kernel.Set<float>(2, 1, -1.0f);
//kernel.Set<float>(1, 0, -1.0f);
//kernel.Set<float>(1, 2, -1.0f);
//Cv2.Filter2D(src, dst, MatType.CV_32F, kernel);
//using (new Window("src image", src))
//using (new Window("dst image", dst))
//{
// Cv2.WaitKey();
//}
#endregion
#region find circles in image
//Mat train = new Mat("../../Images/cartoon-train.png", ImreadModes.GrayScale);
//CircleSegment[] circles;
//Mat dst = new Mat();
//Cv2.GaussianBlur(train, dst, new Size(5, 5), 1.5, 1.5);
//// Note, the minimum distance between concentric circles is 25. Otherwise
//// false circles are detected as a result of the circle's thickness.
//circles = Cv2.HoughCircles(dst, HoughMethods.Gradient, 1, 25, 75, 60, 5, 200);
//for (int i = 0; i < circles.Length; i++)
//{
// Cv2.Circle(dst, circles[i].Center, (int)circles[i].Radius, new Scalar(0), 2);
//}
//using (new Window("Circles", dst))
//{
// Cv2.WaitKey();
//}
#endregion
#region Get corners on image
//Mat src = new Mat("../../Images/building.jpg", ImreadModes.GrayScale);
//// Show Edges
//Mat edges = getEdges(src, 50);
//new Window("Edges", edges);
//// Corner detection
//// Get All Processing Images
//Mat cross = createACross();
//Mat diamond = createADiamond();
//Mat square = createASquare();
//Mat x = createAXShape();
//Mat dst = new Mat();
//// Dilate with a cross
//Cv2.Dilate(src, dst, cross);
//// Erode with a diamond
//Cv2.Erode(dst, dst, diamond);
//Mat dst2 = new Mat();
//// Dilate with a X
//Cv2.Dilate(src, dst2, x);
//// Erode with a square
//Cv2.Erode(dst2, dst2, square);
//// Corners are obtain by differencing the two closed images
//Cv2.Absdiff(dst, dst2, dst);
//applyThreshold(dst, 45);
//// The following code Identifies the founded corners by
//// drawing circle on the src image.
//IDTheCorners(dst, src);
//new Window("Corner on Image", src);
//Cv2.WaitKey();
#endregion
#region Machine Learning
// Translated from C++ article:
// https://docs.opencv.ac.cn/3.1.0/d1/d73/tutorial_introduction_to_svm.html
// Data for visual representation
int width = 512, height = 512;
Mat image = Mat.Zeros(height, width, MatType.CV_8UC3);
// Set up training data
int[] labels = new int[] { 1, -1, -1, -1 };
float[,] trainingData = new float[,] { { 501, 10 }, { 255, 10 }, { 501, 255 }, { 10, 501 } };
Mat trainingDataMat = new Mat(4, 2, MatType.CV_32FC1, trainingData);
Mat labelsMat = new Mat(4, 1, MatType.CV_32SC1, labels);
// Train the SVM
SVM svm = SVM.Create();
svm.Type = SVM.Types.CSvc;
svm.KernelType = SVM.KernelTypes.Linear;
svm.TermCriteria = new TermCriteria(CriteriaType.MaxIter, 100, 1e-6);
svm.Train(trainingDataMat, SampleTypes.RowSample, labelsMat);
// Show the decision regions given by the SVM
Vec3b green = new Vec3b(0, 255, 0), blue = new Vec3b(255, 0, 0);
for (int i = 0; i < image.Rows; ++i)
for (int j = 0; j < image.Cols; ++j)
{
Mat sampleMat = new Mat(1, 2, MatType.CV_32F, new float[] { j, i });
float response = svm.Predict(sampleMat);
if (response == 1)
image.Set<Vec3b>(i, j, green);
else if (response == -1)
image.Set<Vec3b>(i, j, blue);
}
// Show the training data
int thickness = -1;
Cv2.Circle(image, new Point(501, 10), 5, Scalar.Black, thickness);
Cv2.Circle(image, new Point(255, 10), 5, Scalar.White, thickness);
Cv2.Circle(image, new Point(501, 255), 5, Scalar.White, thickness);
Cv2.Circle(image, new Point(10, 501), 5, Scalar.White, thickness);
// Show support vectors
thickness = 2;
Mat sv = svm.GetSupportVectors();
for (int i = 0; i < sv.Rows; ++i)
{
unsafe
{
float* v = (float*)sv.Ptr(i);
Cv2.Circle(image, new Point((int)v[0], (int)v[1]), 6, Scalar.Gray, thickness);
Console.WriteLine("{0:d}, {1:d}", (int)v[0], (int)v[1]);
}
}
Cv2.ImWrite("result.png", image);
// save the image
Cv2.ImShow("SVM Simple Example", image); // show it to the user
Cv2.WaitKey(0);
#endregion
#region Feature SURF flann
//// Token from C++ example:
//// https://docs.opencv.ac.cn/3.1.0/d5/d6f/tutorial_feature_flann_matcher.html
//// As of the writing of this routine,
//// SIFT and SURF is a non-free code and is moved to the
//// contrib repository and then link to the xfeatures2d library.
//// So, you will get runtime error:
//// Unable to find an entry point named 'xfeatures2d_SURF_create'
//// in DLL 'OpenCvSharpExtern'.
//// See these 2 links for an method on how to install the contrib library,
//// it's a bit trick but do-able:
//// https://github.com/shimat/opencvsharp/issues/146
//// https://github.com/shimat/opencvsharp/issues/180
//Mat img_1 = Cv2.ImRead("../../Images/icons.png", ImreadModes.GrayScale);
//Mat img_2 = Cv2.ImRead("../../Images/subIcons.png", ImreadModes.GrayScale);
////-- Step 1: Detect the keypoints using SURF Detector, compute the descriptors
//int minHessian = 400;
//SURF detector = SURF.Create(minHessian);
//KeyPoint[] keypoints_1, keypoints_2;
//Mat descriptors_1 = new Mat(), descriptors_2 = new Mat();
//detector.DetectAndCompute(img_1, new Mat(), out keypoints_1, descriptors_1);
//detector.DetectAndCompute(img_2, new Mat(), out keypoints_2, descriptors_2);
////-- Step 2: Matching descriptor vectors using FLANN matcher
//FlannBasedMatcher matcher = new FlannBasedMatcher();
//DMatch[] matches;
//matches = matcher.Match(descriptors_1, descriptors_2);
//double max_dist = 0; double min_dist = 100;
////-- Quick calculation of max and min distances between keypoints
//for (int i = 0; i < descriptors_1.Rows; i++)
//{
// double dist = matches[i].Distance;
// if (dist < min_dist) min_dist = dist;
// if (dist > max_dist) max_dist = dist;
//}
//Console.WriteLine("-- Max dist : %f", max_dist);
//Console.WriteLine("-- Min dist : %f", min_dist);
////-- Draw only "good" matches (i.e. whose distance is less than 2*min_dist,
////-- or a small arbitrary value ( 0.02 ) in the event that min_dist is very
////-- small) //-- PS.- radiusMatch can also be used here.
//List<DMatch> good_matches = new List<DMatch>();
//for (int i = 0; i < descriptors_1.Rows; i++)
//{
// if (matches[i].Distance <= Math.Max(2 * min_dist, 0.02))
// {
// good_matches.Add(matches[i]);
// }
//}
////-- Draw only "good" matches
//Mat img_matches = new Mat();
//Cv2.DrawMatches(img_1, keypoints_1, img_2, keypoints_2,
// good_matches, img_matches, Scalar.All(-1), Scalar.All(-1),
// new List<byte>(), DrawMatchesFlags.NotDrawSinglePoints);
////-- Show detected matches imshow( "Good Matches", img_matches );
//for (int i = 0; i < (int)good_matches.Count; i++)
//{
// Console.WriteLine("-- Good Match [%d] Keypoint 1: %d -- Keypoint 2: %d", i,
// good_matches[i].QueryIdx, good_matches[i].TrainIdx);
//}
//Cv2.WaitKey(0);
#endregion
}
/***********************************************************************
************************** SUPPORT ROUTINES ***************************
***********************************************************************/
#region Support code for Get Corners on image region
static Mat getEdges(Mat image, int threshold)
{
// Get the gradient image
Mat result = new Mat();
Cv2.MorphologyEx(image, result, MorphTypes.Gradient, new Mat());
applyThreshold(result, threshold);
return result;
}
static Mat createACross()
{
Mat cross = new Mat(5, 5, MatType.CV_8U, new Scalar(0));
// creating the cross-shaped structuring element
for (int i = 0; i < 5; i++)
{
cross.Set<byte>(2, i, 1);
cross.Set<byte>(i, 2, 1);
}
return cross;
}
static Mat createADiamond()
{
Mat diamond = new Mat(5, 5, MatType.CV_8U, new Scalar(1));
// Creating the diamond-shaped structuring element
diamond.Set<byte>(0, 0, 0);
diamond.Set<byte>(1, 0, 0);
diamond.Set<byte>(3, 0, 0);
diamond.Set<byte>(4, 0, 0);
diamond.Set<byte>(0, 1, 0);
diamond.Set<byte>(4, 1, 0);
diamond.Set<byte>(0, 3, 0);
diamond.Set<byte>(4, 3, 0);
diamond.Set<byte>(4, 4, 0);
diamond.Set<byte>(0, 4, 0);
diamond.Set<byte>(1, 4, 0);
diamond.Set<byte>(3, 4, 0);
return diamond;
}
static Mat createASquare()
{
Mat Square = new Mat(5, 5, MatType.CV_8U, new Scalar(1));
return Square;
}
static Mat createAXShape()
{
Mat x = new Mat(5, 5, MatType.CV_8U, new Scalar(0));
// Creating the x-shaped structuring element
for (int i = 0; i < 5; i++)
{
x.Set<byte>(i, i, 1);
x.Set<byte>(4 - i, i, 1);
}
return x;
}
static void applyThreshold(Mat result, int threshold)
{
Cv2.Threshold(result, result, threshold, 255, ThresholdTypes.Binary);
}
static void IDTheCorners(Mat binary, Mat image)
{
for (int r = 0; r < binary.Rows; r++)
for (int c = 0; c < binary.Cols; c++)
if (binary.At<byte>(r, c) != 0)
Cv2.Circle(image, c, r, 5, new Scalar(255));
}
#endregion
#region nearest palette color averaging
/// <summary>
///
/// </summary>
/// <param name="src">input image</param>
/// <param name="dst">output image</param>
/// <param name="binSize">color channel size.
/// i.e., Number of Red shades = floor(255/binSize),
/// where binSize is between 1 and 128.</param>
static void binImage(Mat src, out Mat dst, int binSize = 51)
{
dst = src.Clone();
if (binSize <= 0 || binSize > 255) return;
for (int r = 0; r<src.Rows; r++)
for (int c = 0; c < src.Cols; c++)
{
Vec3b color = src.Get<Vec3b>(r, c);
int binCenter = binSize / 2;
dst.Set<Vec3b>(r, c, new Vec3b(
// nearest color, note the size of the black and white
// bins are only half the size of the other bins.
// Note rounding colors to the nearest color palette
// can "sharply change" a gradient color in a region.
(byte)(((color[0] + binCenter) / binSize) * binSize),
(byte)(((color[1] + binCenter) / binSize) * binSize),
(byte)(((color[2] + binCenter) / binSize) * binSize)
));
}
}
#endregion
}
简要的 C++ 到 C# 演练
关于常用 C++ API 调用的完整讨论可在 opencvexamples.blogspot.com/p/learning-opencv-functions-step-by-step.html 找到。 C# API 与 C++ API 几乎完全匹配。此外，请参阅 opencv 主页，了解方法和类的讨论：docs.opencv.org/3.1.0/ 和 (docs.opencv.org/2.4/ 用于向后兼容。)
C# API 的命名空间位于 shimat.github.io/opencvsharp/ 和 github.com/shimat/opencvsharp/wiki。
本演练的目的是反映从 C++ 转换为 C# 时 C# API 的用法。请注意，我们需要一个 CvTrackbar 的引用... 因为 C# CvTrackbar 不会在输入参数列表中返回修改后的值... 而 C++ 计数器 API 会在输入参数列表中返回一个修改后的值。演练的目的不是解释 C++ 库调用... 而是展示翻译所需的 C# 语法。
using System;
using System.Collections.Generic;
using System.Windows.Forms;
using OpenCvSharp;
/*
* Token from C++ example:
* https://docs.opencv.ac.cn/master/d0/d2a/contours2_8cpp-example.html
* C# APIs:
* http://shimat.github.io/opencvsharp/html/1bb515b3-6278-49e4-9a33-1054bd279323.htm
* Note to, IntelliSense will usually show the APIs arguments.
*/
class Program
{
const int w = 500;
static CvTrackbar Track;
static Point[][] contours; // IntelliSense shows the vector array of vectors in C#
static HierarchyIndex[] hierarchy;
static void on_trackbar(int pos, object usedata) // The C# APIs doc shows void* is an object
{
int _levels = Track.Pos - 3;
Mat cnt_img = Mat.Zeros(w, w, MatType.CV_8UC3);
Cv2.DrawContours( cnt_img, contours, _levels <= 0 ? 3 : -1, Scalar.White,
2, LineTypes.AntiAlias, hierarchy, Math.Abs(_levels) );
Cv2.ImShow("contours", cnt_img);
}
static void Main()
{
Mat img = Mat.Zeros(w, w, MatType.CV_8UC1);
//Draw "6" faces
for( int i = 0; i < 6; i++ )
{
int dx = (i % 2) * 250 - 30;
int dy = (i/2)*150;
Scalar white = Scalar.White;
Scalar black = Scalar.Black;
if( i == 0 )
{
for( int j = 0; j <= 10; j++ )
{
double angle = (j + 5) * Math.PI/21;
Cv2.Line(img,
new Point(Math.Round(dx + 100 + j * 10 - 80 * Math.Cos(angle), 0),
Math.Round(dy + 100 - 90 * Math.Sin(angle), 0)),
new Point(Math.Round(dx + 100 + j * 10 - 30 * Math.Cos(angle), 0),
Math.Round(dy + 100 - 30 * Math.Sin(angle), 0)),
white, 1);
}
}
Cv2.Ellipse(img, new Point(dx + 150, dy + 100), new Size(100, 70), 0, 0, 360, white);
Cv2.Ellipse(img, new Point(dx + 115, dy + 70), new Size( 30, 20), 0, 0, 360, black);
Cv2.Ellipse(img, new Point(dx + 185, dy + 70), new Size( 30, 20), 0, 0, 360, black);
Cv2.Ellipse(img, new Point(dx + 115, dy + 70), new Size( 15, 15), 0, 0, 360, white);
Cv2.Ellipse(img, new Point(dx + 185, dy + 70), new Size( 15, 15), 0, 0, 360, white);
Cv2.Ellipse(img, new Point(dx + 115, dy + 70), new Size( 5, 5), 0, 0, 360, black);
Cv2.Ellipse(img, new Point(dx + 185, dy + 70), new Size( 5, 5), 0, 0, 360, black);
Cv2.Ellipse(img, new Point(dx + 150, dy + 100), new Size( 10, 5), 0, 0, 360, black);
Cv2.Ellipse(img, new Point(dx + 150, dy + 150), new Size( 40, 10), 0, 0, 360, black);
Cv2.Ellipse(img, new Point(dx + 27, dy + 100), new Size( 20, 35), 0, 0, 360, white);
Cv2.Ellipse(img, new Point(dx + 273, dy + 100), new Size( 20, 35), 0, 0, 360, white);
}
//show the faces
Cv2.ImShow( "image", img );
//Extract the contours
Point[][] contours0;
Cv2.FindContours( img, out contours0, out hierarchy,
RetrievalModes.Tree, ContourApproximationModes.ApproxSimple);
contours = new Point[contours0.Length][];
for( int k = 0; k < contours0.Length; k++ )
// Here the C# equivalent is of the C++ API is not a one-to-one match
contours[k] = Cv2.ApproxPolyDP(contours0[k], 3, true);
//show the contours and activate trackbar event control
Cv2.NamedWindow("contours", WindowMode.AutoSize);
Track = new CvTrackbar("levels+3", "contours", 4, 7, on_trackbar);
on_trackbar(0, 0);
Cv2.WaitKey();
}
}
我注意到的另一个有趣的 C++ 到 C# 代码转换是 docs.opencv.org/2.4/doc/tutorials/imgproc/threshold/threshold.html 中的阈值演示。 Threshold_Demo 回调事件需要一个替代窗口定义，指定窗口名称，导致在 C++ 库中执行一个新线程或 null 指针。
using OpenCvSharp;
class Program
{
static void Main()
{
TestCode t = new TestCode();
t.Function();
}
// I did not want to use a lot of static objects so, I put the code
// in a user definite class. The code is derived from:
// https://docs.opencv.ac.cn/2.4/doc/tutorials/imgproc/threshold/threshold.html
// This code shows how to program multiple CvTrackbar controls.
class TestCode
{
int max_BINARY_value = 255;
CvTrackbar CvTrackbarValue;
CvTrackbar CvTrackbarType;
Mat src_gray = new Mat();
Mat dst = new Mat();
Window MyWindow;
public void Function()
{
int threshold_value = 0;
int threshold_type = 3;
int max_value = 255;
int max_type = 4;
string trackbar_type = "Type: \n 0: Binary \n 1:
Binary Inverted \n 2: Truncate \n 3: To Zero \n 4: To Zero Inverted";
string trackbar_value = "Value";
// Load an image
Mat src = new Mat("../../Images/cartoon-train.png", ImreadModes.Color);
/// Convert the image to Gray
Cv2.CvtColor(src, src_gray, ColorConversionCodes.BGR2GRAY);
// Create output window
MyWindow = new Window("Track", WindowMode.AutoSize);
// Whenever the user changes the value of any of the Trackbars,
// the function Threshold_Demo is called. I needed a common global
// window object for Threshold_Demo and Function to prevent a
// internal null reference.
CvTrackbarType = MyWindow.CreateTrackbar(trackbar_type,
threshold_type, max_type, Threshold_Demo);
CvTrackbarValue = MyWindow.CreateTrackbar(trackbar_value,
threshold_value, max_value, Threshold_Demo);
/// Call the function to initialize
Threshold_Demo(0, 0);
/// Wait until user finishes program
while (true)
{
int c;
c = Cv2.WaitKey(20);
if ((char)c == 27)
{ break; }
}
}
void Threshold_Demo(int pos, object userdata)
{
/* 0: Binary
1: Binary Inverted
2: Threshold Truncated
3: Threshold to Zero
4: Threshold to Zero Inverted
*/
Cv2.Threshold(src_gray, dst, CvTrackbarValue.Pos,
max_BINARY_value, (ThresholdTypes)CvTrackbarType.Pos);
MyWindow.Image = dst;
}
}
}
关注点
我重写了一些更常见的 C++ 实现演示代码，以展示它在 OpenCvSharp 中的工作方式。我发现使用 Vision Studio 对象浏览器来查看 OpenCvSharp3 API 方法参数非常有帮助。我还建议查看 https://github.com/shimat/opencvsharp/releases 的代码示例。它说明了额外的 API 调用代码片段和实现。
历史
我添加这篇文章是因为我在此网站上没有找到关于 OpenCvSharp3 的信息。
在第一次更新中：我添加了 2 个额外的代码片段以补充完整性。
在第二次更新中：我修复了 Draw FillPoly 代码段的问题。
在第三次更新中：我只更新了文章内容。可下载的代码片段“最近调色板颜色平均值”有效，但略逊于上方打印输出中列出的代码。下载文件不允许使用亮色。因此，我为基本颜色添加了一个偏移值。这使得调色板颜色更接近真实平均值，并允许纯白色。
此外，由于对“opencvsharp”的 nuget 搜索仅显示版本 2，因此我更新了文本以反映“opencvsharp3”。
在第四次更新中：我在代码集中添加了 2 个更复杂的示例。
在第五次更新中：我添加了不安全代码以修复机器学习代码段部分中的浮点指针转换。
在本次更新中：我添加了一个简单的代码演练部分。