【opencvsharp】sample.windows 形态学变换:开运算/闭运算 膨胀/腐蚀 -滑动条,窗口鼠标事件

本文介绍了如何使用OpenCvSharp进行Windows界面展示、TrackBar滑动事件处理和鼠标回调功能的实现,包括Open/Close操作、鼠标点击事件响应及MorphShapes应用。

 

 

 

using OpenCvSharp;
using System;
using System.Diagnostics;

namespace SamplesCore.Windows
{
    internal class WindowGUISample : ISample
    {
        public void Run()
        {
            Windows_Example();
            TrackBar_Example();
            MouseCallBack_Example();
        }
        /*
        public enum Category
        {
            Windows,
            TrackBar,
            MouseCallBack
        }
        */
        /*
        public void Run(Category category)
        {
            switch (category) {
                case Category.Windows:
                    Windows_Example();
                    break;
                case Category.TrackBar:
                    TrackBar_Example();
                    break;
                case Category.MouseCallBack:
                    MouseCallBack_Example();
                    break;
            }
        }
        */

        public static void Windows_Example()
        {
            using var srcImg = new Mat(FilePath.Image.SurfBoxinscene, ImreadModes.AnyColor);
            using var openCloseWindow = new Window("OpenCVWindow", srcImg);
            Debug.WriteLine(Cv2.WaitKey());
        }

        public void MouseCallBack_Example()
        {
            using var srcImg = new Mat(FilePath.Image.SurfBoxinscene, ImreadModes.AnyColor);
            using var foo = new Window("MouseEvent", srcImg);
            Cv2.SetMouseCallback(foo.Name, CallbackOpenCVAnnotate);
            Cv2.WaitKey();
        }

        private void CallbackOpenCVAnnotate(MouseEventTypes e, int x, int y, MouseEventFlags flags, IntPtr userdata)
        {
            if (e == MouseEventTypes.LButtonDown)
            {
                Debug.WriteLine(x + "," + y + " Down");
            }
            else if (flags.HasFlag(MouseEventFlags.LButton))
            {
                Debug.WriteLine(x + "," + y + " flags");
            }
            else if (e == MouseEventTypes.LButtonUp)
            {
                Debug.WriteLine(x + "," + y + " Up");
            }
            else if (e == MouseEventTypes.MouseWheel)
            {
                Debug.WriteLine(x + "," + y + " Wheel");
            }
        }

        public static void TrackBar_Example()
        {
            using var src = new Mat(FilePath.Image.SurfBoxinscene, ImreadModes.AnyColor);
            using var dst = new Mat();

            src.CopyTo(dst);

            var elementShape = MorphShapes.Rect;
            var maxIterations = 10;

            var openCloseWindow = new Window("Open/Close", image: dst);
            var openCloseTrackbar = openCloseWindow.CreateTrackbar(
                trackbarName: "Iterations",
                initialPos: 10,
                max: maxIterations * 2 + 1,
                callback: pos =>
                {
                    var n = pos - maxIterations;
                    var an = n > 0 ? n : -n;
                    using var element = Cv2.GetStructuringElement(
                            elementShape,
                            new Size(an * 2 + 1, an * 2 + 1),
                            new Point(an, an));

                    if (n < 0)
                    {
                        Cv2.MorphologyEx(src, dst, MorphTypes.Open, element);
                    }
                    else
                    {
                        Cv2.MorphologyEx(src, dst, MorphTypes.Close, element);
                    }

                    Cv2.PutText(dst, (n < 0) ?
                        string.Format("Open/Erosion [{0}]", elementShape)
                        : string.Format("Close/Dilation [{0}]", elementShape),
                        new Point(10, 15), HersheyFonts.HersheyPlain, 1, Scalar.Black);
                    openCloseWindow.Image = dst;
                });


            var erodeDilateWindow = new Window("Erode/Dilate", image: dst);
            var erodeDilateTrackbar = erodeDilateWindow.CreateTrackbar(
                trackbarName: "Iterations",
                initialPos: 10,
                max: maxIterations * 2 + 1,
                callback: pos =>
                {
                    var n = pos - maxIterations;
                    var an = n > 0 ? n : -n;
                    using var element = Cv2.GetStructuringElement(
                            elementShape,
                            new Size(an * 2 + 1, an * 2 + 1),
                            new Point(an, an));
                    if (n < 0)
                    {
                        Cv2.Erode(src, dst, element);
                    }
                    else
                    {
                        Cv2.Dilate(src, dst, element);
                    }

                    Cv2.PutText(dst, (n < 0) ?
                        string.Format("Erode[{0}]", elementShape) :
                        string.Format("Dilate[{0}]", elementShape),
                        new Point(10, 15), HersheyFonts.HersheyPlain, 1, Scalar.Black);
                    erodeDilateWindow.Image = dst;
                });


            for (; ; )
            {
                var key = Cv2.WaitKey();

                if ((char)key == 27) // ESC
                    break;

                switch ((char)key)
                {
                    case 'e':
                        elementShape = MorphShapes.Ellipse;
                        break;
                    case 'r':
                        elementShape = MorphShapes.Rect;
                        break;
                    case 'c':
                        elementShape = MorphShapes.Cross;
                        break;
                }
            }

            openCloseWindow.Dispose();
            erodeDilateWindow.Dispose();
        }

    }
}

e:

 

r:

 

 

c:

 

 

 鼠标事件:

// 示例:加载Sentinel-2影像(2022年,与研究区域匹配) var sentinel2 = ee.ImageCollection('COPERNICUS/S2_HARMONIZED') .filterDate('2019-01-25', '2019-12-31') .filterBounds(geometry) // geometry为用户定义的矢量区域 .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 20)) // 筛选云量低的影像 .median(); // 取中值合成影像 // 以Sentinel-2为例 var ndvi = sentinel2.normalizedDifference(['B8', 'B4']).rename('NDVI'); var ndwi = sentinel2.normalizedDifference(['B3', 'B8']).rename('NDWI'); // 裁剪到研究区域 var ndviClip = ndvi.clip(geometry); var ndwiClip = ndwi.clip(geometry); // NDVI可视化参数(范围-1到1) var ndviParams = {min: -1, max: 1, palette: ['red', 'yellow', 'green']}; // NDWI可视化参数(范围-1到1) var ndwiParams = {min: -1, max: 1, palette: ['white', 'blue']}; Map.centerObject(geometry, 10); Map.addLayer(ndviClip, ndviParams, '2019年NDVI'); Map.addLayer(ndwiClip, ndwiParams, '2019年NDWI'); // 定义通用统计参数 var statsParams = { geometry: geometry, scale: 10, maxPixels: 1e13, bestEffort: true }; // 计算NDVI统计值(显式传递参数) var ndviStats = ndviClip.reduceRegion({ reducer: ee.Reducer.minMax().combine({ reducer2: ee.Reducer.mean(), sharedInputs: true }), geometry: statsParams.geometry, scale: statsParams.scale, maxPixels: statsParams.maxPixels, bestEffort: statsParams.bestEffort }); // 计算NDWI统计值(显式传递参数) var ndwiStats = ndwiClip.reduceRegion({ reducer: ee.Reducer.minMax().combine({ reducer2: ee.Reducer.mean(), sharedInputs: true }), geometry: statsParams.geometry, scale: statsParams.scale, maxPixels: statsParams.maxPixels, bestEffort: statsParams.bestEffort }); // 打印结果 print('2019年NDVI 范围:', ndviStats); print('2019年NDWI 范围:', ndwiStats); // 修正后的MVI计算函数(使用B3、B8、B11波段) function calMvI(img) { var green = img.select("B3"); // B3: 绿光 var nir = img.select("B8"); // B8: 近红外 var swir1 = img.select("B11"); // B11: 短波红外1 var numerator = nir.subtract(green).abs(); // |NIR - Green| var denominator = swir1.subtract(green).abs(); // |SWIR1 - Green| var mvi = numerator.divide(denominator).rename('MVI'); return mvi; } // 加载Sentinel-2影像(去云处理) var sentinel2 = ee.ImageCollection('COPERNICUS/S2_HARMONIZED') .filterDate('2019-01-01', '2019-12-31') .filterBounds(geometry) .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 10)) .median(); // 计算MVI var mviImage = calMvI(sentinel2).clip(geometry); // MVI可视化参数 // 步骤1:计算MVI并可视化(辅助定位) var mviParams = {min: 0, max: 5, palette: ['white', 'yellow', 'green']}; Map.addLayer(mviImage.clip(geometry), mviParams, '2019年MVI 指数'); Map.centerObject(geometry, 10); // 步骤2:点击地图触发查询 Map.onClick(function(lonlat) { // 获取点击位置的坐标 var point = ee.Geometry.Point([lonlat.lon, lonlat.lat]); // 采样MVI值 var mviValue = mviImage.sample(point, 10).first().get('MVI'); // 打印结果 print('点击位置坐标:', point); print('2019年MVI 值:', mviValue); }); //高程值 var delta_dtm = ee.Image("projects/sat-io/open-datasets/DELTARES/deltadtm_v1"); var elevation = delta_dtm.select('b1').clip(geometry); elevation = elevation.updateMask(elevation.neq(10)); var elevationVis = { min: 0, max: 10.0, // cmocean deep palette: ["281a2c", "3f396c", "3e6495", "488e9e", "5dbaa4", "a5dfa7", "fdfecc"] }; Map.addLayer(elevation, elevationVis, 'DeltaDTM'); // 定义点击事件函数 var onClick = function(coords) { var point = ee.Geometry.Point(coords.lon, coords.lat); // 从高程图中提取点击位置的值 var value = elevation.reduceRegion({ reducer: ee.Reducer.first(), geometry: point, scale: 30 // 匹配数据分辨率 }).get('b1'); // 打印结果(单位:米) value.evaluate(function(val) { print('高程值 (米):', val); }); }; // 绑定点击事件 Map.onClick(onClick); // 设定MVI阈值(根据研究区域调整) var mviThreshold =6.5; var elevationMin = 0; var elevationMax = 5; // 生成红树林二值掩膜 var mangrovesMask= mviImage.gt(mviThreshold) .and(elevation.gte(elevationMin)) .and(elevation.lte(elevationMax)) .rename('Mangroves'); // 方法:过滤掉值为0的区域(仅保留红树林区域) var maskedMangroves = mangrovesMask.updateMask(mangrovesMask.neq(0)); var mangroveVis = { min: 1, // 仅显示值为1的区域 max: 1, palette: ['#228B22'] // 直接指定绿色 }; Map.addLayer(maskedMangroves, mangroveVis, '2019年红树林分布'); // 将二值掩膜转换为面积(平方米) var areaImage = mangrovesMask.multiply(ee.Image.pixelArea()); // 统计研究区域内的总面积 var areaStats = areaImage.reduceRegion({ reducer: ee.Reducer.sum(), geometry: geometry, scale: 10, // Sentinel-2分辨率(米) maxPixels: 1e13 // 提高计算上限 }); // 转换为公顷(1公顷=10,000平方米) var areaHectares = ee.Number(areaStats.get('Mangroves')).divide(1e4); print('2019年红树林面积(公顷):', areaHectares); // 示例:加载Sentinel-2影像(2022年,与研究区域匹配) var sentinel2 = ee.ImageCollection('COPERNICUS/S2_HARMONIZED') .filterDate('2022-01-25', '2022-12-31') .filterBounds(geometry) // geometry为用户定义的矢量区域 .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 20)) // 筛选云量低的影像 .median(); // 取中值合成影像 // 以Sentinel-2为例 var ndvi = sentinel2.normalizedDifference(['B8', 'B4']).rename('NDVI'); var ndwi = sentinel2.normalizedDifference(['B3', 'B8']).rename('NDWI'); // 裁剪到研究区域 var ndviClip = ndvi.clip(geometry); var ndwiClip = ndwi.clip(geometry); // NDVI可视化参数(范围-1到1) var ndviParams = {min: -1, max: 1, palette: ['red', 'yellow', 'green']}; // NDWI可视化参数(范围-1到1) var ndwiParams = {min: -1, max: 1, palette: ['white', 'blue']}; Map.centerObject(geometry, 10); Map.addLayer(ndviClip, ndviParams, '2022年NDVI'); Map.addLayer(ndwiClip, ndwiParams, '2022年NDWI'); // 定义通用统计参数 var statsParams = { geometry: geometry, scale: 10, maxPixels: 1e13, bestEffort: true }; // 计算NDVI统计值(显式传递参数) var ndviStats = ndviClip.reduceRegion({ reducer: ee.Reducer.minMax().combine({ reducer2: ee.Reducer.mean(), sharedInputs: true }), geometry: statsParams.geometry, scale: statsParams.scale, maxPixels: statsParams.maxPixels, bestEffort: statsParams.bestEffort }); // 计算NDWI统计值(显式传递参数) var ndwiStats = ndwiClip.reduceRegion({ reducer: ee.Reducer.minMax().combine({ reducer2: ee.Reducer.mean(), sharedInputs: true }), geometry: statsParams.geometry, scale: statsParams.scale, maxPixels: statsParams.maxPixels, bestEffort: statsParams.bestEffort }); // 打印结果 print('2022年NDVI 范围:', ndviStats); print('2022年NDWI 范围:', ndwiStats); // 修正后的MVI计算函数(使用B3、B8、B11波段) function calMvI(img) { var green = img.select("B3"); // B3: 绿光 var nir = img.select("B8"); // B8: 近红外 var swir1 = img.select("B11"); // B11: 短波红外1 var numerator = nir.subtract(green).abs(); // |NIR - Green| var denominator = swir1.subtract(green).abs(); // |SWIR1 - Green| var mvi = numerator.divide(denominator).rename('MVI'); return mvi; } // 加载Sentinel-2影像(去云处理) var sentinel2 = ee.ImageCollection('COPERNICUS/S2_HARMONIZED') .filterDate('2022-01-01', '2022-12-31') .filterBounds(geometry) .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 10)) .median(); // 计算MVI var mviImage = calMvI(sentinel2).clip(geometry); // MVI可视化参数 // 步骤1:计算MVI并可视化(辅助定位) var mviParams = {min: 0, max: 5, palette: ['white', 'yellow', 'green']}; Map.addLayer(mviImage.clip(geometry), mviParams, 'MVI 指数'); Map.centerObject(geometry, 10); // 步骤2:点击地图触发查询 Map.onClick(function(lonlat) { // 获取点击位置的坐标 var point = ee.Geometry.Point([lonlat.lon, lonlat.lat]); // 采样MVI值 var mviValue = mviImage.sample(point, 10).first().get('2022年MVI'); // 打印结果 print('点击位置坐标:', point); print('2022年MVI 值:', mviValue); }); //高程值 var delta_dtm = ee.Image("projects/sat-io/open-datasets/DELTARES/deltadtm_v1"); var elevation = delta_dtm.select('b1').clip(geometry); elevation = elevation.updateMask(elevation.neq(10)); var elevationVis = { min: 0, max: 10.0, // cmocean deep palette: ["281a2c", "3f396c", "3e6495", "488e9e", "5dbaa4", "a5dfa7", "fdfecc"] }; Map.addLayer(elevation, elevationVis, 'DeltaDTM'); // 定义点击事件函数 var onClick = function(coords) { var point = ee.Geometry.Point(coords.lon, coords.lat); // 从高程图中提取点击位置的值 var value = elevation.reduceRegion({ reducer: ee.Reducer.first(), geometry: point, scale: 30 // 匹配数据分辨率 }).get('b1'); // 打印结果(单位:米) value.evaluate(function(val) { print('高程值 (米):', val); }); }; // 绑定点击事件 Map.onClick(onClick); // 设定MVI阈值(根据研究区域调整) var mviThreshold =6.5; var elevationMin = 0; var elevationMax = 5; // 生成红树林二值掩膜 var mangrovesMask= mviImage.gt(mviThreshold) .and(elevation.gte(elevationMin)) .and(elevation.lte(elevationMax)) .rename('Mangroves'); // 方法:过滤掉值为0的区域(仅保留红树林区域) var maskedMangroves = mangrovesMask.updateMask(mangrovesMask.neq(0)); var mangroveVis = { min: 1, // 仅显示值为1的区域 max: 1, palette: ['#228B22'] // 直接指定绿色 }; Map.addLayer(maskedMangroves, mangroveVis, '2022年红树林分布'); // 将二值掩膜转换为面积(平方米) var areaImage = mangrovesMask.multiply(ee.Image.pixelArea()); // 统计研究区域内的总面积 var areaStats = areaImage.reduceRegion({ reducer: ee.Reducer.sum(), geometry: geometry, scale: 10, // Sentinel-2分辨率(米) maxPixels: 1e13 // 提高计算上限 }); // 转换为公顷(1公顷=10,000平方米) var areaHectares = ee.Number(areaStats.get('Mangroves')).divide(1e4); print('2022年红树林面积(公顷):', areaHectares); 现在绘制了2022年和2019年的红树林面积,如何在源代码基础上分别在不同图层绘制19-22年红树林的增加面积,退化面积,稳定面积的图,
最新发布
05-28
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