/// <summary>
/// 対象物の位置をロボット座標系で取得します。
/// </summary>
/// <param name="blob"></param>
/// <returns></returns>
public static (double x, double y) GetTargetRobotCoordinate(SettingsObj obj, ConnectedComponents.Blob blob)
{
var srcList = new List <DenseVector>(4);
var dstList = new List <DenseVector>(4);
// 各座標系の4点をリストに詰める
srcList.Add(DenseVector.OfArray(new double[] { obj.TopLeftArPoseX, obj.TopLeftArPoseY }));
srcList.Add(DenseVector.OfArray(new double[] { obj.TopRightArPoseX, obj.TopRightArPoseY }));
srcList.Add(DenseVector.OfArray(new double[] { obj.BottomRightArPoseX, obj.BottomRightArPoseY }));
srcList.Add(DenseVector.OfArray(new double[] { obj.BottomLeftArPoseX, obj.BottomLeftArPoseY }));
dstList.Add((HomographyHelper.CreateVector2(obj.TopLeftDobotPoseX, obj.TopLeftDobotPoseY)));
dstList.Add(HomographyHelper.CreateVector2(obj.TopRightDobotPoseX, obj.TopRightDobotPoseY));
dstList.Add(HomographyHelper.CreateVector2(obj.BottomRightDobotPoseX, obj.BottomRightDobotPoseY));
dstList.Add(HomographyHelper.CreateVector2(obj.BottomLeftDobotPoseX, obj.BottomLeftDobotPoseY));
// 画像座標系での値
Console.WriteLine($"homography_IMAGE:imgX:{blob.Left + blob.Width / 2}, imgY:{blob.Top + blob.Height / 2}");
// 射影変換行列を求めて
var h**o = HomographyHelper.FindHomography(srcList, dstList);
// 入力平面から出力平面上の座標に変換
var ret = h**o.Translate(blob.Left + blob.Width / 2, blob.Top + blob.Height / 2);
// ロボット座標系での値
Console.WriteLine($"homography_ROBOT:boxX:{ret.dstX}, boxY:{ret.dstY}");
return(ret);
}
/// <summary>
/// 判断两个blob是否相同
/// </summary>
/// <param name="blob1"></param>
/// <param name="blob2"></param>
/// <returns></returns>
private static bool IsSame(ConnectedComponents.Blob blob1, ConnectedComponents.Blob blob2)
{
//如果两个物体 ，宽度 高度，中心点 area 接近则认为它们是相同的
if (Math.Abs(blob1.Width - blob2.Width) >= 40)
{
return(false);
}
if (Math.Abs(blob1.Height - blob2.Height) >= 35)
{
return(false);
}
if (Math.Abs(blob1.Centroid.Y - blob2.Centroid.Y) >= 30)
{
return(false);
}
if (Math.Abs(blob1.Area - blob2.Area) >= 7000)
{
return(false);
}
return(true);
}
private static void Blob()
{
Mat src = new Mat("data/shapes.png", ImreadModes.Color);
Mat gray = src.CvtColor(ColorConversionCodes.BGR2GRAY);
Mat binary = gray.Threshold(0, 255, ThresholdTypes.Otsu | ThresholdTypes.Binary);
Mat labelView = new Mat();
Mat rectView = binary.CvtColor(ColorConversionCodes.GRAY2BGR);
ConnectedComponents cc = Cv2.ConnectedComponentsEx(binary);
if (cc.LabelCount <= 1)
{
return;
}
// draw labels
/*
* Scalar[] colors = cc.Blobs.Select(_ => Scalar.RandomColor()).ToArray();
* int height = cc.Labels.GetLength(0);
* int width = cc.Labels.GetLength(1);
* var labelViewIndexer = labelView.GetGenericIndexer<Vec3b>();
* for (int y = 0; y < height; y++)
* {
* for (int x = 0; x < width; x++)
* {
* int labelValue = cc.Labels[y, x];
* labelViewIndexer[y, x] = colors[labelValue].ToVec3b();
* }
* }
*/
cc.RenderBlobs(labelView);
// draw bonding boxes except background
foreach (var blob in cc.Blobs.Skip(1))
{
rectView.Rectangle(blob.Rect, Scalar.Red);
}
// filter maximum blob
ConnectedComponents.Blob maxBlob = cc.GetLargestBlob();
//cc.Blobs.Skip(1).OrderByDescending(b => b.Area).First();
Mat filtered = new Mat();
cc.FilterByBlob(src, filtered, maxBlob);
using (new Window("src", src))
using (new Window("binary", binary))
using (new Window("labels", labelView))
using (new Window("bonding boxes", rectView))
using (new Window("maximum blob", filtered))
{
Cv2.WaitKey();
}
}
/// <summary>
/// 是否需要保存图片
/// </summary>
/// <param name="mat">指的是刚刚读进来的灰度图片</param>
/// <returns></returns>
public static bool IsNeedToSave(Mat mat)
{
if (mat == null || mat.IsDisposed)
{
return(false);
}
#region 预判断
if ((DateTime.Now - lastCallTime).TotalMilliseconds < 600)
{
//调用间隔至少为600ms
NLogHelper.Trace("调用频繁，不需要截图");
return(false);
}
lastCallTime = DateTime.Now;
#endregion
#region 加载背景图片
if (backgroundMat == null || backgroundMat.IsDisposed)
{
if (!File.Exists(backgroundFile))
{
NLogHelper.Trace("未找到背景图片：" + backgroundFile);
backgroundMat = null;
}
else
{
try
{
backgroundMat = Cv2.ImRead(backgroundFile, ImreadModes.GrayScale);
//去噪
backgroundMat = backgroundMat.MedianBlur(3);
}
catch (Exception ex)
{
NLogHelper.Trace("加载背景图片失败:" + ex);
if (backgroundMat != null && backgroundMat.IsEnabledDispose)
{
backgroundMat.Dispose();
}
backgroundMat = null;
}
}
}
#endregion
#region 图片处理
////进行图像灰度化
if (mat.Channels() > 1)
{
Mat outMat = new Mat();
Cv2.CvtColor(mat, outMat, ColorConversionCodes.BGRA2GRAY);
mat = outMat;
}
//图片去噪
mat = mat.MedianBlur(3);
int width = mat.Width;
int height = mat.Height;
//当前使用的背景图片
Mat usedBackgroundMat = null;
if (backgroundMat != null)
{
if (backgroundMat.Width != width || backgroundMat.Height != height)
{
NLogHelper.Trace("背景图片与原图大小不一样,进行缩放");
usedBackgroundMat = backgroundMat.Resize(new Size(width, height));
}
else
{
usedBackgroundMat = backgroundMat;
}
}
//是否使用了lightMethod 光照模型去除背景， 拍摄同样一张图片但是不带物体
bool usedLightMethod = false;
if (usedBackgroundMat != null && usedBackgroundMat.Width == width && usedBackgroundMat.Height == height)
{
if (lightMethod == 0)
{
mat = usedBackgroundMat - mat;
usedLightMethod = true;
}
else if (lightMethod == 1)
{
mat.ConvertTo(mat, MatType.CV_32F);
usedBackgroundMat.ConvertTo(usedBackgroundMat, MatType.CV_32F);
mat = (1 - (usedBackgroundMat / mat)) * 255;
mat.ConvertTo(mat, MatType.CV_8U);
usedLightMethod = true;
}
}
//二值化图像
if (usedLightMethod)
{
mat = mat.Threshold(30, 255, ThresholdTypes.Binary);
}
else
{
mat = mat.Threshold(140, 255, ThresholdTypes.Binary);
}
#endregion
#region 联通组件
ConnectedComponents components = mat.ConnectedComponentsEx();
List <ConnectedComponents.Blob> blobList = new List <ConnectedComponents.Blob>();
for (int i = 0; i < components.Blobs.Count; i++)
{
if (i == 0)
{
//背景，
continue;
}
ConnectedComponents.Blob blob = components.Blobs[i];
//实际区域大小
if (blob.Area > MinArea && blob.Width > MinWidth && blob.Height > MinHeight)
{
if (blob.Width > width * 0.9 && blob.Height > 0.9)
{
//发现超大物体，此物体有可能是背景或者其它干扰
NLogHelper.Trace("超大物体忽略");
}
else
{
//一瓶矿泉水 width = 227 height=171 area=15907
blobList.Add(blob);
}
}
}
NLogHelper.Trace(string.Format("原图共有{0}个物体", blobList.Count));
#endregion
#region 判断是否需要截图
//获取上一次的blobs
List <ConnectedComponents.Blob> oldLastBlobs = lastBlobList;
lastBlobList = blobList;
if (blobList.Count == 0)
{
//没有图片，不需要保存
NLogHelper.Trace("没有图片，不需要保存");
return(false);
}
//获取最大的宽度的项
ConnectedComponents.Blob maxItem = blobList.OrderByDescending(r => r.Width).First();
if (oldLastBlobs == null || oldLastBlobs.Count == 0)
{
//之前没有图片 或者 中间范围内没有图片
if (maxItem.Width > width * 0.7 && maxItem.Height > height * 0.4)
{
//最大的物体很大
NLogHelper.Trace("之前没有图像，最大的物体很大，进行保存");
return(true);
}
else
{
//查找位于中间的个体数量
List <ConnectedComponents.Blob> middleBlobs = FindMiddleObjects(width, blobList, 0.81);
if (middleBlobs.Count > 0)
{
//中间有物体
NLogHelper.Trace("之前没有图像，中间有物体，进行保存");
return(true);
}
else
{
//中间没有物体或者物体没有完全到中间
NLogHelper.Trace("之前没有图像，中间没有物体或者物体没有完全到中间，进行保存");
return(false);
}
}
}
else
{
//if (maxItem.Width > width*0.7 && maxItem.Height> height*0.4)
//{
// //最大的物体很大
// return true;
//}
//之前图片有物体
List <ConnectedComponents.Blob> newMiddleBlobs = FindMiddleObjects(width, blobList, 0.81);
//获取中间旧的
List <ConnectedComponents.Blob> oldMiddleBlobs = FindMiddleObjects(width, oldLastBlobs, 0.81);
if (newMiddleBlobs.Count == 0)
{
//中间没有，认为不需要截图
NLogHelper.Trace("之前有图像，新图中间没有，认为不需要截图");
return(false);
}
//新的中间有图
if (oldMiddleBlobs.Count == 0)
{
//之前有图片，但图片不在中间,新的又有了
NLogHelper.Trace("之前有图片，但图片不在中间,新的又有了，需要截图");
return(true);
}
else
{
int minDiff = 1;//任务现在和之前相差超过minDiff个物体需要截图
//现在和以前均有图片
if ((newMiddleBlobs.Count - oldMiddleBlobs.Count) > minDiff)
{
//现在跟置前有两个以上的不同图片
NLogHelper.Trace("现在跟之前有两个以上的不同图片，需要截图");
return(true);
}
else
{
////先按最左点排序，再按中心点排序，再按照面积排序 升序
newMiddleBlobs = newMiddleBlobs.OrderBy(r => r.Left).ThenBy(r => r.Width).ThenBy(r => r.Centroid.Y).ThenBy(r => r.Area).ToList();
oldMiddleBlobs = oldMiddleBlobs.OrderBy(r => r.Left).ThenBy(r => r.Width).ThenBy(r => r.Centroid.Y).ThenBy(r => r.Area).ToList();
var lcsTuple = LCS(newMiddleBlobs, oldMiddleBlobs);
List <ConnectedComponents.Blob> commonBlobs = lcsTuple.Item1;
List <ConnectedComponents.Blob> onlyNewBlobs = lcsTuple.Item2;
List <ConnectedComponents.Blob> onlyOldBlobs = lcsTuple.Item3;
if (commonBlobs.Count == 0)
{
//现在和以前没有公共部分，截图
NLogHelper.Trace("现在和以前没有公共部分，需要截图");
return(true);
}
else if (onlyNewBlobs.Count == 0 && onlyOldBlobs.Count == 0)
{
//全部是公共部分
NLogHelper.Trace("现在和以前全部是公共部分，不需要截图");
return(false);
}
else if (onlyOldBlobs.Count == 0)
{
//新的部分多了，除此之外都是公共的
NLogHelper.Trace("新的部分多了，除此之外都是公共的，需要截图");
return(true);
}
else if (onlyNewBlobs.Count == 0)
{
//旧的部分多了，除此之外全是公共的
NLogHelper.Trace("旧的部分多了，除此之外全是公共的，不需要截图");
return(false);
}
else
{
//旧的部分，新的部分，公共的部分都有
NLogHelper.Trace("旧的部分，新的部分，公共的部分都有，需要截图");
return(true);
}
}
}
}
#endregion
}
public static void TestMain()
{
while (true)
{
string fileName = @"./Images/";
Console.WriteLine("输入图片名称");
fileName += Console.ReadLine();
Console.WriteLine(DateTime.Now.ToString("HH:mm:ss fff"));
bool isNeedToSave = BlobUtils.IsNeedToSave(fileName);
Console.WriteLine("IsNeedToSave = " + isNeedToSave);
Console.WriteLine(DateTime.Now.ToString("HH:mm:ss fff"));
}
//图片名称
string imgFile = @"./Images/check1.jpg";
//光线模式文件
string lightPatternFile = @"./Images/blank.jpg";
//移除背景光线的方法 0 different差 1 div 除 (根据测试除的效果略好于差，均大幅好于不用)
int lightMethod = 0;
Console.WriteLine("lightmethod = " + lightMethod);
// 分割的方法 1 connected component 2 connected components with statistic(统计) 3 find contour(轮廓线)
int segmentMethod = 1;
//转换为单通道灰度图
Mat img = Cv2.ImRead(imgFile, ImreadModes.GrayScale);
Mat cloneImg = img.Clone();
//noise removal 噪音去除
img = img.MedianBlur(3);
//使用光照模型去除背景， 拍摄同样一张图片但是不带物体
Mat light = Cv2.ImRead(lightPatternFile, ImreadModes.GrayScale);
light = light.MedianBlur(3);
if (lightMethod == 0)
{
img = light - img;
}
else if (lightMethod == 1)
{
img.ConvertTo(img, MatType.CV_32F);
light.ConvertTo(light, MatType.CV_32F);
img = (1 - (light / img)) * 255;
img.ConvertTo(img, MatType.CV_8U);
}
//二值化图像
if (lightMethod == 0 || lightMethod == 1)
{
img = img.Threshold(30, 255, ThresholdTypes.Binary);
}
else
{
img = img.Threshold(140, 255, ThresholdTypes.BinaryInv);
}
if (segmentMethod == 1)
{
// 1 connected component 2 connected components with statistic(统计) 3 find contour(轮廓线)
//int nLabels = Cv2.ConnectedComponents(img, label, PixelConnectivity.Connectivity8, MatType.CV_32S);
ConnectedComponents components = img.ConnectedComponentsEx();
int nLabels = components.LabelCount;
Point[][] points = img.FindContoursAsArray(RetrievalModes.External, ContourApproximationModes.ApproxSimple);
int findCount = 0;
for (int i = 0; i < points.Length; i++)
{
if (points[i].Length > 100)
{
findCount++;
}
}
findCount--;
Console.WriteLine(points.Length + "=find" + findCount);
Console.WriteLine("number of objects = " + components.LabelCount);
int count = 0;
List <ConnectedComponents.Blob> list = new List <ConnectedComponents.Blob>();
for (int i = 0; i < components.Blobs.Count; i++)
{
if (i == 0)
{
continue;
}
ConnectedComponents.Blob blob = components.Blobs[i];
//实际区域大小
if (blob.Area > 2200 && blob.Width > 50 && blob.Height > 50)
{
//一瓶矿泉水 width = 227 height=171 area=15907
count++;
list.Add(blob);
}
}
list = list.OrderBy(r => r.Centroid.X).ToList();
Console.WriteLine("实际个数是：" + count);
Console.WriteLine("width=" + img.Width + ",height=" + img.Height);
foreach (var blob in list)
{
Console.WriteLine("area=" + blob.Area + ", (" + blob.Centroid.X + "," + blob.Centroid.Y + ") width=" + blob.Width + ",height=" + blob.Height + "left=" + blob.Left);
}
Mat output = Mat.Zeros(img.Rows, img.Cols, MatType.CV_8UC3);
for (int m = 1; m < nLabels; m++)
{
//Mat mask = label.Equals(m);
//output.SetTo(Scalar.RandomColor(),mask);
Scalar scalar = Scalar.RandomColor();
Vec3b vec3B = scalar.ToVec3b();
for (int i = 0; i < img.Rows; i++)
{
for (int j = 0; j < img.Cols; j++)
{
int num = components.Labels[i, j];
if (num == m)
{
output.Set <Vec3b>(i, j, vec3B);
}
}
}
}
using (Window window = new Window("check"))
{
window.ShowImage(output);
Cv2.WaitKey(0);
}
}
using (Window window = new Window("check"))
{
window.ShowImage(cloneImg);
Cv2.WaitKey(0);
}
//Mat src = Cv2.ImRead("./Images/check1.jpg", ImreadModes.GrayScale);
//// Histogram view
//const int Width = 260, Height = 200;
//Mat render = new Mat(new Size(Width, Height), MatType.CV_8UC3, Scalar.All(255));
//// Calculate histogram
//Mat hist = new Mat();
//int[] hdims = { 256 }; // Histogram size for each dimension
//Rangef[] ranges = { new Rangef(0, 256), }; // min/max
//Cv2.CalcHist(
// new Mat[] { src },
// new int[] { 0 },
// null,
// hist,
// 1,
// hdims,
// ranges);
//// Get the max value of histogram
//double minVal, maxVal;
//Cv2.MinMaxLoc(hist, out minVal, out maxVal);
//Scalar color = Scalar.All(100);
//// Scales and draws histogram
//hist = hist * (maxVal != 0 ? Height / maxVal : 0.0);
//for (int j = 0; j < hdims[0]; ++j)
//{
// int binW = (int)((double)Width / hdims[0]);
// render.Rectangle(
// new Point(j * binW, render.Rows - (int)(hist.Get<float>(j))),
// new Point((j + 1) * binW, render.Rows),
// color);
//}
//using (new Window("Image", WindowMode.AutoSize | WindowMode.FreeRatio, src))
//using (new Window("Histogram", WindowMode.AutoSize | WindowMode.FreeRatio, render))
//{
// Cv2.WaitKey();
//}
//Mat mat = new Mat("./Images/check1.jpg",ImreadModes.GrayScale);
//StringBuilder sb = new StringBuilder();
//for (int i = 0; i < mat.Rows; i++)
//{
// for (int j = 0; j < mat.Cols; j++)
// {
// double[] arr = mat.GetArray(i, j);
// sb.Append("(");
// for (int k = 0; k < arr.Length; k++)
// {
// sb.Append(arr[k] + ",");
// }
// sb.Append(")");
// }
// sb.AppendLine();
//}
//File.WriteAllText("1.txt",sb.ToString(),Encoding.UTF8);
//using (Window window = new Window("Lena", WindowMode.Normal, mat))
//{
// window.ShowImage(mat);
// Cv2.WaitKey(100000);
//}
//VideoCapture videoCapture = new VideoCapture(@"D:\BaiduYunDownload\希赛系统架构师视频\3 JG：第03章 系统开发基础.wmv");
//Console.WriteLine(videoCapture.Fps+""+videoCapture.IsOpened());
//int sleepTime = (int)Math.Round(1000/25.0);
//using (Window window = new Window("capture"))
//{
// using (Mat image = new Mat())
// {
// while (true)
// {
// videoCapture.Read(image);
// if (image.Empty())
// {
// break;
// }
// window.ShowImage(image);
// Cv2.WaitKey(sleepTime);
// }
// }
//}
}
