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);
// }
// }
//}
}