标签：lag cal path while ... div pat 矩形区域 img
用过Polar SI9000的都知道,阻抗模型图片可以进行用户鼠标交互，那么它的是如何实现的呢,下面就讲一下如何实现此功能的方法
鼠标点击阻抗模型图片某个像素点, 它可以实现找到离它最近的阻抗参数的文字并用红色框选出来, 并且可以识别文字是哪一个阻抗参数.
解决方法一:
1.将每一种阻抗模型图片中的所有参数在图片中的位置区域信息与参数值记录到数据库中
2.鼠标点击阻抗模型的坐标位置后,再进与数据库中的参数坐标位置匹配
这样就可以实现与Polar阻抗软件相同的效果,但是Polar SI9000有阻抗计算模型93种,要实现的话工作量可不小,所以这种方法排除了。
解决方法二（采用此方法实现）:
1.找最近邻----点击像素点位置，找出离它最近的一个黑色像素点位置
2.聚类----通过一个像素点查找周边相邻的黑色像素点进行聚类
3.识别---截取指定区域文字图片与图像Ocr识别
1.【圈】的遍历方式按下图方式进行
以鼠标点击像素的位置,向像素四周搜索黑色像素点，依次遍历第1圈，第2圈，第3圈.....直到找到黑色像素则终止
2.【段】的遍历方式按下图方式进行
3.C#代码实现,鼠标点击像素点位置,找出离它最近的黑色像素点
/// <summary> /// 通过鼠标点击像素点位置,找出离它最近的一个黑色像素点位置 /// </summary> /// <param name="MousePoint"></param> /// <param name="ArrayBitmap"></param> /// <returns></returns> private Point ArrayLoop(Point MousePoint, Bitmap ArrayBitmap) { Point ClacBlackPoint = new Point(); Color pixel = ArrayBitmap.GetPixel(MousePoint.X, MousePoint.Y); if (pixel.R <= 25 && pixel.G <= 40 && pixel.B <= 60) { return MousePoint; } Point ArraySum = new Point(ArrayBitmap.Size); int[,] ArrayLoopType = new int[,] { { 1, 1 }, { -1, 1 }, { -1, -1 }, { 1, -1 } }; //偏移位移矩阵数组 int LoopLength = getArrayLoopMax(MousePoint, ArraySum);//遍历的圈数计算 int ArayLength = ArrayLoopType.GetLength(0);//计算值为4 for (int k = 1; k <= LoopLength; k++) //按圈遍历 { Point LoopPoint = MousePoint; LoopPoint.Offset(0, -k);//更新圈的起点像素坐标 for (int i = 0; i < ArayLength; i++)//每圈分为4段遍历 { for (int j = 0; j < k; j++) { LoopPoint.X += ArrayLoopType[i, 0]; LoopPoint.Y += ArrayLoopType[i, 1]; if (LoopPoint.X > 0 && LoopPoint.Y > 0 && LoopPoint.X <= ArraySum.X && LoopPoint.Y <= ArraySum.Y)// { pixel = ArrayBitmap.GetPixel(LoopPoint.X - 1, LoopPoint.Y - 1); if (pixel.R <= 25 && pixel.G <= 40 && pixel.B <= 60) { ClacBlackPoint = LoopPoint; goto L1; } } } } } L1: return ClacBlackPoint; } /// <summary> /// 获取遍历圈数最大值 /// </summary> /// <param name="MousePoint"></param> /// <param name="ArraySum"></param> /// <param name="LoopType"></param> /// <returns></returns> private int getArrayLoopMax(Point MousePoint, Point ArraySum, int LoopType = 1) { int LoopLength = 0; if (LoopType == 1) { int TopLeft = (MousePoint.X - 1 + MousePoint.Y - 1); int TopRight = (ArraySum.X - MousePoint.X + MousePoint.Y - 1); int TopMax = Math.Max(TopLeft, TopRight); int BootomLeft = (MousePoint.X - 1 + ArraySum.Y - MousePoint.Y); int BottomRight = (ArraySum.X - MousePoint.X + ArraySum.Y - MousePoint.Y); int BottomMax = Math.Max(BootomLeft, BottomRight); LoopLength = Math.Max(TopMax, BottomMax); } else { int MaxX = Math.Max(MousePoint.X - 1, ArraySum.X - MousePoint.X); int MaxY = Math.Max(MousePoint.Y - 1, ArraySum.Y - MousePoint.Y); LoopLength = Math.Max(MaxX, MaxY); } return LoopLength; }
1.聚类实现方法按下图进行
2.C#代码实现,通过一个像素点查找周边相邻的黑色像素点进行聚类为一个矩形区域
/// <summary> /// 通过一个像素点查找周边相邻的黑色像素点进行聚类为一个矩形区域 /// </summary> /// <param name="CalcPoint"></param> /// <param name="ArrayBitmap"></param> /// <returns></returns> private RangePoint RangeArea(Point CalcPoint, Bitmap ArrayBitmap) { int LoopLength = 10; //搜索周边相邻像素个数 int[,] ArrayLoopType = new int[,] { { 1, 1 }, { -1, 1 }, { -1, -1 }, { 1, -1 } }; Point ArraySum = new Point(ArrayBitmap.Size); RangePoint RangePointArea = new RangePoint(CalcPoint, CalcPoint); HashSet<Point> PointSet = new HashSet<Point>(); HashSet<Point> LoopPointSet = new HashSet<Point>(); Queue<Point> PointQueue = new Queue<Point>(); PointQueue.Enqueue(CalcPoint); while (PointQueue.Count > 0) { var TopPoint = PointQueue.Dequeue(); //TopPoint 以这个点周边范围进行搜索像素 for (int k = 1; k <= LoopLength; k++) { Point LoopPoint = TopPoint; LoopPoint.Offset(0, -k); for (int i = 0; i < 4; i++) { for (int j = 0; j < k; j++) { LoopPoint.X += ArrayLoopType[i, 0]; LoopPoint.Y += ArrayLoopType[i, 1]; if (!LoopPointSet.Contains(LoopPoint)) { LoopPointSet.Add(LoopPoint); if (LoopPoint.X > 0 && LoopPoint.Y > 0 && LoopPoint.X <= ArraySum.X && LoopPoint.Y <= ArraySum.Y) { Color pixel = ArrayBitmap.GetPixel(LoopPoint.X - 1, LoopPoint.Y - 1); if (pixel.R <= 25 && pixel.G <= 40 && pixel.B <= 60) { if (!PointSet.Contains(LoopPoint)) { //找到相似的黑色像素加入队列 PointQueue.Enqueue(LoopPoint); //将周边相似相素 进行扩大合并区域 RangePointArea = RangePointArea.Union(LoopPoint); //加入字典 PointSet.Add(TopPoint); } } } } } } } } return RangePointArea; }
RangePoint Mod类
/// <summary> /// 范围区域点 /// </summary> public class RangePoint { public RangePoint(Point MinPoint, Point MaxPoint) { RangeMinPoint = MinPoint; RangeMaxPoint = MaxPoint; } /// <summary> /// 最小点 /// </summary> public Point RangeMinPoint { get; set; } /// <summary> /// 最大点 /// </summary> public Point RangeMaxPoint { get; set; } /// <summary> /// 范围宽 /// </summary> public int Width { get { return this.RangeMaxPoint.X - this.RangeMinPoint.X + 1; } } /// <summary> /// 范围高 /// </summary> public int Height { get { return this.RangeMaxPoint.Y - this.RangeMinPoint.Y + 1; } } /// <summary> /// 合拼区域 /// </summary> /// <param name="point"></param> /// <returns></returns> public RangePoint Union(Point point) { Point minP = RangeMinPoint; Point MaxP = RangeMaxPoint; if (point.X < minP.X) minP.X = point.X; if (point.Y < minP.Y) minP.Y = point.Y; if (point.X > MaxP.X) MaxP.X = point.X; if (point.Y > MaxP.Y) MaxP.Y = point.Y; RangeMinPoint = minP; RangeMaxPoint = MaxP; return this; } /// <summary> /// 检测点是否在区域内 /// </summary> /// <param name="point"></param> /// <returns></returns> public bool IsRangeInner(Point point) { return (RangeMinPoint.X <= point.X && RangeMinPoint.Y <= point.Y && RangeMaxPoint.X >= point.X && RangeMaxPoint.Y >= point.Y); } /// <summary> /// 获得区域中心点 /// </summary> /// <param name="point"></param> /// <returns></returns> public Point getRangeCenterPoint() { return new Point((int)((this.RangeMinPoint.X + this.RangeMaxPoint.X) * 0.5), (int)((this.RangeMinPoint.Y + this.RangeMaxPoint.Y) * 0.5)); } /// <summary> /// 区域偏移 扩大缩小 /// </summary> /// <param name="point"></param> /// <returns></returns> public RangePoint RangeOffset(int OffsetVal) { this.RangeMinPoint = new Point(this.RangeMinPoint.X - OffsetVal, this.RangeMinPoint.Y - OffsetVal); this.RangeMaxPoint = new Point(this.RangeMaxPoint.X + OffsetVal, this.RangeMaxPoint.Y + OffsetVal); return this; } }
1.通过聚类识别出文本区域坐标进行图像截取,并将截取出来的图像进行图像识别转文字.
2.C#代码实现,截取指定区域文字图片与图像识别
此图像识别用Baidu它们家的Ocr API自己注册一下就能用了,另外一个Tesseract也不错的,可以自行对Ocr字体模型训练,最主要是可以实现本地不联网的情况下也可以Ocr识别
/// <summary> /// 获取图片指定部分 /// </summary> /// <param name="pPath">图片路径</param> /// <param name="pOrigStartPointX">原始图片开始截取处的坐标X值</param> /// <param name="pOrigStartPointY">原始图片开始截取处的坐标Y值</param> /// <param name="pPartWidth">目标图片的宽度</param> /// <param name="pPartHeight">目标图片的高度</param> static System.Drawing.Bitmap GetPart(Image originalImg, int pOrigStartPointX, int pOrigStartPointY, int pPartWidth, int pPartHeight) { System.Drawing.Bitmap partImg = new System.Drawing.Bitmap(pPartWidth, pPartHeight); System.Drawing.Graphics graphics = System.Drawing.Graphics.FromImage(partImg); System.Drawing.Rectangle destRect = new System.Drawing.Rectangle(new System.Drawing.Point(0, 0), new System.Drawing.Size(pPartWidth, pPartHeight));//目标位置 System.Drawing.Rectangle origRect = new System.Drawing.Rectangle(new System.Drawing.Point(pOrigStartPointX, pOrigStartPointY), new System.Drawing.Size(pPartWidth, pPartHeight));//原图位置（默认从原图中截取的图片大小等于目标图片的大小） graphics.DrawImage(originalImg, destRect, origRect, System.Drawing.GraphicsUnit.Pixel); return partImg; } /// <summary> /// Baidu Ocr识别 /// </summary> /// <param name="img"></param> /// <returns></returns> private string OcrGeneralBasic(Image img) { StringBuilder OcrTxt = new StringBuilder(); string baiduAPI_ID = "baiduAPI_ID"; string baiduAPI_key = "baiduAPI_key"; Baidu.Aip.Ocr.Ocr OcrClient = new Baidu.Aip.Ocr.Ocr(baiduAPI_ID, baiduAPI_key); var byteImg = Img2Byte(img); var OcrResult = OcrClient.GeneralBasic(byteImg); var words_result = OcrResult["words_result"]; if (words_result == null) return ""; foreach (var item in words_result) { OcrTxt.AppendLine(item["words"].ToString()); } return OcrTxt.ToString(); } /// <summary> /// 将Image转换成为byte[] /// </summary> /// <param name="img"></param> /// <returns></returns> public byte[] Img2Byte(System.Drawing.Image img) { MemoryStream mstream = new MemoryStream(); img.Save(mstream, System.Drawing.Imaging.ImageFormat.Bmp); byte[] byData = new Byte[mstream.Length]; mstream.Position = 0; mstream.Read(byData, 0, byData.Length); mstream.Close(); return byData; }
1.PictureBox控件显示图片尺寸与实际图片尺寸不一致,会造成PictureBox控件上的鼠标点击像素点位置,并不是实际图片像素点位置,这里给出2种解决方法
方法一.在读入Bitmap图片前提前转换(缩放)图片尺寸和PictureBox控件尺寸一致
/// <summary> /// 图片缩放 /// </summary> /// <param name="bmp"></param> /// <param name="newW"></param> /// <param name="newH"></param> /// <returns></returns> public static Bitmap KiResizeImage(Bitmap bmp, int newW, int newH) { try { Bitmap newBitmap = new Bitmap(newW, newH); Graphics g = Graphics.FromImage(newBitmap); g.InterpolationMode = InterpolationMode.HighQualityBicubic; g.DrawImage(bmp, new Rectangle(0, 0, newW, newH), new Rectangle(0, 0, bmp.Width, bmp.Height), GraphicsUnit.Pixel); g.Dispose(); return newBitmap; } catch { return null; } }
方法二.计算PictureBox图像大小与真实图像大小的比值关系,通过鼠标点击像素位置与屏幕位置的关系,计算得鼠标真实点击的图像像素位置
/// <summary> /// 鼠标移动 查看鼠标位置与实际图片和显示图片关系 /// </summary> /// <param name="sender"></param> /// <param name="e"></param> private void picBox_MouseMove(object sender, MouseEventArgs e) { PictureBox picBox = (PictureBox)sender; int originalWidth = picBox.Image.Width; int originalHeight = picBox.Image.Height; PropertyInfo rectangleProperty = picBox.GetType().GetProperty("ImageRectangle", BindingFlags.Instance | BindingFlags.NonPublic); Rectangle rectangle = (Rectangle)rectangleProperty.GetValue(picBox, null); int currentWidth = rectangle.Width; int currentHeight = rectangle.Height; double rate = (double)currentHeight / (double)originalHeight; int black_left_width = (currentWidth == picBox.Width) ? 0 : (picBox.Width - currentWidth) / 2; int black_top_height = (currentHeight == picBox.Height) ? 0 : (picBox.Height - currentHeight) / 2; int zoom_x = e.X - black_left_width; int zoom_y = e.Y - black_top_height; double original_x = (double)zoom_x / rate; double original_y = (double)zoom_y / rate; StringBuilder sb = new StringBuilder(); sb.AppendFormat("原始尺寸{0}/{1}(宽/高)\r\n", originalWidth, originalHeight); sb.AppendFormat("缩放状态图片尺寸{0}/{1}(宽/高)\r\n", currentWidth, currentHeight); sb.AppendFormat("缩放比率{0}\r\n", rate); sb.AppendFormat("左留白宽度{0}\r\n", black_left_width); sb.AppendFormat("上留白高度{0}\r\n", black_top_height); sb.AppendFormat("当前鼠标坐标{0}/{1}(X/Y)\r\n", e.X, e.Y); sb.AppendFormat("缩放图中鼠标坐标{0}/{1}(X/Y)\r\n", zoom_x, zoom_y); sb.AppendFormat("原始图中鼠标坐标{0}/{1}(X/Y)\r\n", original_x, original_y); string InfoTxt = sb.ToString(); }
标签：lag cal path while ... div pat 矩形区域 img
原文地址：https://www.cnblogs.com/pcbren/p/10346445.html
