【C#】身份证识别（一）：身份证号定位

一、图像预处理

AdaptiveThresholdType

public static Image<Gray,byte> BinImg(Image <Bgr ,byte> img)
{
return img.Convert<Gray, byte>().ThresholdAdaptive(new Gray(255),
AdaptiveThresholdType.GaussianC,
ThresholdType.Binary,
55,
new Gray(55)).Erode(9);
}

二、身份证号区域提取

img.Erode(9)

/// <summary>
/// 获取轮廓
/// </summary>
/// <param name="pic"></param>
/// <returns></returns>
private static  VectorOfVectorOfPoint GetContours(Image<Gray, byte> pic)
{
Image<Gray, byte> p1 = new Image<Gray, byte>(pic.Size);
Image<Gray, byte> p2 = pic.Canny(60, 255);
VectorOfVectorOfPoint contours = contours = new VectorOfVectorOfPoint();
CvInvoke.FindContours(p2, contours, p1, RetrType.Ccomp, ChainApproxMethod.ChainApproxSimple);
return contours;
}

//筛选矩形
public static RotatedRect RotatedRect( Image<Bgr, byte> img)
{
Image<Bgr, byte> a = new Image<Bgr, byte>(img.Size);
VectorOfVectorOfPoint con = GetContours(BinImg(img));
Point[][] con1 = con.ToArrayOfArray();
PointF[][] con2 = Array.ConvertAll<Point[], PointF[]>(con1, new Converter<Point[], PointF[]>(PointToPointF));
for (int i = 0; i < con.Size; i++)
{
RotatedRect rrec = CvInvoke.MinAreaRect(con2[i]);
float w = rrec.Size.Width;
float h = rrec.Size.Height;
if (w / h > 6  &&  w / h < 10 && h > 20)
{

PointF[] pointfs = rrec.GetVertices();
for (int j = 0; j < pointfs.Length; j++)
CvInvoke.Line(a, new Point((int)pointfs[j].X, (int)pointfs[j].Y), new Point((int)pointfs[(j + 1) % 4].X, (int)pointfs[(j + 1) % 4].Y), new MCvScalar(0, 0, 255, 255), 4);
return rrec;
}

}
return new RotatedRect();
}

//绘制矩形
public static void DrawRotatedRect(RotatedRect rrec, Image<Bgr,byte> img)
{
PointF[] pointfs = rrec.GetVertices();
for (int j = 0; j < pointfs.Length; j++)
CvInvoke.Line(img, new Point((int)pointfs[j].X, (int)pointfs[j].Y), new Point((int)pointfs[(j + 1) % 4].X, (int)pointfs[(j + 1) % 4].Y), new MCvScalar(0, 0, 255, 255), 4);
}

三、定位结果