Question

我正试图从2张图片中找到相机外显子。我有CameraCalibration的内在函数，场景已知尺寸（用3DSMAX创建）。

棋盘为1000 * 1000，每个方格为125 * 125。相机位于（0,0,3000）直视着棋盘，棋盘以原点为中心。在第二张图像中，相机被平移（-1500,0，-402）并在Y轴上旋转30°以再次指向棋盘的中心： camera setup

GoodFeaturesToTrack正确识别81个角： chessboards

我创建棋盘角的3d点，cvFindExtrinsicCameraParams2来计算内在函数和cvRodrigues2来获得旋转矩阵。这是代码

Imports Emgu.CV
Imports Emgu.CV.Structure
Imports Emgu.CV.CvInvoke
Imports Emgu.CV.CvEnum
Imports Emgu.CV.UI
Imports System.Drawing
Imports System.IO
Imports System.Diagnostics
Imports System.Math
Module main_

    Sub Main()

        Const MAXFEATURES As Integer = 100
        Dim featuresA(0)() As PointF
        Dim featuresB(0)() As PointF
        Dim features As Integer = 0
        Dim imgA As Emgu.CV.Image(Of Emgu.CV.Structure.Bgr, Byte)
        Dim imgB As Emgu.CV.Image(Of Emgu.CV.Structure.Bgr, Byte)
        Dim grayA As Emgu.CV.Image(Of Emgu.CV.Structure.Gray, Byte)
        Dim grayB As Emgu.CV.Image(Of Emgu.CV.Structure.Gray, Byte)
        Dim pyrBufferA As Emgu.CV.Image(Of Emgu.CV.Structure.Gray, Byte)
        Dim pyrBufferB As Emgu.CV.Image(Of Emgu.CV.Structure.Gray, Byte)
        Dim pointsA As Matrix(Of Single)
        Dim pointsB As Matrix(Of Single)
        Dim flags As Emgu.CV.CvEnum.LKFLOW_TYPE = Emgu.CV.CvEnum.LKFLOW_TYPE.DEFAULT
        Dim imagesize As Size
        Dim termcrit As New Emgu.CV.Structure.MCvTermCriteria(20, 0.03D)
        Dim status As Byte() = Nothing
        Dim errors As Single() = Nothing
        Dim red As Bgr = New Bgr(Color.Red)

        ' Load chessboards
        imgA = New Image(Of [Structure].Bgr, Byte)("chessboard centre.jpg")
        imgB = New Image(Of [Structure].Bgr, Byte)("chessboard left.jpg")
        grayA = imgA.Convert(Of Gray, Byte)()
        grayB = imgB.Convert(Of Gray, Byte)()

        ' setup for feature detection
        imagesize = cvGetSize(grayA)
        pyrBufferA = New Emgu.CV.Image(Of Emgu.CV.Structure.Gray, Byte)(imagesize.Width + 8, imagesize.Height / 3)
        pyrBufferB = New Emgu.CV.Image(Of Emgu.CV.Structure.Gray, Byte)(imagesize.Width + 8, imagesize.Height / 3)
        features = MAXFEATURES

        ' Find features
        featuresA = grayA.GoodFeaturesToTrack(features, 0.01, 25, 3)
        grayA.FindCornerSubPix(featuresA, New System.Drawing.Size(10, 10), New System.Drawing.Size(-1, -1), termcrit)
        features = featuresA(0).Length

        ' Compute optical flow. Not necessary here but good to remember
        Emgu.CV.OpticalFlow.PyrLK(grayA, grayB, pyrBufferA, pyrBufferB, featuresA(0), New Size(25, 25), 3, termcrit, flags, featuresB(0), status, errors)
        Debug.Assert(featuresA(0).GetUpperBound(0) = featuresB(0).GetUpperBound(0))

        ' Copy features to an easier-to-use matrix and get min/max to create 3d points
        Dim minx As Double = Double.MaxValue
        Dim miny As Double = Double.MaxValue
        Dim maxx As Double = Double.MinValue
        Dim maxy As Double = Double.MinValue
        pointsA = New Matrix(Of Single)(features, 2)
        pointsB = New Matrix(Of Single)(features, 2)
        For i As Integer = 0 To features - 1
            pointsA(i, 0) = featuresA(0)(i).X
            pointsA(i, 1) = featuresA(0)(i).Y
            pointsB(i, 0) = featuresB(0)(i).X
            pointsB(i, 1) = featuresB(0)(i).Y
            If pointsA(i, 0) < minx Then
                minx = pointsA(i, 0)
            End If
            If pointsA(i, 1) < miny Then
                miny = pointsA(i, 1)
            End If
            If pointsA(i, 0) > maxx Then
                maxx = pointsA(i, 0)
            End If
            If pointsA(i, 1) > maxy Then
                maxy = pointsA(i, 1)
            End If
        Next

        ' Create 3D object points that correspond to chessboard corners
        ' (The chessboard is 1000*1000, squares are 125*125)
        Dim corners As Integer = Sqrt(features)
        Dim obj As New Matrix(Of Double)(features, 3)
        Dim squaresize2dx As Double = (maxx - minx) / 8 ' pixel width of a chessboard square
        Dim squaresize2dy As Double = (maxy - miny) / 8 ' pixel height of a chessboard square
        For i As Integer = 0 To features - 1
            obj(i, 0) = Math.Round((pointsA(i, 0) - minx) / squaresize2dx) * 125 ' X=0, 125, 250, 375 ... 1000
            obj(i, 1) = Math.Round((pointsA(i, 1) - miny) / squaresize2dy) * 125 ' idem in Y
            obj(i, 2) = 0
            ' Debug.WriteLine(pointsA(i, 0) & " " & pointsA(i, 1) & " " & obj(i, 0) & " " & obj(i, 1) & " " & obj(i, 2)) ' Just to verify
        Next

        ' These were calculated with CalibrateCamera using the same images
        Dim intrinsics As New Matrix(Of Double)({{889.1647, 0.0, 318.3721},
                                                 {0.0, 888.5134, 238.4254},
                                                 {0.0, 0.0, 1.0}})
        ' Find extrinsics
        Dim distortion As New Matrix(Of Double)({-0.036302, 2.008797, -29.674306, -29.674306})
        Dim translation As New Matrix(Of Double)(3, 1)
        Dim rotation As New Matrix(Of Double)(3, 1)
        cvFindExtrinsicCameraParams2(obj, pointsA, intrinsics, distortion, rotation, translation, False)

        ' Convert rotation vector to rotation matrix
        Dim rotmat As New Matrix(Of Double)(3, 3)
        Dim jacobian As New Matrix(Of Double)(9, 3)
        cvRodrigues2(rotation, rotmat, jacobian)

        ' From http://en.wikipedia.org/wiki/Rotation_representation paragraph "Conversion formulae between representations"
        Dim yr As Double = Asin(-rotmat(2, 0))
        Dim xr As Double = Asin(rotmat(2, 1) / Cos(yr))
        Dim zr As Double = Asin(rotmat(1, 0) / Cos(yr))

    End Sub
End Module

结果似乎不正确，我期待翻译/轮换，但我得到了这个：

translation
208.394425348956 
-169.447506344527 
-654.273807995629 
rotation
-0.0224937226554797 
-2.13660350939653 
-1.10542281290682 
rotmat
-0.741100224945266 0.322885083546921 -0.588655824237707 
-0.293966101915684 0.632206237134128 0.716867633983572 
0.603617749499279 0.704315622822328 -0.373610915174894 
xr=1.08307908108382 yr=-0.648031006135158 zr=-0.377625254910525
xr=62.0558602250101° yr=-37.1294416451609° zr=-21.636333343925°

有人知道我做错了什么吗？谢谢！

Answer 1

找到它。失真系数

k1, k2, p1, p2, k3

而不是

k1, k2, k3, k4, k5

正如我编码的那样。当我将它们设置为

时

-0.05716, 2.519006, -0.001674, -0.001021, -33.372798

答案是（大致）正确

Answer 2

请查看针孔相机公式：

[u, v, 1]' ~ A * R|T * [x, y, z, 1]'，其中外在矩阵 R | T的尺寸为3x4。请注意，将其与“理想”或无穷远处的消失点（如[1,0,0,0]相乘）将为您提供相应的R | T列。这只是意味着要查找R | T的所有列，您必须具有至少三个消失点，这些消失点可以从棋盘图案中轻松找到。

现在你只有1，所以如果你的结果看起来合理，你就会很幸运。再试一次，选择校准装置的最小10-20个不同斜面，距离和倾斜度。

相机外部计算错误

2 个答案: