如果您从一个合适的仿射矩阵开始,该仿射矩阵可以被正确分解(如果不是明确的话)成一系列的缩放、旋转、平移,这个方法将执行分解成一个表示平移、旋转(欧拉角)和缩放组件的向量元组:
extension CATransform3D {
func decomposeTRS() -> (float3, float3, float3) {
let m0 = float3(Float(self.m11), Float(self.m12), Float(self.m13))
let m1 = float3(Float(self.m21), Float(self.m22), Float(self.m23))
let m2 = float3(Float(self.m31), Float(self.m32), Float(self.m33))
let m3 = float3(Float(self.m41), Float(self.m42), Float(self.m43))
let t = m3
let sx = length(m0)
let sy = length(m1)
let sz = length(m2)
let s = float3(sx, sy, sz)
let rx = m0 / sx
let ry = m1 / sy
let rz = m2 / sz
let pitch = atan2(ry.z, rz.z)
let yaw = atan2(-rx.z, hypot(ry.z, rz.z))
let roll = atan2(rx.y, rx.x)
let r = float3(pitch, yaw, roll)
return (t, r, s)
}
}
要显示此例程正确提取了各种组件,请构造转换并确保它按预期分解:
let rotationX = CATransform3DMakeRotation(.pi / 2, 1, 0, 0)
let rotationY = CATransform3DMakeRotation(.pi / 3, 0, 1, 0)
let rotationZ = CATransform3DMakeRotation(.pi / 4, 0, 0, 1)
let translation = CATransform3DMakeTranslation(1, 2, 3)
let scale = CATransform3DMakeScale(0.1, 0.2, 0.3)
let transform = CATransform3DConcat(CATransform3DConcat(CATransform3DConcat(CATransform3DConcat(scale, rotationX), rotationY), rotationZ), translation)
let (T, R, S) = transform.decomposeTRS()
print("\(T), \(R), \(S))")
这会产生:
float3(1.0, 2.0, 3.0), float3(1.5708, 1.0472, 0.785398), float3(0.1, 0.2, 0.3))
注意,这个分解假定了一个XYZ的Euler乘法阶,它只是
several possible orderings
.
警告
:对于某些值,这种方法在数值上是不稳定的。我还没有进行足够广泛的测试来知道这些陷阱在哪里,所以
警告清空器
.