Perspective transform from quadrilateral to quadrilateral in Swift using SIMD for matrix operations
19 min readJan 6, 2021
The post from yesterday showed how to calculate a perspective transform (a homography) from one quadrilateral to another quadrilateral in Swift. Custom types defined a few matrices and a minimal set of matrix operations.
Digging into the noodly guts of the math and writing code from scratch can help reinforce one’s understanding. However, as a professor of a friend of mine once said, we must make certain concessions to the brevity of human life. I for one don’t feel the urge to write a matrix library.
For this post I’ve rewritten yesterday’s perspective transform code to use Apple’s SIMD framework. The code uses simd_float2 for 2D vectors (or points), simd_float3 for homogeneous coordinates (x,y,1), float3x3 for affine and perspective transforms in 2D, simd_length() and simd_dot() to calculate the distance between points and angle between vectors, and so on.
Here are other improvements relative to yesterday’s code:
- Colinearity of a set of three points is now calculated using the determinant of a 3x3 matrix. Points are colinear if the determinant is zero. (I’m actually checking near-colinearity: whether the absolute value of the determinant is less than some small number.)
- Affine transforms are calculated following Eberly’s conventions. Eberly calculates the affine transform from the canonical quadrilateral to the two quadrilaterals p and q. Yesterday’s code calculated the affine transforms the other way around.
- Correct ordering of the points in the quadrilaterals is now enforced by an “ordering function” of two algorithms: a convex hull algorithm to provide the points in counterclockwise order, and assignment of the 0th index to the quadrilateral point closest to the origin (0,0).
- The function perspectiveTransform(from:to:) returns a tuple optional with the perspective transform and the quadrilaterals p and q with their points correctly ordered.
- Test code to swap p and q and calculate that perspective transform.
- Test code to randomly reorder points in p and q and thereby exercise the “ordering function.” In whatever way points in p and q are ordered, the perspectiveTransform(from:to:) function should yield the same result.
- Test code to generate random quadrilaterals, including non-convex quadrilaterals for which a perspective mapping will fail. The random x and y values are selected over a range of negative and positive values.
Here’s a rehash of yesterday’s summary of the calculation method.
Dave Eberly’s paper “Perspective Mappings” is a free standalone PDF:
https://geometrictools.com/Documentation/PerspectiveMappings.pdf
The diagram below from Eberly’s paper shows the series of transforms to map quadrilateral p to quadrilateral q via canonical quadrilaterals.
Though I strongly recommend reading Eberly’s paper, here’s my understanding of the chain of calculations to find the perspective transform:
- Start with two quadrilaterals, p and q.
- Ensure the points of each quadrilateral are ordered counterclockwise, with point 00 at lower left.
- Find the affine transform from the canonical quadrilateral to each of p and q. The three points (0,0), (1,0), and (0,1) of the canonical quadrilateral are used to calculate affine transforms.
- Calculate (a,b) for the canonical quadrilateral point corresponding to p11 and (c,d) for the point corresponding to q11.
- Calculate the linear fractional transform from canonical p to canonical q using a, b, c, d. (Eberly calls it the fractional linear transform. Potayto, Potahto.)
- Calculate the perspective transform from p to q as the product of the transforms from p to canonical p, canonical p to canonical q, and canonical q to q.
Copy and paste the following code into an XCode 12 playground and then run it. Scroll down, down, down past the code to see sample console output.
import CoreGraphics
import Foundation
import simd
// Run the whole playground! The perspectiveTransform() function at top depends on types defined below.
// At the bottom is a test function that writes to the console.
/// Find the perspective transform (the homography) from quadrilateral p to quadrilateral q.
/// Algorithm based on PerspectiveMappings.pdf by Dave Eberly:
/// https://geometrictools.com/Documentation/PerspectiveMappings.pdf
/// See also Geometric Tools for Computer Graphics by Schneider & Eberly:
/// https://www.geometrictools.com/Books/Books.html
/// Code uses SIMD types. An extension to CGPoint provides conversions.
func perspectiveTransform(from: Quadrilateral, to: Quadrilateral) -> (transform: float3x3, pOrdered: Quadrilateral, qOrdered: Quadrilateral)? {
//Use the convex hull to order points counterclockwise, then reorder so that index 0 is closest to origin
let p = from.toOrderedQuadrilateral(convexHull(_:))
let q = to.toOrderedQuadrilateral(convexHull(_:))
//points of canonical quadrilateral used to calculate affine transform
let canon = Triangle(simd_float2(0,0), simd_float2(1,0), simd_float2(0,1))
//affine transform from canonical quadrilateral to p using points 00, 10, 01 but not 11
let ptri = Triangle(p.v00, p.v10, p.v01)
guard let Ap = affineTransform(from: canon, to: ptri) else {
print("Could not get affine transform for quadrilateral p")
return nil
}
//affine transform from canonical quadrilateral to q using points 00, 10, 01 but not 11
let qtri = Triangle(q.v00, q.v10, q.v01)
guard let Aq = affineTransform(from: canon, to: qtri) else {
print("Could not get affine transform for quadrilateral q")
return nil
}
let InvAp = Ap.inverse
if InvAp.determinant.isNaN {
print("Could not get inverse of affine transform for quadrilateral p")
return nil
}
let InvAq = Aq.inverse
if InvAq.determinant.isNaN {
print("Could not get inverse of affine transform for quadrilateral q")
return nil
}
// (a,b) is coordinate of p11 in canonical quadrilateral
// (c,d) is coordinate of q11 in canonical quadrilateral
let ap11 = InvAp * p.v11 // (x,y,1)
let aq11 = InvAq * q.v11
let cp11 = ap11.toVector2() // (x,y) in 2D plane
let cq11 = aq11.toVector2()
let a = cp11.x
let b = cp11.y
let c = cq11.x
let d = cq11.y
let s = a + b - 1 // p is convex if s > 0
let t = c + d - 1 // q is convex if t > 0
let pconvex = s > 0
let qconvex = t > 0
if !pconvex || !qconvex {
print("p is \(pconvex ? "convex" : "NOT convex"), s = \(s)")
print("q is \(qconvex ? "convex" : "NOT convex"), t = \(t)")
return nil
}
//fractional linear transformation F from canonical p to canonical q
// F = | bcs 0 0 |
// | 0 ads 0 |
// | b(cs - at) a(ds - bt) abt |
// where
// s = a + b - 1
// t = c + d - 1
// initialize float3x3 by columns
let F = float3x3(
simd_float3(b * c * s, 0, b * (c * s - a * t)),
simd_float3(0, a * d * s, a * (d * s - b * t)),
simd_float3(0, 0, a * b * t)
)
//"The 3 × 3 homography matrix H = Aq * F * Inv(Ap)"
let H = Aq * F * InvAp
//return transform along with ordered quadrilaterals
return (H, p, q)
}
/// Finds the affine transform (translation, rotation, scale, ...) from one triangle to another.
/// Used in perspectiveTransform( ).
func affineTransform(from: Triangle, to: Triangle) -> float3x3? {
// following example from https://stackoverflow.com/questions/18844000/transfer-coordinates-from-one-triangle-to-another-triangle
// M * A = B
// M = B * Inv(A)
let A = from.toMatrix()
let invA = A.inverse
if invA.determinant.isNaN {
return nil
}
let B = to.toMatrix()
let M = B * invA
return M
}
func degreesToRadians(_ degrees: Float) -> Float {
degrees * Float.pi / 180.0
}
func radiansToDegrees(_ radians: Float) -> Float {
180.0 * radians / Float.pi
}
// Returns angle between two 2D vectors in range 0 to 2 * pi [positive]
// a * b = ||a|| ||b|| cos(theta)
// theta = arc cos (a * b / ||a|| ||b||)
func angleBetweenVectors(_ v1: simd_float2, _ v2: simd_float2) -> Float {
acos(simd_dot(v1, v2) / (simd_length(v1) * simd_length(v1)))
}
// Conversions to/from CGPoint for use with CGImage and SIMD matrix operations.
extension CGPoint {
/// A 1x2 vector of the point: (x, y)
var vector2: simd_float2 {
simd_float2(Float(self.x), Float(self.y))
}
/// A 1x3 vector of the point (x, y, 1)
var vector3: simd_float3 {
simd_float3(Float(self.x), Float(self.y), Float(1))
}
/// Returns a point (v.x, v.y)
static func fromVector2(_ v: simd_float2) -> CGPoint {
CGPoint(x: CGFloat(v.x), y: CGFloat(v.y))
}
/// Returns a point (x, y) = (v.x / v.z, v.y / v.z)
/// Returns {x +∞, y +∞} if v.z == 0
static func fromVector3(_ v: simd_float3) -> CGPoint {
CGPoint(x: CGFloat(v.x / v.z), y: CGFloat(v.y / v.z))
}
}
// Conversions between 2D points and 1x3 homogeneous coordinates.
extension simd_float2 {
/// Returns (inf, inf) if v.z == 0
static func fromVector3(_ v: simd_float3) -> simd_float2 {
simd_float2(v.x / v.z, v.y / v.z)
}
/// Returns (x, y, 1)
func toVector3() -> simd_float3 {
simd_float3(self.x, self.y, 1)
}
}
// Conversions between 1x3 homogeneous coordinates and 2D points.
extension simd_float3 {
/// Returns (x,y,1)
static func fromVector2(_ v: simd_float2) -> simd_float3 {
simd_float3(v.x, v.y, 1)
}
/// Returns (inf,inf) if v.z == 0
func toVector2() -> simd_float2 {
simd_float2(self.x / self.z, self.y / self.z)
}
}
extension NumberFormatter {
func string(_ m: simd_float2, _ digits: Int) -> String {
"[\(string(m.x, digits)), \(string(m.y, digits))]"
}
func string(_ m: simd_float3, _ digits: Int) -> String {
"[\(string(m.x, digits)), \(string(m.y, digits)), \(string(m.z, digits))]"
}
func string(_ m: float3x3, _ digits: Int) -> String {
//SIMD: column, row (like x,y)
"\(string(m[0][0], digits)) \(string(m[1][0], digits)) \(string(m[2][0], digits))"
+ "\n\(string(m[0][1], digits)) \(string(m[1][1], digits)) \(string(m[2][1], digits))"
+ "\n\(string(m[0][2], digits)) \(string(m[1][2], digits)) \(string(m[2][2], digits))"
}
func string(_ m: float4x4, _ digits: Int) -> String {
"\(string(m[0][0], digits)) \(string(m[1][0], digits)) \(string(m[2][0], digits)) \(string(m[3][0], digits))"
+ "\n\(string(m[0][1], digits)) \(string(m[1][1], digits)) \(string(m[2][1], digits)) \(string(m[3][1], digits))"
+ "\n\(string(m[0][2], digits)) \(string(m[1][2], digits)) \(string(m[2][2], digits)) \(string(m[3][2], digits))"
+ "\n\(string(m[0][3], digits)) \(string(m[1][3], digits)) \(string(m[2][3], digits)) \(string(m[3][3], digits))"
}
func string(_ t: Triangle, _ digits: Int) -> String {
"\(string(t.point1, digits)), \(string(t.point2, digits)), \(string(t.point3, digits))"
}
func string(_ q: Quadrilateral, _ digits: Int) -> String {
"\(string(q.point1, digits)), \(string(q.point2, digits)), \(string(q.point3, digits)), \(string(q.point4, digits))"
}
func string(_ value: Double, _ digits: Int, failText: String = "[?]") -> String {
minimumFractionDigits = max(0, digits)
maximumFractionDigits = minimumFractionDigits
guard let s = string(from: NSNumber(value: value)) else {
return failText
}
return s
}
func string(_ value: Float, _ digits: Int, failText: String = "[?]") -> String {
minimumFractionDigits = max(0, digits)
maximumFractionDigits = minimumFractionDigits
guard let s = string(from: NSNumber(value: value)) else {
return failText
}
return s
}
}
/// Takes an array of points and returns an array of points representing the "rubberband border" of those points,
/// ordered from the leftmost bottom point and then around counterclockwise.
/// https://en.wikipedia.org/wiki/Convex_hull
/// https://en.wikipedia.org/wiki/Convex_hull_algorithms
func convexHull(_ points: [simd_float2]) -> [simd_float2] {
if points.isEmpty {
return[]
}
// populate list in current order
var pts = points
var hull: [simd_float2] = []
//move (leftmost) point with lowest y from from pts to hull
var n = 0
for i in 1 ..< pts.count {
//here use Float == operator
if pts[i].y < pts[n].y || (pts[i].y == pts[n].y && pts[i].x < pts[n].x) {
n = i
}
}
hull.append(pts.remove(at: n))
//of remaining points, find point for which last hull point to sample point is smallest positive angle
let angle = {
(p1: simd_float2, p2: simd_float2) -> Float in
let a = atan2(p2.y - p1.y, p2.x - p1.x)
return a < 0 ? a + 2 * Float.pi : a
}
while pts.count > 0 {
var n = 0
for i in 0 ..< pts.count {
if angle(hull.last!, pts[i]) < angle(hull.last!, pts[n]) {
n = i
}
}
hull.append(pts.remove(at: n))
}
return hull
}
/// Quadrilaterial defined using terminology of Eberly.
/// NOTE: points must be defined in the correct order! There's currently no convex hull or other method to enforce the correct order.
/// "The first convex quadrilateral has vertices p00, p10, p11 and p01, listed in counterclockwise order."
/// p11 (3rd)
/// p01 (4th)
/// p10 (2nd)
/// p00 (1st)
struct Quadrilateral: CustomStringConvertible {
/// 1x3 vector for point1
var v00: simd_float3
/// 1x3 vector for point2
var v10: simd_float3
/// 1x3 vector for point3
var v11: simd_float3
/// 1x3 vector for point4
var v01: simd_float3
/// Dependent on NumberFormatter extension. Mildly convenient.
var description: String {
let f = NumberFormatter()
return f.string(self, descriptionDigits)
}
/// Digits used in description (e.g. if digits = 1, point1 (2,3) will be displayed as "(2.0, 3.0)"
var descriptionDigits = 1
/// v00 as a 2D point
var point1: simd_float2 {
get { v00.toVector2() }
set { v00 = newValue.toVector3() }
}
/// v10 as a 2D point
var point2: simd_float2 {
get { v10.toVector2() }
set { v10 = newValue.toVector3() }
}
/// v11 as a 2D point
var point3: simd_float2 {
get { v11.toVector2() }
set { v11 = newValue.toVector3() }
}
/// v01 as a 2D point
var point4: simd_float2 {
get { v01.toVector2() }
set { v01 = newValue.toVector3() }
}
var points: [simd_float2] {
[point1, point2, point3, point4]
}
/// Initialize Quadrilateral using Eberly's terminology.
init(v00: simd_float3, v10: simd_float3, v11: simd_float3, v01: simd_float3) {
self.v00 = v00
self.v10 = v10
self.v11 = v11
self.v01 = v01
}
/// Initialize Quadrilateral with 2D points 1, 2, 3, 4 assigned to v00, v10, v11, v01
init(_ point1: simd_float2, _ point2: simd_float2, _ point3: simd_float2, _ point4: simd_float2) {
self.v00 = point1.toVector3()
self.v10 = point2.toVector3()
self.v11 = point3.toVector3()
self.v01 = point4.toVector3()
}
/// Ensure points are ordered counterclosewise, with point1 (p00) at bottom left.
/// This assumes CG coordinates, with the origin at bottom left, +x right, +y up
/// A suitable ordering function would be a traditional convex hull.
mutating func orderPoints(_ orderCounterclockwise: ([simd_float2]) -> [simd_float2], anchor: simd_float2 = simd_float2(0,0)) {
if points.isEmpty {
return
}
let ccw = orderCounterclockwise(points)
/// for 0th index, select point closest to the anchor point
var index = 0
for i in 1 ..< ccw.count {
if simd_length(ccw[i] - anchor) < simd_length(ccw[index] - anchor) {
index = i
}
}
var ordered: [simd_float2] = []
for i in 0 ..< ccw.count {
ordered.append(ccw[(index + i) % ccw.count])
}
//for quick move operations, see https://stackoverflow.com/questions/36541764/how-to-rearrange-item-of-an-array-to-new-position-in-swift
point1 = ordered[0]
point2 = ordered[1]
point3 = ordered[2]
point4 = ordered[3]
}
/// | p1.x p2.x p3.x p4.x |
/// | p1.y p2.y p3.y p4.y |
/// | 1 1 1 1 |
func toMatrix() -> float4x3 {
float4x3(v00, v10, v11, v01)
}
/// Generates a new Quadrilateral with points ordered according to a counterclockwise ordering function and an anchor point.
func toOrderedQuadrilateral(_ orderCounterclockwise: ([simd_float2]) -> [simd_float2], anchor: simd_float2 = simd_float2(0,0)) -> Quadrilateral {
var q = self
q.orderPoints(orderCounterclockwise, anchor: anchor)
return q
}
/// Generates a random quadrilateral with points in the range (-magnitude, -magniture) to (+magnitude, +magnitude).
static func randomQuadrilateral(_ magnitude: Float = 10) -> Quadrilateral {
let randomPoint = { (mag: Float) -> simd_float2 in
simd_float2(Float.random(in: -magnitude...magnitude), Float.random(in: -magnitude...magnitude))
}
return Quadrilateral(randomPoint(magnitude), randomPoint(magnitude), randomPoint(magnitude), randomPoint(magnitude))
}
}
/// Three points nominally defining a triangle, but possibly colinear.
/// Used as an argument to the function SAM.transform(t1:t2:)
struct Triangle: CustomStringConvertible {
var point1: simd_float2
var point2: simd_float2
var point3: simd_float2
/// Dependent on NumberFormatter extension. Mildly convenient.
var description: String {
let f = NumberFormatter()
return f.string(self, descriptionDigits)
}
/// Digits used in description (e.g. if digits = 1, point1 (2,3) will be displayed as "(2.0, 3.0)"
var descriptionDigits = 1
init(_ point1: simd_float2, _ point2: simd_float2, _ point3: simd_float2) {
self.point1 = point1
self.point2 = point2
self.point3 = point3
}
init(_ vector1: simd_float3, _ vector2: simd_float3, _ vector3: simd_float3) {
point1 = vector1.toVector2()
point2 = vector2.toVector2()
point3 = vector3.toVector2()
}
/// Three points are colinear if their determinant is zero. We assume close to colinear might as well be colinear.
/// | x1 x2 x3 |
/// det | y1 y2 y3 | = 0 --> abs( det(M) ) < tolerance ?
/// |1 1 1 |
func colinear(tolerance: Float = 0.01) -> Bool {
let m = toMatrix()
return abs(m.determinant) < tolerance
}
/// | p1.x p2.x p3.x |
/// | p1.y p2.y p3.y |
/// | 1 1 1 |
func toMatrix() -> float3x3 {
float3x3(point1.toVector3(), point2.toVector3(), point3.toVector3())
}
}
/* TEST */
/// Returns a Quadrilateral with points randomly reordered. Used to test ordering functions.
func jumbleOrder(_ q: Quadrilateral) -> Quadrilateral {
var pts = q.points
for _ in 0 ..< 10 {
let oldIndex = Int.random(in: 0 ..< pts.count)
let newIndex = Int.random(in: 0 ..< pts.count)
pts.insert(pts.remove(at: oldIndex), at: newIndex)
}
return Quadrilateral(pts[0], pts[1], pts[2], pts[3])
}
/// Calculate the transform. Print the transform, the quadrilaterals, and transformed points.
func printTransform(from: Quadrilateral, to: Quadrilateral) {
print("p (from): \(from)")
print("q (to): \(to)")
guard let results = perspectiveTransform(from: from, to: to) else {
print("Could not find transform of p and q")
return
}
let H = results.transform
let p = results.pOrdered
let q = results.qOrdered
let f = NumberFormatter()
print()
print("\(#function)")
print("homography (perspective transform): does H * p(vertex) == q(vertex)?\n\(f.string(H, 2))")
//apply 3x3 homography transform to vertices of p as 1x3 homogeneous coordinates (x,y,1)
let hp00 = H * p.v00
let hp10 = H * p.v10
let hp11 = H * p.v11
let hp01 = H * p.v01
//convert to 2D point
let cp00 = hp00.toVector2()
let cp10 = hp10.toVector2()
let cp11 = hp11.toVector2()
let cp01 = hp01.toVector2()
//expected results: the vertices of q as 2D points
let eq00 = q.v00.toVector2()
let eq10 = q.v10.toVector2()
let eq11 = q.v11.toVector2()
let eq01 = q.v01.toVector2()
print()
print("H * p00:")
print("\(f.string(p.v00.toVector2(), 2)) p vertex ")
print("\(f.string(cp00, 2)) transformed")
print("\(f.string(eq00, 2)) expected q vertex")
print()
print("H * p10:")
print("\(f.string(p.v10.toVector2(), 2)) p vertex ")
print("\(f.string(cp10, 2)) transformed")
print("\(f.string(eq10, 2)) expected q vertex")
print()
print("H * p11:")
print("\(f.string(p.v11.toVector2(), 2)) p vertex ")
print("\(f.string(cp11, 2)) transformed")
print("\(f.string(eq11, 2)) expected q vertex")
print()
print("H * p01:")
print("\(f.string(p.v01.toVector2(), 2)) p vertex ")
print("\(f.string(cp01, 2)) calculated q vertex: H * [p vertex]")
print("\(f.string(eq01, 2)) expected q vertex")
}
/// Prints debug information for a number of runs of printTransform(from:to:).
/// For each run the transform is calculated
/// 1. From p to q
/// 2. From p to q, after first randomly jumbling the point orders of the quadrilaterals
/// 3. From q to p
/// The first run uses known points for the quadrilaterals.
/// After the first run, random points are selected.
func testPerspectiveTransforms(_ runs: Int = 5) {
var p = Quadrilateral(
simd_float2(2, 1),
simd_float2(6, 2),
simd_float2(4, 5),
simd_float2(1, 4))
var q = Quadrilateral(
simd_float2(1, 2),
simd_float2(6, 1),
simd_float2(5, 4),
simd_float2(2, 5))
for i in 1 ... runs {
print("\n*** Test \(i) of \(runs) ***")
print("* Test \(i)a: Transform p -> q")
printTransform(from: p, to: q)
print()
print("* Test \(i)b: Jumbled point order")
printTransform(from: jumbleOrder(p), to: jumbleOrder(q))
print()
print("* Test \(i)c: Transform q -> p (swap quadrilaterals)")
printTransform(from: q, to: p)
//after first run, randomize points
p = Quadrilateral.randomQuadrilateral()
q = Quadrilateral.randomQuadrilateral()
}
}
testPerspectiveTransforms(6)Sample console output is shown below. For the very first test run the points of the quadrilaterals p and q are hard coded, but for subsequent runs the points are randomly generated. Randomly generated quadrilaterals may be non-convex, in which case no perspective transform can be found.
*** Test 1 of 6 ***
* Test 1a: Transform p -> q
p (from): [2.0, 1.0], [6.0, 2.0], [4.0, 5.0], [1.0, 4.0]
q (to): [1.0, 2.0], [6.0, 1.0], [5.0, 4.0], [2.0, 5.0]
printTransform(from:to:)
homography (perspective transform): does H * p(vertex) == q(vertex)?
0.64 0.45 -1.31
-0.22 0.60 0.66
0.02 0.06 0.31
H * p00:
[2.00, 1.00] p vertex
[1.00, 2.00] transformed
[1.00, 2.00] expected q vertex
H * p10:
[6.00, 2.00] p vertex
[6.00, 1.00] transformed
[6.00, 1.00] expected q vertex
H * p11:
[4.00, 5.00] p vertex
[5.00, 4.00] transformed
[5.00, 4.00] expected q vertex
H * p01:
[1.00, 4.00] p vertex
[2.00, 5.00] calculated q vertex: H * [p vertex]
[2.00, 5.00] expected q vertex
* Test 1b: Jumbled point order
p (from): [2.0, 1.0], [1.0, 4.0], [4.0, 5.0], [6.0, 2.0]
q (to): [6.0, 1.0], [5.0, 4.0], [1.0, 2.0], [2.0, 5.0]
printTransform(from:to:)
homography (perspective transform): does H * p(vertex) == q(vertex)?
0.64 0.45 -1.31
-0.22 0.60 0.66
0.02 0.06 0.31
H * p00:
[2.00, 1.00] p vertex
[1.00, 2.00] transformed
[1.00, 2.00] expected q vertex
H * p10:
[6.00, 2.00] p vertex
[6.00, 1.00] transformed
[6.00, 1.00] expected q vertex
H * p11:
[4.00, 5.00] p vertex
[5.00, 4.00] transformed
[5.00, 4.00] expected q vertex
H * p01:
[1.00, 4.00] p vertex
[2.00, 5.00] calculated q vertex: H * [p vertex]
[2.00, 5.00] expected q vertex
* Test 1c: Transform q -> p (swap quadrilaterals)
p (from): [1.0, 2.0], [6.0, 1.0], [5.0, 4.0], [2.0, 5.0]
q (to): [2.0, 1.0], [6.0, 2.0], [4.0, 5.0], [1.0, 4.0]
printTransform(from:to:)
homography (perspective transform): does H * p(vertex) == q(vertex)?
0.17 -0.25 1.26
0.09 0.26 -0.16
-0.03 -0.03 0.56
H * p00:
[1.00, 2.00] p vertex
[2.00, 1.00] transformed
[2.00, 1.00] expected q vertex
H * p10:
[6.00, 1.00] p vertex
[6.00, 2.00] transformed
[6.00, 2.00] expected q vertex
H * p11:
[5.00, 4.00] p vertex
[4.00, 5.00] transformed
[4.00, 5.00] expected q vertex
H * p01:
[2.00, 5.00] p vertex
[1.00, 4.00] calculated q vertex: H * [p vertex]
[1.00, 4.00] expected q vertex
*** Test 2 of 6 ***
* Test 2a: Transform p -> q
p (from): [9.2, 0.1], [-8.7, -3.1], [-3.3, -1.6], [5.2, -9.7]
q (to): [-2.7, 2.8], [7.2, 6.0], [-1.5, -5.3], [-5.6, -9.1]
printTransform(from:to:)
homography (perspective transform): does H * p(vertex) == q(vertex)?
8.22 310.99 501.33
-93.19 953.22 1261.78
29.04 -166.41 -163.96
H * p00:
[-3.25, -1.61] p vertex
[-2.70, 2.77] transformed
[-2.70, 2.77] expected q vertex
H * p10:
[-8.69, -3.07] p vertex
[-5.58, -9.07] transformed
[-5.58, -9.07] expected q vertex
H * p11:
[5.16, -9.73] p vertex
[-1.55, -5.29] transformed
[-1.55, -5.29] expected q vertex
H * p01:
[9.22, 0.11] p vertex
[7.23, 6.03] calculated q vertex: H * [p vertex]
[7.23, 6.03] expected q vertex
* Test 2b: Jumbled point order
p (from): [5.2, -9.7], [9.2, 0.1], [-8.7, -3.1], [-3.3, -1.6]
q (to): [-2.7, 2.8], [-1.5, -5.3], [7.2, 6.0], [-5.6, -9.1]
printTransform(from:to:)
homography (perspective transform): does H * p(vertex) == q(vertex)?
8.22 310.99 501.33
-93.19 953.22 1261.78
29.04 -166.41 -163.96
H * p00:
[-3.25, -1.61] p vertex
[-2.70, 2.77] transformed
[-2.70, 2.77] expected q vertex
H * p10:
[-8.69, -3.07] p vertex
[-5.58, -9.07] transformed
[-5.58, -9.07] expected q vertex
H * p11:
[5.16, -9.73] p vertex
[-1.55, -5.29] transformed
[-1.55, -5.29] expected q vertex
H * p01:
[9.22, 0.11] p vertex
[7.23, 6.03] calculated q vertex: H * [p vertex]
[7.23, 6.03] expected q vertex
* Test 2c: Transform q -> p (swap quadrilaterals)
p (from): [-2.7, 2.8], [7.2, 6.0], [-1.5, -5.3], [-5.6, -9.1]
q (to): [9.2, 0.1], [-8.7, -3.1], [-3.3, -1.6], [5.2, -9.7]
printTransform(from:to:)
homography (perspective transform): does H * p(vertex) == q(vertex)?
2.70 -1.63 -4.30
1.07 -0.80 -2.87
-0.61 0.52 1.85
H * p00:
[-2.70, 2.77] p vertex
[-3.25, -1.61] transformed
[-3.25, -1.61] expected q vertex
H * p10:
[-5.58, -9.07] p vertex
[-8.69, -3.07] transformed
[-8.69, -3.07] expected q vertex
H * p11:
[-1.55, -5.29] p vertex
[5.16, -9.73] transformed
[5.16, -9.73] expected q vertex
H * p01:
[7.23, 6.03] p vertex
[9.22, 0.11] calculated q vertex: H * [p vertex]
[9.22, 0.11] expected q vertex
*** Test 3 of 6 ***
* Test 3a: Transform p -> q
p (from): [5.7, -7.9], [0.4, 1.0], [9.6, -5.7], [0.9, 0.3]
q (to): [-0.8, 2.2], [-3.1, 0.9], [-9.3, 0.4], [-0.8, -9.0]
p is NOT convex, s = -85.97753
q is NOT convex, t = -0.6625012
Could not find transform of p and q
* Test 3b: Jumbled point order
p (from): [0.9, 0.3], [9.6, -5.7], [5.7, -7.9], [0.4, 1.0]
q (to): [-0.8, -9.0], [-0.8, 2.2], [-9.3, 0.4], [-3.1, 0.9]
p is NOT convex, s = -85.97753
q is NOT convex, t = -0.6625012
Could not find transform of p and q
* Test 3c: Transform q -> p (swap quadrilaterals)
p (from): [-0.8, 2.2], [-3.1, 0.9], [-9.3, 0.4], [-0.8, -9.0]
q (to): [5.7, -7.9], [0.4, 1.0], [9.6, -5.7], [0.9, 0.3]
p is NOT convex, s = -0.6625012
q is NOT convex, t = -85.97753
Could not find transform of p and q
*** Test 4 of 6 ***
* Test 4a: Transform p -> q
p (from): [-8.2, 5.4], [9.7, -9.9], [-7.2, 7.6], [-4.3, 1.8]
q (to): [-3.6, 7.0], [7.4, -3.5], [-7.7, -1.7], [-1.2, -7.1]
printTransform(from:to:)
homography (perspective transform): does H * p(vertex) == q(vertex)?
-398.26 -634.72 -665.13
1400.17 1693.50 2288.75
174.41 196.82 483.56
H * p00:
[-4.31, 1.83] p vertex
[-1.18, -7.14] transformed
[-1.18, -7.14] expected q vertex
H * p10:
[9.74, -9.89] p vertex
[7.39, -3.53] transformed
[7.39, -3.53] expected q vertex
H * p11:
[-7.22, 7.63] p vertex
[-3.62, 7.03] transformed
[-3.62, 7.03] expected q vertex
H * p01:
[-8.24, 5.36] p vertex
[-7.74, -1.68] calculated q vertex: H * [p vertex]
[-7.74, -1.68] expected q vertex
* Test 4b: Jumbled point order
p (from): [-7.2, 7.6], [-4.3, 1.8], [-8.2, 5.4], [9.7, -9.9]
q (to): [-1.2, -7.1], [7.4, -3.5], [-3.6, 7.0], [-7.7, -1.7]
printTransform(from:to:)
homography (perspective transform): does H * p(vertex) == q(vertex)?
-398.26 -634.72 -665.13
1400.17 1693.50 2288.75
174.41 196.82 483.56
H * p00:
[-4.31, 1.83] p vertex
[-1.18, -7.14] transformed
[-1.18, -7.14] expected q vertex
H * p10:
[9.74, -9.89] p vertex
[7.39, -3.53] transformed
[7.39, -3.53] expected q vertex
H * p11:
[-7.22, 7.63] p vertex
[-3.62, 7.03] transformed
[-3.62, 7.03] expected q vertex
H * p01:
[-8.24, 5.36] p vertex
[-7.74, -1.68] calculated q vertex: H * [p vertex]
[-7.74, -1.68] expected q vertex
* Test 4c: Transform q -> p (swap quadrilaterals)
p (from): [-3.6, 7.0], [7.4, -3.5], [-7.7, -1.7], [-1.2, -7.1]
q (to): [-8.2, 5.4], [9.7, -9.9], [-7.2, 7.6], [-4.3, 1.8]
printTransform(from:to:)
homography (perspective transform): does H * p(vertex) == q(vertex)?
25.84 12.35 -22.89
-19.49 -5.37 -1.39
-1.39 -2.27 15.03
H * p00:
[-1.18, -7.14] p vertex
[-4.31, 1.83] transformed
[-4.31, 1.83] expected q vertex
H * p10:
[7.39, -3.53] p vertex
[9.74, -9.89] transformed
[9.74, -9.89] expected q vertex
H * p11:
[-3.62, 7.03] p vertex
[-7.22, 7.63] transformed
[-7.22, 7.63] expected q vertex
H * p01:
[-7.74, -1.68] p vertex
[-8.24, 5.36] calculated q vertex: H * [p vertex]
[-8.24, 5.36] expected q vertex
*** Test 5 of 6 ***
* Test 5a: Transform p -> q
p (from): [3.8, -6.7], [-9.0, 1.9], [6.6, -0.4], [-7.6, -5.4]
q (to): [-6.8, -0.0], [9.3, -2.0], [0.0, -2.2], [-6.6, -9.9]
p is convex, s = 0.7797686
q is NOT convex, t = -2.3607662
Could not find transform of p and q
* Test 5b: Jumbled point order
p (from): [3.8, -6.7], [6.6, -0.4], [-9.0, 1.9], [-7.6, -5.4]
q (to): [-6.8, -0.0], [-6.6, -9.9], [9.3, -2.0], [0.0, -2.2]
p is convex, s = 0.7797686
q is NOT convex, t = -2.3607662
Could not find transform of p and q
* Test 5c: Transform q -> p (swap quadrilaterals)
p (from): [-6.8, -0.0], [9.3, -2.0], [0.0, -2.2], [-6.6, -9.9]
q (to): [3.8, -6.7], [-9.0, 1.9], [6.6, -0.4], [-7.6, -5.4]
p is NOT convex, s = -2.3607662
q is convex, t = 0.7797686
Could not find transform of p and q
*** Test 6 of 6 ***
* Test 6a: Transform p -> q
p (from): [-8.7, 5.0], [-4.8, 7.6], [-3.1, -8.3], [8.9, 8.6]
q (to): [-9.1, 6.0], [-1.0, -4.7], [7.9, 9.7], [1.0, -0.7]
p is convex, s = 0.12949014
q is NOT convex, t = -3.9814246
Could not find transform of p and q
* Test 6b: Jumbled point order
p (from): [-4.8, 7.6], [8.9, 8.6], [-8.7, 5.0], [-3.1, -8.3]
q (to): [1.0, -0.7], [-1.0, -4.7], [7.9, 9.7], [-9.1, 6.0]
p is convex, s = 0.12949014
q is NOT convex, t = -3.9814246
Could not find transform of p and q
* Test 6c: Transform q -> p (swap quadrilaterals)
p (from): [-9.1, 6.0], [-1.0, -4.7], [7.9, 9.7], [1.0, -0.7]
q (to): [-8.7, 5.0], [-4.8, 7.6], [-3.1, -8.3], [8.9, 8.6]
p is NOT convex, s = -3.9814246
q is convex, t = 0.12949014
Could not find transform of p and q
