138 lines
6 KiB
Swift
138 lines
6 KiB
Swift
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import XCTest
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import ForgeColor
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@testable import ForgeGrade
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final class FlattenerTests: XCTestCase {
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/// Camera-domain stack for flattening: LUT input domain is camera log signal.
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/// Flattened LUT must reproduce stack output for any log-encoded input.
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func makeStack() -> GradeStack {
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var set = CurveSet.identity
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set.master = Curve(points: [.init(x: 0, y: 0.02), .init(x: 0.5, y: 0.45), .init(x: 1, y: 0.98)])
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return GradeStack(nodes: [
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GradeNode(kind: .cdl(CDL(
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slope: SIMD3(1.1, 0.95, 1.02),
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offset: SIMD3(0.01, -0.005, 0),
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power: SIMD3(0.98, 1.03, 1.0),
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saturation: 1.15))),
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GradeNode(kind: .curves(set)),
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GradeNode(kind: .saturation(0.9)),
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])
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}
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// Flattened 33³ LUT matches direct stack eval on random in-domain samples.
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// Tolerance: trilinear error bound is max|f''|·h²/8 per axis (h=1/32). The
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// spline curve node has |f''| up to ~40 near segment junctions -> ~0.005
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// worst case, cross-terms add. 1% (10/1024) is the honest 33³ bound for
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// production-strength grades; 65³ tightens 4x (covered below).
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func testFlattenMatchesDirectEval() {
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let stack = makeStack()
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let look = Flattener.flatten(stack: stack, latticeSize: 33, splitLeadingCDL: false)
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XCTAssertNil(look.cdl)
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guard let lut = look.lut else { return XCTFail("no lut") }
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var rng = SystemRandomNumberGenerator()
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for _ in 0..<300 {
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let px = SIMD3<Float>(
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Float.random(in: 0...1, using: &rng),
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Float.random(in: 0...1, using: &rng),
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Float.random(in: 0...1, using: &rng))
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let direct = stack.evaluate(px)
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let viaLut = lut.sample(px)
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XCTAssertEqual(viaLut.x, direct.x, accuracy: 10.0 / 1024)
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XCTAssertEqual(viaLut.y, direct.y, accuracy: 10.0 / 1024)
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XCTAssertEqual(viaLut.z, direct.z, accuracy: 10.0 / 1024)
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}
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}
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// 65³ lattice: ~2x tighter than 33³. Error scales with h (not h²) because the
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// stack has derivative kinks — curve endpoint pinning and CDL pre-power clamp
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// are C0 surfaces that trilinear crosses. Observed max ~0.004 at 65³.
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func testFlatten65MatchesTighter() {
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let stack = makeStack()
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let look = Flattener.flatten(stack: stack, latticeSize: 65, splitLeadingCDL: false)
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guard let lut = look.lut else { return XCTFail("no lut") }
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var rng = SystemRandomNumberGenerator()
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for _ in 0..<300 {
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let px = SIMD3<Float>(
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Float.random(in: 0...1, using: &rng),
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Float.random(in: 0...1, using: &rng),
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Float.random(in: 0...1, using: &rng))
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let direct = stack.evaluate(px)
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let viaLut = lut.sample(px)
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XCTAssertEqual(viaLut.x, direct.x, accuracy: 5.0 / 1024)
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XCTAssertEqual(viaLut.y, direct.y, accuracy: 5.0 / 1024)
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XCTAssertEqual(viaLut.z, direct.z, accuracy: 5.0 / 1024)
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}
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}
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// Split mode: leading CDL excluded from bake, returned separately.
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// CDL.apply(then lut.sample) must equal full stack.
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func testSplitLeadingCDL() {
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let stack = makeStack()
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let look = Flattener.flatten(stack: stack, latticeSize: 33, splitLeadingCDL: true)
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guard let cdl = look.cdl, let lut = look.lut else { return XCTFail("missing parts") }
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// Returned CDL is stack's first node CDL.
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if case .cdl(let first) = stack.nodes[0].kind {
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XCTAssertEqual(cdl, first)
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} else {
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XCTFail("expected cdl first")
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}
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var rng = SystemRandomNumberGenerator()
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for _ in 0..<300 {
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let px = SIMD3<Float>(
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Float.random(in: 0...1, using: &rng),
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Float.random(in: 0...1, using: &rng),
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Float.random(in: 0...1, using: &rng))
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let direct = stack.evaluate(px)
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let recombined = lut.sample(cdl.apply(px))
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XCTAssertEqual(recombined.x, direct.x, accuracy: 10.0 / 1024)
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XCTAssertEqual(recombined.y, direct.y, accuracy: 10.0 / 1024)
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XCTAssertEqual(recombined.z, direct.z, accuracy: 10.0 / 1024)
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}
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}
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// Split requested but first node not CDL -> no split, full bake.
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func testSplitFallsBackWhenNoLeadingCDL() {
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let stack = GradeStack(nodes: [GradeNode(kind: .saturation(0.8))])
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let look = Flattener.flatten(stack: stack, latticeSize: 17, splitLeadingCDL: true)
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XCTAssertNil(look.cdl)
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XCTAssertNotNil(look.lut)
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}
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// Split requested but leading CDL bypassed -> not split.
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func testSplitIgnoresBypassedCDL() {
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var node = GradeNode(kind: .cdl(CDL(slope: SIMD3(2, 2, 2), offset: .zero, power: .one, saturation: 1)))
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node.isEnabled = false
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let stack = GradeStack(nodes: [node, GradeNode(kind: .saturation(0.8))])
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let look = Flattener.flatten(stack: stack, latticeSize: 17, splitLeadingCDL: true)
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XCTAssertNil(look.cdl)
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}
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// Deterministic: same stack -> byte-identical tables.
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func testDeterministic() {
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let stack = makeStack()
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let a = Flattener.flatten(stack: stack, latticeSize: 17, splitLeadingCDL: false)
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let b = Flattener.flatten(stack: stack, latticeSize: 17, splitLeadingCDL: false)
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XCTAssertEqual(a.lut?.table, b.lut?.table)
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}
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// Empty stack flattens to identity LUT.
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func testEmptyStackIdentityLut() {
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let look = Flattener.flatten(stack: GradeStack(nodes: []), latticeSize: 9, splitLeadingCDL: false)
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guard let lut = look.lut else { return XCTFail() }
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let px = SIMD3<Float>(0.3, 0.6, 0.9)
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let out = lut.sample(px)
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XCTAssertEqual(out.x, px.x, accuracy: 1e-4)
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XCTAssertEqual(out.y, px.y, accuracy: 1e-4)
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XCTAssertEqual(out.z, px.z, accuracy: 1e-4)
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}
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// Lattice size respected.
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func testLatticeSize() {
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let look = Flattener.flatten(stack: GradeStack(nodes: []), latticeSize: 65, splitLeadingCDL: false)
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XCTAssertEqual(look.lut?.size, 65)
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}
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}
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