| /* |
| * Copyright 2020 Google Inc. |
| * |
| * Use of this source code is governed by a BSD-style license that can be |
| * found in the LICENSE file. |
| */ |
| |
| #include "include/core/SkMatrix.h" |
| #include "include/core/SkPoint.h" |
| #include "include/core/SkRect.h" |
| #include "include/core/SkScalar.h" |
| #include "include/core/SkTypes.h" |
| #include "include/private/base/SkVx.h" |
| #include "include/utils/SkRandom.h" |
| #include "src/core/SkGeometry.h" |
| #include "src/gpu/tessellate/Tessellation.h" |
| #include "src/gpu/tessellate/WangsFormula.h" |
| #include "tests/Test.h" |
| |
| #include <algorithm> |
| #include <cmath> |
| #include <functional> |
| #include <limits> |
| #include <string> |
| |
| namespace skgpu::tess { |
| |
| const SkPoint kSerp[4] = { |
| {285.625f, 499.687f}, {411.625f, 808.188f}, {1064.62f, 135.688f}, {1042.63f, 585.187f}}; |
| |
| const SkPoint kLoop[4] = { |
| {635.625f, 614.687f}, {171.625f, 236.188f}, {1064.62f, 135.688f}, {516.625f, 570.187f}}; |
| |
| const SkPoint kQuad[4] = { |
| {460.625f, 557.187f}, {707.121f, 209.688f}, {779.628f, 577.687f}}; |
| |
| static float wangs_formula_quadratic_reference_impl(float precision, const SkPoint p[3]) { |
| float k = (2 * 1) / 8.f * precision; |
| return sqrtf(k * (p[0] - p[1]*2 + p[2]).length()); |
| } |
| |
| static float wangs_formula_cubic_reference_impl(float precision, const SkPoint p[4]) { |
| float k = (3 * 2) / 8.f * precision; |
| return sqrtf(k * std::max((p[0] - p[1]*2 + p[2]).length(), |
| (p[1] - p[2]*2 + p[3]).length())); |
| } |
| |
| // Returns number of segments for linearized quadratic rational. This is an analogue |
| // to Wang's formula, taken from: |
| // |
| // J. Zheng, T. Sederberg. "Estimating Tessellation Parameter Intervals for |
| // Rational Curves and Surfaces." ACM Transactions on Graphics 19(1). 2000. |
| // See Thm 3, Corollary 1. |
| // |
| // Input points should be in projected space. |
| static float wangs_formula_conic_reference_impl(float precision, |
| const SkPoint P[3], |
| const float w) { |
| // Compute center of bounding box in projected space |
| float min_x = P[0].fX, max_x = min_x, |
| min_y = P[0].fY, max_y = min_y; |
| for (int i = 1; i < 3; i++) { |
| min_x = std::min(min_x, P[i].fX); |
| max_x = std::max(max_x, P[i].fX); |
| min_y = std::min(min_y, P[i].fY); |
| max_y = std::max(max_y, P[i].fY); |
| } |
| const SkPoint C = SkPoint::Make(0.5f * (min_x + max_x), 0.5f * (min_y + max_y)); |
| |
| // Translate control points and compute max length |
| SkPoint tP[3] = {P[0] - C, P[1] - C, P[2] - C}; |
| float max_len = 0; |
| for (int i = 0; i < 3; i++) { |
| max_len = std::max(max_len, tP[i].length()); |
| } |
| SkASSERT(max_len > 0); |
| |
| // Compute delta = parametric step size of linearization |
| const float eps = 1 / precision; |
| const float r_minus_eps = std::max(0.f, max_len - eps); |
| const float min_w = std::min(w, 1.f); |
| const float numer = 4 * min_w * eps; |
| const float denom = |
| (tP[2] - tP[1] * 2 * w + tP[0]).length() + r_minus_eps * std::abs(1 - 2 * w + 1); |
| const float delta = sqrtf(numer / denom); |
| |
| // Return corresponding num segments in the interval [tmin,tmax] |
| constexpr float tmin = 0, tmax = 1; |
| SkASSERT(delta > 0); |
| return (tmax - tmin) / delta; |
| } |
| |
| static void for_random_matrices(SkRandom* rand, std::function<void(const SkMatrix&)> f) { |
| SkMatrix m; |
| m.setIdentity(); |
| f(m); |
| |
| for (int i = -10; i <= 30; ++i) { |
| for (int j = -10; j <= 30; ++j) { |
| m.setScaleX(std::ldexp(1 + rand->nextF(), i)); |
| m.setSkewX(0); |
| m.setSkewY(0); |
| m.setScaleY(std::ldexp(1 + rand->nextF(), j)); |
| f(m); |
| |
| m.setScaleX(std::ldexp(1 + rand->nextF(), i)); |
| m.setSkewX(std::ldexp(1 + rand->nextF(), (j + i) / 2)); |
| m.setSkewY(std::ldexp(1 + rand->nextF(), (j + i) / 2)); |
| m.setScaleY(std::ldexp(1 + rand->nextF(), j)); |
| f(m); |
| } |
| } |
| } |
| |
| static void for_random_beziers(int numPoints, SkRandom* rand, |
| std::function<void(const SkPoint[])> f, |
| int maxExponent = 30) { |
| SkASSERT(numPoints <= 4); |
| SkPoint pts[4]; |
| for (int i = -10; i <= maxExponent; ++i) { |
| for (int j = 0; j < numPoints; ++j) { |
| pts[j].set(std::ldexp(1 + rand->nextF(), i), std::ldexp(1 + rand->nextF(), i)); |
| } |
| f(pts); |
| } |
| } |
| |
| // Ensure the optimized "*_log2" versions return the same value as ceil(std::log2(f)). |
| DEF_TEST(wangs_formula_log2, r) { |
| // Constructs a cubic such that the 'length' term in wang's formula == term. |
| // |
| // f = sqrt(k * length(max(abs(p0 - p1*2 + p2), |
| // abs(p1 - p2*2 + p3)))); |
| auto setupCubicLengthTerm = [](int seed, SkPoint pts[], float term) { |
| memset(pts, 0, sizeof(SkPoint) * 4); |
| |
| SkPoint term2d = (seed & 1) ? |
| SkPoint::Make(term, 0) : SkPoint::Make(.5f, std::sqrt(3)/2) * term; |
| seed >>= 1; |
| |
| if (seed & 1) { |
| term2d.fX = -term2d.fX; |
| } |
| seed >>= 1; |
| |
| if (seed & 1) { |
| std::swap(term2d.fX, term2d.fY); |
| } |
| seed >>= 1; |
| |
| switch (seed % 4) { |
| case 0: |
| pts[0] = term2d; |
| pts[3] = term2d * .75f; |
| return; |
| case 1: |
| pts[1] = term2d * -.5f; |
| return; |
| case 2: |
| pts[1] = term2d * -.5f; |
| return; |
| case 3: |
| pts[3] = term2d; |
| pts[0] = term2d * .75f; |
| return; |
| } |
| }; |
| |
| // Constructs a quadratic such that the 'length' term in wang's formula == term. |
| // |
| // f = sqrt(k * length(p0 - p1*2 + p2)); |
| auto setupQuadraticLengthTerm = [](int seed, SkPoint pts[], float term) { |
| memset(pts, 0, sizeof(SkPoint) * 3); |
| |
| SkPoint term2d = (seed & 1) ? |
| SkPoint::Make(term, 0) : SkPoint::Make(.5f, std::sqrt(3)/2) * term; |
| seed >>= 1; |
| |
| if (seed & 1) { |
| term2d.fX = -term2d.fX; |
| } |
| seed >>= 1; |
| |
| if (seed & 1) { |
| std::swap(term2d.fX, term2d.fY); |
| } |
| seed >>= 1; |
| |
| switch (seed % 3) { |
| case 0: |
| pts[0] = term2d; |
| return; |
| case 1: |
| pts[1] = term2d * -.5f; |
| return; |
| case 2: |
| pts[2] = term2d; |
| return; |
| } |
| }; |
| |
| // wangs_formula_cubic and wangs_formula_quadratic both use rsqrt instead of sqrt for speed. |
| // Linearization is all approximate anyway, so as long as we are within ~1/2 tessellation |
| // segment of the reference value we are good enough. |
| constexpr static float kTessellationTolerance = 1/128.f; |
| |
| for (int level = 0; level < 30; ++level) { |
| float epsilon = std::ldexp(SK_ScalarNearlyZero, level * 2); |
| SkPoint pts[4]; |
| |
| { |
| // Test cubic boundaries. |
| // f = sqrt(k * length(max(abs(p0 - p1*2 + p2), |
| // abs(p1 - p2*2 + p3)))); |
| constexpr static float k = (3 * 2) / (8 * (1.f/kPrecision)); |
| float x = std::ldexp(1, level * 2) / k; |
| setupCubicLengthTerm(level << 1, pts, x - epsilon); |
| float referenceValue = wangs_formula_cubic_reference_impl(kPrecision, pts); |
| REPORTER_ASSERT(r, std::ceil(std::log2(referenceValue)) == level); |
| float c = wangs_formula::cubic(kPrecision, pts); |
| REPORTER_ASSERT(r, SkScalarNearlyEqual(c/referenceValue, 1, kTessellationTolerance)); |
| REPORTER_ASSERT(r, wangs_formula::cubic_log2(kPrecision, pts) == level); |
| setupCubicLengthTerm(level << 1, pts, x + epsilon); |
| referenceValue = wangs_formula_cubic_reference_impl(kPrecision, pts); |
| REPORTER_ASSERT(r, std::ceil(std::log2(referenceValue)) == level + 1); |
| c = wangs_formula::cubic(kPrecision, pts); |
| REPORTER_ASSERT(r, SkScalarNearlyEqual(c/referenceValue, 1, kTessellationTolerance)); |
| REPORTER_ASSERT(r, wangs_formula::cubic_log2(kPrecision, pts) == level + 1); |
| } |
| |
| { |
| // Test quadratic boundaries. |
| // f = std::sqrt(k * Length(p0 - p1*2 + p2)); |
| constexpr static float k = 2 / (8 * (1.f/kPrecision)); |
| float x = std::ldexp(1, level * 2) / k; |
| setupQuadraticLengthTerm(level << 1, pts, x - epsilon); |
| float referenceValue = wangs_formula_quadratic_reference_impl(kPrecision, pts); |
| REPORTER_ASSERT(r, std::ceil(std::log2(referenceValue)) == level); |
| float q = wangs_formula::quadratic(kPrecision, pts); |
| REPORTER_ASSERT(r, SkScalarNearlyEqual(q/referenceValue, 1, kTessellationTolerance)); |
| REPORTER_ASSERT(r, wangs_formula::quadratic_log2(kPrecision, pts) == level); |
| setupQuadraticLengthTerm(level << 1, pts, x + epsilon); |
| referenceValue = wangs_formula_quadratic_reference_impl(kPrecision, pts); |
| REPORTER_ASSERT(r, std::ceil(std::log2(referenceValue)) == level+1); |
| q = wangs_formula::quadratic(kPrecision, pts); |
| REPORTER_ASSERT(r, SkScalarNearlyEqual(q/referenceValue, 1, kTessellationTolerance)); |
| REPORTER_ASSERT(r, wangs_formula::quadratic_log2(kPrecision, pts) == level + 1); |
| } |
| } |
| |
| auto check_cubic_log2 = [&](const SkPoint* pts) { |
| float f = std::max(1.f, wangs_formula_cubic_reference_impl(kPrecision, pts)); |
| int f_log2 = wangs_formula::cubic_log2(kPrecision, pts); |
| REPORTER_ASSERT(r, SkScalarCeilToInt(std::log2(f)) == f_log2); |
| float c = std::max(1.f, wangs_formula::cubic(kPrecision, pts)); |
| REPORTER_ASSERT(r, SkScalarNearlyEqual(c/f, 1, kTessellationTolerance)); |
| }; |
| |
| auto check_quadratic_log2 = [&](const SkPoint* pts) { |
| float f = std::max(1.f, wangs_formula_quadratic_reference_impl(kPrecision, pts)); |
| int f_log2 = wangs_formula::quadratic_log2(kPrecision, pts); |
| REPORTER_ASSERT(r, SkScalarCeilToInt(std::log2(f)) == f_log2); |
| float q = std::max(1.f, wangs_formula::quadratic(kPrecision, pts)); |
| REPORTER_ASSERT(r, SkScalarNearlyEqual(q/f, 1, kTessellationTolerance)); |
| }; |
| |
| SkRandom rand; |
| |
| for_random_matrices(&rand, [&](const SkMatrix& m) { |
| SkPoint pts[4]; |
| m.mapPoints(pts, kSerp, 4); |
| check_cubic_log2(pts); |
| |
| m.mapPoints(pts, kLoop, 4); |
| check_cubic_log2(pts); |
| |
| m.mapPoints(pts, kQuad, 3); |
| check_quadratic_log2(pts); |
| }); |
| |
| for_random_beziers(4, &rand, [&](const SkPoint pts[]) { |
| check_cubic_log2(pts); |
| }); |
| |
| for_random_beziers(3, &rand, [&](const SkPoint pts[]) { |
| check_quadratic_log2(pts); |
| }); |
| } |
| |
| // Ensure using transformations gives the same result as pre-transforming all points. |
| DEF_TEST(wangs_formula_vectorXforms, r) { |
| auto check_cubic_log2_with_transform = [&](const SkPoint* pts, const SkMatrix& m){ |
| SkPoint ptsXformed[4]; |
| m.mapPoints(ptsXformed, pts, 4); |
| int expected = wangs_formula::cubic_log2(kPrecision, ptsXformed); |
| int actual = wangs_formula::cubic_log2(kPrecision, pts, wangs_formula::VectorXform(m)); |
| REPORTER_ASSERT(r, actual == expected); |
| }; |
| |
| auto check_quadratic_log2_with_transform = [&](const SkPoint* pts, const SkMatrix& m) { |
| SkPoint ptsXformed[3]; |
| m.mapPoints(ptsXformed, pts, 3); |
| int expected = wangs_formula::quadratic_log2(kPrecision, ptsXformed); |
| int actual = wangs_formula::quadratic_log2(kPrecision, pts, wangs_formula::VectorXform(m)); |
| REPORTER_ASSERT(r, actual == expected); |
| }; |
| |
| SkRandom rand; |
| |
| for_random_matrices(&rand, [&](const SkMatrix& m) { |
| check_cubic_log2_with_transform(kSerp, m); |
| check_cubic_log2_with_transform(kLoop, m); |
| check_quadratic_log2_with_transform(kQuad, m); |
| |
| for_random_beziers(4, &rand, [&](const SkPoint pts[]) { |
| check_cubic_log2_with_transform(pts, m); |
| }); |
| |
| for_random_beziers(3, &rand, [&](const SkPoint pts[]) { |
| check_quadratic_log2_with_transform(pts, m); |
| }); |
| }); |
| } |
| |
| DEF_TEST(wangs_formula_worst_case_cubic, r) { |
| { |
| SkPoint worstP[] = {{0,0}, {100,100}, {0,0}, {0,0}}; |
| REPORTER_ASSERT(r, wangs_formula::worst_case_cubic(kPrecision, 100, 100) == |
| wangs_formula_cubic_reference_impl(kPrecision, worstP)); |
| REPORTER_ASSERT(r, wangs_formula::worst_case_cubic_log2(kPrecision, 100, 100) == |
| wangs_formula::cubic_log2(kPrecision, worstP)); |
| } |
| { |
| SkPoint worstP[] = {{100,100}, {100,100}, {200,200}, {100,100}}; |
| REPORTER_ASSERT(r, wangs_formula::worst_case_cubic(kPrecision, 100, 100) == |
| wangs_formula_cubic_reference_impl(kPrecision, worstP)); |
| REPORTER_ASSERT(r, wangs_formula::worst_case_cubic_log2(kPrecision, 100, 100) == |
| wangs_formula::cubic_log2(kPrecision, worstP)); |
| } |
| auto check_worst_case_cubic = [&](const SkPoint* pts) { |
| SkRect bbox; |
| bbox.setBoundsNoCheck(pts, 4); |
| float worst = wangs_formula::worst_case_cubic(kPrecision, bbox.width(), bbox.height()); |
| int worst_log2 = wangs_formula::worst_case_cubic_log2(kPrecision, bbox.width(), |
| bbox.height()); |
| float actual = wangs_formula_cubic_reference_impl(kPrecision, pts); |
| REPORTER_ASSERT(r, worst >= actual); |
| REPORTER_ASSERT(r, std::ceil(std::log2(std::max(1.f, worst))) == worst_log2); |
| }; |
| SkRandom rand; |
| for (int i = 0; i < 100; ++i) { |
| for_random_beziers(4, &rand, [&](const SkPoint pts[]) { |
| check_worst_case_cubic(pts); |
| }); |
| } |
| // Make sure overflow saturates at infinity (not NaN). |
| constexpr static float inf = std::numeric_limits<float>::infinity(); |
| REPORTER_ASSERT(r, wangs_formula::worst_case_cubic_p4(kPrecision, inf, inf) == inf); |
| REPORTER_ASSERT(r, wangs_formula::worst_case_cubic(kPrecision, inf, inf) == inf); |
| } |
| |
| // Ensure Wang's formula for quads produces max error within tolerance. |
| DEF_TEST(wangs_formula_quad_within_tol, r) { |
| // Wang's formula and the quad math starts to lose precision with very large |
| // coordinate values, so limit the magnitude a bit to prevent test failures |
| // due to loss of precision. |
| constexpr int maxExponent = 15; |
| SkRandom rand; |
| for_random_beziers(3, &rand, [&r](const SkPoint pts[]) { |
| const int nsegs = static_cast<int>( |
| std::ceil(wangs_formula_quadratic_reference_impl(kPrecision, pts))); |
| |
| const float tdelta = 1.f / nsegs; |
| for (int j = 0; j < nsegs; ++j) { |
| const float tmin = j * tdelta, tmax = (j + 1) * tdelta; |
| |
| // Get section of quad in [tmin,tmax] |
| const SkPoint* sectionPts; |
| SkPoint tmp0[5]; |
| SkPoint tmp1[5]; |
| if (tmin == 0) { |
| if (tmax == 1) { |
| sectionPts = pts; |
| } else { |
| SkChopQuadAt(pts, tmp0, tmax); |
| sectionPts = tmp0; |
| } |
| } else { |
| SkChopQuadAt(pts, tmp0, tmin); |
| if (tmax == 1) { |
| sectionPts = tmp0 + 2; |
| } else { |
| SkChopQuadAt(tmp0 + 2, tmp1, (tmax - tmin) / (1 - tmin)); |
| sectionPts = tmp1; |
| } |
| } |
| |
| // For quads, max distance from baseline is always at t=0.5. |
| SkPoint p; |
| p = SkEvalQuadAt(sectionPts, 0.5f); |
| |
| // Get distance of p to baseline |
| const SkPoint n = {sectionPts[2].fY - sectionPts[0].fY, |
| sectionPts[0].fX - sectionPts[2].fX}; |
| const float d = std::abs((p - sectionPts[0]).dot(n)) / n.length(); |
| |
| // Check distance is within specified tolerance |
| REPORTER_ASSERT(r, d <= (1.f / kPrecision) + SK_ScalarNearlyZero); |
| } |
| }, maxExponent); |
| } |
| |
| // Ensure the specialized version for rational quads reduces to regular Wang's |
| // formula when all weights are equal to one |
| DEF_TEST(wangs_formula_rational_quad_reduces, r) { |
| constexpr static float kTessellationTolerance = 1 / 128.f; |
| |
| SkRandom rand; |
| for (int i = 0; i < 100; ++i) { |
| for_random_beziers(3, &rand, [&r](const SkPoint pts[]) { |
| const float rational_nsegs = wangs_formula::conic(kPrecision, pts, 1.f); |
| const float integral_nsegs = wangs_formula_quadratic_reference_impl(kPrecision, pts); |
| REPORTER_ASSERT( |
| r, SkScalarNearlyEqual(rational_nsegs, integral_nsegs, kTessellationTolerance)); |
| }); |
| } |
| } |
| |
| // Ensure the rational quad version (used for conics) produces max error within tolerance. |
| DEF_TEST(wangs_formula_conic_within_tol, r) { |
| constexpr int maxExponent = 24; |
| |
| // Single-precision functions in SkConic/SkGeometry lose too much accuracy with |
| // large-magnitude curves and large weights for this test to pass. |
| using Sk2d = skvx::Vec<2, double>; |
| const auto eval_conic = [](const SkPoint pts[3], float w, float t) -> Sk2d { |
| const auto eval = [](Sk2d A, Sk2d B, Sk2d C, float t) -> Sk2d { |
| return (A * t + B) * t + C; |
| }; |
| |
| const Sk2d p0 = {pts[0].fX, pts[0].fY}; |
| const Sk2d p1 = {pts[1].fX, pts[1].fY}; |
| const Sk2d p1w = p1 * w; |
| const Sk2d p2 = {pts[2].fX, pts[2].fY}; |
| Sk2d numer = eval(p2 - p1w * 2 + p0, (p1w - p0) * 2, p0, t); |
| |
| Sk2d denomC = {1, 1}; |
| Sk2d denomB = {2 * (w - 1), 2 * (w - 1)}; |
| Sk2d denomA = {-2 * (w - 1), -2 * (w - 1)}; |
| Sk2d denom = eval(denomA, denomB, denomC, t); |
| return numer / denom; |
| }; |
| |
| const auto dot = [](const Sk2d& a, const Sk2d& b) -> double { |
| return a[0] * b[0] + a[1] * b[1]; |
| }; |
| |
| const auto length = [](const Sk2d& p) -> double { return sqrt(p[0] * p[0] + p[1] * p[1]); }; |
| |
| SkRandom rand; |
| for (int i = -10; i <= 10; ++i) { |
| const float w = std::ldexp(1 + rand.nextF(), i); |
| for_random_beziers( |
| 3, &rand, |
| [&](const SkPoint pts[]) { |
| const int nsegs = SkScalarCeilToInt(wangs_formula::conic(kPrecision, pts, w)); |
| |
| const float tdelta = 1.f / nsegs; |
| for (int j = 0; j < nsegs; ++j) { |
| const float tmin = j * tdelta, tmax = (j + 1) * tdelta, |
| tmid = 0.5f * (tmin + tmax); |
| |
| Sk2d p0, p1, p2; |
| p0 = eval_conic(pts, w, tmin); |
| p1 = eval_conic(pts, w, tmid); |
| p2 = eval_conic(pts, w, tmax); |
| |
| // Get distance of p1 to baseline (p0, p2). |
| const Sk2d n = {p2[1] - p0[1], p0[0] - p2[0]}; |
| SkASSERT(length(n) != 0); |
| const double d = std::abs(dot(p1 - p0, n)) / length(n); |
| |
| // Check distance is within tolerance |
| REPORTER_ASSERT(r, d <= (1.0 / kPrecision) + SK_ScalarNearlyZero); |
| } |
| }, |
| maxExponent); |
| } |
| } |
| |
| // Ensure the vectorized conic version equals the reference implementation |
| DEF_TEST(wangs_formula_conic_matches_reference, r) { |
| SkRandom rand; |
| for (int i = -10; i <= 10; ++i) { |
| const float w = std::ldexp(1 + rand.nextF(), i); |
| for_random_beziers(3, &rand, [&r, w](const SkPoint pts[]) { |
| const float ref_nsegs = wangs_formula_conic_reference_impl(kPrecision, pts, w); |
| const float nsegs = wangs_formula::conic(kPrecision, pts, w); |
| |
| // Because the Gr version may implement the math differently for performance, |
| // allow different slack in the comparison based on the rough scale of the answer. |
| const float cmpThresh = ref_nsegs * (1.f / (1 << 20)); |
| REPORTER_ASSERT(r, SkScalarNearlyEqual(ref_nsegs, nsegs, cmpThresh)); |
| }); |
| } |
| } |
| |
| // Ensure using transformations gives the same result as pre-transforming all points. |
| DEF_TEST(wangs_formula_conic_vectorXforms, r) { |
| auto check_conic_with_transform = [&](const SkPoint* pts, float w, const SkMatrix& m) { |
| SkPoint ptsXformed[3]; |
| m.mapPoints(ptsXformed, pts, 3); |
| float expected = wangs_formula::conic(kPrecision, ptsXformed, w); |
| float actual = wangs_formula::conic(kPrecision, pts, w, wangs_formula::VectorXform(m)); |
| REPORTER_ASSERT(r, SkScalarNearlyEqual(actual, expected)); |
| }; |
| |
| SkRandom rand; |
| for (int i = -10; i <= 10; ++i) { |
| const float w = std::ldexp(1 + rand.nextF(), i); |
| for_random_beziers(3, &rand, [&](const SkPoint pts[]) { |
| check_conic_with_transform(pts, w, SkMatrix::I()); |
| check_conic_with_transform( |
| pts, w, SkMatrix::Scale(rand.nextRangeF(-10, 10), rand.nextRangeF(-10, 10))); |
| |
| // Random 2x2 matrix |
| SkMatrix m; |
| m.setScaleX(rand.nextRangeF(-10, 10)); |
| m.setSkewX(rand.nextRangeF(-10, 10)); |
| m.setSkewY(rand.nextRangeF(-10, 10)); |
| m.setScaleY(rand.nextRangeF(-10, 10)); |
| check_conic_with_transform(pts, w, m); |
| }); |
| } |
| } |
| |
| } // namespace skgpu::tess |