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Roll skcms from dcb0286a1e17 to 9c30a95f0f16 (1 revision)

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https://skia.googlesource.com/skcms.git/+log/dcb0286a1e17..9c30a95f0f16

2022-05-20 brianosman@google.com Fix "implicit conversion from 'int' to 'float' may lose precision"

If this roll has caused a breakage, revert this CL and stop the roller
using the controls here:
https://autoroll.skia.org/r/skcms-skia-autoroll
Please CC brianosman@google.com,jmbetancourt@google.com on the revert to ensure that a human
is aware of the problem.

To file a bug in Skia: https://bugs.chromium.org/p/skia/issues/entry

To report a problem with the AutoRoller itself, please file a bug:
https://bugs.chromium.org/p/skia/issues/entry?template=Autoroller+Bug

Documentation for the AutoRoller is here:
https://skia.googlesource.com/buildbot/+doc/main/autoroll/README.md

Cq-Include-Trybots: skia/skia.primary:Canary-Chromium
Tbr: brianosman@google.com,jmbetancourt@google.com
Change-Id: Ibd8f1fc3eec9a853fc766979419e9c4ad5af049d
Reviewed-on: https://skia-review.googlesource.com/c/skia/+/542718
Commit-Queue: skia-autoroll <skia-autoroll@skia-public.iam.gserviceaccount.com>
Bot-Commit: skia-autoroll <skia-autoroll@skia-public.iam.gserviceaccount.com>
This commit is contained in:
skia-autoroll 2022-05-20 17:29:31 +00:00 committed by SkCQ
parent 30cec70bef
commit e803e61f4f
2 changed files with 19 additions and 19 deletions
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36
third_party/skcms/skcms.cc vendored
View File

@ -144,7 +144,7 @@ static float TFKind_marker(TFKind kind) {
static TFKind classify(const skcms_TransferFunction& tf, TF_PQish* pq = nullptr
, TF_HLGish* hlg = nullptr) {
if (tf.g < 0 && (int)tf.g == tf.g) {
if (tf.g < 0 && static_cast<float>(static_cast<int>(tf.g)) == tf.g) {
// TODO: soundness checks for PQ/HLG like we do for sRGBish?
switch ((int)tf.g) {
case -PQish: if (pq ) { memcpy(pq , &tf.a, sizeof(*pq )); } return PQish;
@ -235,7 +235,7 @@ static float eval_curve(const skcms_Curve* curve, float x) {
return skcms_TransferFunction_eval(&curve->parametric, x);
}
float ix = fmaxf_(0, fminf_(x, 1)) * (curve->table_entries - 1);
float ix = fmaxf_(0, fminf_(x, 1)) * static_cast<float>(curve->table_entries - 1);
int lo = (int) ix ,
hi = (int)(float)minus_1_ulp(ix + 1.0f);
float t = ix - (float)lo;
@ -258,10 +258,10 @@ static float eval_curve(const skcms_Curve* curve, float x) {
float skcms_MaxRoundtripError(const skcms_Curve* curve, const skcms_TransferFunction* inv_tf) {
uint32_t N = curve->table_entries > 256 ? curve->table_entries : 256;
const float dx = 1.0f / (N - 1);
const float dx = 1.0f / static_cast<float>(N - 1);
float err = 0;
for (uint32_t i = 0; i < N; i++) {
float x = i * dx,
float x = static_cast<float>(i) * dx,
y = eval_curve(curve, x);
err = fmaxf_(err, fabsf_(x - skcms_TransferFunction_eval(inv_tf, y)));
}
@ -330,7 +330,7 @@ static int32_t read_big_i32(const uint8_t* ptr) {
}
static float read_big_fixed(const uint8_t* ptr) {
return read_big_i32(ptr) * (1.0f / 65536.0f);
return static_cast<float>(read_big_i32(ptr)) * (1.0f / 65536.0f);
}
// Maps to an in-memory profile so that fields line up to the locations specified
@ -1063,7 +1063,7 @@ static int fit_linear(const skcms_Curve* curve, int N, float tol,
// Some points' error intervals may intersect the running interval but not lie fully
// within it. So we keep track of the last point we saw that is a valid end point candidate,
// and once the search is done, back up to build the line through *that* point.
const float dx = 1.0f / (N - 1);
const float dx = 1.0f / static_cast<float>(N - 1);
int lin_points = 1;
@ -1078,7 +1078,7 @@ static int fit_linear(const skcms_Curve* curve, int N, float tol,
float slope_min = -INFINITY_;
float slope_max = +INFINITY_;
for (int i = 1; i < N; ++i) {
float x = i * dx;
float x = static_cast<float>(i) * dx;
float y = eval_curve(curve, x);
float slope_max_i = (y + tol - *f) / x,
@ -1098,7 +1098,7 @@ static int fit_linear(const skcms_Curve* curve, int N, float tol,
}
// Set D to the last point that met our tolerance.
*d = (lin_points - 1) * dx;
*d = static_cast<float>(lin_points - 1) * dx;
return lin_points;
}
@ -1108,7 +1108,7 @@ static void canonicalize_identity(skcms_Curve* curve) {
int N = (int)curve->table_entries;
float c = 0.0f, d = 0.0f, f = 0.0f;
if (N == fit_linear(curve, N, 1.0f/(2*N), &c,&d,&f)
if (N == fit_linear(curve, N, 1.0f/static_cast<float>(2*N), &c,&d,&f)
&& c == 1.0f
&& f == 0.0f) {
curve->table_entries = 0;
@ -2031,7 +2031,7 @@ static bool gauss_newton_step(const skcms_Curve* curve,
// We want to evaluate Jf only once, but both lhs and rhs involve Jf^T,
// so we'll have to update lhs and rhs at the same time.
for (int i = 0; i < N; i++) {
float x = x0 + i*dx;
float x = x0 + static_cast<float>(i)*dx;
float dfdP[3] = {0,0,0};
float resid = rg_nonlinear(x,curve,tf, dfdP);
@ -2112,7 +2112,7 @@ static bool fit_nonlinear(const skcms_Curve* curve, int L, int N, skcms_Transfer
}
// No matter where we start, dx should always represent N even steps from 0 to 1.
const float dx = 1.0f / (N-1);
const float dx = 1.0f / static_cast<float>(N-1);
skcms_TransferFunction best_tf = *tf;
float best_max_error = INFINITY_;
@ -2126,7 +2126,7 @@ static bool fit_nonlinear(const skcms_Curve* curve, int L, int N, skcms_Transfer
// As far as we can tell, 1 Gauss-Newton step won't converge, and 3 steps is no better than 2.
for (int j = 0; j < 8; j++) {
if (!gauss_newton_step(curve, tf, L*dx, dx, N-L) || !fixup_tf()) {
if (!gauss_newton_step(curve, tf, static_cast<float>(L)*dx, dx, N-L) || !fixup_tf()) {
*tf = best_tf;
return isfinitef_(best_max_error);
}
@ -2160,7 +2160,7 @@ bool skcms_ApproximateCurve(const skcms_Curve* curve,
}
int N = (int)curve->table_entries;
const float dx = 1.0f / (N - 1);
const float dx = 1.0f / static_cast<float>(N - 1);
*max_error = INFINITY_;
const float kTolerances[] = { 1.5f / 65535.0f, 1.0f / 512.0f };
@ -2182,16 +2182,16 @@ bool skcms_ApproximateCurve(const skcms_Curve* curve,
} else if (L == N - 1) {
// Degenerate case with only two points in the nonlinear segment. Solve directly.
tf.g = 1;
tf.a = (eval_curve(curve, (N-1)*dx) -
eval_curve(curve, (N-2)*dx))
tf.a = (eval_curve(curve, static_cast<float>(N-1)*dx) -
eval_curve(curve, static_cast<float>(N-2)*dx))
/ dx;
tf.b = eval_curve(curve, (N-2)*dx)
- tf.a * (N-2)*dx;
tf.b = eval_curve(curve, static_cast<float>(N-2)*dx)
- tf.a * static_cast<float>(N-2)*dx;
tf.e = 0;
} else {
// Start by guessing a gamma-only curve through the midpoint.
int mid = (L + N) / 2;
float mid_x = mid / (N - 1.0f);
float mid_x = static_cast<float>(mid) / static_cast<float>(N - 1);
float mid_y = eval_curve(curve, mid_x);
tf.g = log2f_(mid_y) / log2f_(mid_x);
tf.a = 1;

2
third_party/skcms/version.sha1 vendored
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@ -1 +1 @@
dcb0286a1e17fcd6a3645281c52aab11e466fb1d
9c30a95f0f167ee1513e5a1ea6846b15a010385c