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// Copyright (c) the JPEG XL Project Authors. All rights reserved.
//
// Use of this source code is governed by a BSD-style
// license that can be found in the LICENSE file.
#include "lib/jxl/enc_detect_dots.h"
#include <jxl/memory_manager.h>
#include <algorithm>
#include <array>
#include <cmath>
#include <cstdint>
#include <cstdio>
#include <utility>
#include <vector>
#undef HWY_TARGET_INCLUDE
#define HWY_TARGET_INCLUDE "lib/jxl/enc_detect_dots.cc"
#include <hwy/foreach_target.h>
#include <hwy/highway.h>
#include "lib/jxl/base/common.h"
#include "lib/jxl/base/compiler_specific.h"
#include "lib/jxl/base/data_parallel.h"
#include "lib/jxl/base/printf_macros.h"
#include "lib/jxl/base/rect.h"
#include "lib/jxl/base/status.h"
#include "lib/jxl/convolve.h"
#include "lib/jxl/enc_linalg.h"
#include "lib/jxl/image.h"
#include "lib/jxl/image_ops.h"
// Set JXL_DEBUG_DOT_DETECT to 1 to enable debugging.
#ifndef JXL_DEBUG_DOT_DETECT
#define JXL_DEBUG_DOT_DETECT 0
#endif
HWY_BEFORE_NAMESPACE();
namespace jxl {
namespace HWY_NAMESPACE {
// These templates are not found via ADL.
using hwy::HWY_NAMESPACE::Add;
using hwy::HWY_NAMESPACE::Mul;
using hwy::HWY_NAMESPACE::Sub;
StatusOr<ImageF> SumOfSquareDifferences(const Image3F& forig,
const Image3F& smooth,
ThreadPool* pool) {
const HWY_FULL(float) d;
const auto color_coef0 = Set(d, 0.0f);
const auto color_coef1 = Set(d, 10.0f);
const auto color_coef2 = Set(d, 0.0f);
JxlMemoryManager* memory_manager = forig.memory_manager();
JXL_ASSIGN_OR_RETURN(
ImageF sum_of_squares,
ImageF::Create(memory_manager, forig.xsize(), forig.ysize()));
const auto process_row = [&](const uint32_t task, size_t thread) -> Status {
const size_t y = static_cast<size_t>(task);
const float* JXL_RESTRICT orig_row0 = forig.Plane(0).ConstRow(y);
const float* JXL_RESTRICT orig_row1 = forig.Plane(1).ConstRow(y);
const float* JXL_RESTRICT orig_row2 = forig.Plane(2).ConstRow(y);
const float* JXL_RESTRICT smooth_row0 = smooth.Plane(0).ConstRow(y);
const float* JXL_RESTRICT smooth_row1 = smooth.Plane(1).ConstRow(y);
const float* JXL_RESTRICT smooth_row2 = smooth.Plane(2).ConstRow(y);
float* JXL_RESTRICT sos_row = sum_of_squares.Row(y);
for (size_t x = 0; x < forig.xsize(); x += Lanes(d)) {
auto v0 = Sub(Load(d, orig_row0 + x), Load(d, smooth_row0 + x));
auto v1 = Sub(Load(d, orig_row1 + x), Load(d, smooth_row1 + x));
auto v2 = Sub(Load(d, orig_row2 + x), Load(d, smooth_row2 + x));
v0 = Mul(Mul(v0, v0), color_coef0);
v1 = Mul(Mul(v1, v1), color_coef1);
v2 = Mul(Mul(v2, v2), color_coef2);
const auto sos = Add(v0, Add(v1, v2)); // weighted sum of square diffs
Store(sos, d, sos_row + x);
}
return true;
};
JXL_RETURN_IF_ERROR(RunOnPool(pool, 0, forig.ysize(), ThreadPool::NoInit,
process_row, "ComputeEnergyImage"));
return sum_of_squares;
}
// NOLINTNEXTLINE(google-readability-namespace-comments)
} // namespace HWY_NAMESPACE
} // namespace jxl
HWY_AFTER_NAMESPACE();
#if HWY_ONCE
namespace jxl {
HWY_EXPORT(SumOfSquareDifferences); // Local function
const int kEllipseWindowSize = 5;
namespace {
struct GaussianEllipse {
double x; // position in x
double y; // position in y
double sigma_x; // scale in x
double sigma_y; // scale in y
double angle; // ellipse rotation in radians
std::array<double, 3> intensity; // intensity in each channel
// The following variables do not need to be encoded
double l2_loss; // error after the Gaussian was fit
double l1_loss;
double ridge_loss; // the l2_loss plus regularization term
double custom_loss; // experimental custom loss
std::array<double, 3> bgColor; // best background color
size_t neg_pixels; // number of negative pixels when subtracting dot
std::array<double, 3> neg_value; // debt due to channel truncation
};
double DotGaussianModel(double dx, double dy, double ct, double st,
double sigma_x, double sigma_y, double intensity) {
double rx = ct * dx + st * dy;
double ry = -st * dx + ct * dy;
double md = (rx * rx / sigma_x) + (ry * ry / sigma_y);
double value = intensity * exp(-0.5 * md);
return value;
}
constexpr bool kOptimizeBackground = true;
// Gaussian that smooths noise but preserves dots
const WeightsSeparable5& WeightsSeparable5Gaussian0_65() {
constexpr float w0 = 0.558311f;
constexpr float w1 = 0.210395f;
constexpr float w2 = 0.010449f;
static constexpr WeightsSeparable5 weights = {
{HWY_REP4(w0), HWY_REP4(w1), HWY_REP4(w2)},
{HWY_REP4(w0), HWY_REP4(w1), HWY_REP4(w2)}};
return weights;
}
// (Iterated) Gaussian that removes dots.
const WeightsSeparable5& WeightsSeparable5Gaussian3() {
constexpr float w0 = 0.222338f;
constexpr float w1 = 0.210431f;
constexpr float w2 = 0.1784f;
static constexpr WeightsSeparable5 weights = {
{HWY_REP4(w0), HWY_REP4(w1), HWY_REP4(w2)},
{HWY_REP4(w0), HWY_REP4(w1), HWY_REP4(w2)}};
return weights;
}
StatusOr<ImageF> ComputeEnergyImage(const Image3F& orig, Image3F* smooth,
ThreadPool* pool) {
JxlMemoryManager* memory_manager = orig.memory_manager();
// Prepare guidance images for dot selection.
JXL_ASSIGN_OR_RETURN(
Image3F forig,
Image3F::Create(memory_manager, orig.xsize(), orig.ysize()));
JXL_ASSIGN_OR_RETURN(
*smooth, Image3F::Create(memory_manager, orig.xsize(), orig.ysize()));
Rect rect(orig);
const auto& weights1 = WeightsSeparable5Gaussian0_65();
const auto& weights3 = WeightsSeparable5Gaussian3();
for (size_t c = 0; c < 3; ++c) {
// Use forig as temporary storage to reduce memory and keep it warmer.
JXL_RETURN_IF_ERROR(
Separable5(orig.Plane(c), rect, weights3, pool, &forig.Plane(c)));
JXL_RETURN_IF_ERROR(
Separable5(forig.Plane(c), rect, weights3, pool, &smooth->Plane(c)));
JXL_RETURN_IF_ERROR(
Separable5(orig.Plane(c), rect, weights1, pool, &forig.Plane(c)));
}
return HWY_DYNAMIC_DISPATCH(SumOfSquareDifferences)(forig, *smooth, pool);
}
struct Pixel {
int x;
int y;
};
Pixel operator+(const Pixel& a, const Pixel& b) {
return Pixel{a.x + b.x, a.y + b.y};
}
// Maximum area in pixels of a ellipse
const size_t kMaxCCSize = 1000;
// Extracts a connected component from a Binary image where seed is part
// of the component
bool ExtractComponent(const Rect& rect, ImageF* img, std::vector<Pixel>* pixels,
const Pixel& seed, double threshold) {
static const std::vector<Pixel> neighbors{{1, -1}, {1, 0}, {1, 1}, {0, -1},
{0, 1}, {-1, -1}, {-1, 1}, {1, 0}};
std::vector<Pixel> q{seed};
while (!q.empty()) {
Pixel current = q.back();
q.pop_back();
pixels->push_back(current);
if (pixels->size() > kMaxCCSize) return false;
for (const Pixel& delta : neighbors) {
Pixel child = current + delta;
if (child.x >= 0 && static_cast<size_t>(child.x) < rect.xsize() &&
child.y >= 0 && static_cast<size_t>(child.y) < rect.ysize()) {
float* value = &rect.Row(img, child.y)[child.x];
if (*value > threshold) {
*value = 0.0;
q.push_back(child);
}
}
}
}
return true;
}
inline bool PointInRect(const Rect& r, const Pixel& p) {
return (static_cast<size_t>(p.x) >= r.x0() &&
static_cast<size_t>(p.x) < (r.x0() + r.xsize()) &&
static_cast<size_t>(p.y) >= r.y0() &&
static_cast<size_t>(p.y) < (r.y0() + r.ysize()));
}
struct ConnectedComponent {
ConnectedComponent(const Rect& bounds, const std::vector<Pixel>&& pixels)
: bounds(bounds), pixels(pixels) {}
Rect bounds;
std::vector<Pixel> pixels;
float maxEnergy;
float meanEnergy;
float varEnergy;
float meanBg;
float varBg;
float score;
Pixel mode;
void CompStats(const ImageF& energy, const Rect& rect, int extra) {
maxEnergy = 0.0;
meanEnergy = 0.0;
varEnergy = 0.0;
meanBg = 0.0;
varBg = 0.0;
int nIn = 0;
int nOut = 0;
mode.x = 0;
mode.y = 0;
for (int sy = -extra; sy < (static_cast<int>(bounds.ysize()) + extra);
sy++) {
int y = sy + static_cast<int>(bounds.y0());
if (y < 0 || static_cast<size_t>(y) >= rect.ysize()) continue;
const float* JXL_RESTRICT erow = rect.ConstRow(energy, y);
for (int sx = -extra; sx < (static_cast<int>(bounds.xsize()) + extra);
sx++) {
int x = sx + static_cast<int>(bounds.x0());
if (x < 0 || static_cast<size_t>(x) >= rect.xsize()) continue;
if (erow[x] > maxEnergy) {
maxEnergy = erow[x];
mode.x = x;
mode.y = y;
}
if (PointInRect(bounds, Pixel{x, y})) {
meanEnergy += erow[x];
varEnergy += erow[x] * erow[x];
nIn++;
} else {
meanBg += erow[x];
varBg += erow[x] * erow[x];
nOut++;
}
}
}
meanEnergy = meanEnergy / nIn;
meanBg = meanBg / nOut;
varEnergy = (varEnergy / nIn) - meanEnergy * meanEnergy;
varBg = (varBg / nOut) - meanBg * meanBg;
score = (meanEnergy - meanBg) / std::sqrt(varBg);
}
};
Rect BoundingRectangle(const std::vector<Pixel>& pixels) {
JXL_DASSERT(!pixels.empty());
int low_x;
int high_x;
int low_y;
int high_y;
low_x = high_x = pixels[0].x;
low_y = high_y = pixels[0].y;
for (const Pixel& p : pixels) {
low_x = std::min(low_x, p.x);
high_x = std::max(high_x, p.x);
low_y = std::min(low_y, p.y);
high_y = std::max(high_y, p.y);
}
return Rect(low_x, low_y, high_x - low_x + 1, high_y - low_y + 1);
}
StatusOr<std::vector<ConnectedComponent>> FindCC(const ImageF& energy,
const Rect& rect, double t_low,
double t_high,
uint32_t maxWindow,
double minScore) {
const int kExtraRect = 4;
JxlMemoryManager* memory_manager = energy.memory_manager();
JXL_ASSIGN_OR_RETURN(
ImageF img,
ImageF::Create(memory_manager, energy.xsize(), energy.ysize()));
JXL_RETURN_IF_ERROR(CopyImageTo(energy, &img));
std::vector<ConnectedComponent> ans;
for (size_t y = 0; y < rect.ysize(); y++) {
float* JXL_RESTRICT row = rect.Row(&img, y);
for (size_t x = 0; x < rect.xsize(); x++) {
if (row[x] > t_high) {
std::vector<Pixel> pixels;
row[x] = 0.0;
Pixel seed = Pixel{static_cast<int>(x), static_cast<int>(y)};
bool success = ExtractComponent(rect, &img, &pixels, seed, t_low);
if (!success) continue;
#if JXL_DEBUG_DOT_DETECT
for (size_t i = 0; i < pixels.size(); i++) {
fprintf(stderr, "(%d,%d) ", pixels[i].x, pixels[i].y);
}
fprintf(stderr, "\n");
#endif // JXL_DEBUG_DOT_DETECT
Rect bounds = BoundingRectangle(pixels);
if (bounds.xsize() < maxWindow && bounds.ysize() < maxWindow) {
ConnectedComponent cc{bounds, std::move(pixels)};
cc.CompStats(energy, rect, kExtraRect);
if (cc.score < minScore) continue;
JXL_DEBUG(JXL_DEBUG_DOT_DETECT,
"cc mode: (%d,%d), max: %f, bgMean: %f bgVar: "
"%f bound:(%" PRIuS ",%" PRIuS ",%" PRIuS ",%" PRIuS ")\n",
cc.mode.x, cc.mode.y, cc.maxEnergy, cc.meanEnergy,
cc.varEnergy, cc.bounds.x0(), cc.bounds.y0(),
cc.bounds.xsize(), cc.bounds.ysize());
ans.push_back(cc);
}
}
}
}
return ans;
}
// TODO(sggonzalez): Adapt this function for the different color spaces or
// remove it if the color space with the best performance does not need it
void ComputeDotLosses(GaussianEllipse* ellipse, const ConnectedComponent& cc,
const Rect& rect, const Image3F& img,
const Image3F& background) {
const int rectBounds = 2;
const double kIntensityR = 0.0; // 0.015;
const double kSigmaR = 0.0; // 0.01;
const double kZeroEpsilon = 0.1; // Tolerance to consider a value negative
double ct = cos(ellipse->angle);
double st = sin(ellipse->angle);
const std::array<double, 3> channelGains{{1.0, 1.0, 1.0}};
int N = 0;
ellipse->l1_loss = 0.0;
ellipse->l2_loss = 0.0;
ellipse->neg_pixels = 0;
ellipse->neg_value.fill(0.0);
double distMeanModeSq = (cc.mode.x - ellipse->x) * (cc.mode.x - ellipse->x) +
(cc.mode.y - ellipse->y) * (cc.mode.y - ellipse->y);
ellipse->custom_loss = 0.0;
for (int c = 0; c < 3; c++) {
for (int sy = -rectBounds;
sy < (static_cast<int>(cc.bounds.ysize()) + rectBounds); sy++) {
int y = sy + cc.bounds.y0();
if (y < 0 || static_cast<size_t>(y) >= rect.ysize()) continue;
const float* JXL_RESTRICT row = rect.ConstPlaneRow(img, c, y);
// bgrow is only used if kOptimizeBackground is false.
// NOLINTNEXTLINE(clang-analyzer-deadcode.DeadStores)
const float* JXL_RESTRICT bgrow = rect.ConstPlaneRow(background, c, y);
for (int sx = -rectBounds;
sx < (static_cast<int>(cc.bounds.xsize()) + rectBounds); sx++) {
int x = sx + cc.bounds.x0();
if (x < 0 || static_cast<size_t>(x) >= rect.xsize()) continue;
double target = row[x];
double dotDelta = DotGaussianModel(
x - ellipse->x, y - ellipse->y, ct, st, ellipse->sigma_x,
ellipse->sigma_y, ellipse->intensity[c]);
if (dotDelta > target + kZeroEpsilon) {
ellipse->neg_pixels++;
ellipse->neg_value[c] += dotDelta - target;
}
double bkg = kOptimizeBackground ? ellipse->bgColor[c] : bgrow[x];
double pred = bkg + dotDelta;
double diff = target - pred;
double l2 = channelGains[c] * diff * diff;
double l1 = channelGains[c] * std::fabs(diff);
ellipse->l2_loss += l2;
ellipse->l1_loss += l1;
double w = DotGaussianModel(x - cc.mode.x, y - cc.mode.y, 1.0, 0.0,
1.0 + ellipse->sigma_x,
1.0 + ellipse->sigma_y, 1.0);
ellipse->custom_loss += w * l2;
N++;
}
}
}
ellipse->l2_loss /= N;
ellipse->custom_loss /= N;
ellipse->custom_loss += 20.0 * distMeanModeSq + ellipse->neg_value[1];
ellipse->l1_loss /= N;
double ridgeTerm = kSigmaR * ellipse->sigma_x + kSigmaR * ellipse->sigma_y;
for (int c = 0; c < 3; c++) {
ridgeTerm += kIntensityR * ellipse->intensity[c] * ellipse->intensity[c];
}
ellipse->ridge_loss = ellipse->l2_loss + ridgeTerm;
}
StatusOr<GaussianEllipse> FitGaussianFast(const ConnectedComponent& cc,
const Rect& rect, const Image3F& img,
const Image3F& background) {
constexpr bool leastSqIntensity = true;
constexpr double kEpsilon = 1e-6;
GaussianEllipse ans;
constexpr int kRectBounds = (kEllipseWindowSize >> 1);
// Compute the 1st and 2nd moments of the CC
double sum = 0.0;
int N = 0;
std::array<double, 3> m1{{0.0, 0.0, 0.0}};
std::array<double, 3> m2{{0.0, 0.0, 0.0}};
std::array<double, 3> color{{0.0, 0.0, 0.0}};
std::array<double, 3> bgColor{{0.0, 0.0, 0.0}};
JXL_DEBUG(JXL_DEBUG_DOT_DETECT,
"%" PRIuS " %" PRIuS " %" PRIuS " %" PRIuS "\n", cc.bounds.x0(),
cc.bounds.y0(), cc.bounds.xsize(), cc.bounds.ysize());
for (int c = 0; c < 3; c++) {
color[c] = rect.ConstPlaneRow(img, c, cc.mode.y)[cc.mode.x] -
rect.ConstPlaneRow(background, c, cc.mode.y)[cc.mode.x];
}
double sign = (color[1] > 0) ? 1 : -1;
for (int sy = -kRectBounds; sy <= kRectBounds; sy++) {
int y = sy + cc.mode.y;
if (y < 0 || static_cast<size_t>(y) >= rect.ysize()) continue;
const float* JXL_RESTRICT row = rect.ConstPlaneRow(img, 1, y);
const float* JXL_RESTRICT bgrow = rect.ConstPlaneRow(background, 1, y);
for (int sx = -kRectBounds; sx <= kRectBounds; sx++) {
int x = sx + cc.mode.x;
if (x < 0 || static_cast<size_t>(x) >= rect.xsize()) continue;
double w = std::max(kEpsilon, sign * (row[x] - bgrow[x]));
sum += w;
m1[0] += w * x;
m1[1] += w * y;
m2[0] += w * x * x;
m2[1] += w * x * y;
m2[2] += w * y * y;
for (int c = 0; c < 3; c++) {
bgColor[c] += rect.ConstPlaneRow(background, c, y)[x];
}
N++;
}
}
JXL_ENSURE(N > 0);
for (int i = 0; i < 3; i++) {
m1[i] /= sum;
m2[i] /= sum;
bgColor[i] /= N;
}
// Some magic constants
constexpr double kSigmaMult = 1.0;
constexpr std::array<double, 3> kScaleMult{{1.1, 1.1, 1.1}};
// Now set the parameters of the Gaussian
ans.x = m1[0];
ans.y = m1[1];
for (int j = 0; j < 3; j++) {
ans.intensity[j] = kScaleMult[j] * color[j];
}
Matrix2x2 Sigma;
Vector2 d;
Matrix2x2 U;
Sigma[0][0] = m2[0] - m1[0] * m1[0];
Sigma[1][1] = m2[2] - m1[1] * m1[1];
Sigma[0][1] = Sigma[1][0] = m2[1] - m1[0] * m1[1];
ConvertToDiagonal(Sigma, d, U);
Vector2& u = U[1];
int p1 = 0;
int p2 = 1;
if (d[0] < d[1]) std::swap(p1, p2);
ans.sigma_x = kSigmaMult * d[p1];
ans.sigma_y = kSigmaMult * d[p2];
ans.angle = std::atan2(u[p1], u[p2]);
ans.l2_loss = 0.0;
ans.bgColor = bgColor;
if (leastSqIntensity) {
GaussianEllipse* ellipse = &ans;
double ct = cos(ans.angle);
double st = sin(ans.angle);
// Estimate intensity with least squares (fixed background)
for (int c = 0; c < 3; c++) {
double gg = 0.0;
double gd = 0.0;
int yc = static_cast<int>(cc.mode.y);
int xc = static_cast<int>(cc.mode.x);
for (int y = yc - kRectBounds; y <= yc + kRectBounds; y++) {
if (y < 0 || static_cast<size_t>(y) >= rect.ysize()) continue;
const float* JXL_RESTRICT row = rect.ConstPlaneRow(img, c, y);
const float* JXL_RESTRICT bgrow = rect.ConstPlaneRow(background, c, y);
for (int x = xc - kRectBounds; x <= xc + kRectBounds; x++) {
if (x < 0 || static_cast<size_t>(x) >= rect.xsize()) continue;
double target = row[x] - bgrow[x];
double gaussian =
DotGaussianModel(x - ellipse->x, y - ellipse->y, ct, st,
ellipse->sigma_x, ellipse->sigma_y, 1.0);
gg += gaussian * gaussian;
gd += gaussian * target;
}
}
ans.intensity[c] = gd / (gg + 1e-6); // Regularized least squares
}
}
ComputeDotLosses(&ans, cc, rect, img, background);
return ans;
}
StatusOr<GaussianEllipse> FitGaussian(const ConnectedComponent& cc,
const Rect& rect, const Image3F& img,
const Image3F& background) {
JXL_ASSIGN_OR_RETURN(GaussianEllipse ellipse,
FitGaussianFast(cc, rect, img, background));
if (ellipse.sigma_x < ellipse.sigma_y) {
std::swap(ellipse.sigma_x, ellipse.sigma_y);
ellipse.angle += kPi / 2.0;
}
ellipse.angle -= kPi * std::floor(ellipse.angle / kPi);
if (fabs(ellipse.angle - kPi) < 1e-6 || fabs(ellipse.angle) < 1e-6) {
ellipse.angle = 0.0;
}
JXL_ENSURE(ellipse.angle >= 0 && ellipse.angle <= kPi &&
ellipse.sigma_x >= ellipse.sigma_y);
JXL_DEBUG(JXL_DEBUG_DOT_DETECT,
"Ellipse mu=(%lf,%lf) sigma=(%lf,%lf) angle=%lf "
"intensity=(%lf,%lf,%lf) bg=(%lf,%lf,%lf) l2_loss=%lf "
"custom_loss=%lf, neg_pix=%" PRIuS ", neg_v=(%lf,%lf,%lf)\n",
ellipse.x, ellipse.y, ellipse.sigma_x, ellipse.sigma_y,
ellipse.angle, ellipse.intensity[0], ellipse.intensity[1],
ellipse.intensity[2], ellipse.bgColor[0], ellipse.bgColor[1],
ellipse.bgColor[2], ellipse.l2_loss, ellipse.custom_loss,
ellipse.neg_pixels, ellipse.neg_value[0], ellipse.neg_value[1],
ellipse.neg_value[2]);
return ellipse;
}
} // namespace
StatusOr<std::vector<PatchInfo>> DetectGaussianEllipses(
const Image3F& opsin, const Rect& rect, const GaussianDetectParams& params,
const EllipseQuantParams& qParams, ThreadPool* pool) {
JxlMemoryManager* memory_manager = opsin.memory_manager();
std::vector<PatchInfo> dots;
JXL_ASSIGN_OR_RETURN(
Image3F smooth,
Image3F::Create(memory_manager, opsin.xsize(), opsin.ysize()));
JXL_ASSIGN_OR_RETURN(ImageF energy, ComputeEnergyImage(opsin, &smooth, pool));
JXL_ASSIGN_OR_RETURN(std::vector<ConnectedComponent> components,
FindCC(energy, rect, params.t_low, params.t_high,
params.maxWinSize, params.minScore));
size_t numCC =
std::min(params.maxCC, (components.size() * params.percCC) / 100);
if (components.size() > numCC) {
std::sort(
components.begin(), components.end(),
[](const ConnectedComponent& a, const ConnectedComponent& b) -> bool {
return a.score > b.score;
});
components.erase(components.begin() + numCC, components.end());
}
for (const auto& cc : components) {
JXL_ASSIGN_OR_RETURN(GaussianEllipse ellipse,
FitGaussian(cc, rect, opsin, smooth));
if (ellipse.x < 0.0 ||
std::ceil(ellipse.x) >= static_cast<double>(rect.xsize()) ||
ellipse.y < 0.0 ||
std::ceil(ellipse.y) >= static_cast<double>(rect.ysize())) {
continue;
}
if (ellipse.neg_pixels > params.maxNegPixels) continue;
double intensity = 0.21 * ellipse.intensity[0] +
0.72 * ellipse.intensity[1] +
0.07 * ellipse.intensity[2];
double intensitySq = intensity * intensity;
// for (int c = 0; c < 3; c++) {
// intensitySq += ellipse.intensity[c] * ellipse.intensity[c];
//}
double sqDistMeanMode = (ellipse.x - cc.mode.x) * (ellipse.x - cc.mode.x) +
(ellipse.y - cc.mode.y) * (ellipse.y - cc.mode.y);
if (ellipse.l2_loss < params.maxL2Loss &&
ellipse.custom_loss < params.maxCustomLoss &&
intensitySq > (params.minIntensity * params.minIntensity) &&
sqDistMeanMode < params.maxDistMeanMode * params.maxDistMeanMode) {
size_t x0 = cc.bounds.x0();
size_t y0 = cc.bounds.y0();
dots.emplace_back();
dots.back().second.emplace_back(x0, y0);
QuantizedPatch& patch = dots.back().first;
patch.xsize = cc.bounds.xsize();
patch.ysize = cc.bounds.ysize();
for (size_t y = 0; y < patch.ysize; y++) {
for (size_t x = 0; x < patch.xsize; x++) {
for (size_t c = 0; c < 3; c++) {
patch.fpixels[c][y * patch.xsize + x] =
rect.ConstPlaneRow(opsin, c, y0 + y)[x0 + x] -
rect.ConstPlaneRow(smooth, c, y0 + y)[x0 + x];
}
}
}
}
}
return dots;
}
} // namespace jxl
#endif // HWY_ONCE