Revert "Gauss filter calculation"

This reverts commit 53ec7dc7cb523f220a9f5cd713b241c706779c81.

Reason for revert: Segv on very specific machines.

Original change's description:
> Gauss filter calculation
> 
> Change-Id: I921ef815d4f788c312aa729f353b6ea154140555
> Reviewed-on: https://skia-review.googlesource.com/67723
> Commit-Queue: Herb Derby <herb@google.com>
> Reviewed-by: Robert Phillips <robertphillips@google.com>

TBR=herb@google.com,robertphillips@google.com

Change-Id: I15164809d081dee0076e815b40fbfdbc6374cfba
No-Presubmit: true
No-Tree-Checks: true
No-Try: true
Reviewed-on: https://skia-review.googlesource.com/69641
Reviewed-by: Herb Derby <herb@google.com>
Commit-Queue: Herb Derby <herb@google.com>

1 files changed, 0 insertions, 152 deletions

diff --git a/src/core/SkGaussFilter.cpp b/src/core/SkGaussFilter.cpp
deleted file mode 100644
index 548ff4398d..0000000000
--- a/src/core/SkGaussFilter.cpp
+++ /dev/null
@@ -1,152 +0,0 @@
-/*
- * Copyright 2017 Google Inc.
- *
- * Use of this source code is governed by a BSD-style license that can be
- * found in the LICENSE file.
- */
-
-
-#include "SkGaussFilter.h"
-
-#include <cmath>
-#include "SkTypes.h"
-
-static constexpr double kPi = 3.14159265358979323846264338327950288;
-
-// The value when we can stop expanding the filter. The spec implies that 3% is acceptable, but
-// we just use 1%.
-static constexpr double kGoodEnough = 1.0 / 100.0;
-
-// Normalize the values of gauss to 1.0, and make sure they add to one.
-// NB if n == 1, then this will force gauss[0] == 1.
-static void normalize(int n, double* gauss) {
-    // Carefully add from smallest to largest to calculate the normalizing sum.
-    double sum = 0;
-    for (int i = n-1; i >= 1; i--) {
-        sum += 2 * gauss[i];
-    }
-    sum += gauss[0];
-
-    // Normalize gauss.
-    for (int i = 0; i < n; i++) {
-        gauss[i] /= sum;
-    }
-
-    // The factors should sum to 1. Take any remaining slop, and add it to gauss[0]. Add the
-    // values in such a way to maintain the most accuracy.
-    sum = 0;
-    for (int i = n - 1; i >= 1; i--) {
-        sum += 2 * gauss[i];
-    }
-
-    gauss[0] = 1 - sum;
-}
-
-static int calculate_bessel_factors(double sigma, double *gauss) {
-    auto var = sigma * sigma;
-
-    // The two functions below come from the equations in "Handbook of Mathematical Functions"
-    // by Abramowitz and Stegun. Specifically, equation 9.6.10 on page 375. Bessel0 is given
-    // explicitly as 9.6.12
-    // BesselI_0 for 0 <= sigma < 2.
-    // NB the k = 0 factor is just sum = 1.0.
-    auto besselI_0 = [](double t) -> double {
-        auto tSquaredOver4 = t * t / 4.0;
-        auto sum = 1.0;
-        auto factor = 1.0;
-        auto k = 1;
-        // Use a variable number of loops. When sigma is small, this only requires 3-4 loops, but
-        // when sigma is near 2, it could require 10 loops. The same holds for BesselI_1.
-        while(factor > 1.0/1000000.0) {
-            factor *= tSquaredOver4 / (k * k);
-            sum += factor;
-            k += 1;
-        }
-        return sum;
-    };
-    // BesselI_1 for 0 <= sigma < 2.
-    auto besselI_1 = [](double t) -> double {
-        auto tSquaredOver4 = t * t / 4.0;
-        auto sum = t / 2.0;
-        auto factor = sum;
-        auto k = 1;
-        while (factor > 1.0/1000000.0) {
-            factor *= tSquaredOver4 / (k * (k + 1));
-            sum += factor;
-            k += 1;
-        }
-        return sum;
-    };
-
-    // The following formula for calculating the Gaussian kernel is from
-    // "Scale-Space for Discrete Signals" by Tony Lindeberg.
-    // gauss(n; var) = besselI_n(var) / (e^var)
-    auto d = std::exp(var);
-    double b[6] = {besselI_0(var), besselI_1(var)};
-    gauss[0] = b[0]/d;
-    gauss[1] = b[1]/d;
-
-    int n = 1;
-    // The recurrence relation below is from "Numerical Recipes" 3rd Edition.
-    // Equation 6.5.16 p.282
-    while (gauss[n] > kGoodEnough) {
-        b[n+1] = -(2*n/var) * b[n] + b[n-1];
-        gauss[n+1] = b[n+1] / d;
-        n += 1;
-    }
-
-    normalize(n, gauss);
-
-    return n;
-}
-
-static int calculate_gauss_factors(double sigma, double* gauss) {
-    SkASSERT(0 <= sigma && sigma < 2);
-
-    // From the SVG blur spec: 8.13 Filter primitive <feGaussianBlur>.
-    // H(x) = exp(-x^2/ (2s^2)) / sqrt(2π * s^2)
-    auto var = sigma * sigma;
-    auto expGaussDenom = -2 * var;
-    auto normalizeDenom = std::sqrt(2 * kPi) * sigma;
-
-    // Use the recursion relation from "Incremental Computation of the Gaussian"  by Ken
-    // Turkowski in GPUGems 3. Page 877.
-    double g0 = 1.0 / normalizeDenom;
-    double g1 = std::exp(1.0 / expGaussDenom);
-    double g2 = g1 * g1;
-
-    gauss[0] = g0;
-    g0 *= g1;
-    g1 *= g2;
-    gauss[1] = g0;
-
-    int n = 1;
-    while (gauss[n] > kGoodEnough) {
-        g0 *= g1;
-        g1 *= g2;
-        gauss[n+1] = g0;
-        n += 1;
-    }
-
-    normalize(n, gauss);
-
-    return n;
-}
-
-SkGaussFilter::SkGaussFilter(double sigma, Type type) {
-    SkASSERT(0 <= sigma && sigma < 2);
-
-    if (type == Type::Bessel) {
-        fN = calculate_bessel_factors(sigma, fBasis);
-    } else {
-        fN = calculate_gauss_factors(sigma, fBasis);
-    }
-}
-
-int SkGaussFilter::filterDouble(double* values) const {
-    for (int i = 0; i < fN; i++) {
-        values[i] = fBasis[i];
-    }
-    return fN;
-}
-