obs-StreamFX/source/util/utility.hpp

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// AUTOGENERATED COPYRIGHT HEADER START
// Copyright (C) 2020-2023 Michael Fabian 'Xaymar' Dirks <info@xaymar.com>
// Copyright (C) 2022 lainon <GermanAizek@yandex.ru>
// AUTOGENERATED COPYRIGHT HEADER END
#pragma once
#include "warning-disable.hpp"
#include <cinttypes>
#include <cstddef>
#include <string>
#include <type_traits>
#include <vector>
#include "warning-enable.hpp"
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extern "C" {
#include "warning-disable.hpp"
#include <obs.h>
#include <graphics/vec2.h>
#include <graphics/vec3.h>
#include <graphics/vec4.h>
#include "warning-enable.hpp"
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}
// Constants
#define S_PI 3.1415926535897932384626433832795 // PI = pi
#define S_PI2 6.283185307179586476925286766559 // 2PI = 2 * pi
#define S_PI2_SQROOT 2.506628274631000502415765284811 // sqrt(2 * pi)
#define S_RAD 57.295779513082320876798154814105 // 180/pi
#define S_DEG 0.01745329251994329576923690768489 // pi/180
#define D_DEG_TO_RAD(x) (x * S_DEG)
#define D_RAD_TO_DEG(x) (x * S_RAD)
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#define D_STR(s) #s
#define D_VSTR(s) D_STR(s)
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namespace streamfx::util {
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bool inline are_property_groups_broken()
{
return obs_get_version() < MAKE_SEMANTIC_VERSION(24, 0, 0);
}
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obs_property_t* obs_properties_add_tristate(obs_properties_t* props, const char* name, const char* desc);
inline bool is_tristate_enabled(int64_t tristate)
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{
return tristate == 1;
}
inline bool is_tristate_disabled(int64_t tristate)
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{
return tristate == 0;
}
inline bool is_tristate_default(int64_t tristate)
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{
return tristate == -1;
}
struct vec2a : public vec2 {
// 16-byte Aligned version of vec2
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static void* operator new(std::size_t count);
static void* operator new[](std::size_t count);
static void operator delete(void* p);
static void operator delete[](void* p);
};
#ifdef _MSC_VER
__declspec(align(16))
#endif
struct vec3a : public vec3 {
// 16-byte Aligned version of vec3
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static void* operator new(std::size_t count);
static void* operator new[](std::size_t count);
static void operator delete(void* p);
static void operator delete[](void* p);
};
#ifdef _MSC_VER
__declspec(align(16))
#endif
struct vec4a : public vec4 {
// 16-byte Aligned version of vec4
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static void* operator new(std::size_t count);
static void* operator new[](std::size_t count);
static void operator delete(void* p);
static void operator delete[](void* p);
};
std::pair<int64_t, int64_t> size_from_string(std::string_view text, bool allowSquare = true);
namespace math {
template<typename T>
inline T pow(T base, T exp)
{
T res = 1;
while (exp) {
if (exp & 1)
res *= base;
exp >>= 1;
base *= base;
}
return res;
}
// Proven by tests to be the fastest implementation on Intel and AMD CPUs.
// Ranking: log10, loop < bitscan < pow
// loop and log10 trade blows, usually almost identical.
// loop is used for integers, log10 for anything else.
template<typename T>
inline bool is_power_of_two(T v)
{
return T(1ull << uint64_t(floor(log10(T(v)) / log10(2.0)))) == v;
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}
template<typename T>
inline bool is_power_of_two_loop(T v)
{
bool have_bit = false;
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for (std::size_t index = 0; index < (sizeof(T) * 8); index++) {
bool cur = (v & (static_cast<T>(1ull) << index)) != 0;
if (cur) {
if (have_bit)
return false;
have_bit = true;
}
}
return true;
}
#pragma push_macro("P_IS_POWER_OF_TWO_AS_LOOP")
#define P_IS_POWER_OF_TWO_AS_LOOP(x) \
template<> \
inline bool is_power_of_two(x v) \
{ \
return is_power_of_two_loop(v); \
}
P_IS_POWER_OF_TWO_AS_LOOP(int8_t)
P_IS_POWER_OF_TWO_AS_LOOP(uint8_t)
P_IS_POWER_OF_TWO_AS_LOOP(int16_t)
P_IS_POWER_OF_TWO_AS_LOOP(uint16_t)
P_IS_POWER_OF_TWO_AS_LOOP(int32_t)
P_IS_POWER_OF_TWO_AS_LOOP(uint32_t)
P_IS_POWER_OF_TWO_AS_LOOP(int64_t)
P_IS_POWER_OF_TWO_AS_LOOP(uint64_t)
#undef P_IS_POWER_OF_TWO_AS_LOOP
#pragma pop_macro("P_IS_POWER_OF_TWO_AS_LOOP")
template<typename T>
inline uint64_t get_power_of_two_exponent_floor(T v)
{
return uint64_t(floor(log10(T(v)) / log10(2.0)));
}
template<typename T>
inline uint64_t get_power_of_two_exponent_ceil(T v)
{
return uint64_t(ceil(log10(T(v)) / log10(2.0)));
}
template<typename T, typename C>
inline bool is_equal(T target, C value)
{
return (target > (value - std::numeric_limits<T>::epsilon())) && (target < (value + std::numeric_limits<T>::epsilon()));
}
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template<typename T>
inline bool is_close(T target, T value, T delta)
{
return (target > (value - delta)) && (target < (value + delta));
}
template<typename T>
inline std::vector<T> pascal_triangle(size_t n)
{
std::vector<T> line;
line.push_back(1);
for (uint64_t k = 0; k < n; k++) {
T v = static_cast<T>(line.at(k) * static_cast<double_t>(n - k) / static_cast<double_t>(k + 1));
line.push_back(v);
}
return line;
}
template<typename T>
inline T gaussian(T x, T o /*, T u = 0*/)
{
// u/µ can be simulated by subtracting that value from x.
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//static const double_t pi = 3.1415926535897932384626433832795;
//static const double_t two_pi = pi * 2.;
static const double_t two_pi_sqroot = 2.506628274631000502415765284811; //sqrt(two_pi);
if (is_equal<double_t>(0, o)) {
return T(std::numeric_limits<double_t>::infinity());
}
// g(x) = (1 / o√(2Π)) * e(-(1/2) * ((x-u)/o)²)
double_t left_e = 1. / (o * two_pi_sqroot);
double_t mid_right_e = ((x /* - u*/) / o);
double_t right_e = -0.5 * mid_right_e * mid_right_e;
double_t final = left_e * exp(right_e);
return T(final);
}
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template<typename T>
inline T lerp(T a, T b, double_t v)
{
return static_cast<T>((static_cast<double_t>(a) * (1.0 - v)) + (static_cast<double_t>(b) * v));
}
template<typename T>
class kalman1D {
T _q_process_noise_covariance;
T _r_measurement_noise_covariance;
T _x_value_of_interest;
T _p_estimation_error_covariance;
T _k_kalman_gain;
public:
kalman1D() : _q_process_noise_covariance(0), _r_measurement_noise_covariance(0), _x_value_of_interest(0), _p_estimation_error_covariance(0), _k_kalman_gain(0.0) {}
kalman1D(T pnc, T mnc, T eec, T value) : _q_process_noise_covariance(pnc), _r_measurement_noise_covariance(mnc), _x_value_of_interest(value), _p_estimation_error_covariance(eec), _k_kalman_gain(0.0) {}
~kalman1D() = default;
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T filter(T measurement)
{
_p_estimation_error_covariance += _q_process_noise_covariance;
_k_kalman_gain = _p_estimation_error_covariance / (_p_estimation_error_covariance + _r_measurement_noise_covariance);
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_x_value_of_interest += _k_kalman_gain * (measurement - _x_value_of_interest);
_p_estimation_error_covariance = (1 - _k_kalman_gain) * _p_estimation_error_covariance;
return _x_value_of_interest;
}
T get()
{
return _x_value_of_interest;
}
};
} // namespace math
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inline std::size_t aligned_offset(std::size_t align, std::size_t pos)
{
return ((pos / align) + 1) * align;
}
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void* malloc_aligned(std::size_t align, std::size_t size);
void free_aligned(void* mem);
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} // namespace streamfx::util