obs-StreamFX/source/util/utility.hpp

// 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 <bitset>
#include <cinttypes>
#include <cstddef>
#include <string>
#include <type_traits>
#include <vector>
#include "warning-enable.hpp"

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"
}

// 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)

#define D_STR(s) #s
#define D_VSTR(s) D_STR(s)

namespace streamfx::util {
	bool inline are_property_groups_broken()
	{
		return obs_get_version() < MAKE_SEMANTIC_VERSION(24, 0, 0);
	}

	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)
	{
		return tristate == 1;
	}

	inline bool is_tristate_disabled(int64_t tristate)
	{
		return tristate == 0;
	}

	inline bool is_tristate_default(int64_t tristate)
	{
		return tristate == -1;
	}

	std::pair<int64_t, int64_t> size_from_string(std::string_view text, bool allowSquare = true);

	namespace math {
		/** Integer exponentiation by squaring.
		 * Complexity: O(log(exp)
		 * Has fall-backs to the normal methods for non-integer types.
		 */
		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;
		}

		template<>
		inline float pow(float base, float exp)
		{
			return ::powf(base, exp);
		}

		template<>
		inline double pow(double base, double exp)
		{
			return ::pow(base, exp);
		}

		template<>
		inline long double pow(long double base, long double exp)
		{
			return ::powl(base, exp);
		}

		/** Fast PoT testing
		 *
		 * This was tested and verified to be the fastest implementation on
		 * Intel and AMD x86 CPUs, in both 32bit and 64bit modes. It is
		 * possible to make it even faster with a bit of SIMD magic (see
		 * json-simd), but it would no longer be generic - and not worth it.
		 *
		 * Ranking: popcnt < log10, loop < bitscan < pow
		 * - log10 version is useful for floating point.
		 * - popcnt and loop are fixed point and integers.
		 * - Pretty clear solution here.
		 * 
		 */
		template<typename T>
		inline bool is_power_of_two(T v)
		{
			static_assert(std::is_integral<T>::value, "Input must be an integral type.");

#if 1 // Optimizes to popcount if available.
			return std::bitset<sizeof(T) * 8>(v).count() == 0;

#elif 0 // Loop Variant 1, uses bit shifts.
			bool bit = false;
			for (std::size_t index = 0; index < (sizeof(T) * 8) || (v == 0); index++) {
				if (bit && (v & 1))
					return false;
				bit = (v & 1);
				v >>= 1;
			}
			return true;
#elif 0 // Loop Variant 2, optimized by compiler to the above.
			bool have_bit = false;
			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;
#endif
		}

		template<typename T>
		inline bool is_power_of_two(float v)
		{
			return T(1ull << uint64_t(floor(log10(v) / log10(2.0f)))) == v;
		}

		template<typename T>
		inline bool is_power_of_two(double v)
		{
			return T(1ull << uint64_t(floor(log10(v) / log10(2.0)))) == v;
		}

		template<typename T>
		inline bool is_power_of_two(long double v)
		{
			return T(1ull << uint64_t(floor(log10(v) / log10(2.0l)))) == v;
		}

		template<typename T>
		inline bool is_power_of_two_loop(T v)
		{
			return is_power_of_two<T>(v);
		}

		/** Retrieves the lower bound on the exponent for a texture that would fit the given size.
		 *
		 */
		template<typename T>
		inline uint64_t get_power_of_two_exponent_floor(T v)
		{
			return uint64_t(floor(log10(T(v)) / log10(2.0l)));
		}

		/** Retrieves the upper bound on the exponent for a texture that would fit the given size.
		 *
		 */
		template<typename T>
		inline uint64_t get_power_of_two_exponent_ceil(T v)
		{
			return uint64_t(ceil(log10(T(v)) / log10(2.0l)));
		}

		template<typename T, typename C>
		inline bool is_close_epsilon(T target, C value)
		{
			return (target > (value - std::numeric_limits<T>::epsilon())) && (target < (value + std::numeric_limits<T>::epsilon()));
		}

		template<typename T>
		inline bool is_close(T target, T value, T epsilon)
		{
			return (target > (value - epsilon)) && (target < (value + epsilon));
		}

		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.
			//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_close_epsilon<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   final       = left_e * exp(right_e);

			return T(final);
		}

		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;

			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);
				_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

	namespace memory {
		inline std::size_t aligned_offset(std::size_t align, std::size_t pos)
		{
			return ((pos / align) + 1) * align;
		}
		void* malloc_aligned(std::size_t align, std::size_t size);
		void  free_aligned(void* mem);
	} // namespace memory
} // namespace streamfx::util