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https://github.com/godotengine/godot
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200 lines
8.2 KiB
C++
200 lines
8.2 KiB
C++
/**************************************************************************/
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/* noise.cpp */
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/**************************************************************************/
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/* This file is part of: */
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/* GODOT ENGINE */
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/* https://godotengine.org */
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/**************************************************************************/
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/* Copyright (c) 2014-present Godot Engine contributors (see AUTHORS.md). */
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/* Copyright (c) 2007-2014 Juan Linietsky, Ariel Manzur. */
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/* */
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/* Permission is hereby granted, free of charge, to any person obtaining */
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/* a copy of this software and associated documentation files (the */
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/* "Software"), to deal in the Software without restriction, including */
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/* without limitation the rights to use, copy, modify, merge, publish, */
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/* distribute, sublicense, and/or sell copies of the Software, and to */
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/* permit persons to whom the Software is furnished to do so, subject to */
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/* the following conditions: */
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/* */
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/* The above copyright notice and this permission notice shall be */
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/* included in all copies or substantial portions of the Software. */
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/* */
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/* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, */
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/* EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF */
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/* MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. */
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/* IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY */
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/* CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, */
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/* TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE */
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/* SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. */
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/**************************************************************************/
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#include "noise.h"
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#include <float.h>
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Vector<Ref<Image>> Noise::_get_seamless_image(int p_width, int p_height, int p_depth, bool p_invert, bool p_in_3d_space, real_t p_blend_skirt, bool p_normalize) const {
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ERR_FAIL_COND_V(p_width <= 0 || p_height <= 0 || p_depth <= 0, Vector<Ref<Image>>());
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int skirt_width = MAX(1, p_width * p_blend_skirt);
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int skirt_height = MAX(1, p_height * p_blend_skirt);
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int skirt_depth = MAX(1, p_depth * p_blend_skirt);
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int src_width = p_width + skirt_width;
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int src_height = p_height + skirt_height;
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int src_depth = p_depth + skirt_depth;
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Vector<Ref<Image>> src = _get_image(src_width, src_height, src_depth, p_invert, p_in_3d_space, p_normalize);
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bool grayscale = (src[0]->get_format() == Image::FORMAT_L8);
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if (grayscale) {
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return _generate_seamless_image<uint8_t>(src, p_width, p_height, p_depth, p_invert, p_blend_skirt);
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} else {
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return _generate_seamless_image<uint32_t>(src, p_width, p_height, p_depth, p_invert, p_blend_skirt);
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}
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}
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Ref<Image> Noise::get_seamless_image(int p_width, int p_height, bool p_invert, bool p_in_3d_space, real_t p_blend_skirt, bool p_normalize) const {
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Vector<Ref<Image>> images = _get_seamless_image(p_width, p_height, 1, p_invert, p_in_3d_space, p_blend_skirt, p_normalize);
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if (images.size() == 0) {
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return Ref<Image>();
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}
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return images[0];
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}
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TypedArray<Image> Noise::get_seamless_image_3d(int p_width, int p_height, int p_depth, bool p_invert, real_t p_blend_skirt, bool p_normalize) const {
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Vector<Ref<Image>> images = _get_seamless_image(p_width, p_height, p_depth, p_invert, true, p_blend_skirt, p_normalize);
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TypedArray<Image> ret;
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ret.resize(images.size());
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for (int i = 0; i < images.size(); i++) {
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ret[i] = images[i];
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}
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return ret;
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}
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// Template specialization for faster grayscale blending.
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template <>
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uint8_t Noise::_alpha_blend<uint8_t>(uint8_t p_bg, uint8_t p_fg, int p_alpha) const {
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uint16_t alpha = p_alpha + 1;
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uint16_t inv_alpha = 256 - p_alpha;
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return (uint8_t)((alpha * p_fg + inv_alpha * p_bg) >> 8);
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}
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Vector<Ref<Image>> Noise::_get_image(int p_width, int p_height, int p_depth, bool p_invert, bool p_in_3d_space, bool p_normalize) const {
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ERR_FAIL_COND_V(p_width <= 0 || p_height <= 0 || p_depth <= 0, Vector<Ref<Image>>());
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Vector<Ref<Image>> images;
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images.resize(p_depth);
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if (p_normalize) {
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// Get all values and identify min/max values.
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LocalVector<real_t> values;
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values.resize(p_width * p_height * p_depth);
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real_t min_val = FLT_MAX;
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real_t max_val = -FLT_MAX;
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int idx = 0;
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for (int d = 0; d < p_depth; d++) {
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for (int y = 0; y < p_height; y++) {
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for (int x = 0; x < p_width; x++) {
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values[idx] = p_in_3d_space ? get_noise_3d(x, y, d) : get_noise_2d(x, y);
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if (values[idx] > max_val) {
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max_val = values[idx];
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}
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if (values[idx] < min_val) {
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min_val = values[idx];
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}
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idx++;
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}
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}
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}
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idx = 0;
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// Normalize values and write to texture.
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for (int d = 0; d < p_depth; d++) {
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Vector<uint8_t> data;
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data.resize(p_width * p_height);
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uint8_t *wd8 = data.ptrw();
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uint8_t ivalue;
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for (int y = 0; y < p_height; y++) {
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for (int x = 0; x < p_width; x++) {
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if (max_val == min_val) {
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ivalue = 0;
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} else {
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ivalue = static_cast<uint8_t>(CLAMP((values[idx] - min_val) / (max_val - min_val) * 255.f, 0, 255));
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}
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if (p_invert) {
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ivalue = 255 - ivalue;
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}
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wd8[x + y * p_width] = ivalue;
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idx++;
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}
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}
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Ref<Image> img = memnew(Image(p_width, p_height, false, Image::FORMAT_L8, data));
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images.write[d] = img;
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}
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} else {
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// Without normalization, the expected range of the noise function is [-1, 1].
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for (int d = 0; d < p_depth; d++) {
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Vector<uint8_t> data;
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data.resize(p_width * p_height);
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uint8_t *wd8 = data.ptrw();
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uint8_t ivalue;
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int idx = 0;
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for (int y = 0; y < p_height; y++) {
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for (int x = 0; x < p_width; x++) {
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float value = (p_in_3d_space ? get_noise_3d(x, y, d) : get_noise_2d(x, y));
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ivalue = static_cast<uint8_t>(CLAMP(value * 127.5f + 127.5f, 0.0f, 255.0f));
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wd8[idx] = p_invert ? (255 - ivalue) : ivalue;
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idx++;
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}
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}
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Ref<Image> img = memnew(Image(p_width, p_height, false, Image::FORMAT_L8, data));
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images.write[d] = img;
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}
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}
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return images;
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}
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Ref<Image> Noise::get_image(int p_width, int p_height, bool p_invert, bool p_in_3d_space, bool p_normalize) const {
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Vector<Ref<Image>> images = _get_image(p_width, p_height, 1, p_invert, p_in_3d_space, p_normalize);
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if (images.is_empty()) {
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return Ref<Image>();
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}
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return images[0];
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}
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TypedArray<Image> Noise::get_image_3d(int p_width, int p_height, int p_depth, bool p_invert, bool p_normalize) const {
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Vector<Ref<Image>> images = _get_image(p_width, p_height, p_depth, p_invert, true, p_normalize);
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TypedArray<Image> ret;
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ret.resize(images.size());
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for (int i = 0; i < images.size(); i++) {
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ret[i] = images[i];
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}
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return ret;
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}
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void Noise::_bind_methods() {
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// Noise functions.
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ClassDB::bind_method(D_METHOD("get_noise_1d", "x"), &Noise::get_noise_1d);
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ClassDB::bind_method(D_METHOD("get_noise_2d", "x", "y"), &Noise::get_noise_2d);
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ClassDB::bind_method(D_METHOD("get_noise_2dv", "v"), &Noise::get_noise_2dv);
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ClassDB::bind_method(D_METHOD("get_noise_3d", "x", "y", "z"), &Noise::get_noise_3d);
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ClassDB::bind_method(D_METHOD("get_noise_3dv", "v"), &Noise::get_noise_3dv);
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// Textures.
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ClassDB::bind_method(D_METHOD("get_image", "width", "height", "invert", "in_3d_space", "normalize"), &Noise::get_image, DEFVAL(false), DEFVAL(false), DEFVAL(true));
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ClassDB::bind_method(D_METHOD("get_seamless_image", "width", "height", "invert", "in_3d_space", "skirt", "normalize"), &Noise::get_seamless_image, DEFVAL(false), DEFVAL(false), DEFVAL(0.1), DEFVAL(true));
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ClassDB::bind_method(D_METHOD("get_image_3d", "width", "height", "depth", "invert", "normalize"), &Noise::get_image_3d, DEFVAL(false), DEFVAL(true));
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ClassDB::bind_method(D_METHOD("get_seamless_image_3d", "width", "height", "depth", "invert", "skirt", "normalize"), &Noise::get_seamless_image_3d, DEFVAL(false), DEFVAL(0.1), DEFVAL(true));
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}
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