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Merge pull request #44832 from nathansmith339/opensimplex-optimization
OpenSimplex data optimization
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55e876777c
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@ -32,7 +32,7 @@
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<argument index="1" name="height" type="int">
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<argument index="1" name="height" type="int">
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</argument>
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</argument>
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<description>
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<description>
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Generate a noise image with the requested [code]width[/code] and [code]height[/code], based on the current noise parameters.
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Generate a noise image in [constant Image.FORMAT_L8] format with the requested [code]width[/code] and [code]height[/code], based on the current noise parameters.
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</description>
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</description>
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</method>
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</method>
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<method name="get_noise_1d" qualifiers="const">
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<method name="get_noise_1d" qualifiers="const">
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@ -108,7 +108,7 @@
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<argument index="0" name="size" type="int">
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<argument index="0" name="size" type="int">
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</argument>
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</argument>
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<description>
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<description>
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Generate a tileable noise image, based on the current noise parameters. Generated seamless images are always square ([code]size[/code] × [code]size[/code]).
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Generate a tileable noise image in [constant Image.FORMAT_L8] format, based on the current noise parameters. Generated seamless images are always square ([code]size[/code] × [code]size[/code]).
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</description>
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</description>
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</method>
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</method>
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</methods>
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</methods>
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@ -104,7 +104,7 @@ void OpenSimplexNoise::set_lacunarity(float p_lacunarity) {
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Ref<Image> OpenSimplexNoise::get_image(int p_width, int p_height) const {
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Ref<Image> OpenSimplexNoise::get_image(int p_width, int p_height) const {
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Vector<uint8_t> data;
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Vector<uint8_t> data;
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data.resize(p_width * p_height * 4);
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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 *wd8 = data.ptrw();
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@ -112,21 +112,17 @@ Ref<Image> OpenSimplexNoise::get_image(int p_width, int p_height) const {
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for (int j = 0; j < p_width; j++) {
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for (int j = 0; j < p_width; j++) {
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float v = get_noise_2d(j, i);
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float v = get_noise_2d(j, i);
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v = v * 0.5 + 0.5; // Normalize [0..1]
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v = v * 0.5 + 0.5; // Normalize [0..1]
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uint8_t value = uint8_t(CLAMP(v * 255.0, 0, 255));
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wd8[(i * p_width + j)] = uint8_t(CLAMP(v * 255.0, 0, 255));
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wd8[(i * p_width + j) * 4 + 0] = value;
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wd8[(i * p_width + j) * 4 + 1] = value;
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wd8[(i * p_width + j) * 4 + 2] = value;
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wd8[(i * p_width + j) * 4 + 3] = 255;
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}
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}
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}
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}
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Ref<Image> image = memnew(Image(p_width, p_height, false, Image::FORMAT_RGBA8, data));
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Ref<Image> image = memnew(Image(p_width, p_height, false, Image::FORMAT_L8, data));
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return image;
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return image;
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}
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}
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Ref<Image> OpenSimplexNoise::get_seamless_image(int p_size) const {
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Ref<Image> OpenSimplexNoise::get_seamless_image(int p_size) const {
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Vector<uint8_t> data;
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Vector<uint8_t> data;
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data.resize(p_size * p_size * 4);
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data.resize(p_size * p_size);
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uint8_t *wd8 = data.ptrw();
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uint8_t *wd8 = data.ptrw();
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@ -147,15 +143,11 @@ Ref<Image> OpenSimplexNoise::get_seamless_image(int p_size) const {
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float v = get_noise_4d(x, y, z, w);
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float v = get_noise_4d(x, y, z, w);
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v = v * 0.5 + 0.5; // Normalize [0..1]
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v = v * 0.5 + 0.5; // Normalize [0..1]
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uint8_t value = uint8_t(CLAMP(v * 255.0, 0, 255));
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wd8[(i * p_size + j)] = uint8_t(CLAMP(v * 255.0, 0, 255));
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wd8[(i * p_size + j) * 4 + 0] = value;
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wd8[(i * p_size + j) * 4 + 1] = value;
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wd8[(i * p_size + j) * 4 + 2] = value;
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wd8[(i * p_size + j) * 4 + 3] = 255;
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}
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}
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}
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}
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Ref<Image> image = memnew(Image(p_size, p_size, false, Image::FORMAT_RGBA8, data));
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Ref<Image> image = memnew(Image(p_size, p_size, false, Image::FORMAT_L8, data));
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return image;
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return image;
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}
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}
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