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Fixed strange behavior of bias in NoiseImageGenerator

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NattoMaki
2023-11-30 23:24:05 +09:00
parent be3205b84b
commit 5d95d3e5fd
1 changed files with 8 additions and 8 deletions
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negi/noise_image_generator.py
+8 -8
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@@ -77,8 +77,8 @@ class NoiseImageGenerator:
"scale": ("FLOAT", {
"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "round": 0.001, "display": "slider"
}),
"bias": ("FLOAT", {
"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01, "round": 0.001, "display": "slider"
"center": ("FLOAT", {
"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01, "round": 0.001, "display": "slider"
}),
"perlin_freq_log2": ("INT", {"default": 4, "min": 1, "max": 11, "step": 1, "display": "slider"}),
"perlin_octaves": ("INT", {"default": 4, "min": 1, "max": 11, "step": 1, "display": "slider"}),
@@ -101,7 +101,7 @@ class NoiseImageGenerator:
return np.fmax(0.0, np.fmin(1.0, image))
def doit(
self, width, height, method, seed, scale, bias,
self, width, height, method, seed, scale, center,
perlin_freq_log2, perlin_octaves, perlin_persistence, image_opt=None):
if image_opt is not None:
@@ -111,13 +111,13 @@ class NoiseImageGenerator:
rand_generator = np.random.default_rng(seed)
image_r = None
if method == "uniform_gray":
image_r = bias + scale * rand_generator.random((1, height, width, 1))
image_r = center + scale * (rand_generator.random((1, height, width, 1)) - 0.5)
elif method == "uniform_color":
image_r = bias + scale * rand_generator.random((1, height, width, 3))
image_r = center + scale * (rand_generator.random((1, height, width, 3)) - 0.5)
elif method == "gaussian_gray":
image_r = bias + scale * rand_generator.normal(bias + scale * 0.5, scale * 0.5, (1, height, width, 1))
image_r = center + scale * rand_generator.normal(0.0, 0.5, (1, height, width, 1))
elif method == "gaussian_color":
image_r = bias + scale * rand_generator.normal(bias + scale * 0.5, scale * 0.5, (1, height, width, 3))
image_r = center + scale * rand_generator.normal(0.0, 0.5, (1, height, width, 3))
elif method.startswith("perlin_"):
shape = _find_shape(width, height)
shape_log2 = int(np.log2(shape[0]))
@@ -136,7 +136,7 @@ class NoiseImageGenerator:
rand_generator, shape, (res, res), perlin_octaves, perlin_persistence)
for _ in range(3)], axis=-1)
image_r = image_r.reshape((1, shape[0], shape[1], -1))[:, :height, :width, :]
image_r = bias + scale * 0.5 + (scale * 0.5) * image_r
image_r = center + (scale * 0.5) * image_r
if image_r is None:
raise ValueError()