clamp image
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@@ -767,9 +767,6 @@ class FrequencySeparate:
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return (t,)
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class RemapRange:
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def __init__(self):
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pass
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@classmethod
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def INPUT_TYPES(s):
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return {
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@@ -805,6 +802,36 @@ class RemapRange:
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return (torch.from_numpy(i_dup),)
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class ClampImage:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"image": ("IMAGE",),
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"blackpoint": ("FLOAT", {
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"default": 0.0,
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"min": 0.0,
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"max": 1.0,
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"step": 0.001
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}),
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"whitepoint": ("FLOAT", {
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"default": 1.0,
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"min": 0.0,
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"max": 1.0,
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"step": 0.001
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}),
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},
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "clamp_image"
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CATEGORY = "image/filters"
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def clamp_image(self, image: torch.Tensor, blackpoint: float, whitepoint: float):
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clamped_image = torch.clamp(torch.nan_to_num(image.detach().clone()), min=blackpoint, max=whitepoint)
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return (clamped_image,)
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Channel_List = ["red", "green", "blue", "alpha", "white", "black"]
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Alpha_List = ["red", "green", "blue", "alpha", "white", "black", "none"]
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class ShuffleChannels:
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@@ -1365,6 +1392,7 @@ class ExposureAdjust:
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if tonemap == "linlog":
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tonemapToLinear(t[...,:3], tonemap_scale)
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elif tonemap == "Reinhard":
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t = np.clip(t, 0, 0.999)
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t[...,:3] = -t[...,:3] / (t[...,:3] - 1)
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exposure(t[...,:3], stops)
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@@ -2068,6 +2096,7 @@ NODE_CLASS_MAPPINGS = {
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"BilateralFilterImage": BilateralFilterImage,
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"BlurImageFast": BlurImageFast,
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"BlurMaskFast": BlurMaskFast,
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"ClampImage": ClampImage,
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"ClampOutliers": ClampOutliers,
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"ColorMatchImage": ColorMatchImage,
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"ConditioningSubtract": ConditioningSubtract,
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@@ -2121,6 +2150,7 @@ NODE_DISPLAY_NAME_MAPPINGS = {
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"BilateralFilterImage": "Bilateral Filter Image",
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"BlurImageFast": "Blur Image (Fast)",
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"BlurMaskFast": "Blur Mask (Fast)",
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"ClampImage": "Clamp Image",
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"ClampOutliers": "Clamp Outliers",
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"ColorMatchImage": "Color Match Image",
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"ConditioningSubtract": "ConditioningSubtract",
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