RemapDepth

Documentation

• Class name: RemapDepth
• Category: Marigold
• Output node: False

The RemapDepth node is designed to adjust the depth values of an image within a specified range and optionally clamp these values for normalization. It plays a crucial role in depth image processing by enabling the fine-tuning of depth perception and enhancing the visual quality of depth maps.

Input types

Required

• image
  • The input image whose depth values are to be remapped. This parameter is essential for defining the source depth map to be adjusted.
  • Comfy dtype: IMAGE
  • Python dtype: torch.Tensor
• min
  • Specifies the minimum value in the remapped depth range, allowing for the adjustment of depth perception by setting a new lower bound.
  • Comfy dtype: FLOAT
  • Python dtype: float
• max
  • Defines the maximum value in the remapped depth range, enabling the customization of depth perception by establishing a new upper bound.
  • Comfy dtype: FLOAT
  • Python dtype: float
• clamp
  • A boolean flag that determines whether the remapped depth values should be clamped within the 0.0 to 1.0 range, ensuring the normalization of depth maps.
  • Comfy dtype: BOOLEAN
  • Python dtype: bool

Output types

• image
  • Comfy dtype: IMAGE
  • The output image with remapped depth values, adjusted according to the specified min, max, and optionally clamped to normalize the depth map.
  • Python dtype: torch.Tensor

Usage tips

• Infra type: GPU
• Common nodes: unknown

Source code

class RemapDepth:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": { 
            "image": ("IMAGE",),
            "min": ("FLOAT", {"default": 0.0,"min": -10.0, "max": 1.0, "step": 0.01}),
            "max": ("FLOAT", {"default": 1.0,"min": 0.0, "max": 10.0, "step": 0.01}),
            "clamp": ("BOOLEAN", {"default": True}),
            },
            }

    RETURN_TYPES = ("IMAGE",)
    FUNCTION = "remap"

    CATEGORY = "Marigold"

    def remap(self, image, min, max, clamp):
        if image.dtype == torch.float16:
            image = image.to(torch.float32)
        image = min + image * (max - min)
        if clamp:
            image = torch.clamp(image, min=0.0, max=1.0)
        return (image, )