Colorize Depthmap

Documentation

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

The ColorizeDepthmap node is designed to transform depth maps into colorized representations, enhancing visual interpretation by applying a colormap. This process facilitates easier understanding and analysis of depth information by converting grayscale depth maps into vibrant, color-coded images.

Input types

Required

• image
  • The depth map to be colorized, which can be either a torch.Tensor or a numpy.ndarray. This map represents the depth information of a scene as a 2D array.
  • Comfy dtype: IMAGE
  • Python dtype: Union[torch.Tensor, numpy.ndarray]
• colorize_method
  • The name of the matplotlib colormap to use for colorizing the depth map.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: str

Output types

• image
  • Comfy dtype: IMAGE
  • The colorized depth map, enhanced with a colormap for better visual interpretation. The output is in the same data type as the input depth map.
  • Python dtype: Union[torch.Tensor, numpy.ndarray]

Usage tips

• Infra type: CPU
• Common nodes: unknown

Source code

class ColorizeDepthmap:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": {  
            "image": ("IMAGE", ),
            "colorize_method": (
            [   
                'Spectral',
                'terrain', 
                'viridis',
                'plasma',
                'inferno',
                'magma',
                'cividis',
                'twilight',
                'rainbow',
                'gist_rainbow',
                'gist_ncar',
                'gist_earth',
                'turbo',
                'jet',
                'afmhot',
                'copper',
                'seismic',
                'hsv',
                'brg',

            ], {
               "default": 'Spectral'
            }),
            },

            }

    RETURN_TYPES = ("IMAGE",)
    RETURN_NAMES =("image",)
    FUNCTION = "color"

    CATEGORY = "Marigold"

    def color(self, image, colorize_method):
        colored_images = []
        for i in range(image.shape[0]):  # Iterate over the batch dimension
            depth_map = image[i].squeeze().permute(2, 0, 1)
            depth_map = depth_map[0]
            depth_map = colorizedepth(depth_map, colorize_method)
            depth_map = torch.from_numpy(depth_map) / 255
            depth_map = depth_map.unsqueeze(0)
            colored_images.append(depth_map)

        # Stack the list of tensors along a new dimension
        colored_images = torch.cat(colored_images, dim=0)
        return (colored_images,)