ColorizeDepthmap¶
Documentation¶
- Class name:
ColorizeDepthmap
- Category:
Marigold
- Output node:
False
The ColorizeDepthmap node is designed to transform depth maps into colorized images. It leverages color mapping techniques to visually represent the depth information contained within a depth map, enhancing interpretability and visual appeal. This process involves adjusting the color intensity based on the depth values, providing a more intuitive understanding of depth variations in the visualized data.
Input types¶
Required¶
image
- The depth map to be colorized. It is the primary input that contains depth information which will be visually enhanced through colorization.
- Comfy dtype:
IMAGE
- Python dtype:
torch.Tensor or numpy.ndarray
colorize_method
- The colormap name to use for colorizing the depth map. It specifies the color scheme applied to represent different depth values.
- Comfy dtype:
COMBO[STRING]
- Python dtype:
str
Output types¶
image
- Comfy dtype:
IMAGE
- The colorized depth map, where depth information is represented through color variations. This output provides a visually enhanced version of the original depth data, making it easier to interpret.
- Python dtype:
torch.Tensor or numpy.ndarray
- Comfy dtype:
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,)