RemapFromInsideParabolas¶
Documentation¶
- Class name:
RemapFromInsideParabolas
- Category:
Bmad/CV/Transform
- Output node:
False
This node is designed to perform a remapping operation from the perspective of inside two parabolas, transforming an image based on specified width and height parameters. It utilizes a source mask defined by two parabolas to apply a unique geometric transformation, aiming to adjust the image's representation for specific visualization or processing needs.
Input types¶
Required¶
src_mask_with_i_parabolas
- Defines the source mask that contains two parabolas, which is crucial for determining the geometric transformation applied to the image.
- Comfy dtype:
MASK
- Python dtype:
torch.Tensor
width
- Specifies the desired width of the output image after remapping, affecting the scale and aspect ratio of the transformation.
- Comfy dtype:
INT
- Python dtype:
int
height
- Determines the height of the output image, influencing the vertical scale and aspect ratio post-transformation.
- Comfy dtype:
INT
- Python dtype:
int
Output types¶
remap
- Comfy dtype:
REMAP
- The result of the remapping operation, providing a transformed image based on the input parameters and the geometric characteristics of the source mask.
- Python dtype:
Dict[str, Any]
- Comfy dtype:
Usage tips¶
- Infra type:
CPU
- Common nodes: unknown
Source code¶
class RemapFromInsideParabolas(RemapBase):
@classmethod
def INPUT_TYPES(cls):
return {"required": {
"src_mask_with_2_parabolas": ("MASK",),
"width": ("INT", {"default": 512, "min": 16, "max": 4096}),
"height": ("INT", {"default": 512, "min": 16, "max": 4096}),
}
}
def send_remap(self, src_mask_with_2_parabolas, width, height):
from .utils.remaps import remap_from_inside_parabolas
return ({
"func": remap_from_inside_parabolas,
"xargs": [tensor2opencv(src_mask_with_2_parabolas, 1), width, height],
"dims": (width, height)
},)