77 lines
2.4 KiB
Markdown
77 lines
2.4 KiB
Markdown
|
|
# Documentation
|
|
- Class name: DistanceTransform
|
|
- Category: Bmad/CV/Thresholding
|
|
- Output node: False
|
|
|
|
DistanceTransform节点用于对二值图像应用距离变换。它将二值图像转换为灰度图像，其中每个像素的亮度与其到最近的二值前景像素的距离成比例。该节点支持不同的距离类型和掩码大小，以便根据需求定制变换效果。
|
|
|
|
# Input types
|
|
## Required
|
|
- binary_image
|
|
- 需要进行距离变换的二值图像。它作为计算到最近前景像素距离的输入。
|
|
- Comfy dtype: IMAGE
|
|
- Python dtype: torch.Tensor
|
|
- distance_type
|
|
- 指定要使用的距离计算类型，允许自定义距离变换效果。
|
|
- Comfy dtype: COMBO[STRING]
|
|
- Python dtype: str
|
|
- mask_size
|
|
- 确定距离变换中使用的掩码大小，影响距离计算的精细程度。
|
|
- Comfy dtype: COMBO[STRING]
|
|
- Python dtype: str
|
|
|
|
# Output types
|
|
- image
|
|
- 距离变换后的结果图像。这是一个灰度图像，其中每个像素的亮度反映了它到最近前景像素的距离。
|
|
- Comfy dtype: IMAGE
|
|
- Python dtype: torch.Tensor
|
|
|
|
|
|
## Usage tips
|
|
- Infra type: `GPU`
|
|
- Common nodes: unknown
|
|
|
|
|
|
## Source code
|
|
```python
|
|
class DistanceTransform:
|
|
distance_types_map = {
|
|
"DIST_L2": cv.DIST_L2,
|
|
"DIST_L1": cv.DIST_L1,
|
|
"DIST_C": cv.DIST_C,
|
|
}
|
|
distance_types = list(distance_types_map.keys())
|
|
|
|
mask_sizes_map = {
|
|
"DIST_MASK_3": cv.DIST_MASK_3,
|
|
"DIST_MASK_5": cv.DIST_MASK_5,
|
|
"DIST_MASK_PRECISE": cv.DIST_MASK_PRECISE
|
|
}
|
|
mask_sizes = list(mask_sizes_map.keys())
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {
|
|
"required": {
|
|
"binary_image": ("IMAGE",),
|
|
"distance_type": (s.distance_types, {"default": s.distance_types[0]}),
|
|
"mask_size": (s.mask_sizes, {"default": s.mask_sizes[0]}),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("IMAGE",)
|
|
FUNCTION = "apply"
|
|
CATEGORY = "Bmad/CV/Thresholding"
|
|
|
|
def apply(self, binary_image, distance_type, mask_size):
|
|
binary_image = tensor2opencv(binary_image, 1)
|
|
distance_transform = cv.distanceTransform(
|
|
binary_image,
|
|
self.distance_types_map[distance_type],
|
|
self.mask_sizes_map[mask_size])
|
|
distance_transform = opencv2tensor(distance_transform)
|
|
return (distance_transform,)
|
|
|
|
```
|