99 lines
3.4 KiB
Markdown
99 lines
3.4 KiB
Markdown
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# Documentation
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- Class name: MorphologicOperation
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- Category: Bmad/CV/Morphology
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- Output node: False
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MorphologicOperation节点提供了一套全面的形态学操作用于图像处理，包括腐蚀、膨胀、开运算、闭运算、梯度、顶帽和底帽变换。它旨在对图像应用这些操作，允许在二值图像上下文中进行各种形式的结构修改和分析。
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# Input types
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## Required
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- src
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- 需要进行形态学操作处理的源图像，根据指定的操作、核类型和迭代次数实现结构性修改。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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- operation
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- 决定要应用的具体形态学操作，如腐蚀或膨胀，影响图像的结构转换。
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- Comfy dtype: COMBO[STRING]
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- Python dtype: str
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- kernel_type
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- 指定形态学操作中使用的核的形状，影响变换的性质。
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- Comfy dtype: COMBO[STRING]
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- Python dtype: str
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- kernel_size_x
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- 核的水平尺寸，影响操作对图像的作用范围和影响程度。
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- Comfy dtype: INT
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- Python dtype: int
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- kernel_size_y
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- 核的垂直尺寸，影响形态学变换的作用范围和深度。
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- Comfy dtype: INT
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- Python dtype: int
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- iterations
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- 形态学操作应用的次数，允许进行渐进的结构变化。
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- Comfy dtype: INT
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- Python dtype: int
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# Output types
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- image
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- 经过形态学操作后的输出图像，反映了所做的结构性变化。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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## Usage tips
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- Infra type: `GPU`
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- Common nodes: unknown
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## Source code
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```python
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class MorphologicOperation:
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# I did not want to make this node, but alas, I found no suit w/ the top/black hat operation
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# so might as well make a generic node w/ all the operations
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# just return as BW and implement convert node
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operation_map = {
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"MORPH_ERODE": cv.MORPH_ERODE,
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"MORPH_DILATE": cv.MORPH_DILATE,
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"MORPH_OPEN": cv.MORPH_OPEN,
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"MORPH_CLOSE": cv.MORPH_CLOSE,
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"MORPH_GRADIENT": cv.MORPH_GRADIENT,
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"MORPH_TOPHAT": cv.MORPH_TOPHAT,
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"MORPH_BLACKHAT": cv.MORPH_BLACKHAT,
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}
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operations = list(operation_map.keys())
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kernel_types_map = {
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"MORPH_RECT": cv.MORPH_RECT,
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"MORPH_ELLIPSE": cv.MORPH_ELLIPSE,
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"MORPH_CROSS": cv.MORPH_CROSS,
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}
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kernel_types = list(kernel_types_map.keys())
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"src": ("IMAGE",),
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"operation": (s.operations, {"default": s.operations[0]}),
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"kernel_type": (s.kernel_types, {"default": s.kernel_types[0]}),
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"kernel_size_x": ("INT", {"default": 4, "min": 2, "step": 2}),
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"kernel_size_y": ("INT", {"default": 4, "min": 2, "step": 2}),
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"iterations": ("INT", {"default": 1, "step": 1}),
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},
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# TODO maybe add optional input for custom kernel
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "apply"
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CATEGORY = "Bmad/CV/Morphology"
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def apply(self, src, operation, kernel_type, kernel_size_x, kernel_size_y, iterations):
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img = tensor2opencv(src, 1)
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kernel = cv.getStructuringElement(self.kernel_types_map[kernel_type], (kernel_size_x + 1, kernel_size_y + 1))
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for i in range(iterations):
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img = cv.morphologyEx(img, self.operation_map[operation], kernel)
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return (opencv2tensor(img),)
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```
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