106 lines
3.9 KiB
Markdown
106 lines
3.9 KiB
Markdown
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# Documentation
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- Class name: `Remap`
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- Category: `Bmad/CV/Transform`
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- Output node: `False`
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Remap节点设计用于通过应用特定的重映射操作来转换图像。它作为更专门的重映射操作的基础类，比如调整透视或以各种方式扭曲图像以实现所需的视觉效果。
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# Input types
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## Required
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- **`remap`**
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- 指定要应用的重映射操作，包括转换所需的函数和参数。它对于定义图像将如何被改变至关重要。
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- Comfy dtype: `REMAP`
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- Python dtype: `Dict[str, Any]`
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- **`src`**
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- 要转换的源图像。它是应用重映射操作的主要输入。
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- Comfy dtype: `IMAGE`
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- Python dtype: `numpy.ndarray`
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- **`interpolation`**
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- 定义重映射过程中使用的插值方法，影响输出图像的质量和外观。
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- Comfy dtype: `COMBO[STRING]`
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- Python dtype: `int`
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## Optional
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- **`src_mask`**
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- 一个可选的掩码，可以应用于源图像，允许基于掩码的覆盖范围进行选择性重映射。
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- Comfy dtype: `MASK`
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- Python dtype: `numpy.ndarray`
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- **`output_with_alpha`**
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- 指示输出图像是否应包含alpha通道，允许透明效果。
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- Comfy dtype: `BOOLEAN`
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- Python dtype: `bool`
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# Output types
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- **`image`**
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- 应用重映射操作后得到的转换图像。
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- Comfy dtype: `IMAGE`
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- Python dtype: `numpy.ndarray`
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- **`mask`**
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- 重映射过程中生成的掩码，对应于源图像的转换区域。
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- Comfy dtype: `MASK`
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- Python dtype: `numpy.ndarray`
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## Usage tips
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- Infra type: `CPU`
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- Common nodes: unknown
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## Source code
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```python
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class Remap:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {
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"remap": ("REMAP", {"forceInput": True}),
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"src": ("IMAGE",),
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"interpolation": (interpolation_types, {"default": interpolation_types[2]}),
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},
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"optional": {
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"src_mask": ("MASK",),
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"output_with_alpha": ("BOOLEAN", {"default": False})
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}
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}
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RETURN_TYPES = ("IMAGE", "MASK",)
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FUNCTION = "transform"
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CATEGORY = "Bmad/CV/Transform"
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def transform(self, src, remap, interpolation, src_mask=None, output_with_alpha=False):
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src = tensor2opencv(src)
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dst_dims = remap["dims"] if "dims" in remap else src.shape[:2]
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func = remap["func"]
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xargs = remap["xargs"]
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# if src_mask is not defined set it to a blank canvas; otherwise, just unwrap it
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src_mask = np.ones(src.shape[:2]) * 255 if src_mask is None else tensor2opencv(src_mask, 1)
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if "custom" not in remap.keys():
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# generic application, using cv.remap
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xs, ys, bb = func(src, *xargs)
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remap_img = cv.remap(src, xs, ys, interpolation_types_map[interpolation])
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mask = cv.remap(src_mask, xs, ys, interpolation_types_map[interpolation])
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else:
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# non-generic application; replaces cv.remap w/ some other function.
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# so far only for user provided homography,
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# to avoid a separate node, since technically it is also a remap and also uses the interpolation argument.
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custom_data = func(src, *xargs)
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remap_img, mask, bb = remap["custom"](custom_data, src, interpolation_types_map[interpolation], src_mask)
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if bb is not None:
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new_img = np.zeros((*dst_dims, 3)) # hope width and height are not swapped
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new_img[bb[1]:bb[3], bb[0]:bb[2], :] = remap_img
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remap_img = new_img
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new_img = np.zeros(dst_dims) # was working previously without the batch dim; unsure if really needed
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new_img[bb[1]:bb[3], bb[0]:bb[2]] = mask
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mask = new_img
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if output_with_alpha:
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new_img = np.zeros((*dst_dims, 4))
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new_img[:, :, 0:3] = remap_img[:, :, :]
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new_img[:, :, 3] = mask[:, :]
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remap_img = new_img
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return (opencv2tensor(remap_img), opencv2tensor(mask))
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```
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