131 lines
4.4 KiB
Markdown
131 lines
4.4 KiB
Markdown
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# Documentation
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- Class name: ImageEffectsLensVignette
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- Category: image/effects/lens
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- Output node: False
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ImageEffectsLensVignette节点用于给图像添加暗角效果，模拟图像边缘与中心相比亮度或饱和度降低的现象。这种效果模仿了某些相机镜头拍摄的照片的特征外观，为图像的中心主体增添了深度感或焦点感。
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# Input types
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## Required
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- images
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- 将要应用暗角效果的输入图像。这个参数对于定义将要进行变换的基础内容至关重要。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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- lens_shape
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- 定义暗角效果的形状，影响亮度或饱和度如何向图像边缘衰减。
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- Comfy dtype: COMBO[STRING]
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- Python dtype: str
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- lens_edge
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- 指定暗角效果的边缘行为，决定效果如何在图像周围或对称地应用。
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- Comfy dtype: COMBO[STRING]
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- Python dtype: str
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- lens_curvy
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- 调整暗角效果的曲率，允许对从中心到边缘的过渡进行更具创意的控制。
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- Comfy dtype: FLOAT
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- Python dtype: float
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- lens_zoom
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- 控制暗角效果的缩放级别，影响不受暗角影响的图像区域。
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- Comfy dtype: FLOAT
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- Python dtype: float
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- brightness
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- 调整图像的整体亮度，通过修改光线水平来增强暗角效果的视觉影响。
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- Comfy dtype: FLOAT
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- Python dtype: float
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- saturation
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- 修改图像的饱和度水平，通过改变颜色的强度来补充暗角效果。
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- Comfy dtype: FLOAT
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- Python dtype: float
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# Output types
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- image
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- 应用暗角效果后的结果图像，通过控制边缘的渐变来突出中心主体的焦点。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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- mask
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- 用于应用暗角效果的遮罩，指示图像中受变换影响的区域。
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- Comfy dtype: MASK
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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 ImageEffectsLensVignette:
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def __init__(self):
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pass
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"images": ("IMAGE",),
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"lens_shape": (["circle", "rectangle"],),
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"lens_edge": (["around", "symmetric"],),
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"lens_curvy": ("FLOAT", {
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"default": 3.0,
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"max": 15.0,
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"step": 0.1,
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}),
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"lens_zoom": ("FLOAT", {
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"default": 0.0,
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"step": 0.1,
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}),
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"brightness": ("FLOAT", {
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"default": 0.25,
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"max": 1.0,
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"step": 0.01
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}),
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"saturation": ("FLOAT", {
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"default": 0.5,
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"max": 1.0,
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"step": 0.01
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}),
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},
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}
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RETURN_TYPES = ("IMAGE", "MASK")
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FUNCTION = "node"
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CATEGORY = "image/effects/lens"
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def node(self, images, lens_shape, lens_edge, lens_curvy, lens_zoom, brightness, saturation):
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tensor = images.clone().detach()
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lens_zoom += 1
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height, width = tensor[0, :, :, 0].shape
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if lens_edge == "around":
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mask = radialspace_2D((height, width), lens_curvy, lens_zoom, lens_shape).unsqueeze(0).unsqueeze(3)
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elif lens_edge == "symmetric":
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if height != width:
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new_height = new_width = max(height, width)
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crop_top_bottom = (new_height - height) // 2
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crop_left_right = (new_width - width) // 2
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mask = radialspace_2D((new_height, new_width), lens_curvy, lens_zoom, lens_shape)[
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crop_top_bottom:-crop_top_bottom or None,
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crop_left_right:-crop_left_right or None
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].unsqueeze(0).unsqueeze(3)
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else:
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mask = radialspace_2D((height, width), lens_curvy, lens_zoom, lens_shape).unsqueeze(0).unsqueeze(3)
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else:
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raise ValueError("Not existing lens_edge parameter.")
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tensor = tensor.permute(0, 3, 1, 2)
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tensor[:, 0:3, :, :] = F.adjust_brightness(tensor[:, 0:3, :, :], brightness)
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tensor[:, 0:3, :, :] = F.adjust_saturation(tensor[:, 0:3, :, :], saturation)
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tensor = tensor.permute(0, 2, 3, 1)
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result = images * (1 - mask) + tensor * mask
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mask = mask.squeeze()
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return result, mask
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```
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