146 lines
5.1 KiB
Markdown
146 lines
5.1 KiB
Markdown
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# Documentation
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- Class name: ImageEffectsLensOpticAxis
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- Category: image/effects/lens
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- Output node: False
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ImageEffectsLensOpticAxis节点用于在图像上应用镜头光轴效果，根据镜头形状、边缘、曲率、缩放、亮度和饱和度等参数操纵图像的外观。它创建一个蒙版来选择性地应用变换，模拟镜头光轴对输入图像的光学效果。
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# Input types
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## Required
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- images
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- 将应用镜头光轴效果的输入图像。这个参数至关重要，因为它作为效果应用的基础。
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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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- 定义应用于图像的镜头效果的形状，影响光轴效果的整体外观。
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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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- 指定镜头效果的边缘行为，决定效果如何在图像边界处淡化或过渡。
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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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- 调整镜头效果的曲率，允许自定义光轴效果的强度和形状。
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- Comfy dtype: FLOAT
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- Python dtype: float
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- lens_zoom
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- 控制镜头效果的缩放级别，影响光轴效果在图像上的比例和强度。
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- Comfy dtype: FLOAT
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- Python dtype: float
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- lens_aperture
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- 决定镜头效果的光圈大小，影响图像中的景深和焦点区域。
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- Comfy dtype: FLOAT
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- Python dtype: float
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- blur_intensity
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- 设置与镜头光轴效果一起应用的模糊效果的强度，有助于整体模拟相机镜头的视觉效果。
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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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- 应用镜头光轴效果后的图像，展示了变换后的外观。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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- mask
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- 用于应用镜头光轴效果的蒙版，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 ImageEffectsLensOpticAxis:
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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", "square", "rectangle", "corners"],),
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"lens_edge": (["around", "symmetric"],),
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"lens_curvy": ("FLOAT", {
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"default": 4.0,
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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": 2.0,
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"step": 0.1,
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}),
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"lens_aperture": ("FLOAT", {
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"default": 0.5,
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"max": 10.0,
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"step": 0.1,
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}),
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"blur_intensity": ("INT", {
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"default": 30,
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"min": 2,
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"step": 2
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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, lens_aperture, blur_intensity):
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blur_intensity -= 1
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lens_zoom += 1
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height, width = images[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, 0.0, 1.0 + lens_curvy).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, 0.0, 1.0 + lens_curvy)[
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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, 0.0, 1.0 + lens_curvy).unsqueeze(0).unsqueeze(3)
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else:
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raise ValueError("Not existing lens_edge parameter.")
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center_x = width // 2
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center_y = height // 2
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y_v, x_v = torch.meshgrid(torch.arange(height), torch.arange(width), indexing='ij')
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dx = x_v - center_x
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dy = y_v - center_y
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distance = torch.sqrt(dx ** 2 + dy ** 2)
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map_x = x_v + mask[0, :, :, 0] * dx / distance * (-lens_aperture * 100)
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map_y = y_v + mask[0, :, :, 0] * dy / distance * (-lens_aperture * 100)
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map_x = map_x.to(torch.float32).numpy()
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map_y = map_y.to(torch.float32).numpy()
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shifted_images = cv2_layer(images, lambda x: cv2.remap(x, map_x, map_y, cv2.INTER_LINEAR))
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shifted_mask = cv2_layer(mask, lambda x: cv2.remap(x, map_x, map_y, cv2.INTER_LINEAR))
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edited_images = cv2_layer(shifted_images, lambda x: cv2.stackBlur(x, (blur_intensity, blur_intensity)))
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mask = torch.clamp(mask, 0.0, 1.0)
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shifted_mask = torch.clamp(shifted_mask, 0.0, 1.0)
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result = shifted_images * (1 - mask) + edited_images * mask
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return result, shifted_mask
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```
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