138 lines
4.6 KiB
Markdown
138 lines
4.6 KiB
Markdown
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# Documentation
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- Class name: ImageEffectsLensBokeh
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- Category: image/effects/lens
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- Output node: False
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ImageEffectsLensBokeh 节点用于为图像添加散景效果，模拟真实相机镜头在对焦区域外产生的美学模糊效果。该节点通过调整与镜头特性和模糊强度相关的参数，创造出视觉上吸引人的效果，模仿真实相机镜头的行为。
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# Input types
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## Required
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- images
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- 需要应用散景效果的输入图像。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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- blades_shape
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- 定义模拟散景效果的镜头形状，影响模糊的整体外观。
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- Comfy dtype: INT
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- Python dtype: int
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- blades_radius
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- 指定镜头光圈叶片的半径，影响散景模糊的大小和形状。
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- Comfy dtype: INT
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- Python dtype: int
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- blades_rotation
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- 确定镜头光圈叶片的旋转角度，影响散景模糊的方向。
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- Comfy dtype: FLOAT
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- Python dtype: float
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- blur_size
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- 设置模糊效果的大小，允许微调散景效果在图像上的显著程度。
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- Comfy dtype: INT
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- Python dtype: int
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- blur_type
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- 指定要应用的模糊类型，提供不同的方法来实现散景效果。
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- Comfy dtype: COMBO[STRING]
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- Python dtype: str
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- method
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- 确定应用镜头模糊的方法，影响散景效果的最终外观。
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- Comfy dtype: COMBO[STRING]
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- Python dtype: str
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# Output types
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- image
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- 应用散景效果后的转换图像，展示通过模拟镜头特性和调整模糊效果所实现的视觉增强。
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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 ImageEffectsLensBokeh:
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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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"blades_shape": ("INT", {
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"default": 5,
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"min": 3,
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}),
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"blades_radius": ("INT", {
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"default": 10,
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"min": 1,
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}),
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"blades_rotation": ("FLOAT", {
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"default": 0.0,
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"min": 0.0,
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"max": 360.0,
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}),
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"blur_size": ("INT", {
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"default": 10,
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"min": 1,
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"step": 2
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}),
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"blur_type": (["bilateral", "stack", "none"],),
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"method": (["dilate", "filter"],),
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},
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "node"
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CATEGORY = "image/effects/lens"
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# noinspection PyUnresolvedReferences
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def lens_blur(self, image, blades_shape, blades_radius, blades_rotation, method):
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angles = np.linspace(0, 2 * np.pi, blades_shape + 1)[:-1] + blades_rotation * np.pi / 180
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x = blades_radius * np.cos(angles) + blades_radius
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y = blades_radius * np.sin(angles) + blades_radius
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pts = np.stack([x, y], axis=1).astype(np.int32)
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mask = np.zeros((blades_radius * 2 + 1, blades_radius * 2 + 1), np.uint8)
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cv2.fillPoly(mask, [pts], 255)
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gaussian_kernel = np.array([[0, -1, 0], [-1, 5, -1], [0, -1, 0]])
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if method == "dilate":
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kernel = cv2.filter2D(mask, -1, gaussian_kernel)
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result = cv2.dilate(image, kernel)
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elif method == "filter":
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height, width = image.shape[:2]
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dilate_size = min(height, width) // 512
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if dilate_size > 0:
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image = cv2.dilate(image, np.ones((dilate_size, dilate_size), np.uint8))
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kernel = mask.astype(np.float32) / np.sum(mask)
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kernel = cv2.filter2D(kernel, -1, gaussian_kernel)
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result = cv2.filter2D(image, -1, kernel)
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else:
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raise ValueError("Unsupported method.")
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return result
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def node(self, images, blades_shape, blades_radius, blades_rotation, blur_size, blur_type, method):
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tensor = images.clone().detach()
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blur_size -= 1
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if blur_type == "bilateral":
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tensor = cv2_layer(tensor, lambda x: cv2.bilateralFilter(x, blur_size, -100, 100))
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elif blur_type == "stack":
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tensor = cv2_layer(tensor, lambda x: cv2.stackBlur(x, (blur_size, blur_size)))
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elif blur_type == "none":
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pass
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else:
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raise ValueError("Unsupported blur type.")
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return (cv2_layer(tensor, lambda x: self.lens_blur(
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x, blades_shape, blades_radius, blades_rotation, method
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)),)
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```
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