146 lines
5.4 KiB
Markdown
146 lines
5.4 KiB
Markdown
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# Documentation
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- Class name: SaltScheduledImageAdjust
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- Category: SALT/Scheduling/Image
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- Output node: False
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- Repo Ref: https://github.com/saltruism/salt-nodes
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SaltScheduledImageAdjust节点专门用于对一批图像应用计划调整，允许随时间动态改变亮度、对比度、饱和度、锐度、高斯模糊和边缘增强。它能够创建图像属性按预定计划演变的序列，从而增强视觉叙事或数据可视化效果。
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# Input types
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## Required
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- images
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- 要调整的图像集合。这个参数是节点操作的核心，它定义了将要进行计划转换的图像集。
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- Comfy dtype: IMAGE
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- Python dtype: List[Image]
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## Optional
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- masks
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- 可选的蒙版，用于在图像上选择性地应用调整。这允许更精确地控制图像的哪些区域受到调整的影响。
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- Comfy dtype: MASK
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- Python dtype: List[Image]
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- brightness_schedule
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- 定义图像亮度如何随时间变化的计划。这通过按预定序列改变图像的亮度来实现动态视觉效果。
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- Comfy dtype: LIST
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- Python dtype: List[float]
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- contrast_schedule
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- 规定随时间变化的对比度调整计划，允许增强或减少图像最暗和最亮部分之间的差异。
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- Comfy dtype: LIST
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- Python dtype: List[float]
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- saturation_schedule
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- 定义图像颜色强度如何随时间变化，使得能够创建鲜艳或柔和的颜色序列。
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- Comfy dtype: LIST
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- Python dtype: List[float]
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- sharpness_schedule
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- 调整图像细节清晰度或模糊度随时间变化的计划，有助于聚焦特定元素或创建梦幻般的序列。
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- Comfy dtype: LIST
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- Python dtype: List[float]
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- gaussian_blur_schedule
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- 确定随时间应用于图像的高斯模糊程度，允许创建柔焦效果或过渡。
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- Comfy dtype: LIST
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- Python dtype: List[float]
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- edge_enhance_schedule
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- 随时间增强图像内边缘的计划，可用于突出细节或创造更具图形感的外观。
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- Comfy dtype: LIST
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- Python dtype: List[float]
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# Output types
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- image
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- 应用计划调整后的图像，展示随时间的动态变化。
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- Comfy dtype: IMAGE
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- Python dtype: List[Image]
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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 SaltScheduledImageAdjust:
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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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},
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"optional": {
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"masks": ("MASK",),
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"brightness_schedule": ("LIST", ),
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"contrast_schedule": ("LIST", ),
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"saturation_schedule": ("LIST", ),
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"sharpness_schedule": ("LIST", ),
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"gaussian_blur_schedule": ("LIST", ),
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"edge_enhance_schedule": ("LIST", ),
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}
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "images_adjust"
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CATEGORY = "SALT/Scheduling/Image"
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def float_value(self, adj_list, idx):
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if isinstance(adj_list, list) and adj_list:
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return adj_list[idx] if idx < len(adj_list) else adj_list[-1]
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else:
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return 1.0
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def images_adjust(
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self,
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images,
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masks=[],
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brightness_schedule=[1.0],
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contrast_schedule=[1.0],
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saturation_schedule=[1.0],
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sharpness_schedule=[1.0],
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gaussian_blur_schedule=[0.0],
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edge_enhance_schedule=[0.0]
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):
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adjusted_images = []
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for idx, img in enumerate(images):
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original_pil_image = tensor2pil(img.unsqueeze(0))
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pil_image = original_pil_image.copy()
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if isinstance(masks, torch.Tensor):
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pil_mask = mask2pil(masks[idx].unsqueeze(0)) if idx < len(masks) else mask2pil(masks[-1].unsqueeze(0))
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pil_mask = pil_mask.resize(original_pil_image.size).convert('L')
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else:
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pil_mask = None
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brightness = self.float_value(brightness_schedule, idx)
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contrast = self.float_value(contrast_schedule, idx)
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saturation = self.float_value(saturation_schedule, idx)
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sharpness = self.float_value(sharpness_schedule, idx)
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gaussian_blur = self.float_value(gaussian_blur_schedule, idx)
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edge_enhance = self.float_value(edge_enhance_schedule, idx)
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if brightness != 1.0:
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pil_image = ImageEnhance.Brightness(pil_image).enhance(brightness)
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if contrast != 1.0:
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pil_image = ImageEnhance.Contrast(pil_image).enhance(contrast)
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if saturation != 1.0:
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pil_image = ImageEnhance.Color(pil_image).enhance(saturation)
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if sharpness != 1.0:
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pil_image = ImageEnhance.Sharpness(pil_image).enhance(sharpness)
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if gaussian_blur > 0.0:
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pil_image = pil_image.filter(ImageFilter.GaussianBlur(radius=gaussian_blur))
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if edge_enhance > 0.0:
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edge_enhanced_img = pil_image.filter(ImageFilter.EDGE_ENHANCE_MORE)
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blend_mask = Image.new("L", pil_image.size, color=int(round(edge_enhance * 255)))
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pil_image = Image.composite(edge_enhanced_img, pil_image, blend_mask)
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if pil_mask:
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pil_image = Image.composite(pil_image, original_pil_image, pil_mask)
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adjusted_images.append(pil2tensor(pil_image))
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return (torch.cat(adjusted_images, dim=0), )
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```
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