75 lines
2.3 KiB
Python
75 lines
2.3 KiB
Python
import torch
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import numpy as np
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from PIL import Image, ImageEnhance
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class SaturationNode:
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"""
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调整图像饱和度的节点
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"""
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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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"image": ("IMAGE",),
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"saturation": ("FLOAT", {
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"default": 1.0,
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"min": 0.0,
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"max": 5.0,
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"step": 0.1
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}),
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},
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "adjust_saturation"
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CATEGORY = "Rui-Node🐶/图像调节🎨"
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def adjust_saturation(self, image, saturation):
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"""
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调整输入图像的饱和度
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参数:
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image: 输入图像张量 (B, H, W, C) 格式
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saturation: 饱和度调整系数，1.0为原始饱和度
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返回:
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调整后的图像张量
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"""
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# 将图像从 PyTorch 张量转换为 PIL 图像进行处理
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batch_size = image.shape[0]
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result = []
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for i in range(batch_size):
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# 将单个图像从 PyTorch 张量转换为 NumPy 数组
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# ComfyUI 中图像格式为 BHWC，值范围为 0-1
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img_np = image[i].cpu().numpy()
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# 确保值范围在 0-1 之间
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img_np = np.clip(img_np, 0, 1)
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# 转换为 PIL 图像 (值范围 0-255)
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img_pil = Image.fromarray((img_np * 255).astype(np.uint8), 'RGB')
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# 使用 PIL 的 ImageEnhance.Color 调整饱和度
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enhancer = ImageEnhance.Color(img_pil)
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enhanced_img = enhancer.enhance(saturation)
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# 将处理后的图像转回 NumPy 数组，然后转为 PyTorch 张量
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enhanced_np = np.array(enhanced_img).astype(np.float32) / 255.0
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enhanced_tensor = torch.from_numpy(enhanced_np)
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result.append(enhanced_tensor)
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# 将结果堆叠为批次
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return (torch.stack(result),)
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# 节点映射字典，用于 ComfyUI 注册节点
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NODE_CLASS_MAPPINGS = {
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"SaturationAdjustment": SaturationNode
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}
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# 节点显示名称映射，用于在 UI 中显示友好名称
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NODE_DISPLAY_NAME_MAPPINGS = {
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"SaturationAdjustment": "调整饱和度 / Saturation Adjustment"
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} |