73 lines
2.5 KiB
Python
73 lines
2.5 KiB
Python
import os
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import uuid
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from PIL import Image
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import numpy as np
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import torch
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class SaveImagePath:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"image_path": ("IMAGE", {"forceInput": True}),
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}
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}
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RETURN_TYPES = ("STRING",)
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FUNCTION = "load"
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CATEGORY = "不忘科技-自定义节点🚩"
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def load(self, image_path):
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# 如果是torch.Tensor类型，转换为numpy数组
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if isinstance(image_path, torch.Tensor):
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image_path = image_path.cpu().numpy()
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# 去除多余的维度，如果形状是(1, 1, height, width, channels)或(1, height, width, channels)等情况
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while len(image_path.shape) > 3:
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image_path = image_path.squeeze(0)
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# 如果是通道优先格式 (C, H, W)，转换为通道最后格式 (H, W, C)
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if len(image_path.shape) == 3 and image_path.shape[0] <= 4:
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image_path = np.transpose(image_path, (1, 2, 0))
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# 如果是单通道图像，转换为3通道
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if len(image_path.shape) == 2:
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image_path = np.stack([image_path] * 3, axis=-1)
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# 数据范围和类型转换 - 这是关键修复
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if image_path.dtype == np.float32 or image_path.dtype == np.float64:
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# ComfyUI图像数据通常是0-1范围的浮点数
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if image_path.max() <= 1.0:
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# 从0-1范围转换到0-255范围
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image_path = (image_path * 255.0).astype(np.uint8)
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else:
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# 如果已经是0-255范围，直接转换类型
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image_path = np.clip(image_path, 0, 255).astype(np.uint8)
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elif image_path.dtype != np.uint8:
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# 其他数据类型，确保在0-255范围内
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image_path = np.clip(image_path, 0, 255).astype(np.uint8)
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pil_image = Image.fromarray(image_path)
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# output_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "output")
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# base_dir = os.path.dirname(os.path.abspath(__file__))
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output_dir = '/root/comfy/ComfyUI/output'
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if not os.path.exists(output_dir):
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os.makedirs(output_dir)
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file_name = "%s.jpg" % str(uuid.uuid4())
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p = os.path.join(output_dir, file_name)
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pil_image.save(p)
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return (p,)
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# 节点类定义结束，以下是用于注册节点的字典结构（通常在实际使用中由ComfyUI等框架来解析和注册）
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NODE_CLASS_MAPPINGS = {
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"SaveImagePath": SaveImagePath
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"SaveImagePath": "保存图片路径"
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}
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