from io import BytesIO

import numpy as np
import requests
import torch
from PIL import Image


# 定义节点类
class LoadNetImg:
    # 定义节点输入类型
    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "image_url": ("STRING", {
                    "default": "https://example.com/sample.jpg",
                    "multiline": False
                }),
            }
        }

    # 定义节点输出类型
    RETURN_TYPES = ("IMAGE",)  # 返回图像数据
    RETURN_NAMES = ("image",)  # 命名返回值
    FUNCTION = "load_image_task"  # 函数标识符，方便注册多个节点功能
    OUTPUT_NODE = False  # 不允许该节点直接作为最终输出节点
    CATEGORY = "image"  # 节点所属类别（在 ComfyUI 界面中分类）

    def load_image_task(self, image_url):
        try:
            if not image_url or not image_url.strip():
                raise ValueError("需要提供图片URL")
            
            # 下载网络图片
            response = requests.get(image_url)
            response.raise_for_status()  # 请求失败时抛出异常
            image = Image.open(BytesIO(response.content)).convert("RGB")

            # 按照官方格式转换图像数据
            # Convert to numpy array and normalize to 0-1
            image_array = np.array(image).astype(np.float32) / 255.0
            # Convert to torch tensor and add batch dimension
            image_tensor = torch.from_numpy(image_array)[None,]

            return (image_tensor,)  # 返回torch张量
        except Exception as e:
            print(f"Error loading image: {e}")
            # 返回一个空的黑色图片作为错误处理
            empty_image = torch.zeros((1, 512, 512, 3), dtype=torch.float32)
            return (empty_image,)




# 映射节点类和名称
NODE_CLASS_MAPPINGS = {
    "LoadNetImg": LoadNetImg,  # 将类映射到节点名称
}
NODE_DISPLAY_NAME_MAPPINGS = {
    "LoadNetImg": "load_net_image",  # 节点显示名称
}