import os
import folder_paths
import hashlib
import torch
import numpy as np
from PIL import Image

def image2mask(image:Image) -> torch.Tensor:
    if image.mode == 'L':
        return torch.tensor([pil2tensor(image)[0, :, :].tolist()])
    else:
        image = image.convert('RGB').split()[0]
        return torch.tensor([pil2tensor(image)[0, :, :].tolist()])

def pil2tensor(image:Image) -> torch.Tensor:
    return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0)

def tensor2pil(t_image: torch.Tensor)  -> Image:
    return Image.fromarray(np.clip(255.0 * t_image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))

def fit_resize_image(image:Image, target_width:int, target_height:int, fit:str, resize_sampler:str, background_color:str = '#000000') -> Image:
    image = image.convert('RGB')
    orig_width, orig_height = image.size
    if image is not None:
        if fit == 'letterbox':
            if orig_width / orig_height > target_width / target_height:  # 更宽，上下留黑
                fit_width = target_width
                fit_height = int(target_width / orig_width * orig_height)
            else:  # 更瘦，左右留黑
                fit_height = target_height
                fit_width = int(target_height / orig_height * orig_width)
            fit_image = image.resize((fit_width, fit_height), resize_sampler)
            ret_image = Image.new('RGB', size=(target_width, target_height), color=background_color)
            ret_image.paste(fit_image, box=((target_width - fit_width)//2, (target_height - fit_height)//2))
        elif fit == 'crop':
            if orig_width / orig_height > target_width / target_height:  # 更宽，裁左右
                fit_width = int(orig_height * target_width / target_height)
                fit_image = image.crop(
                    ((orig_width - fit_width)//2, 0, (orig_width - fit_width)//2 + fit_width, orig_height))
            else:   # 更瘦，裁上下
                fit_height = int(orig_width * target_height / target_width)
                fit_image = image.crop(
                    (0, (orig_height-fit_height)//2, orig_width, (orig_height-fit_height)//2 + fit_height))
            ret_image = fit_image.resize((target_width, target_height), resize_sampler)
        else:
            ret_image = image.resize((target_width, target_height), resize_sampler)
    return  ret_image

def tensor_to_hash(tensor):
    # 将 Tensor 转换为 NumPy 数组
    np_array = tensor.cpu().numpy()
    
    # 将 NumPy 数组转换为字节数据
    byte_data = np_array.tobytes()
    
    # 计算哈希值
    hash_value = hashlib.md5(byte_data).hexdigest()
    
    return hash_value


def create_temp_file(image):
    output_dir = folder_paths.get_temp_directory()

    (
            full_output_folder,
            filename,
            counter,
            subfolder,
            _,
        ) = folder_paths.get_save_image_path('material', output_dir)

    
    image=tensor2pil(image)
 
    image_file = f"{filename}_{counter:05}.png"
     
    image_path=os.path.join(full_output_folder, image_file)

    image.save(image_path,compress_level=4)

    return (image_path,[{
    "filename": image_file,
    "subfolder": subfolder,
    "type": "temp"
    }])