import albumentations as albu
import numpy as np
from iglovikov_helper_functions.utils.image_utils import pad, unpad
from iglovikov_helper_functions.dl.pytorch.utils import tensor_from_rgb_image
import cv2
import torch
from PIL import Image

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

def tensor2np(image):
    return np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8)

class GenImageMask:

    def __init__(self):
        pass
    
    @classmethod
    def INPUT_TYPES(s):
        """
            Return a dictionary which contains config for all input fields.
            Some types (string): "MODEL", "VAE", "CLIP", "CONDITIONING", "LATENT", "IMAGE", "INT", "STRING", "FLOAT".
            Input types "INT", "STRING" or "FLOAT" are special values for fields on the node.
            The type can be a list for selection.

            Returns: `dict`:
                - Key input_fields_group (`string`): Can be either required, hidden or optional. A node class must have property `required`
                - Value input_fields (`dict`): Contains input fields config:
                    * Key field_name (`string`): Name of a entry-point method's argument
                    * Value field_config (`tuple`):
                        + First value is a string indicate the type of field or a list for selection.
                        + Secound value is a config for type "INT", "STRING" or "FLOAT".
        """
        return {"required": {"image": ("IMAGE",), "segm_model": ("SEGM_MODEL",)}}

    CATEGORY = "image"
    RETURN_TYPES = ("IMAGE",)
    RETURN_NAMES = ("BW Mask",)

    FUNCTION = "mask_image"
    def mask_image(self, image, segm_model):

        transform = albu.Compose([albu.Normalize(p=1)], p=1)
        image = tensor2np(image)
        padded_image, pads = pad(image, factor=32, border=cv2.BORDER_CONSTANT)
        x = transform(image=padded_image)["image"]
        x = torch.unsqueeze(tensor_from_rgb_image(x), 0)
        with torch.no_grad():
            prediction = segm_model(x)[0][0]

        mask = (prediction > 0.01).cpu().numpy().astype(np.uint8)

        mask = unpad(mask, pads)

        imag = pil2tensor(Image.fromarray(cv2.cvtColor(mask, cv2.COLOR_GRAY2RGB) * 255))
        return (imag,)


NODE_CLASS_MAPPINGS = {
    "GenImageMask": GenImageMask,
}