import json
from pathlib import Path
from typing import Dict, Optional

import cv2
import numpy as np
from PIL import Image
from loguru import logger
from rich.console import Console
from rich.progress import (
    Progress,
    SpinnerColumn,
    TimeElapsedColumn,
    MofNCompleteColumn,
    TextColumn,
    BarColumn,
    TaskProgressColumn,
)

from iopaint.helper import pil_to_bytes
from iopaint.model.utils import torch_gc
from iopaint.model_manager import ModelManager
from iopaint.schema import InpaintRequest


def glob_images(path: Path) -> Dict[str, Path]:
    # png/jpg/jpeg
    if path.is_file():
        return {path.stem: path}
    elif path.is_dir():
        res = {}
        for it in path.glob("*.*"):
            if it.suffix.lower() in [".png", ".jpg", ".jpeg"]:
                res[it.stem] = it
        return res


def batch_inpaint(
    model: str,
    device,
    image: Path,
    mask: Path,
    output: Path,
    config: Optional[Path] = None,
    concat: bool = False,
):
    if image.is_dir() and output.is_file():
        logger.error(
            "invalid --output: when image is a directory, output should be a directory"
        )
        exit(-1)
    output.mkdir(parents=True, exist_ok=True)

    image_paths = glob_images(image)
    mask_paths = glob_images(mask)
    if len(image_paths) == 0:
        logger.error("invalid --image: empty image folder")
        exit(-1)
    if len(mask_paths) == 0:
        logger.error("invalid --mask: empty mask folder")
        exit(-1)

    if config is None:
        inpaint_request = InpaintRequest()
        logger.info(f"Using default config: {inpaint_request}")
    else:
        with open(config, "r", encoding="utf-8") as f:
            inpaint_request = InpaintRequest(**json.load(f))
        logger.info(f"Using config: {inpaint_request}")

    model_manager = ModelManager(name=model, device=device)
    first_mask = list(mask_paths.values())[0]

    console = Console()

    with Progress(
        SpinnerColumn(),
        TextColumn("[progress.description]{task.description}"),
        BarColumn(),
        TaskProgressColumn(),
        MofNCompleteColumn(),
        TimeElapsedColumn(),
        console=console,
        transient=False,
    ) as progress:
        task = progress.add_task("Batch processing...", total=len(image_paths))
        for stem, image_p in image_paths.items():
            if stem not in mask_paths and mask.is_dir():
                progress.log(f"mask for {image_p} not found")
                progress.update(task, advance=1)
                continue
            mask_p = mask_paths.get(stem, first_mask)

            infos = Image.open(image_p).info

            img = np.array(Image.open(image_p).convert("RGB"))
            mask_img = np.array(Image.open(mask_p).convert("L"))

            if mask_img.shape[:2] != img.shape[:2]:
                progress.log(
                    f"resize mask {mask_p.name} to image {image_p.name} size: {img.shape[:2]}"
                )
                mask_img = cv2.resize(
                    mask_img,
                    (img.shape[1], img.shape[0]),
                    interpolation=cv2.INTER_NEAREST,
                )
            mask_img[mask_img >= 127] = 255
            mask_img[mask_img < 127] = 0

            # bgr
            inpaint_result = model_manager(img, mask_img, inpaint_request)
            inpaint_result = cv2.cvtColor(inpaint_result, cv2.COLOR_BGR2RGB)
            if concat:
                mask_img = cv2.cvtColor(mask_img, cv2.COLOR_GRAY2RGB)
                inpaint_result = cv2.hconcat([img, mask_img, inpaint_result])

            img_bytes = pil_to_bytes(Image.fromarray(inpaint_result), "png", 100, infos)
            save_p = output / f"{stem}.png"
            with open(save_p, "wb") as fw:
                fw.write(img_bytes)

            progress.update(task, advance=1)
            torch_gc()
            # pid = psutil.Process().pid
            # memory_info = psutil.Process(pid).memory_info()
            # memory_in_mb = memory_info.rss / (1024 * 1024)
            # print(f"原图大小：{img.shape},当前进程的内存占用：{memory_in_mb}MB")