lama_cleaner/helper.py

import io
import os
import sys
from typing import List, Optional

from urllib.parse import urlparse
import cv2
from PIL import Image, ImageOps, PngImagePlugin
import numpy as np
import torch
from lama_cleaner.const import MPS_SUPPORT_MODELS
from loguru import logger
from torch.hub import download_url_to_file, get_dir
import hashlib


def md5sum(filename):
    md5 = hashlib.md5()
    with open(filename, "rb") as f:
        for chunk in iter(lambda: f.read(128 * md5.block_size), b""):
            md5.update(chunk)
    return md5.hexdigest()


def switch_mps_device(model_name, device):
    if model_name not in MPS_SUPPORT_MODELS and str(device) == "mps":
        logger.info(f"{model_name} not support mps, switch to cpu")
        return torch.device("cpu")
    return device


def get_cache_path_by_url(url):
    parts = urlparse(url)
    hub_dir = get_dir()
    model_dir = os.path.join(hub_dir, "checkpoints")
    if not os.path.isdir(model_dir):
        os.makedirs(model_dir)
    filename = os.path.basename(parts.path)
    cached_file = os.path.join(model_dir, filename)
    return cached_file


def download_model(url, model_md5: str = None):
    cached_file = get_cache_path_by_url(url)
    if not os.path.exists(cached_file):
        sys.stderr.write('Downloading: "{}" to {}\n'.format(url, cached_file))
        hash_prefix = None
        download_url_to_file(url, cached_file, hash_prefix, progress=True)
        if model_md5:
            _md5 = md5sum(cached_file)
            if model_md5 == _md5:
                logger.info(f"Download model success, md5: {_md5}")
            else:
                try:
                    os.remove(cached_file)
                    logger.error(
                        f"Model md5: {_md5}, expected md5: {model_md5}, wrong model deleted. Please restart lama-cleaner."
                        f"If you still have errors, please try download model manually first https://lama-cleaner-docs.vercel.app/install/download_model_manually.\n"
                    )
                except:
                    logger.error(
                        f"Model md5: {_md5}, expected md5: {model_md5}, please delete {cached_file} and restart lama-cleaner."
                    )
                exit(-1)

    return cached_file


def ceil_modulo(x, mod):
    if x % mod == 0:
        return x
    return (x // mod + 1) * mod


def handle_error(model_path, model_md5, e):
    _md5 = md5sum(model_path)
    if _md5 != model_md5:
        try:
            os.remove(model_path)
            logger.error(
                f"Model md5: {_md5}, expected md5: {model_md5}, wrong model deleted. Please restart lama-cleaner."
                f"If you still have errors, please try download model manually first https://lama-cleaner-docs.vercel.app/install/download_model_manually.\n"
            )
        except:
            logger.error(
                f"Model md5: {_md5}, expected md5: {model_md5}, please delete {model_path} and restart lama-cleaner."
            )
    else:
        logger.error(
            f"Failed to load model {model_path},"
            f"please submit an issue at https://github.com/Sanster/lama-cleaner/issues and include a screenshot of the error:\n{e}"
        )
    exit(-1)


def load_jit_model(url_or_path, device, model_md5: str):
    if os.path.exists(url_or_path):
        model_path = url_or_path
    else:
        model_path = download_model(url_or_path, model_md5)

    logger.info(f"Loading model from: {model_path}")
    try:
        model = torch.jit.load(model_path, map_location="cpu").to(device)
    except Exception as e:
        handle_error(model_path, model_md5, e)
    model.eval()
    return model


def load_model(model: torch.nn.Module, url_or_path, device, model_md5):
    if os.path.exists(url_or_path):
        model_path = url_or_path
    else:
        model_path = download_model(url_or_path, model_md5)

    try:
        logger.info(f"Loading model from: {model_path}")
        state_dict = torch.load(model_path, map_location="cpu")
        model.load_state_dict(state_dict, strict=True)
        model.to(device)
    except Exception as e:
        handle_error(model_path, model_md5, e)
    model.eval()
    return model


def numpy_to_bytes(image_numpy: np.ndarray, ext: str) -> bytes:
    data = cv2.imencode(
        f".{ext}",
        image_numpy,
        [int(cv2.IMWRITE_JPEG_QUALITY), 100, int(cv2.IMWRITE_PNG_COMPRESSION), 0],
    )[1]
    image_bytes = data.tobytes()
    return image_bytes


def pil_to_bytes(pil_img, ext: str, quality: int = 95, exif_infos={}) -> bytes:
    with io.BytesIO() as output:
        kwargs = {k: v for k, v in exif_infos.items() if v is not None}
        if ext == "png" and "parameters" in kwargs:
            pnginfo_data = PngImagePlugin.PngInfo()
            pnginfo_data.add_text("parameters", kwargs["parameters"])
            kwargs["pnginfo"] = pnginfo_data

        pil_img.save(
            output,
            format=ext,
            quality=quality,
            **kwargs,
        )
        image_bytes = output.getvalue()
    return image_bytes


def load_img(img_bytes, gray: bool = False, return_exif: bool = False):
    alpha_channel = None
    image = Image.open(io.BytesIO(img_bytes))

    if return_exif:
        info = image.info or {}
        exif_infos = {"exif": image.getexif(), "parameters": info.get("parameters")}

    try:
        image = ImageOps.exif_transpose(image)
    except:
        pass

    if gray:
        image = image.convert("L")
        np_img = np.array(image)
    else:
        if image.mode == "RGBA":
            np_img = np.array(image)
            alpha_channel = np_img[:, :, -1]
            np_img = cv2.cvtColor(np_img, cv2.COLOR_RGBA2RGB)
        else:
            image = image.convert("RGB")
            np_img = np.array(image)

    if return_exif:
        return np_img, alpha_channel, exif_infos
    return np_img, alpha_channel


def norm_img(np_img):
    if len(np_img.shape) == 2:
        np_img = np_img[:, :, np.newaxis]
    np_img = np.transpose(np_img, (2, 0, 1))
    np_img = np_img.astype("float32") / 255
    return np_img


def resize_max_size(

    np_img, size_limit: int, interpolation=cv2.INTER_CUBIC

) -> np.ndarray:
    # Resize image's longer size to size_limit if longer size larger than size_limit
    h, w = np_img.shape[:2]
    if max(h, w) > size_limit:
        ratio = size_limit / max(h, w)
        new_w = int(w * ratio + 0.5)
        new_h = int(h * ratio + 0.5)
        return cv2.resize(np_img, dsize=(new_w, new_h), interpolation=interpolation)
    else:
        return np_img


def pad_img_to_modulo(

    img: np.ndarray, mod: int, square: bool = False, min_size: Optional[int] = None

):
    """



    Args:

        img: [H, W, C]

        mod:

        square: 是否为正方形

        min_size:



    Returns:



    """
    if len(img.shape) == 2:
        img = img[:, :, np.newaxis]
    height, width = img.shape[:2]
    out_height = ceil_modulo(height, mod)
    out_width = ceil_modulo(width, mod)

    if min_size is not None:
        assert min_size % mod == 0
        out_width = max(min_size, out_width)
        out_height = max(min_size, out_height)

    if square:
        max_size = max(out_height, out_width)
        out_height = max_size
        out_width = max_size

    return np.pad(
        img,
        ((0, out_height - height), (0, out_width - width), (0, 0)),
        mode="symmetric",
    )


def boxes_from_mask(mask: np.ndarray) -> List[np.ndarray]:
    """

    Args:

        mask: (h, w, 1)  0~255



    Returns:



    """
    height, width = mask.shape[:2]
    _, thresh = cv2.threshold(mask, 127, 255, 0)
    contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

    boxes = []
    for cnt in contours:
        x, y, w, h = cv2.boundingRect(cnt)
        box = np.array([x, y, x + w, y + h]).astype(int)

        box[::2] = np.clip(box[::2], 0, width)
        box[1::2] = np.clip(box[1::2], 0, height)
        boxes.append(box)

    return boxes


def only_keep_largest_contour(mask: np.ndarray) -> List[np.ndarray]:
    """

    Args:

        mask: (h, w)  0~255



    Returns:



    """
    _, thresh = cv2.threshold(mask, 127, 255, 0)
    contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

    max_area = 0
    max_index = -1
    for i, cnt in enumerate(contours):
        area = cv2.contourArea(cnt)
        if area > max_area:
            max_area = area
            max_index = i

    if max_index != -1:
        new_mask = np.zeros_like(mask)
        return cv2.drawContours(new_mask, contours, max_index, 255, -1)
    else:
        return mask