lama_cleaner/helper.py

import io
import os
import sys
from typing import List, Optional
from urllib.parse import urlparse

import cv2
import numpy as np
import torch
from PIL import Image, ImageOps
from loguru import logger
from torch.hub import download_url_to_file, get_dir


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):
    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)
    return cached_file


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


def load_jit_model(url_or_path, device):
    # if os.path.exists(url_or_path):
    #     model_path = url_or_path
    # else:
    #     model_path = download_model(url_or_path)
    model_path = os.getcwd()
    logger.info(f"Load model from: {model_path}")
    try:
        model = torch.jit.load(model_path).to(device)
    except:
        logger.error(
            f"Failed to load {model_path}, delete model and restart lama-cleaner"
        )
        exit(-1)
    model.eval()
    return model


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

    try:
        state_dict = torch.load(model_path, map_location='cpu')
        model.load_state_dict(state_dict, strict=True)
        model.to(device)
        logger.info(f"Load model from: {model_path}")
    except:
        logger.error(
            f"Failed to load {model_path}, delete model and restart lama-cleaner"
        )
        exit(-1)
    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 load_img(img_bytes, gray: bool = False):
    alpha_channel = None
    image = Image.open(io.BytesIO(img_bytes))
    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)

    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