lama_cleaner/helper.py

import os
import sys
from typing import List, Optional

from urllib.parse import urlparse
import cv2
import numpy as np
import torch
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(os.path.join(model_dir, "hub", "checkpoints"))
    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)
    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
    nparr = np.frombuffer(img_bytes, np.uint8)
    if gray:
        np_img = cv2.imdecode(nparr, cv2.IMREAD_GRAYSCALE)
    else:
        np_img = cv2.imdecode(nparr, cv2.IMREAD_UNCHANGED)
        if len(np_img.shape) == 3 and np_img.shape[2] == 4:
            alpha_channel = np_img[:, :, -1]
            np_img = cv2.cvtColor(np_img, cv2.COLOR_BGRA2RGB)
        else:
            np_img = cv2.cvtColor(np_img, cv2.COLOR_BGR2RGB)

    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