285 lines
8.1 KiB
Python
285 lines
8.1 KiB
Python
import io
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import os
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import sys
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from typing import List, Optional
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from urllib.parse import urlparse
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import cv2
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from PIL import Image, ImageOps
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import numpy as np
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import torch
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from lama_cleaner.const import MPS_SUPPORT_MODELS
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from loguru import logger
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from torch.hub import download_url_to_file, get_dir
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import hashlib
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def md5sum(filename):
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md5 = hashlib.md5()
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with open(filename, "rb") as f:
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for chunk in iter(lambda: f.read(128 * md5.block_size), b""):
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md5.update(chunk)
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return md5.hexdigest()
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def switch_mps_device(model_name, device):
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if model_name not in MPS_SUPPORT_MODELS and str(device) == "mps":
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logger.info(f"{model_name} not support mps, switch to cpu")
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return torch.device("cpu")
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return device
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def get_cache_path_by_url(url):
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parts = urlparse(url)
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hub_dir = get_dir()
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model_dir = os.path.join(hub_dir, "checkpoints")
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if not os.path.isdir(model_dir):
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os.makedirs(model_dir)
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filename = os.path.basename(parts.path)
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cached_file = os.path.join(model_dir, filename)
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return cached_file
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def download_model(url, model_md5: str = None):
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cached_file = get_cache_path_by_url(url)
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if not os.path.exists(cached_file):
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sys.stderr.write('Downloading: "{}" to {}\n'.format(url, cached_file))
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hash_prefix = None
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download_url_to_file(url, cached_file, hash_prefix, progress=True)
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if model_md5:
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_md5 = md5sum(cached_file)
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if model_md5 == _md5:
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logger.info(f"Download model success, md5: {_md5}")
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else:
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try:
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os.remove(cached_file)
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logger.error(
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f"Model md5: {_md5}, expected md5: {model_md5}, wrong model deleted. Please restart lama-cleaner."
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f"If you still have errors, please try download model manually first https://lama-cleaner-docs.vercel.app/install/download_model_manually.\n"
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)
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except:
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logger.error(
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f"Model md5: {_md5}, expected md5: {model_md5}, please delete {cached_file} and restart lama-cleaner."
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)
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exit(-1)
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return cached_file
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def ceil_modulo(x, mod):
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if x % mod == 0:
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return x
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return (x // mod + 1) * mod
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def handle_error(model_path, model_md5, e):
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_md5 = md5sum(model_path)
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if _md5 != model_md5:
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try:
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os.remove(model_path)
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logger.error(
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f"Model md5: {_md5}, expected md5: {model_md5}, wrong model deleted. Please restart lama-cleaner."
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f"If you still have errors, please try download model manually first https://lama-cleaner-docs.vercel.app/install/download_model_manually.\n"
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)
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except:
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logger.error(
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f"Model md5: {_md5}, expected md5: {model_md5}, please delete {model_path} and restart lama-cleaner."
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)
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else:
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logger.error(
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f"Failed to load model {model_path},"
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f"please submit an issue at https://github.com/Sanster/lama-cleaner/issues and include a screenshot of the error:\n{e}"
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)
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exit(-1)
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def load_jit_model(url_or_path, device, model_md5: str):
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if os.path.exists(url_or_path):
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model_path = url_or_path
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else:
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model_path = download_model(url_or_path, model_md5)
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logger.info(f"Loading model from: {model_path}")
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try:
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model = torch.jit.load(model_path, map_location="cpu").to(device)
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except Exception as e:
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handle_error(model_path, model_md5, e)
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model.eval()
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return model
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def load_model(model: torch.nn.Module, url_or_path, device, model_md5):
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if os.path.exists(url_or_path):
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model_path = url_or_path
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else:
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model_path = download_model(url_or_path, model_md5)
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try:
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logger.info(f"Loading model from: {model_path}")
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state_dict = torch.load(model_path, map_location="cpu")
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model.load_state_dict(state_dict, strict=True)
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model.to(device)
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except Exception as e:
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handle_error(model_path, model_md5, e)
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model.eval()
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return model
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def numpy_to_bytes(image_numpy: np.ndarray, ext: str) -> bytes:
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data = cv2.imencode(
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f".{ext}",
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image_numpy,
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[int(cv2.IMWRITE_JPEG_QUALITY), 100, int(cv2.IMWRITE_PNG_COMPRESSION), 0],
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)[1]
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image_bytes = data.tobytes()
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return image_bytes
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def pil_to_bytes(pil_img, ext: str, exif=None) -> bytes:
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with io.BytesIO() as output:
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pil_img.save(output, format=ext, exif=exif, quality=95)
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image_bytes = output.getvalue()
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return image_bytes
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def load_img(img_bytes, gray: bool = False, return_exif: bool = False):
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alpha_channel = None
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image = Image.open(io.BytesIO(img_bytes))
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try:
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if return_exif:
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exif = image.getexif()
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except:
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exif = None
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logger.error("Failed to extract exif from image")
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try:
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image = ImageOps.exif_transpose(image)
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except:
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pass
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if gray:
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image = image.convert("L")
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np_img = np.array(image)
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else:
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if image.mode == "RGBA":
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np_img = np.array(image)
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alpha_channel = np_img[:, :, -1]
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np_img = cv2.cvtColor(np_img, cv2.COLOR_RGBA2RGB)
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else:
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image = image.convert("RGB")
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np_img = np.array(image)
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if return_exif:
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return np_img, alpha_channel, exif
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return np_img, alpha_channel
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def norm_img(np_img):
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if len(np_img.shape) == 2:
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np_img = np_img[:, :, np.newaxis]
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np_img = np.transpose(np_img, (2, 0, 1))
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np_img = np_img.astype("float32") / 255
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return np_img
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def resize_max_size(
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np_img, size_limit: int, interpolation=cv2.INTER_CUBIC
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) -> np.ndarray:
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# Resize image's longer size to size_limit if longer size larger than size_limit
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h, w = np_img.shape[:2]
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if max(h, w) > size_limit:
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ratio = size_limit / max(h, w)
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new_w = int(w * ratio + 0.5)
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new_h = int(h * ratio + 0.5)
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return cv2.resize(np_img, dsize=(new_w, new_h), interpolation=interpolation)
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else:
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return np_img
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def pad_img_to_modulo(
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img: np.ndarray, mod: int, square: bool = False, min_size: Optional[int] = None
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):
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"""
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Args:
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img: [H, W, C]
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mod:
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square: 是否为正方形
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min_size:
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Returns:
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"""
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if len(img.shape) == 2:
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img = img[:, :, np.newaxis]
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height, width = img.shape[:2]
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out_height = ceil_modulo(height, mod)
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out_width = ceil_modulo(width, mod)
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if min_size is not None:
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assert min_size % mod == 0
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out_width = max(min_size, out_width)
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out_height = max(min_size, out_height)
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if square:
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max_size = max(out_height, out_width)
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out_height = max_size
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out_width = max_size
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return np.pad(
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img,
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((0, out_height - height), (0, out_width - width), (0, 0)),
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mode="symmetric",
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)
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def boxes_from_mask(mask: np.ndarray) -> List[np.ndarray]:
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"""
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Args:
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mask: (h, w, 1) 0~255
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Returns:
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"""
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height, width = mask.shape[:2]
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_, thresh = cv2.threshold(mask, 127, 255, 0)
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contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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boxes = []
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for cnt in contours:
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x, y, w, h = cv2.boundingRect(cnt)
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box = np.array([x, y, x + w, y + h]).astype(int)
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box[::2] = np.clip(box[::2], 0, width)
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box[1::2] = np.clip(box[1::2], 0, height)
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boxes.append(box)
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return boxes
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def only_keep_largest_contour(mask: np.ndarray) -> List[np.ndarray]:
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"""
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Args:
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mask: (h, w) 0~255
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Returns:
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"""
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_, thresh = cv2.threshold(mask, 127, 255, 0)
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contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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max_area = 0
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max_index = -1
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for i, cnt in enumerate(contours):
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area = cv2.contourArea(cnt)
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if area > max_area:
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max_area = area
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max_index = i
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if max_index != -1:
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new_mask = np.zeros_like(mask)
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return cv2.drawContours(new_mask, contours, max_index, 255, -1)
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else:
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return mask
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