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| import os | |
| import random | |
| import cv2 | |
| import numpy as np | |
| import torch | |
| import time | |
| from loguru import logger | |
| from lama_cleaner.helper import get_cache_path_by_url, load_jit_model | |
| from lama_cleaner.model.base import InpaintModel | |
| from lama_cleaner.schema import Config | |
| MANGA_INPAINTOR_MODEL_URL = os.environ.get( | |
| "MANGA_INPAINTOR_MODEL_URL", | |
| "https://github.com/Sanster/models/releases/download/manga/manga_inpaintor.jit", | |
| ) | |
| MANGA_INPAINTOR_MODEL_MD5 = os.environ.get( | |
| "MANGA_INPAINTOR_MODEL_MD5", "7d8b269c4613b6b3768af714610da86c" | |
| ) | |
| MANGA_LINE_MODEL_URL = os.environ.get( | |
| "MANGA_LINE_MODEL_URL", | |
| "https://github.com/Sanster/models/releases/download/manga/erika.jit", | |
| ) | |
| MANGA_LINE_MODEL_MD5 = os.environ.get( | |
| "MANGA_LINE_MODEL_MD5", "0c926d5a4af8450b0d00bc5b9a095644" | |
| ) | |
| class Manga(InpaintModel): | |
| name = "manga" | |
| pad_mod = 16 | |
| def init_model(self, device, **kwargs): | |
| self.inpaintor_model = load_jit_model( | |
| MANGA_INPAINTOR_MODEL_URL, device, MANGA_INPAINTOR_MODEL_MD5 | |
| ) | |
| self.line_model = load_jit_model( | |
| MANGA_LINE_MODEL_URL, device, MANGA_LINE_MODEL_MD5 | |
| ) | |
| self.seed = 42 | |
| def is_downloaded() -> bool: | |
| model_paths = [ | |
| get_cache_path_by_url(MANGA_INPAINTOR_MODEL_URL), | |
| get_cache_path_by_url(MANGA_LINE_MODEL_URL), | |
| ] | |
| return all([os.path.exists(it) for it in model_paths]) | |
| def forward(self, image, mask, config: Config): | |
| """ | |
| image: [H, W, C] RGB | |
| mask: [H, W, 1] | |
| return: BGR IMAGE | |
| """ | |
| seed = self.seed | |
| random.seed(seed) | |
| np.random.seed(seed) | |
| torch.manual_seed(seed) | |
| torch.cuda.manual_seed_all(seed) | |
| gray_img = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY) | |
| gray_img = torch.from_numpy( | |
| gray_img[np.newaxis, np.newaxis, :, :].astype(np.float32) | |
| ).to(self.device) | |
| start = time.time() | |
| lines = self.line_model(gray_img) | |
| torch.cuda.empty_cache() | |
| lines = torch.clamp(lines, 0, 255) | |
| logger.info(f"erika_model time: {time.time() - start}") | |
| mask = torch.from_numpy(mask[np.newaxis, :, :, :]).to(self.device) | |
| mask = mask.permute(0, 3, 1, 2) | |
| mask = torch.where(mask > 0.5, 1.0, 0.0) | |
| noise = torch.randn_like(mask) | |
| ones = torch.ones_like(mask) | |
| gray_img = gray_img / 255 * 2 - 1.0 | |
| lines = lines / 255 * 2 - 1.0 | |
| start = time.time() | |
| inpainted_image = self.inpaintor_model(gray_img, lines, mask, noise, ones) | |
| logger.info(f"image_inpaintor_model time: {time.time() - start}") | |
| cur_res = inpainted_image[0].permute(1, 2, 0).detach().cpu().numpy() | |
| cur_res = (cur_res * 127.5 + 127.5).astype(np.uint8) | |
| cur_res = cv2.cvtColor(cur_res, cv2.COLOR_GRAY2BGR) | |
| return cur_res | |