| import os |
| import random |
|
|
| import cv2 |
| import numpy as np |
| import torch |
| import time |
| from loguru import logger |
|
|
| from iopaint.helper import get_cache_path_by_url, load_jit_model, download_model |
| from .base import InpaintModel |
| from iopaint.schema import InpaintRequest |
|
|
|
|
| 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 |
| is_erase_model = True |
|
|
| 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 |
|
|
| @staticmethod |
| def download(): |
| download_model(MANGA_INPAINTOR_MODEL_URL, MANGA_INPAINTOR_MODEL_MD5) |
| download_model(MANGA_LINE_MODEL_URL, MANGA_LINE_MODEL_MD5) |
|
|
| @staticmethod |
| 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: InpaintRequest): |
| """ |
| 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 |
|
|
