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