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import os |
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import cv2 |
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import numpy as np |
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import torch |
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from loguru import logger |
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from lama_cleaner.helper import download_model, norm_img, get_cache_path_by_url |
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from lama_cleaner.model.base import InpaintModel |
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from lama_cleaner.schema import Config |
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LAMA_MODEL_URL = os.environ.get( |
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"LAMA_MODEL_URL", |
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"https://github.com/Sanster/models/releases/download/add_big_lama/big-lama.pt", |
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) |
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class LaMa(InpaintModel): |
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pad_mod = 8 |
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def init_model(self, device, **kwargs): |
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if os.environ.get("LAMA_MODEL"): |
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model_path = os.environ.get("LAMA_MODEL") |
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if not os.path.exists(model_path): |
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raise FileNotFoundError( |
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f"lama torchscript model not found: {model_path}" |
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) |
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else: |
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model_path = download_model(LAMA_MODEL_URL) |
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logger.info(f"Load LaMa model from: {model_path}") |
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model = torch.jit.load(model_path, map_location="cpu") |
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model = model.to(device) |
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model.eval() |
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self.model = model |
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self.model_path = model_path |
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@staticmethod |
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def is_downloaded() -> bool: |
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return os.path.exists(get_cache_path_by_url(LAMA_MODEL_URL)) |
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def forward(self, image, mask, config: Config): |
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"""Input image and output image have same size |
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image: [H, W, C] RGB |
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mask: [H, W] |
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return: BGR IMAGE |
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""" |
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image = norm_img(image) |
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mask = norm_img(mask) |
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mask = (mask > 0) * 1 |
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image = torch.from_numpy(image).unsqueeze(0).to(self.device) |
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mask = torch.from_numpy(mask).unsqueeze(0).to(self.device) |
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inpainted_image = self.model(image, mask) |
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cur_res = inpainted_image[0].permute(1, 2, 0).detach().cpu().numpy() |
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cur_res = np.clip(cur_res * 255, 0, 255).astype("uint8") |
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cur_res = cv2.cvtColor(cur_res, cv2.COLOR_RGB2BGR) |
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return cur_res |
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