58 lines
1.6 KiB
Python
58 lines
1.6 KiB
Python
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 lama_cleaner.helper import (
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norm_img,
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get_cache_path_by_url,
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load_jit_model,
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download_model,
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)
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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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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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LAMA_MODEL_MD5 = os.environ.get("LAMA_MODEL_MD5", "e3aa4aaa15225a33ec84f9f4bc47e500")
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class LaMa(InpaintModel):
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name = "lama"
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pad_mod = 8
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is_erase_model = True
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@staticmethod
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def download():
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download_model(LAMA_MODEL_URL, LAMA_MODEL_MD5)
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def init_model(self, device, **kwargs):
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self.model = load_jit_model(LAMA_MODEL_URL, device, LAMA_MODEL_MD5).eval()
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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: InpaintRequest):
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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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