Spaces:
Running
on
Zero
Running
on
Zero
File size: 2,009 Bytes
cba094e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 |
import os
import cv2
import numpy as np
import torch
from loguru import logger
from lama_cleaner.helper import pad_img_to_modulo, download_model, norm_img, get_cache_path_by_url
from lama_cleaner.model.base import InpaintModel
from lama_cleaner.schema import Config
LAMA_MODEL_URL = os.environ.get(
"LAMA_MODEL_URL",
"https://github.com/Sanster/models/releases/download/add_big_lama/big-lama.pt",
)
#"https://drive.google.com/file/d/1bMD06F9hkkS1oi8cEmb4cSjXz54Pxs6A/view?usp=sharing" #big-lama.pt file
class LaMa(InpaintModel):
pad_mod = 8
def init_model(self, device, **kwargs):
if os.environ.get("LAMA_MODEL"):
model_path = os.environ.get("LAMA_MODEL")
if not os.path.exists(model_path):
raise FileNotFoundError(
f"lama torchscript model not found: {model_path}"
)
else:
model_path = download_model(LAMA_MODEL_URL)
logger.info(f"Load LaMa model from: {model_path}")
model = torch.jit.load(model_path, map_location="cpu")
model = model.to(device)
model.eval()
self.model = model
self.model_path = model_path
@staticmethod
def is_downloaded() -> bool:
return os.path.exists(get_cache_path_by_url(LAMA_MODEL_URL))
def forward(self, image, mask, config: Config):
"""Input image and output image have same size
image: [H, W, C] RGB
mask: [H, W]
return: BGR IMAGE
"""
image = norm_img(image)
mask = norm_img(mask)
mask = (mask > 0) * 1
image = torch.from_numpy(image).unsqueeze(0).to(self.device)
mask = torch.from_numpy(mask).unsqueeze(0).to(self.device)
inpainted_image = self.model(image, mask)
cur_res = inpainted_image[0].permute(1, 2, 0).detach().cpu().numpy()
cur_res = np.clip(cur_res * 255, 0, 255).astype("uint8")
cur_res = cv2.cvtColor(cur_res, cv2.COLOR_RGB2BGR)
return cur_res
|