Spaces:
Runtime error
Runtime error
File size: 4,376 Bytes
cdb26a4 2b6c2bd cdb26a4 |
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 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 |
import os
import sys
import numpy as np
import torch
import yaml
import glob
import argparse
from PIL import Image
from omegaconf import OmegaConf
from pathlib import Path
os.environ['OMP_NUM_THREADS'] = '1'
os.environ['OPENBLAS_NUM_THREADS'] = '1'
os.environ['MKL_NUM_THREADS'] = '1'
os.environ['VECLIB_MAXIMUM_THREADS'] = '1'
os.environ['NUMEXPR_NUM_THREADS'] = '1'
sys.path.insert(0, str(Path(__file__).resolve().parent / "third_party" / "lama"))
from saicinpainting.evaluation.utils import move_to_device
from saicinpainting.training.trainers import load_checkpoint
from saicinpainting.evaluation.data import pad_tensor_to_modulo
from utils import load_img_to_array, save_array_to_img
@torch.no_grad()
def inpaint_img_with_lama(
img: np.ndarray,
mask: np.ndarray,
config_p: str,
ckpt_p: str,
mod=8,
device="cuda"
):
assert len(mask.shape) == 2
if np.max(mask) == 1:
mask = mask * 255
img = torch.from_numpy(img).float().div(255.)
mask = torch.from_numpy(mask).float()
predict_config = OmegaConf.load(config_p)
predict_config.model.path = ckpt_p
# device = torch.device(predict_config.device)
device = torch.device(device)
train_config_path = os.path.join(
predict_config.model.path, 'config.yaml')
with open(train_config_path, 'r') as f:
train_config = OmegaConf.create(yaml.safe_load(f))
train_config.training_model.predict_only = True
train_config.visualizer.kind = 'noop'
checkpoint_path = os.path.join(
predict_config.model.path, 'models',
predict_config.model.checkpoint
)
model = load_checkpoint(
train_config, checkpoint_path, strict=False, map_location=device)
model.freeze()
if not predict_config.get('refine', False):
model.to(device)
batch = {}
batch['image'] = img.permute(2, 0, 1).unsqueeze(0)
batch['mask'] = mask[None, None]
unpad_to_size = [batch['image'].shape[2], batch['image'].shape[3]]
batch['image'] = pad_tensor_to_modulo(batch['image'], mod)
batch['mask'] = pad_tensor_to_modulo(batch['mask'], mod)
batch = move_to_device(batch, device)
batch['mask'] = (batch['mask'] > 0) * 1
batch = model(batch)
cur_res = batch[predict_config.out_key][0].permute(1, 2, 0)
cur_res = cur_res.detach().cpu().numpy()
if unpad_to_size is not None:
orig_height, orig_width = unpad_to_size
cur_res = cur_res[:orig_height, :orig_width]
cur_res = np.clip(cur_res * 255, 0, 255).astype('uint8')
return cur_res
def setup_args(parser):
parser.add_argument(
"--input_img", type=str, required=True,
help="Path to a single input img",
)
parser.add_argument(
"--input_mask_glob", type=str, required=True,
help="Glob to input masks",
)
parser.add_argument(
"--output_dir", type=str, required=True,
help="Output path to the directory with results.",
)
parser.add_argument(
"--lama_config", type=str,
default="./third_party/lama/configs/prediction/default.yaml",
help="The path to the config file of lama model. "
"Default: the config of big-lama",
)
parser.add_argument(
"--lama_ckpt", type=str, required=True,
help="The path to the lama checkpoint.",
)
if __name__ == "__main__":
"""Example usage:
python lama_inpaint.py \
--input_img FA_demo/FA1_dog.png \
--input_mask_glob "results/FA1_dog/mask*.png" \
--output_dir results \
--lama_config lama/configs/prediction/default.yaml \
--lama_ckpt big-lama
"""
parser = argparse.ArgumentParser()
setup_args(parser)
args = parser.parse_args(sys.argv[1:])
device = "cuda" if torch.cuda.is_available() else "cpu"
img_stem = Path(args.input_img).stem
mask_ps = sorted(glob.glob(args.input_mask_glob))
out_dir = Path(args.output_dir) / img_stem
out_dir.mkdir(parents=True, exist_ok=True)
img = load_img_to_array(args.input_img)
for mask_p in mask_ps:
mask = load_img_to_array(mask_p)
img_inpainted_p = out_dir / f"inpainted_with_{Path(mask_p).name}"
img_inpainted = inpaint_img_with_lama(
img, mask, args.lama_config, args.lama_ckpt, device=device)
save_array_to_img(img_inpainted, img_inpainted_p) |