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from pathlib import Path
import albumentations as A
import cv2
import torch
import tqdm
from albumentations.pytorch.functional import img_to_tensor
from timm.data.constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from torchvision.utils import draw_segmentation_masks, make_grid, save_image
import utils.misc as misc
from models import get_ensemble_model
from opt import get_opt
def demo(folder_path, output_path=Path("tmp")):
opt = get_opt()
model = get_ensemble_model(opt).to(opt.device)
misc.resume_from(model, opt.resume)
with torch.no_grad():
for image_path in tqdm.tqdm(folder_path.glob("*.jpg")):
image = cv2.imread(image_path.as_posix())
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
dsm_image = torch.from_numpy(image).permute(2, 0, 1)
image_size = image.shape[:2]
raw_image = img_to_tensor(image)
image = img_to_tensor(
image,
normalize={"mean": IMAGENET_DEFAULT_MEAN, "std": IMAGENET_DEFAULT_STD},
)
image = image.to(opt.device).unsqueeze(0)
outputs = model(image, seg_size=image_size)
out_map = outputs["ensemble"]["out_map"][0, ...].detach().cpu()
pred = outputs["ensemble"]["out_map"].max().item()
if pred > opt.mask_threshold:
print(f"Found manipulation in {image_path.name}")
else:
print(f"No manipulation found in {image_path.name}")
overlay = draw_segmentation_masks(
dsm_image, masks=out_map[0, ...] > opt.mask_threshold
)
grid_image = make_grid(
[
raw_image,
(out_map.repeat(3, 1, 1) > opt.mask_threshold).float() * 255,
overlay / 255.0,
],
padding=5,
)
image_name = image_path.stem + f"-{pred:.2f}" + image_path.suffix
save_image(grid_image, (output_path / image_name).as_posix())
if __name__ == "__main__":
folder_path = Path("demo")
output_path = Path("tmp")
output_path.mkdir(exist_ok=True, parents=True)
demo(folder_path)
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