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from skimage import io
import torch, os
from PIL import Image
from briarmbg import BriaRMBG
from utilities import preprocess_image, postprocess_image
from huggingface_hub import hf_hub_download
def example_inference():
im_path = f"{os.path.dirname(os.path.abspath(__file__))}/example_input.jpg"
net = BriaRMBG()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
net = BriaRMBG.from_pretrained("briaai/RMBG-1.4")
net.to(device)
net.eval()
# prepare input
model_input_size = [1024,1024]
orig_im = io.imread(im_path)
orig_im_size = orig_im.shape[0:2]
image = preprocess_image(orig_im, model_input_size).to(device)
# inference
result=net(image)
# post process
result_image = postprocess_image(result[0][0], orig_im_size)
# save result
pil_mask_im = Image.fromarray(result_image)
orig_image = Image.open(im_path)
no_bg_image = orig_image.copy()
no_bg_image.putalpha(pil_mask_im)
no_bg_image.save("example_image_no_bg.png")
if __name__ == "__main__":
example_inference() |