from skimage import io
import torch, os
from PIL import Image
from briarmbg import BriaRMBG
from utilities import preprocess_image, postprocess_image

def example_inference():

    model_path = f"{os.path.dirname(__file__)}/model.pth"
    im_path = f"{os.path.dirname(__file__)}/example_input.jpg"

    net = BriaRMBG()
    if torch.cuda.is_available():
        net.load_state_dict(torch.load(model_path)).cuda()
    else:
        net.load_state_dict(torch.load(model_path,map_location="cpu"))
    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)

    if torch.cuda.is_available():
        image=image.cuda()

    # inference 
    result=net(image)

    # post process
    result_image = postprocess_image(result[0][0], orig_im_size)

    # save result
    pil_im = Image.fromarray(result_image)
    no_bg_image = Image.new("RGBA", pil_im.size, (0,0,0,0))
    orig_image = Image.open(im_path)
    no_bg_image.paste(orig_image, mask=pil_im)
    no_bg_image.save("example_image_no_bg.png")


if __name__ == "__main__":
    example_inference()