import torch
import numpy as np
from PIL import Image
from skimage import io
from ormbg import ORMBG
import torch.nn.functional as F


def preprocess_image(im: np.ndarray, model_input_size: list) -> torch.Tensor:
    if len(im.shape) < 3:
        im = im[:, :, np.newaxis]
    im_tensor = torch.tensor(im, dtype=torch.float32).permute(2, 0, 1)
    im_tensor = F.interpolate(
        torch.unsqueeze(im_tensor, 0), size=model_input_size, mode="bilinear"
    ).type(torch.uint8)
    image = torch.divide(im_tensor, 255.0)
    return image


def postprocess_image(result: torch.Tensor, im_size: list) -> np.ndarray:
    result = torch.squeeze(F.interpolate(result, size=im_size, mode="bilinear"), 0)
    ma = torch.max(result)
    mi = torch.min(result)
    result = (result - mi) / (ma - mi)
    im_array = (result * 255).permute(1, 2, 0).cpu().data.numpy().astype(np.uint8)
    im_array = np.squeeze(im_array)
    return im_array


def example_inference():

    image_path = "example.png"
    result_name = "no-background.png"

    net = ORMBG()
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

    if torch.cuda.is_available():
        net.load_state_dict(torch.load("models/ormbg.pth"))
        net = net.cuda()
    else:
        net.load_state_dict(torch.load("models/ormbg.pth", map_location="cpu"))
    net.eval()

    model_input_size = [1024, 1024]
    orig_im = io.imread(image_path)
    orig_im_size = orig_im.shape[0:2]
    image = preprocess_image(orig_im, model_input_size).to(device)

    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(image_path)
    no_bg_image.paste(orig_image, mask=pil_im)
    no_bg_image.save(result_name)


if __name__ == "__main__":
    example_inference()