import os
import sys
import glob
import argparse
import numpy as np

import torch
import torch.nn.functional as F
from torchvision import transforms
from PIL import Image
import utils.utils as utils


def test_samples(args, model, intrins=None, device="cpu"):
    img_paths = glob.glob("./samples/img/*.png") + glob.glob("./samples/img/*.jpg")
    img_paths.sort()

    # normalize
    normalize = transforms.Normalize(
        mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
    )

    with torch.no_grad():
        for img_path in img_paths:
            print(img_path)
            ext = os.path.splitext(img_path)[1]
            img = Image.open(img_path).convert("RGB")
            img = np.array(img).astype(np.float32) / 255.0
            img = torch.from_numpy(img).permute(2, 0, 1).unsqueeze(0).to(device)
            _, _, orig_H, orig_W = img.shape

            # zero-pad the input image so that both the width and height are multiples of 32
            l, r, t, b = utils.pad_input(orig_H, orig_W)
            img = F.pad(img, (l, r, t, b), mode="constant", value=0.0)
            img = normalize(img)

            intrins_path = img_path.replace(ext, ".txt")
            if os.path.exists(intrins_path):
                # NOTE: camera intrinsics should be given as a txt file
                # it should contain the values of fx, fy, cx, cy
                intrins = utils.get_intrins_from_txt(
                    intrins_path, device=device
                ).unsqueeze(0)
            else:
                # NOTE: if intrins is not given, we just assume that the principal point is at the center
                # and that the field-of-view is 60 degrees (feel free to modify this assumption)
                intrins = utils.get_intrins_from_fov(
                    new_fov=60.0, H=orig_H, W=orig_W, device=device
                ).unsqueeze(0)

            intrins[:, 0, 2] += l
            intrins[:, 1, 2] += t

            pred_norm = model(img, intrins=intrins)[-1]
            pred_norm = pred_norm[:, :, t : t + orig_H, l : l + orig_W]

            # save to output folder
            # NOTE: by saving the prediction as uint8 png format, you lose a lot of precision
            # if you want to use the predicted normals for downstream tasks, we recommend saving them as float32 NPY files
            pred_norm_np = (
                pred_norm.cpu().detach().numpy()[0, :, :, :].transpose(1, 2, 0)
            )  # (H, W, 3)
            pred_norm_np = ((pred_norm_np + 1.0) / 2.0 * 255.0).astype(np.uint8)
            target_path = img_path.replace("/img/", "/output/").replace(ext, ".png")
            im = Image.fromarray(pred_norm_np)
            im.save(target_path)


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--ckpt", default="dsine", type=str, help="model checkpoint")
    parser.add_argument("--mode", default="samples", type=str, help="{samples}")
    args = parser.parse_args()

    # define model
    device = torch.device("cpu")

    from models.dsine import DSINE

    model = DSINE().to(device)
    model.pixel_coords = model.pixel_coords.to(device)
    model = utils.load_checkpoint("./checkpoints/%s.pt" % args.ckpt, model)
    model.eval()

    if args.mode == "samples":
        test_samples(args, model, intrins=None, device=device)