import sys
sys.path.append('core')

import argparse
import glob
import numpy as np
import torch
from tqdm import tqdm
from pathlib import Path
from core.defom_stereo import DEFOMStereo
from utils.utils import InputPadder
from PIL import Image
from matplotlib import pyplot as plt


DEVICE = 'cuda'

def load_image(imfile):
    img = np.array(Image.open(imfile)).astype(np.uint8)
    img = torch.from_numpy(img).permute(2, 0, 1).float()
    return img[None].to(DEVICE)

def demo(args):
    model = DEFOMStereo(args)
    checkpoint = torch.load(args.restore_ckpt, map_location='cuda')
    if 'model' in checkpoint:
            model.load_state_dict(checkpoint['model'])
    else:
        model.load_state_dict(checkpoint)

    model.to(DEVICE)
    model.eval()

    output_directory = Path(args.output_directory)
    output_directory.mkdir(exist_ok=True)

    with torch.no_grad():
        left_images = sorted(glob.glob(args.left_imgs, recursive=True))
        right_images = sorted(glob.glob(args.right_imgs, recursive=True))
        print(f"Found {len(left_images)} images. Saving files to {output_directory}/")

        for (imfile1, imfile2) in tqdm(list(zip(left_images, right_images))):
            image1 = load_image(imfile1)
            image2 = load_image(imfile2)

            padder = InputPadder(image1.shape, divis_by=32)
            image1, image2 = padder.pad(image1, image2)

            with torch.no_grad():
                disp_pr = model(image1, image2, iters=args.valid_iters, scale_iters=args.scale_iters, test_mode=True)
            disp_pr = padder.unpad(disp_pr).cpu().squeeze().numpy()

            file_stem = imfile1.split('/')[-1].split('_')[0]+'_'+args.restore_ckpt.split('/')[-1][:-4]
            if args.save_numpy:
                np.save(output_directory / f"{file_stem}.npy", disp_pr)
            plt.imsave(output_directory / f"{file_stem}.png", disp_pr, cmap='jet')


if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--restore_ckpt', help="restore checkpoint", required=True)
    parser.add_argument('--save_numpy', action='store_true', help='save output as numpy arrays')
    parser.add_argument('-l', '--left_imgs', help="path to all first (left) frames", default="demo/*_left.png")
    parser.add_argument('-r', '--right_imgs', help="path to all second (right) frames", default="demo/*_right.png")
    parser.add_argument('--output_directory', help="directory to save output", default="demo")
    parser.add_argument('--mixed_precision', action='store_true', help='use mixed precision')
    parser.add_argument('--valid_iters', type=int, default=32, help='number of flow-field updates during forward pass')
    parser.add_argument('--scale_iters', type=int, default=8, help="number of scaling updates to the disparity field in each forward pass.")

    # Architecture choices
    parser.add_argument('--dinov2_encoder', type=str, default='vitl', choices=['vits', 'vitb', 'vitl', 'vitg'])
    parser.add_argument('--idepth_scale', type=float, default=0.5, help="the scale of inverse depth to initialize disparity")
    parser.add_argument('--hidden_dims', nargs='+', type=int, default=[128]*3, help="hidden state and context dimensions")
    parser.add_argument('--corr_implementation', choices=["reg", "alt", "reg_cuda", "alt_cuda"], default="reg", help="correlation volume implementation")
    parser.add_argument('--shared_backbone', action='store_true', help="use a single backbone for the context and feature encoders")
    parser.add_argument('--corr_levels', type=int, default=2, help="number of levels in the correlation pyramid")
    parser.add_argument('--corr_radius', type=int, default=4, help="width of the correlation pyramid")
    parser.add_argument('--scale_list', type=float, nargs='+', default=[0.125, 0.25, 0.5, 0.75, 1.0, 1.25, 1.5, 2.0],
                        help='the list of scaling factors of disparity')
    parser.add_argument('--scale_corr_radius', type=int, default=2,
                        help="width of the correlation pyramid for scaled disparity")

    parser.add_argument('--n_downsample', type=int, default=2, choices=[2, 3], help="resolution of the disparity field (1/2^K)")
    parser.add_argument('--context_norm', type=str, default="batch", choices=['group', 'batch', 'instance', 'none'], help="normalization of context encoder")
    parser.add_argument('--n_gru_layers', type=int, default=3, help="number of hidden GRU levels")
    
    args = parser.parse_args()

    demo(args)