#!/usr/bin/env python3

# Copyright (C) 2024-present Naver Corporation. All rights reserved.

# Licensed under CC BY-NC-SA 4.0 (non-commercial use only).

#

# --------------------------------------------------------

# mast3r gradio demo executable

# --------------------------------------------------------

import os

import torch

import tempfile

import mast3r.utils.path_to_dust3r # noqa

from dust3r.model import AsymmetricCroCo3DStereo

from mast3r.model import AsymmetricMASt3R

from dust3r.demo import get_args_parser as dust3r_get_args_parser

from dust3r.demo import main_demo

import matplotlib.pyplot as pl

pl.ion()

torch.backends.cuda.matmul.allow_tf32 = True # for gpu >= Ampere and pytorch >= 1.12

def get_args_parser():

return parser

if __name__ == '__main__':

parser = get_args_parser()

args = parser.parse_args()

if args.tmp_dir is not None:

tmp_path = args.tmp_dir

os.makedirs(tmp_path, exist_ok=True)

tempfile.tempdir = tmp_path

if args.server_name is not None:

server_name = args.server_name

else:

server_name = '0.0.0.0' if args.local_network else '127.0.0.1'

if args.weights is not None:

weights_path = args.weights

else:

weights_path = "naver/" + args.model_name

try:

model = AsymmetricMASt3R.from_pretrained(weights_path).to(args.device)

except Exception as e:

model = AsymmetricCroCo3DStereo.from_pretrained(weights_path).to(args.device)

# dust3r will write the 3D model inside tmpdirname

with tempfile.TemporaryDirectory(suffix='dust3r_gradio_demo') as tmpdirname:

if not args.silent:

print('Outputing stuff in', tmpdirname)

main_demo(tmpdirname, model, args.device, args.image_size, server_name, args.server_port, silent=args.silent)