demo.py

#!/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).
#
# --------------------------------------------------------
# gradio demo
# --------------------------------------------------------
import math
import gradio
import os
import torch
import numpy as np
import tempfile
import functools
import trimesh
import copy
from scipy.spatial.transform import Rotation

from mast3r.cloud_opt.sparse_ga import sparse_global_alignment
from mast3r.cloud_opt.tsdf_optimizer import TSDFPostProcess

from mast3r.model import AsymmetricMASt3R
from mast3r.utils.misc import hash_md5
import mast3r.utils.path_to_dust3r  # noqa
from dust3r.image_pairs import make_pairs
from dust3r.utils.image import load_images
from dust3r.utils.device import to_numpy
from dust3r.viz import add_scene_cam, CAM_COLORS, OPENGL, pts3d_to_trimesh, cat_meshes
from dust3r.demo import get_args_parser as dust3r_get_args_parser

import matplotlib.pyplot as pl
pl.ion()

torch.backends.cuda.matmul.allow_tf32 = True  # for gpu >= Ampere and pytorch >= 1.12
batch_size = 1


def get_args_parser():
    parser = dust3r_get_args_parser()
    parser.add_argument('--share', action='store_true')

    actions = parser._actions
    for action in actions:
        if action.dest == 'model_name':
            action.choices = ["MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric"]
    # change defaults
    parser.prog = 'mast3r demo'
    return parser


def _convert_scene_output_to_glb(outdir, imgs, pts3d, mask, focals, cams2world, cam_size=0.05,
                                 cam_color=None, as_pointcloud=False,
                                 transparent_cams=False, silent=False):
    assert len(pts3d) == len(mask) <= len(imgs) <= len(cams2world) == len(focals)
    pts3d = to_numpy(pts3d)
    imgs = to_numpy(imgs)
    focals = to_numpy(focals)
    cams2world = to_numpy(cams2world)

    scene = trimesh.Scene()

    # full pointcloud
    if as_pointcloud:
        pts = np.concatenate([p[m.ravel()] for p, m in zip(pts3d, mask)])
        col = np.concatenate([p[m] for p, m in zip(imgs, mask)])
        pct = trimesh.PointCloud(pts.reshape(-1, 3), colors=col.reshape(-1, 3))
        scene.add_geometry(pct)
    else:
        meshes = []
        for i in range(len(imgs)):
            meshes.append(pts3d_to_trimesh(imgs[i], pts3d[i].reshape(imgs[i].shape), mask[i]))
        mesh = trimesh.Trimesh(**cat_meshes(meshes))
        scene.add_geometry(mesh)

    # add each camera
    for i, pose_c2w in enumerate(cams2world):
        if isinstance(cam_color, list):
            camera_edge_color = cam_color[i]
        else:
            camera_edge_color = cam_color or CAM_COLORS[i % len(CAM_COLORS)]
        add_scene_cam(scene, pose_c2w, camera_edge_color,
                      None if transparent_cams else imgs[i], focals[i],
                      imsize=imgs[i].shape[1::-1], screen_width=cam_size)

    rot = np.eye(4)
    rot[:3, :3] = Rotation.from_euler('y', np.deg2rad(180)).as_matrix()
    scene.apply_transform(np.linalg.inv(cams2world[0] @ OPENGL @ rot))
    outfile = os.path.join(outdir, 'scene.glb')
    if not silent:
        print('(exporting 3D scene to', outfile, ')')
    scene.export(file_obj=outfile)
    return outfile


def get_3D_model_from_scene(outdir, silent, scene, min_conf_thr=2, as_pointcloud=False, mask_sky=False,
                            clean_depth=False, transparent_cams=False, cam_size=0.05, TSDF_thresh=0):
    """
    extract 3D_model (glb file) from a reconstructed scene
    """
    if scene is None:
        return None

    # get optimized values from scene
    rgbimg = scene.imgs
    focals = scene.get_focals().cpu()
    cams2world = scene.get_im_poses().cpu()

    # 3D pointcloud from depthmap, poses and intrinsics
    if TSDF_thresh > 0:
        tsdf = TSDFPostProcess(scene, TSDF_thresh=TSDF_thresh)
        pts3d, _, confs = to_numpy(tsdf.get_dense_pts3d(clean_depth=clean_depth))
    else:
        pts3d, _, confs = to_numpy(scene.get_dense_pts3d(clean_depth=clean_depth))
    msk = to_numpy([c > min_conf_thr for c in confs])
    return _convert_scene_output_to_glb(outdir, rgbimg, pts3d, msk, focals, cams2world, as_pointcloud=as_pointcloud,
                                        transparent_cams=transparent_cams, cam_size=cam_size, silent=silent)


def get_reconstructed_scene(outdir, model, device, silent, image_size, filelist, optim_level, lr1, niter1, lr2, niter2, min_conf_thr,
                            as_pointcloud, mask_sky, clean_depth, transparent_cams, cam_size,
                            scenegraph_type, winsize, win_cyclic, refid, TSDF_thresh, shared_intrinsics, **kw):
    """
    from a list of images, run mast3r inference, sparse global aligner.
    then run get_3D_model_from_scene
    """
    imgs = load_images(filelist, size=image_size, verbose=not silent)
    if len(imgs) == 1:
        imgs = [imgs[0], copy.deepcopy(imgs[0])]
        imgs[1]['idx'] = 1
        filelist = [filelist[0], filelist[0] + '_2']

    scene_graph_params = [scenegraph_type]
    if scenegraph_type in ["swin", "logwin"]:
        scene_graph_params.append(str(winsize))
    elif scenegraph_type == "oneref":
        scene_graph_params.append(str(refid))
    if scenegraph_type in ["swin", "logwin"] and not win_cyclic:
        scene_graph_params.append('noncyclic')
    scene_graph = '-'.join(scene_graph_params)
    pairs = make_pairs(imgs, scene_graph=scene_graph, prefilter=None, symmetrize=True)
    if optim_level == 'coarse':
        niter2 = 0
    # Sparse GA (forward mast3r -> matching -> 3D optim -> 2D refinement -> triangulation)
    scene = sparse_global_alignment(filelist, pairs, os.path.join(outdir, 'cache'),
                                    model, lr1=lr1, niter1=niter1, lr2=lr2, niter2=niter2, device=device,
                                    opt_depth='depth' in optim_level, shared_intrinsics=shared_intrinsics, **kw)
    outfile = get_3D_model_from_scene(outdir, silent, scene, min_conf_thr, as_pointcloud, mask_sky,
                                      clean_depth, transparent_cams, cam_size, TSDF_thresh)
    return scene, outfile


def set_scenegraph_options(inputfiles, win_cyclic, refid, scenegraph_type):
    num_files = len(inputfiles) if inputfiles is not None else 1
    show_win_controls = scenegraph_type in ["swin", "logwin"]
    show_winsize = scenegraph_type in ["swin", "logwin"]
    show_cyclic = scenegraph_type in ["swin", "logwin"]
    max_winsize, min_winsize = 1, 1
    if scenegraph_type == "swin":
        if win_cyclic:
            max_winsize = max(1, math.ceil((num_files - 1) / 2))
        else:
            max_winsize = num_files - 1
    elif scenegraph_type == "logwin":
        if win_cyclic:
            half_size = math.ceil((num_files - 1) / 2)
            max_winsize = max(1, math.ceil(math.log(half_size, 2)))
        else:
            max_winsize = max(1, math.ceil(math.log(num_files, 2)))
    winsize = gradio.Slider(label="Scene Graph: Window Size", value=max_winsize,
                            minimum=min_winsize, maximum=max_winsize, step=1, visible=show_winsize)
    win_cyclic = gradio.Checkbox(value=win_cyclic, label="Cyclic sequence", visible=show_cyclic)
    win_col = gradio.Column(visible=show_win_controls)
    refid = gradio.Slider(label="Scene Graph: Id", value=0, minimum=0,
                          maximum=num_files - 1, step=1, visible=scenegraph_type == 'oneref')
    return win_col, winsize, win_cyclic, refid


def main_demo(tmpdirname, model, device, image_size, server_name, server_port, silent=False, share=False):
    if not silent:
        print('Outputing stuff in', tmpdirname)

    recon_fun = functools.partial(get_reconstructed_scene, tmpdirname, model, device, silent, image_size)
    model_from_scene_fun = functools.partial(get_3D_model_from_scene, tmpdirname, silent)
    with gradio.Blocks(css=""".gradio-container {margin: 0 !important; min-width: 100%};""", title="MASt3R Demo") as demo:
        # scene state is save so that you can change conf_thr, cam_size... without rerunning the inference
        scene = gradio.State(None)
        gradio.HTML('<h2 style="text-align: center;">MASt3R Demo</h2>')
        with gradio.Column():
            inputfiles = gradio.File(file_count="multiple")
            with gradio.Row():
                lr1 = gradio.Slider(label="Coarse LR", value=0.07, minimum=0.01, maximum=0.2, step=0.01)
                niter1 = gradio.Number(value=500, precision=0, minimum=0, maximum=10_000,
                                       label="num_iterations", info="For coarse alignment!")
                lr2 = gradio.Slider(label="Fine LR", value=0.014, minimum=0.005, maximum=0.05, step=0.001)
                niter2 = gradio.Number(value=200, precision=0, minimum=0, maximum=100_000,
                                       label="num_iterations", info="For refinement!")
                optim_level = gradio.Dropdown(["coarse", "refine", "refine+depth"],
                                              value='refine', label="OptLevel",
                                              info="Optimization level")
                shared_intrinsics = gradio.Checkbox(value=False, label="Shared intrinsics",
                                                    info="Only optimize one set of intrinsics for all views")

                scenegraph_type = gradio.Dropdown(["complete", "swin", "logwin", "oneref"],
                                                  value='complete', label="Scenegraph",
                                                  info="Define how to make pairs",
                                                  interactive=True)
                with gradio.Column(visible=False) as win_col:
                    winsize = gradio.Slider(label="Scene Graph: Window Size", value=1,
                                            minimum=1, maximum=1, step=1)
                    win_cyclic = gradio.Checkbox(value=False, label="Cyclic sequence")
                refid = gradio.Slider(label="Scene Graph: Id", value=0, minimum=0, maximum=0, step=1, visible=False)

            run_btn = gradio.Button("Run")

            with gradio.Row():
                # adjust the confidence threshold
                min_conf_thr = gradio.Slider(label="min_conf_thr", value=1.5, minimum=0.0, maximum=10, step=0.1)
                # adjust the camera size in the output pointcloud
                cam_size = gradio.Slider(label="cam_size", value=0.2, minimum=0.001, maximum=1.0, step=0.001)
                TSDF_thresh = gradio.Slider(label="TSDF Threshold", value=0., minimum=0., maximum=1., step=0.01)
            with gradio.Row():
                as_pointcloud = gradio.Checkbox(value=True, label="As pointcloud")
                # two post process implemented
                mask_sky = gradio.Checkbox(value=False, label="Mask sky")
                clean_depth = gradio.Checkbox(value=True, label="Clean-up depthmaps")
                transparent_cams = gradio.Checkbox(value=False, label="Transparent cameras")

            outmodel = gradio.Model3D()

            # events
            scenegraph_type.change(set_scenegraph_options,
                                   inputs=[inputfiles, win_cyclic, refid, scenegraph_type],
                                   outputs=[win_col, winsize, win_cyclic, refid])
            inputfiles.change(set_scenegraph_options,
                              inputs=[inputfiles, win_cyclic, refid, scenegraph_type],
                              outputs=[win_col, winsize, win_cyclic, refid])
            win_cyclic.change(set_scenegraph_options,
                              inputs=[inputfiles, win_cyclic, refid, scenegraph_type],
                              outputs=[win_col, winsize, win_cyclic, refid])
            run_btn.click(fn=recon_fun,
                          inputs=[inputfiles, optim_level, lr1, niter1, lr2, niter2, min_conf_thr, as_pointcloud,
                                  mask_sky, clean_depth, transparent_cams, cam_size,
                                  scenegraph_type, winsize, win_cyclic, refid, TSDF_thresh, shared_intrinsics],
                          outputs=[scene, outmodel])
            min_conf_thr.release(fn=model_from_scene_fun,
                                 inputs=[scene, min_conf_thr, as_pointcloud, mask_sky,
                                         clean_depth, transparent_cams, cam_size, TSDF_thresh],
                                 outputs=outmodel)
            cam_size.change(fn=model_from_scene_fun,
                            inputs=[scene, min_conf_thr, as_pointcloud, mask_sky,
                                    clean_depth, transparent_cams, cam_size, TSDF_thresh],
                            outputs=outmodel)
            TSDF_thresh.change(fn=model_from_scene_fun,
                               inputs=[scene, min_conf_thr, as_pointcloud, mask_sky,
                                       clean_depth, transparent_cams, cam_size, TSDF_thresh],
                               outputs=outmodel)
            as_pointcloud.change(fn=model_from_scene_fun,
                                 inputs=[scene, min_conf_thr, as_pointcloud, mask_sky,
                                         clean_depth, transparent_cams, cam_size, TSDF_thresh],
                                 outputs=outmodel)
            mask_sky.change(fn=model_from_scene_fun,
                            inputs=[scene, min_conf_thr, as_pointcloud, mask_sky,
                                    clean_depth, transparent_cams, cam_size, TSDF_thresh],
                            outputs=outmodel)
            clean_depth.change(fn=model_from_scene_fun,
                               inputs=[scene, min_conf_thr, as_pointcloud, mask_sky,
                                       clean_depth, transparent_cams, cam_size, TSDF_thresh],
                               outputs=outmodel)
            transparent_cams.change(model_from_scene_fun,
                                    inputs=[scene, min_conf_thr, as_pointcloud, mask_sky,
                                            clean_depth, transparent_cams, cam_size, TSDF_thresh],
                                    outputs=outmodel)
    demo.launch(share=False, server_name=server_name, server_port=server_port)


if __name__ == '__main__':
    parser = get_args_parser()
    args = parser.parse_args()

    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

    model = AsymmetricMASt3R.from_pretrained(weights_path).to(args.device)
    chkpt_tag = hash_md5(weights_path)

    # mast3r will write the 3D model inside tmpdirname/chkpt_tag
    if args.tmp_dir is not None:
        tmpdirname = args.tmp_dir
        cache_path = os.path.join(tmpdirname, chkpt_tag)
        os.makedirs(cache_path, exist_ok=True)
        main_demo(cache_path, model, args.device, args.image_size, server_name, args.server_port, silent=args.silent,
                  share=args.share)
    else:
        with tempfile.TemporaryDirectory(suffix='_mast3r_gradio_demo') as tmpdirname:
            cache_path = os.path.join(tmpdirname, chkpt_tag)
            os.makedirs(cache_path, exist_ok=True)
            main_demo(tmpdirname, model, args.device, args.image_size,
                      server_name, args.server_port, silent=args.silent,
                      share=args.share)