mast3r/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).
#
# --------------------------------------------------------
# sparse gradio demo functions
# --------------------------------------------------------
import math
import gradio
import os
import numpy as np
import functools
import trimesh
import copy
from scipy.spatial.transform import Rotation
import tempfile
import shutil

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

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


class SparseGAState():
    def __init__(self, sparse_ga, should_delete=False, cache_dir=None, outfile_name=None):
        self.sparse_ga = sparse_ga
        self.cache_dir = cache_dir
        self.outfile_name = outfile_name
        self.should_delete = should_delete

    def __del__(self):
        if not self.should_delete:
            return
        if self.cache_dir is not None and os.path.isdir(self.cache_dir):
            shutil.rmtree(self.cache_dir)
        self.cache_dir = None
        if self.outfile_name is not None and os.path.isfile(self.outfile_name):
            os.remove(self.outfile_name)
        self.outfile_name = None


def get_args_parser():
    parser = dust3r_get_args_parser()
    parser.add_argument('--share', action='store_true')
    parser.add_argument('--gradio_delete_cache', default=None, type=int,
                        help='age/frequency at which gradio removes the file. If >0, matching cache is purged')

    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(outfile, 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)]).reshape(-1, 3)
        col = np.concatenate([p[m] for p, m in zip(imgs, mask)]).reshape(-1, 3)
        valid_msk = np.isfinite(pts.sum(axis=1))
        pct = trimesh.PointCloud(pts[valid_msk], colors=col[valid_msk])
        scene.add_geometry(pct)
    else:
        meshes = []
        for i in range(len(imgs)):
            pts3d_i = pts3d[i].reshape(imgs[i].shape)
            msk_i = mask[i] & np.isfinite(pts3d_i.sum(axis=-1))
            meshes.append(pts3d_to_trimesh(imgs[i], pts3d_i, msk_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))
    if not silent:
        print('(exporting 3D scene to', outfile, ')')
    scene.export(file_obj=outfile)
    return outfile


def get_3D_model_from_scene(silent, scene_state, 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_state is None:
        return None
    outfile = scene_state.outfile_name
    if outfile is None:
        return None

    # get optimized values from scene
    scene = scene_state.sparse_ga
    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(outfile, rgbimg, pts3d, msk, focals, cams2world, as_pointcloud=as_pointcloud,
                                        transparent_cams=transparent_cams, cam_size=cam_size, silent=silent)


def get_reconstructed_scene(outdir, gradio_delete_cache, model, device, silent, image_size, current_scene_state,
                            filelist, optim_level, lr1, niter1, lr2, niter2, min_conf_thr, matching_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)
    if current_scene_state is not None and \
        not current_scene_state.should_delete and \
            current_scene_state.cache_dir is not None:
        cache_dir = current_scene_state.cache_dir
    elif gradio_delete_cache:
        cache_dir = tempfile.mkdtemp(suffix='_cache', dir=outdir)
    else:
        cache_dir = os.path.join(outdir, 'cache')
    scene = sparse_global_alignment(filelist, pairs, cache_dir,
                                    model, lr1=lr1, niter1=niter1, lr2=lr2, niter2=niter2, device=device,
                                    opt_depth='depth' in optim_level, shared_intrinsics=shared_intrinsics,
                                    matching_conf_thr=matching_conf_thr, **kw)
    if current_scene_state is not None and \
        not current_scene_state.should_delete and \
            current_scene_state.outfile_name is not None:
        outfile_name = current_scene_state.outfile_name
    else:
        outfile_name = tempfile.mktemp(suffix='_scene.glb', dir=outdir)

    scene_state = SparseGAState(scene, gradio_delete_cache, cache_dir, outfile_name)
    outfile = get_3D_model_from_scene(silent, scene_state, min_conf_thr, as_pointcloud, mask_sky,
                                      clean_depth, transparent_cams, cam_size, TSDF_thresh)
    return scene_state, 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, gradio_delete_cache=False):
    if not silent:
        print('Outputing stuff in', tmpdirname)

    recon_fun = functools.partial(get_reconstructed_scene, tmpdirname, gradio_delete_cache, model, device,
                                  silent, image_size)
    model_from_scene_fun = functools.partial(get_3D_model_from_scene, silent)

    def get_context(delete_cache):
        css = """.gradio-container {margin: 0 !important; min-width: 100%};"""
        title = "MASt3R Demo"
        if delete_cache:
            return gradio.Blocks(css=css, title=title, delete_cache=(delete_cache, delete_cache))
        else:
            return gradio.Blocks(css=css, title="MASt3R Demo")  # for compatibility with older versions

    with get_context(gradio_delete_cache) 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():
                with gradio.Column():
                    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")
                    with gradio.Row():
                        matching_conf_thr = gradio.Slider(label="Matching Confidence Thr", value=5.,
                                                          minimum=0., maximum=30., step=0.1,
                                                          info="Before Fallback to Regr3D!")
                        shared_intrinsics = gradio.Checkbox(value=False, label="Shared intrinsics",
                                                            info="Only optimize one set of intrinsics for all views")
                        scenegraph_type = gradio.Dropdown([("complete: all possible image pairs", "complete"),
                                                           ("swin: sliding window", "swin"),
                                                           ("logwin: sliding window with long range", "logwin"),
                                                           ("oneref: match one image with all", "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=[scene, inputfiles, optim_level, lr1, niter1, lr2, niter2, min_conf_thr, matching_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=share, server_name=server_name, server_port=server_port)