third_party/dust3r/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 argparse
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 dust3r.inference import inference
from dust3r.model import AsymmetricCroCo3DStereo
from dust3r.image_pairs import make_pairs
from dust3r.utils.image import load_images, rgb
from dust3r.utils.device import to_numpy
from dust3r.viz import add_scene_cam, CAM_COLORS, OPENGL, pts3d_to_trimesh, cat_meshes
from dust3r.cloud_opt import global_aligner, GlobalAlignerMode

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 = argparse.ArgumentParser()
    parser_url = parser.add_mutually_exclusive_group()
    parser_url.add_argument("--local_network", action='store_true', default=False,
                            help="make app accessible on local network: address will be set to 0.0.0.0")
    parser_url.add_argument("--server_name", type=str, default=None, help="server url, default is 127.0.0.1")
    parser.add_argument("--image_size", type=int, default=512, choices=[512, 224], help="image size")
    parser.add_argument("--server_port", type=int, help=("will start gradio app on this port (if available). "
                                                         "If None, will search for an available port starting at 7860."),
                        default=None)
    parser_weights = parser.add_mutually_exclusive_group(required=True)
    parser_weights.add_argument("--weights", type=str, help="path to the model weights", default=None)
    parser_weights.add_argument("--model_name", type=str, help="name of the model weights",
                                choices=["DUSt3R_ViTLarge_BaseDecoder_512_dpt",
                                         "DUSt3R_ViTLarge_BaseDecoder_512_linear",
                                         "DUSt3R_ViTLarge_BaseDecoder_224_linear"])
    parser.add_argument("--device", type=str, default='cuda', help="pytorch device")
    parser.add_argument("--tmp_dir", type=str, default=None, help="value for tempfile.tempdir")
    parser.add_argument("--silent", action='store_true', default=False,
                        help="silence logs")
    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] 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], 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=3, as_pointcloud=False, mask_sky=False,
                            clean_depth=False, transparent_cams=False, cam_size=0.05):
    """
    extract 3D_model (glb file) from a reconstructed scene
    """
    if scene is None:
        return None
    # post processes
    if clean_depth:
        scene = scene.clean_pointcloud()
    if mask_sky:
        scene = scene.mask_sky()

    # 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
    pts3d = to_numpy(scene.get_pts3d())
    scene.min_conf_thr = float(scene.conf_trf(torch.tensor(min_conf_thr)))
    msk = to_numpy(scene.get_masks())
    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, schedule, niter, min_conf_thr,
                            as_pointcloud, mask_sky, clean_depth, transparent_cams, cam_size,
                            scenegraph_type, winsize, refid):
    """
    from a list of images, run dust3r inference, 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
    if scenegraph_type == "swin":
        scenegraph_type = scenegraph_type + "-" + str(winsize)
    elif scenegraph_type == "oneref":
        scenegraph_type = scenegraph_type + "-" + str(refid)

    pairs = make_pairs(imgs, scene_graph=scenegraph_type, prefilter=None, symmetrize=True)
    output = inference(pairs, model, device, batch_size=batch_size, verbose=not silent)

    mode = GlobalAlignerMode.PointCloudOptimizer if len(imgs) > 2 else GlobalAlignerMode.PairViewer
    scene = global_aligner(output, device=device, mode=mode, verbose=not silent)
    lr = 0.01

    if mode == GlobalAlignerMode.PointCloudOptimizer:
        loss = scene.compute_global_alignment(init='mst', niter=niter, schedule=schedule, lr=lr)

    outfile = get_3D_model_from_scene(outdir, silent, scene, min_conf_thr, as_pointcloud, mask_sky,
                                      clean_depth, transparent_cams, cam_size)

    # also return rgb, depth and confidence imgs
    # depth is normalized with the max value for all images
    # we apply the jet colormap on the confidence maps
    rgbimg = scene.imgs
    depths = to_numpy(scene.get_depthmaps())
    confs = to_numpy([c for c in scene.im_conf])
    cmap = pl.get_cmap('jet')
    depths_max = max([d.max() for d in depths])
    depths = [d/depths_max for d in depths]
    confs_max = max([d.max() for d in confs])
    confs = [cmap(d/confs_max) for d in confs]

    imgs = []
    for i in range(len(rgbimg)):
        imgs.append(rgbimg[i])
        imgs.append(rgb(depths[i]))
        imgs.append(rgb(confs[i]))

    return scene, outfile, imgs


def set_scenegraph_options(inputfiles, winsize, refid, scenegraph_type):
    num_files = len(inputfiles) if inputfiles is not None else 1
    max_winsize = max(1, math.ceil((num_files-1)/2))
    if scenegraph_type == "swin":
        winsize = gradio.Slider(label="Scene Graph: Window Size", value=max_winsize,
                                minimum=1, maximum=max_winsize, step=1, visible=True)
        refid = gradio.Slider(label="Scene Graph: Id", value=0, minimum=0,
                              maximum=num_files-1, step=1, visible=False)
    elif scenegraph_type == "oneref":
        winsize = gradio.Slider(label="Scene Graph: Window Size", value=max_winsize,
                                minimum=1, maximum=max_winsize, step=1, visible=False)
        refid = gradio.Slider(label="Scene Graph: Id", value=0, minimum=0,
                              maximum=num_files-1, step=1, visible=True)
    else:
        winsize = gradio.Slider(label="Scene Graph: Window Size", value=max_winsize,
                                minimum=1, maximum=max_winsize, step=1, visible=False)
        refid = gradio.Slider(label="Scene Graph: Id", value=0, minimum=0,
                              maximum=num_files-1, step=1, visible=False)
    return winsize, refid


def main_demo(tmpdirname, model, device, image_size, server_name, server_port, silent=False):
    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="DUSt3R 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;">DUSt3R Demo</h2>')
        with gradio.Column():
            inputfiles = gradio.File(file_count="multiple")
            with gradio.Row():
                schedule = gradio.Dropdown(["linear", "cosine"],
                                           value='linear', label="schedule", info="For global alignment!")
                niter = gradio.Number(value=300, precision=0, minimum=0, maximum=5000,
                                      label="num_iterations", info="For global alignment!")
                scenegraph_type = gradio.Dropdown(["complete", "swin", "oneref"],
                                                  value='complete', label="Scenegraph",
                                                  info="Define how to make pairs",
                                                  interactive=True)
                winsize = gradio.Slider(label="Scene Graph: Window Size", value=1,
                                        minimum=1, maximum=1, step=1, visible=False)
                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=3.0, minimum=1.0, maximum=20, step=0.1)
                # adjust the camera size in the output pointcloud
                cam_size = gradio.Slider(label="cam_size", value=0.05, minimum=0.001, maximum=0.1, step=0.001)
            with gradio.Row():
                as_pointcloud = gradio.Checkbox(value=False, 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()
            outgallery = gradio.Gallery(label='rgb,depth,confidence', columns=3, height="100%")

            # events
            scenegraph_type.change(set_scenegraph_options,
                                   inputs=[inputfiles, winsize, refid, scenegraph_type],
                                   outputs=[winsize, refid])
            inputfiles.change(set_scenegraph_options,
                              inputs=[inputfiles, winsize, refid, scenegraph_type],
                              outputs=[winsize, refid])
            run_btn.click(fn=recon_fun,
                          inputs=[inputfiles, schedule, niter, min_conf_thr, as_pointcloud,
                                  mask_sky, clean_depth, transparent_cams, cam_size,
                                  scenegraph_type, winsize, refid],
                          outputs=[scene, outmodel, outgallery])
            min_conf_thr.release(fn=model_from_scene_fun,
                                 inputs=[scene, min_conf_thr, as_pointcloud, mask_sky,
                                         clean_depth, transparent_cams, cam_size],
                                 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],
                            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],
                                 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],
                            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],
                               outputs=outmodel)
            transparent_cams.change(model_from_scene_fun,
                                    inputs=[scene, min_conf_thr, as_pointcloud, mask_sky,
                                            clean_depth, transparent_cams, cam_size],
                                    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.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
    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)