app.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 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, load_model
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 plt
plt.ion()

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

def show_mask(mask, ax, random_color=False):
    if random_color:
        color = np.concatenate([np.random.random(3), np.array([0.6])], axis=0)
    else:
        color = np.array([30/255, 144/255, 255/255, 0.6])
    h, w = mask.shape[-2:]
    mask_image = mask.reshape(h, w, 1) * color.reshape(1, 1, -1)
    ax.imshow(mask_image)
    
def show_points(coords, labels, ax, marker_size=375):
    pos_points = coords[labels==1]
    neg_points = coords[labels==0]
    ax.scatter(pos_points[:, 0], pos_points[:, 1], color='green', marker='*', s=marker_size, edgecolor='white', linewidth=1.25)
    ax.scatter(neg_points[:, 0], neg_points[:, 1], color='red', marker='*', s=marker_size, edgecolor='white', linewidth=1.25)   
    
def show_box(box, ax):
    x0, y0 = box[0], box[1]
    w, h = box[2] - box[0], box[3] - box[1]
    ax.add_patch(plt.Rectangle((x0, y0), w, h, edgecolor='green', facecolor=(0,0,0,0), lw=2))    

from SAM import SamPredictor
from SAM.build_sam import sam_model_registry
sam_checkpoint = "checkpoints/sam_vit_b_01ec64.pth"
model_type = "vit_b"

sam = sam_model_registry[model_type](checkpoint=sam_checkpoint)
sam.to(device='cuda')
predictor = SamPredictor(sam)

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.add_argument("--weights", type=str, required=True, help="path to the model weights")
    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")
    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):
    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')
    print('(exporting 3D scene to', outfile, ')')
    scene.export(file_obj=outfile)
    return outfile


def get_3D_model_from_scene(outdir, 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
    # print('SAM step...')
    # predictor.set_image((rgbimg[0] * 255).astype(np.uint8))
    # h,w,c = rgbimg[0].shape
    # input_point = np.array([
    #     [int(w/2), int(h/2)],
    #     [int(w/2), int(h/2)-20]
    # ])
    # input_label = np.array([1,1])
    # masks1, scores, logits = predictor.predict(
    #     point_coords=input_point,
    #     point_labels=input_label,
    #     multimask_output=False,
    # )
    # fig, ax = plt.subplots(4, 2, figsize=(20, 20))
    # show_mask(masks1[0], ax[0][0], random_color=True)
    # show_points(input_point, input_label, ax[0][0])
    # ax[0][1].imshow(rgbimg[0])

    # predictor.set_image((rgbimg[1] * 255).astype(np.uint8))
    # h,w,c = rgbimg[1].shape
    # input_point = np.array([
    #     [int(w/2), int(h/2)],
    #     [int(w/2), int(h/2)-20]
    # ])
    # input_label = np.array([1,1])
    # masks2, scores, logits = predictor.predict(
    #     point_coords=input_point,
    #     point_labels=input_label,
    #     multimask_output=False,
    # )
    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())
    # ax[1][0].imshow(msk[0])
    # msk[0] = msk[0] & masks1[0]
    # ax[1][1].imshow(msk[0])
    # ax[2][1].imshow(rgbimg[1])
    # show_mask(masks2[0], ax[2][0], random_color=True)
    # show_points(input_point, input_label, ax[2][0])
    # ax[3][0].imshow(msk[1])
    # # msk[1] = msk[1] & masks2[0]
    # ax[3][1].imshow(msk[1])
    # plt.savefig("rgb.png")
    # import pdb
    # pdb.set_trace()
    return _convert_scene_output_to_glb(outdir, rgbimg, pts3d, msk, focals, cams2world, as_pointcloud=as_pointcloud,
                                        transparent_cams=transparent_cams, cam_size=cam_size)


def get_reconstructed_scene(outdir, model, device, 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)
    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)

    mode = GlobalAlignerMode.PointCloudOptimizer if len(imgs) > 2 else GlobalAlignerMode.PairViewer
    scene = global_aligner(output, device=device, mode=mode)
    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, 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 = plt.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, (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):
    recon_fun = functools.partial(get_reconstructed_scene, tmpdirname, model, device, image_size)
    model_from_scene_fun = functools.partial(get_3D_model_from_scene, tmpdirname)
    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'

    model = load_model(args.weights, args.device)
    # dust3r will write the 3D model inside tmpdirname
    with tempfile.TemporaryDirectory(suffix='dust3r_gradio_demo') as tmpdirname:
        print('Outputing stuff in', tmpdirname)
        main_demo(tmpdirname, model, args.device, args.image_size, server_name, args.server_port)