File size: 5,334 Bytes
9223079
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
60ad158
 
 
9223079
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
60ad158
 
 
 
 
 
 
 
 
9223079
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
from pathlib import Path
import numpy as np
import torch
import PIL.Image
from tqdm import tqdm
import pycolmap

from ...utils.read_write_model import write_model, read_model


def scene_coordinates(p2D, R_w2c, t_w2c, depth, camera):
    assert len(depth) == len(p2D)
    ret = pycolmap.image_to_world(p2D, camera._asdict())
    p2D_norm = np.asarray(ret["world_points"])
    p2D_h = np.concatenate([p2D_norm, np.ones_like(p2D_norm[:, :1])], 1)
    p3D_c = p2D_h * depth[:, None]
    p3D_w = (p3D_c - t_w2c) @ R_w2c
    return p3D_w


def interpolate_depth(depth, kp):
    h, w = depth.shape
    kp = kp / np.array([[w - 1, h - 1]]) * 2 - 1
    assert np.all(kp > -1) and np.all(kp < 1)
    depth = torch.from_numpy(depth)[None, None]
    kp = torch.from_numpy(kp)[None, None]
    grid_sample = torch.nn.functional.grid_sample

    # To maximize the number of points that have depth:
    # do bilinear interpolation first and then nearest for the remaining points
    interp_lin = grid_sample(depth, kp, align_corners=True, mode="bilinear")[
        0, :, 0
    ]
    interp_nn = torch.nn.functional.grid_sample(
        depth, kp, align_corners=True, mode="nearest"
    )[0, :, 0]
    interp = torch.where(torch.isnan(interp_lin), interp_nn, interp_lin)
    valid = ~torch.any(torch.isnan(interp), 0)

    interp_depth = interp.T.numpy().flatten()
    valid = valid.numpy()
    return interp_depth, valid


def image_path_to_rendered_depth_path(image_name):
    parts = image_name.split("/")
    name = "_".join(["".join(parts[0].split("-")), parts[1]])
    name = name.replace("color", "pose")
    name = name.replace("png", "depth.tiff")
    return name


def project_to_image(p3D, R, t, camera, eps: float = 1e-4, pad: int = 1):
    p3D = (p3D @ R.T) + t
    visible = p3D[:, -1] >= eps  # keep points in front of the camera
    p2D_norm = p3D[:, :-1] / p3D[:, -1:].clip(min=eps)
    ret = pycolmap.world_to_image(p2D_norm, camera._asdict())
    p2D = np.asarray(ret["image_points"])
    size = np.array([camera.width - pad - 1, camera.height - pad - 1])
    valid = np.all((p2D >= pad) & (p2D <= size), -1)
    valid &= visible
    return p2D[valid], valid


def correct_sfm_with_gt_depth(sfm_path, depth_folder_path, output_path):
    cameras, images, points3D = read_model(sfm_path)
    for imgid, img in tqdm(images.items()):
        image_name = img.name
        depth_name = image_path_to_rendered_depth_path(image_name)

        depth = PIL.Image.open(Path(depth_folder_path) / depth_name)
        depth = np.array(depth).astype("float64")
        depth = depth / 1000.0  # mm to meter
        depth[(depth == 0.0) | (depth > 1000.0)] = np.nan

        R_w2c, t_w2c = img.qvec2rotmat(), img.tvec
        camera = cameras[img.camera_id]
        p3D_ids = img.point3D_ids
        p3Ds = np.stack([points3D[i].xyz for i in p3D_ids[p3D_ids != -1]], 0)

        p2Ds, valids_projected = project_to_image(p3Ds, R_w2c, t_w2c, camera)
        invalid_p3D_ids = p3D_ids[p3D_ids != -1][~valids_projected]
        interp_depth, valids_backprojected = interpolate_depth(depth, p2Ds)
        scs = scene_coordinates(
            p2Ds[valids_backprojected],
            R_w2c,
            t_w2c,
            interp_depth[valids_backprojected],
            camera,
        )
        invalid_p3D_ids = np.append(
            invalid_p3D_ids,
            p3D_ids[p3D_ids != -1][valids_projected][~valids_backprojected],
        )
        for p3did in invalid_p3D_ids:
            if p3did == -1:
                continue
            else:
                obs_imgids = points3D[p3did].image_ids
                invalid_imgids = list(np.where(obs_imgids == img.id)[0])
                points3D[p3did] = points3D[p3did]._replace(
                    image_ids=np.delete(obs_imgids, invalid_imgids),
                    point2D_idxs=np.delete(
                        points3D[p3did].point2D_idxs, invalid_imgids
                    ),
                )

        new_p3D_ids = p3D_ids.copy()
        sub_p3D_ids = new_p3D_ids[new_p3D_ids != -1]
        valids = np.ones(np.count_nonzero(new_p3D_ids != -1), dtype=bool)
        valids[~valids_projected] = False
        valids[valids_projected] = valids_backprojected
        sub_p3D_ids[~valids] = -1
        new_p3D_ids[new_p3D_ids != -1] = sub_p3D_ids
        img = img._replace(point3D_ids=new_p3D_ids)

        assert len(img.point3D_ids[img.point3D_ids != -1]) == len(
            scs
        ), f"{len(scs)}, {len(img.point3D_ids[img.point3D_ids != -1])}"
        for i, p3did in enumerate(img.point3D_ids[img.point3D_ids != -1]):
            points3D[p3did] = points3D[p3did]._replace(xyz=scs[i])
        images[imgid] = img

    output_path.mkdir(parents=True, exist_ok=True)
    write_model(cameras, images, points3D, output_path)


if __name__ == "__main__":
    dataset = Path("datasets/7scenes")
    outputs = Path("outputs/7Scenes")

    SCENES = [
        "chess",
        "fire",
        "heads",
        "office",
        "pumpkin",
        "redkitchen",
        "stairs",
    ]
    for scene in SCENES:
        sfm_path = outputs / scene / "sfm_superpoint+superglue"
        depth_path = dataset / f"depth/7scenes_{scene}/train/depth"
        output_path = outputs / scene / "sfm_superpoint+superglue+depth"
        correct_sfm_with_gt_depth(sfm_path, depth_path, output_path)