File size: 3,115 Bytes
efe5745
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
import json
import numpy as np
from numpy.linalg import inv
from pathlib import Path
import imageio
import open3d as o3d

from hc3d.vis import CameraCone
from hc3d.render import compute_intrinsics, unproject
from hc3d.utils import batch_img_resize
from fabric.utils.seed import seed_everything


def get_K(H=500, W=500, fov=60):
    K = compute_intrinsics(W / H, fov, H)
    return K


def shoot_rays(K, pose):
    h = 200
    pixs = np.array([
        [10, h],
        [200, h],
        [400, h]
    ])
    pts = unproject(K, pixs, depth=1.0)
    pts = np.concatenate([
        pts,
        np.array([0, 0, 0, 1]).reshape(1, -1),
    ], axis=0)  # origin, followed by 4 img corners
    pts = pts @ pose.T
    pts = pts[:, :3]
    pts = pts.astype(np.float32)

    n = len(pixs)
    lines = np.array([
        [i, n] for i in range(n)
    ], dtype=np.int32)

    color = [1, 1, 0]
    colors = np.array([color] * len(lines), dtype=np.float32)

    lset = o3d.t.geometry.LineSet()
    lset.point['positions'] = pts
    lset.line['indices'] = lines
    lset.line['colors'] = colors

    return lset


def test_rays(H, W, K):
    xs, ys = np.meshgrid(
        np.arange(W, dtype=np.float32),
        np.arange(H, dtype=np.float32), indexing='xy'
    )
    xys = np.stack([xs, ys], axis=-1)
    my_rays = unproject(K, xys.reshape(-1, 2))
    my_rays = my_rays.reshape(int(H), int(W), 4)[:, :, :3]
    return


def plot_inward_facing_views():
    # from run_sjc import get_train_poses
    from math import pi
    from pose import Poser
    H, W = 64, 64
    poser = Poser(H, W, FoV=60, R=4)
    # K, poses = poser.sample_test(100)
    K, poses, _ = poser.sample_train(1000)
    K = K[0]

    cam_locs = poses[:, :3, -1]
    # radius = np.linalg.norm(cam_locs, axis=1)
    # print(f"scene radius {radius}")

    # test_rays(H, W, K)

    # K = get_K(H, W, 50)
    # NeRF blender actually follows OpenGL camera convention (except top-left corner); nice
    # but its world coordinate is z up. I find it strange.

    def generate_cam(po, color, im=None):
        cone = CameraCone(K, po, W, H, scale=0.1,
                          top_left_corner=(0, 0), color=color)
        lset = cone.as_line_set()
        if im is None:
            return [lset]
        else:
            # o3d img tsr requires contiguous array
            im = np.ascontiguousarray(im)
            view_plane = cone.as_view_plane(im)
            return [lset, view_plane]

    cones = []

    for i in range(len(poses)):
        po = poses[i]
        geom = generate_cam(po, [1, 0, 0])
        cones.extend(geom)
        # rays = shoot_rays(K, po)
        # cones.extend([rays])

    o3d.visualization.draw(cones, show_skybox=False)


def blend_rgba(img):
    img = img[..., :3] * img[..., -1:] + (1. - img[..., -1:])  # blend A to RGB
    return img


def compare():
    import math
    import matplotlib.pyplot as plt

    vs = np.linspace(1e-5, math.pi - 1e-5, 500)
    phi = np.arccos(1 - 2 * (vs / math.pi))
    plt.plot(vs, phi)
    plt.show()


if __name__ == "__main__":
    seed_everything(0)
    plot_inward_facing_views()
    # compare()