DeepMultiCap/lib/sample_util.py at main · DSaurus/DeepMultiCap · GitHub

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
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
import numpy as np
import torch
from .geometry import batch_cross_3d

def max_min_norm(x, b_min, b_max):
    '''
    :param x [B, 3, N]
    :param b_min [3] list
    :param b_max [3] list
    '''
    b_min = torch.FloatTensor(b_min).to(x.device).reshape(1, 3, 1)
    b_max = torch.FloatTensor(b_max).to(x.device).reshape(1, 3, 1)
    return (x - b_min) / (b_max - b_min)

def obj_sample_surface_wo_tex(obj, count):
    with torch.no_grad():
        vert = torch.FloatTensor(obj['vi'][:, :3])
        faces = torch.LongTensor(obj['f']).permute(0, 2, 1)
        v_tri = torch.zeros((faces.shape[0], 3, 3))
        for i in range(3):
            v_tri[:, i, :] = vert[faces[:, 0, i], :]
        v_ori = v_tri[:, 0, :].clone()
        v_vec = v_tri[:, 1:, :].clone() - v_ori.unsqueeze(1)

        area = torch.zeros((faces.shape[0], 1))
        cross = batch_cross_3d(v_vec[:, 0, :], v_vec[:, 1, :])
        area = torch.abs(torch.sum(cross, dim=1).unsqueeze(1)).numpy()

        area_sum = np.sum(area)
        area_cum = np.cumsum(area)
        face_pick = np.random.random(count) * area_sum
        face_index = np.searchsorted(area_cum, face_pick)-1

        v_ori = v_ori[face_index, :]
        v_vec = v_vec[face_index, :]

        random_lengths = np.random.random((count, 2, 1))

        # points will be distributed on a quadrilateral if we use 2 0-1 samples
        # if the two scalar components sum less than 1.0 the point will be
        # inside the triangle, so we find vectors longer than 1.0 and
        # transform them to be inside the triangle
        random_test = random_lengths.sum(axis=1).reshape(-1) > 1.0
        random_lengths[random_test] -= 1.0
        random_lengths = torch.FloatTensor(np.abs(random_lengths))

        # multiply triangle edge vectors by the random lengths and sum
        sample_v_vec = (v_vec * random_lengths).sum(dim=1)

        # finally, offset by the origin to generate
        v_samples = sample_v_vec + v_ori

    return v_samples

def obj_sample_surface(obj, count):
    with torch.no_grad():
        vert = torch.FloatTensor(obj['vi'])
        tex = torch.FloatTensor(obj['vt'])
        faces = torch.LongTensor(obj['f']).permute(0, 2, 1)
        v_tri = torch.zeros((faces.shape[0], 3, 3))
        t_tri = torch.zeros((faces.shape[0], 3, 2))
        for i in range(3):
            v_tri[:, i, :] = vert[faces[:, 0, i], :]
            t_tri[:, i, :] = tex[faces[:, 1, i], :]
        v_ori = v_tri[:, 0, :].clone()
        t_ori = t_tri[:, 0, :].clone()
        v_vec = v_tri[:, 1:, :].clone() - v_ori.unsqueeze(1)
        t_vec = t_tri[:, 1:, :].clone() - t_ori.unsqueeze(1)

        area = torch.zeros((faces.shape[0], 1))
        cross = batch_cross_3d(v_vec[:, 0, :], v_vec[:, 1, :])
        area = torch.abs(torch.sum(cross, dim=1).unsqueeze(1)).numpy()
        normal = cross / torch.sum(torch.sqrt(cross**2), dim=1).unsqueeze(1)

        area_sum = np.sum(area)
        # cumulative area (len(mesh.faces))
        area_cum = np.cumsum(area)
        face_pick = np.random.random(count) * area_sum
        face_index = np.searchsorted(area_cum, face_pick)-1

        v_ori, t_ori = v_ori[face_index, :], t_ori[face_index, :]
        v_vec, t_vec = v_vec[face_index, :], t_vec[face_index, :]

        random_lengths = np.random.random((count, 2, 1))

        # points will be distributed on a quadrilateral if we use 2 0-1 samples
        # if the two scalar components sum less than 1.0 the point will be
        # inside the triangle, so we find vectors longer than 1.0 and
        # transform them to be inside the triangle
        random_test = random_lengths.sum(axis=1).reshape(-1) > 1.0
        random_lengths[random_test] -= 1.0
        random_lengths = torch.FloatTensor(np.abs(random_lengths))

        # multiply triangle edge vectors by the random lengths and sum
        sample_v_vec = (v_vec * random_lengths).sum(dim=1)
        sample_t_vec = (t_vec * random_lengths).sum(dim=1)

        # finally, offset by the origin to generate
        v_samples = sample_v_vec + v_ori
        t_samples = sample_t_vec + t_ori
        n_samples = normal[face_index, :]

    return v_samples, t_samples, n_samples

def save_samples_truncted_prob_obj(fname, points, prob, continue_write=False, pts_num=0):
    '''
    Save the visualization of sampling to a obj file.
    Red points represent positive predictions.
    Green points represent negative predictions.
    :param fname: File name to save
    :param points: [N, 3] array of points
    :param prob: [N, 1] array of predictions in the range [0~1]
    :return:
    '''
    r = (prob).reshape([-1, 1]) * 255
    g = (1-prob).reshape([-1, 1]) * 255
    b = np.zeros(r.shape)
    if pts_num == 0:
        pts_num = points.shape[0]

    to_save = np.concatenate([points, r, g, b], axis=-1)
    if continue_write:
        f = open(fname, mode='a')
    else:
        f = open(fname, mode='w')
        # f.write('ply\nformat ascii 1.0\nelement vertex %d\nproperty float x\nproperty float y\nproperty float z\nproperty uchar red\nproperty uchar green\nproperty uchar blue\nend_header\n' % pts_num)
    for i in range(points.shape[0]):
        f.write("v %f %f %f %d %d %d\n" % (points[i, 0], points[i, 1], points[i, 2], r[i], g[i], b[i]))

def save_samples_truncted_prob(fname, points, prob):
    '''
    Save the visualization of sampling to a ply file.
    Red points represent positive predictions.
    Green points represent negative predictions.
    :param fname: File name to save
    :param points: [N, 3] array of points
    :param prob: [N, 1] array of predictions in the range [0~1]
    :return:
    '''
    r = (prob).reshape([-1, 1]) * 255
    g = (1-prob).reshape([-1, 1]) * 255
    b = np.zeros(r.shape)

    to_save = np.concatenate([points, r, g, b], axis=-1)
    return np.savetxt(fname,
                      to_save,
                      fmt='%.6f %.6f %.6f %d %d %d',
                      comments='',
                      header=(
                          'ply\nformat ascii 1.0\nelement vertex {:d}\nproperty float x\nproperty float y\nproperty float z\nproperty uchar red\nproperty uchar green\nproperty uchar blue\nend_header').format(
                          points.shape[0])
                      )


def save_samples_rgb(fname, points, rgb):
    '''
    Save the visualization of sampling to a ply file.
    Red points represent positive predictions.
    Green points represent negative predictions.
    :param fname: File name to save
    :param points: [N, 3] array of points
    :param rgb: [N, 3] array of rgb values in the range [0~1]
    :return:
    '''
    to_save = np.concatenate([points, rgb * 255], axis=-1)
    return np.savetxt(fname,
                      to_save,
                      fmt='%.6f %.6f %.6f %d %d %d',
                      comments='',
                      header=(
                          'ply\nformat ascii 1.0\nelement vertex {:d}\nproperty float x\nproperty float y\nproperty float z\nproperty uchar red\nproperty uchar green\nproperty uchar blue\nend_header').format(
                          points.shape[0])
                      )