PanocontextFormer/parse_endpoint2.py at develop · fangchuan/PanocontextFormer · 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
import os
import math
import trimesh
import torch

from glob import glob
import numpy as np
from pytorch3d.structures import Meshes
from shapely.geometry import Polygon
from pytorch3d.renderer import TexturesVertex

from igibson.model_util_igbson import IGbsonDatasetConfig
from utils.softargmax import softargmax1d
from pytorch3d.transforms import RotateAxisAngle,Translate
from pytorch3d.io import save_obj,load_obj
import matplotlib.pyplot as plt

from pytorch3d.renderer import (
    look_at_view_transform,
    FoVOrthographicCameras,
    RasterizationSettings,
    MeshRenderer,
    MeshRasterizer,
    TexturesVertex,
)

from utils.custom_sharder import CustomShader

Num_heading_bin = 12

def get_standard_bbox(device = 'cpu'):

    transformation = np.eye(4)
    transformation[2, 3] = -0.5
    corners = np.array([[-0.5,-0.5],[-0.5,0.5],[0.5,0.5],[0.5,-0.5]])
    polygon = Polygon(corners.tolist())
    mesh = trimesh.creation.extrude_polygon(polygon, height=1, transform=transformation)
    verts = torch.tensor(mesh.vertices, dtype=torch.float32)
    faces_verts_idx = torch.tensor(mesh.faces)
    verts_rgb = torch.ones_like(verts)[None]  # (1, V, 3)
    textures = TexturesVertex(verts_rgb)
    textured_mesh = Meshes(
        verts=[verts.to(device)],
        faces=[faces_verts_idx.to(device)],
        textures=textures.to(device)
    )
    return textured_mesh

if __name__ == '__main__':
    end_points = {}
    all_filename = glob(os.path.join("./endpoint/"+"*.npy"))
    for i in range(len(all_filename)):
        key = all_filename[i].split("/")[-1][:-4]
        try:
            end_points[key] = torch.from_numpy(np.load(all_filename[i],allow_pickle=True))
        except:
            print("can not load ",key)
            pass
    image_size = 200

    DATASET_CONFIG = IGbsonDatasetConfig()

    prefix = 'last_'
    bs = end_points[f'{prefix}center'].shape[0]

    pred_center = torch.tensor(end_points[f'{prefix}center'],requires_grad=True)
    pred_size = torch.tensor(end_points[f'{prefix}pred_size'],requires_grad=True)
    pred_heading_residual =  torch.tensor(end_points[f'{prefix}heading_residuals'],requires_grad=True)
    heading_scores = torch.tensor(end_points[f'{prefix}heading_scores'],requires_grad=True)

    pred_heading_class_argmax = torch.argmax(heading_scores, -1) # B,num_proposal
    pred_heading_residual = torch.gather(pred_heading_residual, 2, pred_heading_class_argmax.unsqueeze(-1)) # B,num_proposal,1
    pred_heading_class = softargmax1d(heading_scores.view(-1,Num_heading_bin)).view(bs,-1).unsqueeze(-1)

    pred_heading_gap = 2 * math.pi / float(Num_heading_bin)
    pred_angle = (pred_heading_gap * pred_heading_class + pred_heading_residual).squeeze(2)

    bbox_num = bs * 256
    batch_bbox_mesh = get_standard_bbox()
    batch_bbox_mesh = batch_bbox_mesh.extend(bbox_num)

    # apply_size
    all_size = pred_size.view(-1,3)
    batch_bbox_mesh = batch_bbox_mesh.scale_verts(all_size)

    # apply_rotation
    mesh_rotation = RotateAxisAngle(pred_angle.view(-1), degrees=False, axis="Z")
    # apply_translation
    mesh_translation = Translate(pred_center.view(-1,3))
    transform = mesh_rotation.compose(mesh_translation)
    updated_verts = transform.transform_points(batch_bbox_mesh.verts_padded())
    batch_bbox_mesh = batch_bbox_mesh.update_padded(updated_verts)

    batch_bbox_mesh_verts = batch_bbox_mesh.verts_padded()
    batch_bbox_mesh_faces = batch_bbox_mesh.faces_padded()

    # for bbox_idx in range(bbox_num):
    #     save_obj("dump/"+str(bbox_idx)+".obj",verts=batch_bbox_mesh_verts[bbox_idx],faces=batch_bbox_mesh_faces[bbox_idx])


    # render bbox
    R, T = look_at_view_transform(10, 0, 0)
    cameras = FoVOrthographicCameras(scale_xyz = ((0.12,0.12,0.12),), device='cpu', R=R, T=T)
    raster_settings = RasterizationSettings(
        image_size=image_size,
        blur_radius=0.0,
        faces_per_pixel=1,
    )
    rasterizer = MeshRasterizer(
        cameras=cameras,
        raster_settings=raster_settings
    )
    shader = CustomShader(device='cpu', cameras=cameras)
    renderer = MeshRenderer(rasterizer, shader)
    renderer_bbox_image = renderer(batch_bbox_mesh)
    renderer_bbox_image = renderer_bbox_image.view(bs,-1,image_size,image_size,1)

    # print(image.shape)
    # for i in range(512):
    #     print(i)
    #     plt.imshow(image[i,:,:].detach().numpy())
    #     plt.show()

    # render layout_mesh
    mesh_filepath = "/Users/yuandong/Downloads/layout_mesh_2stage_551/Beechwood_1_int_00016_refine.obj"
    verts, faces, aux = load_obj(mesh_filepath)
    faces_verts_idx = faces.verts_idx
    verts_rgb = torch.ones_like(verts)[None]  # (1, V, 3)
    textures = TexturesVertex(verts_rgb.to('cpu'))

    textured_mesh = Meshes(
        verts=[torch.tensor(verts,requires_grad=True)],
        faces=[faces_verts_idx],
        textures=textures
    )
    textured_mesh = textured_mesh.extend(renderer_bbox_image.shape[0])

    renderer_layout_image = renderer(textured_mesh).unsqueeze(1)


    outside_mask = renderer_layout_image<0.5
    outside_bbox_image = outside_mask * renderer_bbox_image
    outside_bbox_image_1d = outside_bbox_image.view(bs,256,-1)
    inters_sum = torch.sum(outside_bbox_image_1d,2)

    obj_ass_mask = end_points[f'{prefix}objectness_label'].float()

    mask_and = torch.sum(torch.logical_and(renderer_layout_image>0.5, renderer_bbox_image>0.5).view(bs,256,-1),2)
    renderer_bbox_image_mask = torch.sum((renderer_bbox_image>0.5).view(bs,256,-1),2)
    inside_mask = (mask_and==renderer_bbox_image_mask)

    inv_mask_and = torch.sum(torch.logical_and(renderer_layout_image<0.5, renderer_bbox_image>0.5).view(bs,256,-1),2)
    outside_mask = (inv_mask_and==renderer_bbox_image_mask)

    inside_outside_mask = ~torch.logical_or(inside_mask,outside_mask)

    final_mask = inside_outside_mask * obj_ass_mask

    physical_loss = torch.sum(inters_sum * final_mask)/(torch.sum(final_mask) + 1e-6)/100