File size: 10,820 Bytes
4d85df4
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
e3dcfcf
4d85df4
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
e3dcfcf
4d85df4
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
97b5aba
4d85df4
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
import numpy as np
import torch
import fusion
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go

def read_calib(calib_path):
        """
        Modify from https://github.com/utiasSTARS/pykitti/blob/d3e1bb81676e831886726cc5ed79ce1f049aef2c/pykitti/utils.py#L68
        :param calib_path: Path to a calibration text file.
        :return: dict with calibration matrices.
        """
        calib_all = {}
        with open(calib_path, "r") as f:
            for line in f.readlines():
                if line == "\n":
                    break
                key, value = line.split(":", 1)
                calib_all[key] = np.array([float(x) for x in value.split()])

        # reshape matrices
        calib_out = {}
        # 3x4 projection matrix for left camera
        calib_out["P2"] = calib_all["P2"].reshape(3, 4)
        calib_out["Tr"] = np.identity(4)  # 4x4 matrix
        calib_out["Tr"][:3, :4] = calib_all["Tr"].reshape(3, 4)
        return calib_out


def vox2pix(cam_E, cam_k, 
            vox_origin, voxel_size, 
            img_W, img_H, 
            scene_size):
    """
    compute the 2D projection of voxels centroids
    
    Parameters:
    ----------
    cam_E: 4x4
       =camera pose in case of NYUv2 dataset
       =Transformation from camera to lidar coordinate in case of SemKITTI
    cam_k: 3x3
        camera intrinsics
    vox_origin: (3,)
        world(NYU)/lidar(SemKITTI) cooridnates of the voxel at index (0, 0, 0)
    img_W: int
        image width
    img_H: int
        image height
    scene_size: (3,)
        scene size in meter: (51.2, 51.2, 6.4) for SemKITTI and (4.8, 4.8, 2.88) for NYUv2
    
    Returns
    -------
    projected_pix: (N, 2)
        Projected 2D positions of voxels
    fov_mask: (N,)
        Voxels mask indice voxels inside image's FOV 
    pix_z: (N,)
        Voxels'distance to the sensor in meter
    """
    # Compute the x, y, z bounding of the scene in meter
    vol_bnds = np.zeros((3,2))
    vol_bnds[:,0] = vox_origin
    vol_bnds[:,1] = vox_origin + np.array(scene_size)

    # Compute the voxels centroids in lidar cooridnates
    vol_dim = np.ceil((vol_bnds[:,1]- vol_bnds[:,0])/ voxel_size).copy(order='C').astype(int)
    xv, yv, zv = np.meshgrid(
            range(vol_dim[0]),
            range(vol_dim[1]),
            range(vol_dim[2]),
            indexing='ij'
          )
    vox_coords = np.concatenate([
            xv.reshape(1,-1),
            yv.reshape(1,-1),
            zv.reshape(1,-1)
          ], axis=0).astype(int).T

    # Project voxels'centroid from lidar coordinates to camera coordinates
    cam_pts = fusion.TSDFVolume.vox2world(vox_origin, vox_coords, voxel_size)
    cam_pts = fusion.rigid_transform(cam_pts, cam_E)

    # Project camera coordinates to pixel positions
    projected_pix = fusion.TSDFVolume.cam2pix(cam_pts, cam_k)
    pix_x, pix_y = projected_pix[:, 0], projected_pix[:, 1]

    # Eliminate pixels outside view frustum
    pix_z = cam_pts[:, 2]
    fov_mask = np.logical_and(pix_x >= 0,
                np.logical_and(pix_x < img_W,
                np.logical_and(pix_y >= 0,
                np.logical_and(pix_y < img_H,
                pix_z > 0))))


    return torch.from_numpy(projected_pix), torch.from_numpy(fov_mask), torch.from_numpy(pix_z)



def get_grid_coords(dims, resolution):
    """
    :param dims: the dimensions of the grid [x, y, z] (i.e. [256, 256, 32])
    :return coords_grid: is the center coords of voxels in the grid
    """

    g_xx = np.arange(0, dims[0] + 1)
    g_yy = np.arange(0, dims[1] + 1)
    sensor_pose = 10
    g_zz = np.arange(0, dims[2] + 1)

    # Obtaining the grid with coords...
    xx, yy, zz = np.meshgrid(g_xx[:-1], g_yy[:-1], g_zz[:-1])
    coords_grid = np.array([xx.flatten(), yy.flatten(), zz.flatten()]).T
    coords_grid = coords_grid.astype(np.float)

    coords_grid = (coords_grid * resolution) + resolution / 2

    temp = np.copy(coords_grid)
    temp[:, 0] = coords_grid[:, 1]
    temp[:, 1] = coords_grid[:, 0]
    coords_grid = np.copy(temp)

    return coords_grid

def get_projections(img_W, img_H):
    scale_3ds = [2, 4]
    data = {}
    for scale_3d in scale_3ds:
        scene_size = (51.2, 51.2, 6.4)
        vox_origin = np.array([0, -25.6, -2])
        voxel_size = 0.2
        
        calib = read_calib("calib.txt")    
        cam_k = calib["P2"][:3, :3]
        T_velo_2_cam = calib["Tr"]
        
        # compute the 3D-2D mapping
        projected_pix, fov_mask, pix_z = vox2pix(
            T_velo_2_cam,
            cam_k,
            vox_origin,
            voxel_size * scale_3d,
            img_W,
            img_H,
            scene_size,
        )            

        data["projected_pix_{}".format(scale_3d)] = projected_pix
        data["pix_z_{}".format(scale_3d)] = pix_z
        data["fov_mask_{}".format(scale_3d)] = fov_mask 
    return data


def majority_pooling(grid, k_size=2):
    result = np.zeros(
        (grid.shape[0] // k_size, grid.shape[1] // k_size, grid.shape[2] // k_size)
    )
    for xx in range(0, int(np.floor(grid.shape[0] / k_size))):
        for yy in range(0, int(np.floor(grid.shape[1] / k_size))):
            for zz in range(0, int(np.floor(grid.shape[2] / k_size))):

                sub_m = grid[
                    (xx * k_size) : (xx * k_size) + k_size,
                    (yy * k_size) : (yy * k_size) + k_size,
                    (zz * k_size) : (zz * k_size) + k_size,
                ]
                unique, counts = np.unique(sub_m, return_counts=True)
                if True in ((unique != 0) & (unique != 255)):
                    # Remove counts with 0 and 255
                    counts = counts[((unique != 0) & (unique != 255))]
                    unique = unique[((unique != 0) & (unique != 255))]
                else:
                    if True in (unique == 0):
                        counts = counts[(unique != 255)]
                        unique = unique[(unique != 255)]
                value = unique[np.argmax(counts)]
                result[xx, yy, zz] = value
    return result


def draw(
    voxels,
    # T_velo_2_cam,
    # vox_origin,
    fov_mask,
    # img_size,
    # f,
    voxel_size=0.4,
    # d=7,  # 7m - determine the size of the mesh representing the camera
):

    fov_mask = fov_mask.reshape(-1)
    # Compute the voxels coordinates
    grid_coords = get_grid_coords(
        [voxels.shape[0], voxels.shape[1], voxels.shape[2]], voxel_size
    )


    # Attach the predicted class to every voxel
    grid_coords = np.vstack([grid_coords.T, voxels.reshape(-1)]).T

    # Get the voxels inside FOV
    fov_grid_coords = grid_coords[fov_mask, :]

    # Get the voxels outside FOV
    outfov_grid_coords = grid_coords[~fov_mask, :]

    # Remove empty and unknown voxels
    fov_voxels = fov_grid_coords[
        (fov_grid_coords[:, 3] > 0) & (fov_grid_coords[:, 3] < 255), :
    ]
    # print(np.unique(fov_voxels[:, 3], return_counts=True))
    outfov_voxels = outfov_grid_coords[
        (outfov_grid_coords[:, 3] > 0) & (outfov_grid_coords[:, 3] < 255), :
    ]

    # figure = mlab.figure(size=(1400, 1400), bgcolor=(1, 1, 1))
    colors = np.array(
        [
            [0,0,0],
            [100, 150, 245],
            [100, 230, 245],
            [30, 60, 150],
            [80, 30, 180],
            [100, 80, 250],
            [255, 30, 30],
            [255, 40, 200],
            [150, 30, 90],
            [255, 0, 255],
            [255, 150, 255],
            [75, 0, 75],
            [175, 0, 75],
            [255, 200, 0],
            [255, 120, 50],
            [0, 175, 0],
            [135, 60, 0],
            [150, 240, 80],
            [255, 240, 150],
            [255, 0, 0],
        ]
    ).astype(np.uint8)

    pts_colors = [f'rgb({colors[int(i)][0]}, {colors[int(i)][1]}, {colors[int(i)][2]})' for i in fov_voxels[:, 3]]
    out_fov_colors = [f'rgb({colors[int(i)][0]//3*2}, {colors[int(i)][1]//3*2}, {colors[int(i)][2]//3*2})' for i in outfov_voxels[:, 3]]
    pts_colors = pts_colors + out_fov_colors
    
    fov_voxels = np.concatenate([fov_voxels, outfov_voxels], axis=0)
    x = fov_voxels[:, 0].flatten()
    y = fov_voxels[:, 1].flatten()
    z = fov_voxels[:, 2].flatten()
    # label = fov_voxels[:, 3].flatten()
    fig = go.Figure(data=[go.Scatter3d(x=x, y=y, z=z,mode='markers',
                    marker=dict(
                            size=2,
                            color=pts_colors,                # set color to an array/list of desired values
                            # colorscale='Viridis',   # choose a colorscale
                            opacity=1.0,
                            symbol='square'
                        ))])
    fig.update_layout(
    scene = dict(
        aspectmode='data',
        xaxis = dict(
            backgroundcolor="rgb(255, 255, 255)",
            gridcolor="black",
            showbackground=True,
            zerolinecolor="black",
            nticks=4, 
            visible=False,
            range=[-1,55],),
        yaxis = dict(
            backgroundcolor="rgb(255, 255, 255)",
            gridcolor="black",
            showbackground=True,
            zerolinecolor="black",
            visible=False,
            nticks=4, range=[-1,55],),
        zaxis = dict(
            backgroundcolor="rgb(255, 255, 255)",
            gridcolor="black",
            showbackground=True,
            zerolinecolor="black",
            visible=False,
            nticks=4, range=[-1,7],),
        bgcolor="black",
    ),
        
    )

    # fig = px.scatter_3d(
    #     fov_voxels, 
    #     x=fov_voxels[:, 0], y="y", z="z", color="label")
    # Draw occupied inside FOV voxels
    # plt_plot_fov = mlab.points3d(
    #     fov_voxels[:, 0],
    #     fov_voxels[:, 1],
    #     fov_voxels[:, 2],
    #     fov_voxels[:, 3],
    #     colormap="viridis",
    #     scale_factor=voxel_size - 0.05 * voxel_size,
    #     mode="cube",
    #     opacity=1.0,
    #     vmin=1,
    #     vmax=19,
    # )

    # # Draw occupied outside FOV voxels
    # plt_plot_outfov = mlab.points3d(
    #     outfov_voxels[:, 0],
    #     outfov_voxels[:, 1],
    #     outfov_voxels[:, 2],
    #     outfov_voxels[:, 3],
    #     colormap="viridis",
    #     scale_factor=voxel_size - 0.05 * voxel_size,
    #     mode="cube",
    #     opacity=1.0,
    #     vmin=1,
    #     vmax=19,
    # )

    

    # plt_plot_fov.glyph.scale_mode = "scale_by_vector"
    # plt_plot_outfov.glyph.scale_mode = "scale_by_vector"

    # plt_plot_fov.module_manager.scalar_lut_manager.lut.table = colors

    # outfov_colors = colors
    # outfov_colors[:, :3] = outfov_colors[:, :3] // 3 * 2
    # plt_plot_outfov.module_manager.scalar_lut_manager.lut.table = outfov_colors

    # mlab.show()
    return fig