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indoor.py
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indoor.py
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# Copyright (c) Chris Choy (chrischoy@ai.stanford.edu).
#
# Permission is hereby granted, free of charge, to any person obtaining a copy of
# this software and associated documentation files (the "Software"), to deal in
# the Software without restriction, including without limitation the rights to
# use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies
# of the Software, and to permit persons to whom the Software is furnished to do
# so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
#
# Please cite "4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural
# Networks", CVPR'19 (https://arxiv.org/abs/1904.08755) if you use any part
# of the code.
import os
import argparse
import numpy as np
from urllib.request import urlretrieve
try:
import open3d as o3d
except ImportError:
raise ImportError('Please install open3d with `pip install open3d`.')
import torch
import MinkowskiEngine as ME
from examples.minkunet import MinkUNet34C
# Check if the weights and file exist and download
if not os.path.isfile('weights.pth'):
print('Downloading weights...')
urlretrieve("https://bit.ly/2O4dZrz", "weights.pth")
if not os.path.isfile("1.ply"):
print('Downloading an example pointcloud...')
urlretrieve("https://bit.ly/3c2iLhg", "1.ply")
parser = argparse.ArgumentParser()
parser.add_argument('--file_name', type=str, default='1.ply')
parser.add_argument('--weights', type=str, default='weights.pth')
parser.add_argument('--use_cpu', action='store_true')
CLASS_LABELS = ('wall', 'floor', 'cabinet', 'bed', 'chair', 'sofa', 'table',
'door', 'window', 'bookshelf', 'picture', 'counter', 'desk',
'curtain', 'refrigerator', 'shower curtain', 'toilet', 'sink',
'bathtub', 'otherfurniture')
VALID_CLASS_IDS = [
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 16, 24, 28, 33, 34, 36, 39
]
SCANNET_COLOR_MAP = {
0: (0., 0., 0.),
1: (174., 199., 232.),
2: (152., 223., 138.),
3: (31., 119., 180.),
4: (255., 187., 120.),
5: (188., 189., 34.),
6: (140., 86., 75.),
7: (255., 152., 150.),
8: (214., 39., 40.),
9: (197., 176., 213.),
10: (148., 103., 189.),
11: (196., 156., 148.),
12: (23., 190., 207.),
14: (247., 182., 210.),
15: (66., 188., 102.),
16: (219., 219., 141.),
17: (140., 57., 197.),
18: (202., 185., 52.),
19: (51., 176., 203.),
20: (200., 54., 131.),
21: (92., 193., 61.),
22: (78., 71., 183.),
23: (172., 114., 82.),
24: (255., 127., 14.),
25: (91., 163., 138.),
26: (153., 98., 156.),
27: (140., 153., 101.),
28: (158., 218., 229.),
29: (100., 125., 154.),
30: (178., 127., 135.),
32: (146., 111., 194.),
33: (44., 160., 44.),
34: (112., 128., 144.),
35: (96., 207., 209.),
36: (227., 119., 194.),
37: (213., 92., 176.),
38: (94., 106., 211.),
39: (82., 84., 163.),
40: (100., 85., 144.),
}
def load_file(file_name):
pcd = o3d.io.read_point_cloud(file_name)
coords = np.array(pcd.points)
colors = np.array(pcd.colors)
return coords, colors, pcd
def normalize_color(color: torch.Tensor, is_color_in_range_0_255: bool = False) -> torch.Tensor:
r"""
Convert color in range [0, 1] to [-0.5, 0.5]. If the color is in range [0,
255], use the argument `is_color_in_range_0_255=True`.
`color` (torch.Tensor): Nx3 color feature matrix
`is_color_in_range_0_255` (bool): If the color is in range [0, 255] not [0, 1], normalize the color to [0, 1].
"""
if is_color_in_range_0_255:
color /= 255
color -= 0.5
return color.float()
if __name__ == '__main__':
config = parser.parse_args()
device = torch.device('cuda' if (
torch.cuda.is_available() and not config.use_cpu) else 'cpu')
print(f"Using {device}")
# Define a model and load the weights
model = MinkUNet34C(3, 20).to(device)
model_dict = torch.load(config.weights)
model.load_state_dict(model_dict)
model.eval()
coords, colors, pcd = load_file(config.file_name)
# Measure time
with torch.no_grad():
voxel_size = 0.02
# Feed-forward pass and get the prediction
in_field = ME.TensorField(
features=normalize_color(torch.from_numpy(colors)),
coordinates=ME.utils.batched_coordinates([coords / voxel_size], dtype=torch.float32),
quantization_mode=ME.SparseTensorQuantizationMode.UNWEIGHTED_AVERAGE,
minkowski_algorithm=ME.MinkowskiAlgorithm.SPEED_OPTIMIZED,
device=device,
)
# Convert to a sparse tensor
sinput = in_field.sparse()
# Output sparse tensor
soutput = model(sinput)
# get the prediction on the input tensor field
out_field = soutput.slice(in_field)
logits = out_field.F
_, pred = logits.max(1)
pred = pred.cpu().numpy()
# Create a point cloud file
pred_pcd = o3d.geometry.PointCloud()
# Map color
colors = np.array([SCANNET_COLOR_MAP[VALID_CLASS_IDS[l]] for l in pred])
pred_pcd.points = o3d.utility.Vector3dVector(coords)
pred_pcd.colors = o3d.utility.Vector3dVector(colors / 255)
pred_pcd.estimate_normals()
# Move the original point cloud
pcd.points = o3d.utility.Vector3dVector(
np.array(pcd.points) + np.array([0, 5, 0]))
# Visualize the input point cloud and the prediction
o3d.visualization.draw_geometries([pcd, pred_pcd])