MinkowskiEngine语义分割

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

from examples.common import Timer

# Check if the weights and file exist and download

if not os.path.isfile('weights.pth'):

print('Downloading weights and a room ply file...')

urlretrieve("http://cvgl.stanford.edu/data2/minkowskiengine/weights.pth",

'weights.pth')

urlretrieve("http://cvgl.stanford.edu/data2/minkowskiengine/1.ply", '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

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=torch.from_numpy(colors).float(),

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])