代码在git
import open3d as o3d
import numpy as np
def read_pcd(file_path):
pcd = o3d.io.read_point_cloud(file_path)
# print(np.asarray(pcd.points))
colors = np.asarray(pcd.colors) * 255
points = np.asarray(pcd.points)
# print(points.shape, colors.shape)
# return np.concatenate([points, colors], axis=-1)
return points
a=read_pcd('b1.pcd')
print(a)
#第二种
def read_pcd(pcd_path):
lines = []
num_points = None
with open(pcd_path, 'r') as f:
for line in f:
lines.append(line.strip())
if line.startswith('POINTS'):
num_points = int(line.split()[-1])
assert num_points is not None
points = []
for line in lines[-num_points:]:
x, y, z, i = list(map(float, line.split()))
#这里没有把i放进去，也是为了后面 x, y, z 做矩阵变换的时候方面
#但这个理解我选择保留， 因为可能把i放进去也不影响代码的简易程度
points.append((np.array([x, y, z, i])))
return points
a0=read_pcd('b1.pcd')
a=np.array(a0)
print(a)
#第三种
def load_pcd_to_ndarray(pcd_path):
with open(pcd_path) as f:
while True:
ln = f.readline().strip()
if ln.startswith('DATA'):
break
points = np.loadtxt(f)
points = points[:, 0:4]
return points
a0=read_pcd('b1.pcd')
a=np.array(a0)
print(a)
#第四种（需要源码安装pypcd）链接在底部
# import argparse
# from pypcd import pypcd
# import numpy as np
# parser = argparse.ArgumentParser()
# parser.add_argument('--pcd_path', default='', type=str)
# args = parser.parse_args()
# def read_pcd(pcd_path):
# pcd = pypcd.PointCloud.from_path(pcd_path)
# pcd_np_points = np.zeros((pcd.points, 5), dtype=np.float32)
# print(pcd.pc_data['x'])
# pcd_np_points[:, 0] = np.transpose(pcd.pc_data['x'])
# pcd_np_points[:, 1] = np.transpose(pcd.pc_data['y'])
# pcd_np_points[:, 2] = np.transpose(pcd.pc_data['z'])
# pcd_np_points[:, 3] = np.transpose(pcd.pc_data['intensity'])
# pcd_np_points[:, 4] = np.transpose(pcd.pc_data['is_ground'])
# del_index = np.where(np.isnan(pcd_np_points))[0]
# pcd_np_points = np.delete(pcd_np_points, del_index, axis=0)
# return pcd_np_points
# read_pcd(args.pcd_path)
# #第一种
# # pip3 install python-pcl
# import pcl
# #pcd_ndarray = pcl.load(args.pcd_path).to_array()[:, :3] #不要intensity
# pcd_ndarray = pcl.load_XYZI(args.pcd_path).to_array()[:, :4]
# point_num = pcd_ndarray.shape[0]
# # shape属性可以获取矩阵的形状（例如二维数组的行列），获取的结果是一个元组
