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showpc_memo1.py
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showpc_memo1.py
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'''
***point cloud lectern***
=========memo 1=========
╰(●’◡’●)╮
to show point cloud for classification task. 分类 通过颜色区分不同的类
take data_whu and data_isprs as examples.
========2019.12.9========
'''
from __future__ import print_function
import os
import sys
import numpy as np
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
ROOT_DIR = os.path.dirname(BASE_DIR) # 根路径
sys.path.append(os.path.join(ROOT_DIR, 'mayavi'))
try:
raw_input # Python 2
except NameError:
raw_input = input # Python 3
# 类class
class pointcloud_object(object):
'''Load and parse object data into a usable format.'''
def __init__(self, root_dir):
self.lidar_dir = root_dir
def get_lidar(self, idx):
# assert(idx<data_num)
lidar_filename = os.path.join(self.lidar_dir, '%03d.txt'%(idx))#bin 获取文件路径 000 要有三位
return load_velo_scan(lidar_filename)
# 加载TXT文件
def load_velo_scan(velo_filename):
scan = np.loadtxt(velo_filename) # 加载txt文件 不同文件使用不同的加载函数！！！
print('the shape of scan is: ', scan.shape)
return scan
# 绘画
def draw_lidar(pc, fig=None, bgcolor=(0,0,0), pts_scale=1, pts_mode='point', pts_color=None):
''' Draw lidar points
Args:
pc: numpy array (n,3) of XYZ
fig: mayavi figure handler, if None create new one otherwise will use it
Returns:
fig: created or used fig
'''
if fig is None: fig = mlab.figure(figure=None, bgcolor=bgcolor, fgcolor=None, engine=None, size=(1600, 1000))
# category label r g b
# vegetation 0 0 255 0
# building 1 255 0 0
# car 2 0 0 255
# pedestrian 3 255 255 0
# lamp 4 255 97 0
# fence 5 155 74 18
# others 6 106 90 205
# 不同类别，不同颜色
color_list = [(0, 1, 0),
(1, 0, 0),
(0, 0, 1),
(1, 1, 0),
(1, 97/255, 0),
(155/255, 97/255, 18/255),
(0, 1, 1),
(106/255, 90/255, 205/255)]
without_semantic_label = False#True# True： 点云颜色相同 False： 点云颜色不同
if without_semantic_label:
mlab.points3d(pc[:,0], pc[:,1], pc[:,2], color=color_list[0], mode=pts_mode, colormap = 'gnuplot', scale_factor=pts_scale, figure=fig)
else:
# pc0_mask = pc[:,3]==0
# pc1_mask = pc[:,3]==1
# pc2_mask = pc[:,3]==2
# pc3_mask = pc[:,3]==3
# pc4_mask = pc[:,3]==4
# pc5_mask = pc[:,3]==5
# pc6_mask = pc[:,3]==6
pc0 = pc[pc[:,3]==0] # 第四位表示label类别，将每一类都分好 一共7类
pc1 = pc[pc[:,3]==1]
pc2 = pc[pc[:,3]==2]
pc3 = pc[pc[:,3]==3]
pc4 = pc[pc[:,3]==4]
pc5 = pc[pc[:,3]==5]
pc6 = pc[pc[:,3]==6]
print('136: the size of pc0 is:', len(pc0)) # 点的数量 849168
print('136: the size of pc1 is:', len(pc1)) # 21484
print('136: the size of pc2 is:', len(pc2)) # 101805
print('136: the size of pc3 is:', len(pc3)) # 6048
print('136: the size of pc4 is:', len(pc4)) # 4783
print('136: the size of pc5 is:', len(pc5)) # 63868
print('136: the size of pc6 is:', len(pc6)) # 2626
mlab.points3d(pc0[:,0], pc0[:,1], pc0[:,2], color=color_list[0], mode=pts_mode, colormap = 'gnuplot', scale_factor=pts_scale, figure=fig)
mlab.points3d(pc1[:,0], pc1[:,1], pc1[:,2], color=color_list[1], mode=pts_mode, colormap = 'gnuplot', scale_factor=pts_scale, figure=fig)
mlab.points3d(pc2[:,0], pc2[:,1], pc2[:,2], color=color_list[2], mode=pts_mode, colormap = 'gnuplot', scale_factor=pts_scale, figure=fig)
mlab.points3d(pc3[:,0], pc3[:,1], pc3[:,2], color=color_list[3], mode=pts_mode, colormap = 'gnuplot', scale_factor=pts_scale, figure=fig)
mlab.points3d(pc4[:,0], pc4[:,1], pc4[:,2], color=color_list[4], mode=pts_mode, colormap = 'gnuplot', scale_factor=pts_scale, figure=fig)
mlab.points3d(pc5[:,0], pc5[:,1], pc5[:,2], color=color_list[5], mode=pts_mode, colormap = 'gnuplot', scale_factor=pts_scale, figure=fig)
mlab.points3d(pc6[:,0], pc6[:,1], pc6[:,2], color=color_list[6], mode=pts_mode, colormap = 'gnuplot', scale_factor=pts_scale, figure=fig)
# mlab.view(azimuth=180, elevation=90, focalpoint=[ 12.0909996 , -1.04700089, -2.03249991], distance=62.0, figure=fig)
mlab.view(azimuth=270, elevation=0, focalpoint=[ pc[0,0], pc[0,1], pc[0,2]], distance=10.0, figure=fig)
return fig
# 显示lidar
def show_lidar(pc_velo, img_fov=False):
''' Show all LiDAR points.
Draw 3d box in LiDAR point cloud (in velo coord system) '''
if 'mlab' not in sys.modules: import mayavi.mlab as mlab
print(('All point num: ', pc_velo.shape[0]))
fig = mlab.figure(figure=None, bgcolor=(1,1,1),
fgcolor=None, engine=None, size=(1000, 500))
draw_lidar(pc_velo, fig=fig) # 绘画
mlab.show(1)
def dataset_viz():
dataset = pointcloud_object(os.path.join(ROOT_DIR, 'data-whu')) # 路径
# dataset = pointcloud_object(os.path.join(ROOT_DIR, 'data-isprs'))
print('143:', dataset)
data_num = 10 # data-whu文件夹下最多10 TXT文件
for data_idx in range(data_num):
pc_velo = dataset.get_lidar(data_idx)[:,0:4]
print('the len of dataset is: ', data_num, 'data_idx is: ', data_idx)
show_lidar(pc_velo, False) # 显示点云函数
raw_input()
if __name__=='__main__':
import mayavi.mlab as mlab
# from viz_util import draw_lidar_simple, draw_lidar, draw_gt_boxes3d
dataset_viz()