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make_pcl.py
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import pickle
import rospy
from util import get_rgba_pcd_msg
from sensor_msgs.msg import PointCloud2,Image
import json
import numpy as np
import pandas as pd
import argparse
from pclpy import pcl
import tf
from tf.transformations import quaternion_from_euler as qfe
from nav_msgs.msg import Odometry
from geometry_msgs.msg import TransformStamped
from geometry_msgs.msg import PoseStamped
from cv_bridge import CvBridge
import time
import glob
import cv2
import re
from predict import get_colors
global sempcd
global args
global index
global poses
global br
global savepcd
global odom_trans
def class2color(cls,alpha = False):
c = color_classes[cls]
if not alpha:
return np.array(c).astype(np.uint8)
else:
return np.array([*c, 255]).astype(np.uint8)
def save_nppc(nparr,fname):
s = nparr.shape
if s[1] == 4:#rgb
tmp = pcl.PointCloud.PointXYZRGBA(nparr[:,:3],np.array([color_classes[int(i)] for i in nparr[:,3]]))
else:
tmp = pcl.PointCloud.PointXYZ(nparr)
pcl.io.save(fname,tmp)
return tmp
def process():
global sempcd
global args
global index
global poses
global br
if args.filters:
sempcd = sempcd[np.in1d(sempcd[:, 3], args.filters)]
sem_msg = get_rgba_pcd_msg(sempcd)
sem_msg.header.frame_id = 'world'
semanticCloudPubHandle.publish(sem_msg)
if args.trajectory:
p = poses[index]
rotation = pd.Series(p[3:7], index=['x', 'y', 'z', 'w'])
br.sendTransform((p[0], p[1], p[2]), rotation, rospy.Time(time.time()), 'odom', 'world')
index += 1
if args.semantic and index < len(simgs):
simg = cv2.imread(simgs[index],0)
r,c = simg.shape
semimg = colors[simg.flatten()].reshape((*simg.shape,3))
semimgPubHandle.publish(bri.cv2_to_imgmsg(semimg, 'bgr8'))
if args.origin and index < len(imgs):
imgPubHandle.publish(bri.cv2_to_imgmsg(cv2.imread(imgs[index]), 'bgr8'))
return sempcd
parser = argparse.ArgumentParser(description='Rebuild semantic point cloud')
parser.add_argument('-c','--config',help='The config file path, recommand use this method to start the tool')
parser.add_argument('-i','--input',type=argparse.FileType('rb'))
parser.add_argument('-m','--mode',choices=['indoor','outdoor'],help="Depend on the way to store the pickle file")
parser.add_argument('-f','--filters', default=None,nargs='+',type=int,help='Default to show all the classes, the meaning of each class refers to class.json')
parser.add_argument('-s','--save',default=None,help='Save to pcd file')
parser.add_argument('-t','--trajectory',default=None,help='Trajectory file, use to follow the camera')
parser.add_argument('--semantic',default=None,help='Semantic photos folder')
parser.add_argument('--origin',default=None,help='Origin photos folder')
args = parser.parse_args()
if args.config:
with open(args.config,'r') as f:
config = json.load(f)
args.input = (args.input or open(config['save_folder']+(config['mode'] == 'indoor' and '/indoor.pkl' or '/outdoor.pkl'),'rb'))
args.mode = (args.mode or config['mode'])
args.trajectory = (args.trajectory or config['save_folder']+'/pose.csv')
args.save = (args.save or config['save_folder']+'/result.pcd')
args.semantic = (args.semantic or config['save_folder']+'/sempics')
args.origin = (args.origin or config['save_folder']+'/originpics')
color_classes = get_colors(config['cmap'])
rospy.init_node('fix_distortion', anonymous=False, log_level=rospy.DEBUG)
odomPubHandle = rospy.Publisher('Odom',Odometry,queue_size = 5)
posePubHandle = rospy.Publisher('Pose',PoseStamped,queue_size = 5)
imgPubHandle = rospy.Publisher('Img',Image,queue_size = 5)
semanticCloudPubHandle = rospy.Publisher('SemanticCloud', PointCloud2, queue_size=5)
vecPubHandle = rospy.Publisher('VectorCloud', PointCloud2, queue_size=5)
testPubHandle = rospy.Publisher('TestCloud', PointCloud2, queue_size=5)
semimgPubHandle = rospy.Publisher('SemanticImg',Image,queue_size = 5)
savepcd = []
bri = CvBridge()
if args.semantic:
simgs = glob.glob(args.semantic+'/*')
simgs.sort(key = lambda x:int(re.findall('[0-9]{3,7}',x)[0]))
colors = color_classes.astype('uint8')
if args.origin:
imgs = glob.glob(args.origin+'/*')
imgs.sort(key = lambda x:int(re.findall('[0-9]{3,7}',x)[0]))
index = 0
br = tf.TransformBroadcaster()
if args.trajectory:
poses = np.loadtxt(args.trajectory, delimiter=',')
if args.mode == 'indoor':
sempcds = pickle.load(args.input)
for sempcd in sempcds:
pcd = process()
savepcd.append(pcd)
savepcd = np.concatenate(savepcd)
elif args.mode == 'outdoor':
try:
while True:
sempcd = pickle.load(args.input)
pcd = process()
savepcd.append(pcd)
except EOFError:
print('done')
savepcd = np.concatenate(savepcd)
if args.save is not None:
save_nppc(savepcd,args.save)