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jfxmap_python
Commit 053bf233 authored by oscar's avatar oscar
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Showing with 640 additions and 10 deletions
script/trans_and_export_data.py
View file @ 053bf233
......@@ -460,6 +460,7 @@ class NumpyEncoder(json.JSONEncoder):
g_saveFileType = 1 #0为pcd文件，1是bin文件
g_showCloud = 0 #0不显示，1显示
g_pcd_info = {}
def Save_Cloud_File(cloud,json):
#去除boxx内的点
......@@ -504,7 +505,7 @@ def Save_Cloud_File(cloud,json):
axis_pcd = o3d.geometry.TriangleMesh.create_coordinate_frame(size=5, origin=[0, 0, 0])
drawpointcloud += [axis_pcd]
custom_draw_geometry_with_key_callback(drawpointcloud, "")
g_pcd_info[cloud["pcdName"]] = len(cloud["boxes"])
......@@ -532,10 +533,14 @@ def Check_Add_Cloud_box(save_cloud,bbox,pcd,pcdName,path,index):
break;
if add_idx == -1 :
#需要保存数据了
if index > 1 and len(save_cloud) >= 20 and len(save_cloud[0]["boxes"]) >= 5 :
if index > 1 and len(save_cloud) >= 50 and len(save_cloud[0]["boxes"]) >= 10 :
save_cloud[0]["isSave"] = 1
elif index > 1 and len(save_cloud) > 0 and len(save_cloud[0]["boxes"]) > 20 :
save_cloud[0]["isSave"] = 1
elif index > 1 and len(save_cloud) > 0 and len(save_cloud[0]["boxes"]) > 10 :
elif index > 1 and len(save_cloud) >= 100 :
save_cloud[0]["isSave"] = 1
if len(save_cloud[0]["boxes"]) < 10:
print("save cloud file = ",save_cloud[0]["pcdName"], " box num = ", len(save_cloud[0]["boxes"]))
if add_idx == -1:
new_cloud = {}
new_cloud["np_pcd"] = np.array(pcd.points)
......@@ -634,10 +639,10 @@ if __name__ == '__main__':
for bbox in bboxes:
# exportCenterBL = get_loc_from_origin([bbox[4][0], bbox[4][1], bbox[4][2]], 0, generate_Trans)
lidar_loc_ex,out_BL_ex,exportCenterBL = get_loc([bbox[4][0], bbox[4][1], bbox[4][2],bbox[6][0],bbox[6][1],bbox[6][2],bbox[2]], 0, generate_Trans,generate_kitti2origin)
print(exportCenterBL)
# print(exportCenterBL)
angle2 = car_yaw_cal(generate_car_yaw_cal_angle,bbox[2])
mapInfoExport = get_map_data(exportCenterBL[0],exportCenterBL[1],angle2)
print("call get ex data isInMap = ",mapInfoExport)
# print("call get ex data isInMap = ",mapInfoExport)
if mapInfoExport[0] != 1:
continue;
laneAngle = mapInfoExport[10];
......@@ -646,8 +651,8 @@ if __name__ == '__main__':
detaAngel -= 360
while detaAngel < -180:
detaAngel += 360
print("angle = ",angle2," laneAngle = ",laneAngle," detaAngel = ",detaAngel)
if abs(detaAngel) > 30:
# print("angle = ",angle2," laneAngle = ",laneAngle," detaAngel = ",detaAngel)
if bbox[0] != "pedestrian" and abs(detaAngel) > 30:
continue;
#获取到汽车点云符合条件，可以添加到新点云里
# f1.write('%f,%f,%f,%f,%f,%f,%f,%f,%f,%f\n'%(exportCenterBL[0],exportCenterBL[1],bbox[4][0], bbox[4][1], bbox[4][2],bbox[6][0], bbox[6][1], bbox[6][2],angle2,laneAngle))
......@@ -666,15 +671,17 @@ if __name__ == '__main__':
dz = np.array([0,0,delta_z])
pickcloud = np.add(pickcloud,dz)
print(pickcloud)
# print(pickcloud)
bbox[1] += dz
Add_Cloud_box(save_cloud_list,idx,bbox,pickcloud,save_json)
if isDeal == 1:
index += 1
if index >= 10:
break
if index % 10 == 0:
print("while index = ",index)
# if index >= 10:
# break
else:
isStop = 1
for cloud in save_cloud_list:
......@@ -682,6 +689,9 @@ if __name__ == '__main__':
jsn_path = os.path.join(generate_root_path,generate_child_dir + ".json")
with open(jsn_path,'w') as file_obj:
json.dump(save_json,file_obj,cls=NumpyEncoder)
for file, num in g_pcd_info.items():
if num < 10:
print("file = ",file,", box num = ",num)
# for dir in dirs:
# if os.path.isdir(origin_root_path + "/" + dir) == False:
# continue
......
script/trans_data_load.py 0 → 100644
View file @ 053bf233
import os
import open3d as o3d
import json
import glob
import numpy as np
import math
import random
import shutil as sh
import copy
from scipy.spatial.transform import Rotation as R
import cv2
from scipy.spatial import Delaunay
import scipy
from utils.getloc_coll import get_loc, get_world_loc,get_loc_from_origin
from easydict import EasyDict
import yaml
from jfxmap_lib.jfxmap import init_jfxmap, get_map_data
from utils.compute_yaw import compute_yaw,car_yaw_cal
from seg_ww_ground import ground_segmentation
def compute_pitch(A,B,C):
origin_direction = [0,0,1]
nor = [A,B,C]
angle = np.arccos(np.dot(origin_direction,nor) / np.linalg.norm(nor))
print(angle,angle* 180/ np.pi)
return angle
#绕z轴旋转：欧拉角到旋转矩阵
def rotz(t):
"""Rotation about the z-axis."""
c = np.cos(t)
s = np.sin(t)
return np.array([[c, -s, 0],
[s, c, 0],
[0, 0, 1]])
def compute_rot(A,B,C):
origin_direction = np.array([0,0,1])
nor = np.array([A,B,C])
mod = np.linalg.norm(nor)
nor = nor / mod
angle = np.arccos(np.dot(nor,origin_direction))
print(angle,angle* 180/ np.pi)
p_rot = np.cross(nor,origin_direction)
p_rot = p_rot / np.linalg.norm(p_rot)
sin_ang = np.sin(angle)
cos_ang = np.cos(angle)
rotMat = np.zeros([4,4])
rotMat[0,0] = cos_ang + p_rot[0] * p_rot[0] * (1 - cos_ang)
rotMat[0,1] = p_rot[0] * p_rot[1] * ( 1 - cos_ang - p_rot[2] * sin_ang)
rotMat[0,2] = p_rot[1] * sin_ang + p_rot[0] * p_rot[2] * (1-cos_ang)
rotMat[1,0] = p_rot[2] * sin_ang + p_rot[0] * p_rot[1] * (1 - cos_ang)
rotMat[1,1] = cos_ang + p_rot[1] * p_rot[1] * (1 - cos_ang)
rotMat[1,2] = -p_rot[0] * sin_ang + p_rot[1] * p_rot[2] * ( 1 - cos_ang)
rotMat[2,0] = -p_rot[1] * sin_ang + p_rot[0] * p_rot[2] * (1 - cos_ang)
rotMat[2,1] = p_rot[0] * sin_ang + p_rot[1] * p_rot[2] * ( 1 - cos_ang)
rotMat[2,2] = cos_ang + p_rot[2] * p_rot[2] * (1 - cos_ang)
rotMat[3,3] = 1
return angle,rotMat
rotMat = {}
def gen_rotMat():
global rotMat
#生成N7_1这段数据的全局转换矩阵
A,B,C,D = 5.10151553e-04,1.30740918e-03,1.89664435e-01,9.81847968e-01
#A,B,C,D = -9.39865821e-04, 1.04783823e-03, 1.85715376e-01, 9.82602574e-01
ang,hori_rotMat = compute_rot(A,B,C)
print(ang,hori_rotMat)
#ext_theta = 0.5 * np.pi
ext_theta = 90 / 180.0 * np.pi
kitti_Rot = np.zeros([4,4])
kitti_Rot[:3,:3] = rotz(ext_theta)
kitti_Rot[2,3] = -1
kitti_Rot[3,3] = 1
print(kitti_Rot)
tmp = np.dot(kitti_Rot,hori_rotMat)
rotMat['N7_1'] = tmp
print(tmp)
inverse_tmp = np.linalg.inv(tmp)
print(inverse_tmp)
N2_1_mat = np.array([[0.05045908306549516, -0.997741728436212, 0.04433197799906369, 0.0],
[0.9959209468126726, 0.05359234819149573, 0.07259013648610471, 0.0], [-0.0748020630460102,
0.04048831377621809, 0.9963761076077744, 0.2859260540868016], [0.0, 0.0, 0.0,
1.0]])
rotMat['N2_1'] = N2_1_mat
N2_2_mat = np.array([[0.07510626206747571, -0.9946697513726462, 0.07064796601834904, 0.0],
[0.9968676257061473, 0.07313389683723709, -0.03010597868179836, 0.0], [0.024778745271707994,
0.07268781767035316, 0.997046887034447, -0.7000000000000002], [0.0, 0.0, 0.0,
1.0]])
rotMat['N2_2'] = N2_2_mat
N2_3_mat = np.array([[0.07568437565247627, -0.9968308870840116, -0.02449607800031248,
0.0], [0.9971317454228593, 0.07565174868629727, 0.002257252902847979, 0.0],
[-0.0003969282768393359, -0.024596655789107878, 0.9996973776958381, 0.9000000000000004],
[0.0, 0.0, 0.0, 1.0]])
rotMat['N2_3'] = N2_3_mat
N2_4_mat = np.array([[0.07516802438907474, -0.996779910756179, 0.0279209172905782, 0.0],
[0.9961881280280445, 0.07630731297561882, 0.042265914901034, 0.0], [-0.044260385057339445,
0.024637441006419476, 0.9987161833149752, 0.09999999999999964], [0.0, 0.0, 0.0,
1.0]])
rotMat['N2_4'] = N2_4_mat
N3_1_mat = np.array([[0.10282893175566048, -0.9927983688656276, 0.06146226136206685, 0.0],
[0.9946815801416514, 0.10226437626202323, -0.01226994191701453, 0.0], [0.0058961784994428,
0.06239708427073043, 0.9980339868729993, 0.7844127162859094], [0.0, 0.0, 0.0,
1.0]])
rotMat['N3_1'] = N3_1_mat
N3_2_mat = np.array([[0.9995180204153945, 0.028485544660163925, -0.012341013341895022,
10.0], [-0.02812481342444661, 0.9991986491976914, 0.02847901528053492, 0.0],
[0.013142364122603362, -0.02811820027887063, 0.9995182064766736, 0.7000000000000002],
[0.0, 0.0, 0.0, 1.0]])
rotMat['N3_2'] = N3_2_mat
N4_1_mat = np.array([[-0.444341735964156, -0.8947739313681952, 0.044045810518876544, 0.0],
[0.8953720752482885, -0.4419458502434798, 0.054705688169885476, 0.0], [-0.029483360492542885,
0.06374540921880009, 0.9975305781065533, -0.09999999999999964], [0.0, 0.0, 0.0,
1.0]])
rotMat['N4_1'] = N4_1_mat
N4_2_mat = np.array([[0.9988523295913587, 0.04671359708300838, -0.010576555085760117,
10.0], [-0.04648606911938285, 0.9987018755893003, 0.020823281975752502, 0.0],
[0.011535555805583356, -0.02030772124045653, 0.9997272265024476, 0.7999999999999998],
[0.0, 0.0, 0.0, 1.0]])
rotMat['N4_2'] = N4_2_mat
N5_1_mat = np.array([[-0.08986377281989212, -0.9954705408424266, 0.031030704946254054,
0.0], [0.9958706079834863, -0.09021596031025624, -0.010139657780214443, 0.0],
[0.012893195460257717, 0.029991379097749554, 0.9994669993004465, 0.5747271756415682],
[0.0, 0.0, 0.0, 1.0]])
rotMat['N5_1'] = N5_1_mat
N5_2_mat = np.array([[-0.09764111729780293, -0.9944741458664936, 0.038566636046517395,
0.0], [0.9945996324606676, -0.09887670911792698, -0.03154310547669095, 0.0],
[0.035182144930937416, 0.03527845797532399, 0.9987580523234553, 1.2727123965813156],
[0.0, 0.0, 0.0, 1.0]])
rotMat['N5_2'] = N5_2_mat
N5_3_mat = np.array([[0.11376454710271973, -0.9928370794231558, 0.03649878826770976, 0.0],
[0.9918680257870178, 0.11560991762950971, 0.05321810187391049, 0.0], [-0.05705652674214411,
0.030147647805359817, 0.9979156638152981, -0.404451678835378], [0.0, 0.0, 0.0,
1.0]])
rotMat['N5_3'] = N5_3_mat
N6_1_mat = np.array([[0.9994973004805413, -0.03086596626934255, 0.007241440351907325,
10.0], [0.03082860898771179, 0.9995110809882194, 0.0052149640117460745, 0.0],
[-0.007398864777328993, -0.004989098918726587, 0.9999601820532585, 0.9000000000000004],
[0.0, 0.0, 0.0, 1.0]])
rotMat['N6_1'] = N6_1_mat
N6_2_mat = np.array([[0.9988722862129534, -0.04214125427326351, -0.021869396973478106,
10.0], [0.0409985649537926, 0.99789214163878, -0.05030299521200135, 0.0], [
0.023943130694148426, 0.04934965393845363, 0.9984945358632253, 0.9000000000000004],
[0.0, 0.0, 0.0, 1.0]])
rotMat['N6_2'] = N6_2_mat
N6_3_mat = np.array([[-0.04889424680162646, -0.9987854830493128, -0.006075481845301646,
0.0], [0.9987878973799329, -0.048858057728727665, -0.005968772264798985, 0.0],
[0.00566468684698101, -0.00635995636205657, 0.9999637304812602, -0.6553131698852468],
[0.0, 0.0, 0.0, 1.0]])
rotMat['N6_3'] = N6_3_mat
N8_1_mat = np.array([[0.06742395876958965, -0.9977243948218384, 0.00020435565303055666,
0.0], [0.9976344970690704, 0.06741513105551401, -0.013439135482274529, 0.0],
[0.013394776652850061, 0.001109991965790168, 0.9999096698583608, 0.7999999999999998],
[0.0, 0.0, 0.0, 1.0]])
rotMat['N8_1'] = N8_1_mat
N9_1_mat = np.array([[-0.19809481806349166, -0.9794499176397107, 0.03789857374567175,
0.0], [0.9799804151970427, -0.19712000934332657, 0.02796583177233762, 0.0],
[-0.01992056441529652, 0.04267974639159458, 0.998890189340813, 0.9980420980703562],
[0.0, 0.0, 0.0, 1.0]])
rotMat['N9_1'] = N9_1_mat
N9_2_mat = np.array([[-0.03297378854159967, -0.9961526066188378, 0.08119552694397052,
0.0], [0.9993813319576447, -0.03186786767058145, 0.014879258875841538, 0.0],
[-0.01223448420563244, 0.0816359194020958, 0.9965871231656551, -0.11066447945547608],
[0.0, 0.0, 0.0, 1.0]])
rotMat['N9_2'] = N9_2_mat
N10_1_mat = np.array([[-0.10138137931389381, -0.9920819078535333, 0.07413031794148582,
0.0], [0.9946640529021992, -0.10251230933181928, -0.011603805394292926, 0.0],
[0.019111195477614955, 0.07255835269044347, 0.9971810505932539, -0.09999999999999964],
[0.0, 0.0, 0.0, 1.0]])
rotMat['N10_1'] = N10_1_mat
N10_2_mat = np.array([[0.051858864388487746, -0.9973430294431006, 0.05116189798663601,
0.0], [0.9980146776291314, 0.049924120800806875, -0.0383964243008054, 0.0],
[0.03574019335646457, 0.05305152008684534, 0.9979519902256409, 1.259040914953271],
[0.0, 0.0, 0.0, 1.0]])
rotMat['N10_2'] = N10_2_mat
gen_rotMat()
#根据quart和计算出的原始坐标系的corners_3d来
#计算转换到新虚拟坐标系下的heading和heading平面
def my_compute_heading_plane(quart,corners_3d):
rot = R.from_quat(quart)
euler = rot.as_euler('zxy', degrees=False) #航向角
#点的方向按照逆时针顺序给出，保证算出的法向量垂直于平面向外
six_planes = [[corners_3d[1],corners_3d[5],corners_3d[6],corners_3d[2]],\
[corners_3d[0],corners_3d[4],corners_3d[5],corners_3d[1]],\
[corners_3d[3],corners_3d[7],corners_3d[4],corners_3d[0]],\
[corners_3d[2],corners_3d[6],corners_3d[7],corners_3d[3]],\
[corners_3d[0],corners_3d[1],corners_3d[2],corners_3d[3]],\
[corners_3d[7],corners_3d[6],corners_3d[5],corners_3d[4]] ]
normals = []
for it in six_planes:
it = np.array(it)
nor = np.cross(it[1] - it[2],it[0] - it[1])
normals.append(nor)
origin_direction = [1,0,0]
ret_ind = -1
for ind,nor in enumerate(normals):
direction = np.arccos(np.dot(origin_direction,nor) / np.linalg.norm(nor))
if abs(direction - abs(euler[0])) < 0.5:
ret_ind = ind
#print('debug:',direction,ind)
break
try:
assert(ret_ind == 0)
except:
print("wrong annotation")
return False, [], []
#new_heading = origin_lidar_heading_to_new_virtual(euler[0])
new_heading = euler[0]
new_heading_plane = copy.deepcopy(six_planes[ret_ind])
'''
nhp_len = len(new_heading_plane)
for ii in range(nhp_len):
#TODO cur lidar -> virtual kitti lidar
new_heading_plane[ii][0] = new_heading_plane[ii][0] - 15.0
new_heading_plane[ii][1] = new_heading_plane[ii][1] - 7.0
'''
return True,new_heading, new_heading_plane
#根据标注的3d框的center、quart、size计算3d bbox的8个顶点
#此处计算的是原始lidar坐标系下的坐标
def my_compute_box_3d(center, quart, size):
# P' = T * R * S * p
#其中p为box3d局部坐标，P'为变换后的世界坐标，假设初始box3d局部坐标系与lidar世界坐标系重合
#标准的box边长为1，正方体
x_corners = [-1,1,1,-1,-1,1,1,-1]
y_corners = [1,1,-1,-1,1,1,-1,-1]
z_corners = [1,1,1,1,-1,-1,-1,-1]
tmp = np.vstack([x_corners, y_corners, z_corners]) / 2
corners_3d = np.ones([4,8])
corners_3d[:3,:] = tmp
S = np.diag([size[0],size[1],size[2],1])
rot = R.from_quat(quart)
TR = rot.as_matrix()
Trans = np.zeros([4,4])
Trans[:3,:3] = TR
Trans[0,3] = center[0]
Trans[1,3] = center[1]
Trans[2,3] = center[2]
tmp4x4 = np.dot(S,corners_3d)
world_corners_3d = np.dot(Trans,tmp4x4)
return np.transpose(world_corners_3d[:3,:])
#读入原始lidar坐标系的标注信息
#将原始lidar坐标系转化为标准kitti坐标系
#转换方式：
# 所有corners_3d、center坐标按照“ln(x) = -l(y);ln(y) = l(x);ln(z) = l(z)”来转换
# 新的虚拟new_heading = np.pi + heading if -np.pi < heading < 0;
# 新的虚拟new_heading = heading - np.pi if 0 < heading < np.pi;
########
#保持不变的信息：
# category、size
def read_all_3d_bboxes(box3d,rotMat):
bboxes = []
for item in box3d:
center = [item['box3D']['translation']['x'],item['box3D']['translation']['y'],item['box3D']['translation']['z']]
center = np.array(center, np.float32)
center = np.transpose(np.dot(rotMat[:3,:3],np.transpose(center))) + rotMat[:3,3]
quart = [item['box3D']['rotation']['x'],item['box3D']['rotation']['y'],item['box3D']['rotation']['z'],item['box3D']['rotation']['w']]
origin_rot = R.from_quat(quart)
origin_rot_mat = origin_rot.as_matrix()
new_rot_mat = np.dot(rotMat[:3,:3],origin_rot_mat)
tmp = R.from_matrix(new_rot_mat)
quart = tmp.as_quat()
size = [item['box3D']['size']['x'],item['box3D']['size']['y'],item['box3D']['size']['z']]
size = np.array(size, np.float32)
if size[0] < 0.4 or size[1] < 0.4 or size[2] < 0.4:
continue
#计算原始lidar坐标系中的corners_3d
corners_3d = my_compute_box_3d(center, quart, size)
category = item['category']
assert_flag, heading, heading_plane = my_compute_heading_plane(quart,corners_3d)
if assert_flag == False:
return False, []
#以上为原始lidar坐标系的数据，下面要把bbox转成虚拟lidar坐标系的数据
#TODO cur lidar -> virtual kitti lidar
#new x = old x - 15 new y = old y - 7
#corners_3d[:,0] = corners_3d[:,0] - 15.0
#corners_3d[:,1] = corners_3d[:,1] - 7.0
#TODO cur lidar -> virtual kitti lidar
#center[0] = center[0] - 15.0
#center[1] = center[1] - 7.0
#特别注意，下面的heading和heading_plane是虚拟lidar坐标系下的heading
bboxes.append([category, corners_3d, heading, heading_plane, center, quart, size])
return True, bboxes
#按照文本来解析pcd点云文件
#解析过程中将原始lidar坐标系转换为新虚拟lidar坐标系
#转换方式：
# ln(x) = -l(y);ln(y) = l(x);ln(z) = l(z)
# intensity = intensity / 255.0
def parse_pandarmind_pcd(pandar_pcd_path,rotMat):
'''
pandar_fp = open(pandar_pcd_path,'r')
origin_data = []
pc = []
for line in pandar_fp.readlines()[11:]:
#TODO cur lidar -> virtual kitti lidar
x = float(line.split(' ')[1]) * -1 + 5 #将原点移到铁丝网后5米，这样可以把剔除掉的对象拉回来
y = float(line.split(' ')[0]) * 1 + 22 #将原点右移22米
z = float(line.split(' ')[2])
intensity = int(line.split(' ')[3])
origin_data.append([x,y,z,intensity/255.0])
pc.append([x,y,z])
origin_data = np.array(origin_data,dtype=np.float32)
pc = np.array(pc,dtype=np.float32)
pcd = o3d.geometry.PointCloud()
pcd.points = o3d.utility.Vector3dVector(pc)
return origin_data, pcd
'''
pcd = o3d.io.read_point_cloud(pandar_pcd_path)
converted_pcd = copy.deepcopy(pcd)
xyz = np.array(pcd.points)
# convert points to horizontal plane
xyz_h = copy.deepcopy(xyz)
xyz_h = np.transpose(np.dot(rotMat[:3,:3], np.transpose(xyz_h))) + rotMat[:3,3]
xyzi = np.zeros([xyz_h.shape[0],4])
xyzi[:,:3] = xyz_h[:,:]
converted_pcd.points = o3d.utility.Vector3dVector(xyzi[:,:3])
'''
xyzi[:,0] = xyz[:,0] - 15
xyzi[:,1] = xyz[:,1] - 7
xyzi[:,2] = xyz[:,2]
pcd.points = o3d.utility.Vector3dVector(xyzi[:,:3])
'''
return xyzi,pcd,converted_pcd
#根据corners_3d来生成open3d渲染要用的12条边
def gen_o3d_3dbboxes(corners_):
bbox_lines = [[0, 1], [1, 2], [2, 3], [3, 0], [4, 5], [5, 6], [6, 7], [7, 4], [0, 4], [1, 5], [2, 6], [3, 7]]
colors = [[1, 0, 0] for _ in range(len(bbox_lines))] #red
bbox = o3d.geometry.LineSet()
bbox.lines = o3d.utility.Vector2iVector(bbox_lines)
bbox.colors = o3d.utility.Vector3dVector(colors)
bbox.points = o3d.utility.Vector3dVector(corners_)
return bbox
def custom_draw_geometry_with_key_callback(pcd, file_path):
def save_pcd(vis):
#io.write_point_cloud(file_path, pcd, write_ascii=True)
#print(file_path)
return False
key_to_callback = {}
key_to_callback[ord("S")] = save_pcd
o3d.visualization.draw_geometries_with_key_callbacks(pcd, key_to_callback)
def in_hull(p, hull):
"""
:param p: (N, K) test points
:param hull: (M, K) M corners of a box
:return (N) bool
"""
try:
if not isinstance(hull, Delaunay):
hull = Delaunay(hull)
flag = hull.find_simplex(p) >= 0
except scipy.spatial.qhull.QhullError:
print('Warning: not a hull %s' % str(hull))
flag = np.zeros(p.shape[0], dtype=np.bool)
return flag
def merge_new_config(config, new_config):
for key, val in new_config.items():
if not isinstance(val, dict):
config[key] = val
continue
if key not in config:
config[key] = EasyDict()
merge_new_config(config[key], val)
return config
def read_yaml(path):
with open(path, 'r',encoding='utf-8') as f:
new_config = yaml.load(f, Loader=yaml.FullLoader)
config = EasyDict()
merge_new_config(config=config, new_config=new_config)
return config
def shadow_casting(points,bbox):
bbox_shp = bbox.shape
assert bbox_shp[0] == 8, " must have 8 points"
assert bbox_shp[1] == 3, " each point must have 3 coordinates"
far_points = bbox * 1.5
hull = np.concatenate((bbox,far_points),axis=0)
flag = in_hull(points,hull)
#print("X"*100, np.count_nonzero(flag))
#print(points[flag])
return flag
if __name__ == '__main__':
return
origin_root_path = "/home/oscar/ros/git/jfxmap_python/script/data/10-1/"
generate_root_path = "/home/oscar/ros/git/jfxmap_python/script/data/5-3/"
save_root_path = "/home/oscar/ros/git/jfxmap_python/script/"
origin_child_dir = "N10_1"
generate_child_dir = "N5_3"
origin_cfg_path = origin_root_path + "config/10-1.yaml"
origin_cfg = read_yaml(origin_cfg_path)
origin_Trans=origin_cfg.TRACKING.TRANS
origin_Trans = np.array(origin_Trans)
origin_kitti2origin=origin_cfg.POINTPILLARS.KITTI2ORIGIN
origin_kitti2origin = np.array(origin_kitti2origin)
generate_cfg_path = generate_root_path + "config/5-3.yaml"
generate_cfg = read_yaml(generate_cfg_path)
generate_Trans=generate_cfg.TRACKING.TRANS
generate_Trans = np.array(generate_Trans)
generate_kitti2origin=generate_cfg.POINTPILLARS.KITTI2ORIGIN
generate_kitti2origin = np.array(generate_kitti2origin)
#读标注的json文件
anno = {}
saveName = {}
jsn_file = os.path.join(origin_root_path, "origin/" + origin_child_dir + ".json")
print(jsn_file)
p_dir = "/home/oscar/ros/git/jfxmap_python/jfxmap/"
f_path = "./maps/qichecheng/mapconfig.json"
ret = init_jfxmap(p_dir,f_path)
print(ret)
base_file_path = os.path.join(generate_root_path, "base/1633640721.417269000.pcd")
g_xyzi,g_pcd,g_converted_pcd = parse_pandarmind_pcd(base_file_path, rotMat[generate_child_dir])
# converted_points = np.array(g_converted_pcd.points)
# b = np.where( (converted_points[:,2] <= -5.0) & (converted_points[:,2] > -7) )
# cropped_cropped = converted_points[b]
# print(cropped_cropped.shape)
# seg,m = ground_segmentation(data=cropped_cropped[:,:3])
m = [ 1.11119401e-03, -8.66741252e-04, 1.60358817e-01, 9.87057781e-01]
g_np_cloud = np.array(g_converted_pcd.points)
#g_np_flag = np.ones([g_np_cloud.shape[0]], dtype=bool)
vis = True
with open(jsn_file, 'r',encoding='utf-8',errors='ignore') as fp:
jsn = json.load(fp)
annos = jsn["annotations"]
for an in annos:
fileuri = "origin/pcds/" + an['fileuri'].split("/")[-1]
fileuri_path = os.path.join(origin_root_path,fileuri)
# print(fileuri_path)
if os.path.isfile(fileuri_path):
print(fileuri_path," ",fileuri)
anno[fileuri_path] = an['labels_box3D']
pcdname = an['fileuri'].split("/")[-1]
saveName[fileuri_path] = pcdname[0:pcdname.find('.pcd')]
num = 1
for file_path,boxx in anno.items():
svaeFile = save_root_path + saveName[file_path] + ".csv"
f1 = open(svaeFile, 'w')
f1.write('lat,lon,x,y,z,l,w,h,rot_y,laneAngle\n')
# print(file_path)
# print(boxx)
# bbox_flag,bboxes = read_all_3d_bboxes(boxx, np.identity(4))
bbox_flag,bboxes = read_all_3d_bboxes(boxx, rotMat[origin_child_dir])
if bbox_flag == False or len(bboxes) < 1:
continue
# print(bbox_flag)
# print(bboxes)
# 地面转成水平面，地面点的z值几乎差不多
xyzi,pcd,converted_pcd = parse_pandarmind_pcd(file_path, rotMat[origin_child_dir])
print(xyzi)
print(pcd)
print(converted_pcd)
exportDrawCloud = []
xyz = np.array(converted_pcd.points)
savecloud = o3d.geometry.PointCloud()
drawpointcloud = []
merge_geos = [converted_pcd]
for bbox in bboxes:
# out_center_BL = get_loc_from_origin([bbox[4][0], bbox[4][1], bbox[4][2]], 0, origin_Trans)
lidar_loc,out_BL,out_center_BL = get_loc([bbox[4][0], bbox[4][1], bbox[4][2],bbox[6][0],bbox[6][1],bbox[6][2],bbox[2]], 0, origin_Trans,origin_kitti2origin)
print(out_center_BL)
angle = car_yaw_cal(131.419987,bbox[2])
mapInfo = get_map_data(out_center_BL[0],out_center_BL[1],angle)
print("call get data isInMap = ",mapInfo)
# exportCenterBL = get_loc_from_origin([bbox[4][0], bbox[4][1], bbox[4][2]], 0, generate_Trans)
lidar_loc_ex,out_BL_ex,exportCenterBL = get_loc([bbox[4][0], bbox[4][1], bbox[4][2],bbox[6][0],bbox[6][1],bbox[6][2],bbox[2]], 0, generate_Trans,generate_kitti2origin)
print(exportCenterBL)
angle2 = car_yaw_cal(79.89299572540227,bbox[2])
mapInfoExport = get_map_data(exportCenterBL[0],exportCenterBL[1],angle2)
print("call get ex data isInMap = ",mapInfoExport)
if mapInfoExport[0] != 1:
continue;
laneAngle = mapInfoExport[10];
detaAngel = laneAngle - angle2;
while detaAngel > 180:
detaAngel -= 360
while detaAngel < -180:
detaAngel += 360
print("angle = ",angle2," laneAngle = ",laneAngle," detaAngel = ",detaAngel)
if abs(detaAngel) > 30:
continue;
f1.write('%f,%f,%f,%f,%f,%f,%f,%f,%f,%f\n'%(exportCenterBL[0],exportCenterBL[1],bbox[4][0], bbox[4][1], bbox[4][2],bbox[6][0], bbox[6][1], bbox[6][2],angle2,laneAngle))
flag = in_hull(xyz,bbox[1])
pickcloud = xyz[flag]
bottom_center = (bbox[1][5,:] + bbox[1][7,:]) / 2.0
ground_z = (-m[3] - m[0] * bottom_center[0] - m[1] * bottom_center[1]) / m[2]
delta_z = ground_z - bottom_center[2]
dz = np.array([0,0,delta_z])
addz = np.add(pickcloud,dz)
print(pickcloud)
print(addz)
point_cloud = o3d.geometry.PointCloud()
color_ = [[0, 1, 1]]
point_cloud.colors = o3d.utility.Vector3dVector(color_)
point_cloud.points = o3d.utility.Vector3dVector(addz)
drawpointcloud += [point_cloud]
exportDrawCloud += [point_cloud]
savecloud += point_cloud
drawboxesorgin = gen_o3d_3dbboxes(bbox[1])
bbox[1] += dz
drawboxes = gen_o3d_3dbboxes(bbox[1])
drawpointcloud += [drawboxes]
exportDrawCloud += [drawboxes]
#去除boxx内的点
flag_del = shadow_casting(g_np_cloud,bbox[1])
#g_np_flag = g_np_flag & (~flag_del)
g_np_cloud = g_np_cloud[~flag_del]
#tmp = g_converted_pcd.select_by_index(flag_del)
#g_converted_pcd = tmp
#渲染bbox
merge_geos += [drawboxesorgin]
heading_point = (bbox[3][0] + bbox[3][2]) / 2
heading_pcd = o3d.geometry.PointCloud()
heading_pcd.points = o3d.utility.Vector3dVector(np.array([heading_point]))
colors = [[0, 1, 0]]
heading_pcd.colors = o3d.utility.Vector3dVector(colors)
#渲染车辆形式方向的点
merge_geos += [heading_pcd]
drawpointcloud += [heading_pcd]
exportDrawCloud += [heading_pcd]
# savecloud += heading_pcd
pcd_g_np = o3d.geometry.PointCloud()
pcd_g_np.points = o3d.utility.Vector3dVector(g_np_cloud)
savecloud += pcd_g_np
exportDrawCloud += [pcd_g_np]
f1.close()
if vis:
axis_pcd = o3d.geometry.TriangleMesh.create_coordinate_frame(size=5, origin=[0, 0, 0])
merge_geos += [axis_pcd]
drawpointcloud += [axis_pcd]
exportDrawCloud += [axis_pcd]
#pcd.paint_uniform_color([1.00, 0, 0])
savefile = "/home/oscar/ros/git/jfxmap_python/pointcloud" + str(num) + ".pcd"
o3d.io.write_point_cloud(savefile,savecloud)
num += 1
#custom_draw_geometry_with_key_callback(merge_geos, file_path)
# custom_draw_geometry_with_key_callback(drawpointcloud, file_path)
custom_draw_geometry_with_key_callback(exportDrawCloud, file_path)
# pcd_list = glob.glob("D:/work/git_workspace/jfxmap_python/script/data_aug_samples/10-1_pcds/*.pcd")
# print(pcd_list)
\ No newline at end of file
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