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get_icp_pt2pl_err.py
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get_icp_pt2pl_err.py
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import open3d as o3d
import numpy as np
import time
import copy
from sklearn.neighbors import KDTree
import matplotlib.pyplot as plt
def file2matrix(filename):
fr = open(filename)
numberOfLines = len(fr.readlines()) #get the number of lines in the file
trans = np.eye(4) #prepare matrix to return
truth = [] #prepare labels return
fr = open(filename)
index = 0
for line in fr.readlines():
line = line.strip()
# listFromLine = line.split('\t')
listFromLine = line.split()
listFromLine = [float(x) for x in listFromLine]
if(index % 5 ==0):
index = 0
elif(index % 5 ==1):
trans[0, :] = np.array(listFromLine)
elif(index % 5 ==2):
trans[1,:] = np.array(listFromLine)
elif(index % 5 ==3):
trans[2,:] = np.array(listFromLine)
elif(index % 5 ==4):
trans[3,:] = np.array(listFromLine)
truth.append(trans.copy())#这里不用copy的话，，，每个元素都是一样的
index += 1
return truth
if __name__ == '__main__':
root_path = '/Bill/DataSet/RedWood/'
dataset_names = ['loft', 'lobby', 'apartment','bedroom','boardroom']
root_save_path = '/icp_pt2pl/src2ref'
dataset_numbers = [252,199,319,219,243]
# for i in range(len(dataset_names)):
for i in range(1):
file_path = root_path + dataset_names[i]
end = dataset_numbers[i]
save_path = dataset_names[i] + root_save_path
print(file_path)
groud_truth = file2matrix(file_path + '/reg_output.log')
voxel_size = 0.05 # means 5cm for this dataset
err_R = []
err_T = []
trans_all = []
fail_list = []
start = 0
# end = 251
for j in range(start, end):
print(
'j',j
)
# index_src = j + 1
# index_ref = j
index_src = j
index_ref = j + 1
current_transformation = np.identity(4)
source_show = o3d.io.read_point_cloud(file_path + "/mesh_%s.ply"%(index_src))
target_show = o3d.io.read_point_cloud(file_path + "/mesh_%s.ply"%(index_ref))
source = source_show.voxel_down_sample(voxel_size)
target = target_show.voxel_down_sample(voxel_size)
source.estimate_normals(
o3d.geometry.KDTreeSearchParamHybrid(radius=0.15, max_nn=30))
target.estimate_normals(
o3d.geometry.KDTreeSearchParamHybrid(radius=0.15, max_nn=30))
result_icp = o3d.pipelines.registration.registration_icp(
source, target, 0.2, current_transformation, # 后边这个距离阈值，就是大于这个距离的就去掉不计算
o3d.pipelines.registration.TransformationEstimationPointToPlane())
print(result_icp.transformation)
total_trans = result_icp.transformation
R = total_trans[:3,:3].reshape(3,3)
t = total_trans[:3,3].reshape(-1,1)
if index_src > index_ref:
err_R.append(np.arccos((np.trace(R.T @ groud_truth[j][:3,:3]) - 1) / 2) * 180 / np.pi )
err_T.append(np.linalg.norm(t - groud_truth[j][:3,3].reshape(-1,1), ord=2,axis=0))
trans_all.append((total_trans))
else:
err_R.append( np.arccos( (np.trace(R @ groud_truth[j][:3,:3] ) - 1) / 2) * 180 / np.pi )
err_T.append(np.linalg.norm(-R.T @ t - groud_truth[j][:3,3].reshape(-1,1), ord=2,axis=0))
trans_all.append((total_trans))
# print(total_trans[:3,:3] @ groud_truth[j][:3,:3], np.trace(total_trans[:3,:3] @ groud_truth[j][:3,:3] - np.eye(3)))
# print(total_trans, groud_truth[j])
print('err_R err_T', err_R[j - start], err_T[j - start],total_trans)
if index_src > index_ref:
#
# location = str(start) + '_' + str(end)
err_all = [err_R, err_T]
plt.figure("ERR_R ref2src") # 图像窗口名称
plt.plot(err_R)
plt.savefig(save_path + '/%s_%s_err_All_ref2src.jpg'%(start, end))
plt.show()
plt.figure("ERR_T ref2src") # 图像窗口名称
plt.plot(err_T)
plt.savefig(save_path + '/%s_%s_trans_all_ref2src.jpg' % (start, end))
plt.show()
np.savetxt(save_path + '/%s_%s_fail_list_ref2src.txt'%(start, end), fail_list)
np.save(save_path + '/%s_%s_err_All_ref2src.npy'%(start, end), err_all)
np.savetxt(save_path + '/%s_%s_err_All_ref2src.txt' % (start, end), err_all)
np.save(save_path + '/%s_%s_trans_all_ref2src.npy'%(start, end), trans_all)
np.savetxt(save_path + '/%s_%s_trans_all_ref2src.txt'%(start, end), np.array(trans_all).reshape(-1,4),fmt='%0.8f')
else:
err_all = [err_R, err_T]
plt.figure("ERR_R src2ref") # 图像窗口名称
plt.plot(err_R)
plt.savefig(save_path + '/%s_%serr_All_src2ref.jpg'%(start, end))
plt.show()
plt.figure("ERR_T src2ref") # 图像窗口名称
plt.plot(err_T)
plt.savefig(save_path + '/%s_%strans_all_src2ref.jpg' % (start, end))
plt.show()
np.savetxt(save_path + '/%s_%s_fail_list_src2ref.txt'%(start, end), fail_list)
np.savetxt(save_path + '/%s_%serr_All_src2ref.txt' % (start, end), err_all)
np.save(save_path + '/%s_%serr_All_src2ref.npy'%(start, end), err_all)
np.save(save_path + '/%s_%strans_all_src2ref.npy'%(start, end), trans_all)
np.savetxt(save_path + '/%s_%strans_all_src2ref.txt'%(start, end), np.array(trans_all).reshape(-1,4),fmt='%0.8f')