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get_FGR_err.py
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get_FGR_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 draw_registration_result(source, target, transformation):
source_temp = copy.deepcopy(source)
target_temp = copy.deepcopy(target)
source_temp.estimate_normals(
o3d.geometry.KDTreeSearchParamHybrid(radius=0.15, max_nn=30))
target_temp.estimate_normals(
o3d.geometry.KDTreeSearchParamHybrid(radius=0.15, max_nn=30))
source_temp.paint_uniform_color([1, 0.706, 0])
target_temp.paint_uniform_color([0, 0.651, 0.929])
source_temp.transform(transformation)
o3d.visualization.draw_geometries([source_temp, target_temp])
def execute_fast_global_registration(source_down, target_down, source_fpfh,
target_fpfh, voxel_size):
distance_threshold = voxel_size * 0.5
print(":: Apply fast global registration with distance threshold %.3f" \
% distance_threshold)
result = o3d.pipelines.registration.registration_fast_based_on_feature_matching(
source_down, target_down, source_fpfh, target_fpfh,
o3d.pipelines.registration.FastGlobalRegistrationOption(
maximum_correspondence_distance=distance_threshold))
return result
def preprocess_point_cloud(pcd, voxel_size):
print(":: Downsample with a voxel size %.3f." % voxel_size)
pcd_down = pcd.voxel_down_sample(voxel_size)
radius_normal = voxel_size * 2
print(":: Estimate normal with search radius %.3f." % radius_normal)
pcd_down.estimate_normals(
o3d.geometry.KDTreeSearchParamHybrid(radius=radius_normal, max_nn=30))
radius_feature = voxel_size * 5
print(":: Compute FPFH feature with search radius %.3f." % radius_feature)
pcd_fpfh = o3d.pipelines.registration.compute_fpfh_feature(
pcd_down,
o3d.geometry.KDTreeSearchParamHybrid(radius=radius_feature, max_nn=100))
return pcd_down, pcd_fpfh
def prepare_dataset(source, target, voxel_size):
source_down, source_fpfh = preprocess_point_cloud(source, voxel_size)
target_down, target_fpfh = preprocess_point_cloud(target, voxel_size)
# print('source_fpfh', source_fpfh.num, target_fpfh.num)
# print('source_fpfh',source_fpfh,np.asarray(source_fpfh.data))
return source, target, source_down, target_down, source_fpfh, target_fpfh
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__':
file_path = '/Bill/DataSet/RedWood/loft/'
save_path = 'loft/fgr/src2ref'
end = 252
# file_path = '/Bill/DataSet/RedWood/lobby/'
# save_path = 'lobby/fgr/src2ref'
# end = 199
# file_path = '/Bill/DataSet/RedWood/apartment/'
# save_path = 'apartment/fgr/src2ref'
# end = 319
# file_path = '/Bill/DataSet/RedWood/bedroom/'
# save_path = 'bedroom/fgr/src2ref'
# end = 219
# file_path = '/Bill/DataSet/RedWood/boardroom/'
# save_path = 'boardroom/fgr/src2ref'
# end = 243
# file_path = 'D:\PointCloud_DataSet\RedWood\\loft\\loft\\'
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
print(
'file_path', file_path
)
# end = 244
for j in range(start, end):
print(
'j',j
)
# index_src = j + 1
# index_ref = j
index_src = j
index_ref = j + 1
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, target, source_down, target_down, source_fpfh, target_fpfh = prepare_dataset(source_show, target_show, voxel_size)
result_fast = execute_fast_global_registration(source_down, target_down,
source_fpfh, target_fpfh,
voxel_size)
# print(result_fast.transformation)
total_trans = result_fast.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')