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evaluate_calib.py
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evaluate_calib.py
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# -------------------------------------------------------------------
# Copyright (C) 2020 Università degli studi di Milano-Bicocca, iralab
# Author: Daniele Cattaneo ([email protected])
# Released under Creative Commons
# Attribution-NonCommercial-ShareAlike 4.0 International License.
# http://creativecommons.org/licenses/by-nc-sa/4.0/
# -------------------------------------------------------------------
# Modified Author: Xudong Lv
# based on github.com/cattaneod/CMRNet/blob/master/evaluate_iterative_single_CALIB.py
import csv
import random
import open3d as o3
import cv2
import mathutils
# import matplotlib
# matplotlib.use('Qt5Agg')
import os
import matplotlib.pyplot as plt
import numpy as np
import torch
import torch.nn.functional as F
import torch.nn.parallel
import torch.utils.data
from sacred import Experiment
from sacred.utils import apply_backspaces_and_linefeeds
from skimage import io
from tqdm import tqdm
import time
from models.LCCNet import LCCNet
from DatasetLidarCamera import DatasetLidarCameraKittiOdometry
from quaternion_distances import quaternion_distance
from utils import (mat2xyzrpy, merge_inputs, overlay_imgs, quat2mat,
quaternion_from_matrix, rotate_back, rotate_forward,
tvector2mat)
from torch.utils.data import Dataset
from pykitti import odometry
import pandas as pd
from PIL import Image
from math import radians
from utils import invert_pose
from torchvision import transforms
# import matplotlib
# matplotlib.rc("font",family='AR PL UMing CN')
plt.rcParams['axes.unicode_minus'] = False
# plt.rc('font',family='Times New Roman')
font_EN = {'family': 'Times New Roman', 'weight': 'normal', 'size': 16}
font_CN = {'family': 'AR PL UMing CN', 'weight': 'normal', 'size': 16}
plt_size = 10.5
ex = Experiment("LCCNet-evaluate-iterative")
ex.captured_out_filter = apply_backspaces_and_linefeeds
os.environ['CUDA_VISIBLE_DEVICES'] = '1'
# noinspection PyUnusedLocal
@ex.config
def config():
dataset = 'kitti/odom'
data_folder = '/data/kitti_odometry/dataset'
test_sequence = 0
use_prev_output = False
max_t = 1.5
max_r = 20.
occlusion_kernel = 5
occlusion_threshold = 3.0
network = 'Res_f1'
norm = 'bn'
show = False
use_reflectance = False
weight = None # List of weights' path, for iterative refinement
save_name = None
# Set to True only if you use two network, the first for rotation and the second for translation
rot_transl_separated = False
random_initial_pose = False
save_log = False
dropout = 0.0
max_depth = 80.
iterative_method = 'multi_range' # ['multi_range', 'single_range', 'single']
output = '../output'
save_image = False
outlier_filter = True
outlier_filter_th = 10
out_fig_lg = 'EN' # [EN, CN]
weights = [
'./pretrained/kitti_iter1.tar',
'./pretrained/kitti_iter2.tar',
'./pretrained/kitti_iter3.tar',
'./pretrained/kitti_iter4.tar',
'./pretrained/kitti_iter5.tar',
]
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
EPOCH = 1
def _init_fn(worker_id, seed):
seed = seed + worker_id + EPOCH * 100
print(f"Init worker {worker_id} with seed {seed}")
torch.manual_seed(seed)
np.random.seed(seed)
random.seed(seed)
def get_2D_lidar_projection(pcl, cam_intrinsic):
pcl_xyz = cam_intrinsic @ pcl.T
pcl_xyz = pcl_xyz.T
pcl_z = pcl_xyz[:, 2]
pcl_xyz = pcl_xyz / (pcl_xyz[:, 2, None] + 1e-10)
pcl_uv = pcl_xyz[:, :2]
return pcl_uv, pcl_z
def lidar_project_depth(pc_rotated, cam_calib, img_shape):
pc_rotated = pc_rotated[:3, :].detach().cpu().numpy()
cam_intrinsic = cam_calib.numpy()
pcl_uv, pcl_z = get_2D_lidar_projection(pc_rotated.T, cam_intrinsic)
mask = (pcl_uv[:, 0] > 0) & (pcl_uv[:, 0] < img_shape[1]) & (pcl_uv[:, 1] > 0) & (
pcl_uv[:, 1] < img_shape[0]) & (pcl_z > 0)
pcl_uv = pcl_uv[mask]
pcl_z = pcl_z[mask]
pcl_uv = pcl_uv.astype(np.uint32)
pcl_z = pcl_z.reshape(-1, 1)
depth_img = np.zeros((img_shape[0], img_shape[1], 1))
depth_img[pcl_uv[:, 1], pcl_uv[:, 0]] = pcl_z
depth_img = torch.from_numpy(depth_img.astype(np.float32))
depth_img = depth_img.cuda()
depth_img = depth_img.permute(2, 0, 1)
pc_valid = pc_rotated.T[mask]
return depth_img, pcl_uv, pc_valid
@ex.automain
def main(_config, seed):
global EPOCH, weights
if _config['weight'] is not None:
weights = _config['weight']
if _config['iterative_method'] == 'single':
weights = [weights[0]]
dataset_class = DatasetLidarCameraKittiOdometry
# dataset_class = DatasetTest
img_shape = (384, 1280)
input_size = (256, 512)
# split = 'test'
if _config['random_initial_pose']:
split = 'test_random'
if _config['test_sequence'] is None:
raise TypeError('test_sequences cannot be None')
else:
if isinstance(_config['test_sequence'], int):
_config['test_sequence'] = f"{_config['test_sequence']:02d}"
dataset_val = dataset_class(_config['data_folder'], max_r=_config['max_r'], max_t=_config['max_t'],
split='test', use_reflectance=_config['use_reflectance']
,config=_config, img_shape = img_shape)
np.random.seed(seed)
torch.random.manual_seed(seed)
def init_fn(x):
return _init_fn(x, seed)
num_worker = 0
batch_size = 1
TestImgLoader = torch.utils.data.DataLoader(dataset=dataset_val,
shuffle=False,
batch_size=batch_size,
num_workers=num_worker,
worker_init_fn=init_fn,
collate_fn=merge_inputs,
drop_last=False,
pin_memory=False)
print(len(TestImgLoader))
models = [] # iterative model
for i in range(len(weights)):
# network choice and settings
if _config['network'].startswith('Res'):
feat = 1
md = 4
split = _config['network'].split('_')
for item in split[1:]:
if item.startswith('f'):
feat = int(item[-1])
elif item.startswith('md'):
md = int(item[2:])
assert 0 < feat < 7, "Feature Number from PWC have to be between 1 and 6"
assert 0 < md, "md must be positive"
model = LCCNet(input_size, use_feat_from=feat, md=md,
use_reflectance=_config['use_reflectance'], dropout=_config['dropout'])
else:
raise TypeError("Network unknown")
checkpoint = torch.load(weights[i], map_location='cpu')
saved_state_dict = checkpoint['state_dict']
model.load_state_dict(saved_state_dict)
model = model.to(device)
model.eval()
models.append(model)
if _config['save_log']:
log_file = f'./results_for_paper/log_seq{_config["test_sequence"]}.csv'
log_file = open(log_file, 'w')
log_file = csv.writer(log_file)
header = ['frame']
for i in range(len(weights) + 1):
header += [f'iter{i}_error_t', f'iter{i}_error_r', f'iter{i}_error_x', f'iter{i}_error_y',
f'iter{i}_error_z', f'iter{i}_error_r', f'iter{i}_error_p', f'iter{i}_error_y']
log_file.writerow(header)
show = _config['show']
# save image to the output path
_config['output'] = os.path.join(_config['output'], _config['iterative_method'])
rgb_path = os.path.join(_config['output'], 'rgb')
if not os.path.exists(rgb_path):
os.makedirs(rgb_path)
depth_path = os.path.join(_config['output'], 'depth')
if not os.path.exists(depth_path):
os.makedirs(depth_path)
input_path = os.path.join(_config['output'], 'input')
if not os.path.exists(input_path):
os.makedirs(input_path)
gt_path = os.path.join(_config['output'], 'gt')
if not os.path.exists(gt_path):
os.makedirs(gt_path)
if _config['out_fig_lg'] == 'EN':
results_path = os.path.join(_config['output'], 'results_en')
elif _config['out_fig_lg'] == 'CN':
results_path = os.path.join(_config['output'], 'results_cn')
if not os.path.exists(results_path):
os.makedirs(results_path)
pred_path = os.path.join(_config['output'], 'pred')
for it in range(len(weights)):
if not os.path.exists(os.path.join(pred_path, 'iteration_'+str(it+1))):
os.makedirs(os.path.join(pred_path, 'iteration_'+str(it+1)))
# save pointcloud to the output path
pc_lidar_path = os.path.join(_config['output'], 'pointcloud', 'lidar')
if not os.path.exists(pc_lidar_path):
os.makedirs(pc_lidar_path)
pc_input_path = os.path.join(_config['output'], 'pointcloud', 'input')
if not os.path.exists(pc_input_path):
os.makedirs(pc_input_path)
pc_pred_path = os.path.join(_config['output'], 'pointcloud', 'pred')
if not os.path.exists(pc_pred_path):
os.makedirs(pc_pred_path)
errors_r = []
errors_t = []
errors_t2 = []
errors_xyz = []
errors_rpy = []
all_RTs = []
mis_calib_list = []
total_time = 0
prev_tr_error = None
prev_rot_error = None
for i in range(len(weights) + 1):
errors_r.append([])
errors_t.append([])
errors_t2.append([])
errors_rpy.append([])
for batch_idx, sample in enumerate(tqdm(TestImgLoader)):
N = 100 # 500
# if batch_idx > 200:
# break
log_string = [str(batch_idx)]
lidar_input = []
rgb_input = []
lidar_gt = []
shape_pad_input = []
real_shape_input = []
pc_rotated_input = []
RTs = []
shape_pad = [0, 0, 0, 0]
outlier_filter = False
if batch_idx == 0 or not _config['use_prev_output']:
# Qui dare posizione di input del frame corrente rispetto alla GT
sample['tr_error'] = sample['tr_error'].cuda()
sample['rot_error'] = sample['rot_error'].cuda()
else:
sample['tr_error'] = prev_tr_error
sample['rot_error'] = prev_rot_error
for idx in range(len(sample['rgb'])):
# ProjectPointCloud in RT-pose
real_shape = [sample['rgb'][idx].shape[1], sample['rgb'][idx].shape[2], sample['rgb'][idx].shape[0]]
sample['point_cloud'][idx] = sample['point_cloud'][idx].cuda() # 变换到相机坐标系下的激光雷达点云
pc_lidar = sample['point_cloud'][idx].clone()
if _config['max_depth'] < 80.:
pc_lidar = pc_lidar[:, pc_lidar[0, :] < _config['max_depth']].clone()
depth_gt, uv_gt, pc_gt_valid = lidar_project_depth(pc_lidar, sample['calib'][idx], real_shape) # image_shape
depth_gt /= _config['max_depth']
if _config['save_image']:
# save the Lidar pointcloud
pcl_lidar = o3.PointCloud()
pc_lidar = pc_lidar.detach().cpu().numpy()
pcl_lidar.points = o3.Vector3dVector(pc_lidar.T[:, :3])
# o3.draw_geometries(downpcd)
o3.write_point_cloud(pc_lidar_path + '/{}.pcd'.format(batch_idx), pcl_lidar)
R = quat2mat(sample['rot_error'][idx])
T = tvector2mat(sample['tr_error'][idx])
RT_inv = torch.mm(T, R)
RT = RT_inv.clone().inverse()
pc_rotated = rotate_back(sample['point_cloud'][idx], RT_inv) # Pc` = RT * Pc
if _config['max_depth'] < 80.:
pc_rotated = pc_rotated[:, pc_rotated[0, :] < _config['max_depth']].clone()
depth_img, uv_input, pc_input_valid = lidar_project_depth(pc_rotated, sample['calib'][idx], real_shape) # image_shape
depth_img /= _config['max_depth']
if _config['outlier_filter'] and uv_input.shape[0] <= _config['outlier_filter_th']:
outlier_filter = True
else:
outlier_filter = False
if _config['save_image']:
# save the RGB input pointcloud
img = cv2.imread(sample['img_path'][0])
R = img[uv_input[:, 1], uv_input[:, 0], 0] / 255
G = img[uv_input[:, 1], uv_input[:, 0], 1] / 255
B = img[uv_input[:, 1], uv_input[:, 0], 2] / 255
pcl_input = o3.PointCloud()
pcl_input.points = o3.Vector3dVector(pc_input_valid[:, :3])
pcl_input.colors = o3.Vector3dVector(np.vstack((R, G, B)).T)
# o3.draw_geometries(downpcd)
o3.write_point_cloud(pc_input_path + '/{}.pcd'.format(batch_idx), pcl_input)
# PAD ONLY ON RIGHT AND BOTTOM SIDE
rgb = sample['rgb'][idx].cuda()
shape_pad = [0, 0, 0, 0]
shape_pad[3] = (img_shape[0] - rgb.shape[1]) # // 2
shape_pad[1] = (img_shape[1] - rgb.shape[2]) # // 2 + 1
rgb = F.pad(rgb, shape_pad)
depth_img = F.pad(depth_img, shape_pad)
depth_gt = F.pad(depth_gt, shape_pad)
rgb_input.append(rgb)
lidar_input.append(depth_img)
lidar_gt.append(depth_gt)
real_shape_input.append(real_shape)
shape_pad_input.append(shape_pad)
pc_rotated_input.append(pc_rotated)
RTs.append(RT)
if outlier_filter:
continue
lidar_input = torch.stack(lidar_input)
rgb_input = torch.stack(rgb_input)
rgb_resize = F.interpolate(rgb_input, size=[256, 512], mode="bilinear")
lidar_resize = F.interpolate(lidar_input, size=[256, 512], mode="bilinear")
if _config['save_image']:
out0 = overlay_imgs(rgb_input[0], lidar_input)
out0 = out0[:376, :1241, :]
cv2.imwrite(os.path.join(input_path, sample['rgb_name'][0]), out0[:, :, [2, 1, 0]]*255)
out1 = overlay_imgs(rgb_input[0], lidar_gt[0].unsqueeze(0))
out1 = out1[:376, :1241, :]
cv2.imwrite(os.path.join(gt_path, sample['rgb_name'][0]), out1[:, :, [2, 1, 0]]*255)
depth_img = depth_img.detach().cpu().numpy()
depth_img = (depth_img / np.max(depth_img)) * 255
cv2.imwrite(os.path.join(depth_path, sample['rgb_name'][0]), depth_img[0, :376, :1241])
if show:
out0 = overlay_imgs(rgb_input[0], lidar_input)
out1 = overlay_imgs(rgb_input[0], lidar_gt[0].unsqueeze(0))
cv2.imshow("INPUT", out0[:, :, [2, 1, 0]])
cv2.imshow("GT", out1[:, :, [2, 1, 0]])
cv2.waitKey(1)
rgb = rgb_input.to(device)
lidar = lidar_input.to(device)
rgb_resize = rgb_resize.to(device)
lidar_resize = lidar_resize.to(device)
target_transl = sample['tr_error'].to(device)
target_rot = sample['rot_error'].to(device)
# the initial calibration errors before sensor calibration
RT1 = RTs[0]
mis_calib = torch.stack(sample['initial_RT'])[1:]
mis_calib_list.append(mis_calib)
T_composed = RT1[:3, 3]
R_composed = quaternion_from_matrix(RT1)
errors_t[0].append(T_composed.norm().item())
errors_t2[0].append(T_composed)
errors_r[0].append(quaternion_distance(R_composed.unsqueeze(0),
torch.tensor([1., 0., 0., 0.], device=R_composed.device).unsqueeze(0),
R_composed.device))
# rpy_error = quaternion_to_tait_bryan(R_composed)
rpy_error = mat2xyzrpy(RT1)[3:]
rpy_error *= (180.0 / 3.141592)
errors_rpy[0].append(rpy_error)
log_string += [str(errors_t[0][-1]), str(errors_r[0][-1]), str(errors_t2[0][-1][0].item()),
str(errors_t2[0][-1][1].item()), str(errors_t2[0][-1][2].item()),
str(errors_rpy[0][-1][0].item()), str(errors_rpy[0][-1][1].item()),
str(errors_rpy[0][-1][2].item())]
# if batch_idx == 0.:
# print(f'Initial T_erorr: {errors_t[0]}')
# print(f'Initial R_erorr: {errors_r[0]}')
start = 0
# t1 = time.time()
# Run model
with torch.no_grad():
for iteration in range(start, len(weights)):
# Run the i-th network
t1 = time.time()
if _config['iterative_method'] == 'single_range' or _config['iterative_method'] == 'single':
T_predicted, R_predicted = models[0](rgb_resize, lidar_resize)
elif _config['iterative_method'] == 'multi_range':
T_predicted, R_predicted = models[iteration](rgb_resize, lidar_resize)
run_time = time.time() - t1
if _config['rot_transl_separated'] and iteration == 0:
T_predicted = torch.tensor([[0., 0., 0.]], device='cuda')
if _config['rot_transl_separated'] and iteration == 1:
R_predicted = torch.tensor([[1., 0., 0., 0.]], device='cuda')
# Project the points in the new pose predicted by the i-th network
R_predicted = quat2mat(R_predicted[0])
T_predicted = tvector2mat(T_predicted[0])
RT_predicted = torch.mm(T_predicted, R_predicted)
RTs.append(torch.mm(RTs[iteration], RT_predicted)) # inv(H_gt)*H_pred_1*H_pred_2*.....H_pred_n
if iteration == 0:
rotated_point_cloud = pc_rotated_input[0]
else:
rotated_point_cloud = rotated_point_cloud
rotated_point_cloud = rotate_forward(rotated_point_cloud, RT_predicted) # H_pred*X_init
depth_img_pred, uv_pred, pc_pred_valid = lidar_project_depth(rotated_point_cloud, sample['calib'][0], real_shape_input[0]) # image_shape
depth_img_pred /= _config['max_depth']
depth_pred = F.pad(depth_img_pred, shape_pad_input[0])
lidar = depth_pred.unsqueeze(0)
lidar_resize = F.interpolate(lidar, size=[256, 512], mode="bilinear")
if iteration == len(weights)-1 and _config['save_image']:
# save the RGB pointcloud
img = cv2.imread(sample['img_path'][0])
R = img[uv_pred[:, 1], uv_pred[:, 0], 0] / 255
G = img[uv_pred[:, 1], uv_pred[:, 0], 1] / 255
B = img[uv_pred[:, 1], uv_pred[:, 0], 2] / 255
pcl_pred = o3.PointCloud()
pcl_pred.points = o3.Vector3dVector(pc_pred_valid[:, :3])
pcl_pred.colors = o3.Vector3dVector(np.vstack((R, G, B)).T)
# o3.draw_geometries(downpcd)
o3.write_point_cloud(pc_pred_path + '/{}.pcd'.format(batch_idx), pcl_pred)
if _config['save_image']:
out2 = overlay_imgs(rgb_input[0], lidar)
out2 = out2[:376, :1241, :]
cv2.imwrite(os.path.join(os.path.join(pred_path, 'iteration_'+str(iteration+1)),
sample['rgb_name'][0]), out2[:, :, [2, 1, 0]]*255)
if show:
out2 = overlay_imgs(rgb_input[0], lidar)
cv2.imshow(f'Pred_Iter_{iteration}', out2[:, :, [2, 1, 0]])
cv2.waitKey(1)
# inv(H_init)*H_pred
T_composed = RTs[iteration + 1][:3, 3]
R_composed = quaternion_from_matrix(RTs[iteration + 1])
errors_t[iteration + 1].append(T_composed.norm().item())
errors_t2[iteration + 1].append(T_composed)
errors_r[iteration + 1].append(quaternion_distance(R_composed.unsqueeze(0),
torch.tensor([1., 0., 0., 0.], device=R_composed.device).unsqueeze(0),
R_composed.device))
# rpy_error = quaternion_to_tait_bryan(R_composed)
rpy_error = mat2xyzrpy(RTs[iteration + 1])[3:]
rpy_error *= (180.0 / 3.141592)
errors_rpy[iteration + 1].append(rpy_error)
log_string += [str(errors_t[iteration + 1][-1]), str(errors_r[iteration + 1][-1]),
str(errors_t2[iteration + 1][-1][0].item()), str(errors_t2[iteration + 1][-1][1].item()),
str(errors_t2[iteration + 1][-1][2].item()), str(errors_rpy[iteration + 1][-1][0].item()),
str(errors_rpy[iteration + 1][-1][1].item()), str(errors_rpy[iteration + 1][-1][2].item())]
# run_time = time.time() - t1
total_time += run_time
# final calibration error
all_RTs.append(RTs[-1])
prev_RT = RTs[-1].inverse()
prev_tr_error = prev_RT[:3, 3].unsqueeze(0)
prev_rot_error = quaternion_from_matrix(prev_RT).unsqueeze(0)
if _config['save_log']:
log_file.writerow(log_string)
# Yaw（偏航）：欧拉角向量的y轴
# Pitch（俯仰）：欧拉角向量的x轴
# Roll（翻滚）： 欧拉角向量的z轴
# mis_calib_input[transl_x, transl_y, transl_z, rotx, roty, rotz] Nx6
mis_calib_input = torch.stack(mis_calib_list)[:, :, 0]
if _config['save_log']:
log_file.close()
print("Iterative refinement: ")
for i in range(len(weights) + 1):
errors_r[i] = torch.tensor(errors_r[i]).abs() * (180.0 / 3.141592)
errors_t[i] = torch.tensor(errors_t[i]).abs() * 100
for k in range(len(errors_rpy[i])):
# errors_rpy[i][k] = torch.tensor(errors_rpy[i][k])
# errors_t2[i][k] = torch.tensor(errors_t2[i][k]) * 100
errors_rpy[i][k] = errors_rpy[i][k].clone().detach().abs()
errors_t2[i][k] = errors_t2[i][k].clone().detach().abs() * 100
print(f"Iteration {i}: \tMean Translation Error: {errors_t[i].mean():.4f} cm "
f" Mean Rotation Error: {errors_r[i].mean():.4f} degree")
print(f"Iteration {i}: \tMedian Translation Error: {errors_t[i].median():.4f} cm "
f" Median Rotation Error: {errors_r[i].median():.4f} degree")
print(f"Iteration {i}: \tStd. Translation Error: {errors_t[i].std():.4f} cm "
f" Std. Rotation Error: {errors_r[i].std():.4f} degree\n")
# translation xyz
print(f"Iteration {i}: \tMean Translation X Error: {errors_t2[i][0].mean():.4f} cm "
f" Median Translation X Error: {errors_t2[i][0].median():.4f} cm "
f" Std. Translation X Error: {errors_t2[i][0].std():.4f} cm ")
print(f"Iteration {i}: \tMean Translation Y Error: {errors_t2[i][1].mean():.4f} cm "
f" Median Translation Y Error: {errors_t2[i][1].median():.4f} cm "
f" Std. Translation Y Error: {errors_t2[i][1].std():.4f} cm ")
print(f"Iteration {i}: \tMean Translation Z Error: {errors_t2[i][2].mean():.4f} cm "
f" Median Translation Z Error: {errors_t2[i][2].median():.4f} cm "
f" Std. Translation Z Error: {errors_t2[i][2].std():.4f} cm \n")
# rotation rpy
print(f"Iteration {i}: \tMean Rotation Roll Error: {errors_rpy[i][0].mean(): .4f} degree"
f" Median Rotation Roll Error: {errors_rpy[i][0].median():.4f} degree"
f" Std. Rotation Roll Error: {errors_rpy[i][0].std():.4f} degree")
print(f"Iteration {i}: \tMean Rotation Pitch Error: {errors_rpy[i][1].mean(): .4f} degree"
f" Median Rotation Pitch Error: {errors_rpy[i][1].median():.4f} degree"
f" Std. Rotation Pitch Error: {errors_rpy[i][1].std():.4f} degree")
print(f"Iteration {i}: \tMean Rotation Yaw Error: {errors_rpy[i][2].mean(): .4f} degree"
f" Median Rotation Yaw Error: {errors_rpy[i][2].median():.4f} degree"
f" Std. Rotation Yaw Error: {errors_rpy[i][2].std():.4f} degree\n")
with open(os.path.join(_config['output'], 'results.txt'),
'a', encoding='utf-8') as f:
f.write(f"Iteration {i}: \n")
f.write("Translation Error && Rotation Error:\n")
f.write(f"Iteration {i}: \tMean Translation Error: {errors_t[i].mean():.4f} cm "
f" Mean Rotation Error: {errors_r[i].mean():.4f} degree\n")
f.write(f"Iteration {i}: \tMedian Translation Error: {errors_t[i].median():.4f} cm "
f" Median Rotation Error: {errors_r[i].median():.4f} degree\n")
f.write(f"Iteration {i}: \tStd. Translation Error: {errors_t[i].std():.4f} cm "
f" Std. Rotation Error: {errors_r[i].std():.4f} degree\n\n")
# translation xyz
f.write("Translation Error XYZ:\n")
f.write(f"Iteration {i}: \tMean Translation X Error: {errors_t2[i][0].mean():.4f} cm "
f" Median Translation X Error: {errors_t2[i][0].median():.4f} cm "
f" Std. Translation X Error: {errors_t2[i][0].std():.4f} cm \n")
f.write(f"Iteration {i}: \tMean Translation Y Error: {errors_t2[i][1].mean():.4f} cm "
f" Median Translation Y Error: {errors_t2[i][1].median():.4f} cm "
f" Std. Translation Y Error: {errors_t2[i][1].std():.4f} cm \n")
f.write(f"Iteration {i}: \tMean Translation Z Error: {errors_t2[i][2].mean():.4f} cm "
f" Median Translation Z Error: {errors_t2[i][2].median():.4f} cm "
f" Std. Translation Z Error: {errors_t2[i][2].std():.4f} cm \n\n")
# rotation rpy
f.write("Rotation Error RPY:\n")
f.write(f"Iteration {i}: \tMean Rotation Roll Error: {errors_rpy[i][0].mean(): .4f} degree"
f" Median Rotation Roll Error: {errors_rpy[i][0].median():.4f} degree"
f" Std. Rotation Roll Error: {errors_rpy[i][0].std():.4f} degree\n")
f.write(f"Iteration {i}: \tMean Rotation Pitch Error: {errors_rpy[i][1].mean(): .4f} degree"
f" Median Rotation Pitch Error: {errors_rpy[i][1].median():.4f} degree"
f" Std. Rotation Pitch Error: {errors_rpy[i][1].std():.4f} degree\n")
f.write(f"Iteration {i}: \tMean Rotation Yaw Error: {errors_rpy[i][2].mean(): .4f} degree"
f" Median Rotation Yaw Error: {errors_rpy[i][2].median():.4f} degree"
f" Std. Rotation Yaw Error: {errors_rpy[i][2].std():.4f} degree\n\n\n")
for i in range(len(errors_t2)):
errors_t2[i] = torch.stack(errors_t2[i]).abs() / 100
errors_rpy[i] = torch.stack(errors_rpy[i]).abs()
# mis_calib_input
# t_x = mis_calib_input[:, 0]
# t_y = mis_calib_input[:, 1]
# t_z = mis_calib_input[:, 2]
# r_roll = mis_calib_input[:, 5]
# r_pitch = mis_calib_input[:, 3]
# r_yaw = mis_calib_input[:, 4]
# plot_error
# plot_x = errors_t2[:, 0]
# plot_y = errors_t2[:, 1]
# plot_z = errors_t2[:, 2]
# plot_roll = errors_rpy[:, 0]
# plot_pitch = errors_rpy[:, 1]
# plot_yaw = errors_rpy[:, 2]
# translation error
# fig = plt.figure(figsize=(6, 3)) # 设置图大小 figsize=(6,3)
# plt.title('Calibration Translation Error')
plot_x = np.zeros((mis_calib_input.shape[0], 2))
plot_x[:, 0] = mis_calib_input[:, 0].cpu().numpy()
plot_x[:, 1] = errors_t2[-1][:, 0].cpu().numpy()
plot_x = plot_x[np.lexsort(plot_x[:, ::-1].T)]
plot_y = np.zeros((mis_calib_input.shape[0], 2))
plot_y[:, 0] = mis_calib_input[:, 1].cpu().numpy()
plot_y[:, 1] = errors_t2[-1][:, 1].cpu().numpy()
plot_y = plot_y[np.lexsort(plot_y[:, ::-1].T)]
plot_z = np.zeros((mis_calib_input.shape[0], 2))
plot_z[:, 0] = mis_calib_input[:, 2].cpu().numpy()
plot_z[:, 1] = errors_t2[-1][:, 2].cpu().numpy()
plot_z = plot_z[np.lexsort(plot_z[:, ::-1].T)]
N_interval = plot_x.shape[0] // N
plot_x = plot_x[::N_interval]
plot_y = plot_y[::N_interval]
plot_z = plot_z[::N_interval]
plt.plot(plot_x[:, 0], plot_x[:, 1], c='red', label='X')
plt.plot(plot_y[:, 0], plot_y[:, 1], c='blue', label='Y')
plt.plot(plot_z[:, 0], plot_z[:, 1], c='green', label='Z')
# plt.legend(loc='best')
if _config['out_fig_lg'] == 'EN':
plt.xlabel('Miscalibration (m)', font_EN)
plt.ylabel('Absolute Error (m)', font_EN)
plt.legend(loc='best', prop=font_EN)
elif _config['out_fig_lg'] == 'CN':
plt.xlabel('初始标定外参偏差/米', font_CN)
plt.ylabel('绝对误差/米', font_CN)
plt.legend(loc='best', prop=font_CN)
plt.xticks(fontproperties='Times New Roman', size=plt_size)
plt.yticks(fontproperties='Times New Roman', size=plt_size)
plt.savefig(os.path.join(results_path, 'xyz_plot.png'))
plt.close('all')
errors_t = errors_t[-1].numpy()
errors_t = np.sort(errors_t, axis=0)[:-10] # 去掉一些异常值
# plt.title('Calibration Translation Error Distribution')
plt.hist(errors_t / 100, bins=50)
# ax = plt.gca()
# ax.set_xlabel('Absolute Translation Error (m)')
# ax.set_ylabel('Number of instances')
# ax.set_xticks([0.00, 0.25, 0.00, 0.25, 0.50])
if _config['out_fig_lg'] == 'EN':
plt.xlabel('Absolute Translation Error (m)', font_EN)
plt.ylabel('Number of instances', font_EN)
elif _config['out_fig_lg'] == 'CN':
plt.xlabel('绝对平移误差/米', font_CN)
plt.ylabel('实验序列数目/个', font_CN)
plt.xticks(fontproperties='Times New Roman', size=plt_size)
plt.yticks(fontproperties='Times New Roman', size=plt_size)
plt.savefig(os.path.join(results_path, 'translation_error_distribution.png'))
plt.close('all')
# rotation error
# fig = plt.figure(figsize=(6, 3)) # 设置图大小 figsize=(6,3)
# plt.title('Calibration Rotation Error')
plot_pitch = np.zeros((mis_calib_input.shape[0], 2))
plot_pitch[:, 0] = mis_calib_input[:, 3].cpu().numpy() * (180.0 / 3.141592)
plot_pitch[:, 1] = errors_rpy[-1][:, 1].cpu().numpy()
plot_pitch = plot_pitch[np.lexsort(plot_pitch[:, ::-1].T)]
plot_yaw = np.zeros((mis_calib_input.shape[0], 2))
plot_yaw[:, 0] = mis_calib_input[:, 4].cpu().numpy() * (180.0 / 3.141592)
plot_yaw[:, 1] = errors_rpy[-1][:, 2].cpu().numpy()
plot_yaw = plot_yaw[np.lexsort(plot_yaw[:, ::-1].T)]
plot_roll = np.zeros((mis_calib_input.shape[0], 2))
plot_roll[:, 0] = mis_calib_input[:, 5].cpu().numpy() * (180.0 / 3.141592)
plot_roll[:, 1] = errors_rpy[-1][:, 0].cpu().numpy()
plot_roll = plot_roll[np.lexsort(plot_roll[:, ::-1].T)]
N_interval = plot_roll.shape[0] // N
plot_pitch = plot_pitch[::N_interval]
plot_yaw = plot_yaw[::N_interval]
plot_roll = plot_roll[::N_interval]
# Yaw（偏航）：欧拉角向量的y轴
# Pitch（俯仰）：欧拉角向量的x轴
# Roll（翻滚）： 欧拉角向量的z轴
if _config['out_fig_lg'] == 'EN':
plt.plot(plot_yaw[:, 0], plot_yaw[:, 1], c='red', label='Yaw(Y)')
plt.plot(plot_pitch[:, 0], plot_pitch[:, 1], c='blue', label='Pitch(X)')
plt.plot(plot_roll[:, 0], plot_roll[:, 1], c='green', label='Roll(Z)')
plt.xlabel('Miscalibration (degree)', font_EN)
plt.ylabel('Absolute Error (degree)', font_EN)
plt.legend(loc='best', prop=font_EN)
elif _config['out_fig_lg'] == 'CN':
plt.plot(plot_yaw[:, 0], plot_yaw[:, 1], c='red', label='偏航角')
plt.plot(plot_pitch[:, 0], plot_pitch[:, 1], c='blue', label='俯仰角')
plt.plot(plot_roll[:, 0], plot_roll[:, 1], c='green', label='翻滚角')
plt.xlabel('初始标定外参偏差/度', font_CN)
plt.ylabel('绝对误差/度', font_CN)
plt.legend(loc='best', prop=font_CN)
plt.xticks(fontproperties='Times New Roman', size=plt_size)
plt.yticks(fontproperties='Times New Roman', size=plt_size)
plt.savefig(os.path.join(results_path, 'rpy_plot.png'))
plt.close('all')
errors_r = errors_r[-1].numpy()
errors_r = np.sort(errors_r, axis=0)[:-10] # 去掉一些异常值
# np.savetxt('rot_error.txt', arr_, fmt='%0.8f')
# print('max rotation_error: {}'.format(max(errors_r)))
# plt.title('Calibration Rotation Error Distribution')
plt.hist(errors_r, bins=50)
#plt.xlim([0, 1.5]) # x轴边界
#plt.xticks([0.0, 0.3, 0.6, 0.9, 1.2, 1.5]) # 设置x刻度
# ax = plt.gca()
if _config['out_fig_lg'] == 'EN':
plt.xlabel('Absolute Rotation Error (degree)', font_EN)
plt.ylabel('Number of instances', font_EN)
elif _config['out_fig_lg'] == 'CN':
plt.xlabel('绝对旋转误差/度', font_CN)
plt.ylabel('实验序列数目/个', font_CN)
plt.xticks(fontproperties='Times New Roman', size=plt_size)
plt.yticks(fontproperties='Times New Roman', size=plt_size)
plt.savefig(os.path.join(results_path, 'rotation_error_distribution.png'))
plt.close('all')
if _config["save_name"] is not None:
torch.save(torch.stack(errors_t).cpu().numpy(), f'./results_for_paper/{_config["save_name"]}_errors_t')
torch.save(torch.stack(errors_r).cpu().numpy(), f'./results_for_paper/{_config["save_name"]}_errors_r')
torch.save(torch.stack(errors_t2).cpu().numpy(), f'./results_for_paper/{_config["save_name"]}_errors_t2')
torch.save(torch.stack(errors_rpy).cpu().numpy(), f'./results_for_paper/{_config["save_name"]}_errors_rpy')
avg_time = total_time / len(TestImgLoader)
print("average runing time on {} iteration: {} s".format(len(weights), avg_time))
print("End!")