535 lines
22 KiB
Python
535 lines
22 KiB
Python
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# -- coding: utf-8 --
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import aurco_3d_detect as aurco3d
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import cloud_3d_detect as cloud3d
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import svd
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import utils
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import os
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import sys
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from utils import Log
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from config import *
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CAMERA_FILE = os.path.dirname(__file__) + "\\samples\\1\\output.png"
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LIDAR_FILE = os.path.dirname(__file__) + "\\samples\\1\\output.ply"
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sys.path.append(r"./")
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_dict = [
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"LEFT",
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"TOP",
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"RIGHT",
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"BOTTOM",
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]
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def do_calibration_with_one_sample(camera_file_path = CAMERA_FILE, lidar_file_path = LIDAR_FILE):
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global CAMERA_FILE, LIDAR_FILE
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if CAMERA_FILE != camera_file_path :
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CAMERA_FILE = camera_file_path
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if LIDAR_FILE != lidar_file_path :
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LIDAR_FILE = lidar_file_path
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frame, aurco_marks_conners = aurco3d.cal_camera_marks_3d_conners(CAMERA_FILE)
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# 分割标定板
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marks_pcd = cloud3d.seg_mark_cloud(LIDAR_FILE, kk, show=False)
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if len(marks_pcd) != MARK_COUNT:
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return -1, None, None
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pcd_marks_conners = cloud3d.cal_marks_3d_conners(show=False)
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pcd_marks_conners_new = []
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for i in range(4):
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tmmp = []
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for j in range(4):
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tmmp.append(pcd_marks_conners[i].points[j])
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pcd_marks_conners_new.append(tmmp)
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# 排除异常点
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__exclude = []
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while True:
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__max = 0
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__min = 10
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__mean = 0
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__cnt = 0
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__conner_mean = 0
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__conner_cnt = 0
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__conners_mean_distance = []
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for i in range(16):
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src_aruco_mark_conner = aurco_marks_conners[int(i/4)][i%4]
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src_cloud_mark_conner = pcd_marks_conners_new[int(i/4)][i%4]
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for j in range(16):
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if i in __exclude or j in __exclude:
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continue
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dst_aruco_mark_conner = aurco_marks_conners[int(j/4)][j%4]
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dst_cloud_mark_conner = pcd_marks_conners_new[int(j/4)][j%4]
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aurco_distance = np.linalg.norm(src_aruco_mark_conner - dst_aruco_mark_conner)
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lidar_distance = np.linalg.norm(src_cloud_mark_conner - dst_cloud_mark_conner)
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diff_distance = abs(aurco_distance - lidar_distance)
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if diff_distance > __max:
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__max = diff_distance
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if diff_distance < __min:
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__min = diff_distance
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__cnt += 1
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__mean += diff_distance
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__conner_cnt += 1
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__conner_mean += diff_distance
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# print(str(int(i/4)) + "-" + _dict[i%4] + " : " + str(int(j/4)) + "-" + _dict[j%4]
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# + " = " + str(diff_distance))
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if __conner_cnt == 0: tmp=0
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else : tmp = __conner_mean/__conner_cnt
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__conners_mean_distance.append(tmp)
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__conner_cnt = 0
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__conner_mean = 0
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__mean = __mean/__cnt
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Log.info("16点对比结果")
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print("__max:" + str(__max))
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print("__min:" + str(__min))
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print("__cnt:" + str(__cnt))
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print("__mean:" + str(__mean))
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print("__conners_mean_distance:")
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# for test
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# for i in range(len(__conners_mean_distance)):
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# print(__conners_mean_distance[i])
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max_index = max(enumerate(__conners_mean_distance), key=lambda x: x[1])[0]
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if len(__exclude) < conf_max_exclude_num:
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__exclude.append(max_index)
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else:
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break
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# 初始化SVD数据
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list_marks_aurco = []
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list_marks_pcd = []
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for i in range(16):
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# for test
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print(aurco_marks_conners[int(i/4)][i%4][0] - pcd_marks_conners_new[int(i/4)][i%4][0],
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aurco_marks_conners[int(i/4)][i%4][1] - pcd_marks_conners_new[int(i/4)][i%4][1],
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aurco_marks_conners[int(i/4)][i%4][2] - pcd_marks_conners_new[int(i/4)][i%4][2])
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if i in __exclude:
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continue
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list_marks_aurco.append(aurco_marks_conners[int(i/4)][i%4])
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list_marks_pcd.append(pcd_marks_conners_new[int(i/4)][i%4])
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# SVD 分解得到 RT
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_R, _t, _ ,_ = svd.scipy_svd(np.asarray(list_marks_pcd), np.asarray(list_marks_aurco))
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# _r, _p, _y = utils.rotation_matrix_to_euler_angles(_R)
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_r, _p, _y = utils.rotation_matrix_to_rpy(_R)
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print(_R)
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print(_t)
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print(_r, _p, _y)
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return 0, [_t[0],_t[1],_t[2]], [_r, _p, _y], __mean
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g_marks_pcd = []
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def step1_get_aruco_and_lidar_marks_and_conners(
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camera_file_path = CAMERA_FILE,
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lidar_file_path = LIDAR_FILE,
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times = 1,
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simple=True):
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global CAMERA_FILE, LIDAR_FILE, g_marks_pcd
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if CAMERA_FILE != camera_file_path :
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CAMERA_FILE = camera_file_path
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if LIDAR_FILE != lidar_file_path :
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LIDAR_FILE = lidar_file_path
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frame, aurco_marks_conners = aurco3d.cal_camera_marks_3d_conners(CAMERA_FILE)
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if simple:
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# 分割标定板
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if times == 1: # 第一次执行时分割
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g_marks_pcd = cloud3d.seg_mark_cloud_simple(LIDAR_FILE, kk, show=False)
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if len(g_marks_pcd) != MARK_COUNT:
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return None, None
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pcd_marks_conners = cloud3d.cal_marks_3d_conners_simple(g_marks_pcd, show=False)
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else:
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# 分割标定板
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if times == 1: # 第一次执行时分割
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g_marks_pcd = cloud3d.seg_mark_cloud(LIDAR_FILE, kk, show=False)
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if len(g_marks_pcd) != MARK_COUNT:
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return None, None
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pcd_marks_conners = cloud3d.cal_marks_3d_conners(show=False)
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pcd_marks_conners_new = []
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for i in range(4):
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tmmp = []
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for j in range(4):
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tmmp.append(pcd_marks_conners[i].points[j])
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pcd_marks_conners_new.append(tmmp)
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return aurco_marks_conners, pcd_marks_conners_new
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def step1_get_aruco_and_lidar_marks_and_conners_v2(
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camera_file_path = CAMERA_FILE,
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lidar_file_path = LIDAR_FILE,
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times = 1,
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simple=True):
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global CAMERA_FILE, LIDAR_FILE, g_marks_pcd
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if CAMERA_FILE != camera_file_path :
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CAMERA_FILE = camera_file_path
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if LIDAR_FILE != lidar_file_path :
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LIDAR_FILE = lidar_file_path
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frame, aurco_marks_conners = aurco3d.cal_camera_marks_3d_conners_v2(CAMERA_FILE)
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frame, pcd_marks_conners = aurco3d.cal_lidar_marks_3d_conner_v2(LIDAR_FILE)
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pcd_marks_conners_new = []
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for i in range(4):
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tmmp = []
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for j in range(4):
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tmmp.append(pcd_marks_conners[i][j])
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pcd_marks_conners_new.append(tmmp)
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return aurco_marks_conners, pcd_marks_conners_new
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def step2_exclude_bad_conners(aurco_marks_conners, pcd_marks_conners):
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# 排除异常点
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__exclude = []
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while True:
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__max = 0
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__min = 10
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__mean = 0
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__cnt = 0
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__conner_mean = 0
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__conner_cnt = 0
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__conners_mean_distance = []
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for i in range(16):
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src_aruco_mark_conner = aurco_marks_conners[int(i/4)][i%4]
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src_cloud_mark_conner = pcd_marks_conners[int(i/4)][i%4]
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for j in range(16):
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if i in __exclude or j in __exclude:
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continue
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dst_aruco_mark_conner = aurco_marks_conners[int(j/4)][j%4]
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dst_cloud_mark_conner = pcd_marks_conners[int(j/4)][j%4]
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aurco_distance = np.linalg.norm(src_aruco_mark_conner - dst_aruco_mark_conner)
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lidar_distance = np.linalg.norm(src_cloud_mark_conner - dst_cloud_mark_conner)
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diff_distance = abs(aurco_distance - lidar_distance)
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if diff_distance > __max:
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__max = diff_distance
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if diff_distance < __min:
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__min = diff_distance
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__cnt += 1
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__mean += diff_distance
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__conner_cnt += 1
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__conner_mean += diff_distance
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# print(str(int(i/4)) + "-" + _dict[i%4] + " : " + str(int(j/4)) + "-" + _dict[j%4]
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# + " = " + str(diff_distance))
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if __conner_cnt == 0: tmp=0
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else : tmp = __conner_mean/__conner_cnt
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__conners_mean_distance.append(tmp)
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__conner_cnt = 0
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__conner_mean = 0
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__mean = __mean/__cnt
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Log.info("16点对比结果")
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print("__max:" + str(__max))
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print("__min:" + str(__min))
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print("__cnt:" + str(__cnt))
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print("__mean:" + str(__mean))
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print("__conners_mean_distance:")
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# for test
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# for i in range(len(__conners_mean_distance)):
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# print(__conners_mean_distance[i])
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max_index = max(enumerate(__conners_mean_distance), key=lambda x: x[1])[0]
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if len(__exclude) < conf_max_exclude_num:
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__exclude.append(max_index)
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else:
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break
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# 初始化SVD数据
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list_marks_aurco = []
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list_marks_pcd = []
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for i in range(16):
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# for test
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print(aurco_marks_conners[int(i/4)][i%4][0] - pcd_marks_conners[int(i/4)][i%4][0],
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aurco_marks_conners[int(i/4)][i%4][1] - pcd_marks_conners[int(i/4)][i%4][1],
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aurco_marks_conners[int(i/4)][i%4][2] - pcd_marks_conners[int(i/4)][i%4][2])
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if i in __exclude:
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continue
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list_marks_aurco.append(aurco_marks_conners[int(i/4)][i%4])
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list_marks_pcd.append(pcd_marks_conners[int(i/4)][i%4])
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return list_marks_aurco, list_marks_pcd, __mean
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def step2_exclude_bad_conners_v2(aurco_marks_conners, pcd_marks_conners):
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# 排除异常点
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__exclude = []
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while True:
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__max = 0
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__min = 10
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__mean = 0
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__cnt = 0
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__conner_mean = 0
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__conner_cnt = 0
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__conners_mean_distance = []
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for i in range(16):
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src_aruco_mark_conner = aurco_marks_conners[int(i/4)][i%4]
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src_cloud_mark_conner = pcd_marks_conners[int(i/4)][i%4]
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for j in range(16):
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if i in __exclude or j in __exclude:
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continue
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dst_aruco_mark_conner = aurco_marks_conners[int(j/4)][j%4]
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dst_cloud_mark_conner = pcd_marks_conners[int(j/4)][j%4]
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aurco_distance = np.linalg.norm(src_aruco_mark_conner - dst_aruco_mark_conner)
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lidar_distance = np.linalg.norm(src_cloud_mark_conner - dst_cloud_mark_conner)
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diff_distance = abs(aurco_distance - lidar_distance)
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if diff_distance > __max:
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__max = diff_distance
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if diff_distance < __min:
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__min = diff_distance
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__cnt += 1
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__mean += diff_distance
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__conner_cnt += 1
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__conner_mean += diff_distance
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# print(str(int(i/4)) + "-" + _dict[i%4] + " : " + str(int(j/4)) + "-" + _dict[j%4]
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# + " = " + str(diff_distance))
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if __conner_cnt == 0: tmp=0
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else : tmp = __conner_mean/__conner_cnt
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__conners_mean_distance.append(tmp)
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__conner_cnt = 0
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__conner_mean = 0
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__mean = __mean/__cnt
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Log.info("16点对比结果")
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print("__max:" + str(__max))
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print("__min:" + str(__min))
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print("__cnt:" + str(__cnt))
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print("__mean:" + str(__mean))
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print("__conners_mean_distance:")
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# for test
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# for i in range(len(__conners_mean_distance)):
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# print(__conners_mean_distance[i])
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max_index = max(enumerate(__conners_mean_distance), key=lambda x: x[1])[0]
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if len(__exclude) < conf_max_exclude_num:
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|||
|
|
__exclude.append(max_index)
|
|||
|
|
else:
|
|||
|
|
break
|
|||
|
|
|
|||
|
|
# 初始化SVD数据
|
|||
|
|
list_marks_aurco = []
|
|||
|
|
list_marks_pcd = []
|
|||
|
|
Log.info("16点对比结果")
|
|||
|
|
for i in range(16):
|
|||
|
|
# for test
|
|||
|
|
print(aurco_marks_conners[int(i/4)][i%4][0] - pcd_marks_conners[int(i/4)][i%4][0],
|
|||
|
|
aurco_marks_conners[int(i/4)][i%4][1] - pcd_marks_conners[int(i/4)][i%4][1],
|
|||
|
|
aurco_marks_conners[int(i/4)][i%4][2] - pcd_marks_conners[int(i/4)][i%4][2])
|
|||
|
|
list_marks_aurco.append(aurco_marks_conners[int(i/4)][i%4])
|
|||
|
|
list_marks_pcd.append(pcd_marks_conners[int(i/4)][i%4])
|
|||
|
|
|
|||
|
|
return list_marks_aurco, list_marks_pcd, __mean
|
|||
|
|
|
|||
|
|
import open3d as o3d
|
|||
|
|
def create_spheres(points, sizes=None, color=[0, 0.706, 1]):
|
|||
|
|
"""
|
|||
|
|
创建一系列球体以替代点云中的点，允许每个点有不同的大小。
|
|||
|
|
|
|||
|
|
参数:
|
|||
|
|
points: N x 3 数组，表示N个点的位置。
|
|||
|
|
sizes: N维数组或列表，表示对应点的半径大小。如果为None，则所有球体大小相同。
|
|||
|
|
color: RGB颜色值，默认是橙色。
|
|||
|
|
|
|||
|
|
返回:
|
|||
|
|
spheres: 包含所有球体的Open3D几何对象列表。
|
|||
|
|
"""
|
|||
|
|
spheres = []
|
|||
|
|
if sizes is None:
|
|||
|
|
sizes = [0.006] * len(points) # 如果没有提供大小，则默认所有球体大小相同
|
|||
|
|
for i, point in enumerate(points):
|
|||
|
|
sphere = o3d.geometry.TriangleMesh.create_sphere(radius=sizes[i])
|
|||
|
|
sphere.translate(point) # 将球体移动到点的位置
|
|||
|
|
sphere.paint_uniform_color(color)
|
|||
|
|
spheres.append(sphere)
|
|||
|
|
return spheres
|
|||
|
|
|
|||
|
|
def step3_cal_aruco_and_lidar_marks_rt(aurco_marks_conners, pcd_marks_conners):
|
|||
|
|
# SVD 分解得到 RT
|
|||
|
|
array_aurco_marks_conners = np.asarray(aurco_marks_conners)
|
|||
|
|
array_pcd_marks_conners = np.asarray(pcd_marks_conners)
|
|||
|
|
_R, _t, _ ,_ = svd.scipy_svd(array_pcd_marks_conners, array_aurco_marks_conners)
|
|||
|
|
# _r, _p, _y = utils.rotation_matrix_to_euler_angles(_R)
|
|||
|
|
_r, _p, _y = utils.rotation_matrix_to_rpy(_R)
|
|||
|
|
print(_R)
|
|||
|
|
print(_t)
|
|||
|
|
print(_r, _p, _y)
|
|||
|
|
|
|||
|
|
# for test
|
|||
|
|
_pdc_rt = (_R @ np.asarray(array_pcd_marks_conners).T).T + _t
|
|||
|
|
if True:
|
|||
|
|
# 角点显示的圆球半径，单位（米）
|
|||
|
|
spheres_2d = create_spheres(array_aurco_marks_conners[:16], None, color=[0, 0.706, 1])
|
|||
|
|
spheres_3d = create_spheres(_pdc_rt[:16], None, color=[0, 0, 0])
|
|||
|
|
points_list = []
|
|||
|
|
for i in range(len(array_aurco_marks_conners[:16])):
|
|||
|
|
points_list.append(spheres_2d[i])
|
|||
|
|
points_list.append(spheres_3d[i])
|
|||
|
|
# o3d.visualization.draw_geometries(points_list)
|
|||
|
|
return 0, [_t[0],_t[1],_t[2]], [_r, _p, _y]
|
|||
|
|
|
|||
|
|
def calibration_by_one_samples_points():
|
|||
|
|
calibration_result = []
|
|||
|
|
bad_samples = []
|
|||
|
|
samples_path_root = os.path.dirname(__file__) + "\\samples\\calibration"
|
|||
|
|
|
|||
|
|
# 标定使用的样本数量
|
|||
|
|
samples_size = 1
|
|||
|
|
# 每个样本执行的次数
|
|||
|
|
times_per_one_sample = 2
|
|||
|
|
for subdir in utils.list_subdirs(samples_path_root):
|
|||
|
|
paths = utils.traverse_folder(samples_path_root + "\\" + subdir)
|
|||
|
|
if len(paths) != 2:
|
|||
|
|
print("[ERROR]\t Data Error Path : " + paths)
|
|||
|
|
times = 0
|
|||
|
|
while times < times_per_one_sample:
|
|||
|
|
times += 1
|
|||
|
|
ret, _t, _rpy, __mean = do_calibration_with_one_sample(paths[1], paths[0]) # 0 png, 1 ply
|
|||
|
|
if ret != 0:
|
|||
|
|
bad_samples.append(subdir)
|
|||
|
|
continue
|
|||
|
|
# 合并得到最终得旋转角
|
|||
|
|
_new_rpy = utils.combine_rpy(roll, pitch, yaw, _rpy[0], _rpy[1], _rpy[2])
|
|||
|
|
calibration_result.append([subdir, str(times), __mean, _t, _rpy, _new_rpy])
|
|||
|
|
samples_size -= 1
|
|||
|
|
if samples_size == 0:
|
|||
|
|
break
|
|||
|
|
for i in range(len(calibration_result)):
|
|||
|
|
str_mean = str(calibration_result[i][2])
|
|||
|
|
str_t = str(calibration_result[i][3])
|
|||
|
|
str_ypr = str(calibration_result[i][4])
|
|||
|
|
str_new_rpy = str(calibration_result[i][5])
|
|||
|
|
print(calibration_result[i][0], calibration_result[i][1], str_mean, str_t, str_ypr, str_new_rpy)
|
|||
|
|
for i in range(len(bad_samples)):
|
|||
|
|
print(bad_samples[i])
|
|||
|
|
utils.save_to_json(os.path.dirname(__file__) + '\\record\\record.json', calibration_result)
|
|||
|
|
|
|||
|
|
|
|||
|
|
def calibration_by_all_samples_points(samples_path_root, samples_size=1, times_per_one_sample=2):
|
|||
|
|
calibration_result = []
|
|||
|
|
bad_samples = []
|
|||
|
|
all_points = []
|
|||
|
|
|
|||
|
|
# 定义总体的 aurco_marks_conners, pcd_marks_conners
|
|||
|
|
aurco_marks_conners_all = []
|
|||
|
|
pcd_marks_conners_all = []
|
|||
|
|
for subdir in utils.list_subdirs(samples_path_root):
|
|||
|
|
paths = utils.traverse_folder(samples_path_root + "\\" + subdir)
|
|||
|
|
if len(paths) != 2:
|
|||
|
|
print("[ERROR]\t Data Error Path : " + paths)
|
|||
|
|
times = 0
|
|||
|
|
while times < times_per_one_sample:
|
|||
|
|
times += 1
|
|||
|
|
aurco_marks_conners, pcd_marks_conners = \
|
|||
|
|
step1_get_aruco_and_lidar_marks_and_conners(paths[1], paths[0], times) # 0 png, 1 ply
|
|||
|
|
if aurco_marks_conners is None:
|
|||
|
|
Log.error("subdir is " + subdir + " aurco_marks_conners has ERROR")
|
|||
|
|
break
|
|||
|
|
aurco_marks_conners, pcd_marks_conners, __mean = \
|
|||
|
|
step2_exclude_bad_conners(aurco_marks_conners, pcd_marks_conners)
|
|||
|
|
_, _t, _rpy = step3_cal_aruco_and_lidar_marks_rt(aurco_marks_conners, pcd_marks_conners)
|
|||
|
|
_new_rpy = utils.combine_rpy(roll, pitch, yaw, _rpy[0], _rpy[1], _rpy[2])
|
|||
|
|
calibration_result.append([subdir, str(times), __mean, _t, _rpy, _new_rpy])
|
|||
|
|
|
|||
|
|
for it in range(len(aurco_marks_conners)):
|
|||
|
|
aurco_marks_conners_all.append(aurco_marks_conners[it].tolist())
|
|||
|
|
pcd_marks_conners_all.append(pcd_marks_conners[it].tolist())
|
|||
|
|
samples_size -= 1
|
|||
|
|
if samples_size == 0:
|
|||
|
|
break
|
|||
|
|
|
|||
|
|
# 将多样本多次计算的综合点统一进行SVD分解
|
|||
|
|
if len(aurco_marks_conners_all) == 0:
|
|||
|
|
return -1, None, None, None
|
|||
|
|
_, _t, _rpy = step3_cal_aruco_and_lidar_marks_rt(aurco_marks_conners_all, pcd_marks_conners_all)
|
|||
|
|
#
|
|||
|
|
_new_rpy = utils.combine_rpy(roll, pitch, yaw, _rpy[0], _rpy[1], _rpy[2])
|
|||
|
|
calibration_result.append(["all", str(len(aurco_marks_conners_all)) + " & " + str(len(pcd_marks_conners_all)), \
|
|||
|
|
__mean, _t, _rpy, _new_rpy])
|
|||
|
|
|
|||
|
|
for i in range(len(calibration_result)):
|
|||
|
|
str_mean = str(calibration_result[i][2])
|
|||
|
|
str_t = str(calibration_result[i][3])
|
|||
|
|
str_ypr = str(calibration_result[i][4])
|
|||
|
|
str_new_rpy = str(calibration_result[i][5])
|
|||
|
|
print(calibration_result[i][0], calibration_result[i][1], str_mean, str_t, str_ypr, str_new_rpy)
|
|||
|
|
for i in range(len(bad_samples)):
|
|||
|
|
print(bad_samples[i])
|
|||
|
|
utils.save_to_json(os.path.dirname(__file__) + '\\record\\record.json', calibration_result)
|
|||
|
|
|
|||
|
|
all_points.append(aurco_marks_conners_all)
|
|||
|
|
all_points.append(pcd_marks_conners_all)
|
|||
|
|
utils.save_to_json(os.path.dirname(__file__) + '\\record\\points.json', all_points)
|
|||
|
|
|
|||
|
|
return 0, _t, _rpy, __mean
|
|||
|
|
|
|||
|
|
def calibration_by_all_samples_points_v2(samples_path_root, samples_size=1, times_per_one_sample=2):
|
|||
|
|
calibration_result = []
|
|||
|
|
bad_samples = []
|
|||
|
|
all_points = []
|
|||
|
|
|
|||
|
|
# 定义总体的 aurco_marks_conners, pcd_marks_conners
|
|||
|
|
aurco_marks_conners_all = []
|
|||
|
|
pcd_marks_conners_all = []
|
|||
|
|
for subdir in utils.list_subdirs(samples_path_root):
|
|||
|
|
paths = utils.traverse_folder(samples_path_root + "\\" + subdir)
|
|||
|
|
if len(paths) != 2:
|
|||
|
|
print("[ERROR]\t Data Error Path : " + paths)
|
|||
|
|
times = 0
|
|||
|
|
while times < times_per_one_sample:
|
|||
|
|
times += 1
|
|||
|
|
aurco_marks_conners, pcd_marks_conners = \
|
|||
|
|
step1_get_aruco_and_lidar_marks_and_conners_v2(paths[1], paths[0], times) # 0 png, 1 ply
|
|||
|
|
if aurco_marks_conners is None:
|
|||
|
|
Log.error("subdir is " + subdir + " aurco_marks_conners has ERROR")
|
|||
|
|
break
|
|||
|
|
aurco_marks_conners, pcd_marks_conners, __mean = \
|
|||
|
|
step2_exclude_bad_conners(aurco_marks_conners, pcd_marks_conners)
|
|||
|
|
_, _t, _rpy = step3_cal_aruco_and_lidar_marks_rt(aurco_marks_conners, pcd_marks_conners)
|
|||
|
|
_new_rpy = utils.combine_rpy(roll, pitch, yaw, _rpy[0], _rpy[1], _rpy[2])
|
|||
|
|
calibration_result.append([subdir, str(times), __mean, _t, _rpy, _new_rpy])
|
|||
|
|
|
|||
|
|
for it in range(len(aurco_marks_conners)):
|
|||
|
|
aurco_marks_conners_all.append(aurco_marks_conners[it].tolist())
|
|||
|
|
pcd_marks_conners_all.append(pcd_marks_conners[it].tolist())
|
|||
|
|
samples_size -= 1
|
|||
|
|
if samples_size == 0:
|
|||
|
|
break
|
|||
|
|
|
|||
|
|
# 将多样本多次计算的综合点统一进行SVD分解
|
|||
|
|
if len(aurco_marks_conners_all) == 0:
|
|||
|
|
return -1, None, None, None
|
|||
|
|
_, _t, _rpy = step3_cal_aruco_and_lidar_marks_rt(aurco_marks_conners_all, pcd_marks_conners_all)
|
|||
|
|
_new_rpy = utils.combine_rpy(roll, pitch, yaw, _rpy[0], _rpy[1], _rpy[2])
|
|||
|
|
calibration_result.append(["all", str(len(aurco_marks_conners_all)) + " & " + str(len(pcd_marks_conners_all)), \
|
|||
|
|
__mean, _t, _rpy, _new_rpy])
|
|||
|
|
|
|||
|
|
for i in range(len(calibration_result)):
|
|||
|
|
str_mean = str(calibration_result[i][2])
|
|||
|
|
str_t = str(calibration_result[i][3])
|
|||
|
|
str_ypr = str(calibration_result[i][4])
|
|||
|
|
str_new_rpy = str(calibration_result[i][5])
|
|||
|
|
print(calibration_result[i][0], calibration_result[i][1], str_mean, str_t, str_ypr, str_new_rpy)
|
|||
|
|
for i in range(len(bad_samples)):
|
|||
|
|
print(bad_samples[i])
|
|||
|
|
utils.save_to_json(os.path.dirname(__file__) + '\\record\\record.json', calibration_result)
|
|||
|
|
|
|||
|
|
all_points.append(aurco_marks_conners_all)
|
|||
|
|
all_points.append(pcd_marks_conners_all)
|
|||
|
|
utils.save_to_json(os.path.dirname(__file__) + '\\record\\points.json', all_points)
|
|||
|
|
|
|||
|
|
return 0, _t, _rpy, __mean
|
|||
|
|
|
|||
|
|
|
|||
|
|
if __name__ == '__main__':
|
|||
|
|
# calibration_by_one_samples_points()
|
|||
|
|
samples_size = 5
|
|||
|
|
times_per_one_sample = 1
|
|||
|
|
# samples_path_root = os.path.dirname(__file__) + "\\ssh_tmp"
|
|||
|
|
samples_path_root = os.path.dirname(__file__) + "\\samples\\20250214"
|
|||
|
|
ret, _t, _rpy, __mean = calibration_by_all_samples_points(samples_path_root, samples_size, times_per_one_sample)
|
|||
|
|
# ret, _t, _rpy, __mean = calibration_by_all_samples_points_v2(samples_path_root, samples_size, times_per_one_sample)
|
|||
|
|
# rt = utils.read_from_json(os.path.dirname(__file__) + '\\record\\record.json')
|
|||
|
|
# print(rt)
|