159 lines
4.5 KiB
Markdown
159 lines
4.5 KiB
Markdown
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# 拍照流程
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1 执行 capture.py
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2 执行完成后 \ssh_tmp 目录下会生成 outpu.png 和 output.ply
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3 执行 calibration_mian.py
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4 查看执行的消息
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# 问题
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def detect_marks_roi(im_project, show=False):
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masks = []
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# ???
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offset = 40
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# camera_driver
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相机驱动代码
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## 编译
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拷贝到虚拟机编译环境
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cmake ..
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make -j8
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## 执行
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将可执行文件 camera_driver 拷贝到目标机器
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/root/calibration
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chmod 777 ./camera_driver
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./camera_driver
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执行完成后在当前目录下生成 output.png 文件
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# lidar_driver
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雷达驱动代码
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## 编译
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拷贝到虚拟机编译环境
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cmake ..
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make -j8
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## 执行
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将可执行文件 lidar_driver 拷贝到目标机器
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/root/calibration
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chmod 777 ./lidar_driver
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./lidar_driver
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执行完成后在当前目录下生成 output.ply 文件
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# sh 脚本
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## 安装
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安装前需关闭代理
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pip install paramiko scp -i https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple
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capture.py
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def cameraCap():
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def lidarCap():
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# samples
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数据目录
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# error record
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## 1
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ypr
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==>
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rpy
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## 2
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if point_3d[0] > 0: point_3d[0] = point_3d[0] + point_3d[0] / _r * kk
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else: point_3d[0] = point_3d[0] - point_3d[0] / _r * kk
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==>
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if point_3d[0] > 0: point_3d[0] = point_3d[0] + point_3d[0] / _r * kk
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else: point_3d[0] = point_3d[0] + point_3d[0] / _r * kk
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## 3
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if cv2.pointPolygonTest(np.asarray(mark_rois[i]), (x,y), False) > 0:
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==>
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if cv2.pointPolygonTest(np.asarray(mark_rois[i]), (x,y), False) >= 0:
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# 执行条件
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## num 4
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### z < 3.5 该值设置过大，会增加误差
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conf_max_exclude_num = 4
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svd.scipy_svd
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samples_size = 15
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times_per_one_sample = 5
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box3
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kk = 0.0
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### distance_threshold=0.01 ransac_n=100
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def correct_mark_cloud(mark_pcd):
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plane_model, inliers = mark_pcd.segment_plane(distance_threshold=0.01,
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ransac_n=100,
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num_iterations=1500)
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inlier_cloud = mark_pcd.select_by_index(inliers)
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inlier_cloud = inlier_cloud.paint_uniform_color([1.0, 0, 0])
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outlier_cloud = mark_pcd.select_by_index(inliers, invert=True)
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cv2.findHomography(np.array(pts_3d), np.array(pts_hom), cv2.RANSAC, ransacReprojThreshold)
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ransacReprojThreshold = 0.5
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## num 5
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### z < 3.5 该值设置过大，会增加误差
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conf_max_exclude_num = 4
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svd.scipy_svd
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samples_size = 10
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times_per_one_sample = 4
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box3
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kk = 0.0
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def correct_mark_cloud(mark_pcd):
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### distance_threshold=0.015 ransac_n=2000
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plane_model, inliers = mark_pcd.segment_plane(distance_threshold=0.015,
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ransac_n=2000,
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num_iterations=1500)
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inlier_cloud = mark_pcd.select_by_index(inliers)
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inlier_cloud = inlier_cloud.paint_uniform_color([1.0, 0, 0])
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outlier_cloud = mark_pcd.select_by_index(inliers, invert=True)
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## num 6
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### z < 3.5 该值设置过大，会增加误差
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conf_max_exclude_num = 4
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svd.scipy_svd
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### samples_size = 10
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### times_per_one_sample = 4
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box3
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kk = 0.0
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def correct_mark_cloud(mark_pcd):
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### distance_threshold=0.01 ransac_n=2000
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plane_model, inliers = mark_pcd.segment_plane(distance_threshold=0.015,
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ransac_n=2000,
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num_iterations=1500)
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## num 7
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### z < 3.5 该值设置过大，会增加误差
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conf_max_exclude_num = 4
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svd.scipy_svd
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### samples_size = 10
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### times_per_one_sample = 4
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box3
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kk = 0.0
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def correct_mark_cloud(mark_pcd):
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### distance_threshold=0.015 ransac_n=100
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plane_model, inliers = mark_pcd.segment_plane(distance_threshold=0.015,
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ransac_n=2000,
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num_iterations=1500)
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## num 8
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ransacReprojThreshold = 3.0
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## point.json
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main
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calibration_by_all_samples_points
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遍历子文件夹
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遍历循环次数
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step1_get_aruco_and_lidar_marks_and_conners（重写）
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aurco3d.cal_camera_marks_3d_conners
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cloud3d.cal_marks_3d_conners_simple
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return aurco_marks_conners, pcd_marks_conners_new
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step2_exclude_bad_conners
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去除坏点
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step3_cal_aruco_and_lidar_marks_rt
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_R, _t, _ ,_ = svd.scipy_svd(array_pcd_marks_conners, array_aurco_marks_conners)
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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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calibration_result.append([subdir, str(times), __mean, _t, _rpy, _new_rpy])
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