不收费的网站有哪些,公司网站制作教程,四川城乡建设厅官网,做网站买主机还是服务器零、概要
注意#xff1a;该文章是手写ai自动驾驶#xff0c;Nuscenes数据集的笔记。
首先#xff0c;学习需要使用到 nuScenes 数据集。python 工具需要使用到 nuscenes-devkit、pyquaternion
from nuscenes.nuscenes import NuScenes
from pyquaternion import Quatern…零、概要
注意该文章是手写ai自动驾驶Nuscenes数据集的笔记。
首先学习需要使用到 nuScenes 数据集。python 工具需要使用到 nuscenes-devkit、pyquaternion
from nuscenes.nuscenes import NuScenes
from pyquaternion import Quaternion # 四元数操作的包https://github.com/nutonomy/nuscenes-devkit/tree/master 官网https://github.com/nutonomy/nuscenes-devkit/blob/master/python-sdk/tutorials/nuscenes_tutorial.ipynb可以看非常好 https://www.nuscenes.org/nuscenes#explore该网址能够可视化了解nuscenes数据集。
一、提出疑问
如何将Lidar的点与图片对应画出 二、代码大体了解
# pip install nuscenes-devkit # tingfeng1 需要包 nuscenes-devkit
from nuscenes.nuscenes import NuScenes
from nuscenes.utils.data_classes import Box
from pyquaternion import Quaternion
import numpy as np
import cv2
import osnp.set_printoptions(precision3, suppressTrue)version mini
dataroot /data/mini
nuscenes NuScenes(versionv1.0-{}.format(version), datarootdataroot, verboseFalse) # tingfeng2 实例化类NuScenescameras [CAM_FRONT_LEFT, CAM_FRONT, CAM_FRONT_RIGHT, CAM_BACK_LEFT, CAM_BACK, CAM_BACK_RIGHT]
sample nuscenes.sample[0] # tingfeng3获取第一个样本def to_matrix4x4_2(rotation, translation, inverseTrue):output np.eye(4)output[:3, :3] rotationoutput[:3, 3] translationif inverse:output np.linalg.inv(output)return outputdef to_matrix4x4(m):output np.eye(4)output[:3, :3] mreturn output# tingfeng4 拿到样本后获取Lidar的信息
# Lidar的点云是基于Lidar坐标系的
# 需要将lidar的坐标转换到ego然后ego到global
lidar_sample_data nuscenes.get(sample_data, sample[data][LIDAR_TOP])
lidar_file os.path.join(nuscenes.dataroot, lidar_sample_data[filename])
lidar_pointcloud np.fromfile(lidar_file, dtypenp.float32).reshape(-1, 5)
ego_pose nuscenes.get(ego_pose, lidar_sample_data[ego_pose_token])
ego_to_global to_matrix4x4_2(Quaternion(ego_pose[rotation]).rotation_matrix, np.array(ego_pose[translation]), False)
lidar_sensor nuscenes.get(calibrated_sensor, lidar_sample_data[calibrated_sensor_token])
lidar_to_ego to_matrix4x4_2(Quaternion(lidar_sensor[rotation]).rotation_matrix, np.array(lidar_sensor[translation]), False)
lidar_to_global ego_to_global lidar_to_ego
lidar_points np.concatenate([lidar_pointcloud[:, :3], np.ones((len(lidar_pointcloud), 1))], axis1)
lidar_points lidar_points lidar_to_global.T # tingfeng5 点云转到global坐标系# tingfeng6 获取每一个camera的信息
# 1. annotation标注是global坐标系的
# 2. 由于时间戳的缘故每个camera/lidar/radar都有ego_pose用于修正差异
for camera in cameras:camera_sample_data nuscenes.get(sample_data, sample[data][camera])image_file os.path.join(nuscenes.dataroot, camera_sample_data[filename])ego_pose nuscenes.get(ego_pose, camera_sample_data[ego_pose_token])global_to_ego to_matrix4x4_2(Quaternion(ego_pose[rotation]).rotation_matrix, np.array(ego_pose[translation]))camera_sensor nuscenes.get(calibrated_sensor, camera_sample_data[calibrated_sensor_token]) camera_intrinsic to_matrix4x4(camera_sensor[camera_intrinsic])ego_to_camera to_matrix4x4_2(Quaternion(camera_sensor[rotation]).rotation_matrix, np.array(camera_sensor[translation]))global_to_image camera_intrinsic ego_to_camera global_to_ego # tingfeng7 根据相机中得到的信息得到的三个矩阵将global坐标系的坐标转换到image上image cv2.imread(image_file) for annotation_token in sample[anns]: # tingfeng8 标注的框的信息在sample[anns]中遍历标注框instance nuscenes.get(sample_annotation, annotation_token)box Box(instance[translation], instance[size], Quaternion(instance[rotation]))corners np.ones((4, 8))corners[:3, :] box.corners() corners (global_to_image corners)[:3] # tingfeng11 注意角点是global坐标这里转换到image上corners[:2] / corners[[2]]corners corners.T.astype(int) # tingfeng9 到此是计算框的角点ix, iy [0, 1, 2, 3, 0, 1, 2, 3, 4, 5, 6, 7], [4, 5, 6, 7, 1, 2, 3, 0, 5, 6, 7, 4]for p0, p1 in zip(corners[ix], corners[iy]):if p0[2] 0 or p1[2] 0: continuecv2.line(image, (p0[0], p0[1]), (p1[0], p1[1]), (0, 255, 0), 2, 16) # tingfeng10 到此是把角点划到image上image_based_points lidar_points global_to_image.T # tingfeng12 雷达的信息也投射到image上image_based_points[:, :2] / image_based_points[:, [2]]show_points image_based_points[image_based_points[:, 2] 0][:, :2].astype(np.int32)for x, y in show_points:cv2.circle(image, (x, y), 3, (255, 0, 0), -1, 16) # # tingfeng13 lidar的信息画到image上cv2.imwrite(f{camera}.jpg, image)三、手写
3.1 了解 NuScenes 的实例对象 了解如何实例化参数需要什么 第一个参数 version。目前理解是给 nuscenes 下载下来。那个有一堆 json 文件的文件夹名字。 通常是 v1.0-{mini, test, trainval} 第二个参数是数据集根目录。图片上是 nuscenes 了解如何获取数据数据是什么样 如下实例化后。就能简单查看里面的数据了。
from nuscenes.nuscenes import NuScenes
import numpy as np
import cv2
import os# 1. 实例化NuScenes
version v1.0-mini # 因为我们是用的是mini数据集
dataroot /home/shenlan09/tingfeng/BEVFusion/BEVfusion/bevfusion/configs/nuscenes
# 我的数据集放这里了我用的绝对路径
nuscenes NuScenes(versionversion, datarootdataroot, verboseFalse)
# print(len(nuscenes.sample)) # 404sample nuscenes.sample[0] # 读取sample中第一个数据具体了解
print(nuscenes)另外。官方教程上拿到第一个 sample 可以用
%matplotlib inline
from nuscenes.nuscenes import NuScenes
nusc NuScenes(versionv1.0-mini, dataroot/home/shenlan09/tingfeng/BEVFusion/BEVfusion/bevfusion/configs/nuscenes, verboseTrue)Loading NuScenes tables for version v1.0-mini...
23 category,
8 attribute,
4 visibility,
911 instance,
12 sensor,
120 calibrated_sensor,
31206 ego_pose,
8 log,
10 scene,
404 sample,
31206 sample_data,
18538 sample_annotation,
4 map,
Done loading in 0.5 seconds.
Reverse indexing ...
Done reverse indexing in 0.1 seconds.nusc.list_scenes()
scene-0061, Parked truck, construction, intersectio... [18-07-24 03:28:47] 19s, singapore-onenorth, #anns:4622
scene-0103, Many peds right, wait for turning car, ... [18-08-01 19:26:43] 19s, boston-seaport, #anns:2046
scene-0655, Parking lot, parked cars, jaywalker, be... [18-08-27 15:51:32] 20s, boston-seaport, #anns:2332
scene-0553, Wait at intersection, bicycle, large tr... [18-08-28 20:48:16] 20s, boston-seaport, #anns:1950
scene-0757, Arrive at busy intersection, bus, wait ... [18-08-30 19:25:08] 20s, boston-seaport, #anns:592
scene-0796, Scooter, peds on sidewalk, bus, cars, t... [18-10-02 02:52:24] 20s, singapore-queensto, #anns:708
scene-0916, Parking lot, bicycle rack, parked bicyc... [18-10-08 07:37:13] 20s, singapore-queensto, #anns:2387
scene-1077, Night, big street, bus stop, high speed... [18-11-21 11:39:27] 20s, singapore-hollandv, #anns:890
scene-1094, Night, after rain, many peds, PMD, ped ... [18-11-21 11:47:27] 19s, singapore-hollandv, #anns:1762
scene-1100, Night, peds in sidewalk, peds cross cro... [18-11-21 11:49:47] 19s, singapore-hollandv, #anns:935my_scene nusc.scene[0]{token: cc8c0bf57f984915a77078b10eb33198,
log_token: 7e25a2c8ea1f41c5b0da1e69ecfa71a2,
nbr_samples: 39,
first_sample_token: ca9a282c9e77460f8360f564131a8af5,
last_sample_token: ed5fc18c31904f96a8f0dbb99ff069c0,
name: scene-0061,
description: Parked truck, construction, intersection, turn left, following a van}
first_sample_token my_scene[first_sample_token]# The rendering command below is commented out because it tends to crash in notebooksnusc.render_sample(first_sample_token) # 能够在 ipynb 文件中渲染
3.1.1 数据格式
nuscenes.sample[0]的打印输出信息nuscenes.sample[0]是指第一个样本。是某时刻的所有数据 包含多个相机数据token、Lidar数据对应的token。anns是3D标注边框的token
{token: ca9a282c9e77460f8360f564131a8af5,
timestamp: 1532402927647951,
prev: ,
next: 39586f9d59004284a7114a68825e8eec,
scene_token: cc8c0bf57f984915a77078b10eb33198,
data传感器信息: {RADAR_FRONT: 每个里面的字符串叫token根据这个就能找到位置的信息37091c75b9704e0daa829ba56dfa0906, RADAR_FRONT_LEFT: 11946c1461d14016a322916157da3c7d, RADAR_FRONT_RIGHT: 491209956ee3435a9ec173dad3aaf58b, RADAR_BACK_LEFT: 312aa38d0e3e4f01b3124c523e6f9776, RADAR_BACK_RIGHT: 07b30d5eb6104e79be58eadf94382bc1, LIDAR_TOP: 9d9bf11fb0e144c8b446d54a8a00184f, CAM_FRONT: e3d495d4ac534d54b321f50006683844, CAM_FRONT_RIGHT: aac7867ebf4f446395d29fbd60b63b3b, CAM_BACK_RIGHT: 79dbb4460a6b40f49f9c150cb118247e, CAM_BACK: 03bea5763f0f4722933508d5999c5fd8, CAM_BACK_LEFT: 43893a033f9c46d4a51b5e08a67a1eb7, CAM_FRONT_LEFT: fe5422747a7d4268a4b07fc396707b23}, anns: [ef63a697930c4b20a6b9791f423351da, 6b89da9bf1f84fd6a5fbe1c3b236f809, 924ee6ac1fed440a9d9e3720aac635a0, 91e3608f55174a319246f361690906ba, cd051723ed9c40f692b9266359f547af, 36d52dfedd764b27863375543c965376, 70af124fceeb433ea73a79537e4bea9e, 63b89fe17f3e41ecbe28337e0e35db8e, e4a3582721c34f528e3367f0bda9485d, fcb2332977ed4203aa4b7e04a538e309, a0cac1c12246451684116067ae2611f6, 02248ff567e3497c957c369dc9a1bd5c, 9db977e264964c2887db1e37113cddaa, ca9c5dd6cf374aa980fdd81022f016fd, 179b8b54ee74425893387ebc09ee133d, 5b990ac640bf498ca7fd55eaf85d3e12, 16140fbf143d4e26a4a7613cbd3aa0e8, 54939f11a73d4398b14aeef500bf0c23, 83d881a6b3d94ef3a3bc3b585cc514f8, 74986f1604f047b6925d409915265bf7, e86330c5538c4858b8d3ffe874556cc5, a7bd5bb89e27455bbb3dba89a576b6a1, fbd9d8c939b24f0eb6496243a41e8c41, 198023a1fb5343a5b6fad033ab8b7057, ffeafb90ecd5429cba23d0be9a5b54ee, cc636a58e27e446cbdd030c14f3718fd, 076a7e3ec6244d3b84e7df5ebcbac637, 0603fbaef1234c6c86424b163d2e3141, d76bd5dcc62f4c57b9cece1c7bcfabc5, 5acb6c71bcd64aa188804411b28c4c8f, 49b74a5f193c4759b203123b58ca176d, 77519174b48f4853a895f58bb8f98661, c5e9455e98bb42c0af7d1990db1df0c9, fcc5b4b5c4724179ab24962a39ca6d65, 791d1ca7e228433fa50b01778c32449a, 316d20eb238c43ef9ee195642dd6e3fe, cda0a9085607438c9b1ea87f4360dd64, e865152aaa194f22b97ad0078c012b21, 7962506dbc24423aa540a5e4c7083dad, 29cca6a580924b72a90b9dd6e7710d3e, a6f7d4bb60374f868144c5ba4431bf4c, f1ae3f713ba946069fa084a6b8626fbf, d7af8ede316546f68d4ab4f3dbf03f88, 91cb8f15ed4444e99470d43515e50c1d, bc638d33e89848f58c0b3ccf3900c8bb, 26fb370c13f844de9d1830f6176ebab6, 7e66fdf908d84237943c833e6c1b317a, 67c5dbb3ddcc4aff8ec5140930723c37, eaf2532c820740ae905bb7ed78fb1037, 3e2d17fa9aa5484d9cabc1dfca532193, de6bd5ffbed24aa59c8891f8d9c32c44, 9d51d699f635478fbbcd82a70396dd62, b7cbc6d0e80e4dfda7164871ece6cb71, 563a3f547bd64a2f9969278c5ef447fd, df8917888b81424f8c0670939e61d885, bb3ef5ced8854640910132b11b597348, a522ce1d7f6545d7955779f25d01783b, 1fafb2468af5481ca9967407af219c32, 05de82bdb8484623906bb9d97ae87542, bfedb0d85e164b7697d1e72dd971fb72, ca0f85b4f0d44beb9b7ff87b1ab37ff5, bca4bbfdef3d4de980842f28be80b3ca, a834fb0389a8453c810c3330e3503e16, 6c804cb7d78943b195045082c5c2d7fa, adf1594def9e4722b952fea33b307937, 49f76277d07541c5a584aa14c9d28754, 15a3b4d60b514db5a3468e2aef72a90c, 18cc2837f2b9457c80af0761a0b83ccc, 2bfcc693ae9946daba1d9f2724478fd4]}直接使用下方代码可以直接打印
nuscenes.list_sample(sample[token])Sample: ca9a282c9e77460f8360f564131a8af5sample_data_token: 37091c75b9704e0daa829ba56dfa0906, mod: radar, channel: RADAR_FRONT
sample_data_token: 11946c1461d14016a322916157da3c7d, mod: radar, channel: RADAR_FRONT_LEFT
sample_data_token: 491209956ee3435a9ec173dad3aaf58b, mod: radar, channel: RADAR_FRONT_RIGHT
sample_data_token: 312aa38d0e3e4f01b3124c523e6f9776, mod: radar, channel: RADAR_BACK_LEFT
sample_data_token: 07b30d5eb6104e79be58eadf94382bc1, mod: radar, channel: RADAR_BACK_RIGHT
sample_data_token: 9d9bf11fb0e144c8b446d54a8a00184f, mod: lidar, channel: LIDAR_TOP
sample_data_token: e3d495d4ac534d54b321f50006683844, mod: camera, channel: CAM_FRONT
sample_data_token: aac7867ebf4f446395d29fbd60b63b3b, mod: camera, channel: CAM_FRONT_RIGHT
sample_data_token: 79dbb4460a6b40f49f9c150cb118247e, mod: camera, channel: CAM_BACK_RIGHT
sample_data_token: 03bea5763f0f4722933508d5999c5fd8, mod: camera, channel: CAM_BACK
sample_data_token: 43893a033f9c46d4a51b5e08a67a1eb7, mod: camera, channel: CAM_BACK_LEFT
sample_data_token: fe5422747a7d4268a4b07fc396707b23, mod: camera, channel: CAM_FRONT_LEFTsample_annotation_token: ef63a697930c4b20a6b9791f423351da, category: human.pedestrian.adult
sample_annotation_token: 6b89da9bf1f84fd6a5fbe1c3b236f809, category: human.pedestrian.adult
sample_annotation_token: 924ee6ac1fed440a9d9e3720aac635a0, category: vehicle.car
sample_annotation_token: 91e3608f55174a319246f361690906ba, category: human.pedestrian.adult
sample_annotation_token: cd051723ed9c40f692b9266359f547af, category: movable_object.trafficcone
sample_annotation_token: 36d52dfedd764b27863375543c965376, category: vehicle.bicycle
sample_annotation_token: 70af124fceeb433ea73a79537e4bea9e, category: human.pedestrian.adult
sample_annotation_token: 63b89fe17f3e41ecbe28337e0e35db8e, category: vehicle.car
sample_annotation_token: e4a3582721c34f528e3367f0bda9485d, category: human.pedestrian.adult
sample_annotation_token: fcb2332977ed4203aa4b7e04a538e309, category: movable_object.barrier
...
sample_annotation_token: 49f76277d07541c5a584aa14c9d28754, category: vehicle.car
sample_annotation_token: 15a3b4d60b514db5a3468e2aef72a90c, category: movable_object.barrier
sample_annotation_token: 18cc2837f2b9457c80af0761a0b83ccc, category: movable_object.barrier
sample_annotation_token: 2bfcc693ae9946daba1d9f2724478fd4, category: movable_object.barrier3.1.2 数据格式–官网图例
这个图例学完下面的代码回来看。就能取到自己想要的数据。 得到sample_data获取不同数据需使用不同代码 通过sample_data 的filename 定位到具体文件。用 np.fromfile读取数据通过sample_data 的calibrated_sensor_token用nuscenes定位定位calibrated_sensor拿到标定数据
3.1.2.1 上图理解–sample_token 获取 sample_data
sample[data][LIDAR_TOP] sample[data]中存储了 RADAR LIDAR CAM的信息sample[data][LIDAR_TOP]能拿到相机对应的sample_token图中的弧线箭头我理解为通过这个 sample_token 是能够获得 sample_data的sample_data nuscenes.get(sample_data, lidar_sample_token)是通过sample_token 获得 sample_data的具体方法 当得到了 sample_data 我们就能得到 sample_data 中的其他信息print(sample_data)
{token: 9d9bf11fb0e144c8b446d54a8a00184f,
sample_token: ca9a282c9e77460f8360f564131a8af5,
ego_pose_token: 9d9bf11fb0e144c8b446d54a8a00184f,
calibrated_sensor_token: a183049901c24361a6b0b11b8013137c,
timestamp: 1532402927647951,
fileformat: pcd,
is_key_frame: True,
height: 0,
width: 0,
filename: samples/LIDAR_TOP/n015-2018-07-24-11-22-450800__LIDAR_TOP__1532402927647951.pcd.bin,
prev: ,
next: 0cedf1d2d652468d92d23491136b5d15,
sensor_modality: lidar,
channel: LIDAR_TOP}可视化
会输出名字为123.png的图像 nuscenes.render_sample_data(lidar_sample_data[token], out_path./123)
3.1.2.2 通过sampe_data中的filename拿到具体的雷达数据.pcd.bin文件
lidar_filename sample_data[filename] # samples/LIDAR_TOP/n015-2018-07-24-11-22-450800__LIDAR_TOP__1532402927647951.pcd.bin
data np.fromfile(os.path.join(dataroot, lidar_filename), dtypenp.float32).reshape(-1, 5)
print(data.shape) # (34688, 5) #x y z intensity ring_index实际上拿到的是某种路径samples/LIDAR_TOP/n015-2018-07-24-11-22-450800__LIDAR_TOP__1532402927647951.pcd.bin暂时理解为 os.path.join(dataroot, lidar_filename)
3.2 坐标系的理解
3. 坐标系3.1 全局坐标系global coordinate- 可以简单的认为车辆在t0时刻的位置认为是全局坐标系的原点 3.2 车体坐标系ego_pose. ego coordinate- 以车体为原点的坐标系3.3 传感器坐标系- lidar 的坐标系- camera 的坐标系- radar 的坐标系3.3 标定的理解
第一个理解。整个过程如下 即将 lidar 点云转换到 global再转换到图片上
过程为 timestamp t0 时的lidar_points - ego_pose0 - global - ego_pose1 - camera - intrinsic - image第二个理解拿到 4*4 变换矩阵的过程 4. 标定calibater
lidar的标定获得的结果是lidar相对于ego而言的位置(translation)和旋转(rotation) - translation 可以用3个float数字表示位置- 相对于ego而言的位置- rotation则是用4个float表示旋转用的是四元数camera的标定获得的结果是camera相对于ego而言的位置 (translation)和旋转(rotation) - translation 可以用3个float数字表示位置- 相对于ego而言的位置- rotation则是用4个float表示旋转用的是四元数- carmera 还多了一个camera_intrinsic 相机的内参3d-2d平面- 相机畸变参数目前nuScenes数据集不考虑通过 lidar_calibrated_data nuscenes.get(quot;calibrated_sensorquot;, lidar_sample_data[quot;calibrated_sensor_tokenquot;]) 类似的语句就能拿到传感器的标定
3.3.1 LIDAR calibrated_sensor
主要完成 lidar_points - ego_pose0 - global
拿到 lidar 相对 ego 的标定数据
通过 lidar_calibrated_data nuscenes.get(calibrated_sensor, lidar_sample_data[calibrated_sensor_token]) 拿到 lidar 的标定数据。 标定数据 rotation 四元数 转 旋 转矩阵的过程。 # lidar_calibrated_data nuscenes.get(calibrated_sensor, lidar_sample_data[calibrated_sensor_token])
# calibrated_sensor 中的rotation属性使用四元数
lidar_calibrated_data[rotation] ----》[0.7077955119163518, -0.006492242056004365, 0.010646214713995808, -0.7063073142877817]Quaternion(lidar_calibrated_data[rotation]).rotation_matrix- 使用四元数转换成旋转矩阵如下[[ 0.00203327 0.99970406 0.02424172][-0.99998053 0.00217566 -0.00584864][-0.00589965 -0.02422936 0.99968902]]旋转矩阵平移矩阵合并
理解lidar 旋转矩阵 与 平移矩阵 合并
旋转矩阵是 3*3 与 平移矩阵合并就是创建一个4*4 的对角矩阵把前三行前三列替换为旋转矩阵。前三行第四列替换为二维数据的矩阵一般22就搞定旋转和平移合并就变成33.这里用二维简单理解3维为啥要变成4*4。 [ s c a l e 0 x 0 s c a l e y 0 0 1 ] [ x 1 y 1 1 ] [ x 2 y 2 1 ] \begin{bmatrix} scale0x\\ 0scaley \\ 001\end{bmatrix} \begin{bmatrix} x1\\ y1 \\ 1\end{bmatrix} \begin{bmatrix} x2\\ y2 \\ 1\end{bmatrix} scale000scale0xy1 x1y11 x2y21 代码
def get_matrix(calibrated_data):output np.eye(4)output[:3, :3] Quaternion(calibrated_data[rotation]).rotation_matrixoutput[:3, 3] calibrated_data[translation]return outputlidar_calibrated_data nuscenes.get(calibrated_sensor, lidar_sample_data[calibrated_sensor_token])
# 6.1.2
# lidar_to_ego_matrix 是基于ego而言的。
# point lidar_to_ego_matrix lidar_points.T 代表了lidar - ego 的过程。
lidar_to_ego_matrix get_matrix(lidar_calibrated_data)
print(lidar_to_ego_matrix) [[ 0.00203327 0.99970406 0.02424172 0.943713 ][-0.99998053 0.00217566 -0.00584864 0. ][-0.00589965 -0.02422936 0.99968902 1.84023 ][ 0. 0. 0. 1. ]]
- $$\begin{bmatrix} scale_1scale_2scale_3x\\ scale_4scale_5scale_6y \\ scale_7scale_8scale_9z \\
0001\end{bmatrix} \begin{bmatrix} x1\\ y1 \\ z1 \\1\end{bmatrix} \begin{bmatrix} x2\\ y2 \\z2\\ 1\end{bmatrix} $$lidar_points.T 是方便矩阵乘法
[图片]#### 得到 lidar_to_ego_matrixpython
lidar_calibrated_data nuscenes.get(calibrated_sensor, lidar_sample_data[calibrated_sensor_token])
lidar_to_ego_matrix get_matrix(lidar_calibrated_data)
print(lidar_to_ego_matrix) [[ 0.00203327 0.99970406 0.02424172 0.943713 ][-0.99998053 0.00217566 -0.00584864 0. ][-0.00589965 -0.02422936 0.99968902 1.84023 ][ 0. 0. 0. 1. ]]拿到 ego 相对于 global 的标定
ego_pose_data0 nuscenes.get(ego_pose, lidar_sample_data[ego_pose_token])
# print(ego_pose_data0){token: 9d9bf11fb0e144c8b446d54a8a00184f,
timestamp: 1532402927647951,
rotation: [0.5720320396729045, -0.0016977771610471074, 0.011798001930183783, -0.8201446642457809],
translation: [411.3039349319818, 1180.8903791765097, 0.0]}拿到 ego_to_global_matrix
go_to_global_matrix get_matrix(ego_pose_data0)
print(ego_to_global_matrix)[[-3.45552926e-01 9.38257989e-01 1.62825160e-02 4.11303935e02][-9.38338111e-01 -3.45280305e-01 -1.74097708e-02 1.18089038e03][-1.07128245e-02 -2.12945025e-02 9.99715849e-01 0.00000000e00][ 0.00000000e00 0.00000000e00 0.00000000e00 1.00000000e00]]具体变换
lidar_to_global_matrix ego_to_global_matrix lidar_to_ego_matrix # lidar-ego-global
global_points lidar_point lidar_to_global_matrix.T # 真正变换点代码
def get_matrix(calibrated_data, inverseFalse):args:calibrated_data : calibrated_sensor对象。一般通过nuscenes.get(calibrated_sensortoken..)得到inverse : 是否取逆矩阵。具体根据calibrated_sensor对象里面的 rotation 与 translation 计算出一个4*4的旋转平移矩阵。如果inverse设置为ture。则对这个矩阵逆变换output np.eye(4)output[:3, :3] Quaternion(calibrated_data[rotation]).rotation_matrixoutput[:3, 3] calibrated_data[translation]if inverse:output np.linalg.inv(output)return outputlidar_calibrated_data nuscenes.get(calibrated_sensor, lidar_sample_data[calibrated_sensor_token])# 6.1.2
# lidar_to_ego_matrix 是基于ego而言的。
# point lidar_to_ego_matrix lidar_points.T 代表了lidar - ego 的过程。
lidar_to_ego_matrix get_matrix(lidar_calibrated_data)
print(lidar_to_ego_matrix) [[ 0.00203327 0.99970406 0.02424172 0.943713 ][-0.99998053 0.00217566 -0.00584864 0. ][-0.00589965 -0.02422936 0.99968902 1.84023 ][ 0. 0. 0. 1. ]]
# 6.2 timestamp t0 时的ego_pose0 - global
# ego_to_global_matrix 是基于ego而言的。
# point ego_to_global_matrix lidar_points.T
ego_pose_data0 nuscenes.get(ego_pose, lidar_sample_data[ego_pose_token])
# print(ego_pose_data0){token: 9d9bf11fb0e144c8b446d54a8a00184f,
timestamp: 1532402927647951,
rotation: [0.5720320396729045, -0.0016977771610471074, 0.011798001930183783, -0.8201446642457809],
translation: [411.3039349319818, 1180.8903791765097, 0.0]}ego_to_global_matrix get_matrix(ego_pose_data0)
print(ego_to_global_matrix)[[-3.45552926e-01 9.38257989e-01 1.62825160e-02 4.11303935e02][-9.38338111e-01 -3.45280305e-01 -1.74097708e-02 1.18089038e03][-1.07128245e-02 -2.12945025e-02 9.99715849e-01 0.00000000e00][ 0.00000000e00 0.00000000e00 0.00000000e00 1.00000000e00]]3.3.2 相机 camera 的标定
主要完成 global - ego_pose1 - camera - intrinsic - imagecamera 有很多要循环处理相机标定注意点。求得的变换矩阵后求逆矩阵
拿到 camera 相对 ego 的标定数据
通过 lidar_calibrated_data nuscenes.get(calibrated_sensor, lidar_sample_data[calibrated_sensor_token]) 拿到 lidar 的标定数据。
代码 camera_token sample[data][cam]
camera_data nuscenes.get(sample_data, camera_token)image_file os.path.join(dataroot, camera_data[filename])
image cv2.imread(image_file)camera_calibrated_token camera_data[calibrated_sensor_token]
camera_calibrated_data nuscenes.get(calibrated_sensor, camera_calibrated_token)
camera多了camera_intrinsic 所以写下来。
{token: 75ad8e2a8a3f4594a13db2398430d097,
sensor_token: ec4b5d41840a509984f7ec36419d4c09,
translation: [1.52387798135, 0.494631336551, 1.50932822144],
rotation: [0.6757265034669446, -0.6736266522251881, 0.21214015046209478, -0.21122827103904068],
camera_intrinsic: [[1272.5979470598488, 0.0, 826.6154927353808], [0.0, 1272.5979470598488, 479.75165386361925], [0.0, 0.0, 1.0]]}
得到camera_to_ego_matrix然后取逆得到ego_to_camera_matrix
ego_to_camera_matrix get_matrix(camera_calibrated_data, True) # 不加True得到的是camera_to_ego_matrix拿到 ego 相对于 global 的标定
camera_ego_pose nuscenes.get(ego_pose, camera_data[ego_pose_token])拿到 ego_to_global_matrix然后取逆得到 global_to_ego_matrix
global_to_ego_matrix get_matrix(camera_ego_pose, True) # 不加True得到的是ego_to_global_matrix相比 lidar新增相机内参矩阵
## 7.2 新增步骤处理camera_intrinsiccamera_intrinsic np.eye(4)camera_intrinsic[:3, :3] camera_calibrated_data[camera_intrinsic] # shape 【3 * 3】具体变换
global_to_image camera_intrinsic ego_to_camera_matrix global_to_ego_matrix3.3.3 画框
for token in sample[anns]:annotation nuscenes.get(sample_annotation, token)box Box(annotation[translation], annotation[size], Quaternion(annotation[rotation]))corners box.corners().T # box.corners()形状是[3, 8]global_corners np.concatenate((corners, np.ones((len(corners), 1))), axis1)image_base_corners global_corners global_to_image.Timage_base_corners[:, :2] / image_base_corners[:, [2]]image_base_corners image_base_corners.astype(np.int32)ix, iy [0, 1, 2, 3, 0, 1, 2, 3, 4, 5, 6, 7], [4, 5, 6, 7, 1, 2, 3, 0, 5, 6, 7, 4]for p0, p1 in zip(image_base_corners[ix], image_base_corners[iy]):if p0[2] 0 or p1[2] 0: continuecv2.line(image, (p0[0], p0[1]), (p1[0], p1[1]), (0, 255, 0), 2, 16) 循环表示画0 与 4 的边 画1 与 5 的边 画2 与 6 的边 画3 与 7 的边 画0 与 1 的边 画1 与 2 的边 画2 与 3 的边 画3 与 0 的边 画4 与 5 的边 画5 与 6 的边 画6 与 7 的边 画7 与 4 的边 0 ------ 1/ | / |4 ------ 5 || 3 ---|-- 2 | / | /7 ------ 6可视化
nuscenes.render_annotation(token, out_path./234)
