本文分享在DAIR-V2X-V數(shù)據(jù)集中，將標(biāo)簽轉(zhuǎn)為Kitti格式，并可視化3D檢測效果。

一、將標(biāo)簽轉(zhuǎn)為Kitti格式

DAIR-V2X包括不同類型的數(shù)據(jù)集：

• DAIR-V2X-I
• DAIR-V2X-V
• DAIR-V2X-C
• V2X-Seq-SPD
• V2X-Seq-TFD
• DAIR-V2X-C-Example:?google_drive_link
• V2X-Seq-SPD-Example:?google_drive_link
• V2X-Seq-TFD-Example:?google_drive_link

本文選擇DAIR-V2X-V作為示例。

1、下載DAIR-V2X工程

?DAIR-V2X開源地址：https://github.com/AIR-THU/DAIR-V2X

2、存放數(shù)據(jù)

可以將數(shù)據(jù)存放到data目錄中，比如：data/DAIR-V2X-V/single-vehicle-side，里面包含兩個(gè)關(guān)鍵目錄和一個(gè)文件

calib/

label/

data_info.json

3、修復(fù)bug

在tools/dataset_converter/gen_kitti/label_json2kitti.py中的22行，將 i15 = str(-eval(item["rotation"])) 改為：

i15 = str(-float(item["rotation"]))

如何不修改會報(bào)錯(cuò)的；

DAIR-V2X-gp/tools/dataset_converter/gen_kitti/label_json2kitti.py", line 22, in write_kitti_in_txt
? ? i15 = str(-eval(item["rotation"]))
TypeError: eval() arg 1 must be a string, bytes or code object

將tools/dataset_converter/gen_kitti/label_json2kitti.py復(fù)制到根目錄中，避免找不到tool庫。

4、修改配置參數(shù)

label_json2kitti.py中，可以將rawdata_copy和kitti_pcd2bin注釋掉。

這樣節(jié)約時(shí)間，不用程序拷貝圖像、點(diǎn)云數(shù)據(jù)，只需生成標(biāo)簽即可。

if __name__ == "__main__":print("================ Start to Convert ================")args = parser.parse_args()source_root = args.source_roottarget_root = args.target_rootprint("================ Start to Copy Raw Data ================")mdkir_kitti(target_root)# rawdata_copy(source_root, target_root)# kitti_pcd2bin(target_root)

5、轉(zhuǎn)換數(shù)據(jù)

執(zhí)行如下命令

python dair2kitti.py --source-root ./data/DAIR-V2X-V/single-vehicle-side --target-root ./data/DAIR-V2X-V/single-vehicle-side   --split-path ./data/split_datas/single-vehicle-split-data.json   --label-type camera --sensor-view vehicle

會打印信息

================ Start to Convert ================
================ Start to Copy Raw Data ================
================ Start to Generate Label ================
================ Start to Generate Calibration Files ================
15627 15627
================ Start to Generate ImageSet Files ================

查看目錄：data/DAIR-V2X-V/single-vehicle-side，生成了3個(gè)目錄

ImageSets

testing

training

其中，testing目錄是空的

ImageSets目錄包含：

training目錄包含：

6、查看生成數(shù)據(jù)格式

查看calib中的相機(jī)標(biāo)定文件，比如 000000.txt

P2: 3996.487567 0.0 955.58618 0.0 0.0 3963.430994 527.646219 0.0 0.0 0.0 1.0 0.0
P2: 3996.487567 0.0 955.58618 0.0 0.0 3963.430994 527.646219 0.0 0.0 0.0 1.0 0.0
P2: 3996.487567 0.0 955.58618 0.0 0.0 3963.430994 527.646219 0.0 0.0 0.0 1.0 0.0
P2: 3996.487567 0.0 955.58618 0.0 0.0 3963.430994 527.646219 0.0 0.0 0.0 1.0 0.0
R0_rect: 1 0 0 0 1 0 0 0 1
Tr_velo_to_cam: 0.006283 -0.999979 -0.001899 -0.298036 -0.005334 0.001865 -0.999984 -0.666812 0.999966 0.006293 -0.005322 -0.516927
Tr_velo_to_cam: 0.006283 -0.999979 -0.001899 -0.298036 -0.005334 0.001865 -0.999984 -0.666812 0.999966 0.006293 -0.005322 -0.516927

查看lable_2中的標(biāo)簽，比如 000000.txt

Car 0 0 0.33888581543844903 0 527.938232 69.723068 637.4556269999999 0.850836 4.337498 2.073565 -9.601712831407 0.8624079931420001 32.383280568744 1.615145

二、可視化3D框

?使用Kitti的方式，實(shí)現(xiàn)可視化推理結(jié)果，上面生成的結(jié)果，和kitii標(biāo)簽格式是一致的。

在新建一個(gè)vis目錄包括：

dataset? ? ? ? ? ? ? ? ? ? 存放相機(jī)標(biāo)定數(shù)據(jù)、圖片、標(biāo)簽

save_3d_output? 存放可視化圖片

kitti_3d_vis.py? ? ?可視化運(yùn)行此代碼

kitti_util.py? ? ? ? ? ? 依賴代碼

具體的目錄結(jié)構(gòu)：

主代碼 kitti_3d_vis.py

# kitti_3d_vis.pyfrom __future__ import print_functionimport os
import sys
import cv2
import random
import os.path
import shutil
from PIL import Image
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
ROOT_DIR = os.path.dirname(BASE_DIR)
sys.path.append(BASE_DIR)
sys.path.append(os.path.join(ROOT_DIR, 'mayavi'))
from kitti_util import *def visualization():import mayavi.mlab as mlabdataset = kitti_object(r'./dataset/')path = r'./dataset/testing/label_2/'Save_Path = r'./save_3d_output/'files = os.listdir(path)for file in files:name = file.split('.')[0]save_path = Save_Path + name + '.png'data_idx = int(name)# Load data from datasetobjects = dataset.get_label_objects(data_idx)img = dataset.get_image(data_idx)img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)calib = dataset.get_calibration(data_idx)print(' ------------ save image with 3D bounding box ------- ')print('name:', name)show_image_with_boxes(img, objects, calib, save_path, True)if __name__=='__main__':visualization()

依賴代碼?kitti_util.py

# kitti_util.pyfrom __future__ import print_functionimport os
import sys
import cv2
import numpy as np
from PIL import Image
import matplotlib.pyplot as plt
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
ROOT_DIR = os.path.dirname(BASE_DIR)
sys.path.append(os.path.join(ROOT_DIR, 'mayavi'))class kitti_object(object):def __init__(self, root_dir, split='testing'):self.root_dir = root_dirself.split = splitself.split_dir = os.path.join(root_dir, split)if split == 'training':self.num_samples = 7481elif split == 'testing':self.num_samples = 7518else:print('Unknown split: %s' % (split))exit(-1)self.image_dir = os.path.join(self.split_dir, 'image_2')self.calib_dir = os.path.join(self.split_dir, 'calib')self.label_dir = os.path.join(self.split_dir, 'label_2')def __len__(self):return self.num_samplesdef get_image(self, idx):assert(idx<self.num_samples) img_filename = os.path.join(self.image_dir, '%06d.png'%(idx))return load_image(img_filename)def get_calibration(self, idx):assert(idx<self.num_samples) calib_filename = os.path.join(self.calib_dir, '%06d.txt'%(idx))return Calibration(calib_filename)def get_label_objects(self, idx):# assert(idx<self.num_samples and self.split=='training') label_filename = os.path.join(self.label_dir, '%06d.txt'%(idx))return read_label(label_filename)def show_image_with_boxes(img, objects, calib, save_path, show3d=True):''' Show image with 2D bounding boxes '''img1 = np.copy(img) # for 2d bboximg2 = np.copy(img) # for 3d bboxfor obj in objects:if obj.type=='DontCare':continuecv2.rectangle(img1, (int(obj.xmin),int(obj.ymin)), (int(obj.xmax),int(obj.ymax)), (0,255,0), 2) # 畫2D框box3d_pts_2d, box3d_pts_3d = compute_box_3d(obj, calib.P) # 獲取3D框-圖像(8*2)、3D框-相機(jī)坐標(biāo)系(8*3)img2 = draw_projected_box3d(img2, box3d_pts_2d) # 在圖像上畫3D框if show3d:Image.fromarray(img2).save(save_path) # 保存帶有3D框的圖像# Image.fromarray(img2).show()else:Image.fromarray(img1).save(save_path) # 保存帶有2D框的圖像# Image.fromarray(img1).show()class Object3d(object):''' 3d object label '''def __init__(self, label_file_line):data = label_file_line.split(' ')data[1:] = [float(x) for x in data[1:]]# extract label, truncation, occlusionself.type = data[0] # 'Car', 'Pedestrian', ...self.truncation = data[1] # truncated pixel ratio [0..1]self.occlusion = int(data[2]) # 0=visible, 1=partly occluded, 2=fully occluded, 3=unknownself.alpha = data[3] # object observation angle [-pi..pi]# extract 2d bounding box in 0-based coordinatesself.xmin = data[4] # leftself.ymin = data[5] # topself.xmax = data[6] # rightself.ymax = data[7] # bottomself.box2d = np.array([self.xmin,self.ymin,self.xmax,self.ymax])# extract 3d bounding box informationself.h = data[8] # box heightself.w = data[9] # box widthself.l = data[10] # box length (in meters)self.t = (data[11],data[12],data[13]) # location (x,y,z) in camera coord.self.ry = data[14] # yaw angle (around Y-axis in camera coordinates) [-pi..pi]def print_object(self):print('Type, truncation, occlusion, alpha: %s, %d, %d, %f' % \(self.type, self.truncation, self.occlusion, self.alpha))print('2d bbox (x0,y0,x1,y1): %f, %f, %f, %f' % \(self.xmin, self.ymin, self.xmax, self.ymax))print('3d bbox h,w,l: %f, %f, %f' % \(self.h, self.w, self.l))print('3d bbox location, ry: (%f, %f, %f), %f' % \(self.t[0],self.t[1],self.t[2],self.ry))class Calibration(object):''' Calibration matrices and utils3d XYZ in <label>.txt are in rect camera coord.2d box xy are in image2 coordPoints in <lidar>.bin are in Velodyne coord.y_image2 = P^2_rect * x_recty_image2 = P^2_rect * R0_rect * Tr_velo_to_cam * x_velox_ref = Tr_velo_to_cam * x_velox_rect = R0_rect * x_refP^2_rect = [f^2_u,  0,      c^2_u,  -f^2_u b^2_x;0,      f^2_v,  c^2_v,  -f^2_v b^2_y;0,      0,      1,      0]= K * [1|t]image2 coord:----> x-axis (u)||v y-axis (v)velodyne coord:front x, left y, up zrect/ref camera coord:right x, down y, front zRef (KITTI paper): http://www.cvlibs.net/publications/Geiger2013IJRR.pdfTODO(rqi): do matrix multiplication only once for each projection.'''def __init__(self, calib_filepath, from_video=False):if from_video:calibs = self.read_calib_from_video(calib_filepath)else:calibs = self.read_calib_file(calib_filepath)# Projection matrix from rect camera coord to image2 coordself.P = calibs['P2'] self.P = np.reshape(self.P, [3,4])# Rigid transform from Velodyne coord to reference camera coordself.V2C = calibs['Tr_velo_to_cam']self.V2C = np.reshape(self.V2C, [3,4])self.C2V = inverse_rigid_trans(self.V2C)# Rotation from reference camera coord to rect camera coordself.R0 = calibs['R0_rect']self.R0 = np.reshape(self.R0,[3,3])# Camera intrinsics and extrinsicsself.c_u = self.P[0,2]self.c_v = self.P[1,2]self.f_u = self.P[0,0]self.f_v = self.P[1,1]self.b_x = self.P[0,3]/(-self.f_u) # relative self.b_y = self.P[1,3]/(-self.f_v)def read_calib_file(self, filepath):''' Read in a calibration file and parse into a dictionary.'''data = {}with open(filepath, 'r') as f:for line in f.readlines():line = line.rstrip()if len(line)==0: continuekey, value = line.split(':', 1)# The only non-float values in these files are dates, which# we don't care about anywaytry:data[key] = np.array([float(x) for x in value.split()])except ValueError:passreturn datadef read_calib_from_video(self, calib_root_dir):''' Read calibration for camera 2 from video calib files.there are calib_cam_to_cam and calib_velo_to_cam under the calib_root_dir'''data = {}cam2cam = self.read_calib_file(os.path.join(calib_root_dir, 'calib_cam_to_cam.txt'))velo2cam = self.read_calib_file(os.path.join(calib_root_dir, 'calib_velo_to_cam.txt'))Tr_velo_to_cam = np.zeros((3,4))Tr_velo_to_cam[0:3,0:3] = np.reshape(velo2cam['R'], [3,3])Tr_velo_to_cam[:,3] = velo2cam['T']data['Tr_velo_to_cam'] = np.reshape(Tr_velo_to_cam, [12])data['R0_rect'] = cam2cam['R_rect_00']data['P2'] = cam2cam['P_rect_02']return datadef cart2hom(self, pts_3d):''' Input: nx3 points in CartesianOupput: nx4 points in Homogeneous by pending 1'''n = pts_3d.shape[0]pts_3d_hom = np.hstack((pts_3d, np.ones((n,1))))return pts_3d_hom# =========================== # ------- 3d to 3d ---------- # =========================== def project_velo_to_ref(self, pts_3d_velo):pts_3d_velo = self.cart2hom(pts_3d_velo) # nx4return np.dot(pts_3d_velo, np.transpose(self.V2C))def project_ref_to_velo(self, pts_3d_ref):pts_3d_ref = self.cart2hom(pts_3d_ref) # nx4return np.dot(pts_3d_ref, np.transpose(self.C2V))def project_rect_to_ref(self, pts_3d_rect):''' Input and Output are nx3 points '''return np.transpose(np.dot(np.linalg.inv(self.R0), np.transpose(pts_3d_rect)))def project_ref_to_rect(self, pts_3d_ref):''' Input and Output are nx3 points '''return np.transpose(np.dot(self.R0, np.transpose(pts_3d_ref)))def project_rect_to_velo(self, pts_3d_rect):''' Input: nx3 points in rect camera coord.Output: nx3 points in velodyne coord.''' pts_3d_ref = self.project_rect_to_ref(pts_3d_rect)return self.project_ref_to_velo(pts_3d_ref)def project_velo_to_rect(self, pts_3d_velo):pts_3d_ref = self.project_velo_to_ref(pts_3d_velo)return self.project_ref_to_rect(pts_3d_ref)def corners3d_to_img_boxes(self, corners3d):""":param corners3d: (N, 8, 3) corners in rect coordinate:return: boxes: (None, 4) [x1, y1, x2, y2] in rgb coordinate:return: boxes_corner: (None, 8) [xi, yi] in rgb coordinate"""sample_num = corners3d.shape[0]corners3d_hom = np.concatenate((corners3d, np.ones((sample_num, 8, 1))), axis=2)  # (N, 8, 4)img_pts = np.matmul(corners3d_hom, self.P.T)  # (N, 8, 3)x, y = img_pts[:, :, 0] / img_pts[:, :, 2], img_pts[:, :, 1] / img_pts[:, :, 2]x1, y1 = np.min(x, axis=1), np.min(y, axis=1)x2, y2 = np.max(x, axis=1), np.max(y, axis=1)boxes = np.concatenate((x1.reshape(-1, 1), y1.reshape(-1, 1), x2.reshape(-1, 1), y2.reshape(-1, 1)), axis=1)boxes_corner = np.concatenate((x.reshape(-1, 8, 1), y.reshape(-1, 8, 1)), axis=2)return boxes, boxes_corner# =========================== # ------- 3d to 2d ---------- # =========================== def project_rect_to_image(self, pts_3d_rect):''' Input: nx3 points in rect camera coord.Output: nx2 points in image2 coord.'''pts_3d_rect = self.cart2hom(pts_3d_rect)pts_2d = np.dot(pts_3d_rect, np.transpose(self.P)) # nx3pts_2d[:,0] /= pts_2d[:,2]pts_2d[:,1] /= pts_2d[:,2]return pts_2d[:,0:2]def project_velo_to_image(self, pts_3d_velo):''' Input: nx3 points in velodyne coord.Output: nx2 points in image2 coord.'''pts_3d_rect = self.project_velo_to_rect(pts_3d_velo)return self.project_rect_to_image(pts_3d_rect)# =========================== # ------- 2d to 3d ---------- # =========================== def project_image_to_rect(self, uv_depth):''' Input: nx3 first two channels are uv, 3rd channelis depth in rect camera coord.Output: nx3 points in rect camera coord.'''n = uv_depth.shape[0]x = ((uv_depth[:,0]-self.c_u)*uv_depth[:,2])/self.f_u + self.b_xy = ((uv_depth[:,1]-self.c_v)*uv_depth[:,2])/self.f_v + self.b_ypts_3d_rect = np.zeros((n,3))pts_3d_rect[:,0] = xpts_3d_rect[:,1] = ypts_3d_rect[:,2] = uv_depth[:,2]return pts_3d_rectdef project_image_to_velo(self, uv_depth):pts_3d_rect = self.project_image_to_rect(uv_depth)return self.project_rect_to_velo(pts_3d_rect)def rotx(t):''' 3D Rotation about the x-axis. '''c = np.cos(t)s = np.sin(t)return np.array([[1,  0,  0],[0,  c, -s],[0,  s,  c]])def roty(t):''' Rotation about the y-axis. '''c = np.cos(t)s = np.sin(t)return np.array([[c,  0,  s],[0,  1,  0],[-s, 0,  c]])def rotz(t):''' Rotation about the z-axis. '''c = np.cos(t)s = np.sin(t)return np.array([[c, -s,  0],[s,  c,  0],[0,  0,  1]])def transform_from_rot_trans(R, t):''' Transforation matrix from rotation matrix and translation vector. '''R = R.reshape(3, 3)t = t.reshape(3, 1)return np.vstack((np.hstack([R, t]), [0, 0, 0, 1]))def inverse_rigid_trans(Tr):''' Inverse a rigid body transform matrix (3x4 as [R|t])[R'|-R't; 0|1]'''inv_Tr = np.zeros_like(Tr) # 3x4inv_Tr[0:3,0:3] = np.transpose(Tr[0:3,0:3])inv_Tr[0:3,3] = np.dot(-np.transpose(Tr[0:3,0:3]), Tr[0:3,3])return inv_Trdef read_label(label_filename):lines = [line.rstrip() for line in open(label_filename)]objects = [Object3d(line) for line in lines]return objectsdef load_image(img_filename):return cv2.imread(img_filename)def load_velo_scan(velo_filename):scan = np.fromfile(velo_filename, dtype=np.float32)scan = scan.reshape((-1, 4))return scandef project_to_image(pts_3d, P):'''將3D坐標(biāo)點(diǎn)投影到圖像平面上，生成2D坐pts_3d是一個(gè)nx3的矩陣，包含了待投影的3D坐標(biāo)點(diǎn)（每行一個(gè)點(diǎn)），P是相機(jī)的投影矩陣，通常是一個(gè)3x4的矩陣。函數(shù)返回一個(gè)nx2的矩陣，包含了投影到圖像平面上的2D坐標(biāo)點(diǎn)。'''''' Project 3d points to image plane.Usage: pts_2d = projectToImage(pts_3d, P)input: pts_3d: nx3 matrixP:      3x4 projection matrixoutput: pts_2d: nx2 matrixP(3x4) dot pts_3d_extended(4xn) = projected_pts_2d(3xn)=> normalize projected_pts_2d(2xn)<=> pts_3d_extended(nx4) dot P'(4x3) = projected_pts_2d(nx3)=> normalize projected_pts_2d(nx2)'''n = pts_3d.shape[0] # 獲取3D點(diǎn)的數(shù)量pts_3d_extend = np.hstack((pts_3d, np.ones((n,1)))) # 將每個(gè)3D點(diǎn)的坐標(biāo)擴(kuò)展為齊次坐標(biāo)形式（4D），通過在每個(gè)點(diǎn)的末尾添加1，創(chuàng)建了一個(gè)nx4的矩陣。# print(('pts_3d_extend shape: ', pts_3d_extend.shape))pts_2d = np.dot(pts_3d_extend, np.transpose(P)) # 將擴(kuò)展的3D坐標(biāo)點(diǎn)矩陣與投影矩陣P相乘，得到一個(gè)nx3的矩陣，其中每一行包含了3D點(diǎn)在圖像平面上的投影坐標(biāo)。每個(gè)點(diǎn)的坐標(biāo)表示為[x, y, z]。pts_2d[:,0] /= pts_2d[:,2] # 將投影坐標(biāo)中的x坐標(biāo)除以z坐標(biāo)，從而獲得2D圖像上的x坐標(biāo)。pts_2d[:,1] /= pts_2d[:,2] # 將投影坐標(biāo)中的y坐標(biāo)除以z坐標(biāo)，從而獲得2D圖像上的y坐標(biāo)。return pts_2d[:,0:2] # 返回一個(gè)nx2的矩陣,其中包含了每個(gè)3D點(diǎn)在2D圖像上的坐標(biāo)。def compute_box_3d(obj, P):'''計(jì)算對象的3D邊界框在圖像平面上的投影輸入: obj代表一個(gè)物體標(biāo)簽信息,  P代表相機(jī)的投影矩陣-內(nèi)參。輸出: 返回兩個(gè)值, corners_3d表示3D邊界框在 相機(jī)坐標(biāo)系 的8個(gè)角點(diǎn)的坐標(biāo)-3D坐標(biāo)。corners_2d表示3D邊界框在 圖像上 的8個(gè)角點(diǎn)的坐標(biāo)-2D坐標(biāo)。'''# compute rotational matrix around yaw axis# 計(jì)算一個(gè)繞Y軸旋轉(zhuǎn)的旋轉(zhuǎn)矩陣R，用于將3D坐標(biāo)從世界坐標(biāo)系轉(zhuǎn)換到相機(jī)坐標(biāo)系。obj.ry是對象的偏航角R = roty(obj.ry)    # 3d bounding box dimensions# 物體實(shí)際的長、寬、高l = obj.l;w = obj.w;h = obj.h;# 3d bounding box corners# 存儲了3D邊界框的8個(gè)角點(diǎn)相對于對象中心的坐標(biāo)。這些坐標(biāo)定義了3D邊界框的形狀。x_corners = [l/2,l/2,-l/2,-l/2,l/2,l/2,-l/2,-l/2];y_corners = [0,0,0,0,-h,-h,-h,-h];z_corners = [w/2,-w/2,-w/2,w/2,w/2,-w/2,-w/2,w/2];# rotate and translate 3d bounding box# 1、將3D邊界框的角點(diǎn)坐標(biāo)從對象坐標(biāo)系轉(zhuǎn)換到相機(jī)坐標(biāo)系。它使用了旋轉(zhuǎn)矩陣Rcorners_3d = np.dot(R, np.vstack([x_corners,y_corners,z_corners]))# 3D邊界框的坐標(biāo)進(jìn)行平移corners_3d[0,:] = corners_3d[0,:] + obj.t[0];corners_3d[1,:] = corners_3d[1,:] + obj.t[1];corners_3d[2,:] = corners_3d[2,:] + obj.t[2];# 2、檢查對象是否在相機(jī)前方，因?yàn)橹挥性谙鄼C(jī)前方的對象才會被繪制。# 如果對象的Z坐標(biāo)（深度）小于0.1，就意味著對象在相機(jī)后方，那么corners_2d將被設(shè)置為None，函數(shù)將返回None。if np.any(corners_3d[2,:]<0.1):corners_2d = Nonereturn corners_2d, np.transpose(corners_3d)# project the 3d bounding box into the image plane# 3、將相機(jī)坐標(biāo)系下的3D邊界框的角點(diǎn)，投影到圖像平面上，得到它們在圖像上的2D坐標(biāo)。corners_2d = project_to_image(np.transpose(corners_3d), P);return corners_2d, np.transpose(corners_3d)def compute_orientation_3d(obj, P):''' Takes an object and a projection matrix (P) and projects the 3dobject orientation vector into the image plane.Returns:orientation_2d: (2,2) array in left image coord.orientation_3d: (2,3) array in in rect camera coord.'''# compute rotational matrix around yaw axisR = roty(obj.ry)# orientation in object coordinate systemorientation_3d = np.array([[0.0, obj.l],[0,0],[0,0]])# rotate and translate in camera coordinate system, project in imageorientation_3d = np.dot(R, orientation_3d)orientation_3d[0,:] = orientation_3d[0,:] + obj.t[0]orientation_3d[1,:] = orientation_3d[1,:] + obj.t[1]orientation_3d[2,:] = orientation_3d[2,:] + obj.t[2]# vector behind image plane?if np.any(orientation_3d[2,:]<0.1):orientation_2d = Nonereturn orientation_2d, np.transpose(orientation_3d)# project orientation into the image planeorientation_2d = project_to_image(np.transpose(orientation_3d), P);return orientation_2d, np.transpose(orientation_3d)def draw_projected_box3d(image, qs, color=(0,60,255), thickness=2):'''qs: 包含8個(gè)3D邊界框角點(diǎn)坐標(biāo)的數(shù)組, 形狀為(8, 2)。圖像坐標(biāo)下的3D框, 8個(gè)頂點(diǎn)坐標(biāo)。'''''' Draw 3d bounding box in imageqs: (8,2) array of vertices for the 3d box in following order:1 -------- 0/|         /|2 -------- 3 .| |        | |. 5 -------- 4|/         |/6 -------- 7'''qs = qs.astype(np.int32) # 將輸入的頂點(diǎn)坐標(biāo)轉(zhuǎn)換為整數(shù)類型，以便在圖像上繪制。# 這個(gè)循環(huán)迭代4次，每次處理一個(gè)邊界框的一條邊。for k in range(0,4):# Ref: http://docs.enthought.com/mayavi/mayavi/auto/mlab_helper_functions.html# 定義了要繪制的邊的起始點(diǎn)和結(jié)束點(diǎn)的索引。在這個(gè)循環(huán)中，它用于繪制邊界框的前四條邊。i,j=k,(k+1)%4cv2.line(image, (qs[i,0],qs[i,1]), (qs[j,0],qs[j,1]), color, thickness)# 定義了要繪制的邊的起始點(diǎn)和結(jié)束點(diǎn)的索引。在這個(gè)循環(huán)中，它用于繪制邊界框的后四條邊，與前四條邊平行i,j=k+4,(k+1)%4 + 4cv2.line(image, (qs[i,0],qs[i,1]), (qs[j,0],qs[j,1]), color, thickness)# 定義了要繪制的邊的起始點(diǎn)和結(jié)束點(diǎn)的索引。在這個(gè)循環(huán)中，它用于繪制連接前四條邊和后四條邊的邊界框的邊。i,j=k,k+4cv2.line(image, (qs[i,0],qs[i,1]), (qs[j,0],qs[j,1]), color, thickness)return image

運(yùn)行后會在save_3d_output中保存可視化的圖像。

模型推理結(jié)果可視化效果：

這個(gè)數(shù)據(jù)集的部分標(biāo)簽不準(zhǔn)確！！！

總結(jié)：有些失望，不準(zhǔn)確的標(biāo)簽占比較大；本來還想著用它替換Kitti的數(shù)據(jù)集。

只能用來做預(yù)訓(xùn)練，或者人工挑選標(biāo)簽做數(shù)據(jù)清洗。