kitti数据label的2d与3d坐标转为像素坐标方法与教程(代码实现)

前言

kitti数据是一个通用数据，但里面标定文件或标签文件等相互关心很有可能把大家陷入其中。为此，本文分享kitti数据的label标签内容转换，特别是标签的3d坐标转换到图像像素坐标，这也是本文重点介绍内容。而本文与其它文章不太相同，我们不注重kitti原理介绍，而是使用代码将其转换，并给出完整代码。

一、kitti标签label坐标转换的主函数

1、主函数调用代码

先给出调用的主函数，然后在分别讲解，直接看代码如下：

if __name__ == '__main__':path = r'C:\Users\Administrator\Desktop\kitti_data\KITTI\training'index = '000008'calib_path = os.path.join(path, 'calib', index + '.txt')  # 获得标定文件image_2_path = os.path.join(path, 'image_2', index + '.png')  # 获得图像label_2_path = os.path.join(path, 'label_2', index + '.txt')  # 获得标签leblimg = get_image(image_2_path)  # 读取图像objects = read_label(label_2_path)  # 处理标签calib = Calibration(calib_path)  # 处理标定文件img_bbox2d, img_bbox3d, box3d_to_pixel2d = labels_boxes2pixel_in_image(img, objects, calib)  # 重点内容，集成转换cv2.imwrite('./bbox2d.png',img_bbox2d)cv2.imwrite('./bbox3d.png',img_bbox3d)

2、数据格式示意图

二、kitti数据获取

1、图像获取

因为要将其2d与3d坐标展现到图像中，需要获取图像数据，其代码如下：

def get_image(img_path):img = cv2.imread(img_path)return img

2、label标签数据获取

读取标签txt文件内容，将其每个目标内容赋值给Object3d类中，构建一个列表，如下：

# 读取label标签
def read_label(label_filename):lines = [line.rstrip() for line in open(label_filename)]objects = [Object3d(line) for line in lines]return objects

而Object类是对标签的解析，如下：

# 转换kitti数据标签labels
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]

后面标签内容我不在解析了。

3、标定文件数据获取

其代码如下：

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 = self.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)  # relativeself.b_y = self.P[1, 3] / (-self.f_v)

旋转可参考：
https://zhuanlan.zhihu.com/p/86223712

            1 -------- 0/|         /|2 -------- 3 .| |        | |. 5 -------- 4|/         |/6 -------- 7

三、kitti标签坐标转换方法

1、集成主函数-labels_boxes2pixel_in_image

我先给出坐标转换集成主函数，该函数实现了kitti标签2d与3d坐标转换为对应像素坐标实现方法，也将其kitti的标签2d坐标与3d转换坐标显示在图中，其代码如下：

def labels_boxes2pixel_in_image(img, objects, calib):"""Show image with 2D bounding boxes:param img: 图像内容:param objects: label标签内容:param calib: 标定文件内容:return: img1, img2,box3d_to_pixel2d,第一个值是2d坐标图像，第二个值是3d坐标转到像素画的图像，第三个值是相机坐标与像素坐标对应点"""img1 = np.copy(img)  # for 2d bboximg2 = np.copy(img)  # for 3d bbox# TODO: change the color of boxesbox3d_to_pixel2d = {"corners_3d":[],"box3d_pts_2d":[]}for obj in objects:# 画2d坐标if obj.type == "DontCare":continueelse:cv2.rectangle(img1,(int(obj.xmin),int(obj.ymin)),(int(obj.xmax), int(obj.ymax)),(0, 255, 0),2,)cv2.putText(img1, str(obj.type), (int(obj.xmin), int(obj.ymin)), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)# 这个非常重要，该代码就是将其label的3d坐标转为像素2d坐标关键函数box3d_pts_2d, corners_3d = compute_box_3d(obj, calib.P)box3d_to_pixel2d['corners_3d'].append(corners_3d)box3d_to_pixel2d['box3d_pts_2d'].append(box3d_pts_2d)if box3d_pts_2d is None:print("something wrong in the 3D box.")continueimg2 = draw_projected_box3d(img2, box3d_pts_2d, color=(0, 255, 0))return img1, img2,box3d_to_pixel2d

在这个函数中只解读compute_box_3d(obj, calib.P)函数，其它画框啥的我不在说明了。

2、标签3d坐标转像素坐标-compute_box_3d(obj, calib.P)

1、函数输入内容

输入是kitti标签内容，已被类给实例化或处理了，输入是标定文件相机内参内容，calib.P是第二个相机内参。

2、旋转角转换

kitti标签最后一个数字是旋转角的转换，该角是绕y轴的旋转，其调用函数是R = roty(obj.ry)。至于该函数实现如下代码：

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]])

不要问我为什么，请看坐标转换矩阵：

你也可以参考知乎：https://zhuanlan.zhihu.com/p/86223712

3、原点基准获取3d坐标（l w h）

假设XYZ坐标轴以(0,0,0)原点为中心，通过kitti标签的长宽高得到八个点实际坐标，如下代码：

 # 3d bounding box dimensionsl = obj.lw = obj.wh = obj.h# 3d bounding box cornersx_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]

上面点坐标依次是下图数字编号顺序，如下：

4、绕Y轴坐标旋转

这个不在解释，如下代码直接np.dot实现旋转，如下：

# rotate and translate 3d bounding boxcorners_3d = np.dot(R, np.vstack([x_corners, y_corners, z_corners]))

5、获得目标3d坐标

旋转之后，直接加上标签XYZ坐标获取3d坐标，如下：

    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]

6、3d坐标转像素坐标

该函数是3d坐标转像素坐标方法调用函数，如下：

# project the 3d bounding box into the image plane，相机坐标转像素坐标corners_2d = project_to_image(np.transpose(corners_3d), P)

该函数就是实现np.dot(P内参,3d坐标)，我也不在解释，代码如下：

def project_to_image(pts_3d, P):""" 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]pts_3d_extend = np.hstack((pts_3d, np.ones((n, 1))))# print(('pts_3d_extend shape: ', pts_3d_extend.shape))# pts_2d = np.dot(P, pts_3d_extend.T).T # 这一句与下面一句是等价的pts_2d = np.dot(pts_3d_extend, np.transpose(P))  # nx3pts_2d[:, 0] /= pts_2d[:, 2]pts_2d[:, 1] /= pts_2d[:, 2]return pts_2d[:, 0:2]

四、完整代码与结果现实

1、结果现实

2、完整代码

这个代码可以直接使用，如下：

import os
import cv2
import numpy as np
import matplotlib.pyplot as plt
def get_image(img_path):img = cv2.imread(img_path)return img
def show_img(img):plt.imshow(img)plt.show()
# 转换kitti数据标签labels
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 estimate_diffculty(self):""" Function that estimate difficulty to detect the object as defined in kitti website"""# height of the bounding boxbb_height = np.abs(self.xmax - self.xmin)if bb_height >= 40 and self.occlusion == 0 and self.truncation <= 0.15:return "Easy"elif bb_height >= 25 and self.occlusion in [0, 1] and self.truncation <= 0.30:return "Moderate"elif (bb_height >= 25 and self.occlusion in [0, 1, 2] and self.truncation <= 0.50):return "Hard"else:return "Unknown"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))print("Difficulty of estimation: {}".format(self.estimate_diffculty()))
def project_to_image(pts_3d, P):""" 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]pts_3d_extend = np.hstack((pts_3d, np.ones((n, 1))))# print(('pts_3d_extend shape: ', pts_3d_extend.shape))# pts_2d = np.dot(P, pts_3d_extend.T).T # 这一句与下面一句是等价的pts_2d = np.dot(pts_3d_extend, np.transpose(P))  # nx3pts_2d[:, 0] /= pts_2d[:, 2]pts_2d[:, 1] /= pts_2d[:, 2]return pts_2d[:, 0:2]
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 compute_box_3d(obj, P):""" Takes an object and a projection matrix (P) and projects the 3dbounding box into the image plane.Returns:corners_2d: (8,2) array in left image coord.corners_3d: (8,3) array in in rect camera coord."""# compute rotational matrix around yaw axisR = roty(obj.ry)# 3d bounding box dimensionsl = obj.lw = obj.wh = obj.h# 3d bounding box cornersx_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 boxcorners_3d = np.dot(R, np.vstack([x_corners, y_corners, z_corners]))# print corners_3d.shapecorners_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]# print 'cornsers_3d: ', corners_3d# only draw 3d bounding box for objs in front of the cameraif 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，相机坐标转像素坐标corners_2d = project_to_image(np.transpose(corners_3d), P)# print 'corners_2d: ', corners_2dreturn corners_2d, np.transpose(corners_3d)
# 读取label标签
def read_label(label_filename):lines = [line.rstrip() for line in open(label_filename)]objects = [Object3d(line) for line in lines]return objects
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 = self.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)  # relativeself.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.Ref: https://github.com/utiasSTARS/pykitti/blob/master/pykitti/utils.py"""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)# ===========================# ------- 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)def project_8p_to_4p(self, pts_2d):x0 = np.min(pts_2d[:, 0])x1 = np.max(pts_2d[:, 0])y0 = np.min(pts_2d[:, 1])y1 = np.max(pts_2d[:, 1])x0 = max(0, x0)# x1 = min(x1, proj.image_width)y0 = max(0, y0)# y1 = min(y1, proj.image_height)return np.array([x0, y0, x1, y1])def project_velo_to_4p(self, pts_3d_velo):""" Input: nx3 points in velodyne coord.Output: 4 points in image2 coord."""pts_2d_velo = self.project_velo_to_image(pts_3d_velo)return self.project_8p_to_4p(pts_2d_velo)# ===========================# ------- 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 inverse_rigid_trans(self,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_Tr
def draw_projected_box3d(image, qs, color=(0, 255, 0), thickness=2):""" Draw 3d bounding box in imageqs: (8,3) array of vertices for the 3d box in following order:1 -------- 0/|         /|2 -------- 3 .| |        | |. 5 -------- 4|/         |/6 -------- 7"""qs = qs.astype(np.int32)for k in range(0, 4):# Ref: http://docs.enthought.com/mayavi/mayavi/auto/mlab_helper_functions.htmli, j = k, (k + 1) % 4# use LINE_AA for opencv3# cv2.line(image, (qs[i,0],qs[i,1]), (qs[j,0],qs[j,1]), color, thickness, cv2.CV_AA)cv2.line(image, (qs[i, 0], qs[i, 1]), (qs[j, 0], qs[j, 1]), color, thickness)i, j = k + 4, (k + 1) % 4 + 4cv2.line(image, (qs[i, 0], qs[i, 1]), (qs[j, 0], qs[j, 1]), color, thickness)i, j = k, k + 4cv2.line(image, (qs[i, 0], qs[i, 1]), (qs[j, 0], qs[j, 1]), color, thickness)return image
def labels_boxes2pixel_in_image(img, objects, calib):"""Show image with 2D bounding boxes:param img: 图像内容:param objects: label标签内容:param calib: 标定文件内容:return: img1, img2,box3d_to_pixel2d,第一个值是2d坐标图像，第二个值是3d坐标转到像素画的图像，第三个值是相机坐标与像素坐标对应点"""img1 = np.copy(img)  # for 2d bboximg2 = np.copy(img)  # for 3d bbox# TODO: change the color of boxesbox3d_to_pixel2d = {"corners_3d":[],"box3d_pts_2d":[]}for obj in objects:# 画2d坐标if obj.type == "DontCare":continueelse:cv2.rectangle(img1,(int(obj.xmin),int(obj.ymin)),(int(obj.xmax), int(obj.ymax)),(0, 255, 0),2,)cv2.putText(img1, str(obj.type), (int(obj.xmin), int(obj.ymin)), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)# 这个非常重要，该代码就是将其label的3d坐标转为像素2d坐标关键函数box3d_pts_2d, corners_3d = compute_box_3d(obj, calib.P)box3d_to_pixel2d['corners_3d'].append(corners_3d)box3d_to_pixel2d['box3d_pts_2d'].append(box3d_pts_2d)if box3d_pts_2d is None:print("something wrong in the 3D box.")continueimg2 = draw_projected_box3d(img2, box3d_pts_2d, color=(0, 255, 0))return img1, img2,box3d_to_pixel2dif __name__ == '__main__':path = r'C:\Users\Administrator\Desktop\kitti_data\KITTI\training'index = '000019'calib_path = os.path.join(path, 'calib', index + '.txt')  # 获得标定文件image_2_path = os.path.join(path, 'image_2', index + '.png')  # 获得图像label_2_path = os.path.join(path, 'label_2', index + '.txt')  # 获得标签leblimg = get_image(image_2_path)  # 读取图像objects = read_label(label_2_path)  # 处理标签calib = Calibration(calib_path)  # 处理标定文件img_bbox2d, img_bbox3d, box3d_to_pixel2d = labels_boxes2pixel_in_image(img, objects, calib)  # 重点内容，集成转换cv2.imwrite('./bbox2d.png',img_bbox2d)cv2.imwrite('./bbox3d.png',img_bbox3d)