AttributeError: 'PoseTransform' object has no attribute 'orientation'

时间: 2023-10-04 20:04:25 浏览: 85
这个错误是由于在一个名为'PoseTransform'的对象中调用了一个名为'orientation'的属性,但该属性在该对象中不存在。这意味着你要使用'PoseTransform'对象的'orientation'属性时发生了错误。根据提供的引用内容,我无法确定具体的上下文和代码,所以我不能给出更具体的解决方案。但是,通常出现这个错误可能是因为对象的属性名称拼写错误、属性不存在或者属性类型不正确。请检查你的代码,确保'PoseTransform'对象中确实存在'orientation'属性,并且属性名称拼写正确。如果属性类型不正确,你可能需要对其进行类型转换或者修改代码以适应正确的属性类型。<span class="em">1</span><span class="em">2</span><span class="em">3</span> #### 引用[.reference_title] - *1* [【Python错误】AttributeError: 'bool' object has no attribute '__call__'](https://blog.csdn.net/lzhui1987/article/details/53236451)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v93^chatsearchT3_1"}}] [.reference_item style="max-width: 33.333333333333336%"] - *2* [python报错: list object has no attribute shape的解决](https://download.csdn.net/download/weixin_38748721/13708867)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v93^chatsearchT3_1"}}] [.reference_item style="max-width: 33.333333333333336%"] - *3* [Python报错:AttributeError: ‘int‘ object has no attribute ‘decode](https://blog.csdn.net/m0_58429499/article/details/126079534)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v93^chatsearchT3_1"}}] [.reference_item style="max-width: 33.333333333333336%"] [ .reference_list ]
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--------------------------------------------------------------------------- AttributeError Traceback (most recent call last) <ipython-input-36-6da7a0d23674> in <module> 13 height=2500 14 ) ---> 15 wordcloud.fit_words(num)#传入词频 16 17 #展示词云 C:\ProgramData\Anaconda3\lib\site-packages\wordcloud\wordcloud.py in fit_words(self, frequencies) 387 self 388 """ --> 389 return self.generate_from_frequencies(frequencies) 390 391 def generate_from_frequencies(self, frequencies, max_font_size=None): # noqa: C901 C:\ProgramData\Anaconda3\lib\site-packages\wordcloud\wordcloud.py in generate_from_frequencies(self, frequencies, max_font_size) 451 font_size = self.height 452 else: --> 453 self.generate_from_frequencies(dict(frequencies[:2]), 454 max_font_size=self.height) 455 # find font sizes C:\ProgramData\Anaconda3\lib\site-packages\wordcloud\wordcloud.py in generate_from_frequencies(self, frequencies, max_font_size) 506 font, orientation=orientation) 507 # get size of resulting text --> 508 box_size = draw.textbbox((0, 0), word, font=transposed_font, anchor="lt") 509 # find possible places using integral image: 510 result = occupancy.sample_position(box_size[3] + self.margin, C:\ProgramData\Anaconda3\lib\site-packages\PIL\ImageDraw.py in textbbox(self, xy, text, font, anchor, spacing, align, direction, features, language, stroke_width, embedded_color) 565 font = self.getfont() 566 mode = "RGBA" if embedded_color else self.fontmode --> 567 bbox = font.getbbox( 568 text, mode, direction, features, language, stroke_width, anchor 569 ) AttributeError: 'TransposedFont' object has no attribute 'getbbox'

--------------------------------------------------------------------------- AttributeError Traceback (most recent call last) <ipython-input-21-d305d74bc36f> in <module> 20 21 ---> 22 word_cloud.generate(data_cut) 23 24 plt.subplots(figsize=(12,8)) C:\ProgramData\Anaconda3\lib\site-packages\wordcloud\wordcloud.py in generate(self, text) 640 self 641 """ --> 642 return self.generate_from_text(text) 643 644 def _check_generated(self): C:\ProgramData\Anaconda3\lib\site-packages\wordcloud\wordcloud.py in generate_from_text(self, text) 622 """ 623 words = self.process_text(text) --> 624 self.generate_from_frequencies(words) 625 return self 626 C:\ProgramData\Anaconda3\lib\site-packages\wordcloud\wordcloud.py in generate_from_frequencies(self, frequencies, max_font_size) 451 font_size = self.height 452 else: --> 453 self.generate_from_frequencies(dict(frequencies[:2]), 454 max_font_size=self.height) 455 # find font sizes C:\ProgramData\Anaconda3\lib\site-packages\wordcloud\wordcloud.py in generate_from_frequencies(self, frequencies, max_font_size) 509 font, orientation=orientation) 510 # get size of resulting text --> 511 box_size = draw.textbbox((0, 0), word, font=transposed_font, anchor="lt") 512 # find possible places using integral image: 513 result = occupancy.sample_position(box_size[3] + self.margin, C:\ProgramData\Anaconda3\lib\site-packages\PIL\ImageDraw.py in textbbox(self, xy, text, font, anchor, spacing, align, direction, features, language, stroke_width, embedded_color) 655 features, 656 language, --> 657 stroke_width, 658 anchor, 659 ink, AttributeError: 'TransposedFont' object has no attribute 'getbbox

E:\anconda\envs\paddleocr\python.exe F:\AI_learning\OCR\ppocr\app.py F:\AI_learning\OCR\ppocr\app.py:8: DeprecationWarning: The parameter use_angle_cls has been deprecated and will be removed in the future. Please use use_textline_orientation instead. ocr = PaddleOCR(use_angle_cls=True, lang="en") Creating model: ('PP-LCNet_x1_0_doc_ori', None) Using official model (PP-LCNet_x1_0_doc_ori), the model files will be automatically downloaded and saved in C:\Users\Administrator\.paddlex\official_models. Traceback (most recent call last): File "F:\AI_learning\OCR\ppocr\app.py", line 8, in <module> ocr = PaddleOCR(use_angle_cls=True, lang="en") ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "E:\anconda\envs\paddleocr\Lib\site-packages\paddleocr\_pipelines\ocr.py", line 144, in __init__ super().__init__(**base_params) File "E:\anconda\envs\paddleocr\Lib\site-packages\paddleocr\_pipelines\base.py", line 73, in __init__ self.paddlex_pipeline = self._create_paddlex_pipeline() ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "E:\anconda\envs\paddleocr\Lib\site-packages\paddleocr\_pipelines\base.py", line 107, in _create_paddlex_pipeline return create_pipeline(config=self._merged_paddlex_config, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "E:\anconda\envs\paddleocr\Lib\site-packages\paddlex\inference\pipelines\__init__.py", line 165, in create_pipeline pipeline = BasePipeline.get(pipeline_name)( ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "E:\anconda\envs\paddleocr\Lib\site-packages\paddlex\utils\deps.py", line 195, in _wrapper return old_init_func(self, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "E:\anconda\envs\paddleocr\Lib\site-packages\paddlex\inference\pipelines\_parallel.py", line 103, in __init__ self._pipeline = self._create_internal_pipeline(config, self.device) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "E:\anconda\envs\paddleocr\Lib\site-packages\paddlex\inference\pipelines\_parallel.py", line 158, in _create_internal_pipeline return self._pipeline_cls( ^^^^^^^^^^^^^^^^^^^ File "E:\anconda\envs\paddleocr\Lib\site-packages\paddlex\inference\pipelines\ocr\pipeline.py", line 73, in __init__ self.doc_preprocessor_pipeline = self.create_pipeline( ^^^^^^^^^^^^^^^^^^^^^ File "E:\anconda\envs\paddleocr\Lib\site-packages\paddlex\inference\pipelines\base.py", line 134, in create_pipeline pipeline = create_pipeline( ^^^^^^^^^^^^^^^^ File "E:\anconda\envs\paddleocr\Lib\site-packages\paddlex\inference\pipelines\__init__.py", line 165, in create_pipeline pipeline = BasePipeline.get(pipeline_name)( ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "E:\anconda\envs\paddleocr\Lib\site-packages\paddlex\utils\deps.py", line 195, in _wrapper return old_init_func(self, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "E:\anconda\envs\paddleocr\Lib\site-packages\paddlex\inference\pipelines\_parallel.py", line 103, in __init__ self._pipeline = self._create_internal_pipeline(config, self.device) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "E:\anconda\envs\paddleocr\Lib\site-packages\paddlex\inference\pipelines\_parallel.py", line 158, in _create_internal_pipeline return self._pipeline_cls( ^^^^^^^^^^^^^^^^^^^ File "E:\anconda\envs\paddleocr\Lib\site-packages\paddlex\inference\pipelines\doc_preprocessor\pipeline.py", line 67, in __init__ self.doc_ori_classify_model = self.create_model(doc_ori_classify_config) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "E:\anconda\envs\paddleocr\Lib\site-packages\paddlex\inference\pipelines\base.py", line 99, in create_model model = create_predictor( ^^^^^^^^^^^^^^^^^ File "E:\anconda\envs\paddleocr\Lib\site-packages\paddlex\inference\models\__init__.py", line 77, in create_predictor return BasePredictor.get(model_name)( ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "E:\anconda\envs\paddleocr\Lib\site-packages\paddlex\inference\models\image_classification\predictor.py", line 49, in __init__ self.preprocessors, self.infer, self.postprocessors = self._build() ^^^^^^^^^^^^^ File "E:\anconda\envs\paddleocr\Lib\site-packages\paddlex\inference\models\image_classification\predictor.py", line 82, in _build infer = self.create_static_infer() ^^^^^^^^^^^^^^^^^^^^^^^^^^ File "E:\anconda\envs\paddleocr\Lib\site-packages\paddlex\inference\models\base\predictor\base_predictor.py", line 240, in create_static_infer return PaddleInfer(self.model_dir, self.MODEL_FILE_PREFIX, self._pp_option) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "E:\anconda\envs\paddleocr\Lib\site-packages\paddlex\inference\models\common\static_infer.py", line 274, in __init__ self.predictor = self._create() ^^^^^^^^^^^^^^ File "E:\anconda\envs\paddleocr\Lib\site-packages\paddlex\inference\models\common\static_infer.py", line 427, in _create config.set_optimization_level(3) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ AttributeError: 'paddle.fluid.libpaddle.AnalysisConfig' object has no attribute 'set_optimization_level' 进程已结束,退出代码为 1 根据这个错误修改代码

import math import os import struct from argparse import ArgumentParser import av import numpy as np import open3d as o3d import rosbag import yaml from sensor_msgs import point_cloud2 import subprocess from protoc.octopudata_localizationinfo_pb2 import LocalizationInfoFrame, LocalizationInfo from protoc.octopudata_trackedobject_pb2 import TrackedObjectFrame, Object, TrackedObject from protoc.octopusdata_controlcommand_pb2 import CommandFrame, ControlCommand from protoc.octopusdata_gnss_pb2 import GnssPoint, GnssPoints from protoc.octopusdata_plantrajectory_pb2 import Trajectory, TrajectoryPoint, PlanTrajectory from protoc.octopusdata_predictionobstacles_pb2 import PerceptionObstacle, \ Obstacle, PredictionTrajectory, PathPoint, PredictionObstacles from protoc.octopusdata_routingpath_pb2 import RoutingPath, Path, Point, RoutingFrames from protoc.octopusdata_vehicleinfo_pb2 import VehicleFrame, VehicleInfo av.logging.set_level(av.logging.PANIC) codec_ctx = av.codec.Codec('hevc','r') h265_code = codec_ctx.create() class Pose: def __init__(self, q0, q1, q2, q3, x, y, z): self.q0 = q0 self.q1 = q1 self.q2 = q2 self.q3 = q3 self.x = x self.y = y self.z = z def get_ts(secs, nsecs): return int(secs * 1000 + nsecs / 1000000) def get_modulo(x, y, z): return math.sqrt(x * x + y * y + z * z) def yaw_from_quaternions(w, x, y, z): a = 2 * (w * z + x * y) b = 1 - 2 * (y * y + z * z) return math.atan2(a, b) def pose_has_nan(p): return math.isnan(p.x) or math.isnan(p.y) or math.isnan(p.z) or \ math.isnan(p.q0) or math.isnan(p.q1) or math.isnan(p.q2) or \ math.isnan(p.q3) def get_t(q0, q1, q2, q3, x, y, z): aa = 1 - 2 * (q2 * q2 + q3 * q3) ab = 2 * (q1 * q2 - q0 * q3) ac = 2 * (q1 * q3 + q0 * q2) ba = 2 * (q1 * q2 + q0 * q3) bb = 1 - 2 * (q1 * q1 + q3 * q3) bc = 2 * (q2 * q3 - q0 * q1) ca = 2 * (q1 * q3 - q0 * q2) cb = 2 * (q2 * q3 + q0 * q1) cc = 1 - 2 * (q1 * q1 + q2 * q2) t = np.mat([[aa, ab, ac, x], [ba, bb, bc, y], [ca, cb, cc, z], [0, 0, 0, 1]]) return t def get_label(perception_typ, subtype): if perception_typ == 3: return 'Pedestrian' elif perception_typ == 4: return 'Bi_Tricycle' elif perception_typ == 5: if subtype == 5: return 'Truck' elif subtype == 6: return 'Bus' else: return 'Car' else: return 'unknow' def main(args): for file in os.listdir(args.input): if file.endswith('.bag'): bag_path = os.path.join(args.input, file) bag = rosbag.Bag(bag_path, "r") output_dir = os.getenv('output_dir') if not os.path.exists(os.path.join(output_dir, 'innoPtClound_A4')): os.makedirs(os.path.join(output_dir, 'innoPtClound_A4')) if not os.path.exists(os.path.join(output_dir, 'innoPtClound_B2')): os.makedirs(os.path.join(output_dir, 'innoPtClound_B2')) if not os.path.exists(os.path.join(output_dir, 'radar_track_array_0')): os.makedirs(os.path.join(output_dir, 'radar_track_array_0')) if not os.path.exists(os.path.join(output_dir, 'radar_track_array_3')): os.makedirs(os.path.join(output_dir, 'radar_track_array_3')) if not os.path.exists(os.path.join(output_dir, 'mdc_camera_instance_74')): os.makedirs(os.path.join(output_dir, 'mdc_camera_instance_74')) if not os.path.exists(os.path.join(output_dir, 'mdc_camera_instance_73')): os.makedirs(os.path.join(output_dir, 'mdc_camera_instance_73')) if not os.path.exists(os.path.join(output_dir, 'mdc_camera_instance_72')): os.makedirs(os.path.join(output_dir, 'mdc_camera_instance_72')) if not os.path.exists(os.path.join(output_dir, 'mdc_camera_instance_71')): os.makedirs(os.path.join(output_dir, 'mdc_camera_instance_71')) routes = [] controls = [] plans = [] preds = [] gnss = [] vehs = [] locs = [] objs = [] ego_pose = None has_camera_71 = False has_camera_72 = False has_camera_73 = False has_camera_74 = False has_lidar_A4 = False has_lidar_B2 = False has_radar_0 = False has_radar_3 = False lidar_num = 0 image_num = 0 radar_num = 0 for topic, msg, t in bag.read_messages(): time_stamp = int(t.to_sec() * 1000) # 以 rosbag 时间戳(t)为基准,转换为 13 位时间戳 if topic == '/innoPtClound_A4': ### 图达通 时间辍应该是13位数字,图达通雷达8位,华为96线6位 # file_path = os.path.join(output_dir, 'innoPtClound_A4', '{}.pcd'.format(int(msg.header.stamp.secs * 1000 + # msg.header.stamp.nsecs / 1000000))) file_path = os.path.join(output_dir, 'innoPtClound_A4', '{}.pcd'.format(time_stamp)) # print(file_path) # 提取点云数据,包括 x, y, z points = list(point_cloud2.read_points(msg, field_names=["x", "y", "z", "intensity"], skip_nans=True)) if points: # 转换为 numpy 数组,添加 intensity, ring, timestamp 字段 np_points = np.array(points) # (N, 3), 包含 x, y, z # 转换为 Open3D 格式点云 pcd = o3d.geometry.PointCloud() pcd.points = o3d.utility.Vector3dVector(np_points[:, :3]) # x, y, z pcd.colors = o3d.utility.Vector3dVector(np.tile(np_points[:, 3:4] / np_points[:, 3:4].max(), (1, 3))) # 用 intensity 作为灰度颜色 o3d.io.write_point_cloud(file_path, pcd) lidar_num += 1 has_lidar_A4 = True elif topic == '/innoPtClound_B2': ### 图达通 # file_path = os.path.join(output_dir, 'innoPtClound_B2', '{}.pcd'.format(int(msg.header.stamp.secs * 1000 + # msg.header.stamp.nsecs / 1000000))) file_path = os.path.join(output_dir, 'innoPtClound_B2', '{}.pcd'.format(time_stamp)) # print(file_path) # 提取点云数据,包括 x, y, z points = list(point_cloud2.read_points(msg, field_names=["x", "y", "z", "intensity"], skip_nans=True)) if points: # 转换为 numpy 数组,添加 intensity, ring, timestamp 字段 np_points = np.array(points) # (N, 3), 包含 x, y, z # 转换为 Open3D 格式点云 pcd = o3d.geometry.PointCloud() pcd.points = o3d.utility.Vector3dVector(np_points[:, :3]) # x, y, z pcd.colors = o3d.utility.Vector3dVector(np.tile(np_points[:, 3:4] / np_points[:, 3:4].max(), (1, 3))) # 用 intensity 作为灰度颜色 o3d.io.write_point_cloud(file_path, pcd) lidar_num += 1 has_lidar_B2 = True elif topic == 'mdc_camera_instance_74': ### 相机 时间辍应该是13位数字 # time_stamp = int(msg.header.stamp.secs * 1000 + # msg.header.stamp.nsecs / 1000000) file_path = os.path.join(output_dir, 'mdc_camera_instance_74', '{}.jpg'.format(time_stamp)) packet = av.packet.Packet(msg.data) try: out = h265_code.decode(packet) for frame in out: if frame.format.name != 'rgb24': frame = frame.reformat(format='rgb24') img = frame.to_image() img.save(file_path) image_num += 1 has_camera_74 = True except Exception as e: print("{} frame can not trans to jpg".format(time_stamp), e) elif topic == 'mdc_camera_instance_73': ### 相机 时间辍应该是13位数字 # time_stamp = int(msg.header.stamp.secs * 1000 + # msg.header.stamp.nsecs / 1000000) file_path = os.path.join(output_dir, 'mdc_camera_instance_73', '{}.jpg'.format(time_stamp)) packet = av.packet.Packet(msg.data) try: out = h265_code.decode(packet) for frame in out: if frame.format.name != 'rgb24': frame = frame.reformat(format='rgb24') img = frame.to_image() img.save(file_path) image_num += 1 has_camera_73 = True except Exception as e: print("{} frame can not trans to jpg".format(time_stamp), e) elif topic == 'mdc_camera_instance_72': ### 相机 # time_stamp = int(msg.header.stamp.secs * 1000 + # msg.header.stamp.nsecs / 1000000) file_path = os.path.join(output_dir, 'mdc_camera_instance_72', '{}.jpg'.format(time_stamp)) packet = av.packet.Packet(msg.data) try: out = h265_code.decode(packet) for frame in out: if frame.format.name != 'rgb24': frame = frame.reformat(format='rgb24') img = frame.to_image() img.save(file_path) image_num += 1 has_camera_72 = True except Exception as e: print("{} frame can not trans to jpg".format(time_stamp), e) elif topic == 'mdc_camera_instance_71': ### 相机 # time_stamp = int(msg.header.stamp.secs * 1000 + # msg.header.stamp.nsecs / 1000000) file_path = os.path.join(output_dir, 'mdc_camera_instance_71', '{}.jpg'.format(time_stamp)) packet = av.packet.Packet(msg.data) try: out = h265_code.decode(packet) for frame in out: if frame.format.name != 'rgb24': frame = frame.reformat(format='rgb24') img = frame.to_image() img.save(file_path) image_num += 1 has_camera_71 = True except Exception as e: print("{} frame can not trans to jpg".format(time_stamp), e) elif topic == '/radar_track_array_0': ### 大陆408 时间辍应该是13位数字 # file_path = os.path.join(output_dir, 'radar_track_array_0', '{}.pcd'.format(int(msg.header.stamp.secs * 1000 + # msg.header.stamp.nsecs / 1000000))) file_path = os.path.join(output_dir, 'radar_track_array_0', '{}.pcd'.format(time_stamp)) points = [] for track in msg.trackList: x, y, z = track.x, track.y, track.z vx, vy, ax, ay = track.vx, track.vy, track.ax, track.ay rcs, snr, yawRate = track.rcs, track.snr, track.yawRate obj_id, trackType, lifetime = track.id, track.trackType, track.lifetime # 过滤无效点 if np.isnan(x) or np.isnan(y) or np.isnan(z): continue points.append([x, y, z, vx, vy, ax, ay, rcs, snr, yawRate, obj_id, trackType, lifetime]) if not points: print("没有有效点云数据") continue points = np.array(points) # **写入 PCD 文件** with open(file_path, 'w') as f: # **写入 PCD 头部** f.write("# .PCD v0.7 - Point Cloud Data file format\n") f.write("VERSION 0.7\n") f.write(f"FIELDS x y z vx vy ax ay rcs snr yawRate id trackType lifetime\n") f.write("SIZE 4 4 4 4 4 4 4 4 4 4 4 4 4\n") f.write("TYPE F F F F F F F F F F I I I\n") # F = float, I = int f.write("COUNT 1 1 1 1 1 1 1 1 1 1 1 1 1\n") f.write(f"WIDTH {points.shape[0]}\n") f.write("HEIGHT 1\n") f.write("VIEWPOINT 0 0 0 1 0 0 0\n") f.write(f"POINTS {points.shape[0]}\n") f.write("DATA ascii\n") # **写入点云数据** np.savetxt(f, points, fmt="%.6f %.6f %.6f %.6f %.6f %.6f %.6f %.6f %.6f %.6f %d %d %d") radar_num += 1 has_radar_0 = True elif topic == '/radar_track_array_3': ### 大陆408 # file_path = os.path.join(output_dir, 'radar_track_array_3', '{}.pcd'.format(int(msg.header.stamp.secs * 1000 + # msg.header.stamp.nsecs / 1000000))) file_path = os.path.join(output_dir, 'radar_track_array_3', '{}.pcd'.format(time_stamp)) points = [] for track in msg.trackList: x, y, z = track.x, track.y, track.z vx, vy, ax, ay = track.vx, track.vy, track.ax, track.ay rcs, snr, yawRate = track.rcs, track.snr, track.yawRate obj_id, trackType, lifetime = track.id, track.trackType, track.lifetime # 过滤无效点 if np.isnan(x) or np.isnan(y) or np.isnan(z): continue points.append([x, y, z, vx, vy, ax, ay, rcs, snr, yawRate, obj_id, trackType, lifetime]) if not points: print("没有有效点云数据") continue points = np.array(points) # **写入 PCD 文件** with open(file_path, 'w') as f: # **写入 PCD 头部** f.write("# .PCD v0.7 - Point Cloud Data file format\n") f.write("VERSION 0.7\n") f.write(f"FIELDS x y z vx vy ax ay rcs snr yawRate id trackType lifetime\n") f.write("SIZE 4 4 4 4 4 4 4 4 4 4 4 4 4\n") f.write("TYPE F F F F F F F F F F I I I\n") # F = float, I = int f.write("COUNT 1 1 1 1 1 1 1 1 1 1 1 1 1\n") f.write(f"WIDTH {points.shape[0]}\n") f.write("HEIGHT 1\n") f.write("VIEWPOINT 0 0 0 1 0 0 0\n") f.write(f"POINTS {points.shape[0]}\n") f.write("DATA ascii\n") # **写入点云数据** np.savetxt(f, points, fmt="%.6f %.6f %.6f %.6f %.6f %.6f %.6f %.6f %.6f %.6f %d %d %d") radar_num += 1 has_radar_3 = True elif topic == '/routing/routing_response_viz': rv = RoutingPath() rv.timestamp = int(t.secs * 1000 + t.nsecs / 1000000) rv.stamp_secs = t.secs 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point_pb.z = trajectory_point.path_point.point.z point_pb.theta = trajectory_point.path_point.theta point_pb.kappa = trajectory_point.path_point.kappa point_pb.lane_id = trajectory_point.path_point.lane_id point_pb.v = trajectory_point.v point_pb.a = trajectory_point.a point_pb.relative_time = trajectory_point.relative_time points_pbs.append(point_pb) traj_pb.path_point.extend(points_pbs) traj_pbs.append(traj_pb) ob_pb.prediction_trajectory.extend(traj_pbs) obj_list.append(ob_pb) tr_pb.obstacle_info.extend(obj_list) preds.append(tr_pb) elif topic == '/inspvax': pb_loc_gnss = GnssPoint() pb_loc_gnss.stamp_secs = msg.header.stamp.secs # 1 pb_loc_gnss.stamp_nsecs = msg.header.stamp.nsecs # 2 pb_loc_gnss.timestamp = get_ts(msg.header.stamp.secs, msg.header.stamp.nsecs) pb_loc_gnss.latitude = msg.latitude # 3 pb_loc_gnss.longitude = msg.longitude # 4 pb_loc_gnss.elevation = msg.altitude gnss.append(pb_loc_gnss) elif topic == '/holo/VehicleInfoMagotan': veh_pb = VehicleFrame() 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