做展示型企业网站,仿牌外贸网站推广,骏域网站建设,体育评论做的好的网站目录 一、平台 二、数据 三、代码 3.1 文件组织结构 3.2 lasDataLoader.py 读取数据 3.3 修改原始模型的通道数量 3.4 lasTrainSS.py【训练】 3.5 lasTestSS.py【预测】 一、平台 Windows 10 GPU RTX 3090 CUDA 11.1 cudnn 8.9.6 Python 3.9 Torch 1.9.1 cu111…目录 一、平台 二、数据 三、代码 3.1 文件组织结构 3.2 lasDataLoader.py 读取数据 3.3 修改原始模型的通道数量 3.4 lasTrainSS.py【训练】 3.5 lasTestSS.py【预测】 一、平台 Windows 10 GPU RTX 3090 CUDA 11.1 cudnn 8.9.6 Python 3.9 Torch 1.9.1 cu111 所用的原始代码https://github.com/yanx27/Pointnet_Pointnet2_pytorch 二、数据 有Classification属性的已经分类的LAS点云 三、代码 分享给有需要的人代码质量勿喷。 对原始代码进行了简化和注释。 分割结果保存成txt或者利用 laspy 生成点云。 别问为啥在C盘问就是2T的三星980Pro 完整代码https://download.csdn.net/download/xinjiang666/88755213?spm1001.2014.3001.5501 3.1 文件组织结构 3.2 lasDataLoader.py 读取数据 # 6通道特征块相对坐标全局相对坐标有效果# ### 分类的类别 classNumber 2 #0-未分类1-路面# 训练用 class lasDataset(Dataset):def __init__(self, splittrain, data_rootdataset, train_ratio0.6,val_ratio0.2,test_ratio0.2, num_point1024, block_size1.0, sample_rate1.0, transformNone):# 局部坐标XYZ(m) rgbpoints np.transpose(np.array([las.X*lasHeader.scales[0],las.Y*lasHeader.scales[1],las.Z*lasHeader.scales[2],las.red,las.green,las.blue]))self.las_points.append(points)coordMIN, coordMAX np.amin(points, axis0)[:3], np.amax(points, axis0)[:3]self.las_coord_MIN.append(coordMIN), self.las_coord_MAX.append(coordMAX)# labellabels np.transpose(np.array([las.classification]))labels[labels 11] 1self.las_labels.append(labels)num_point_all.append(labels.size)# 标签的统计直方图tmp, _ np.histogram(labels, range(classNumber 1))labelweights tmpdef __getitem__(self, idx):las_idx self.las_idxs[idx]points self.las_points[las_idx] # N * 6labels self.las_labels[las_idx] # NN_points points.shape[0]# # normalize old 9通道特征# selected_points points[selected_point_idxs, :] # num_point * 6# current_points np.zeros((self.num_point, 9)) # num_point * 9# current_points[:, 6] selected_points[:, 0] / self.las_coord_MAX[las_idx][0]# current_points[:, 7] selected_points[:, 1] / self.las_coord_MAX[las_idx][1]# current_points[:, 8] selected_points[:, 2] / self.las_coord_MAX[las_idx][2]# selected_points[:, 0] selected_points[:, 0] - center[0] # 相对块中心的x# selected_points[:, 1] selected_points[:, 1] - center[1] # 相对块中心的y# selected_points[:, 3:6] / 255.0# current_points[:, 0:6] selected_points# region ### normalize 6通道特征块相对坐标全局相对坐标无颜色selected_points points[selected_point_idxs, 0:3] # num_point * 3block_points points[selected_point_idxs, 0:3]block_points[:, 0] selected_points[:, 0] - center[0] # 相对块中心的xblock_points[:, 1] selected_points[:, 1] - center[1] # 相对块中心的yblock_points[:, 2] selected_points[:, 2]current_points np.zeros((self.num_point, 6)) # num_point * 6current_points[:, 0:3] block_pointscurrent_points[:, 3] (selected_points[:, 0]-self.las_coord_MIN[las_idx][0]) / (self.las_coord_MAX[las_idx][0]-self.las_coord_MIN[las_idx][0])current_points[:, 4] (selected_points[:, 1]-self.las_coord_MIN[las_idx][1]) / (self.las_coord_MAX[las_idx][1]-self.las_coord_MIN[las_idx][1])current_points[:, 5] (selected_points[:, 2]-self.las_coord_MIN[las_idx][2]) / (self.las_coord_MAX[las_idx][2]-self.las_coord_MIN[las_idx][2])# endregion# 测试用 class testDatasetToPred():# prepare to give prediction on each pointsdef __init__(self, data_root, block_points1024, splittest, stride0.5, block_size1.0, padding0.001):for file in self.file_list:# las文件的绝对路径pathLAS os.path.join(data_root, file)# 读取文件ndarrya:点数量7(xyz rgb l)las laspy.read(pathLAS)# 头文件信息 偏移和尺度lasHeader las.headerself.las_offset.append(lasHeader.offsets), self.las_scales.append(lasHeader.scales)# 局部坐标XYZ(m) rgb 真实标签data np.transpose(np.array([las.X * lasHeader.scales[0], las.Y * lasHeader.scales[1], las.Z * lasHeader.scales[2],las.red,las.green,las.blue, las.classification])) # ndarray点数量,7# 局部坐标XYZ(m)points data[:, :3]coordMIN, coordMAX np.amin(points, axis0)[:3], np.amax(points, axis0)[:3]self.las_coord_MIN.append(coordMIN), self.las_coord_MAX.append(coordMAX)self.scene_points_list.append(data[:, :6])# 真实标签labels data[:, 6]labels[labels 11] 1self.semantic_labels_list.append(labels)def __getitem__(self, index):for index_x in range(0, grid_x):# region ### 6通道特征块相对坐标全局相对坐标无颜色data_batch[:, 0] data_batch[:, 0] - (s_x self.block_size / 2.0)data_batch[:, 1] data_batch[:, 1] - (s_y self.block_size / 2.0)normlized_xyz np.zeros((point_size, 3))temp points[point_idxs, :]normlized_xyz[:, 0] (temp[:, 0]-coordMIN[0]) / (coordMAX[0]-coordMIN[0])normlized_xyz[:, 1] (temp[:, 1]-coordMIN[1]) / (coordMAX[1]-coordMIN[1])normlized_xyz[:, 2] (temp[:, 2]-coordMIN[2]) / (coordMAX[2]-coordMIN[2])### 6通道特征组合data_batch np.concatenate((data_batch[:,0:3], normlized_xyz), axis1)#endregion 3.3 修改原始模型的通道数量 pointnet_sem_seg.pyclass get_model(nn.Module):def __init__(self, num_class):super(get_model, self).__init__()self.k num_classself.feat PointNetEncoder(global_featFalse, feature_transformTrue, channel6) ###6通道特征 pointnet2_sem_seg.pyclass get_model(nn.Module):def __init__(self, num_classes):super(get_model, self).__init__()self.sa1 PointNetSetAbstraction(1024, 0.1, 32, 6 3, [32, 32, 64], False)### 6代表输入网络的通道数量 3.4 lasTrainSS.py【训练】 # 参考 # https://github.com/yanx27/Pointnet_Pointnet2_pytorch # 先在Terminal运行python -m visdom.server # 再运行本文件# True为PointNet PN2bool True # PN2bool False# 当前文件的路径 ROOT_DIR os.path.dirname(os.path.abspath(__file__))# 输出 PointNet训练模型的路径: PointNet dirModel1 ROOT_DIR /trainModel/pointnet_model if not os.path.exists(dirModel1):os.makedirs(dirModel1) # 输出 PointNet训练模型的路径 dirModel2 ROOT_DIR /trainModel/PointNet2_model if not os.path.exists(dirModel2):os.makedirs(dirModel2)# 日志的路径 pathLog os.path.join(ROOT_DIR, LOG_train.txt)# 训练数据集的路径 pathDataset os.path.join(ROOT_DIR, dataset/lasDatasetClassification/)# 点云语义分割的类别名称这里只分了2类 classNumber 2 classes [un, rs] class2label {cls: i for i, cls in enumerate(classes)} seg_classes class2label seg_label_to_cat {} for i, cat in enumerate(seg_classes.keys()):seg_label_to_cat[i] cat 3.5 lasTestSS.py【预测】 # 参考 # https://github.com/yanx27/Pointnet_Pointnet2_pytorch# True为PointNet PN2bool True # PN2bool False# save to LAS import laspy def SaveResultLAS(newLasPath, las_offsets, las_scales,point_np, rgb_np, label1, label2):# datanewx point_np[:, 0]las_offsets[0]newy point_np[:, 1]las_offsets[1]newz point_np[:, 2]las_offsets[2]newred rgb_np[:, 0]newgreen rgb_np[:, 1]newblue rgb_np[:, 2]newclassification label1newuserdata label2minx min(newx)miny min(newy)minz min(newz)# create a new headernewheader laspy.LasHeader(point_format3, version1.2)newheader.scales np.array([0.0001, 0.0001, 0.0001])newheader.offsets np.array([minx, miny, minz])newheader.add_extra_dim(laspy.ExtraBytesParams(nameClassification, typenp.uint8))newheader.add_extra_dim(laspy.ExtraBytesParams(nameUserData, typenp.uint8))# create a Lasnewlas laspy.LasData(newheader)newlas.x newxnewlas.y newynewlas.z newznewlas.red newrednewlas.green newgreennewlas.blue newbluenewlas.Classification newclassificationnewlas.UserData newuserdata# writenewlas.write(newLasPath)# 当前文件的路径 ROOT_DIR os.path.dirname(os.path.abspath(__file__))# 模型的路径 pathTrainModel os.path.join(ROOT_DIR, trainModel/pointnet_model) if PN2bool:pathTrainModel os.path.join(ROOT_DIR, trainModel/PointNet2_model)# 预测结果路径 visual_dir ROOT_DIR /testResultPN/ if PN2bool:visual_dir ROOT_DIR /testResultPN2/ visual_dir Path(visual_dir) visual_dir.mkdir(exist_okTrue)# 日志的路径 pathLog os.path.join(ROOT_DIR, LOG_test_eval.txt)# 测试数据的路径 pathDataset os.path.join(ROOT_DIR, dataset/lasDatasetClassification2/)# 点云语义分割的类别名称这里只分了2类 classNumber 2 classes [un, rs] class2label {cls: i for i, cls in enumerate(classes)} seg_classes class2label seg_label_to_cat {} for i, cat in enumerate(seg_classes.keys()):seg_label_to_cat[i] cat