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main_btf_raw.py
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import open3d as o3d
from dataset_pc import Dataset3dad_train,Dataset3dad_test
from torch.utils.data import Dataset
from torch.utils.data import DataLoader
from feature_extractors.fpfh_pc_features import PC_FPFHFeatures
import torch
import torch.nn.functional as F
from feature_extractors.ransac_position import get_registration_np,get_registration_refine_np
from utils.visualization import vis_pointcloud_np_two
import os
real_3d_classes = ['airplane','car','candybar','chicken',
'diamond','duck','fish','gemstone',
'seahorse','shell','starfish','toffees']
# data_dir = "/ssd2/m3lab/data/3DAD/3dad_demo_pcd_tiny/"
root_dir = './data'
save_dir = './benchmark/btf_raw/'
# data_dir = root_dir + 'airplane'
print('Task start: BTF_Raw')
for real_class in real_3d_classes:
train_loader = DataLoader(Dataset3dad_train(root_dir, real_class, 1024, True), num_workers=1,
batch_size=1, shuffle=False, drop_last=True)
voxel_size = 0.5
train_sampling_ratio = 100
test_sampling_ratio = 500
pc_pfph = PC_FPFHFeatures()
for data, mask, label, path in train_loader:
# print(path)
# path_list = path[0].split('/')
# print(path_list)
basic_template = data.squeeze(0).cpu().numpy()
break
for data, mask, label, path in train_loader:
reg_data = data.squeeze(0).cpu().numpy()
# reg_data = get_registration_refine_np(data.squeeze(0).cpu().numpy(),basic_template)
# vis_pointcloud_np_two(reg_data,basic_template)
# print(data.shape) torch.Size([1, 190339, 3])
# o3d_pc = o3d.geometry.PointCloud(o3d.utility.Vector3dVector(data.squeeze(0).numpy()))
# radius_normal = voxel_size * 2
# o3d_pc.estimate_normals(o3d.geometry.KDTreeSearchParamHybrid(radius=radius_normal, max_nn=30))
# radius_feature = voxel_size * 5
# pcd_fpfh = o3d.pipelines.registration.compute_fpfh_feature(o3d_pc, o3d.geometry.KDTreeSearchParamHybrid
# (radius=radius_feature, max_nn=100))
# fpfh = torch.from_numpy(pcd_fpfh.data.T)
fpfh = torch.from_numpy(reg_data)
length = fpfh.shape[0]
idx_list = [i*train_sampling_ratio for i in range(int(length/train_sampling_ratio))]
new_fpfh = fpfh[idx_list,:]
# pc_pfph.patch_lib.append(fpfh)
pc_pfph.patch_lib.append(new_fpfh)
# print(fpfh.shape) (190339, 33)
#1. test datalodaer 2. predict anomaly map 3. metrics point_cloud_auc point_cloud_ap
# print(data)
# print(label)
# Pipeline 1. 存储train data feature到memory 2.抽取test data feature，与meomory中feature比较 3.对每个点计算metrics
test_loader = DataLoader(Dataset3dad_test(root_dir, real_class, 1024, True), num_workers=1,
batch_size=1, shuffle=True, drop_last=False)
if( not os.path.exists(save_dir+real_class)):
os.makedirs(save_dir+real_class)
for data, mask, label, path in test_loader:
reg_data = data.squeeze(0).cpu().numpy()
# reg_data = get_registration_refine_np(data.squeeze(0).cpu().numpy(),basic_template)
# vis_pointcloud_np_two(reg_data,basic_template)
# print('data shape:{}'.format(str(data.shape)))
# o3d_pc = o3d.geometry.PointCloud(o3d.utility.Vector3dVector(data.squeeze(0).numpy()))
# radius_normal = voxel_size * 2
# o3d_pc.estimate_normals(o3d.geometry.KDTreeSearchParamHybrid(radius=radius_normal, max_nn=30))
# radius_feature = voxel_size * 5
# pcd_fpfh = o3d.pipelines.registration.compute_fpfh_feature(o3d_pc, o3d.geometry.KDTreeSearchParamHybrid
# (radius=radius_feature, max_nn=100))
# fpfh = pcd_fpfh.data.T
# fpfh = torch.from_numpy(pcd_fpfh.data.T)
fpfh = torch.from_numpy(reg_data)
length = fpfh.shape[0]
idx_list = [i*test_sampling_ratio for i in range(int(length/test_sampling_ratio))]
new_fpfh = fpfh[idx_list,:]
# new_mask = mask[:,idx_list]
path_list = path[0].split('/')
target_path = os.path.join(save_dir,real_class,path_list[-1])
pc_pfph.compute_s_s_map(new_fpfh,33,mask,label,reg_data,idx_list,path[0],target_path)
# pass
# print(data.shape) torch.Size([1, 366855, 3])
# print(label.shape) torch.Size([1, 366855])
pc_pfph.calculate_metrics()
image_rocaucs = round(pc_pfph.image_rocauc, 3)
image_aupr = round(pc_pfph.image_aupr, 3)
pixel_rocaucs = round(pc_pfph.pixel_rocauc, 3)
pixel_aupr = round(pc_pfph.pixel_aupr, 3)
# au_pros = round(pc_pfph.au_pro, 3)
# print(real_class)
# print('image_rocaucs:'+str(image_rocaucs))
# print('image_aupr:'+str(image_aupr))
# print('pixel_rocaucs:'+str(pixel_rocaucs))
# print('pixel_aupr:'+str(pixel_aupr))
print('Task:{}, object_auroc:{}, point_auroc:{}, object_aupr:{}, point_aupr:{}'.format
(cls,image_rocaucs,pixel_rocaucs,image_aupr,pixel_aupr))