diff --git a/.gitignore b/.gitignore
index 485dee6..1c8bd59 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1 +1,13 @@
-.idea
+.vscode
+
+*.o
+*.so
+*.cpp
+*.egg-info
+
+__pycache__/
+
+data/
+results/
+test/
+*log*
\ No newline at end of file
diff --git a/LICENSE b/LICENSE
deleted file mode 100644
index 6d37ae8..0000000
--- a/LICENSE
+++ /dev/null
@@ -1,176 +0,0 @@
-
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diff --git a/README.md b/README.md
index 8fbd586..302aef2 100644
--- a/README.md
+++ b/README.md
@@ -1,180 +1,27 @@
-[](https://paperswithcode.com/sota/semantic-segmentation-on-semantic3d?p=191111236)
-[](https://paperswithcode.com/sota/3d-semantic-segmentation-on-semantickitti?p=191111236)
-[](https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode)
-
-# RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds (CVPR 2020)
-
-This is the official implementation of **RandLA-Net** (CVPR2020, Oral presentation), a simple and efficient neural architecture for semantic segmentation of large-scale 3D point clouds. For technical details, please refer to:
-
-**RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds**
-[Qingyong Hu](https://www.cs.ox.ac.uk/people/qingyong.hu/), [Bo Yang*](https://yang7879.github.io/), [Linhai Xie](https://www.cs.ox.ac.uk/people/linhai.xie/), [Stefano Rosa](https://www.cs.ox.ac.uk/people/stefano.rosa/), [Yulan Guo](http://yulanguo.me/), [Zhihua Wang](https://www.cs.ox.ac.uk/people/zhihua.wang/), [Niki Trigoni](https://www.cs.ox.ac.uk/people/niki.trigoni/), [Andrew Markham](https://www.cs.ox.ac.uk/people/andrew.markham/).
-**[[Paper](https://arxiv.org/abs/1911.11236)] [[Video](https://youtu.be/Ar3eY_lwzMk)] [[Blog](https://zhuanlan.zhihu.com/p/105433460)] [[Project page](http://randla-net.cs.ox.ac.uk/)]**
-
-
- 
-
-
-
-### (1) Setup
-This code has been tested with Python 3.5, Tensorflow 1.11, CUDA 9.0 and cuDNN 7.4.1 on Ubuntu 16.04.
-
-- Clone the repository
-```
-git clone --depth=1 https://github.com/QingyongHu/RandLA-Net && cd RandLA-Net
-```
-- Setup python environment
-```
-conda create -n randlanet python=3.5
-source activate randlanet
-pip install -r helper_requirements.txt
-sh compile_op.sh
-```
-
-**Update 03/21/2020, pre-trained models and results are available now.**
-You can download the pre-trained models and results [here](https://drive.google.com/open?id=1iU8yviO3TP87-IexBXsu13g6NklwEkXB).
-Note that, please specify the model path in the main function (e.g., `main_S3DIS.py`) if you want to use the pre-trained model and have a quick try of our RandLA-Net.
-
-### (2) S3DIS
-S3DIS dataset can be found
-here.
-Download the files named "Stanford3dDataset_v1.2_Aligned_Version.zip". Uncompress the folder and move it to
-`/data/S3DIS`.
-
-- Preparing the dataset:
-```
-python utils/data_prepare_s3dis.py
-```
-- Start 6-fold cross validation:
-```
-sh jobs_6_fold_cv_s3dis.sh
-```
-- Move all the generated results (*.ply) in `/test` folder to `/data/S3DIS/results`, calculate the final mean IoU results:
-```
-python utils/6_fold_cv.py
-```
-
-Quantitative results of different approaches on S3DIS dataset (6-fold cross-validation):
-
-
-
-Qualitative results of our RandLA-Net:
-
-|  |  |
-| ------------------------------ | ---------------------------- |
-
-
-
-### (3) Semantic3D
-7zip is required to uncompress the raw data in this dataset, to install p7zip:
-```
-sudo apt-get install p7zip-full
-```
-- Download and extract the dataset. First, please specify the path of the dataset by changing the `BASE_DIR` in "download_semantic3d.sh"
-```
-sh utils/download_semantic3d.sh
-```
-- Preparing the dataset:
-```
-python utils/data_prepare_semantic3d.py
-```
-- Start training:
-```
-python main_Semantic3D.py --mode train --gpu 0
-```
-- Evaluation:
-```
-python main_Semantic3D.py --mode test --gpu 0
-```
-Quantitative results of different approaches on Semantic3D (reduced-8):
-
-
-
-Qualitative results of our RandLA-Net:
-
-|  |  |
-| -------------------------------- | ------------------------------- |
-|  |  |
-
-
-
-**Note:**
-- Preferably with more than 64G RAM to process this dataset due to the large volume of point cloud
-
-
-### (4) SemanticKITTI
-
-SemanticKITTI dataset can be found here. Download the files
- related to semantic segmentation and extract everything into the same folder. Uncompress the folder and move it to
-`/data/semantic_kitti/dataset`.
-
-- Preparing the dataset:
-```
-python utils/data_prepare_semantickitti.py
-```
-
-- Start training:
-```
-python main_SemanticKITTI.py --mode train --gpu 0
-```
-
-- Evaluation:
-```
-sh jobs_test_semantickitti.sh
-```
-
-Quantitative results of different approaches on SemanticKITTI dataset:
-
-
-
-Qualitative results of our RandLA-Net:
-
-
-
-
-### (5) Demo
-
- 
-
-
-### Citation
-If you find our work useful in your research, please consider citing:
-
- @article{hu2019randla,
- title={RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds},
- author={Hu, Qingyong and Yang, Bo and Xie, Linhai and Rosa, Stefano and Guo, Yulan and Wang, Zhihua and Trigoni, Niki and Markham, Andrew},
- journal={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
- year={2020}
- }
-
- @article{hu2021learning,
- title={Learning Semantic Segmentation of Large-Scale Point Clouds with Random Sampling},
- author={Hu, Qingyong and Yang, Bo and Xie, Linhai and Rosa, Stefano and Guo, Yulan and Wang, Zhihua and Trigoni, Niki and Markham, Andrew},
- journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
- year={2021},
- publisher={IEEE}
- }
-
-
-### Acknowledgment
-- Part of our code refers to nanoflann library and the the recent work KPConv.
-- We use blender to make the video demo.
-
-
-### License
-Licensed under the CC BY-NC-SA 4.0 license, see [LICENSE](./LICENSE).
-
-
-### Updates
-* 21/03/2020: Updating all experimental results
-* 21/03/2020: Adding pretrained models and results
-* 02/03/2020: Code available!
-* 15/11/2019: Initial release！
-
-## Related Repos
-1. [SoTA-Point-Cloud: Deep Learning for 3D Point Clouds: A Survey](https://github.com/QingyongHu/SoTA-Point-Cloud) 
-2. [SensatUrban: Learning Semantics from Urban-Scale Photogrammetric Point Clouds](https://github.com/QingyongHu/SpinNet) 
-3. [3D-BoNet: Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds](https://github.com/Yang7879/3D-BoNet) 
-4. [SpinNet: Learning a General Surface Descriptor for 3D Point Cloud Registration](https://github.com/QingyongHu/SpinNet) 
-5. [SQN: Weakly-Supervised Semantic Segmentation of Large-Scale 3D Point Clouds with 1000x Fewer Labels](https://github.com/QingyongHu/SQN) 
-
+# Toronto-3D and OpenGF dataset code for RandLA-Net
+
+Code for [Toronto-3D](https://github.com/WeikaiTan/Toronto-3D.git) has been uploaded. Try it for building your own network.
+
+Will release code for OpenGF later
+
+## Train and test RandLA-Net on Toronto-3D
+1. Set up environment and compile the operations - exactly the same as the RandLA-Net environment
+1. Create a folder called `data` and move the `.ply` files into `data/Tronto_3D/original_ply/`
+1. Change parameters according to your preference in `data_prepare_toronto3d.py` and run to preprocess point clouds
+1. Change parameters according to your preference in `helper_tool.ply` to build the network
+1. Train the network by running `python main_Toronto3D.py --mode train`
+1. Test and evaluate on `L002` by running `python main_Toronto3D.py --mode test --test_eval True`
+1. Modify the code to find a good parameter set or test on your own data
+
+## Sample results of Toronto-3D
+The highest results reported are from RandLA-Net in [Hu et al. (2021)](https://doi.org/10.1109/TPAMI.2021.3083288). Here are some results I got on my code with the default parameters. The largest factor in mIoU is the accuracy of *Road Markings*, which is impossible to be classified with XYZ only.
+
+| Features | OA | mIoU | Road | Road mrk. | Natural | Bldg | Util. line | Pole | Car | Fence |
+|----------|----|------|------|-----------|---------|------|------------|------|-----|-------|
+| [Hu et al. (2021)](https://doi.org/10.1109/TPAMI.2021.3083288) | 92.95 | 77.71 | 94.61 | 42.62 | 96.89 | 93.01 | 86.51 | 78.07 | 92.85 | 37.12 |
+| [Hu et al. (2021)](https://doi.org/10.1109/TPAMI.2021.3083288) with RGB| 94.37 | 81.77 | 96.69 | 64.21 | 96.92 | 94.24 | 88.06 | 77.84 | 93.37 | 42.86 |
+|XYZRGBI | 96.57 | 81.00 | 95.61 | 58.04 | 97.22 | 93.45 | 87.58 | 82.64 | 91.06 | 42.39 |
+|XYZRGB | 96.71 | 80.89 | 95.88 | 60.75 | 97.02 | 94.04 | 86.71 | 83.30 | 87.66 | 41.80 |
+|XYZI | 95.65 | 80.03 | 94.59 | 50.14 | 95.90 | 92.76 | 87.70 | 77.77 | 91.10 | 50.30 |
+|XYZ | 94.94 | 74.13 | 93.53 | 12.52 | 96.67 | 92.34 | 86.25 | 80.10 | 88.04 | 43.57 |
diff --git a/RandLANet.py b/RandLANet.py
index bf90997..22bff36 100755
--- a/RandLANet.py
+++ b/RandLANet.py
@@ -7,6 +7,8 @@
import helper_tf_util
import time
+import warnings
+warnings.simplefilter(action="ignore", category=FutureWarning)
def log_out(out_str, f_out):
f_out.write(out_str + '\n')
diff --git a/data_prepare_toronto3d.py b/data_prepare_toronto3d.py
new file mode 100644
index 0000000..8444d0f
--- /dev/null
+++ b/data_prepare_toronto3d.py
@@ -0,0 +1,54 @@
+from sklearn.neighbors import KDTree
+from os.path import join, exists, dirname, abspath
+import numpy as np
+import os, pickle
+import sys
+
+BASE_DIR = dirname(abspath(__file__))
+ROOT_DIR = dirname(BASE_DIR)
+sys.path.append(BASE_DIR)
+sys.path.append(ROOT_DIR)
+from helper_ply import write_ply, read_ply
+from helper_tool import DataProcessing as DP
+
+grid_size = 0.06
+dataset_path = 'data/Toronto_3D'
+train_files = ['L001', 'L003', 'L004']
+val_files = ['L002']
+UTM_OFFSET = [627285, 4841948, 0]
+original_pc_folder = join(dataset_path, 'original_ply')
+sub_pc_folder = join(dataset_path, 'input_{:.3f}'.format(grid_size))
+os.mkdir(sub_pc_folder) if not exists(sub_pc_folder) else None
+
+for pc_path in [join(original_pc_folder, fname + '.ply') for fname in train_files + val_files]:
+ print(pc_path)
+ file_name = pc_path.split('/')[-1][:-4]
+
+ pc = read_ply(pc_path)
+ labels = pc['scalar_Label'].astype(np.uint8)
+ xyz = np.vstack((pc['x'] - UTM_OFFSET[0], pc['y'] - UTM_OFFSET[1], pc['z'] - UTM_OFFSET[2])).T.astype(np.float32)
+ color = np.vstack((pc['red'], pc['green'], pc['blue'])).T.astype(np.uint8)
+ intensity = pc['scalar_Intensity'].astype(np.uint8).reshape(-1,1)
+ # Subsample to save space
+ sub_xyz, sub_colors, sub_labels = DP.grid_sub_sampling(xyz, color, labels, grid_size)
+ _, sub_intensity = DP.grid_sub_sampling(xyz, features=intensity, grid_size=grid_size)
+
+ sub_colors = sub_colors / 255.0
+ sub_intensity = sub_intensity[:,0] / 255.0
+ sub_ply_file = join(sub_pc_folder, file_name + '.ply')
+ write_ply(sub_ply_file, [sub_xyz, sub_colors, sub_intensity, sub_labels], ['x', 'y', 'z', 'red', 'green', 'blue', 'intensity', 'class'])
+
+ search_tree = KDTree(sub_xyz, leaf_size=50)
+ kd_tree_file = join(sub_pc_folder, file_name + '_KDTree.pkl')
+ with open(kd_tree_file, 'wb') as f:
+ pickle.dump(search_tree, f)
+
+ if file_name not in train_files:
+ proj_idx = np.squeeze(search_tree.query(xyz, return_distance=False))
+ proj_idx = proj_idx.astype(np.int32)
+ proj_save = join(sub_pc_folder, file_name + '_proj.pkl')
+ with open(proj_save, 'wb') as f:
+ pickle.dump([proj_idx, labels], f)
+
+
+
diff --git a/helper_tool.py b/helper_tool.py
index 6354f9f..b6f95f7 100755
--- a/helper_tool.py
+++ b/helper_tool.py
@@ -1,4 +1,4 @@
-from open3d import linux as open3d
+import open3d
from os.path import join
import numpy as np
import colorsys, random, os, sys
@@ -14,7 +14,38 @@
import cpp_wrappers.cpp_subsampling.grid_subsampling as cpp_subsampling
import nearest_neighbors.lib.python.nearest_neighbors as nearest_neighbors
+class ConfigToronto3D:
+ k_n = 16 # KNN
+ num_layers = 5 # Number of layers
+ num_points = 65536 # Number of input points
+ num_classes = 8 # Number of valid classes
+ sub_grid_size = 0.06 # preprocess_parameter
+ use_rgb = False # Use RGB
+ use_intensity = False # Use intensity
+
+ batch_size = 4 # batch_size during training
+ val_batch_size = 14 # batch_size during validation and test
+ train_steps = 500 # Number of steps per epochs
+ val_steps = 25 # Number of validation steps per epoch
+
+ sub_sampling_ratio = [4, 4, 4, 4, 2] # sampling ratio of random sampling at each layer
+ d_out = [16, 64, 128, 256, 512] # feature dimension
+
+ noise_init = 3.5 # noise initial parameter
+ max_epoch = 100 # maximum epoch during training
+ learning_rate = 1e-2 # initial learning rate
+ lr_decays = {i: 0.95 for i in range(0, 500)} # decay rate of learning rate
+
+ train_sum_dir = 'train_log'
+ saving = True
+ saving_path = None
+ augment_scale_anisotropic = True
+ augment_symmetries = [True, False, False]
+ augment_rotation = 'vertical'
+ augment_scale_min = 0.8
+ augment_scale_max = 1.2
+ augment_noise = 0.001
class ConfigSemanticKITTI:
k_n = 16 # KNN
num_layers = 4 # Number of layers
@@ -255,7 +286,9 @@ def get_class_weights(dataset_name):
elif dataset_name is 'SemanticKITTI':
num_per_class = np.array([55437630, 320797, 541736, 2578735, 3274484, 552662, 184064, 78858,
240942562, 17294618, 170599734, 6369672, 230413074, 101130274, 476491114,
- 9833174, 129609852, 4506626, 1168181])
+ 9833174, 129609852, 4506626, 1168181], dtype=np.int32)
+ elif dataset_name is 'Toronto3D':
+ num_per_class = np.array([35391894, 1449308, 4650919, 18252779, 589856, 743579, 4311631, 356463], dtype=np.int32)
weight = num_per_class / float(sum(num_per_class))
ce_label_weight = 1 / (weight + 0.02)
return np.expand_dims(ce_label_weight, axis=0)
diff --git a/imgs/S3DIS_area2.gif b/imgs/S3DIS_area2.gif
deleted file mode 100644
index 1df595b..0000000
Binary files a/imgs/S3DIS_area2.gif and /dev/null differ
diff --git a/imgs/S3DIS_area3.gif b/imgs/S3DIS_area3.gif
deleted file mode 100644
index c754f9d..0000000
Binary files a/imgs/S3DIS_area3.gif and /dev/null differ
diff --git a/imgs/Semantic3D-1.gif b/imgs/Semantic3D-1.gif
deleted file mode 100644
index 02f1a95..0000000
Binary files a/imgs/Semantic3D-1.gif and /dev/null differ
diff --git a/imgs/Semantic3D-3.gif b/imgs/Semantic3D-3.gif
deleted file mode 100644
index a93c4b3..0000000
Binary files a/imgs/Semantic3D-3.gif and /dev/null differ
diff --git a/imgs/Semantic3D-4.gif b/imgs/Semantic3D-4.gif
deleted file mode 100644
index 628f718..0000000
Binary files a/imgs/Semantic3D-4.gif and /dev/null differ
diff --git a/imgs/SemanticKITTI-2.gif b/imgs/SemanticKITTI-2.gif
deleted file mode 100644
index 006fadd..0000000
Binary files a/imgs/SemanticKITTI-2.gif and /dev/null differ
diff --git a/jobs_6_fold_cv_s3dis.sh b/jobs_6_fold_cv_s3dis.sh
deleted file mode 100644
index eb14c8c..0000000
--- a/jobs_6_fold_cv_s3dis.sh
+++ /dev/null
@@ -1,14 +0,0 @@
-python -B main_S3DIS.py --gpu 0 --mode train --test_area 1
-python -B main_S3DIS.py --gpu 0 --mode test --test_area 1
-python -B main_S3DIS.py --gpu 0 --mode train --test_area 2
-python -B main_S3DIS.py --gpu 0 --mode test --test_area 2
-python -B main_S3DIS.py --gpu 0 --mode train --test_area 3
-python -B main_S3DIS.py --gpu 0 --mode test --test_area 3
-python -B main_S3DIS.py --gpu 0 --mode train --test_area 4
-python -B main_S3DIS.py --gpu 0 --mode test --test_area 4
-python -B main_S3DIS.py --gpu 0 --mode train --test_area 5
-python -B main_S3DIS.py --gpu 0 --mode test --test_area 5
-python -B main_S3DIS.py --gpu 0 --mode train --test_area 6
-python -B main_S3DIS.py --gpu 0 --mode test --test_area 6
-
-
diff --git a/jobs_test_semantickitti.sh b/jobs_test_semantickitti.sh
deleted file mode 100644
index 340c66c..0000000
--- a/jobs_test_semantickitti.sh
+++ /dev/null
@@ -1,12 +0,0 @@
-python -B main_SemanticKITTI.py --gpu 0 --mode test --test_area 11
-python -B main_SemanticKITTI.py --gpu 0 --mode test --test_area 12
-python -B main_SemanticKITTI.py --gpu 0 --mode test --test_area 13
-python -B main_SemanticKITTI.py --gpu 0 --mode test --test_area 14
-python -B main_SemanticKITTI.py --gpu 0 --mode test --test_area 15
-python -B main_SemanticKITTI.py --gpu 0 --mode test --test_area 16
-python -B main_SemanticKITTI.py --gpu 0 --mode test --test_area 17
-python -B main_SemanticKITTI.py --gpu 0 --mode test --test_area 18
-python -B main_SemanticKITTI.py --gpu 0 --mode test --test_area 19
-python -B main_SemanticKITTI.py --gpu 0 --mode test --test_area 20
-python -B main_SemanticKITTI.py --gpu 0 --mode test --test_area 21
-
diff --git a/main_S3DIS.py b/main_S3DIS.py
deleted file mode 100755
index 7e21834..0000000
--- a/main_S3DIS.py
+++ /dev/null
@@ -1,269 +0,0 @@
-from os.path import join
-from RandLANet import Network
-from tester_S3DIS import ModelTester
-from helper_ply import read_ply
-from helper_tool import ConfigS3DIS as cfg
-from helper_tool import DataProcessing as DP
-from helper_tool import Plot
-import tensorflow as tf
-import numpy as np
-import time, pickle, argparse, glob, os
-
-
-class S3DIS:
- def __init__(self, test_area_idx):
- self.name = 'S3DIS'
- self.path = '/data/S3DIS'
- self.label_to_names = {0: 'ceiling',
- 1: 'floor',
- 2: 'wall',
- 3: 'beam',
- 4: 'column',
- 5: 'window',
- 6: 'door',
- 7: 'table',
- 8: 'chair',
- 9: 'sofa',
- 10: 'bookcase',
- 11: 'board',
- 12: 'clutter'}
- self.num_classes = len(self.label_to_names)
- self.label_values = np.sort([k for k, v in self.label_to_names.items()])
- self.label_to_idx = {l: i for i, l in enumerate(self.label_values)}
- self.ignored_labels = np.array([])
-
- self.val_split = 'Area_' + str(test_area_idx)
- self.all_files = glob.glob(join(self.path, 'original_ply', '*.ply'))
-
- # Initiate containers
- self.val_proj = []
- self.val_labels = []
- self.possibility = {}
- self.min_possibility = {}
- self.input_trees = {'training': [], 'validation': []}
- self.input_colors = {'training': [], 'validation': []}
- self.input_labels = {'training': [], 'validation': []}
- self.input_names = {'training': [], 'validation': []}
- self.load_sub_sampled_clouds(cfg.sub_grid_size)
-
- def load_sub_sampled_clouds(self, sub_grid_size):
- tree_path = join(self.path, 'input_{:.3f}'.format(sub_grid_size))
- for i, file_path in enumerate(self.all_files):
- t0 = time.time()
- cloud_name = file_path.split('/')[-1][:-4]
- if self.val_split in cloud_name:
- cloud_split = 'validation'
- else:
- cloud_split = 'training'
-
- # Name of the input files
- kd_tree_file = join(tree_path, '{:s}_KDTree.pkl'.format(cloud_name))
- sub_ply_file = join(tree_path, '{:s}.ply'.format(cloud_name))
-
- data = read_ply(sub_ply_file)
- sub_colors = np.vstack((data['red'], data['green'], data['blue'])).T
- sub_labels = data['class']
-
- # Read pkl with search tree
- with open(kd_tree_file, 'rb') as f:
- search_tree = pickle.load(f)
-
- self.input_trees[cloud_split] += [search_tree]
- self.input_colors[cloud_split] += [sub_colors]
- self.input_labels[cloud_split] += [sub_labels]
- self.input_names[cloud_split] += [cloud_name]
-
- size = sub_colors.shape[0] * 4 * 7
- print('{:s} {:.1f} MB loaded in {:.1f}s'.format(kd_tree_file.split('/')[-1], size * 1e-6, time.time() - t0))
-
- print('\nPreparing reprojected indices for testing')
-
- # Get validation and test reprojected indices
- for i, file_path in enumerate(self.all_files):
- t0 = time.time()
- cloud_name = file_path.split('/')[-1][:-4]
-
- # Validation projection and labels
- if self.val_split in cloud_name:
- proj_file = join(tree_path, '{:s}_proj.pkl'.format(cloud_name))
- with open(proj_file, 'rb') as f:
- proj_idx, labels = pickle.load(f)
- self.val_proj += [proj_idx]
- self.val_labels += [labels]
- print('{:s} done in {:.1f}s'.format(cloud_name, time.time() - t0))
-
- # Generate the input data flow
- def get_batch_gen(self, split):
- if split == 'training':
- num_per_epoch = cfg.train_steps * cfg.batch_size
- elif split == 'validation':
- num_per_epoch = cfg.val_steps * cfg.val_batch_size
-
- self.possibility[split] = []
- self.min_possibility[split] = []
- # Random initialize
- for i, tree in enumerate(self.input_colors[split]):
- self.possibility[split] += [np.random.rand(tree.data.shape[0]) * 1e-3]
- self.min_possibility[split] += [float(np.min(self.possibility[split][-1]))]
-
- def spatially_regular_gen():
- # Generator loop
- for i in range(num_per_epoch):
-
- # Choose the cloud with the lowest probability
- cloud_idx = int(np.argmin(self.min_possibility[split]))
-
- # choose the point with the minimum of possibility in the cloud as query point
- point_ind = np.argmin(self.possibility[split][cloud_idx])
-
- # Get all points within the cloud from tree structure
- points = np.array(self.input_trees[split][cloud_idx].data, copy=False)
-
- # Center point of input region
- center_point = points[point_ind, :].reshape(1, -1)
-
- # Add noise to the center point
- noise = np.random.normal(scale=cfg.noise_init / 10, size=center_point.shape)
- pick_point = center_point + noise.astype(center_point.dtype)
-
- # Check if the number of points in the selected cloud is less than the predefined num_points
- if len(points) < cfg.num_points:
- # Query all points within the cloud
- queried_idx = self.input_trees[split][cloud_idx].query(pick_point, k=len(points))[1][0]
- else:
- # Query the predefined number of points
- queried_idx = self.input_trees[split][cloud_idx].query(pick_point, k=cfg.num_points)[1][0]
-
- # Shuffle index
- queried_idx = DP.shuffle_idx(queried_idx)
- # Get corresponding points and colors based on the index
- queried_pc_xyz = points[queried_idx]
- queried_pc_xyz = queried_pc_xyz - pick_point
- queried_pc_colors = self.input_colors[split][cloud_idx][queried_idx]
- queried_pc_labels = self.input_labels[split][cloud_idx][queried_idx]
-
- # Update the possibility of the selected points
- dists = np.sum(np.square((points[queried_idx] - pick_point).astype(np.float32)), axis=1)
- delta = np.square(1 - dists / np.max(dists))
- self.possibility[split][cloud_idx][queried_idx] += delta
- self.min_possibility[split][cloud_idx] = float(np.min(self.possibility[split][cloud_idx]))
-
- # up_sampled with replacement
- if len(points) < cfg.num_points:
- queried_pc_xyz, queried_pc_colors, queried_idx, queried_pc_labels = \
- DP.data_aug(queried_pc_xyz, queried_pc_colors, queried_pc_labels, queried_idx, cfg.num_points)
-
- if True:
- yield (queried_pc_xyz.astype(np.float32),
- queried_pc_colors.astype(np.float32),
- queried_pc_labels,
- queried_idx.astype(np.int32),
- np.array([cloud_idx], dtype=np.int32))
-
- gen_func = spatially_regular_gen
- gen_types = (tf.float32, tf.float32, tf.int32, tf.int32, tf.int32)
- gen_shapes = ([None, 3], [None, 3], [None], [None], [None])
- return gen_func, gen_types, gen_shapes
-
- @staticmethod
- def get_tf_mapping2():
- # Collect flat inputs
- def tf_map(batch_xyz, batch_features, batch_labels, batch_pc_idx, batch_cloud_idx):
- batch_features = tf.concat([batch_xyz, batch_features], axis=-1)
- input_points = []
- input_neighbors = []
- input_pools = []
- input_up_samples = []
-
- for i in range(cfg.num_layers):
- neighbour_idx = tf.py_func(DP.knn_search, [batch_xyz, batch_xyz, cfg.k_n], tf.int32)
- sub_points = batch_xyz[:, :tf.shape(batch_xyz)[1] // cfg.sub_sampling_ratio[i], :]
- pool_i = neighbour_idx[:, :tf.shape(batch_xyz)[1] // cfg.sub_sampling_ratio[i], :]
- up_i = tf.py_func(DP.knn_search, [sub_points, batch_xyz, 1], tf.int32)
- input_points.append(batch_xyz)
- input_neighbors.append(neighbour_idx)
- input_pools.append(pool_i)
- input_up_samples.append(up_i)
- batch_xyz = sub_points
-
- input_list = input_points + input_neighbors + input_pools + input_up_samples
- input_list += [batch_features, batch_labels, batch_pc_idx, batch_cloud_idx]
-
- return input_list
-
- return tf_map
-
- def init_input_pipeline(self):
- print('Initiating input pipelines')
- cfg.ignored_label_inds = [self.label_to_idx[ign_label] for ign_label in self.ignored_labels]
- gen_function, gen_types, gen_shapes = self.get_batch_gen('training')
- gen_function_val, _, _ = self.get_batch_gen('validation')
- self.train_data = tf.data.Dataset.from_generator(gen_function, gen_types, gen_shapes)
- self.val_data = tf.data.Dataset.from_generator(gen_function_val, gen_types, gen_shapes)
-
- self.batch_train_data = self.train_data.batch(cfg.batch_size)
- self.batch_val_data = self.val_data.batch(cfg.val_batch_size)
- map_func = self.get_tf_mapping2()
-
- self.batch_train_data = self.batch_train_data.map(map_func=map_func)
- self.batch_val_data = self.batch_val_data.map(map_func=map_func)
-
- self.batch_train_data = self.batch_train_data.prefetch(cfg.batch_size)
- self.batch_val_data = self.batch_val_data.prefetch(cfg.val_batch_size)
-
- iter = tf.data.Iterator.from_structure(self.batch_train_data.output_types, self.batch_train_data.output_shapes)
- self.flat_inputs = iter.get_next()
- self.train_init_op = iter.make_initializer(self.batch_train_data)
- self.val_init_op = iter.make_initializer(self.batch_val_data)
-
-
-if __name__ == '__main__':
- parser = argparse.ArgumentParser()
- parser.add_argument('--gpu', type=int, default=0, help='the number of GPUs to use [default: 0]')
- parser.add_argument('--test_area', type=int, default=5, help='Which area to use for test, option: 1-6 [default: 5]')
- parser.add_argument('--mode', type=str, default='train', help='options: train, test, vis')
- parser.add_argument('--model_path', type=str, default='None', help='pretrained model path')
- FLAGS = parser.parse_args()
-
- os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
- os.environ['CUDA_VISIBLE_DEVICES'] = str(FLAGS.gpu)
- os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
- Mode = FLAGS.mode
-
- test_area = FLAGS.test_area
- dataset = S3DIS(test_area)
- dataset.init_input_pipeline()
-
- if Mode == 'train':
- model = Network(dataset, cfg)
- model.train(dataset)
- elif Mode == 'test':
- cfg.saving = False
- model = Network(dataset, cfg)
- if FLAGS.model_path is not 'None':
- chosen_snap = FLAGS.model_path
- else:
- chosen_snapshot = -1
- logs = np.sort([os.path.join('results', f) for f in os.listdir('results') if f.startswith('Log')])
- chosen_folder = logs[-1]
- snap_path = join(chosen_folder, 'snapshots')
- snap_steps = [int(f[:-5].split('-')[-1]) for f in os.listdir(snap_path) if f[-5:] == '.meta']
- chosen_step = np.sort(snap_steps)[-1]
- chosen_snap = os.path.join(snap_path, 'snap-{:d}'.format(chosen_step))
- tester = ModelTester(model, dataset, restore_snap=chosen_snap)
- tester.test(model, dataset)
- else:
- ##################
- # Visualize data #
- ##################
-
- with tf.Session() as sess:
- sess.run(tf.global_variables_initializer())
- sess.run(dataset.train_init_op)
- while True:
- flat_inputs = sess.run(dataset.flat_inputs)
- pc_xyz = flat_inputs[0]
- sub_pc_xyz = flat_inputs[1]
- labels = flat_inputs[21]
- Plot.draw_pc_sem_ins(pc_xyz[0, :, :], labels[0, :])
- Plot.draw_pc_sem_ins(sub_pc_xyz[0, :, :], labels[0, 0:np.shape(sub_pc_xyz)[1]])
diff --git a/main_SemanticKITTI.py b/main_SemanticKITTI.py
deleted file mode 100644
index 07fd6c5..0000000
--- a/main_SemanticKITTI.py
+++ /dev/null
@@ -1,240 +0,0 @@
-from helper_tool import DataProcessing as DP
-from helper_tool import ConfigSemanticKITTI as cfg
-from helper_tool import Plot
-from os.path import join
-from RandLANet import Network
-from tester_SemanticKITTI import ModelTester
-import tensorflow as tf
-import numpy as np
-import os, argparse, pickle
-
-
-class SemanticKITTI:
- def __init__(self, test_id):
- self.name = 'SemanticKITTI'
- self.dataset_path = '/data/semantic_kitti/dataset/sequences_0.06'
- self.label_to_names = {0: 'unlabeled',
- 1: 'car',
- 2: 'bicycle',
- 3: 'motorcycle',
- 4: 'truck',
- 5: 'other-vehicle',
- 6: 'person',
- 7: 'bicyclist',
- 8: 'motorcyclist',
- 9: 'road',
- 10: 'parking',
- 11: 'sidewalk',
- 12: 'other-ground',
- 13: 'building',
- 14: 'fence',
- 15: 'vegetation',
- 16: 'trunk',
- 17: 'terrain',
- 18: 'pole',
- 19: 'traffic-sign'}
- self.num_classes = len(self.label_to_names)
- self.label_values = np.sort([k for k, v in self.label_to_names.items()])
- self.label_to_idx = {l: i for i, l in enumerate(self.label_values)}
- self.ignored_labels = np.sort([0])
-
- self.val_split = '08'
-
- self.seq_list = np.sort(os.listdir(self.dataset_path))
- self.test_scan_number = str(test_id)
- self.train_list, self.val_list, self.test_list = DP.get_file_list(self.dataset_path,
- self.test_scan_number)
- self.train_list = DP.shuffle_list(self.train_list)
- self.val_list = DP.shuffle_list(self.val_list)
-
- self.possibility = []
- self.min_possibility = []
-
- # Generate the input data flow
- def get_batch_gen(self, split):
- if split == 'training':
- num_per_epoch = int(len(self.train_list) / cfg.batch_size) * cfg.batch_size
- path_list = self.train_list
- elif split == 'validation':
- num_per_epoch = int(len(self.val_list) / cfg.val_batch_size) * cfg.val_batch_size
- cfg.val_steps = int(len(self.val_list) / cfg.batch_size)
- path_list = self.val_list
- elif split == 'test':
- num_per_epoch = int(len(self.test_list) / cfg.val_batch_size) * cfg.val_batch_size * 4
- path_list = self.test_list
- for test_file_name in path_list:
- points = np.load(test_file_name)
- self.possibility += [np.random.rand(points.shape[0]) * 1e-3]
- self.min_possibility += [float(np.min(self.possibility[-1]))]
-
- def spatially_regular_gen():
- # Generator loop
- for i in range(num_per_epoch):
- if split != 'test':
- cloud_ind = i
- pc_path = path_list[cloud_ind]
- pc, tree, labels = self.get_data(pc_path)
- # crop a small point cloud
- pick_idx = np.random.choice(len(pc), 1)
- selected_pc, selected_labels, selected_idx = self.crop_pc(pc, labels, tree, pick_idx)
- else:
- cloud_ind = int(np.argmin(self.min_possibility))
- pick_idx = np.argmin(self.possibility[cloud_ind])
- pc_path = path_list[cloud_ind]
- pc, tree, labels = self.get_data(pc_path)
- selected_pc, selected_labels, selected_idx = self.crop_pc(pc, labels, tree, pick_idx)
-
- # update the possibility of the selected pc
- dists = np.sum(np.square((selected_pc - pc[pick_idx]).astype(np.float32)), axis=1)
- delta = np.square(1 - dists / np.max(dists))
- self.possibility[cloud_ind][selected_idx] += delta
- self.min_possibility[cloud_ind] = np.min(self.possibility[cloud_ind])
-
- if True:
- yield (selected_pc.astype(np.float32),
- selected_labels.astype(np.int32),
- selected_idx.astype(np.int32),
- np.array([cloud_ind], dtype=np.int32))
-
- gen_func = spatially_regular_gen
- gen_types = (tf.float32, tf.int32, tf.int32, tf.int32)
- gen_shapes = ([None, 3], [None], [None], [None])
-
- return gen_func, gen_types, gen_shapes
-
- def get_data(self, file_path):
- seq_id = file_path.split('/')[-3]
- frame_id = file_path.split('/')[-1][:-4]
- kd_tree_path = join(self.dataset_path, seq_id, 'KDTree', frame_id + '.pkl')
- # Read pkl with search tree
- with open(kd_tree_path, 'rb') as f:
- search_tree = pickle.load(f)
- points = np.array(search_tree.data, copy=False)
- # Load labels
- if int(seq_id) >= 11:
- labels = np.zeros(np.shape(points)[0], dtype=np.uint8)
- else:
- label_path = join(self.dataset_path, seq_id, 'labels', frame_id + '.npy')
- labels = np.squeeze(np.load(label_path))
- return points, search_tree, labels
-
- @staticmethod
- def crop_pc(points, labels, search_tree, pick_idx):
- # crop a fixed size point cloud for training
- center_point = points[pick_idx, :].reshape(1, -1)
- select_idx = search_tree.query(center_point, k=cfg.num_points)[1][0]
- select_idx = DP.shuffle_idx(select_idx)
- select_points = points[select_idx]
- select_labels = labels[select_idx]
- return select_points, select_labels, select_idx
-
- @staticmethod
- def get_tf_mapping2():
-
- def tf_map(batch_pc, batch_label, batch_pc_idx, batch_cloud_idx):
- features = batch_pc
- input_points = []
- input_neighbors = []
- input_pools = []
- input_up_samples = []
-
- for i in range(cfg.num_layers):
- neighbour_idx = tf.py_func(DP.knn_search, [batch_pc, batch_pc, cfg.k_n], tf.int32)
- sub_points = batch_pc[:, :tf.shape(batch_pc)[1] // cfg.sub_sampling_ratio[i], :]
- pool_i = neighbour_idx[:, :tf.shape(batch_pc)[1] // cfg.sub_sampling_ratio[i], :]
- up_i = tf.py_func(DP.knn_search, [sub_points, batch_pc, 1], tf.int32)
- input_points.append(batch_pc)
- input_neighbors.append(neighbour_idx)
- input_pools.append(pool_i)
- input_up_samples.append(up_i)
- batch_pc = sub_points
-
- input_list = input_points + input_neighbors + input_pools + input_up_samples
- input_list += [features, batch_label, batch_pc_idx, batch_cloud_idx]
-
- return input_list
-
- return tf_map
-
- def init_input_pipeline(self):
- print('Initiating input pipelines')
- cfg.ignored_label_inds = [self.label_to_idx[ign_label] for ign_label in self.ignored_labels]
- gen_function, gen_types, gen_shapes = self.get_batch_gen('training')
- gen_function_val, _, _ = self.get_batch_gen('validation')
- gen_function_test, _, _ = self.get_batch_gen('test')
-
- self.train_data = tf.data.Dataset.from_generator(gen_function, gen_types, gen_shapes)
- self.val_data = tf.data.Dataset.from_generator(gen_function_val, gen_types, gen_shapes)
- self.test_data = tf.data.Dataset.from_generator(gen_function_test, gen_types, gen_shapes)
-
- self.batch_train_data = self.train_data.batch(cfg.batch_size)
- self.batch_val_data = self.val_data.batch(cfg.val_batch_size)
- self.batch_test_data = self.test_data.batch(cfg.val_batch_size)
-
- map_func = self.get_tf_mapping2()
-
- self.batch_train_data = self.batch_train_data.map(map_func=map_func)
- self.batch_val_data = self.batch_val_data.map(map_func=map_func)
- self.batch_test_data = self.batch_test_data.map(map_func=map_func)
-
- self.batch_train_data = self.batch_train_data.prefetch(cfg.batch_size)
- self.batch_val_data = self.batch_val_data.prefetch(cfg.val_batch_size)
- self.batch_test_data = self.batch_test_data.prefetch(cfg.val_batch_size)
-
- iter = tf.data.Iterator.from_structure(self.batch_train_data.output_types, self.batch_train_data.output_shapes)
- self.flat_inputs = iter.get_next()
- self.train_init_op = iter.make_initializer(self.batch_train_data)
- self.val_init_op = iter.make_initializer(self.batch_val_data)
- self.test_init_op = iter.make_initializer(self.batch_test_data)
-
-
-if __name__ == '__main__':
- parser = argparse.ArgumentParser()
- parser.add_argument('--gpu', type=int, default=0, help='the number of GPUs to use [default: 0]')
- parser.add_argument('--mode', type=str, default='train', help='options: train, test, vis')
- parser.add_argument('--test_area', type=str, default='14', help='options: 08, 11,12,13,14,15,16,17,18,19,20,21')
- parser.add_argument('--model_path', type=str, default='None', help='pretrained model path')
- FLAGS = parser.parse_args()
-
- os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
- os.environ['CUDA_VISIBLE_DEVICES'] = str(FLAGS.gpu)
- os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
- Mode = FLAGS.mode
-
- test_area = FLAGS.test_area
- dataset = SemanticKITTI(test_area)
- dataset.init_input_pipeline()
-
- if Mode == 'train':
- model = Network(dataset, cfg)
- model.train(dataset)
- elif Mode == 'test':
- cfg.saving = False
- model = Network(dataset, cfg)
- if FLAGS.model_path is not 'None':
- chosen_snap = FLAGS.model_path
- else:
- chosen_snapshot = -1
- logs = np.sort([os.path.join('results', f) for f in os.listdir('results') if f.startswith('Log')])
- chosen_folder = logs[-1]
- snap_path = join(chosen_folder, 'snapshots')
- snap_steps = [int(f[:-5].split('-')[-1]) for f in os.listdir(snap_path) if f[-5:] == '.meta']
- chosen_step = np.sort(snap_steps)[-1]
- chosen_snap = os.path.join(snap_path, 'snap-{:d}'.format(chosen_step))
- tester = ModelTester(model, dataset, restore_snap=chosen_snap)
- tester.test(model, dataset)
- else:
- ##################
- # Visualize data #
- ##################
-
- with tf.Session() as sess:
- sess.run(tf.global_variables_initializer())
- sess.run(dataset.train_init_op)
- while True:
- flat_inputs = sess.run(dataset.flat_inputs)
- pc_xyz = flat_inputs[0]
- sub_pc_xyz = flat_inputs[1]
- labels = flat_inputs[17]
- Plot.draw_pc_sem_ins(pc_xyz[0, :, :], labels[0, :])
- Plot.draw_pc_sem_ins(sub_pc_xyz[0, :, :], labels[0, 0:np.shape(sub_pc_xyz)[1]])
diff --git a/main_Semantic3D.py b/main_Toronto3D.py
similarity index 70%
rename from main_Semantic3D.py
rename to main_Toronto3D.py
index 6eb9c7b..3d14f7d 100644
--- a/main_Semantic3D.py
+++ b/main_Toronto3D.py
@@ -1,60 +1,45 @@
from os.path import join, exists
from RandLANet import Network
-from tester_Semantic3D import ModelTester
+from tester_Toronto3D import ModelTester
from helper_ply import read_ply
from helper_tool import Plot
from helper_tool import DataProcessing as DP
-from helper_tool import ConfigSemantic3D as cfg
+from helper_tool import ConfigToronto3D as cfg
import tensorflow as tf
import numpy as np
import pickle, argparse, os
-class Semantic3D:
- def __init__(self):
- self.name = 'Semantic3D'
- self.path = '/data/semantic3d'
- self.label_to_names = {0: 'unlabeled',
- 1: 'man-made terrain',
- 2: 'natural terrain',
- 3: 'high vegetation',
- 4: 'low vegetation',
- 5: 'buildings',
- 6: 'hard scape',
- 7: 'scanning artefacts',
- 8: 'cars'}
+class Toronto3D:
+ def __init__(self, mode='train'):
+ self.name = 'Toronto3D'
+ self.path = 'data/Toronto_3D'
+ self.label_to_names = {0: 'unclassified',
+ 1: 'Ground',
+ 2: 'Road marking',
+ 3: 'Natural',
+ 4: 'Building',
+ 5: 'Utility line',
+ 6: 'Pole',
+ 7: 'Car',
+ 8: 'Fence'}
self.num_classes = len(self.label_to_names)
self.label_values = np.sort([k for k, v in self.label_to_names.items()])
self.label_to_idx = {l: i for i, l in enumerate(self.label_values)}
self.ignored_labels = np.sort([0])
- self.original_folder = join(self.path, 'original_data')
self.full_pc_folder = join(self.path, 'original_ply')
- self.sub_pc_folder = join(self.path, 'input_{:.3f}'.format(cfg.sub_grid_size))
-
- # Following KPConv to do the train-validation split
- self.all_splits = [0, 1, 4, 5, 3, 4, 3, 0, 1, 2, 3, 4, 2, 0, 5]
- self.val_split = 1
# Initial training-validation-testing files
- self.train_files = []
- self.val_files = []
- self.test_files = []
- cloud_names = [file_name[:-4] for file_name in os.listdir(self.original_folder) if file_name[-4:] == '.txt']
- for pc_name in cloud_names:
- if exists(join(self.original_folder, pc_name + '.labels')):
- self.train_files.append(join(self.sub_pc_folder, pc_name + '.ply'))
- else:
- self.test_files.append(join(self.full_pc_folder, pc_name + '.ply'))
-
- self.train_files = np.sort(self.train_files)
- self.test_files = np.sort(self.test_files)
+ self.train_files = ['L001', 'L003', 'L004']
+ self.val_files = ['L002']
+ self.test_files = ['L002']
- for i, file_path in enumerate(self.train_files):
- if self.all_splits[i] == self.val_split:
- self.val_files.append(file_path)
+ self.val_split = 3
- self.train_files = np.sort([x for x in self.train_files if x not in self.val_files])
+ self.train_files = [os.path.join(self.full_pc_folder, files + '.ply') for files in self.train_files]
+ self.val_files = [os.path.join(self.full_pc_folder, files + '.ply') for files in self.val_files]
+ self.test_files = [os.path.join(self.full_pc_folder, files + '.ply') for files in self.test_files]
# Initiate containers
self.val_proj = []
@@ -69,44 +54,26 @@ def __init__(self):
self.input_colors = {'training': [], 'validation': [], 'test': []}
self.input_labels = {'training': [], 'validation': []}
- # Ascii files dict for testing
- self.ascii_files = {
- 'MarketplaceFeldkirch_Station4_rgb_intensity-reduced.ply': 'marketsquarefeldkirch4-reduced.labels',
- 'sg27_station10_rgb_intensity-reduced.ply': 'sg27_10-reduced.labels',
- 'sg28_Station2_rgb_intensity-reduced.ply': 'sg28_2-reduced.labels',
- 'StGallenCathedral_station6_rgb_intensity-reduced.ply': 'stgallencathedral6-reduced.labels',
- 'birdfountain_station1_xyz_intensity_rgb.ply': 'birdfountain1.labels',
- 'castleblatten_station1_intensity_rgb.ply': 'castleblatten1.labels',
- 'castleblatten_station5_xyz_intensity_rgb.ply': 'castleblatten5.labels',
- 'marketplacefeldkirch_station1_intensity_rgb.ply': 'marketsquarefeldkirch1.labels',
- 'marketplacefeldkirch_station4_intensity_rgb.ply': 'marketsquarefeldkirch4.labels',
- 'marketplacefeldkirch_station7_intensity_rgb.ply': 'marketsquarefeldkirch7.labels',
- 'sg27_station10_intensity_rgb.ply': 'sg27_10.labels',
- 'sg27_station3_intensity_rgb.ply': 'sg27_3.labels',
- 'sg27_station6_intensity_rgb.ply': 'sg27_6.labels',
- 'sg27_station8_intensity_rgb.ply': 'sg27_8.labels',
- 'sg28_station2_intensity_rgb.ply': 'sg28_2.labels',
- 'sg28_station5_xyz_intensity_rgb.ply': 'sg28_5.labels',
- 'stgallencathedral_station1_intensity_rgb.ply': 'stgallencathedral1.labels',
- 'stgallencathedral_station3_intensity_rgb.ply': 'stgallencathedral3.labels',
- 'stgallencathedral_station6_intensity_rgb.ply': 'stgallencathedral6.labels'}
-
- self.load_sub_sampled_clouds(cfg.sub_grid_size)
-
- def load_sub_sampled_clouds(self, sub_grid_size):
+ self.load_sub_sampled_clouds(cfg.sub_grid_size, mode)
+
+ def load_sub_sampled_clouds(self, sub_grid_size, mode):
tree_path = join(self.path, 'input_{:.3f}'.format(sub_grid_size))
- files = np.hstack((self.train_files, self.val_files, self.test_files))
+ if mode == 'test':
+ files = self.test_files
+ else:
+ files = np.hstack((self.train_files, self.val_files))
for i, file_path in enumerate(files):
cloud_name = file_path.split('/')[-1][:-4]
print('Load_pc_' + str(i) + ': ' + cloud_name)
- if file_path in self.val_files:
- cloud_split = 'validation'
- elif file_path in self.train_files:
- cloud_split = 'training'
- else:
+ if mode == 'test':
cloud_split = 'test'
+ else:
+ if file_path in self.val_files:
+ cloud_split = 'validation'
+ else:
+ cloud_split = 'training'
# Name of the input files
kd_tree_file = join(tree_path, '{:s}_KDTree.pkl'.format(cloud_name))
@@ -114,7 +81,15 @@ def load_sub_sampled_clouds(self, sub_grid_size):
# read ply with data
data = read_ply(sub_ply_file)
- sub_colors = np.vstack((data['red'], data['green'], data['blue'])).T
+ # read RGB / intensity accoring to configuration
+ if cfg.use_rgb and cfg.use_intensity:
+ sub_colors = np.vstack((data['red'], data['green'], data['blue'], data['intensity'])).T
+ elif cfg.use_rgb and not cfg.use_intensity:
+ sub_colors = np.vstack((data['red'], data['green'], data['blue'])).T
+ elif not cfg.use_rgb and cfg.use_intensity:
+ sub_colors = data['intensity'].reshape(-1,1)
+ else:
+ sub_colors = np.ones((data.shape[0],1))
if cloud_split == 'test':
sub_labels = None
else:
@@ -129,29 +104,15 @@ def load_sub_sampled_clouds(self, sub_grid_size):
if cloud_split in ['training', 'validation']:
self.input_labels[cloud_split] += [sub_labels]
- # Get validation and test re_projection indices
- print('\nPreparing reprojection indices for validation and test')
-
- for i, file_path in enumerate(files):
-
- # get cloud name and split
- cloud_name = file_path.split('/')[-1][:-4]
-
- # Validation projection and labels
- if file_path in self.val_files:
- proj_file = join(tree_path, '{:s}_proj.pkl'.format(cloud_name))
- with open(proj_file, 'rb') as f:
- proj_idx, labels = pickle.load(f)
- self.val_proj += [proj_idx]
- self.val_labels += [labels]
-
- # Test projection
- if file_path in self.test_files:
+ # Get test re_projection indices
+ if cloud_split == 'test':
+ print('\nPreparing reprojection indices for {}'.format(cloud_name))
proj_file = join(tree_path, '{:s}_proj.pkl'.format(cloud_name))
with open(proj_file, 'rb') as f:
proj_idx, labels = pickle.load(f)
self.test_proj += [proj_idx]
self.test_labels += [labels]
+
print('finished')
return
@@ -163,6 +124,13 @@ def get_batch_gen(self, split):
num_per_epoch = cfg.val_steps * cfg.val_batch_size
elif split == 'test':
num_per_epoch = cfg.val_steps * cfg.val_batch_size
+
+ # assign number of features according to input
+ n_features = 1 # use xyz only by default
+ if cfg.use_rgb and cfg.use_intensity:
+ n_features = 4
+ elif cfg.use_rgb and not cfg.use_intensity:
+ n_features = 3
# Reset possibility
self.possibility[split] = []
@@ -214,6 +182,7 @@ def spatially_regular_gen():
queried_pc_labels = self.input_labels[split][cloud_idx][query_idx]
queried_pc_labels = np.array([self.label_to_idx[l] for l in queried_pc_labels])
queried_pt_weight = np.array([self.class_weight[split][0][n] for n in queried_pc_labels])
+
# Update the possibility of the selected points
dists = np.sum(np.square((points[query_idx] - pick_point).astype(np.float32)), axis=1)
@@ -230,7 +199,7 @@ def spatially_regular_gen():
gen_func = spatially_regular_gen
gen_types = (tf.float32, tf.float32, tf.int32, tf.int32, tf.int32)
- gen_shapes = ([None, 3], [None, 3], [None], [None], [None])
+ gen_shapes = ([None, 3], [None, n_features], [None], [None], [None])
return gen_func, gen_types, gen_shapes
def get_tf_mapping(self):
@@ -299,45 +268,53 @@ def tf_augment_input(inputs):
noise = tf.random_normal(tf.shape(transformed_xyz), stddev=cfg.augment_noise)
transformed_xyz = transformed_xyz + noise
- rgb = features[:, :3]
- stacked_features = tf.concat([transformed_xyz, rgb], axis=-1)
+ stacked_features = tf.concat([transformed_xyz, features], axis=-1)
return stacked_features
- def init_input_pipeline(self):
- print('Initiating input pipelines')
+ def init_train_pipeline(self):
+ print('Initiating training pipelines')
cfg.ignored_label_inds = [self.label_to_idx[ign_label] for ign_label in self.ignored_labels]
gen_function, gen_types, gen_shapes = self.get_batch_gen('training')
gen_function_val, _, _ = self.get_batch_gen('validation')
- gen_function_test, _, _ = self.get_batch_gen('test')
self.train_data = tf.data.Dataset.from_generator(gen_function, gen_types, gen_shapes)
self.val_data = tf.data.Dataset.from_generator(gen_function_val, gen_types, gen_shapes)
- self.test_data = tf.data.Dataset.from_generator(gen_function_test, gen_types, gen_shapes)
self.batch_train_data = self.train_data.batch(cfg.batch_size)
self.batch_val_data = self.val_data.batch(cfg.val_batch_size)
- self.batch_test_data = self.test_data.batch(cfg.val_batch_size)
map_func = self.get_tf_mapping()
self.batch_train_data = self.batch_train_data.map(map_func=map_func)
self.batch_val_data = self.batch_val_data.map(map_func=map_func)
- self.batch_test_data = self.batch_test_data.map(map_func=map_func)
self.batch_train_data = self.batch_train_data.prefetch(cfg.batch_size)
self.batch_val_data = self.batch_val_data.prefetch(cfg.val_batch_size)
- self.batch_test_data = self.batch_test_data.prefetch(cfg.val_batch_size)
iter = tf.data.Iterator.from_structure(self.batch_train_data.output_types, self.batch_train_data.output_shapes)
self.flat_inputs = iter.get_next()
self.train_init_op = iter.make_initializer(self.batch_train_data)
self.val_init_op = iter.make_initializer(self.batch_val_data)
- self.test_init_op = iter.make_initializer(self.batch_test_data)
+ def init_test_pipeline(self):
+ print('Initiating testing pipelines')
+ cfg.ignored_label_inds = [self.label_to_idx[ign_label] for ign_label in self.ignored_labels]
+ gen_function_test,gen_types, gen_shapes = self.get_batch_gen('test')
+
+ self.test_data = tf.data.Dataset.from_generator(gen_function_test, gen_types, gen_shapes)
+ self.batch_test_data = self.test_data.batch(cfg.val_batch_size)
+ map_func = self.get_tf_mapping()
+ self.batch_test_data = self.batch_test_data.map(map_func=map_func)
+ self.batch_test_data = self.batch_test_data.prefetch(cfg.val_batch_size)
+
+ iter = tf.data.Iterator.from_structure(self.batch_test_data.output_types, self.batch_test_data.output_shapes)
+ self.flat_inputs = iter.get_next()
+ self.test_init_op = iter.make_initializer(self.batch_test_data)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--gpu', type=int, default=0, help='the number of GPUs to use [default: 0]')
- parser.add_argument('--mode', type=str, default='train', help='options: train, test, vis')
+ parser.add_argument('--mode', type=str, default='test', help='options: train, test, vis')
parser.add_argument('--model_path', type=str, default='None', help='pretrained model path')
+ parser.add_argument('--test_eval', type=bool, default=True, help='evaluate test result on L002')
FLAGS = parser.parse_args()
GPU_ID = FLAGS.gpu
@@ -346,14 +323,15 @@ def init_input_pipeline(self):
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
Mode = FLAGS.mode
- dataset = Semantic3D()
- dataset.init_input_pipeline()
+ dataset = Toronto3D(mode=Mode)
if Mode == 'train':
+ dataset.init_train_pipeline()
model = Network(dataset, cfg)
model.train(dataset)
elif Mode == 'test':
cfg.saving = False
+ dataset.init_test_pipeline()
model = Network(dataset, cfg)
if FLAGS.model_path is not 'None':
chosen_snap = FLAGS.model_path
@@ -363,10 +341,13 @@ def init_input_pipeline(self):
chosen_folder = logs[-1]
snap_path = join(chosen_folder, 'snapshots')
snap_steps = [int(f[:-5].split('-')[-1]) for f in os.listdir(snap_path) if f[-5:] == '.meta']
- chosen_step = np.sort(snap_steps)[-1]
+ chosen_step = np.sort(snap_steps)[chosen_snapshot]
chosen_snap = os.path.join(snap_path, 'snap-{:d}'.format(chosen_step))
- tester = ModelTester(model, dataset, restore_snap=chosen_snap)
- tester.test(model, dataset)
+ tester = ModelTester(model, dataset, cfg, restore_snap=chosen_snap)
+ if FLAGS.test_eval:
+ tester.test(model, dataset, eval=True)
+ else:
+ tester.test(model, dataset)
else:
##################
diff --git a/tester_S3DIS.py b/tester_S3DIS.py
deleted file mode 100755
index 47a8183..0000000
--- a/tester_S3DIS.py
+++ /dev/null
@@ -1,178 +0,0 @@
-from os import makedirs
-from os.path import exists, join
-from helper_ply import write_ply
-from sklearn.metrics import confusion_matrix
-from helper_tool import DataProcessing as DP
-import tensorflow as tf
-import numpy as np
-import time
-
-
-def log_out(out_str, log_f_out):
- log_f_out.write(out_str + '\n')
- log_f_out.flush()
- print(out_str)
-
-
-class ModelTester:
- def __init__(self, model, dataset, restore_snap=None):
- my_vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)
- self.saver = tf.train.Saver(my_vars, max_to_keep=100)
- self.Log_file = open('log_test_' + str(dataset.val_split) + '.txt', 'a')
-
- # Create a session for running Ops on the Graph.
- on_cpu = False
- if on_cpu:
- c_proto = tf.ConfigProto(device_count={'GPU': 0})
- else:
- c_proto = tf.ConfigProto()
- c_proto.gpu_options.allow_growth = True
- self.sess = tf.Session(config=c_proto)
- self.sess.run(tf.global_variables_initializer())
-
- # Load trained model
- if restore_snap is not None:
- self.saver.restore(self.sess, restore_snap)
- print("Model restored from " + restore_snap)
-
- self.prob_logits = tf.nn.softmax(model.logits)
-
- # Initiate global prediction over all test clouds
- self.test_probs = [np.zeros(shape=[l.shape[0], model.config.num_classes], dtype=np.float32)
- for l in dataset.input_labels['validation']]
-
- def test(self, model, dataset, num_votes=100):
-
- # Smoothing parameter for votes
- test_smooth = 0.95
-
- # Initialise iterator with validation/test data
- self.sess.run(dataset.val_init_op)
-
- # Number of points per class in validation set
- val_proportions = np.zeros(model.config.num_classes, dtype=np.float32)
- i = 0
- for label_val in dataset.label_values:
- if label_val not in dataset.ignored_labels:
- val_proportions[i] = np.sum([np.sum(labels == label_val) for labels in dataset.val_labels])
- i += 1
-
- # Test saving path
- saving_path = time.strftime('results/Log_%Y-%m-%d_%H-%M-%S', time.gmtime())
- test_path = join('test', saving_path.split('/')[-1])
- makedirs(test_path) if not exists(test_path) else None
- makedirs(join(test_path, 'val_preds')) if not exists(join(test_path, 'val_preds')) else None
-
- step_id = 0
- epoch_id = 0
- last_min = -0.5
-
- while last_min < num_votes:
- try:
- ops = (self.prob_logits,
- model.labels,
- model.inputs['input_inds'],
- model.inputs['cloud_inds'],
- )
-
- stacked_probs, stacked_labels, point_idx, cloud_idx = self.sess.run(ops, {model.is_training: False})
- correct = np.sum(np.argmax(stacked_probs, axis=1) == stacked_labels)
- acc = correct / float(np.prod(np.shape(stacked_labels)))
- print('step' + str(step_id) + ' acc:' + str(acc))
- stacked_probs = np.reshape(stacked_probs, [model.config.val_batch_size, model.config.num_points,
- model.config.num_classes])
-
- for j in range(np.shape(stacked_probs)[0]):
- probs = stacked_probs[j, :, :]
- p_idx = point_idx[j, :]
- c_i = cloud_idx[j][0]
- self.test_probs[c_i][p_idx] = test_smooth * self.test_probs[c_i][p_idx] + (1 - test_smooth) * probs
- step_id += 1
-
- except tf.errors.OutOfRangeError:
-
- new_min = np.min(dataset.min_possibility['validation'])
- log_out('Epoch {:3d}, end. Min possibility = {:.1f}'.format(epoch_id, new_min), self.Log_file)
-
- if last_min + 1 < new_min:
-
- # Update last_min
- last_min += 1
-
- # Show vote results (On subcloud so it is not the good values here)
- log_out('\nConfusion on sub clouds', self.Log_file)
- confusion_list = []
-
- num_val = len(dataset.input_labels['validation'])
-
- for i_test in range(num_val):
- probs = self.test_probs[i_test]
- preds = dataset.label_values[np.argmax(probs, axis=1)].astype(np.int32)
- labels = dataset.input_labels['validation'][i_test]
-
- # Confs
- confusion_list += [confusion_matrix(labels, preds, dataset.label_values)]
-
- # Regroup confusions
- C = np.sum(np.stack(confusion_list), axis=0).astype(np.float32)
-
- # Rescale with the right number of point per class
- C *= np.expand_dims(val_proportions / (np.sum(C, axis=1) + 1e-6), 1)
-
- # Compute IoUs
- IoUs = DP.IoU_from_confusions(C)
- m_IoU = np.mean(IoUs)
- s = '{:5.2f} | '.format(100 * m_IoU)
- for IoU in IoUs:
- s += '{:5.2f} '.format(100 * IoU)
- log_out(s + '\n', self.Log_file)
-
- if int(np.ceil(new_min)) % 1 == 0:
-
- # Project predictions
- log_out('\nReproject Vote #{:d}'.format(int(np.floor(new_min))), self.Log_file)
- proj_probs_list = []
-
- for i_val in range(num_val):
- # Reproject probs back to the evaluations points
- proj_idx = dataset.val_proj[i_val]
- probs = self.test_probs[i_val][proj_idx, :]
- proj_probs_list += [probs]
-
- # Show vote results
- log_out('Confusion on full clouds', self.Log_file)
- confusion_list = []
- for i_test in range(num_val):
- # Get the predicted labels
- preds = dataset.label_values[np.argmax(proj_probs_list[i_test], axis=1)].astype(np.uint8)
-
- # Confusion
- labels = dataset.val_labels[i_test]
- acc = np.sum(preds == labels) / len(labels)
- log_out(dataset.input_names['validation'][i_test] + ' Acc:' + str(acc), self.Log_file)
-
- confusion_list += [confusion_matrix(labels, preds, dataset.label_values)]
- name = dataset.input_names['validation'][i_test] + '.ply'
- write_ply(join(test_path, 'val_preds', name), [preds, labels], ['pred', 'label'])
-
- # Regroup confusions
- C = np.sum(np.stack(confusion_list), axis=0)
-
- IoUs = DP.IoU_from_confusions(C)
- m_IoU = np.mean(IoUs)
- s = '{:5.2f} | '.format(100 * m_IoU)
- for IoU in IoUs:
- s += '{:5.2f} '.format(100 * IoU)
- log_out('-' * len(s), self.Log_file)
- log_out(s, self.Log_file)
- log_out('-' * len(s) + '\n', self.Log_file)
- print('finished \n')
- self.sess.close()
- return
-
- self.sess.run(dataset.val_init_op)
- epoch_id += 1
- step_id = 0
- continue
-
- return
diff --git a/tester_SemanticKITTI.py b/tester_SemanticKITTI.py
deleted file mode 100644
index 6f3a99b..0000000
--- a/tester_SemanticKITTI.py
+++ /dev/null
@@ -1,167 +0,0 @@
-from os import makedirs
-from os.path import exists, join, isfile, dirname, abspath
-from helper_tool import DataProcessing as DP
-from sklearn.metrics import confusion_matrix
-import tensorflow as tf
-import numpy as np
-import yaml
-import pickle
-
-BASE_DIR = dirname(abspath(__file__))
-
-data_config = join(BASE_DIR, 'utils', 'semantic-kitti.yaml')
-DATA = yaml.safe_load(open(data_config, 'r'))
-remap_dict = DATA["learning_map_inv"]
-
-# make lookup table for mapping
-max_key = max(remap_dict.keys())
-remap_lut = np.zeros((max_key + 100), dtype=np.int32)
-remap_lut[list(remap_dict.keys())] = list(remap_dict.values())
-
-remap_dict_val = DATA["learning_map"]
-max_key = max(remap_dict_val.keys())
-remap_lut_val = np.zeros((max_key + 100), dtype=np.int32)
-remap_lut_val[list(remap_dict_val.keys())] = list(remap_dict_val.values())
-
-
-def log_out(out_str, f_out):
- f_out.write(out_str + '\n')
- f_out.flush()
- print(out_str)
-
-
-class ModelTester:
- def __init__(self, model, dataset, restore_snap=None):
- my_vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)
- self.saver = tf.train.Saver(my_vars, max_to_keep=100)
- self.Log_file = open('log_test_' + dataset.name + '.txt', 'a')
-
- # Create a session for running Ops on the Graph.
- on_cpu = False
- if on_cpu:
- c_proto = tf.ConfigProto(device_count={'GPU': 0})
- else:
- c_proto = tf.ConfigProto()
- c_proto.gpu_options.allow_growth = True
- self.sess = tf.Session(config=c_proto)
- self.sess.run(tf.global_variables_initializer())
-
- # Name of the snapshot to restore to (None if you want to start from beginning)
- if restore_snap is not None:
- self.saver.restore(self.sess, restore_snap)
- print("Model restored from " + restore_snap)
-
- self.prob_logits = tf.nn.softmax(model.logits)
- self.test_probs = 0
- self.idx = 0
-
- def test(self, model, dataset):
-
- # Initialise iterator with train data
- self.sess.run(dataset.test_init_op)
- self.test_probs = [np.zeros(shape=[len(l), model.config.num_classes], dtype=np.float16)
- for l in dataset.possibility]
-
- test_path = join('test', 'sequences')
- makedirs(test_path) if not exists(test_path) else None
- save_path = join(test_path, dataset.test_scan_number, 'predictions')
- makedirs(save_path) if not exists(save_path) else None
- test_smooth = 0.98
- epoch_ind = 0
-
- while True:
- try:
- ops = (self.prob_logits,
- model.labels,
- model.inputs['input_inds'],
- model.inputs['cloud_inds'])
- stacked_probs, labels, point_inds, cloud_inds = self.sess.run(ops, {model.is_training: False})
- if self.idx % 10 == 0:
- print('step ' + str(self.idx))
- self.idx += 1
- stacked_probs = np.reshape(stacked_probs, [model.config.val_batch_size,
- model.config.num_points,
- model.config.num_classes])
- for j in range(np.shape(stacked_probs)[0]):
- probs = stacked_probs[j, :, :]
- inds = point_inds[j, :]
- c_i = cloud_inds[j][0]
- self.test_probs[c_i][inds] = test_smooth * self.test_probs[c_i][inds] + (1 - test_smooth) * probs
-
- except tf.errors.OutOfRangeError:
- new_min = np.min(dataset.min_possibility)
- log_out('Epoch {:3d}, end. Min possibility = {:.1f}'.format(epoch_ind, new_min), self.Log_file)
- if np.min(dataset.min_possibility) > 0.5: # 0.5
- log_out(' Min possibility = {:.1f}'.format(np.min(dataset.min_possibility)), self.Log_file)
- print('\nReproject Vote #{:d}'.format(int(np.floor(new_min))))
-
- # For validation set
- num_classes = 19
- gt_classes = [0 for _ in range(num_classes)]
- positive_classes = [0 for _ in range(num_classes)]
- true_positive_classes = [0 for _ in range(num_classes)]
- val_total_correct = 0
- val_total_seen = 0
-
- for j in range(len(self.test_probs)):
- test_file_name = dataset.test_list[j]
- frame = test_file_name.split('/')[-1][:-4]
- proj_path = join(dataset.dataset_path, dataset.test_scan_number, 'proj')
- proj_file = join(proj_path, str(frame) + '_proj.pkl')
- if isfile(proj_file):
- with open(proj_file, 'rb') as f:
- proj_inds = pickle.load(f)
- probs = self.test_probs[j][proj_inds[0], :]
- pred = np.argmax(probs, 1)
- if dataset.test_scan_number == '08':
- label_path = join(dirname(dataset.dataset_path), 'sequences', dataset.test_scan_number,
- 'labels')
- label_file = join(label_path, str(frame) + '.label')
- labels = DP.load_label_kitti(label_file, remap_lut_val)
- invalid_idx = np.where(labels == 0)[0]
- labels_valid = np.delete(labels, invalid_idx)
- pred_valid = np.delete(pred, invalid_idx)
- labels_valid = labels_valid - 1
- correct = np.sum(pred_valid == labels_valid)
- val_total_correct += correct
- val_total_seen += len(labels_valid)
- conf_matrix = confusion_matrix(labels_valid, pred_valid, np.arange(0, num_classes, 1))
- gt_classes += np.sum(conf_matrix, axis=1)
- positive_classes += np.sum(conf_matrix, axis=0)
- true_positive_classes += np.diagonal(conf_matrix)
- else:
- store_path = join(test_path, dataset.test_scan_number, 'predictions',
- str(frame) + '.label')
- pred = pred + 1
- pred = pred.astype(np.uint32)
- upper_half = pred >> 16 # get upper half for instances
- lower_half = pred & 0xFFFF # get lower half for semantics
- lower_half = remap_lut[lower_half] # do the remapping of semantics
- pred = (upper_half << 16) + lower_half # reconstruct full label
- pred = pred.astype(np.uint32)
- pred.tofile(store_path)
- log_out(str(dataset.test_scan_number) + ' finished', self.Log_file)
- if dataset.test_scan_number=='08':
- iou_list = []
- for n in range(0, num_classes, 1):
- iou = true_positive_classes[n] / float(
- gt_classes[n] + positive_classes[n] - true_positive_classes[n])
- iou_list.append(iou)
- mean_iou = sum(iou_list) / float(num_classes)
-
- log_out('eval accuracy: {}'.format(val_total_correct / float(val_total_seen)), self.Log_file)
- log_out('mean IOU:{}'.format(mean_iou), self.Log_file)
-
- mean_iou = 100 * mean_iou
- print('Mean IoU = {:.1f}%'.format(mean_iou))
- s = '{:5.2f} | '.format(mean_iou)
- for IoU in iou_list:
- s += '{:5.2f} '.format(100 * IoU)
- print('-' * len(s))
- print(s)
- print('-' * len(s) + '\n')
- self.sess.close()
- return
- self.sess.run(dataset.test_init_op)
- epoch_ind += 1
- continue
diff --git a/tester_Semantic3D.py b/tester_Toronto3D.py
similarity index 60%
rename from tester_Semantic3D.py
rename to tester_Toronto3D.py
index ebcc900..7b4992c 100644
--- a/tester_Semantic3D.py
+++ b/tester_Toronto3D.py
@@ -1,9 +1,12 @@
from os import makedirs
from os.path import exists, join
+
+from tensorflow import data
from helper_ply import read_ply, write_ply
import tensorflow as tf
import numpy as np
import time
+from sklearn.metrics import confusion_matrix
def log_string(out_str, log_out):
@@ -13,7 +16,7 @@ def log_string(out_str, log_out):
class ModelTester:
- def __init__(self, model, dataset, restore_snap=None):
+ def __init__(self, model, dataset, config, restore_snap=None):
# Tensorflow Saver definition
my_vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)
self.saver = tf.train.Saver(my_vars, max_to_keep=100)
@@ -37,9 +40,10 @@ def __init__(self, model, dataset, restore_snap=None):
self.test_probs = [np.zeros((l.data.shape[0], model.config.num_classes), dtype=np.float16)
for l in dataset.input_trees['test']]
+ self.config = config
self.log_out = open('log_test_' + dataset.name + '.txt', 'a')
- def test(self, model, dataset, num_votes=100):
+ def test(self, model, dataset, num_votes=100, eval=False):
# Smoothing parameter for votes
test_smooth = 0.98
@@ -52,7 +56,7 @@ def test(self, model, dataset, num_votes=100):
test_path = join('test', saving_path.split('/')[-1])
makedirs(test_path) if not exists(test_path) else None
makedirs(join(test_path, 'predictions')) if not exists(join(test_path, 'predictions')) else None
- makedirs(join(test_path, 'probs')) if not exists(join(test_path, 'probs')) else None
+ # makedirs(join(test_path, 'probs')) if not exists(join(test_path, 'probs')) else None
#####################
# Network predictions
@@ -61,6 +65,7 @@ def test(self, model, dataset, num_votes=100):
step_id = 0
epoch_id = 0
last_min = -0.5
+ t0 = time.time()
while last_min < num_votes:
@@ -89,8 +94,9 @@ def test(self, model, dataset, num_votes=100):
new_min = np.min(dataset.min_possibility['test'])
log_string('Epoch {:3d}, end. Min possibility = {:.1f}'.format(epoch_id, new_min), self.log_out)
- if last_min + 4 < new_min:
+ if last_min + 1 < new_min:
+ print('Prediction done in {:.1f} s\n'.format(time.time() - t0))
print('Saving clouds')
# Update last_min
@@ -103,8 +109,10 @@ def test(self, model, dataset, num_votes=100):
i_test = 0
for i, file_path in enumerate(files):
# Get file
- points = self.load_evaluation_points(file_path)
- points = points.astype(np.float16)
+ if eval:
+ points, gt = self.load_evaluation_points(file_path)
+ else:
+ points = self.load_test_points(file_path)
# Reproject probs
probs = np.zeros(shape=[np.shape(points)[0], 8], dtype=np.float16)
@@ -123,15 +131,18 @@ def test(self, model, dataset, num_votes=100):
# Save plys
cloud_name = file_path.split('/')[-1]
+ ply_name = join(test_path, 'predictions', cloud_name)
+ write_ply(ply_name, [points, preds], ['x', 'y', 'z', 'preds'])
+ log_string(ply_name + ' has saved', self.log_out)
+
+ # evaluate prediction results
+ if eval:
+ self.evaluate(preds, gt)
- # Save ascii preds
- ascii_name = join(test_path, 'predictions', dataset.ascii_files[cloud_name])
- np.savetxt(ascii_name, preds, fmt='%d')
- log_string(ascii_name + 'has saved', self.log_out)
i_test += 1
t2 = time.time()
- print('Done in {:.1f} s\n'.format(t2 - t1))
+ print('Reprojection and saving done in {:.1f} s\n'.format(t2 - t1))
self.sess.close()
return
@@ -142,6 +153,60 @@ def test(self, model, dataset, num_votes=100):
return
@staticmethod
- def load_evaluation_points(file_path):
+ def load_test_points(file_path):
data = read_ply(file_path)
return np.vstack((data['x'], data['y'], data['z'])).T
+
+ @staticmethod
+ def load_evaluation_points(file_path):
+ data = read_ply(file_path)
+ xyz = np.vstack((data['x'], data['y'], data['z'])).T
+ label = data['scalar_Label'].astype(np.uint8)
+ return xyz, label
+
+ def evaluate(self, pred, gt):
+ gt_classes = [0 for _ in range(self.config.num_classes)]
+ positive_classes = [0 for _ in range(self.config.num_classes)]
+ true_positive_classes = [0 for _ in range(self.config.num_classes)]
+ val_total_correct = 0
+ val_total_seen = 0
+
+ if not self.config.ignored_label_inds:
+ pred_valid = pred
+ labels_valid = gt
+ else:
+ invalid_idx = np.where(gt == self.config.ignored_label_inds)[0]
+ labels_valid = np.delete(gt, invalid_idx)
+ labels_valid = labels_valid - 1
+ pred_valid = np.delete(pred, invalid_idx)
+ pred_valid = pred_valid - 1
+
+ correct = np.sum(pred_valid == labels_valid)
+ val_total_correct += correct
+ val_total_seen += len(labels_valid)
+
+ conf_matrix = confusion_matrix(labels_valid, pred_valid, np.arange(0, self.config.num_classes, 1))
+ gt_classes += np.sum(conf_matrix, axis=1)
+ positive_classes += np.sum(conf_matrix, axis=0)
+ true_positive_classes += np.diagonal(conf_matrix)
+
+ iou_list = []
+ for n in range(0, self.config.num_classes, 1):
+ iou = true_positive_classes[n] / float(gt_classes[n] + positive_classes[n] - true_positive_classes[n])
+ iou_list.append(iou)
+ mean_iou = sum(iou_list) / float(self.config.num_classes)
+
+ log_string('eval accuracy: {}'.format(val_total_correct / float(val_total_seen)), self.log_out)
+ log_string('mean IOU:{}'.format(mean_iou), self.log_out)
+
+ mean_iou = 100 * mean_iou
+ log_string('Mean IoU = {:.1f}%'.format(mean_iou), self.log_out)
+ s = '{:5.2f} | '.format(mean_iou)
+ for IoU in iou_list:
+ s += '{:5.2f} '.format(100 * IoU)
+ log_string('-' * len(s), self.log_out)
+ log_string(s, self.log_out)
+ log_string('-' * len(s) + '\n', self.log_out)
+ return mean_iou
+
+
diff --git a/utils/6_fold_cv.py b/utils/6_fold_cv.py
deleted file mode 100644
index 7393efd..0000000
--- a/utils/6_fold_cv.py
+++ /dev/null
@@ -1,66 +0,0 @@
-import numpy as np
-import glob, os, sys
-
-BASE_DIR = os.path.dirname(os.path.abspath(__file__))
-ROOT_DIR = os.path.dirname(BASE_DIR)
-sys.path.append(ROOT_DIR)
-from helper_ply import read_ply
-from helper_tool import Plot
-
-if __name__ == '__main__':
- base_dir = '/data/S3DIS/results'
- original_data_dir = '/data/S3DIS/original_ply'
- data_path = glob.glob(os.path.join(base_dir, '*.ply'))
- data_path = np.sort(data_path)
-
- test_total_correct = 0
- test_total_seen = 0
- gt_classes = [0 for _ in range(13)]
- positive_classes = [0 for _ in range(13)]
- true_positive_classes = [0 for _ in range(13)]
- visualization = False
-
- for file_name in data_path:
- pred_data = read_ply(file_name)
- pred = pred_data['pred']
- original_data = read_ply(os.path.join(original_data_dir, file_name.split('/')[-1][:-4] + '.ply'))
- labels = original_data['class']
- points = np.vstack((original_data['x'], original_data['y'], original_data['z'])).T
-
- ##################
- # Visualize data #
- ##################
- if visualization:
- colors = np.vstack((original_data['red'], original_data['green'], original_data['blue'])).T
- xyzrgb = np.concatenate([points, colors], axis=-1)
- Plot.draw_pc(xyzrgb) # visualize raw point clouds
- Plot.draw_pc_sem_ins(points, labels) # visualize ground-truth
- Plot.draw_pc_sem_ins(points, pred) # visualize prediction
-
- correct = np.sum(pred == labels)
- print(str(file_name.split('/')[-1][:-4]) + '_acc:' + str(correct / float(len(labels))))
- test_total_correct += correct
- test_total_seen += len(labels)
-
- for j in range(len(labels)):
- gt_l = int(labels[j])
- pred_l = int(pred[j])
- gt_classes[gt_l] += 1
- positive_classes[pred_l] += 1
- true_positive_classes[gt_l] += int(gt_l == pred_l)
-
- iou_list = []
- for n in range(13):
- iou = true_positive_classes[n] / float(gt_classes[n] + positive_classes[n] - true_positive_classes[n])
- iou_list.append(iou)
- mean_iou = sum(iou_list) / 13.0
- print('eval accuracy: {}'.format(test_total_correct / float(test_total_seen)))
- print('mean IOU:{}'.format(mean_iou))
- print(iou_list)
-
- acc_list = []
- for n in range(13):
- acc = true_positive_classes[n] / float(gt_classes[n])
- acc_list.append(acc)
- mean_acc = sum(acc_list) / 13.0
- print('mAcc value is :{}'.format(mean_acc))
diff --git a/utils/data_prepare_s3dis.py b/utils/data_prepare_s3dis.py
deleted file mode 100644
index f0394a5..0000000
--- a/utils/data_prepare_s3dis.py
+++ /dev/null
@@ -1,80 +0,0 @@
-from sklearn.neighbors import KDTree
-from os.path import join, exists, dirname, abspath
-import numpy as np
-import pandas as pd
-import os, sys, glob, pickle
-
-BASE_DIR = dirname(abspath(__file__))
-ROOT_DIR = dirname(BASE_DIR)
-sys.path.append(BASE_DIR)
-sys.path.append(ROOT_DIR)
-from helper_ply import write_ply
-from helper_tool import DataProcessing as DP
-
-dataset_path = '/data/S3DIS/Stanford3dDataset_v1.2_Aligned_Version'
-anno_paths = [line.rstrip() for line in open(join(BASE_DIR, 'meta/anno_paths.txt'))]
-anno_paths = [join(dataset_path, p) for p in anno_paths]
-
-gt_class = [x.rstrip() for x in open(join(BASE_DIR, 'meta/class_names.txt'))]
-gt_class2label = {cls: i for i, cls in enumerate(gt_class)}
-
-sub_grid_size = 0.04
-original_pc_folder = join(dirname(dataset_path), 'original_ply')
-sub_pc_folder = join(dirname(dataset_path), 'input_{:.3f}'.format(sub_grid_size))
-os.mkdir(original_pc_folder) if not exists(original_pc_folder) else None
-os.mkdir(sub_pc_folder) if not exists(sub_pc_folder) else None
-out_format = '.ply'
-
-
-def convert_pc2ply(anno_path, save_path):
- """
- Convert original dataset files to ply file (each line is XYZRGBL).
- We aggregated all the points from each instance in the room.
- :param anno_path: path to annotations. e.g. Area_1/office_2/Annotations/
- :param save_path: path to save original point clouds (each line is XYZRGBL)
- :return: None
- """
- data_list = []
-
- for f in glob.glob(join(anno_path, '*.txt')):
- class_name = os.path.basename(f).split('_')[0]
- if class_name not in gt_class: # note: in some room there is 'staris' class..
- class_name = 'clutter'
- pc = pd.read_csv(f, header=None, delim_whitespace=True).values
- labels = np.ones((pc.shape[0], 1)) * gt_class2label[class_name]
- data_list.append(np.concatenate([pc, labels], 1)) # Nx7
-
- pc_label = np.concatenate(data_list, 0)
- xyz_min = np.amin(pc_label, axis=0)[0:3]
- pc_label[:, 0:3] -= xyz_min
-
- xyz = pc_label[:, :3].astype(np.float32)
- colors = pc_label[:, 3:6].astype(np.uint8)
- labels = pc_label[:, 6].astype(np.uint8)
- write_ply(save_path, (xyz, colors, labels), ['x', 'y', 'z', 'red', 'green', 'blue', 'class'])
-
- # save sub_cloud and KDTree file
- sub_xyz, sub_colors, sub_labels = DP.grid_sub_sampling(xyz, colors, labels, sub_grid_size)
- sub_colors = sub_colors / 255.0
- sub_ply_file = join(sub_pc_folder, save_path.split('/')[-1][:-4] + '.ply')
- write_ply(sub_ply_file, [sub_xyz, sub_colors, sub_labels], ['x', 'y', 'z', 'red', 'green', 'blue', 'class'])
-
- search_tree = KDTree(sub_xyz)
- kd_tree_file = join(sub_pc_folder, str(save_path.split('/')[-1][:-4]) + '_KDTree.pkl')
- with open(kd_tree_file, 'wb') as f:
- pickle.dump(search_tree, f)
-
- proj_idx = np.squeeze(search_tree.query(xyz, return_distance=False))
- proj_idx = proj_idx.astype(np.int32)
- proj_save = join(sub_pc_folder, str(save_path.split('/')[-1][:-4]) + '_proj.pkl')
- with open(proj_save, 'wb') as f:
- pickle.dump([proj_idx, labels], f)
-
-
-if __name__ == '__main__':
- # Note: there is an extra character in the v1.2 data in Area_5/hallway_6. It's fixed manually.
- for annotation_path in anno_paths:
- print(annotation_path)
- elements = str(annotation_path).split('/')
- out_file_name = elements[-3] + '_' + elements[-2] + out_format
- convert_pc2ply(annotation_path, join(original_pc_folder, out_file_name))
diff --git a/utils/data_prepare_semantic3d.py b/utils/data_prepare_semantic3d.py
deleted file mode 100644
index 31bc1a3..0000000
--- a/utils/data_prepare_semantic3d.py
+++ /dev/null
@@ -1,83 +0,0 @@
-from sklearn.neighbors import KDTree
-from os.path import join, exists, dirname, abspath
-import numpy as np
-import os, glob, pickle
-import sys
-
-BASE_DIR = dirname(abspath(__file__))
-ROOT_DIR = dirname(BASE_DIR)
-sys.path.append(BASE_DIR)
-sys.path.append(ROOT_DIR)
-from helper_ply import write_ply
-from helper_tool import DataProcessing as DP
-
-grid_size = 0.06
-dataset_path = '/data/semantic3d/original_data'
-original_pc_folder = join(dirname(dataset_path), 'original_ply')
-sub_pc_folder = join(dirname(dataset_path), 'input_{:.3f}'.format(grid_size))
-os.mkdir(original_pc_folder) if not exists(original_pc_folder) else None
-os.mkdir(sub_pc_folder) if not exists(sub_pc_folder) else None
-
-for pc_path in glob.glob(join(dataset_path, '*.txt')):
- print(pc_path)
- file_name = pc_path.split('/')[-1][:-4]
-
- # check if it has already calculated
- if exists(join(sub_pc_folder, file_name + '_KDTree.pkl')):
- continue
-
- pc = DP.load_pc_semantic3d(pc_path)
- # check if label exists
- label_path = pc_path[:-4] + '.labels'
- if exists(label_path):
- labels = DP.load_label_semantic3d(label_path)
- full_ply_path = join(original_pc_folder, file_name + '.ply')
-
- # Subsample to save space
- sub_points, sub_colors, sub_labels = DP.grid_sub_sampling(pc[:, :3].astype(np.float32),
- pc[:, 4:7].astype(np.uint8), labels, 0.01)
- sub_labels = np.squeeze(sub_labels)
-
- write_ply(full_ply_path, (sub_points, sub_colors, sub_labels), ['x', 'y', 'z', 'red', 'green', 'blue', 'class'])
-
- # save sub_cloud and KDTree file
- sub_xyz, sub_colors, sub_labels = DP.grid_sub_sampling(sub_points, sub_colors, sub_labels, grid_size)
- sub_colors = sub_colors / 255.0
- sub_labels = np.squeeze(sub_labels)
- sub_ply_file = join(sub_pc_folder, file_name + '.ply')
- write_ply(sub_ply_file, [sub_xyz, sub_colors, sub_labels], ['x', 'y', 'z', 'red', 'green', 'blue', 'class'])
-
- search_tree = KDTree(sub_xyz, leaf_size=50)
- kd_tree_file = join(sub_pc_folder, file_name + '_KDTree.pkl')
- with open(kd_tree_file, 'wb') as f:
- pickle.dump(search_tree, f)
-
- proj_idx = np.squeeze(search_tree.query(sub_points, return_distance=False))
- proj_idx = proj_idx.astype(np.int32)
- proj_save = join(sub_pc_folder, file_name + '_proj.pkl')
- with open(proj_save, 'wb') as f:
- pickle.dump([proj_idx, labels], f)
-
- else:
- full_ply_path = join(original_pc_folder, file_name + '.ply')
- write_ply(full_ply_path, (pc[:, :3].astype(np.float32), pc[:, 4:7].astype(np.uint8)),
- ['x', 'y', 'z', 'red', 'green', 'blue'])
-
- # save sub_cloud and KDTree file
- sub_xyz, sub_colors = DP.grid_sub_sampling(pc[:, :3].astype(np.float32), pc[:, 4:7].astype(np.uint8),
- grid_size=grid_size)
- sub_colors = sub_colors / 255.0
- sub_ply_file = join(sub_pc_folder, file_name + '.ply')
- write_ply(sub_ply_file, [sub_xyz, sub_colors], ['x', 'y', 'z', 'red', 'green', 'blue'])
- labels = np.zeros(pc.shape[0], dtype=np.uint8)
-
- search_tree = KDTree(sub_xyz, leaf_size=50)
- kd_tree_file = join(sub_pc_folder, file_name + '_KDTree.pkl')
- with open(kd_tree_file, 'wb') as f:
- pickle.dump(search_tree, f)
-
- proj_idx = np.squeeze(search_tree.query(pc[:, :3].astype(np.float32), return_distance=False))
- proj_idx = proj_idx.astype(np.int32)
- proj_save = join(sub_pc_folder, file_name + '_proj.pkl')
- with open(proj_save, 'wb') as f:
- pickle.dump([proj_idx, labels], f)
diff --git a/utils/data_prepare_semantickitti.py b/utils/data_prepare_semantickitti.py
deleted file mode 100644
index 9ba7640..0000000
--- a/utils/data_prepare_semantickitti.py
+++ /dev/null
@@ -1,76 +0,0 @@
-import pickle, yaml, os, sys
-import numpy as np
-from os.path import join, exists, dirname, abspath
-from sklearn.neighbors import KDTree
-
-BASE_DIR = dirname(abspath(__file__))
-ROOT_DIR = dirname(BASE_DIR)
-sys.path.append(BASE_DIR)
-sys.path.append(ROOT_DIR)
-from helper_tool import DataProcessing as DP
-
-data_config = os.path.join(BASE_DIR, 'semantic-kitti.yaml')
-DATA = yaml.safe_load(open(data_config, 'r'))
-remap_dict = DATA["learning_map"]
-max_key = max(remap_dict.keys())
-remap_lut = np.zeros((max_key + 100), dtype=np.int32)
-remap_lut[list(remap_dict.keys())] = list(remap_dict.values())
-
-grid_size = 0.06
-dataset_path = '/data/semantic_kitti/dataset/sequences'
-output_path = '/data/semantic_kitti/dataset/sequences' + '_' + str(grid_size)
-seq_list = np.sort(os.listdir(dataset_path))
-
-for seq_id in seq_list:
- print('sequence' + seq_id + ' start')
- seq_path = join(dataset_path, seq_id)
- seq_path_out = join(output_path, seq_id)
- pc_path = join(seq_path, 'velodyne')
- pc_path_out = join(seq_path_out, 'velodyne')
- KDTree_path_out = join(seq_path_out, 'KDTree')
- os.makedirs(seq_path_out) if not exists(seq_path_out) else None
- os.makedirs(pc_path_out) if not exists(pc_path_out) else None
- os.makedirs(KDTree_path_out) if not exists(KDTree_path_out) else None
-
- if int(seq_id) < 11:
- label_path = join(seq_path, 'labels')
- label_path_out = join(seq_path_out, 'labels')
- os.makedirs(label_path_out) if not exists(label_path_out) else None
- scan_list = np.sort(os.listdir(pc_path))
- for scan_id in scan_list:
- print(scan_id)
- points = DP.load_pc_kitti(join(pc_path, scan_id))
- labels = DP.load_label_kitti(join(label_path, str(scan_id[:-4]) + '.label'), remap_lut)
- sub_points, sub_labels = DP.grid_sub_sampling(points, labels=labels, grid_size=grid_size)
- search_tree = KDTree(sub_points)
- KDTree_save = join(KDTree_path_out, str(scan_id[:-4]) + '.pkl')
- np.save(join(pc_path_out, scan_id)[:-4], sub_points)
- np.save(join(label_path_out, scan_id)[:-4], sub_labels)
- with open(KDTree_save, 'wb') as f:
- pickle.dump(search_tree, f)
- if seq_id == '08':
- proj_path = join(seq_path_out, 'proj')
- os.makedirs(proj_path) if not exists(proj_path) else None
- proj_inds = np.squeeze(search_tree.query(points, return_distance=False))
- proj_inds = proj_inds.astype(np.int32)
- proj_save = join(proj_path, str(scan_id[:-4]) + '_proj.pkl')
- with open(proj_save, 'wb') as f:
- pickle.dump([proj_inds], f)
- else:
- proj_path = join(seq_path_out, 'proj')
- os.makedirs(proj_path) if not exists(proj_path) else None
- scan_list = np.sort(os.listdir(pc_path))
- for scan_id in scan_list:
- print(scan_id)
- points = DP.load_pc_kitti(join(pc_path, scan_id))
- sub_points = DP.grid_sub_sampling(points, grid_size=0.06)
- search_tree = KDTree(sub_points)
- proj_inds = np.squeeze(search_tree.query(points, return_distance=False))
- proj_inds = proj_inds.astype(np.int32)
- KDTree_save = join(KDTree_path_out, str(scan_id[:-4]) + '.pkl')
- proj_save = join(proj_path, str(scan_id[:-4]) + '_proj.pkl')
- np.save(join(pc_path_out, scan_id)[:-4], sub_points)
- with open(KDTree_save, 'wb') as f:
- pickle.dump(search_tree, f)
- with open(proj_save, 'wb') as f:
- pickle.dump([proj_inds], f)
diff --git a/utils/download_semantic3d.sh b/utils/download_semantic3d.sh
deleted file mode 100644
index 806bd39..0000000
--- a/utils/download_semantic3d.sh
+++ /dev/null
@@ -1,57 +0,0 @@
-BASE_DIR=${1-/data/semantic3d/original_data}
-
-# Training data
-wget -c -N http://semantic3d.net/data/point-clouds/training1/bildstein_station1_xyz_intensity_rgb.7z -P $BASE_DIR
-wget -c -N http://semantic3d.net/data/point-clouds/training1/bildstein_station3_xyz_intensity_rgb.7z -P $BASE_DIR
-wget -c -N http://semantic3d.net/data/point-clouds/training1/bildstein_station5_xyz_intensity_rgb.7z -P $BASE_DIR
-wget -c -N http://semantic3d.net/data/point-clouds/training1/domfountain_station1_xyz_intensity_rgb.7z -P $BASE_DIR
-wget -c -N http://semantic3d.net/data/point-clouds/training1/domfountain_station2_xyz_intensity_rgb.7z -P $BASE_DIR/
-wget -c -N http://semantic3d.net/data/point-clouds/training1/domfountain_station3_xyz_intensity_rgb.7z -P $BASE_DIR
-wget -c -N http://semantic3d.net/data/point-clouds/training1/neugasse_station1_xyz_intensity_rgb.7z -P $BASE_DIR
-wget -c -N http://semantic3d.net/data/point-clouds/training1/sg27_station1_intensity_rgb.7z -P $BASE_DIR
-wget -c -N http://semantic3d.net/data/point-clouds/training1/sg27_station2_intensity_rgb.7z -P $BASE_DIR
-wget -c -N http://semantic3d.net/data/point-clouds/training1/sg27_station4_intensity_rgb.7z -P $BASE_DIR/
-wget -c -N http://semantic3d.net/data/point-clouds/training1/sg27_station5_intensity_rgb.7z -P $BASE_DIR
-wget -c -N http://semantic3d.net/data/point-clouds/training1/sg27_station9_intensity_rgb.7z -P $BASE_DIR
-wget -c -N http://semantic3d.net/data/point-clouds/training1/sg28_station4_intensity_rgb.7z -P $BASE_DIR
-wget -c -N http://semantic3d.net/data/point-clouds/training1/untermaederbrunnen_station1_xyz_intensity_rgb.7z -P $BASE_DIR
-wget -c -N http://semantic3d.net/data/point-clouds/training1/untermaederbrunnen_station3_xyz_intensity_rgb.7z -P $BASE_DIR/
-wget -c -N http://semantic3d.net/data/sem8_labels_training.7z -P $BASE_DIR
-
-
-# Test data
-wget -c -N http://semantic3d.net/data/point-clouds/testing1/birdfountain_station1_xyz_intensity_rgb.7z -P $BASE_DIR
-wget -c -N http://semantic3d.net/data/point-clouds/testing1/castleblatten_station1_intensity_rgb.7z -P $BASE_DIR
-wget -c -N http://semantic3d.net/data/point-clouds/testing1/castleblatten_station5_xyz_intensity_rgb.7z -P $BASE_DIR
-wget -c -N http://semantic3d.net/data/point-clouds/testing1/marketplacefeldkirch_station1_intensity_rgb.7z -P $BASE_DIR
-wget -c -N http://semantic3d.net/data/point-clouds/testing1/marketplacefeldkirch_station4_intensity_rgb.7z -P $BASE_DIR
-wget -c -N http://semantic3d.net/data/point-clouds/testing1/marketplacefeldkirch_station7_intensity_rgb.7z -P $BASE_DIR
-wget -c -N http://semantic3d.net/data/point-clouds/testing1/sg27_station10_intensity_rgb.7z -P $BASE_DIR
-wget -c -N http://semantic3d.net/data/point-clouds/testing1/sg27_station3_intensity_rgb.7z -P $BASE_DIR
-wget -c -N http://semantic3d.net/data/point-clouds/testing1/sg27_station6_intensity_rgb.7z -P $BASE_DIR
-wget -c -N http://semantic3d.net/data/point-clouds/testing1/sg27_station8_intensity_rgb.7z -P $BASE_DIR
-wget -c -N http://semantic3d.net/data/point-clouds/testing1/sg28_station2_intensity_rgb.7z -P $BASE_DIR
-wget -c -N http://semantic3d.net/data/point-clouds/testing1/sg28_station5_xyz_intensity_rgb.7z -P $BASE_DIR
-wget -c -N http://semantic3d.net/data/point-clouds/testing1/stgallencathedral_station1_intensity_rgb.7z -P $BASE_DIR
-wget -c -N http://semantic3d.net/data/point-clouds/testing1/stgallencathedral_station3_intensity_rgb.7z -P $BASE_DIR
-wget -c -N http://semantic3d.net/data/point-clouds/testing1/stgallencathedral_station6_intensity_rgb.7z -P $BASE_DIR
-
-# reduced-8
-wget -c -N http://semantic3d.net/data/point-clouds/testing2/MarketplaceFeldkirch_Station4_rgb_intensity-reduced.txt.7z -P $BASE_DIR
-wget -c -N http://semantic3d.net/data/point-clouds/testing2/StGallenCathedral_station6_rgb_intensity-reduced.txt.7z -P $BASE_DIR
-wget -c -N http://semantic3d.net/data/point-clouds/testing2/sg27_station10_rgb_intensity-reduced.txt.7z -P $BASE_DIR
-wget -c -N http://semantic3d.net/data/point-clouds/testing2/sg28_Station2_rgb_intensity-reduced.txt.7z -P $BASE_DIR
-
-
-
-for entry in "$BASE_DIR"/*
-do
- 7z x "$entry" -o$(dirname "$entry") -y
-done
-
-mv $BASE_DIR/station1_xyz_intensity_rgb.txt $BASE_DIR/neugasse_station1_xyz_intensity_rgb.txt
-
-for entry in "$BASE_DIR"/*.7z
-do
- rm "$entry"
-done
\ No newline at end of file
diff --git a/utils/meta/anno_paths.txt b/utils/meta/anno_paths.txt
deleted file mode 100644
index 0ad2f25..0000000
--- a/utils/meta/anno_paths.txt
+++ /dev/null
@@ -1,272 +0,0 @@
-Area_1/conferenceRoom_1/Annotations
-Area_1/conferenceRoom_2/Annotations
-Area_1/copyRoom_1/Annotations
-Area_1/hallway_1/Annotations
-Area_1/hallway_2/Annotations
-Area_1/hallway_3/Annotations
-Area_1/hallway_4/Annotations
-Area_1/hallway_5/Annotations
-Area_1/hallway_6/Annotations
-Area_1/hallway_7/Annotations
-Area_1/hallway_8/Annotations
-Area_1/office_10/Annotations
-Area_1/office_11/Annotations
-Area_1/office_12/Annotations
-Area_1/office_13/Annotations
-Area_1/office_14/Annotations
-Area_1/office_15/Annotations
-Area_1/office_16/Annotations
-Area_1/office_17/Annotations
-Area_1/office_18/Annotations
-Area_1/office_19/Annotations
-Area_1/office_1/Annotations
-Area_1/office_20/Annotations
-Area_1/office_21/Annotations
-Area_1/office_22/Annotations
-Area_1/office_23/Annotations
-Area_1/office_24/Annotations
-Area_1/office_25/Annotations
-Area_1/office_26/Annotations
-Area_1/office_27/Annotations
-Area_1/office_28/Annotations
-Area_1/office_29/Annotations
-Area_1/office_2/Annotations
-Area_1/office_30/Annotations
-Area_1/office_31/Annotations
-Area_1/office_3/Annotations
-Area_1/office_4/Annotations
-Area_1/office_5/Annotations
-Area_1/office_6/Annotations
-Area_1/office_7/Annotations
-Area_1/office_8/Annotations
-Area_1/office_9/Annotations
-Area_1/pantry_1/Annotations
-Area_1/WC_1/Annotations
-Area_2/auditorium_1/Annotations
-Area_2/auditorium_2/Annotations
-Area_2/conferenceRoom_1/Annotations
-Area_2/hallway_10/Annotations
-Area_2/hallway_11/Annotations
-Area_2/hallway_12/Annotations
-Area_2/hallway_1/Annotations
-Area_2/hallway_2/Annotations
-Area_2/hallway_3/Annotations
-Area_2/hallway_4/Annotations
-Area_2/hallway_5/Annotations
-Area_2/hallway_6/Annotations
-Area_2/hallway_7/Annotations
-Area_2/hallway_8/Annotations
-Area_2/hallway_9/Annotations
-Area_2/office_10/Annotations
-Area_2/office_11/Annotations
-Area_2/office_12/Annotations
-Area_2/office_13/Annotations
-Area_2/office_14/Annotations
-Area_2/office_1/Annotations
-Area_2/office_2/Annotations
-Area_2/office_3/Annotations
-Area_2/office_4/Annotations
-Area_2/office_5/Annotations
-Area_2/office_6/Annotations
-Area_2/office_7/Annotations
-Area_2/office_8/Annotations
-Area_2/office_9/Annotations
-Area_2/storage_1/Annotations
-Area_2/storage_2/Annotations
-Area_2/storage_3/Annotations
-Area_2/storage_4/Annotations
-Area_2/storage_5/Annotations
-Area_2/storage_6/Annotations
-Area_2/storage_7/Annotations
-Area_2/storage_8/Annotations
-Area_2/storage_9/Annotations
-Area_2/WC_1/Annotations
-Area_2/WC_2/Annotations
-Area_3/conferenceRoom_1/Annotations
-Area_3/hallway_1/Annotations
-Area_3/hallway_2/Annotations
-Area_3/hallway_3/Annotations
-Area_3/hallway_4/Annotations
-Area_3/hallway_5/Annotations
-Area_3/hallway_6/Annotations
-Area_3/lounge_1/Annotations
-Area_3/lounge_2/Annotations
-Area_3/office_10/Annotations
-Area_3/office_1/Annotations
-Area_3/office_2/Annotations
-Area_3/office_3/Annotations
-Area_3/office_4/Annotations
-Area_3/office_5/Annotations
-Area_3/office_6/Annotations
-Area_3/office_7/Annotations
-Area_3/office_8/Annotations
-Area_3/office_9/Annotations
-Area_3/storage_1/Annotations
-Area_3/storage_2/Annotations
-Area_3/WC_1/Annotations
-Area_3/WC_2/Annotations
-Area_4/conferenceRoom_1/Annotations
-Area_4/conferenceRoom_2/Annotations
-Area_4/conferenceRoom_3/Annotations
-Area_4/hallway_10/Annotations
-Area_4/hallway_11/Annotations
-Area_4/hallway_12/Annotations
-Area_4/hallway_13/Annotations
-Area_4/hallway_14/Annotations
-Area_4/hallway_1/Annotations
-Area_4/hallway_2/Annotations
-Area_4/hallway_3/Annotations
-Area_4/hallway_4/Annotations
-Area_4/hallway_5/Annotations
-Area_4/hallway_6/Annotations
-Area_4/hallway_7/Annotations
-Area_4/hallway_8/Annotations
-Area_4/hallway_9/Annotations
-Area_4/lobby_1/Annotations
-Area_4/lobby_2/Annotations
-Area_4/office_10/Annotations
-Area_4/office_11/Annotations
-Area_4/office_12/Annotations
-Area_4/office_13/Annotations
-Area_4/office_14/Annotations
-Area_4/office_15/Annotations
-Area_4/office_16/Annotations
-Area_4/office_17/Annotations
-Area_4/office_18/Annotations
-Area_4/office_19/Annotations
-Area_4/office_1/Annotations
-Area_4/office_20/Annotations
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diff --git a/utils/meta/class_names.txt b/utils/meta/class_names.txt
deleted file mode 100644
index ca1d178..0000000
--- a/utils/meta/class_names.txt
+++ /dev/null
@@ -1,13 +0,0 @@
-ceiling
-floor
-wall
-beam
-column
-window
-door
-table
-chair
-sofa
-bookcase
-board
-clutter
diff --git a/utils/semantic-kitti.yaml b/utils/semantic-kitti.yaml
deleted file mode 100644
index 6281065..0000000
--- a/utils/semantic-kitti.yaml
+++ /dev/null
@@ -1,211 +0,0 @@
-# This file is covered by the LICENSE file in the root of this project.
-labels:
- 0 : "unlabeled"
- 1 : "outlier"
- 10: "car"
- 11: "bicycle"
- 13: "bus"
- 15: "motorcycle"
- 16: "on-rails"
- 18: "truck"
- 20: "other-vehicle"
- 30: "person"
- 31: "bicyclist"
- 32: "motorcyclist"
- 40: "road"
- 44: "parking"
- 48: "sidewalk"
- 49: "other-ground"
- 50: "building"
- 51: "fence"
- 52: "other-structure"
- 60: "lane-marking"
- 70: "vegetation"
- 71: "trunk"
- 72: "terrain"
- 80: "pole"
- 81: "traffic-sign"
- 99: "other-object"
- 252: "moving-car"
- 253: "moving-bicyclist"
- 254: "moving-person"
- 255: "moving-motorcyclist"
- 256: "moving-on-rails"
- 257: "moving-bus"
- 258: "moving-truck"
- 259: "moving-other-vehicle"
-color_map: # bgr
- 0 : [0, 0, 0]
- 1 : [0, 0, 255]
- 10: [245, 150, 100]
- 11: [245, 230, 100]
- 13: [250, 80, 100]
- 15: [150, 60, 30]
- 16: [255, 0, 0]
- 18: [180, 30, 80]
- 20: [255, 0, 0]
- 30: [30, 30, 255]
- 31: [200, 40, 255]
- 32: [90, 30, 150]
- 40: [255, 0, 255]
- 44: [255, 150, 255]
- 48: [75, 0, 75]
- 49: [75, 0, 175]
- 50: [0, 200, 255]
- 51: [50, 120, 255]
- 52: [0, 150, 255]
- 60: [170, 255, 150]
- 70: [0, 175, 0]
- 71: [0, 60, 135]
- 72: [80, 240, 150]
- 80: [150, 240, 255]
- 81: [0, 0, 255]
- 99: [255, 255, 50]
- 252: [245, 150, 100]
- 256: [255, 0, 0]
- 253: [200, 40, 255]
- 254: [30, 30, 255]
- 255: [90, 30, 150]
- 257: [250, 80, 100]
- 258: [180, 30, 80]
- 259: [255, 0, 0]
-content: # as a ratio with the total number of points
- 0: 0.018889854628292943
- 1: 0.0002937197336781505
- 10: 0.040818519255974316
- 11: 0.00016609538710764618
- 13: 2.7879693665067774e-05
- 15: 0.00039838616015114444
- 16: 0.0
- 18: 0.0020633612104619787
- 20: 0.0016218197275284021
- 30: 0.00017698551338515307
- 31: 1.1065903904919655e-08
- 32: 5.532951952459828e-09
- 40: 0.1987493871255525
- 44: 0.014717169549888214
- 48: 0.14392298360372
- 49: 0.0039048553037472045
- 50: 0.1326861944777486
- 51: 0.0723592229456223
- 52: 0.002395131480328884
- 60: 4.7084144280367186e-05
- 70: 0.26681502148037506
- 71: 0.006035012012626033
- 72: 0.07814222006271769
- 80: 0.002855498193863172
- 81: 0.0006155958086189918
- 99: 0.009923127583046915
- 252: 0.001789309418528068
- 253: 0.00012709999297008662
- 254: 0.00016059776092534436
- 255: 3.745553104802113e-05
- 256: 0.0
- 257: 0.00011351574470342043
- 258: 0.00010157861367183268
- 259: 4.3840131989471124e-05
-# classes that are indistinguishable from single scan or inconsistent in
-# ground truth are mapped to their closest equivalent
-learning_map:
- 0 : 0 # "unlabeled"
- 1 : 0 # "outlier" mapped to "unlabeled" --------------------------mapped
- 10: 1 # "car"
- 11: 2 # "bicycle"
- 13: 5 # "bus" mapped to "other-vehicle" --------------------------mapped
- 15: 3 # "motorcycle"
- 16: 5 # "on-rails" mapped to "other-vehicle" ---------------------mapped
- 18: 4 # "truck"
- 20: 5 # "other-vehicle"
- 30: 6 # "person"
- 31: 7 # "bicyclist"
- 32: 8 # "motorcyclist"
- 40: 9 # "road"
- 44: 10 # "parking"
- 48: 11 # "sidewalk"
- 49: 12 # "other-ground"
- 50: 13 # "building"
- 51: 14 # "fence"
- 52: 0 # "other-structure" mapped to "unlabeled" ------------------mapped
- 60: 9 # "lane-marking" to "road" ---------------------------------mapped
- 70: 15 # "vegetation"
- 71: 16 # "trunk"
- 72: 17 # "terrain"
- 80: 18 # "pole"
- 81: 19 # "traffic-sign"
- 99: 0 # "other-object" to "unlabeled" ----------------------------mapped
- 252: 1 # "moving-car" to "car" ------------------------------------mapped
- 253: 7 # "moving-bicyclist" to "bicyclist" ------------------------mapped
- 254: 6 # "moving-person" to "person" ------------------------------mapped
- 255: 8 # "moving-motorcyclist" to "motorcyclist" ------------------mapped
- 256: 5 # "moving-on-rails" mapped to "other-vehicle" --------------mapped
- 257: 5 # "moving-bus" mapped to "other-vehicle" -------------------mapped
- 258: 4 # "moving-truck" to "truck" --------------------------------mapped
- 259: 5 # "moving-other"-vehicle to "other-vehicle" ----------------mapped
-learning_map_inv: # inverse of previous map
- 0: 0 # "unlabeled", and others ignored
- 1: 10 # "car"
- 2: 11 # "bicycle"
- 3: 15 # "motorcycle"
- 4: 18 # "truck"
- 5: 20 # "other-vehicle"
- 6: 30 # "person"
- 7: 31 # "bicyclist"
- 8: 32 # "motorcyclist"
- 9: 40 # "road"
- 10: 44 # "parking"
- 11: 48 # "sidewalk"
- 12: 49 # "other-ground"
- 13: 50 # "building"
- 14: 51 # "fence"
- 15: 70 # "vegetation"
- 16: 71 # "trunk"
- 17: 72 # "terrain"
- 18: 80 # "pole"
- 19: 81 # "traffic-sign"
-learning_ignore: # Ignore classes
- 0: True # "unlabeled", and others ignored
- 1: False # "car"
- 2: False # "bicycle"
- 3: False # "motorcycle"
- 4: False # "truck"
- 5: False # "other-vehicle"
- 6: False # "person"
- 7: False # "bicyclist"
- 8: False # "motorcyclist"
- 9: False # "road"
- 10: False # "parking"
- 11: False # "sidewalk"
- 12: False # "other-ground"
- 13: False # "building"
- 14: False # "fence"
- 15: False # "vegetation"
- 16: False # "trunk"
- 17: False # "terrain"
- 18: False # "pole"
- 19: False # "traffic-sign"
-split: # sequence numbers
- train:
- - 0
- - 1
- - 2
- - 3
- - 4
- - 5
- - 6
- - 7
- - 9
- - 10
- valid:
- - 8
- test:
- - 11
- - 12
- - 13
- - 14
- - 15
- - 16
- - 17
- - 18
- - 19
- - 20
- - 21