From a22980d08f9551f9165f1c45ae67aaa743a4d456 Mon Sep 17 00:00:00 2001 From: MartinHahner Date: Thu, 16 Jun 2022 14:42:27 +0200 Subject: [PATCH] Code release --- .github/workflows/close_stale_issues.yml | 26 + .gitignore | 18 + .gitmodules | 15 + LICENSE | 408 +++ README.md | 139 +- calib/20171102_64E_S3.yaml | 242 ++ pointcloud_viewer.py | 3403 ++++++++++++++++++++++ teaser.gif | Bin 0 -> 6935029 bytes tools/snowfall/geometry.py | 223 ++ tools/snowfall/precompute.py | 106 + tools/snowfall/sampling.py | 413 +++ tools/snowfall/simulation.py | 608 ++++ tools/wet_ground/augmentation.py | 333 +++ tools/wet_ground/phy_equations.py | 281 ++ tools/wet_ground/planes.py | 80 + tools/wet_ground/utils.py | 103 + 16 files changed, 6390 insertions(+), 8 deletions(-) create mode 100644 .github/workflows/close_stale_issues.yml create mode 100644 .gitignore create mode 100644 .gitmodules create mode 100644 LICENSE create mode 100644 calib/20171102_64E_S3.yaml create mode 100644 pointcloud_viewer.py create mode 100644 teaser.gif create mode 100644 tools/snowfall/geometry.py create mode 100644 tools/snowfall/precompute.py create mode 100644 tools/snowfall/sampling.py create mode 100644 tools/snowfall/simulation.py create mode 100644 tools/wet_ground/augmentation.py create mode 100644 tools/wet_ground/phy_equations.py create mode 100644 tools/wet_ground/planes.py create mode 100644 tools/wet_ground/utils.py diff --git a/.github/workflows/close_stale_issues.yml b/.github/workflows/close_stale_issues.yml new file mode 100644 index 0000000..7d228b9 --- /dev/null +++ b/.github/workflows/close_stale_issues.yml @@ -0,0 +1,26 @@ +# https://github.com/actions/stale +# https://github.com/marketplace/actions/close-stale-issues +# https://docs.github.com/en/actions/managing-issues-and-pull-requests/closing-inactive-issues + +name: close stale issues +on: + schedule: + - cron: "30 1 * * *" + +jobs: + close-issues: + runs-on: ubuntu-latest + permissions: + issues: write + pull-requests: write + steps: + - uses: actions/stale@v4.1.0 + with: + days-before-issue-stale: 30 + days-before-issue-close: 14 + stale-issue-label: "stale" + stale-issue-message: "This issue is stale because it has been open for 30 days with no activity." + close-issue-message: "This issue was closed because it has been inactive for 14 days since being marked as stale." + days-before-pr-stale: -1 + days-before-pr-close: -1 + repo-token: ${{ secrets.GITHUB_TOKEN }} diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..d9d359c --- /dev/null +++ b/.gitignore @@ -0,0 +1,18 @@ +# PyCharm +.idea/ + +# DROR download folder +alpha_0.45/ + +# experiments folder +experiments/ + +# snowflake download folder +npy/ + +# wet ground example folder +tools/wet_ground/example_data/ + +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] diff --git a/.gitmodules b/.gitmodules new file mode 100644 index 0000000..a66f309 --- /dev/null +++ b/.gitmodules @@ -0,0 +1,15 @@ +[submodule "lib/LiDAR_fog_sim"] + path = lib/LiDAR_fog_sim + url = git@github.com:MartinHahner/LiDAR_fog_sim +[submodule "lib/LISA"] + path = lib/LISA + url = git@github.com:MartinHahner/LISA +[submodule "lib/cadc_devkit"] + path = lib/cadc_devkit + url = git@github.com:MartinHahner/cadc_devkit +[submodule "lib/OpenPCDet"] + path = lib/OpenPCDet + url = git@github.com:MartinHahner/OpenPCDet +[submodule "lib/pa_aug"] + path = lib/pa_aug + url = git@github.com:MartinHahner/pa-aug.pytorch diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..b21e8d1 --- /dev/null +++ b/LICENSE @@ -0,0 +1,408 @@ +Attribution-NonCommercial 4.0 International + +======================================================================= + +Creative Commons Corporation ("Creative Commons") is not a law firm and +does not provide legal services or legal advice. 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For +the avoidance of doubt, this paragraph does not form part of the +public licenses. + +Creative Commons may be contacted at creativecommons.org. + diff --git a/README.md b/README.md index 8226371..e988b74 100644 --- a/README.md +++ b/README.md @@ -1,7 +1,5 @@ Created by [Martin Hahner](https://sites.google.com/view/martinhahner/home) at the [Computer Vision Lab](https://vision.ee.ethz.ch/) of [ETH Zurich](https://ethz.ch/). -☝️ ___Our code will be published no later than June 2022.___ - [![Support Ukraine](https://img.shields.io/badge/Support-Ukraine-FFD500?style=flat&labelColor=005BBB)](https://opensource.fb.com/support-ukraine) [![arXiv](https://img.shields.io/badge/arXiv-2203.15118-00ff00.svg)](https://arxiv.org/abs/2203.15118) ![visitors](https://visitor-badge.laobi.icu/badge?page_id=SysCV.LiDAR_snow_sim) [![PapersWithCode](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/lidar-snowfall-simulation-for-robust-3d/3d-object-detection-on-clear-weather)](https://paperswithcode.com/sota/3d-object-detection-on-clear-weather?p=lidar-snowfall-simulation-for-robust-3d)
@@ -15,6 +13,56 @@ Created by [Martin Hahner](https://sites.google.com/view/martinhahner/home) at t πŸ“£ Oral at [CVPR 2022](https://cvpr2022.thecvf.com/).
Please visit our [paper website](https://trace.ethz.ch/lidar_snow_sim) for more details. + + +## Overview + + . + β”œβ”€β”€ calib # contains the LiDAR sensor calibration file used in STF + β”‚ └── ... + β”œβ”€β”€ lib # contains external libraries as submodules + β”‚ └── ... + β”œβ”€β”€ splits # contains the splits we used for our experiments + β”‚ └── ... + β”œβ”€β”€ tools # contains our snowfall and wet ground simulation code + β”‚ β”œβ”€β”€ snowfall + β”‚ β”‚ β”œβ”€β”€ geometry.py + β”‚ β”‚ β”œβ”€β”€ precompute.py + β”‚ β”‚ β”œβ”€β”€ sampling.py + β”‚ β”‚ └── simulation.py + β”‚ └── wet_ground + β”‚ β”œβ”€β”€ augmentation.py + β”‚ β”œβ”€β”€ phy_equations.py + β”‚ β”œβ”€β”€ planes.py + β”‚ └── utils.py + β”œβ”€β”€ .gitignore + β”œβ”€β”€ .gitmodules + β”œβ”€β”€ LICENSE + β”œβ”€β”€ pointcloud_viewer.py # to visualize LiDAR point clouds and apply various augmentations + β”œβ”€β”€ README.md + └── teaser.gif + +**Datasets supported by [pointcloud_viewer.py](pointcloud_viewer.py):** +- [H3D](https://usa.honda-ri.com/H3D) +- [A2D2](https://www.a2d2.audi/a2d2/en.html) +- [KITTI](http://www.cvlibs.net/datasets/kitti/eval_object.php?obj_benchmark=3d) +- [LyftL5](https://self-driving.lyft.com/level5/prediction/) +- [Pandaset](https://pandaset.org/) +- [nuScenes](https://www.nuscenes.org/nuscenes) +- [Argoverse](https://www.argoverse.org/data.html#tracking-link) +- [ApolloScape](http://apolloscape.auto/tracking.html) +- **[SeeingThroughFog](https://www.cs.princeton.edu/~fheide/AdverseWeatherFusion/)**  :arrow_left: works best +- [WaymoOpenDataset](https://waymo.com/open/) (via [waymo_kitti_converter](https://github.com/caizhongang/waymo_kitti_converter)) + +**Note:
The snowfall and wet ground simulation is only tested on the [SeeingThroughFog](https://www.cs.princeton.edu/~fheide/AdverseWeatherFusion/) (STF) dataset.**
+To support other datasets as well, code changes are required. + +## License + +This software is made available for non-commercial use under a Creative Commons [License](LICENSE).
+A summary of the license can be found [here](https://creativecommons.org/licenses/by-nc/4.0/). + + ## Citation(s) If you find this work useful, please consider citing our paper. @@ -38,18 +86,94 @@ You may also want to check out our earlier work
} ``` +## Getting Started + +### Setup + +1) Install [anaconda](https://docs.anaconda.com/anaconda/install/). + +2) Execute the following commands. +```bash +# Create a new conda environment. +conda create --name snowy_lidar python=3.9 -y + +# Activate the newly created conda environment. +conda activate snowy_lidar + +# Install dependencies. +conda install matplotlib pandas plyfile pyaml pyopengl pyqt pyqtgraph scipy scikit-learn tqdm -c conda-forge -y +pip install PyMieScatt pyquaternion + +# Clone this repository (including submodules!). +git clone git@github.com:SysCV/LiDAR_snow_sim.git --recursive +cd LiDAR_snow_sim +``` + +3) If you want to use our precomputed snowflake patterns, you can download them (2.3GB) as mentioned below. +```bash +wget https://www.trace.ethz.ch/publications/2022/lidar_snow_simulation/snowflakes.zip +unzip snowflakes.zip +rm snowflakes.zip +``` + +4) If you want to use [DROR](https://ieeexplore.ieee.org/document/8575761) as well,
+you need to install [PCL](https://pointclouds.org/) or download the point indices (215MB) as mentioned below. +```bash +wget https://www.trace.ethz.ch/publications/2022/lidar_snow_simulation/DROR.zip +unzip DROR.zip +rm DROR.zip +``` + +5) Enjoy [pointcloud_viewer.py](pointcloud_viewer.py). +```bash +python pointcloud_viewer.py +``` + +6) If you also want to run inference on the [STF dataset](https://www.cs.princeton.edu/~fheide/AdverseWeatherFusion/), a couple of extra steps are required.
+Note: For unknown reasons, this can roughly slow down the augmentation(s) by a factor of two. +```bash +# Download our checkpoints (265MB) +wget https://www.trace.ethz.ch/publications/2022/lidar_snow_simulation/experiments.zip +unzip experiments.zip +rm experiments.zip + +# Install PyTorch. +conda install pytorch==1.10.1 torchvision==0.11.2 torchaudio==0.10.1 cudatoolkit=11.3 -c conda-forge -c pytorch -y + +# Install spconv +pip install spconv-cu113 + +# build pcdet +cd lib/OpenPCDet +python setup.py develop +cd ../.. +``` + + +### Disclaimer + +The code has been successfully tested on +- Ubuntu 18.04.6 LTS + CUDA 11.3 + conda 4.13.0 +- Debian GNU/Linux 10 (buster) + conda 4.13.0 +- MacOS Big Sur 11.6.6 + conda 4.13.0 + + +## Contributions +Please feel free to suggest improvements to this repository.
+We are always open to merge useful pull request. + ## Acknowledgments **This work is supported by [Toyota](https://www.toyota-europe.com/) via the [TRACE](https://www.trace.ethz.ch/) project.** The work also received funding by the [AI-SEE](https://www.ai-see.eu/) project with national funding from -- the [Austrian Research Promotion Agency (FFG)](https://www.ffg.at/), -- [Business Finland](https://www.businessfinland.fi/), -- [Federal Ministry of Education and Research (BMBF)](https://www.bmbf.de/bmbf/en/home/home_node.html) and -- [National Research Council of Canada Industrial Research Assistance Program (NRC-IRAP)](https://nrc.canada.ca/en/support-technology-innovation). +- the [Austrian Research Promotion Agency (FFG)](https://www.ffg.at/), +- [Business Finland](https://www.businessfinland.fi/), +- [Federal Ministry of Education and Research (BMBF)](https://www.bmbf.de/bmbf/en/home/home_node.html) and +- [National Research Council of Canada Industrial Research Assistance Program (NRC-IRAP)](https://nrc.canada.ca/en/support-technology-innovation). We also thank the [Federal Ministry for Economic Affairs and Energy](https://www.bmwi.de/Navigation/EN/Home/home.html) for support within
-[*VVM-Verification and Validation Methods for Automated Vehicles Level 4 and 5*](https://www.vvm-projekt.de/en/project), a [PEGASUS](https://pegasus-family.de/) family project. +[*VVM-Verification and Validation Methods for Automated Vehicles Level 4 and 5*](https://www.vvm-projekt.de/en/project), a [PEGASUS](https://pegasus-family.de/) family project. [Felix Heide](https://www.cs.princeton.edu/~fheide/) was supported by an [NSF CAREER Award](https://www.cs.princeton.edu/news/national-science-foundation-awarded-professor-felix-heide-nsf-career-award) (2047359),
a [Sony Young Faculty Award](https://www.sony.com/en/SonyInfo/research-award-program/), and a [Project X Innovation Award](https://aspire-report.princeton.edu/engineering/project-x-fund). @@ -57,4 +181,3 @@ a [Sony Young Faculty Award](https://www.sony.com/en/SonyInfo/research-award-pro We thank Emmanouil Sakaridis for verifying our derivation of occlusion angles in our snowfall simulation. [](https://ethz.ch)           [](https://www.princeton.edu)           [](https://www.mpi-inf.mpg.de)           [](https://www.kuleuven.be) - diff --git a/calib/20171102_64E_S3.yaml b/calib/20171102_64E_S3.yaml new file mode 100644 index 0000000..b82aeda --- /dev/null +++ b/calib/20171102_64E_S3.yaml @@ -0,0 +1,242 @@ +lasers: +- {dist_correction: 1.3249773999999999, dist_correction_x: 1.3634254000000001, dist_correction_y: 1.3542392, + focal_distance: 8.0, focal_slope: 0.94999999, horiz_offset_correction: 0.025999999, + laser_id: 0, min_intensity: 40, rot_correction: -0.08012567834864294, vert_correction: -0.12658417533780103, + vert_offset_correction: 0.21572407} +- {dist_correction: 1.4987132, dist_correction_x: 1.5412885, dist_correction_y: 1.5473203000000002, + focal_distance: 12.5, focal_slope: 1.0, horiz_offset_correction: -0.025999999, laser_id: 1, + min_intensity: 40, rot_correction: -0.04335935423363529, vert_correction: -0.12175346699676513, + vert_offset_correction: 0.21537130000000002} +- {dist_correction: 1.4120155, dist_correction_x: 1.4835945000000001, dist_correction_y: 1.4903978, + focal_distance: 24.0, focal_slope: 0.69999999, horiz_offset_correction: 0.025999999, + laser_id: 2, min_intensity: 40, rot_correction: 0.05670294788917557, vert_correction: 0.00356118725906175, + vert_offset_correction: 0.20631704} +- {dist_correction: 1.4353603000000001, dist_correction_x: 1.4418828, dist_correction_y: 1.4739595, + focal_distance: 24.0, focal_slope: 0.75, horiz_offset_correction: -0.025999999, + laser_id: 3, min_intensity: 40, rot_correction: 0.09596194076990601, vert_correction: 0.008262563386399281, + vert_offset_correction: 0.20597898} +- {dist_correction: 1.3399507, dist_correction_x: 1.3756326, dist_correction_y: 1.3914072, + focal_distance: 18.0, focal_slope: 0.5, horiz_offset_correction: 0.025999999, laser_id: 4, + min_intensity: 40, rot_correction: -0.004783008667111809, vert_correction: -0.11611041958999802, + vert_offset_correction: 0.21495975} +- {dist_correction: 1.4128778, dist_correction_x: 1.4329786999999998, dist_correction_y: 1.4480529999999998, + focal_distance: 24.0, focal_slope: 0.75, horiz_offset_correction: -0.025999999, + laser_id: 5, min_intensity: 40, rot_correction: 0.03468324921814289, vert_correction: -0.10965216205088012, + vert_offset_correction: 0.2144894} +- {dist_correction: 1.4114821000000002, dist_correction_x: 1.4616875999999999, dist_correction_y: 1.4136073, + focal_distance: 13.0, focal_slope: 2.0, horiz_offset_correction: 0.025999999, laser_id: 6, + min_intensity: 40, rot_correction: -0.01781467690420274, vert_correction: -0.14884568763136827, + vert_offset_correction: 0.21735559} +- {dist_correction: 1.4525862, dist_correction_x: 1.4784868, dist_correction_y: 1.4998865000000001, + focal_distance: 24.0, focal_slope: 0.75, horiz_offset_correction: -0.025999999, + laser_id: 7, min_intensity: 40, rot_correction: 0.02027819518075872, vert_correction: -0.14504505690689615, + vert_offset_correction: 0.21707632000000002} +- {dist_correction: 1.3909828, dist_correction_x: 1.4337270000000002, dist_correction_y: 1.4284450000000002, + focal_distance: 23.5, focal_slope: 0.40000001, horiz_offset_correction: 0.025999999, + laser_id: 8, min_intensity: 40, rot_correction: 0.07043605889118819, vert_correction: -0.10318468393732395, + vert_offset_correction: 0.21401903} +- {dist_correction: 1.4907301000000002, dist_correction_x: 1.5137833, dist_correction_y: 1.5527275, + focal_distance: 19.5, focal_slope: 0.60000002, horiz_offset_correction: -0.025999999, + laser_id: 9, min_intensity: 40, rot_correction: 0.10915132236872782, vert_correction: -0.09772098481916251, + vert_offset_correction: 0.21362219} +- {dist_correction: 1.3168228, dist_correction_x: 1.3538705, dist_correction_y: 1.3585283000000001, + focal_distance: 6.5, focal_slope: 1.2, horiz_offset_correction: 0.025999999, laser_id: 10, + min_intensity: 40, rot_correction: 0.056593009599592445, vert_correction: -0.13923599552700858, + vert_offset_correction: 0.21665007} +- {dist_correction: 1.4264073, dist_correction_x: 1.4625987, dist_correction_y: 1.5047078999999999, + focal_distance: 21.0, focal_slope: 1.1, horiz_offset_correction: -0.025999999, laser_id: 11, + min_intensity: 40, rot_correction: 0.0953288505090131, vert_correction: -0.13261418667434482, + vert_offset_correction: 0.21616503} +- {dist_correction: 1.4213582, dist_correction_x: 1.4525093000000002, dist_correction_y: 1.4399548, + focal_distance: 24.0, focal_slope: 0.69999999, horiz_offset_correction: 0.025999999, + laser_id: 12, min_intensity: 40, rot_correction: -0.08031205856880491, vert_correction: -0.05525412215182704, + vert_offset_correction: 0.21055021000000002} +- {dist_correction: 1.490439, dist_correction_x: 1.5103398000000001, dist_correction_y: 1.5188097999999999, + focal_distance: 24.0, focal_slope: 0.85000002, horiz_offset_correction: -0.025999999, + laser_id: 13, min_intensity: 40, rot_correction: -0.04013736996936938, vert_correction: -0.04975012163590781, + vert_offset_correction: 0.21015335000000002} +- {dist_correction: 1.4129089, dist_correction_x: 1.4646819, dist_correction_y: 1.4545599000000002, + focal_distance: 14.0, focal_slope: 1.2, horiz_offset_correction: 0.025999999, laser_id: 14, + min_intensity: 20, rot_correction: -0.09241976189172774, vert_correction: -0.0910352183760594, + vert_offset_correction: 0.21313713} +- {dist_correction: 1.4274017, dist_correction_x: 1.4373315, dist_correction_y: 1.4452847, + focal_distance: 24.0, focal_slope: 0.75, horiz_offset_correction: -0.025999999, + laser_id: 15, min_intensity: 40, rot_correction: -0.0542433740567291, vert_correction: -0.08495012793898118, + vert_offset_correction: 0.21269617000000002} +- {dist_correction: 1.3633778, dist_correction_x: 1.4071463, dist_correction_y: 1.3955342, + focal_distance: 24.0, focal_slope: 0.64999998, horiz_offset_correction: 0.025999999, + laser_id: 16, min_intensity: 40, rot_correction: -0.005597129714009329, vert_correction: -0.043427018903405855, + vert_offset_correction: 0.20969770000000001} +- {dist_correction: 1.4362886000000001, dist_correction_x: 1.5241066, dist_correction_y: 1.5385965, + focal_distance: 24.0, focal_slope: 0.80000001, horiz_offset_correction: -0.025999999, + laser_id: 17, min_intensity: 40, rot_correction: 0.03409468230646836, vert_correction: -0.03812106264303967, + vert_offset_correction: 0.20931555} +- {dist_correction: 1.3805224999999999, dist_correction_x: 1.4738669, dist_correction_y: 1.4555945, + focal_distance: 24.0, focal_slope: 0.69999999, horiz_offset_correction: 0.025999999, + laser_id: 18, min_intensity: 40, rot_correction: -0.019057166326721987, vert_correction: -0.07926501984257026, + vert_offset_correction: 0.21228462} +- {dist_correction: 1.3949545, dist_correction_x: 1.4371518, dist_correction_y: 1.4429382000000002, + focal_distance: 24.0, focal_slope: 0.75, horiz_offset_correction: -0.025999999, + laser_id: 19, min_intensity: 40, rot_correction: 0.020683261665537075, vert_correction: -0.07438795730193064, + vert_offset_correction: 0.21193186} +- {dist_correction: 1.2772563, dist_correction_x: 1.3631271, dist_correction_y: 1.3662206000000001, + focal_distance: 24.0, focal_slope: 0.64999998, horiz_offset_correction: 0.025999999, + laser_id: 20, min_intensity: 40, rot_correction: 0.06949383591318203, vert_correction: -0.032200364768206785, + vert_offset_correction: 0.20888927} +- {dist_correction: 1.4789717, dist_correction_x: 1.5019594, dist_correction_y: 1.5250914, + focal_distance: 24.0, focal_slope: 0.75, horiz_offset_correction: -0.025999999, + laser_id: 21, min_intensity: 20, rot_correction: 0.10837607504025998, vert_correction: -0.027094473854667452, + vert_offset_correction: 0.20852182} +- {dist_correction: 1.3653679, dist_correction_x: 1.4059218000000002, dist_correction_y: 1.3970361, + focal_distance: 24.0, focal_slope: 0.69999999, horiz_offset_correction: 0.025999999, + laser_id: 22, min_intensity: 10, rot_correction: 0.05591855503542727, vert_correction: -0.06808320147052566, + vert_offset_correction: 0.21147621000000003} +- {dist_correction: 1.4182037, dist_correction_x: 1.4552274, dist_correction_y: 1.4624287, + focal_distance: 24.0, focal_slope: 0.69999999, horiz_offset_correction: -0.025999999, + laser_id: 23, min_intensity: 40, rot_correction: 0.09491605919696541, vert_correction: -0.06218028850077059, + vert_offset_correction: 0.21104996} +- {dist_correction: 1.4491635, dist_correction_x: 1.4693596, dist_correction_y: 1.5063879, + focal_distance: 11.0, focal_slope: 1.35, horiz_offset_correction: 0.025999999, laser_id: 24, + min_intensity: 10, rot_correction: -0.07926942330827302, vert_correction: 0.015211696348436898, + vert_offset_correction: 0.20547923999999998} +- {dist_correction: 1.4477327, dist_correction_x: 1.4680737, dist_correction_y: 1.4867723000000002, + focal_distance: 21.0, focal_slope: 0.64999998, horiz_offset_correction: -0.025999999, + laser_id: 25, min_intensity: 40, rot_correction: -0.04114040021851501, vert_correction: 0.021137769837365927, + vert_offset_correction: 0.20505299000000002} +- {dist_correction: 1.4569492, dist_correction_x: 1.4763495, dist_correction_y: 1.4794533, + focal_distance: 24.0, focal_slope: 1.2, horiz_offset_correction: 0.025999999, laser_id: 26, + min_intensity: 10, rot_correction: -0.09175772534519049, vert_correction: -0.02014825541794774, + vert_offset_correction: 0.20802208} +- {dist_correction: 1.4202034000000001, dist_correction_x: 1.4434087, dist_correction_y: 1.4641794, + focal_distance: 24.0, focal_slope: 1.0, horiz_offset_correction: -0.025999999, laser_id: 27, + min_intensity: 40, rot_correction: -0.05478131594812029, vert_correction: -0.013608856125641236, + vert_offset_correction: 0.20755173} +- {dist_correction: 1.4624274, dist_correction_x: 1.4689874, dist_correction_y: 1.4884387000000001, + focal_distance: 24.0, focal_slope: 0.60000002, horiz_offset_correction: 0.025999999, + laser_id: 28, min_intensity: 20, rot_correction: -0.006154807614049144, vert_correction: 0.027062359796430756, + vert_offset_correction: 0.20462672999999998} +- {dist_correction: 1.4818254, dist_correction_x: 1.4883812, dist_correction_y: 1.5065907000000003, + focal_distance: 24.0, focal_slope: 0.64999998, horiz_offset_correction: -0.025999999, + laser_id: 29, min_intensity: 10, rot_correction: 0.033634548938460324, vert_correction: 0.03257665775493676, + vert_offset_correction: 0.20422987} +- {dist_correction: 1.4566035, dist_correction_x: 1.4917917, dist_correction_y: 1.495023, + focal_distance: 24.0, focal_slope: 0.80000001, horiz_offset_correction: 0.025999999, + laser_id: 30, min_intensity: 40, rot_correction: -0.019391916986595998, vert_correction: -0.008703503368623128, + vert_offset_correction: 0.20719896} +- {dist_correction: 1.4302849, dist_correction_x: 1.4821237, dist_correction_y: 1.4928844000000001, + focal_distance: 24.0, focal_slope: 0.75, horiz_offset_correction: -0.025999999, + laser_id: 31, min_intensity: 10, rot_correction: 0.020325608795218393, vert_correction: -0.0019580150746423583, + vert_offset_correction: 0.20671391} +- {dist_correction: 1.3751733, dist_correction_x: 1.381003, dist_correction_y: 1.3791220000000002, + focal_distance: 12.0, focal_slope: 2.0, horiz_offset_correction: 0.025999999, laser_id: 32, + min_intensity: 40, rot_correction: -0.13289416017564498, vert_correction: -0.39527185409579646, + vert_offset_correction: 0.16007624} +- {dist_correction: 1.2954352, dist_correction_x: 1.3348222, dist_correction_y: 1.3424704, + focal_distance: 0.25, focal_slope: 0.94999999, horiz_offset_correction: -0.025999999, + laser_id: 33, min_intensity: 10, rot_correction: -0.07196062668208551, vert_correction: -0.3877619815771452, + vert_offset_correction: 0.15953004} +- {dist_correction: 1.3848502999999999, dist_correction_x: 1.4212219, dist_correction_y: 1.4204210000000002, + focal_distance: 12.0, focal_slope: 0.40000001, horiz_offset_correction: 0.025999999, + laser_id: 34, rot_correction: 0.08484039734357905, vert_correction: -0.19930022938936873, + vert_offset_correction: 0.14670095} +- {dist_correction: 1.2735551, dist_correction_x: 1.2946091000000002, dist_correction_y: 1.3373412, + focal_distance: 9.5, focal_slope: 1.45, horiz_offset_correction: -0.025999999, laser_id: 35, + rot_correction: 0.14132308991714068, vert_correction: -0.18847487706375146, vert_offset_correction: 0.14600232999999999} +- {dist_correction: 1.3155770999999998, dist_correction_x: 1.3754842999999999, dist_correction_y: 1.3599864, + focal_distance: 10.0, focal_slope: 1.55, horiz_offset_correction: 0.025999999, laser_id: 36, + rot_correction: -0.010367785987873072, vert_correction: -0.38038196155484233, vert_offset_correction: 0.15899656} +- {dist_correction: 1.2383937, dist_correction_x: 1.2847844000000002, dist_correction_y: 1.2975331, + focal_distance: 9.5, focal_slope: 1.5, horiz_offset_correction: -0.025999999, laser_id: 37, + rot_correction: 0.04917377551965573, vert_correction: -0.3699409132772216, vert_offset_correction: 0.15824712999999999} +- {dist_correction: 1.2692068, dist_correction_x: 1.3555504, dist_correction_y: 1.3500606, + focal_distance: 9.0, focal_slope: 1.35, horiz_offset_correction: 0.025999999, laser_id: 38, + rot_correction: -0.033663681974334614, vert_correction: -0.4331233680891581, vert_offset_correction: 0.1628834} +- {dist_correction: 1.2704299000000001, dist_correction_x: 1.256096, dist_correction_y: 1.2917195000000001, + focal_distance: 0.25, focal_slope: 1.0, horiz_offset_correction: -0.025999999, laser_id: 39, + rot_correction: 0.02970128540814142, vert_correction: -0.42365160586492506, vert_offset_correction: 0.16217209000000002} +- {dist_correction: 1.3242505, dist_correction_x: 1.3679602, dist_correction_y: 1.3602597, + focal_distance: 0.25, focal_slope: 0.80000001, horiz_offset_correction: 0.025999999, + laser_id: 40, rot_correction: 0.11045904172671112, vert_correction: -0.3610257714580346, + vert_offset_correction: 0.15761203000000001} +- {dist_correction: 1.2417036000000001, dist_correction_x: 1.2753799, dist_correction_y: 1.3160570999999999, + focal_distance: 12.0, focal_slope: 1.95, horiz_offset_correction: -0.025999999, + laser_id: 41, rot_correction: 0.16898065062173281, vert_correction: -0.3497067702067833, + vert_offset_correction: 0.1568118} +- {dist_correction: 1.2770083, dist_correction_x: 1.294035, dist_correction_y: 1.2799448, + focal_distance: 0.25, focal_slope: 1.05, horiz_offset_correction: 0.025999999, laser_id: 42, + rot_correction: 0.08720617543669408, vert_correction: -0.41563902616182186, vert_offset_correction: 0.16157509} +- {dist_correction: 1.2715452999999999, dist_correction_x: 1.2586041000000001, dist_correction_y: 1.3063615, + focal_distance: 8.0, focal_slope: 1.45, horiz_offset_correction: -0.025999999, laser_id: 43, + rot_correction: 0.14953862523231207, vert_correction: -0.4037726372972675, vert_offset_correction: 0.16069862000000001} +- {dist_correction: 1.3691966, dist_correction_x: 1.4248035, dist_correction_y: 1.4160633999999999, + focal_distance: 9.5, focal_slope: 1.5, horiz_offset_correction: 0.025999999, laser_id: 44, + rot_correction: -0.1289848477986512, vert_correction: -0.2870427654491517, vert_offset_correction: 0.15249309} +- {dist_correction: 1.2590324, dist_correction_x: 1.2952177, dist_correction_y: 1.3026451000000001, + focal_distance: 9.5, focal_slope: 0.40000001, horiz_offset_correction: -0.025999999, + laser_id: 45, rot_correction: -0.06861990766880643, vert_correction: -0.2787493448161101, + vert_offset_correction: 0.1519342} +- {dist_correction: 1.3576372, dist_correction_x: 1.4220195000000002, dist_correction_y: 1.4047495, + focal_distance: 11.5, focal_slope: 2.0, horiz_offset_correction: 0.025999999, laser_id: 46, + rot_correction: -0.15311368611359788, vert_correction: -0.3424711063669128, vert_offset_correction: 0.15630372} +- {dist_correction: 1.2192308, dist_correction_x: 1.2838873000000002, dist_correction_y: 1.2723692, + focal_distance: 0.25, focal_slope: 0.80000001, horiz_offset_correction: -0.025999999, + laser_id: 47, rot_correction: -0.09182823315631256, vert_correction: -0.3313646164053843, + vert_offset_correction: 0.15552889} +- {dist_correction: 1.3319777, dist_correction_x: 1.3851605, dist_correction_y: 1.3619548000000001, + focal_distance: 14.5, focal_slope: 0.5, horiz_offset_correction: 0.025999999, laser_id: 48, + rot_correction: -0.009677803578418753, vert_correction: -0.2715549929196343, vert_offset_correction: 0.15145152} +- {dist_correction: 1.2615283, dist_correction_x: 1.2949489, dist_correction_y: 1.3105116, + focal_distance: 11.5, focal_slope: 1.7, horiz_offset_correction: -0.025999999, laser_id: 49, + rot_correction: 0.04752737152966444, vert_correction: -0.2620447461853948, vert_offset_correction: 0.15081642} +- {dist_correction: 1.3043471, dist_correction_x: 1.3546584, dist_correction_y: 1.3258238, + focal_distance: 10.0, focal_slope: 1.5, horiz_offset_correction: 0.025999999, laser_id: 50, + rot_correction: -0.031876049431930446, vert_correction: -0.32697130851884926, vert_offset_correction: 0.15522404} +- {dist_correction: 1.217234, dist_correction_x: 1.2595786, dist_correction_y: 1.2660506, + focal_distance: 14.5, focal_slope: 0.40000001, horiz_offset_correction: -0.025999999, + laser_id: 51, rot_correction: 0.02734981552423422, vert_correction: -0.3168539490043034, + vert_offset_correction: 0.15452543000000002} +- {dist_correction: 1.3343082999999998, dist_correction_x: 1.3773648, dist_correction_y: 1.3680313000000002, + focal_distance: 9.0, focal_slope: 0.40000001, horiz_offset_correction: 0.025999999, + laser_id: 52, rot_correction: 0.1059183442755036, vert_correction: -0.25363534842355817, + vert_offset_correction: 0.15025753} +- {dist_correction: 1.2802379, dist_correction_x: 1.2970529, dist_correction_y: 1.3454172, + focal_distance: 12.5, focal_slope: 1.7, horiz_offset_correction: -0.025999999, laser_id: 53, + rot_correction: 0.1627021811941921, vert_correction: -0.2409525119882134, vert_offset_correction: 0.14941919} +- {dist_correction: 1.3556595999999999, dist_correction_x: 1.405239, dist_correction_y: 1.3963416000000002, + focal_distance: 6.5, focal_slope: 0.40000001, horiz_offset_correction: 0.025999999, + laser_id: 54, rot_correction: 0.08622890275664939, vert_correction: -0.3075976651295216, + vert_offset_correction: 0.15389032} +- {dist_correction: 1.2126501, dist_correction_x: 1.2667055999999999, dist_correction_y: 1.2983803, + focal_distance: 12.0, focal_slope: 1.65, horiz_offset_correction: -0.025999999, + laser_id: 55, rot_correction: 0.14526436799938475, vert_correction: -0.29492149585191946, + vert_offset_correction: 0.15302658} +- {dist_correction: 1.4643387, dist_correction_x: 1.4668213, dist_correction_y: 1.4981345, + focal_distance: 9.5, focal_slope: 1.6, horiz_offset_correction: 0.025999999, laser_id: 56, + rot_correction: -0.1254947425640354, vert_correction: -0.17958427457384749, vert_offset_correction: 0.14543074} +- {dist_correction: 1.3163063, dist_correction_x: 1.32084, dist_correction_y: 1.344314, + focal_distance: 0.25, focal_slope: 0.60000002, horiz_offset_correction: -0.025999999, + laser_id: 57, rot_correction: -0.06616441441615112, vert_correction: -0.1716573102518716, + vert_offset_correction: 0.14492266} +- {dist_correction: 1.4791275, dist_correction_x: 1.5032370000000002, dist_correction_y: 1.504231, + focal_distance: 10.0, focal_slope: 1.9, horiz_offset_correction: 0.025999999, laser_id: 58, + rot_correction: -0.14904011556471117, vert_correction: -0.2343876828996395, vert_offset_correction: 0.14898732} +- {dist_correction: 1.3451407, dist_correction_x: 1.3635945, dist_correction_y: 1.3745059000000002, + focal_distance: 0.25, focal_slope: 0.55000001, horiz_offset_correction: -0.025999999, + laser_id: 59, rot_correction: -0.08869881941024317, vert_correction: -0.22547306467922804, + vert_offset_correction: 0.14840302} +- {dist_correction: 1.4042865, dist_correction_x: 1.4230287000000001, dist_correction_y: 1.4323932, + focal_distance: 5.0, focal_slope: 0.80000001, horiz_offset_correction: 0.025999999, + laser_id: 60, rot_correction: -0.011033481617187117, vert_correction: -0.1647032640134567, + vert_offset_correction: 0.14447808} +- {dist_correction: 1.3322758, dist_correction_x: 1.3656628000000002, dist_correction_y: 1.3536118, + focal_distance: 0.25, focal_slope: 0.44999999, horiz_offset_correction: -0.025999999, + laser_id: 61, rot_correction: 0.04691559872025539, vert_correction: -0.15454155742794698, + vert_offset_correction: 0.14383028} +- {dist_correction: 1.4418548999999998, dist_correction_x: 1.4882887, dist_correction_y: 1.4795813000000002, + focal_distance: 10.0, focal_slope: 1.1, horiz_offset_correction: 0.025999999, laser_id: 62, + rot_correction: -0.03167581478816615, vert_correction: -0.2184708037202268, vert_offset_correction: 0.14794575000000001} +- {dist_correction: 1.3281212, dist_correction_x: 1.3694792000000002, dist_correction_y: 1.3788945000000001, + focal_distance: 15.0, focal_slope: 0.40000001, horiz_offset_correction: -0.025999999, + laser_id: 63, rot_correction: 0.025768587364500155, vert_correction: -0.2083180489284506, + vert_offset_correction: 0.14728524} +num_lasers: 64 diff --git a/pointcloud_viewer.py b/pointcloud_viewer.py new file mode 100644 index 0000000..9f18cd0 --- /dev/null +++ b/pointcloud_viewer.py @@ -0,0 +1,3403 @@ +__author__ = "Martin Hahner" +__contact__ = "martin.hahner@pm.me" +__license__ = "CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)" + +# GUI adapted from +# https://memotut.com/create-a-3d-model-viewer-with-pyqt5-and-pyqtgraph-b3916/ and +# https://matplotlib.org/3.1.1/gallery/user_interfaces/embedding_in_qt_sgskip.html + +import multiprocessing as mp +if __name__ == '__main__': + mp.set_start_method("spawn") + +import os +import sys +import copy +import gzip +import socket +import pandas +import logging +import argparse +import tempfile +import warnings + +warnings.simplefilter(action='ignore', category=DeprecationWarning) +warnings.simplefilter(action='ignore', category=FutureWarning) +warnings.simplefilter(action='ignore', category=UserWarning) + +import numpy as np +import pickle as pkl +import matplotlib as mpl +import matplotlib.cm as cm +import pyqtgraph.opengl as gl + +from glob import glob +from pathlib import Path +from pprint import pprint +from plyfile import PlyData +from typing import List, Dict + +from PyQt5.QtGui import * +from PyQt5.QtCore import * +from PyQt5.QtWidgets import * +from pyqtgraph.Qt import QtGui + +from lib.LISA.python.lisa import LISA + +from lib.cadc_devkit.other.create_image_sets import DROR_LEVELS + +from lib.OpenPCDet.pcdet.utils import calibration_kitti + + +from lib.LiDAR_fog_sim.fog_simulation import ParameterSet, simulate_fog +from lib.LiDAR_fog_sim.SeeingThroughFog.tools.DatasetViewer.lib.read import load_calib_data, read_label +from lib.LiDAR_fog_sim.SeeingThroughFog.tools.DatasetFoggification.beta_modification import BetaRadomization +from lib.LiDAR_fog_sim.SeeingThroughFog.tools.DatasetFoggification.lidar_foggification import haze_point_cloud + +from tools.wet_ground.augmentation import ground_water_augmentation + +from tools.snowfall.simulation import augment +from tools.snowfall.sampling import snowfall_rate_to_rainfall_rate, compute_occupancy + +try: + + import torch + + CUDA_AVAILABLE = torch.cuda.is_available() + + from pcdet.utils import common_utils + from pcdet.datasets import build_dataloader + from pcdet.config import cfg, cfg_from_yaml_file + from pcdet.models import build_network, load_data_to_gpu + +except ImportError as e: + + CUDA_AVAILABLE = False + + if __name__ == '__main__': + print(f'{e} => Inference not available') + +try: + + from lib.cadc_devkit.other.dror import dynamic_radius_outlier_filter, get_cube_mask + + live_DROR_available = True + +except ImportError as e: + + if __name__ == '__main__': + print(f'{e} => live DROR not available') + + live_DROR_available = False + + + def get_cube_mask() -> None: + raise NotImplementedError + + + def dynamic_radius_outlier_filter(sensor: str, signal: str, variant: str, before: int, filename: str) -> np.ndarray: + + path = f'{DROR}/alpha_0.45/all/{sensor}/{signal}/{variant}' + name = Path(filename).name.replace('.bin', '') + file = f'{path}/{name}.pkl' + + with open(file, 'rb') as f: + snow_indices = pkl.load(f) + + mask = np.ones(before, dtype=bool) + + mask[snow_indices] = False + + return mask + +MIN_DIST = 3 # in m -> to hide "the ring" around the sensor + +CAMERA_READY = True +SAVE_IMAGES = False +ROTATE = 0 + +BLACK = ( 0, 0, 0, 255) +WHITE = (255, 255, 255, 255) + + # R, G, B, alpha +COLORS = [( 0, 255, 0, 255), # cars in green + (255, 0, 0, 255), # pedestrian in red + (255, 255, 0, 255)] # cyclists in yellow + +if CAMERA_READY: + DET_COLORS = [BLACK, # cars + BLACK, # pedestrian + BLACK] # cyclists + + GRAY = (0, 0, 0, 100) + +else: + # R, G, B, alpha + DET_COLORS = [( 0, 255, 0, 100), # cars in green + (255, 0, 0, 100), # pedestrian in red + (255, 255, 0, 100)] # cyclists in yellow + + GRAY = (255, 255, 255, 100) + + +parser = argparse.ArgumentParser() +parser.add_argument('-d', '--datasets', type=str, help='path to where you store your datasets', + default=str(Path.home() / 'datasets')) +parser.add_argument('-e', '--experiments', type=str, help='path to where you store your OpenPCDet experiments', + default=str(Path(__file__).parent.absolute() / 'experiments')) +args = parser.parse_args() + +DATASETS_ROOT = Path(args.datasets) +EXPERIMENTS_ROOT = Path(args.experiments) + +DROR = Path(__file__).parent.absolute() + +AUDI = DATASETS_ROOT / 'A2D2/camera_lidar_semantic_bboxes' +LYFT = DATASETS_ROOT / 'LyftLevel5/Perception/train_lidar' +ARGO = DATASETS_ROOT / 'Argoverse' +PANDA = DATASETS_ROOT / 'PandaSet' +DENSE = DATASETS_ROOT / 'DENSE/SeeingThroughFog/lidar_hdl64_strongest' +KITTI = DATASETS_ROOT / 'KITTI/3D/training/velodyne' +WAYMO = DATASETS_ROOT / 'WaymoOpenDataset/WOD/train/velodyne' +HONDA = DATASETS_ROOT / 'Honda_3D/scenarios' +APOLLO = DATASETS_ROOT / 'Apollo3D' +NUSCENES = DATASETS_ROOT / 'nuScenes/sweeps/LIDAR_TOP' + +HOSTNAME = socket.gethostname() + +if HOSTNAME == 'beauty' or HOSTNAME == 'beast': + DENSE = Path.home() / 'datasets_local' / 'DENSE/SeeingThroughFog/lidar_hdl64_strongest' + + +def get_calib(sensor: str = 'hdl64'): + calib_file = Path(__file__).parent.absolute() / 'lib' / 'OpenPCDet' / 'data' / 'dense' / f'calib_{sensor}.txt' + assert calib_file.exists(), f'{calib_file} not found' + return calibration_kitti.Calibration(calib_file) + + +def get_fov_flag(pts_rect, img_shape, calib): + pts_img, pts_rect_depth = calib.rect_to_img(pts_rect) + val_flag_1 = np.logical_and(pts_img[:, 0] >= 0, pts_img[:, 0] < img_shape[1]) + val_flag_2 = np.logical_and(pts_img[:, 1] >= 0, pts_img[:, 1] < img_shape[0]) + val_flag_merge = np.logical_and(val_flag_1, val_flag_2) + pts_valid_flag = np.logical_and(val_flag_merge, pts_rect_depth >= 0) + + return pts_valid_flag + + +def close_all_windows(exit_code: int = 0) -> None: + sys.exit(exit_code) + + +class Namespace: + def __init__(self, **kwargs): + self.__dict__.update(kwargs) + + +class QHLine(QFrame): + + def __init__(self): + super(QHLine, self).__init__() + self.setFrameShape(QFrame.HLine) + self.setFrameShadow(QFrame.Sunken) + + +class QVLine(QFrame): + + def __init__(self): + super(QVLine, self).__init__() + self.setFrameShape(QFrame.VLine) + self.setFrameShadow(QFrame.Sunken) + + +class ImageWindow(QMainWindow): + + def __init__(self) -> None: + + super(ImageWindow, self).__init__() + + self.monitor = QDesktopWidget().screenGeometry(0) + + self.setGeometry(self.monitor) + + self.centerWidget = QWidget() + self.setCentralWidget(self.centerWidget) + + self.layout = QGridLayout() + self.centerWidget.setLayout(self.layout) + + self.image_label = QLabel() + self.layout.addWidget(self.image_label, 0, 0) + + +# noinspection PyUnresolvedReferences +class LidarWindow(QMainWindow): + + def __init__(self) -> None: + + super(LidarWindow, self).__init__() + + self.show_img = False + self.show_temp = False + self.image_window = ImageWindow() + + self.boxes = {} + self.label = None + self.model = None + self.config = None + self.logger = None + self.sampler = None + self.eval_set = None + self.eval_loader = None + self.predictions = None + self.prediction_boxes = [] + self.result_dict = {} + self.show_predictions = False + self.prediction_threshold = 50 + + self.gain = True + self.noise_variant = 'v4' + + self.noise = 10 + + self.min_fog_response = -1 + self.max_fog_response = -1 + self.num_fog_responses = -1 + + self.dror_alpha = 0.45 # HDL64 Spec Sheet: Angular Resolution (Horizontal/Azimuth): 0.08Β° – 0.35Β° + self.dror_beta = 3 + self.dror_k_min = 3 + self.dror_sr_min = 4 # in cm + + self.dror_alpha_scale = 100 + + self.p = ParameterSet(gamma=0.000001, + gamma_min=0.0000001, + gamma_max=0.00001, + gamma_scale=10000000) + + self.p.beta_0 = self.p.gamma / np.pi + self.row_height = 20 + + self.monitor = QDesktopWidget().screenGeometry(0) + + self.setGeometry(self.monitor) + + self.setAcceptDrops(True) + + self.simulated_fog = False + self.simulated_fog_pc = None + + self.simulated_fog_dense = False + + self.color_dict = {0: 'x', + 1: 'y', + 2: 'z', + 3: 'intensity', + 4: 'distance', + 5: 'angle', + 6: 'channel'} + + self.min_value = 0 + self.max_value = 63 + self.point_size = 3 + self.line_width = 3 + + if CAMERA_READY: + self.color_feature = 3 + else: + self.color_feature = 2 + + self.threshold = 50 + self.num_features = 5 + self.dataset = None + self.success = False + self.extension = 'bin' + self.d_type = np.float32 + self.intensity_multiplier = 1 + self.color_name = self.color_dict[self.color_feature] + + self.min_height = -400 # in cm + self.max_distance = 80 # in m + + self.lastDir = None + self.droppedFilename = None + + self.current_pc = None + self.current_mesh = None + + self.temporal_pcs = [] + self.temporal_meshes = [] + + self.file_name = None + self.file_list = None + self.index = -1 + + self.centerWidget = QWidget() + self.setCentralWidget(self.centerWidget) + + self.layout = QGridLayout() + self.centerWidget.setLayout(self.layout) + + self.num_columns = 6 + self.current_row = 0 + + self.viewer = gl.GLViewWidget() + if CAMERA_READY: + self.viewer.setBackgroundColor('w') + self.viewer.setWindowTitle('drag & drop point cloud viewer') + self.viewer.opts['center'] = QVector3D(15, 0, 0) + self.viewer.opts['distance'] = 30 + self.viewer.opts['elevation'] = 5 + self.viewer.opts['azimuth'] = 180 + + self.layout.addWidget(self.viewer, 0, 6, 30, 170) + + self.show_dror_cube = False + try: + self.dror_cube = gl.GLBoxItem(QtGui.QVector3D(1, 1, 1), color=GRAY, line_width=self.line_width) + except TypeError: + self.dror_cube = gl.GLBoxItem(QtGui.QVector3D(1, 1, 1), color=GRAY) + self.dror_cube.setSize(10, 2, 2) + self.dror_cube.translate(3, -1, -1) + + self.current_row += 0 + + self.num_info = QLabel("") + self.num_info.setAlignment(Qt.AlignCenter) + self.num_info.setMaximumSize(self.monitor.width(), self.row_height) + self.layout.addWidget(self.num_info, self.current_row, 0) + + self.file_name_label = QLabel() + self.file_name_label.setAlignment(Qt.AlignCenter) + self.file_name_label.setMaximumSize(self.monitor.width(), 20) + self.layout.addWidget(self.file_name_label, self.current_row, 1) + + self.inference_btn = QPushButton('run inference') + self.inference_btn.setEnabled(False) + self.inference_btn.clicked.connect(self.run_inference) + self.layout.addWidget(self.inference_btn, self.current_row, 3, 1, 2) + + self.minus6 = QCheckBox("-6") + self.minus6.setEnabled(False) + self.minus6.stateChanged.connect(self.update_temporal_clouds) + self.layout.addWidget(self.minus6, self.current_row, 5) + + self.current_row += 1 + + self.choose_dir_btn = QPushButton("choose custom directory") + self.choose_dir_btn.clicked.connect(self.show_directory_dialog) + self.layout.addWidget(self.choose_dir_btn, self.current_row, 1) + + self.prev_btn = QPushButton("<-") + self.next_btn = QPushButton("->") + + self.prev_btn.clicked.connect(self.decrement_index) + self.next_btn.clicked.connect(self.increment_index) + + self.layout.addWidget(self.prev_btn, self.current_row, 0) + self.layout.addWidget(self.next_btn, self.current_row, 2) + + self.load_kitti_btn = QPushButton("KITTI") + self.load_kitti_btn.clicked.connect(self.load_kitti) + self.layout.addWidget(self.load_kitti_btn, self.current_row, 3) + + self.load_nuscenes_btn = QPushButton("nuScenes") + self.load_nuscenes_btn.clicked.connect(self.load_nuscenes) + self.layout.addWidget(self.load_nuscenes_btn, self.current_row, 4) + + self.minus5 = QCheckBox("-5") + self.minus5.setEnabled(False) + self.minus5.stateChanged.connect(self.update_temporal_clouds) + self.layout.addWidget(self.minus5, self.current_row, 5) + + self.current_row += 1 + + if self.show_predictions: + self.visualize_predictions_btn = QPushButton('hide predictions', self) + else: + self.visualize_predictions_btn = QPushButton('show predictions', self) + + self.visualize_predictions_btn.setEnabled(False) + self.layout.addWidget(self.visualize_predictions_btn, self.current_row, 0) + self.visualize_predictions_btn.clicked.connect(self.toggle_predictions) + + self.experiment_path_box = QLineEdit(self) + self.experiment_path_box.setText('snow+wet') + self.layout.addWidget(self.experiment_path_box, self.current_row, 1) + self.current_experiment = self.experiment_path_box.text() + + self.load_experiment_path_btn = QPushButton('load results', self) + self.layout.addWidget(self.load_experiment_path_btn, self.current_row, 2) + self.load_experiment_path_btn.clicked.connect(self.load_results) + + self.load_dense_btn = QPushButton("DENSE") + self.load_dense_btn.clicked.connect(self.load_dense) + self.layout.addWidget(self.load_dense_btn, self.current_row, 3) + + self.load_lyft_btn = QPushButton("LyftL5") + self.load_lyft_btn.clicked.connect(self.load_lyft) + self.layout.addWidget(self.load_lyft_btn, self.current_row, 4) + + self.minus4 = QCheckBox("-4") + self.minus4.setEnabled(False) + self.minus4.stateChanged.connect(self.update_temporal_clouds) + self.layout.addWidget(self.minus4, self.current_row, 5) + + self.current_row += 1 + + self.prediction_threshold_title = QLabel("prediction confidence") + self.prediction_threshold_title.setAlignment(Qt.AlignCenter) + self.layout.addWidget(self.prediction_threshold_title, self.current_row, 0) + + self.prediction_threshold_label = QLabel(str(self.prediction_threshold)) + self.prediction_threshold_label.setAlignment(Qt.AlignCenter) + self.layout.addWidget(self.prediction_threshold_label, self.current_row, 2) + + self.prediction_threshold_slider = QSlider(Qt.Horizontal) + self.prediction_threshold_slider.setMinimum(0) + self.prediction_threshold_slider.setMaximum(100) + self.prediction_threshold_slider.setValue(self.prediction_threshold) + self.prediction_threshold_slider.setTickPosition(QSlider.TicksBelow) + self.prediction_threshold_slider.setTickInterval(10) + self.prediction_threshold_slider.setEnabled(False) + + self.layout.addWidget(self.prediction_threshold_slider, self.current_row, 1) + self.prediction_threshold_slider.valueChanged.connect(self.prediction_threshold_slider_change) + + self.load_honda_btn = QPushButton("H3D") + self.load_honda_btn.clicked.connect(self.load_honda) + self.layout.addWidget(self.load_honda_btn, self.current_row, 3) + + self.load_argo_btn = QPushButton("Argoverse") + self.load_argo_btn.clicked.connect(self.load_argo) + self.layout.addWidget(self.load_argo_btn, self.current_row, 4) + + self.minus3 = QCheckBox("-3") + self.minus3.setEnabled(False) + self.minus3.stateChanged.connect(self.update_temporal_clouds) + self.layout.addWidget(self.minus3, self.current_row, 5) + + self.current_row += 1 + + self.mor_label = QLabel(f'meteorological optical range (MOR) = {round(self.p.mor, 2)}m') + self.mor_label.setAlignment(Qt.AlignCenter) + self.mor_label.setMaximumSize(self.monitor.width(), self.row_height) + self.layout.addWidget(self.mor_label, self.current_row, 1) + + self.load_audi_btn = QPushButton("A2D2") + self.load_audi_btn.clicked.connect(self.load_audi) + self.layout.addWidget(self.load_audi_btn, self.current_row, 3) + + self.load_waymo_btn = QPushButton("Waymo") + self.load_waymo_btn.clicked.connect(self.load_waymo) + self.layout.addWidget(self.load_waymo_btn, self.current_row, 4) + + self.minus2 = QCheckBox("-2") + self.minus2.setEnabled(False) + self.minus2.stateChanged.connect(self.update_temporal_clouds) + self.layout.addWidget(self.minus2, self.current_row, 5) + + self.current_row += 1 + + self.alpha_title = QLabel('attenuation coefficient') + self.alpha_title.setAlignment(Qt.AlignRight) + self.layout.addWidget(self.alpha_title, self.current_row, 0) + + self.alpha_slider = QSlider(Qt.Horizontal) + self.alpha_slider.setMinimum(int(self.p.alpha_min * self.p.alpha_scale)) + self.alpha_slider.setMaximum(int(self.p.alpha_max * self.p.alpha_scale)) + self.alpha_slider.setValue(int(self.p.alpha * self.p.alpha_scale)) + + self.layout.addWidget(self.alpha_slider, self.current_row, 1) + self.alpha_slider.valueChanged.connect(self.update_labels) + + self.alpha_label = QLabel(f"\u03B1 = {self.p.alpha}") + self.alpha_label.setAlignment(Qt.AlignLeft) + self.layout.addWidget(self.alpha_label, self.current_row, 2) + + self.load_panda_btn = QPushButton("PandaSet") + self.load_panda_btn.clicked.connect(self.load_panda) + self.layout.addWidget(self.load_panda_btn, self.current_row, 3) + + self.load_apollo_btn = QPushButton("Apollo") + self.load_apollo_btn.clicked.connect(self.load_apollo) + self.layout.addWidget(self.load_apollo_btn, self.current_row, 4) + + self.minus1 = QCheckBox("-1") + self.minus1.setEnabled(False) + self.minus1.stateChanged.connect(self.update_temporal_clouds) + self.layout.addWidget(self.minus1, self.current_row, 5) + + self.current_row += 1 + + self.beta_title = QLabel('backscattering coefficient') + self.beta_title.setAlignment(Qt.AlignRight) + self.layout.addWidget(self.beta_title, self.current_row, 0) + + self.beta_slider = QSlider(Qt.Horizontal) + self.beta_slider.setMinimum(int(self.p.beta_min * self.p.beta_scale)) + self.beta_slider.setMaximum(int(self.p.beta_max * self.p.beta_scale)) + self.beta_slider.setValue(int(self.p.beta * self.p.beta_scale)) + + self.layout.addWidget(self.beta_slider, self.current_row, 1) + self.beta_slider.valueChanged.connect(self.update_labels) + + self.beta_label = QLabel(f"\u03B2 = {round(self.p.beta * self.p.mor, 3)} / MOR") + self.beta_label.setAlignment(Qt.AlignLeft) + self.layout.addWidget(self.beta_label, self.current_row, 2) + + self.toggle_img_btn = QPushButton("show image") + self.toggle_img_btn.setEnabled(False) + self.toggle_img_btn.clicked.connect(self.toggle_image_visibility) + self.layout.addWidget(self.toggle_img_btn, self.current_row, 3) + + self.toggle_temp_btn = QPushButton("show temporal") + self.toggle_temp_btn.setEnabled(False) + self.toggle_temp_btn.clicked.connect(self.toggle_temp_visibility) + self.layout.addWidget(self.toggle_temp_btn, self.current_row, 4) + + self.zero = QCheckBox("0") + self.zero.setEnabled(False) + self.zero.setChecked(True) + self.zero.stateChanged.connect(self.update_temporal_clouds) + self.layout.addWidget(self.zero, self.current_row, 5) + + self.current_row += 1 + + self.gamma_title = QLabel("reflextivity of the hard target") + self.gamma_title.setAlignment(Qt.AlignRight) + self.layout.addWidget(self.gamma_title, self.current_row, 0) + + self.gamma_slider = QSlider(Qt.Horizontal) + self.gamma_slider.setMinimum(int(self.p.gamma_min * self.p.gamma_scale)) + self.gamma_slider.setMaximum(int(self.p.gamma_max * self.p.gamma_scale)) + self.gamma_slider.setValue(int(self.p.gamma * self.p.gamma_scale)) + + self.layout.addWidget(self.gamma_slider, self.current_row, 1) + self.gamma_slider.valueChanged.connect(self.update_labels) + + self.gamma_label = QLabel(f"\u0393 = {self.p.gamma}") + self.gamma_label.setAlignment(Qt.AlignLeft) + self.layout.addWidget(self.gamma_label, self.current_row, 2) + + self.show_fov_only = True + self.show_fov_only_btn = QPushButton('show full 360Β°' if self.show_fov_only else 'show camera FOV only') + self.show_fov_only_btn.setEnabled(False) + self.show_fov_only_btn.clicked.connect(self.toggle_fov) + self.layout.addWidget(self.show_fov_only_btn, self.current_row, 3, 1, 2) + + self.plus1 = QCheckBox("+1") + self.plus1.setEnabled(False) + self.plus1.stateChanged.connect(self.update_temporal_clouds) + self.layout.addWidget(self.plus1, self.current_row, 5) + + self.current_row += 1 + + self.plus2 = QCheckBox("+2") + self.plus2.setEnabled(False) + self.plus2.stateChanged.connect(self.update_temporal_clouds) + self.layout.addWidget(self.plus2, self.current_row, 5) + + self.log_info = QLabel("") + self.log_info.setAlignment(Qt.AlignCenter) + self.log_info.setMaximumSize(self.monitor.width(), self.row_height) + self.layout.addWidget(self.log_info, self.current_row, 1) + + self.dense_split_paths = [] + + self.cb_splits = QComboBox() + # self.cb_splits.setEditable(True) + # self.cb_splits.lineEdit().setReadOnly(True) + # self.cb_splits.lineEdit().setAlignment(Qt.AlignCenter) + self.cb_splits.addItems(self.populate_dense_splits()) + self.cb_splits.currentIndexChanged.connect(self.split_selection_change) + self.cb_splits.setEnabled(False) + self.layout.addWidget(self.cb_splits, self.current_row, 3, 1, 2) + + self.current_row += 1 + + self.color_title = QLabel("color code") + self.color_title.setAlignment(Qt.AlignCenter) + self.layout.addWidget(self.color_title, self.current_row, 0) + + self.color_label = QLabel(self.color_name) + self.color_label.setAlignment(Qt.AlignCenter) + self.layout.addWidget(self.color_label, self.current_row, 2) + + self.color_slider = QSlider(Qt.Horizontal) + self.color_slider.setMinimum(0) + self.color_slider.setMaximum(6) + self.color_slider.setValue(self.color_feature) + self.color_slider.setTickPosition(QSlider.TicksBelow) + self.color_slider.setTickInterval(1) + + self.layout.addWidget(self.color_slider, self.current_row, 1) + self.color_slider.valueChanged.connect(self.color_slider_change) + + self.sensor = 'hdl64' + self.cb_sensors = QComboBox() + self.cb_sensors.addItems(['hdl64', 'vlp32']) + self.cb_sensors.currentIndexChanged.connect(self.sensor_selection_change) + self.cb_sensors.setEnabled(False) + self.layout.addWidget(self.cb_sensors, self.current_row, 3) + + self.signal = 'strongest' + self.cb_signals = QComboBox() + self.cb_signals.addItems(['strongest', 'last']) + self.cb_signals.currentIndexChanged.connect(self.signal_selection_change) + self.cb_signals.setEnabled(False) + self.layout.addWidget(self.cb_signals, self.current_row, 4) + + self.plus3 = QCheckBox("+3") + self.plus3.setEnabled(False) + self.plus3.stateChanged.connect(self.update_temporal_clouds) + self.layout.addWidget(self.plus3, self.current_row, 5) + + self.current_row += 1 + + self.min_height_title = QLabel("minimum height") + self.min_height_title.setAlignment(Qt.AlignCenter) + self.layout.addWidget(self.min_height_title, self.current_row, 0) + + self.min_height_label = QLabel(f'{self.min_height} cm') + self.min_height_label.setAlignment(Qt.AlignCenter) + self.layout.addWidget(self.min_height_label, self.current_row, 2) + + self.min_height_slider = QSlider(Qt.Horizontal) + self.min_height_slider.setMinimum(-400) + self.min_height_slider.setMaximum(0) + self.min_height_slider.setValue(self.min_height) + self.min_height_slider.setTickPosition(QSlider.TicksBelow) + self.min_height_slider.setTickInterval(10) + + self.layout.addWidget(self.min_height_slider, self.current_row, 1) + self.min_height_slider.valueChanged.connect(self.min_height_slider_change) + + if self.simulated_fog: + self.alpha_slider.setEnabled(True) + self.beta_slider.setEnabled(True) + self.gamma_slider.setEnabled(True) + self.toggle_simulated_fog_btn = QPushButton("remove our fog") + else: + self.alpha_slider.setEnabled(False) + self.beta_slider.setEnabled(False) + self.gamma_slider.setEnabled(False) + self.toggle_simulated_fog_btn = QPushButton("add our fog") + + self.toggle_simulated_fog_btn.clicked.connect(self.toggle_simulated_fog) + self.layout.addWidget(self.toggle_simulated_fog_btn, self.current_row, 3) + + self.toggle_simulated_fog_btn.setEnabled(False) + + if self.simulated_fog_dense: + self.toggle_simulated_fog_dense_btn = QPushButton("remove STF fog") + else: + self.toggle_simulated_fog_dense_btn = QPushButton("add STF fog") + + self.toggle_simulated_fog_dense_btn.clicked.connect(self.toggle_simulated_fog_dense) + self.layout.addWidget(self.toggle_simulated_fog_dense_btn, self.current_row, 4) + self.toggle_simulated_fog_dense_btn.setEnabled(False) + + self.plus4 = QCheckBox("+4") + self.plus4.setEnabled(False) + self.plus4.stateChanged.connect(self.update_temporal_clouds) + self.layout.addWidget(self.plus4, self.current_row, 5) + + self.current_row += 1 + + self.max_distance_title = QLabel("maximum distance") + self.max_distance_title.setAlignment(Qt.AlignCenter) + self.layout.addWidget(self.max_distance_title, self.current_row, 0) + + self.max_distance_label = QLabel(f'{self.max_distance} m') + self.max_distance_label.setAlignment(Qt.AlignCenter) + self.layout.addWidget(self.max_distance_label, self.current_row, 2) + + self.max_distance_slider = QSlider(Qt.Horizontal) + self.max_distance_slider.setMinimum(0) + self.max_distance_slider.setMaximum(200) + self.max_distance_slider.setValue(self.max_distance) + self.max_distance_slider.setTickPosition(QSlider.TicksBelow) + self.max_distance_slider.setTickInterval(10) + + self.layout.addWidget(self.max_distance_slider, self.current_row, 1) + self.max_distance_slider.valueChanged.connect(self.max_distance_slider_change) + + self.current_row += 1 + + self.layout.addWidget(QHLine(), self.current_row, 0, 1, self.num_columns) + + self.current_row += 1 + + self.dror_intensity = 'DROR' + + self.dror_headline = QLabel(self.dror_intensity) + self.dror_headline.setAlignment(Qt.AlignCenter) + self.dror_headline.setMaximumSize(self.monitor.width(), 20) + self.layout.addWidget(self.dror_headline, self.current_row, 1) + + self.apply_dror = False + + self.toggle_cube_btn = QPushButton('hide cube' if self.show_dror_cube else 'show_cube') + self.toggle_cube_btn.setEnabled(False) + self.toggle_cube_btn.clicked.connect(self.toggle_dror_cube) + self.layout.addWidget(self.toggle_cube_btn, self.current_row, 3) + + self.toggle_dror_btn = QPushButton("apply DROR") + self.toggle_dror_btn.clicked.connect(self.toggle_dror) + self.layout.addWidget(self.toggle_dror_btn, self.current_row, 4) + self.toggle_dror_btn.setEnabled(False) + + self.current_row += 1 + + self.dror_alpha_title = QLabel("horizontal angular resolution") + self.dror_alpha_title.setAlignment(Qt.AlignCenter) + self.layout.addWidget(self.dror_alpha_title, self.current_row, 0) + + self.dror_alpha_label = QLabel(f"\u03B1 = {self.dror_alpha}") + self.dror_alpha_label.setAlignment(Qt.AlignLeft) + self.layout.addWidget(self.dror_alpha_label, self.current_row, 2) + + self.dror_alpha_slider = QSlider(Qt.Horizontal) + self.dror_alpha_slider.setMinimum(0) + self.dror_alpha_slider.setMaximum(100) + self.dror_alpha_slider.setValue(int(self.dror_alpha * self.dror_alpha_scale)) + self.dror_alpha_slider.setTickPosition(QSlider.TicksBelow) + self.dror_alpha_slider.setTickInterval(10) + self.dror_alpha_slider.setEnabled(False) + + self.layout.addWidget(self.dror_alpha_slider, self.current_row, 1) + self.dror_alpha_slider.valueChanged.connect(self.dror_change) + + self.snowfall_rate_title = QLabel('snowfall rate') + self.snowfall_rate_title.setAlignment(Qt.AlignCenter) + self.layout.addWidget(self.snowfall_rate_title, self.current_row, 3) + + self.velocity_title = QLabel('velocity') + self.velocity_title.setAlignment(Qt.AlignCenter) + self.layout.addWidget(self.velocity_title, self.current_row, 4) + + self.current_row += 1 + + self.dror_beta_title = QLabel("multiplication factor") + self.dror_beta_title.setAlignment(Qt.AlignCenter) + self.layout.addWidget(self.dror_beta_title, self.current_row, 0) + + self.dror_beta_label = QLabel(f"\u03B2 = {self.dror_beta}") + self.dror_beta_label.setAlignment(Qt.AlignLeft) + self.layout.addWidget(self.dror_beta_label, self.current_row, 2) + + self.dror_beta_slider = QSlider(Qt.Horizontal) + self.dror_beta_slider.setMinimum(0) + self.dror_beta_slider.setMaximum(10) + self.dror_beta_slider.setValue(int(self.dror_beta)) + self.dror_beta_slider.setTickPosition(QSlider.TicksBelow) + self.dror_beta_slider.setTickInterval(1) + self.dror_beta_slider.setEnabled(False) + + self.layout.addWidget(self.dror_beta_slider, self.current_row, 1) + self.dror_beta_slider.valueChanged.connect(self.dror_change) + + self.cb_snowfall_rate = QComboBox() + self.cb_snowfall_rate.addItems(['0.5', '1', '1.5', '2', '2.5']) + self.cb_snowfall_rate.currentIndexChanged.connect(self.snowfall_change) + self.cb_snowfall_rate.setEnabled(False) + self.layout.addWidget(self.cb_snowfall_rate, self.current_row, 3) + + self.cb_velocity = QComboBox() + self.cb_velocity.addItems(['0.2', '0.4', '0.6', '0.8', '1', '1.2', '1.4', '1.6', '1.8', '2']) + self.cb_velocity.currentIndexChanged.connect(self.snowfall_change) + self.cb_velocity.setEnabled(False) + self.layout.addWidget(self.cb_velocity, self.current_row, 4) + + self.current_row += 1 + + self.dror_k_min_title = QLabel("min. number of neighbors") + self.dror_k_min_title.setAlignment(Qt.AlignCenter) + self.layout.addWidget(self.dror_k_min_title, self.current_row, 0) + + self.dror_k_min_label = QLabel(f"k_min = {self.dror_k_min}") + self.dror_k_min_label.setAlignment(Qt.AlignLeft) + self.layout.addWidget(self.dror_k_min_label, self.current_row, 2) + + self.dror_k_min_slider = QSlider(Qt.Horizontal) + self.dror_k_min_slider.setMinimum(0) + self.dror_k_min_slider.setMaximum(10) + self.dror_k_min_slider.setValue(self.dror_k_min) + self.dror_k_min_slider.setTickPosition(QSlider.TicksBelow) + self.dror_k_min_slider.setTickInterval(1) + self.dror_k_min_slider.setEnabled(False) + + self.layout.addWidget(self.dror_k_min_slider, self.current_row, 1) + self.dror_k_min_slider.valueChanged.connect(self.dror_change) + + self.rainfall_rate_title = QLabel('') + self.rainfall_rate_title.setAlignment(Qt.AlignCenter) + self.layout.addWidget(self.rainfall_rate_title, self.current_row, 3, 1, 2) + + self.current_row += 1 + + self.dror_sr_min_title = QLabel('min. search radius') + self.dror_sr_min_title.setAlignment(Qt.AlignCenter) + self.layout.addWidget(self.dror_sr_min_title, self.current_row, 0) + + self.dror_sr_min_label = QLabel(f'sr_min = {self.dror_sr_min}cm') + self.dror_sr_min_label.setAlignment(Qt.AlignLeft) + self.layout.addWidget(self.dror_sr_min_label, self.current_row, 2) + + self.dror_sr_min_slider = QSlider(Qt.Horizontal) + self.dror_sr_min_slider.setMinimum(0) + self.dror_sr_min_slider.setMaximum(100) + self.dror_sr_min_slider.setValue(self.dror_sr_min) + self.dror_sr_min_slider.setTickPosition(QSlider.TicksBelow) + self.dror_sr_min_slider.setTickInterval(10) + self.dror_sr_min_slider.setEnabled(False) + + self.layout.addWidget(self.dror_sr_min_slider, self.current_row, 1) + self.dror_sr_min_slider.valueChanged.connect(self.dror_change) + + self.toggle_snow_btn = QPushButton("apply snow") + self.toggle_snow_btn.setEnabled(False) + self.toggle_snow_btn.clicked.connect(self.toggle_snow) + self.layout.addWidget(self.toggle_snow_btn, self.current_row, 4) + + self.current_row += 1 + + self.layout.addWidget(QHLine(), self.current_row, 0, 1, self.num_columns) + + self.current_row += 1 + + ######## + # SNOW # + ######## + + self.lisa = None + self.tmp_value = None + + self.fixed_seed = True + self.apply_wet = False + self.apply_snow = False + self.apply_lisa = False + + self.mode = 'gunn' + self.rain_rate = 71 # (mm/hr) + self.wavelength = 905 # (nm) + self.r_min = 0.9 # (m) + self.r_min_scale = 10 + self.r_max = 120 # (m) + self.beam_divergence = 0.003 # (rad) + self.beam_divergence_scale = 10000 + self.min_diameter = 0.05 # (mm) + self.min_diameter_scale = 100 + self.range_accuracy = 0.09 # (m) + self.range_accuracy_scale = 100 + self.snowfall_rate = '0.5' + self.terminal_velocity = '0.2' + + self.rain_rate_title = QLabel('rain rate') + self.rain_rate_title.setAlignment(Qt.AlignCenter) + self.layout.addWidget(self.rain_rate_title, self.current_row, 0) + + self.rain_rate_label = QLabel(f'Rr = {self.rain_rate}mm/hr') + self.rain_rate_label.setAlignment(Qt.AlignLeft) + self.layout.addWidget(self.rain_rate_label, self.current_row, 2) + + self.rain_rate_slider = QSlider(Qt.Horizontal) + self.rain_rate_slider.setMinimum(1) + self.rain_rate_slider.setMaximum(200) + self.rain_rate_slider.setValue(self.rain_rate) + self.rain_rate_slider.setTickPosition(QSlider.TicksBelow) + self.rain_rate_slider.setTickInterval(50) + self.rain_rate_slider.setEnabled(False) + + self.layout.addWidget(self.rain_rate_slider, self.current_row, 1) + self.rain_rate_slider.valueChanged.connect(self.snowfall_change) + + self.lisa_alpha_label = QLabel('') + self.lisa_alpha_label.setAlignment(Qt.AlignCenter) + self.layout.addWidget(self.lisa_alpha_label, self.current_row, 3, 1, 2) + + self.current_row += 1 + + self.wavelength_title = QLabel('wavelength') + self.wavelength_title.setAlignment(Qt.AlignCenter) + self.layout.addWidget(self.wavelength_title, self.current_row, 0) + + self.wavelength_label = QLabel(f'\u03BB = {self.wavelength}nm') + self.wavelength_label.setAlignment(Qt.AlignLeft) + self.layout.addWidget(self.wavelength_label, self.current_row, 2) + + self.wavelength_slider = QSlider(Qt.Horizontal) + self.wavelength_slider.setMinimum(905) + self.wavelength_slider.setMaximum(1550) + self.wavelength_slider.setValue(self.wavelength) + self.wavelength_slider.setTickPosition(QSlider.TicksBelow) + self.wavelength_slider.setTickInterval(15) + self.wavelength_slider.setEnabled(False) + + self.layout.addWidget(self.wavelength_slider, self.current_row, 1) + self.wavelength_slider.valueChanged.connect(self.snowfall_change) + + self.cb_lisa = QComboBox() + self.cb_lisa.addItems(['gunn', 'sekhon', 'rain', + 'chu_hogg_fog', 'strong_advection_fog', 'moderate_advection_fog', + 'coast_haze', 'continental_haze', 'moderate_spray', 'strong_spray', 'goodin et al.']) + self.cb_lisa.currentIndexChanged.connect(self.snowfall_change) + self.cb_lisa.setEnabled(False) + self.layout.addWidget(self.cb_lisa, self.current_row, 3, 1, 2) + + self.current_row += 1 + + self.r_min_title = QLabel('r_min') + self.r_min_title.setAlignment(Qt.AlignCenter) + self.layout.addWidget(self.r_min_title, self.current_row, 0) + + self.r_min_label = QLabel(f'r_min = {self.r_min}m') + self.r_min_label.setAlignment(Qt.AlignLeft) + self.layout.addWidget(self.r_min_label, self.current_row, 2) + + self.r_min_slider = QSlider(Qt.Horizontal) + self.r_min_slider.setMinimum(1 * self.r_min_scale) + self.r_min_slider.setMaximum(10 * self.r_min_scale) + self.r_min_slider.setValue(int(self.r_min * self.r_min_scale)) + self.r_min_slider.setTickPosition(QSlider.TicksBelow) + self.r_min_slider.setTickInterval(5) + self.r_min_slider.setEnabled(False) + + self.layout.addWidget(self.r_min_slider, self.current_row, 1) + self.r_min_slider.valueChanged.connect(self.snowfall_change) + + self.toggle_seed_btn = QPushButton('seed fixed' if self.fixed_seed else 'seed not fixed') + self.toggle_seed_btn.setEnabled(False) + self.toggle_seed_btn.clicked.connect(self.toggle_seed) + self.layout.addWidget(self.toggle_seed_btn, self.current_row, 3) + + self.toggle_lisa_btn = QPushButton("apply LISA") + self.toggle_lisa_btn.setEnabled(False) + self.toggle_lisa_btn.clicked.connect(self.toggle_lisa) + self.layout.addWidget(self.toggle_lisa_btn, self.current_row, 4) + + self.current_row += 1 + + self.r_max_title = QLabel('r_max') + self.r_max_title.setAlignment(Qt.AlignCenter) + self.layout.addWidget(self.r_max_title, self.current_row, 0) + + self.r_max_label = QLabel(f'r_max = {self.r_max}m') + self.r_max_label.setAlignment(Qt.AlignLeft) + self.layout.addWidget(self.r_max_label, self.current_row, 2) + + self.r_max_slider = QSlider(Qt.Horizontal) + self.r_max_slider.setMinimum(80) + self.r_max_slider.setMaximum(200) + self.r_max_slider.setValue(self.r_max) + self.r_max_slider.setTickPosition(QSlider.TicksBelow) + self.r_max_slider.setTickInterval(10) + self.r_max_slider.setEnabled(False) + + self.layout.addWidget(self.r_max_slider, self.current_row, 1) + self.r_max_slider.valueChanged.connect(self.snowfall_change) + + self.num_unchanged_label = QLabel('') + self.num_unchanged_label.setAlignment(Qt.AlignLeft) + self.layout.addWidget(self.num_unchanged_label, self.current_row, 3, 1, 2) + + self.current_row += 1 + + self.beam_divergence_title = QLabel('beam divergence') + self.beam_divergence_title.setAlignment(Qt.AlignCenter) + self.layout.addWidget(self.beam_divergence_title, self.current_row, 0) + + self.beam_divergence_label = QLabel(f'\u2221 = {round(self.beam_divergence * 180 / np.pi, 2)}Β°') + self.beam_divergence_label.setAlignment(Qt.AlignLeft) + self.layout.addWidget(self.beam_divergence_label, self.current_row, 2) + + self.beam_divergence_slider = QSlider(Qt.Horizontal) + self.beam_divergence_slider.setMinimum(int(0.01 / 180 * np.pi * self.beam_divergence_scale)) + self.beam_divergence_slider.setMaximum(int(1 / 180 * np.pi * self.beam_divergence_scale)) + self.beam_divergence_slider.setValue(int(self.beam_divergence * self.beam_divergence_scale)) + self.beam_divergence_slider.setTickPosition(QSlider.TicksBelow) + self.beam_divergence_slider.setTickInterval(10) + self.beam_divergence_slider.setEnabled(False) + + self.layout.addWidget(self.beam_divergence_slider, self.current_row, 1) + self.beam_divergence_slider.valueChanged.connect(self.snowfall_change) + + self.num_removed_label = QLabel('') + self.num_removed_label.setAlignment(Qt.AlignLeft) + self.layout.addWidget(self.num_removed_label, self.current_row, 3, 1, 2) + + self.current_row += 1 + + self.min_diameter_title = QLabel('min_diameter') + self.min_diameter_title.setAlignment(Qt.AlignCenter) + self.layout.addWidget(self.min_diameter_title, self.current_row, 0) + + self.min_diameter_label = QLabel(f'\u2300 = {self.min_diameter}mm') + self.min_diameter_label.setAlignment(Qt.AlignLeft) + self.layout.addWidget(self.min_diameter_label, self.current_row, 2) + + self.min_diameter_slider = QSlider(Qt.Horizontal) + self.min_diameter_slider.setMinimum(int(0.01 * self.min_diameter_scale)) + self.min_diameter_slider.setMaximum(int(5 * self.min_diameter_scale)) + self.min_diameter_slider.setValue(int(self.min_diameter * self.min_diameter_scale)) + self.min_diameter_slider.setTickPosition(QSlider.TicksBelow) + self.min_diameter_slider.setTickInterval(100) + self.min_diameter_slider.setEnabled(False) + + self.layout.addWidget(self.min_diameter_slider, self.current_row, 1) + self.min_diameter_slider.valueChanged.connect(self.snowfall_change) + + self.num_scattered_label = QLabel('') + self.num_scattered_label.setAlignment(Qt.AlignLeft) + self.layout.addWidget(self.num_scattered_label, self.current_row, 3, 1, 2) + + self.current_row += 1 + + self.range_accuracy_title = QLabel('range accuracy') + self.range_accuracy_title.setAlignment(Qt.AlignCenter) + self.layout.addWidget(self.range_accuracy_title, self.current_row, 0) + + self.range_accuracy_label = QLabel(f'~ = {int(self.range_accuracy * 100)}cm') + self.range_accuracy_label.setAlignment(Qt.AlignLeft) + self.layout.addWidget(self.range_accuracy_label, self.current_row, 2) + + self.range_accuracy_slider = QSlider(Qt.Horizontal) + self.range_accuracy_slider.setMinimum(int(0.01 * self.range_accuracy_scale)) + self.range_accuracy_slider.setMaximum(int(0.1 * self.range_accuracy_scale)) + self.range_accuracy_slider.setValue(int(self.range_accuracy * self.range_accuracy_scale)) + self.range_accuracy_slider.setTickPosition(QSlider.TicksBelow) + self.range_accuracy_slider.setTickInterval(1) + self.range_accuracy_slider.setEnabled(False) + + self.layout.addWidget(self.range_accuracy_slider, self.current_row, 1) + self.range_accuracy_slider.valueChanged.connect(self.snowfall_change) + + self.num_attenuated_label = QLabel('') + self.num_attenuated_label.setAlignment(Qt.AlignLeft) + self.layout.addWidget(self.num_attenuated_label, self.current_row, 3, 1, 2) + + self.current_row += 1 + + self.layout.addWidget(QHLine(), self.current_row, 0, 1, self.num_columns) + + self.current_row += 1 + + self.water_height = 0.0008 + self.water_height_scale = 10000 + self.pavement_height = 0.001 + self.pavement_height_scale = 100000 + self.noise_floor = 0.7 + self.noise_floor_scale = 100 + self.power_factor = 15 + self.flat_earth = False + self.estimation = 'linear' + + self.water_height_title = QLabel('water height') + self.water_height_title.setAlignment(Qt.AlignCenter) + self.layout.addWidget(self.water_height_title, self.current_row, 0) + + self.water_height_label = QLabel(f'h_w = {round(self.water_height * 1000, 1)}mm') + self.water_height_label.setAlignment(Qt.AlignLeft) + self.layout.addWidget(self.water_height_label, self.current_row, 2) + + self.water_height_slider = QSlider(Qt.Horizontal) + self.water_height_slider.setMinimum(0) + self.water_height_slider.setMaximum(int(0.01 * self.water_height_scale)) + self.water_height_slider.setValue(int(self.water_height * self.water_height_scale)) + self.water_height_slider.setTickPosition(QSlider.TicksBelow) + self.water_height_slider.setTickInterval(10) + self.water_height_slider.setEnabled(False) + + self.layout.addWidget(self.water_height_slider, self.current_row, 1) + self.water_height_slider.valueChanged.connect(self.snowfall_change) + + self.estimation_title = QLabel('estimation') + self.estimation_title.setAlignment(Qt.AlignCenter) + self.layout.addWidget(self.estimation_title, self.current_row, 4) + + self.current_row += 1 + + self.pavement_height_title = QLabel('pavement height') + self.pavement_height_title.setAlignment(Qt.AlignCenter) + self.layout.addWidget(self.pavement_height_title, self.current_row, 0) + + self.pavement_height_label = QLabel(f'h_p = {round(self.pavement_height * 1000, 2)}mm') + self.pavement_height_label.setAlignment(Qt.AlignLeft) + self.layout.addWidget(self.pavement_height_label, self.current_row, 2) + + self.pavement_height_slider = QSlider(Qt.Horizontal) + self.pavement_height_slider.setMinimum(int(0.0001 * self.pavement_height_scale)) + self.pavement_height_slider.setMaximum(int(0.01 * self.pavement_height_scale)) + self.pavement_height_slider.setValue(round(self.pavement_height * self.pavement_height_scale)) + self.pavement_height_slider.setTickPosition(QSlider.TicksBelow) + self.pavement_height_slider.setTickInterval(100) + self.pavement_height_slider.setEnabled(False) + + self.layout.addWidget(self.pavement_height_slider, self.current_row, 1) + self.pavement_height_slider.valueChanged.connect(self.snowfall_change) + + self.cb_estimation = QComboBox() + self.cb_estimation.addItems(['linear', 'poly']) + self.cb_estimation.currentIndexChanged.connect(self.snowfall_change) + self.cb_estimation.setEnabled(False) + self.layout.addWidget(self.cb_estimation, self.current_row, 4) + + self.current_row += 1 + + self.noise_floor_title = QLabel('noise floor') + self.noise_floor_title.setAlignment(Qt.AlignCenter) + self.layout.addWidget(self.noise_floor_title, self.current_row, 0) + + self.noise_floor_label = QLabel(f'n_f = {int(self.noise_floor * 100)}%') + self.noise_floor_label.setAlignment(Qt.AlignLeft) + self.layout.addWidget(self.noise_floor_label, self.current_row, 2) + + self.noise_floor_slider = QSlider(Qt.Horizontal) + self.noise_floor_slider.setMinimum(0) + self.noise_floor_slider.setMaximum(100) + self.noise_floor_slider.setValue(round(self.noise_floor * self.noise_floor_scale)) + self.noise_floor_slider.setTickPosition(QSlider.TicksBelow) + self.noise_floor_slider.setTickInterval(10) + self.noise_floor_slider.setEnabled(False) + + self.layout.addWidget(self.noise_floor_slider, self.current_row, 1) + self.noise_floor_slider.valueChanged.connect(self.snowfall_change) + + self.flat_earth_btn = QPushButton('flat earch' if self.flat_earth else 'no flat earth') + self.flat_earth_btn.setEnabled(False) + self.flat_earth_btn.clicked.connect(self.toggle_flat_earth) + self.layout.addWidget(self.flat_earth_btn, self.current_row, 3) + + self.toggle_wet_btn = QPushButton("apply wet") + self.toggle_wet_btn.setEnabled(False) + self.toggle_wet_btn.clicked.connect(self.toggle_wet) + self.layout.addWidget(self.toggle_wet_btn, self.current_row, 4) + + self.current_row += 1 + + self.power_factor_title = QLabel('power factor') + self.power_factor_title.setAlignment(Qt.AlignCenter) + self.layout.addWidget(self.power_factor_title, self.current_row, 0) + + self.power_factor_label = QLabel(f'p_f = {self.power_factor}m') + self.power_factor_label.setAlignment(Qt.AlignLeft) + self.layout.addWidget(self.power_factor_label, self.current_row, 2) + + self.power_factor_slider = QSlider(Qt.Horizontal) + self.power_factor_slider.setMinimum(10) + self.power_factor_slider.setMaximum(20) + self.power_factor_slider.setValue(self.power_factor) + self.power_factor_slider.setTickPosition(QSlider.TicksBelow) + self.power_factor_slider.setTickInterval(1) + self.power_factor_slider.setEnabled(False) + + self.layout.addWidget(self.power_factor_slider, self.current_row, 1) + self.power_factor_slider.valueChanged.connect(self.snowfall_change) + + self.current_row += 1 + + self.snowfall_change() + + def closeEvent(self, event: QCloseEvent) -> None: + + close_all_windows() + + def add_temporal_cloud(self, index: int): + + assert index in [-6, -5, -4, -3, -2, -1, 1, 2, 3, 4] + + filename = self.file_name + folder = filename.split('/')[-2] + + filename = filename.replace('SeeingThroughFog', f'SeeingThroughFog/temporal_data') + filename = filename.replace(folder, f'lidar_{self.sensor}_{self.signal}_history_{index}') + filename = filename.replace('vlp32', 'vlp32c') + + pc = np.fromfile(filename, dtype=self.d_type) + pc = pc.reshape((-1, self.num_features)) + + pc[:, 3] = np.round(pc[:, 3] * self.intensity_multiplier) + + # filter camera FOV + if self.show_fov_only: + calib = get_calib(self.sensor) + + pts_rect = calib.lidar_to_rect(pc[:, 0:3]) + fov_flag = get_fov_flag(pts_rect, (1024, 1920), calib) + + pc = pc[fov_flag] + + min_dist_mask = np.linalg.norm(pc[:, 0:3], axis=1) > MIN_DIST + pc = pc[min_dist_mask, :] + + self.temporal_pcs.append(copy.deepcopy(pc)) + + max_dist_mask = np.linalg.norm(pc[:, 0:3], axis=1) < self.max_distance + pc = pc[max_dist_mask, :] + + min_height_mask = pc[:, 2] > (self.min_height / 100) # in m + pc = pc[min_height_mask, :] + + colors = self.get_colors(pc) + + mesh = gl.GLScatterPlotItem(pos=np.asarray(pc[:, 0:3]), size=self.point_size, color=colors, + glOptions='translucent') + self.temporal_meshes.append(mesh) + + def update_available_temporal_clouds(self, filename: str): + + if self.dataset == 'DENSE': + + # frame = Path(filename).stem + # sensor = 'vlp32c' if self.sensor == 'vlp32' else 'hdl64' + # signal = self.signal + # + # future = self.temporal_info[frame][sensor][signal]['future'] + # past = self.temporal_info[frame][sensor][signal]['past'] + + past = [0, 0, 0, 0, 0, 0] + future = [0, 0, 0, 0] + + self.minus1.setEnabled(bool(past[0])) + self.minus2.setEnabled(bool(past[1])) + self.minus3.setEnabled(bool(past[2])) + self.minus4.setEnabled(bool(past[3])) + self.minus5.setEnabled(bool(past[4])) + self.minus6.setEnabled(bool(past[5])) + + self.plus1.setEnabled(bool(future[0])) + self.plus2.setEnabled(bool(future[1])) + self.plus3.setEnabled(bool(future[2])) + self.plus4.setEnabled(bool(future[3])) + + # uncheck unavailable temporal frames + + if not past[0]: + self.minus1.setChecked(False) + + if not past[1]: + self.minus2.setChecked(False) + + if not past[2]: + self.minus3.setChecked(False) + + if not past[3]: + self.minus4.setChecked(False) + + if not past[4]: + self.minus5.setChecked(False) + + if not past[5]: + self.minus6.setChecked(False) + + if not future[0]: + self.plus1.setChecked(False) + + if not future[1]: + self.plus2.setChecked(False) + + if not future[2]: + self.plus3.setChecked(False) + + if not future[3]: + self.plus4.setChecked(False) + + else: + + self.minus1.setEnabled(False) + self.minus2.setEnabled(False) + self.minus3.setEnabled(False) + self.minus4.setEnabled(False) + self.minus5.setEnabled(False) + self.minus6.setEnabled(False) + + self.minus1.setChecked(False) + self.minus2.setChecked(False) + self.minus3.setChecked(False) + self.minus4.setChecked(False) + self.minus5.setChecked(False) + self.minus6.setChecked(False) + + self.plus1.setEnabled(False) + self.plus2.setEnabled(False) + self.plus3.setEnabled(False) + self.plus4.setEnabled(False) + + self.plus1.setChecked(False) + self.plus2.setChecked(False) + self.plus3.setChecked(False) + self.plus4.setChecked(False) + + def update_temporal_clouds(self) -> None: + + if self.current_mesh: + try: + self.viewer.removeItem(self.current_mesh) + except ValueError: + pass + + if self.zero.isChecked(): + self.viewer.addItem(self.current_mesh) + + for mesh in self.temporal_meshes: + try: + self.viewer.removeItem(mesh) + except ValueError: + pass + + self.temporal_pcs = [] + self.temporal_meshes = [] + + if self.show_temp: + + if self.minus6.isChecked(): + self.add_temporal_cloud(-6) + + if self.minus5.isChecked(): + self.add_temporal_cloud(-5) + + if self.minus4.isChecked(): + self.add_temporal_cloud(-4) + + if self.minus3.isChecked(): + self.add_temporal_cloud(-3) + + if self.minus2.isChecked(): + self.add_temporal_cloud(-2) + + if self.minus1.isChecked(): + self.add_temporal_cloud(-1) + + if self.plus1.isChecked(): + self.add_temporal_cloud(1) + + if self.plus2.isChecked(): + self.add_temporal_cloud(2) + + if self.plus3.isChecked(): + self.add_temporal_cloud(3) + + if self.plus4.isChecked(): + self.add_temporal_cloud(4) + + for mesh in self.temporal_meshes: + self.viewer.addItem(mesh) + + def load_results(self) -> None: + + exp_dir = EXPERIMENTS_ROOT / self.experiment_path_box.text() + + test_folders = [x[0] for x in os.walk(exp_dir) if 'epoch' in x[0] and 'test' in x[0]] + + self.result_dict = {} + + for test_folder in test_folders: + key = test_folder.split('/')[-1] + + pkl_path = Path(test_folder) / 'result.pkl' + + with open(pkl_path, 'rb') as f: + list_to_be_sorted = pkl.load(f) + + self.result_dict[key] = sorted(list_to_be_sorted, key=lambda d: d['frame_id']) + + if self.result_dict is not None: + self.visualize_predictions_btn.setEnabled(True) + self.prediction_threshold_slider.setEnabled(True) + + def visualize_predictions(self) -> None: + + split = self.cb_splits.currentText() + + if self.cb_sensors.currentText() == 'vlp32': + split = f'{split}_vlp32' + + if 'test' in split: + split = split.replace('_dror', '') + + pred_dict = self.result_dict[split][self.index] + + assert self.file_name.split('/')[-1].split('.')[0] == pred_dict['frame_id'], f'frame missmatch ' \ + f"{self.file_name.split('/')[-1].split('.')[0]} != {pred_dict['frame_id']}" + + lookup = {'Car': 0, + 'Pedestrian': 1, + 'Cyclist': 2} + + predictions = np.zeros((pred_dict['boxes_lidar'].shape[0], 9)) + predictions[:, 0:-2] = pred_dict['boxes_lidar'] + predictions[:, 7] = np.array([lookup[name] for name in pred_dict['name']]) + predictions[:, 8] = pred_dict['score'] + + self.predictions = predictions + + self.add_predictions() + + def add_predictions(self) -> None: + + self.show_predictions = True + self.visualize_predictions_btn.setText('hide predictions') + self.visualize_predictions_btn.setEnabled(True) + self.prediction_threshold_slider.setEnabled(True) + + for prediction in self.predictions: + + x, y, z, w, l, h, rotation, category, score = prediction + + dist = np.sqrt(x ** 2 + y ** 2 + z ** 2) + + rotation = np.rad2deg(rotation) + 90 + + try: + color = DET_COLORS[int(category)] + except IndexError: + if CAMERA_READY: + color = BLACK + else: + color = WHITE + + try: + box = gl.GLBoxItem(QtGui.QVector3D(1, 1, 1), color=color, line_width=self.line_width) + except TypeError: + box = gl.GLBoxItem(QtGui.QVector3D(1, 1, 1), color=color) + + box.setSize(l, w, h) + + box.translate(-l / 2, -w / 2, -h / 2) + box.rotate(angle=rotation, x=0, y=0, z=1) + box.translate(x, y, z) + + self.prediction_boxes.append((dist, score, box)) + + for dist, score, box in self.prediction_boxes: + if score * 100 > self.prediction_threshold: + self.viewer.addItem(box) + + + def populate_dense_splits(self) -> List[str]: + + split_folder = Path(__file__).parent.absolute() / 'lib' / 'LiDAR_fog_sim' / 'SeeingThroughFog' / 'splits' + + splits = [] + + for file in os.listdir(split_folder): + + if file.endswith('.txt') and 'FOVstrongest3000' in file \ + and '0.35' not in file \ + and '_day' not in file \ + and 'vlp32' not in file \ + and '_night' not in file \ + and 'test_wet' not in file \ + and 'FOVlast3000' not in file: + string = file.replace('.txt', '') + string = string.replace('_alpha_0.45', '') + string = string.replace('_FOVstrongest3000', '') + + splits.append(string) + + self.dense_split_paths.append(split_folder / file) + + self.dense_split_paths = sorted(self.dense_split_paths) + + return sorted(splits) + + def split_selection_change(self) -> None: + + self.reset_dense_features() + + self.file_list = [] + + sensor_flag = 'vlp32' if self.sensor == 'vlp32' else '' + + split = self.cb_splits.currentText() + + if 'dror' in split: + split = split.replace('dror', f'{sensor_flag}_FOV{self.signal}3000_dror_alpha_0.45') + else: + split = f'{split}_{sensor_flag}_FOV{self.signal}3000' + + split = split.replace('__', '_') + + # open file and read the content in a list + with open(f'lib/LiDAR_fog_sim/SeeingThroughFog/splits/{split}.txt', 'r') as filehandle: + for line in filehandle: + # remove linebreak which is the last character of the string + file_path = Path(DENSE) / f'{line[:-1].replace(",", "_")}.bin' + + # add item to the list + self.file_list.append(str(file_path)) + + self.file_list = sorted(self.file_list) + + self.index = 0 + self.set_dense() + self.show_pointcloud(self.file_list[self.index]) + + def sensor_selection_change(self) -> None: + + # reset live inference + self.config = None + self.model = None + + self.sensor = self.cb_sensors.currentText() + self.show_pointcloud(self.file_list[self.index], force_reload=True) + + def signal_selection_change(self) -> None: + + # reset live inference + self.config = None + self.model = None + + self.signal = self.cb_signals.currentText() + self.show_pointcloud(self.file_list[self.index], force_reload=True) + + def update_labels(self) -> None: + + self.p.alpha = self.alpha_slider.value() / self.p.alpha_scale + self.alpha_label.setText(f"\u03B1 = {self.p.alpha}") + + self.p.mor = np.log(20) / self.p.alpha + self.mor_label.setText(f'meteorological optical range (MOR) = {round(self.p.mor, 2)}m') + + self.p.beta_scale = 1000 * self.p.mor + self.p.beta = self.beta_slider.value() / self.p.beta_scale + self.beta_label.setText(f"\u03B2 = {round(self.p.beta * self.p.mor, 3)} / MOR") + + self.p.gamma = self.gamma_slider.value() / self.p.gamma_scale + self.gamma_label.setText(f"\u0393 = {self.p.gamma}") + self.p.beta_0 = self.p.gamma / np.pi + + if self.file_list: + self.show_pointcloud(self.file_list[self.index]) + + def reset_dense_features(self) -> None: + + self.boxes = {} + + self.alpha_slider.setEnabled(False) + self.beta_slider.setEnabled(False) + self.gamma_slider.setEnabled(False) + + self.simulated_fog = False + self.simulated_fog_dense = False + self.toggle_simulated_fog_btn.setText('add our fog') + self.toggle_simulated_fog_dense_btn.setText('add STF fog') + + self.toggle_temp_btn.setEnabled(False) + + self.minus1.setEnabled(False) + self.minus2.setEnabled(False) + self.minus3.setEnabled(False) + self.minus4.setEnabled(False) + self.minus5.setEnabled(False) + self.minus6.setEnabled(False) + + self.plus1.setEnabled(False) + self.plus2.setEnabled(False) + self.plus3.setEnabled(False) + self.plus4.setEnabled(False) + + def reset_custom_values(self) -> None: + + self.sensor = 'hdl64' + self.signal = 'strongest' + self.toggle_img_btn.setEnabled(False) + self.show_fov_only_btn.setEnabled(False) + + self.min_value = 0 + self.max_value = 63 + self.num_features = 5 + self.dataset = None + self.success = False + self.d_type = np.float32 + self.intensity_multiplier = 1 + self.color_name = self.color_dict[self.color_feature] + + def dror_change(self) -> None: + + self.dror_alpha = self.dror_alpha_slider.value() / self.dror_alpha_scale + self.dror_alpha_label.setText(f"\u03B1 = {self.dror_alpha}") + + self.dror_beta = self.dror_beta_slider.value() + self.dror_beta_label.setText(f"\u03B2 = {self.dror_beta}") + + self.dror_k_min = self.dror_k_min_slider.value() + self.dror_k_min_label.setText(f"k_min = {self.dror_k_min}") + + self.dror_sr_min = self.dror_sr_min_slider.value() + self.dror_sr_min_label.setText(f"sr_min = {self.dror_sr_min}cm") + + if self.file_list: + self.show_pointcloud(self.file_list[self.index], force_reload=True) + + def snowfall_change(self) -> None: + + ######## + # LISA # + ######## + + self.mode = self.cb_lisa.currentText() + + self.rain_rate = self.rain_rate_slider.value() + self.rain_rate_label.setText(f"Rr = {self.rain_rate}mm/hr") + + self.wavelength = self.wavelength_slider.value() + self.wavelength_label.setText(f"\u03BB = {self.wavelength}nm") + + self.r_min = self.r_min_slider.value() / self.r_min_scale + self.r_min_label.setText(f'r_min = {self.r_min}m') + + self.r_max = self.r_max_slider.value() + self.r_max_label.setText(f'r_max = {self.r_max}m') + + self.beam_divergence = self.beam_divergence_slider.value() / self.beam_divergence_scale + self.beam_divergence_label.setText(f'\u2221 = {round(self.beam_divergence * 180 / np.pi, 2)}Β°') + + self.min_diameter = self.min_diameter_slider.value() / self.min_diameter_scale + self.min_diameter_label.setText(f'\u2300 = {self.min_diameter}mm') + + self.range_accuracy = self.range_accuracy_slider.value() / self.range_accuracy_scale + self.range_accuracy_label.setText(f'~ = {int(self.range_accuracy * 100)}cm') + + self.lisa = LISA(wavelength=self.wavelength, mode=self.mode, r_min=self.r_min, r_max=self.r_max, + beam_divergence=self.beam_divergence, min_diameter=self.min_diameter, + range_accuracy=self.range_accuracy, signal=self.signal, show_progressbar=True) + + ######## + # ours # + ######## + + self.water_height = self.water_height_slider.value() / self.water_height_scale + self.water_height_label.setText(f'h_w = {round(self.water_height * 1000, 1)}mm') + + self.pavement_height = self.pavement_height_slider.value() / self.pavement_height_scale + self.pavement_height_label.setText(f'h_p = {round(self.pavement_height * 1000, 2)}mm') + + self.noise_floor = self.noise_floor_slider.value() / self.noise_floor_scale + self.noise_floor_label.setText(f'n_f = {int(self.noise_floor * 100)}%') + + self.power_factor = self.power_factor_slider.value() + self.power_factor_label.setText(f'p_f = {self.power_factor}') + + self.estimation = self.cb_estimation.currentText() + self.snowfall_rate = self.cb_snowfall_rate.currentText() + self.terminal_velocity = self.cb_velocity.currentText() + + if self.file_list: + self.show_pointcloud(self.file_list[self.index], force_reload=True) + + def prediction_threshold_slider_change(self) -> None: + + self.prediction_threshold = self.prediction_threshold_slider.value() + self.prediction_threshold_label.setText(str(self.prediction_threshold)) + + if self.file_list: + self.show_pointcloud(self.file_list[self.index]) + + def max_distance_slider_change(self) -> None: + + self.max_distance = self.max_distance_slider.value() + self.max_distance_label.setText(f'{self.max_distance} m') + + if self.file_list: + self.show_pointcloud(self.file_list[self.index]) + + def min_height_slider_change(self) -> None: + + self.min_height = self.min_height_slider.value() + self.min_height_label.setText(f'{self.min_height} cm') + + if self.file_list: + self.show_pointcloud(self.file_list[self.index]) + + def color_slider_change(self) -> None: + + self.color_feature = self.color_slider.value() + + self.color_name = self.color_dict[self.color_feature] + self.color_label.setText(self.color_name) + + if self.current_mesh: + + if Path(self.file_name).suffix == '.pickle': + self.show_pcdet_dict(self.file_name) + else: + self.show_pointcloud(self.file_name, needs_reload=False) + + def check_index_overflow(self) -> None: + + if self.index == -1: + self.index = len(self.file_list) - 1 + + if self.index >= len(self.file_list): + self.index = 0 + + def decrement_index(self) -> None: + + self.predictions = None + self.prediction_boxes = [] + + if self.index != -1: + + self.index -= 1 + self.check_index_overflow() + + if Path(self.file_list[self.index]).suffix == ".pickle": + self.show_pcdet_dict(self.file_list[self.index]) + else: + self.show_pointcloud(self.file_list[self.index]) + + def increment_index(self) -> None: + + self.predictions = None + self.prediction_boxes = [] + + if SAVE_IMAGES and ROTATE: + self.viewer.opts['azimuth'] = (self.viewer.opts['azimuth'] + ROTATE) % 360 # rotate camera each frame + + if self.index != -1: + + self.index += 1 + self.check_index_overflow() + + if Path(self.file_list[self.index]).suffix == ".pickle": + self.show_pcdet_dict(self.file_list[self.index]) + else: + self.show_pointcloud(self.file_list[self.index]) + + def set_pc_det(self, before: bool) -> None: + + self.min_value = 0 + self.max_value = 63 + self.extension = 'pickle' + self.d_type = np.float32 + self.intensity_multiplier = 1 + + if before: + + self.dataset = 'before' + self.num_features = 5 + self.color_dict[6] = 'channel' + + else: + + self.dataset = 'after' + self.num_features = 4 + self.color_dict[6] = 'not available' + + def set_kitti(self) -> None: + self.dataset = 'KITTI' + self.min_value = -1 + self.max_value = -1 + self.num_features = 4 + self.extension = 'bin' + self.d_type = np.float32 + self.show_fov_only = False + self.intensity_multiplier = 255 + self.color_dict[6] = 'not available' + + def load_kitti(self) -> None: + + self.reset_dense_features() + self.image_window.hide() + self.viewer.setCameraPosition(pos=QVector3D(10, 0, 0), distance=20, elevation=15, azimuth=180) + + self.file_list = [] + + # open file and read the content in a list + with open('lib/LiDAR_fog_sim/file_lists/KITTI.txt', 'r') as filehandle: + for line in filehandle: + # remove linebreak which is the last character of the string + file_path = Path(KITTI) / line[:-1] + + # add item to the list + self.file_list.append(str(file_path)) + + self.index = 0 + self.set_kitti() + self.show_pointcloud(self.file_list[self.index]) + + def set_audi(self) -> None: + + self.dataset = 'A2D2' + self.min_value = 0 + self.max_value = 4 + self.num_features = 5 + self.extension = 'npz' + self.d_type = np.float32 + self.show_fov_only = False + self.intensity_multiplier = 1 + self.color_dict[6] = 'lidar_id' + + def load_audi(self) -> None: + + self.reset_dense_features() + self.image_window.hide() + + self.viewer.setCameraPosition(pos=QVector3D(45, 0, 0), distance=95, elevation=15, azimuth=180) + + self.file_list = [] + + # open file and read the content in a list + with open('lib/LiDAR_fog_sim/file_lists/A2D2.txt', 'r') as filehandle: + for line in filehandle: + # remove linebreak which is the last character of the string + file_path = Path(AUDI) / line[:-1] + + # add item to the list + self.file_list.append(str(file_path)) + + self.index = 0 + self.set_audi() + self.show_pointcloud(self.file_list[self.index]) + + def set_honda(self) -> None: + + self.dataset = 'Honda3D' + self.min_value = 0 + self.max_value = 63 + self.num_features = 5 + self.extension = 'ply' + self.d_type = np.float32 + self.show_fov_only = False + self.intensity_multiplier = 1 + self.color_dict[6] = 'channel' + + def load_honda(self) -> None: + + self.reset_dense_features() + self.image_window.hide() + + self.viewer.setCameraPosition(pos=QVector3D(10, 0, 0), distance=20, elevation=15, azimuth=180) + + self.file_list = [] + + # open file and read the content in a list + with open('lib/LiDAR_fog_sim/file_lists/Honda3D.txt', 'r') as filehandle: + for line in filehandle: + # remove linebreak which is the last character of the string + file_path = Path(HONDA) / line[:-1] + + # add item to the list + self.file_list.append(str(file_path)) + + self.index = 0 + self.set_honda() + self.show_pointcloud(self.file_list[self.index]) + + def set_argo(self) -> None: + + self.dataset = 'Argoverse' + self.min_value = 0 + self.max_value = 31 + self.num_features = 5 + self.extension = 'ply' + self.d_type = np.float32 + self.show_fov_only = False + self.intensity_multiplier = 1 + self.color_dict[6] = 'channel' + + def load_argo(self) -> None: + + self.reset_dense_features() + self.image_window.hide() + + self.viewer.setCameraPosition(pos=QVector3D(10, 0, 0), distance=85, elevation=20, azimuth=180) + + self.file_list = [] + + # open file and read the content in a list + with open('lib/LiDAR_fog_sim/file_lists/Argoverse.txt', 'r') as filehandle: + for line in filehandle: + # remove linebreak which is the last character of the string + file_path = Path(ARGO) / line[:-1] + + # add item to the list + self.file_list.append(str(file_path)) + + self.index = 0 + self.set_argo() + self.show_pointcloud(self.file_list[self.index]) + + def set_dense(self) -> None: + + self.dataset = 'DENSE' + self.min_value = 0 + self.max_value = 63 + self.num_features = 5 + self.extension = 'bin' + self.d_type = np.float32 + self.intensity_multiplier = 1 + self.color_dict[6] = 'channel' + + self.toggle_img_btn.setEnabled(True) + self.show_fov_only_btn.setEnabled(True) + + self.toggle_temp_btn.setEnabled(True) + + self.sensor = self.cb_sensors.currentText() + self.signal = self.cb_signals.currentText() + + self.cb_splits.setEnabled(True) + self.cb_sensors.setEnabled(True) + self.cb_signals.setEnabled(True) + + def load_dense(self) -> None: + + self.reset_dense_features() + + if self.show_img: + self.image_window.show() + else: + self.image_window.hide() + + self.viewer.setCameraPosition(pos=QVector3D(15, 0, 0), distance=22.55, elevation=30, azimuth=180) + + self.file_list = [] + + # open file and read the content in a list + with open('lib/LiDAR_fog_sim/file_lists/DENSE.txt', 'r') as filehandle: + for line in filehandle: + # remove linebreak which is the last character of the string + file_path = Path(DENSE) / line[:-1] + + # add item to the list + self.file_list.append(str(file_path)) + + scene = '' + # scene = '2018-02-12_14-32-26_00450' # teaser scene + index = [idx for idx, s in enumerate(self.file_list) if scene in s][0] + + self.index = index if scene else 0 + self.set_dense() + self.cb_splits.setCurrentText('all') + self.show_pointcloud(self.file_list[self.index]) + + def set_nuscenes(self) -> None: + + self.dataset = 'nuScenes' + self.min_value = 0 + self.max_value = 31 + self.num_features = 5 + self.extension = 'bin' + self.intensity_multiplier = 1 + self.color_dict[6] = 'channel' + + def load_nuscenes(self) -> None: + + self.viewer.setCameraPosition(pos=QVector3D(2, 5, 0), distance=60, elevation=25, azimuth=270) + + self.reset_dense_features() + self.image_window.hide() + + self.file_list = [] + + with open('lib/LiDAR_fog_sim/file_lists/nuScenes.pkl', 'rb') as f: + file_list = pkl.load(f) + + for file in file_list: + self.file_list.append(str(NUSCENES) + file) + + self.index = 0 + self.set_nuscenes() + self.show_pointcloud(self.file_list[self.index]) + + def set_lyft(self) -> None: + + self.dataset = 'LyftL5' + self.min_value = 0 + self.max_value = 16 + self.num_features = 5 + self.extension = 'bin' + self.intensity_multiplier = 1 + self.color_dict[6] = 'channel' + + def load_lyft(self) -> None: + + self.reset_dense_features() + self.image_window.hide() + + self.viewer.setCameraPosition(pos=QVector3D(0, 0, 0), distance=80, elevation=20, azimuth=0) + + self.file_list = [] + + # open file and read the content in a list + with open('lib/LiDAR_fog_sim/file_lists/LyftL5.txt', 'r') as filehandle: + for line in filehandle: + # remove linebreak which is the last character of the string + file_path = Path(LYFT) / line[:-1] + + # add item to the list + self.file_list.append(str(file_path)) + + self.index = 0 + self.set_lyft() + self.show_pointcloud(self.file_list[self.index]) + + def set_waymo(self) -> None: + + self.dataset = 'WaymoOpenDataset' + self.min_value = -1 + self.max_value = -1 + self.num_features = 4 + self.extension = 'bin' + self.d_type = np.float32 + self.show_fov_only = False + self.intensity_multiplier = 255 + self.color_dict[6] = 'not available' + + def load_waymo(self) -> None: + + self.reset_dense_features() + self.image_window.hide() + + self.viewer.setCameraPosition(pos=QVector3D(5, 0, 0), distance=45, elevation=25, azimuth=180) + + self.file_list = [] + + # open file and read the content in a list + with open('lib/LiDAR_fog_sim/file_lists/WAYMO.txt', 'r') as filehandle: + for line in filehandle: + # remove linebreak which is the last character of the string + file_path = Path(WAYMO) / line[:-1] + + # add item to the list + self.file_list.append(str(file_path)) + + self.index = 0 + self.set_waymo() + self.show_pointcloud(self.file_list[self.index]) + + def set_panda(self) -> None: + + self.dataset = 'PandaSet' + self.min_value = 0 + self.max_value = 1 + self.num_features = 5 + self.extension = 'pkl.gz' + self.d_type = np.float32 + self.show_fov_only = False + self.intensity_multiplier = 1 + self.color_dict[6] = 'lidar_id' + + def load_panda(self) -> None: + + self.reset_dense_features() + self.image_window.hide() + + self.viewer.setCameraPosition(pos=QVector3D(20, 10, 0), distance=95, elevation=20, azimuth=222) + + self.file_list = [] + + # open file and read the content in a list + with open('lib/LiDAR_fog_sim/file_lists/PandaSet.txt', 'r') as filehandle: + for line in filehandle: + # remove linebreak which is the last character of the string + file_path = Path(PANDA) / line[:-1] + + # add item to the list + self.file_list.append(str(file_path)) + + self.index = 0 + self.set_panda() + self.show_pointcloud(self.file_list[self.index]) + + def set_apollo(self) -> None: + + self.dataset = 'Apollo' + self.min_value = -1 + self.max_value = -1 + self.num_features = 4 + self.extension = 'bin' + self.d_type = np.float32 + self.show_fov_only = False + self.intensity_multiplier = 255 + self.color_dict[6] = 'not available' + + def load_apollo(self) -> None: + + self.reset_dense_features() + self.image_window.hide() + + self.viewer.setCameraPosition(pos=QVector3D(10, 0, 0), distance=20, elevation=15, azimuth=180) + + self.file_list = [] + + # open file and read the content in a list + with open('lib/LiDAR_fog_sim/file_lists/Apollo.txt', 'r') as filehandle: + for line in filehandle: + # remove linebreak which is the last character of the string + file_path = Path(APOLLO) / line[:-1] + + # add item to the list + self.file_list.append(str(file_path)) + + self.index = 0 + self.set_apollo() + self.show_pointcloud(self.file_list[self.index]) + + def show_directory_dialog(self) -> None: + + self.reset_dense_features() + + # directory = Path(os.getenv("HOME")) / 'Downloads' + directory = '/scratch/mhahner/LiDARSnowDemoVideos/2018-02-05_12-06-04/lidar_hdl64_s3/velodyne_pointclouds/strongest_echo_egomotion' + + if self.lastDir: + directory = self.lastDir + + dir_name = QFileDialog.getExistingDirectory(self, "Open Directory", str(directory), + QFileDialog.ShowDirsOnly | QFileDialog.DontResolveSymlinks) + + if dir_name: + self.lastDir = Path(dir_name) + self.create_file_list(dir_name) + + def get_index(self, filename: str) -> int: + + try: + return self.file_list.index(str(filename)) + + except ValueError: + logging.warning(f'{filename} not found in self.file_list') + return -1 + + def toggle_fov(self) -> None: + + self.show_fov_only = not self.show_fov_only + self.show_fov_only_btn.setText('show full 360Β°' if self.show_fov_only else 'show camera FOV only') + self.show_pointcloud(self.file_list[self.index], force_reload=True) + + def toggle_snow(self) -> None: + + self.apply_snow = not self.apply_snow + self.toggle_snow_btn.setText('remove snow' if self.apply_snow else 'apply snow') + self.show_pointcloud(self.file_list[self.index], force_reload=True) + + def toggle_wet(self) -> None: + + self.apply_wet = not self.apply_wet + self.toggle_wet_btn.setText('remove wet' if self.apply_wet else 'apply wet') + self.show_pointcloud(self.file_list[self.index], force_reload=True) + + def toggle_flat_earth(self) -> None: + + self.flat_earth = not self.flat_earth + self.flat_earth_btn.setText('flat earth' if self.flat_earth else 'no flat earth') + self.snowfall_change() + + def toggle_seed(self) -> None: + + self.fixed_seed = not self.fixed_seed + self.toggle_seed_btn.setText('seed fixed' if self.fixed_seed else 'seed not fixed') + self.snowfall_change() + + def toggle_lisa(self) -> None: + + self.apply_lisa = not self.apply_lisa + self.toggle_lisa_btn.setText('remove LISA' if self.apply_lisa else 'apply lisa') + self.show_pointcloud(self.file_list[self.index], force_reload=True) + + + def toggle_dror_cube(self) -> None: + + self.show_dror_cube = not self.show_dror_cube + + if self.show_dror_cube: + self.toggle_cube_btn.setText('hide cube') + self.viewer.addItem(self.dror_cube) + else: + self.toggle_cube_btn.setText('show cube') + self.viewer.removeItem(self.dror_cube) + + def toggle_temp_visibility(self) -> None: + + self.show_temp = not self.show_temp + self.toggle_temp_btn.setText('hide temporal' if self.show_temp else 'show temporal') + self.update_temporal_clouds() + + def toggle_image_visibility(self) -> None: + + self.show_img = not self.show_img + + if self.show_img: + + self.image_window.show() + self.toggle_img_btn.setText('hide image') + self.populate_image(self.file_list[self.index]) + + else: + + self.image_window.hide() + self.toggle_img_btn.setText('show image') + + def toggle_simulated_fog(self) -> None: + + if self.file_list is not None and 'extraction' not in self.file_name: + + self.simulated_fog = not self.simulated_fog + + if self.simulated_fog: + + self.toggle_simulated_fog_btn.setText('remove our fog') + self.alpha_slider.setEnabled(True) + self.beta_slider.setEnabled(True) + self.gamma_slider.setEnabled(True) + + else: + + self.toggle_simulated_fog_btn.setText('add our fog') + self.alpha_slider.setEnabled(False) + self.beta_slider.setEnabled(False) + self.gamma_slider.setEnabled(False) + + self.show_pointcloud(self.file_list[self.index], force_reload=True) + + def get_dror_mask(self, filename: str, pc: np.ndarray) -> np.ndarray: + + before = len(pc) + + if live_DROR_available: + + pc_copy = copy.deepcopy(pc) + + mask = dynamic_radius_outlier_filter(pc, + alpha=self.dror_alpha, + beta=self.dror_beta, + k_min=self.dror_k_min, + sr_min=self.dror_sr_min / 100) # convert to meters + + cube_mask = get_cube_mask(pc_copy) + pc_copy = pc_copy[cube_mask] + + cube_indices = dynamic_radius_outlier_filter(pc_copy, + alpha=self.dror_alpha, + beta=self.dror_beta, + k_min=self.dror_k_min, + sr_min=self.dror_sr_min / 100) # convert to meters + + self.update_dror_title(cube_indices=cube_indices) + + else: # try loading precomputed dict + + mask = dynamic_radius_outlier_filter(sensor=self.sensor, signal=self.signal, before=before, + variant='full', filename=filename) + + self.update_dror_title(filename=filename) + + after = (mask == True).sum() + + diff = before - after + + if diff > 0: + percent = 100 - int(after / (before / 100)) + text = f'{self.dror_intensity}: {diff:,} (~{percent}%) out of {before:,} points filtered'.replace(',', '.') + self.dror_headline.setText(text) + + return mask + + def toggle_dror(self) -> None: + + self.apply_dror = not self.apply_dror + + if self.apply_dror: + self.toggle_dror_btn.setText('revert DROR') + else: + self.toggle_dror_btn.setText('apply DROR') + self.dror_headline.setText(self.dror_intensity) + + self.show_pointcloud(self.file_list[self.index], force_reload=True) + + def toggle_simulated_fog_dense(self) -> None: + + if self.file_list is not None and 'extraction' not in self.file_name: + + self.simulated_fog_dense = not self.simulated_fog_dense + + if self.simulated_fog_dense: + + self.toggle_simulated_fog_dense_btn.setText('remove STF fog') + self.alpha_slider.setEnabled(True) + self.beta_slider.setEnabled(True) + self.gamma_slider.setEnabled(True) + + else: + + self.toggle_simulated_fog_dense_btn.setText('add STF fog') + self.alpha_slider.setEnabled(False) + self.beta_slider.setEnabled(False) + self.gamma_slider.setEnabled(False) + + self.show_pointcloud(self.file_list[self.index], force_reload=True) + + def toggle_predictions(self) -> None: + + self.show_predictions = not self.show_predictions + + if self.show_predictions: + self.visualize_predictions_btn.setText('hide predictions') + else: + self.visualize_predictions_btn.setText('show predictions') + + if self.file_list: + self.show_pointcloud(self.file_list[self.index]) + + def create_file_list(self, dirname: str, filename: str = None, extension: str = None) -> None: + + if extension: + file_list = [y for x in os.walk(dirname) for y in glob(os.path.join(x[0], f'*.{extension}'))] + else: + file_list = [y for x in os.walk(dirname) for y in glob(os.path.join(x[0], f'*.{self.extension}'))] + + self.file_list = sorted(file_list) + + # with open('lib/LiDAR_fog_sim/file_lists/Argoverse.txt', 'w') as filehandle: + # filehandle.writelines(f'{Path(file).parent.parent.parent.parent.name}/' + # f'{Path(file).parent.parent.parent.name}/' + # f'{Path(file).parent.parent.name}/' + # f'{Path(file).parent.name}/' + # f'{Path(file).name}\n' for file in self.file_list) + + if len(self.file_list) > 0: + + if filename is None: + filename = self.file_list[0] + + self.index = self.get_index(filename) + + if Path(self.file_list[self.index]).suffix == ".pickle": + self.show_pcdet_dict(self.file_list[self.index]) + else: + self.show_pointcloud(self.file_list[self.index]) + + def reset_viewer(self) -> None: + + self.num_info.setText(f'sequence_size: {len(self.file_list)}') + + self.min_fog_response = np.inf + self.max_fog_response = 0 + self.num_fog_responses = 0 + + self.viewer.items = [] + + if self.show_dror_cube: + self.viewer.addItem(self.dror_cube) + + def run_inference(self) -> None: + + if self.current_experiment != self.experiment_path_box.text(): + self.config = None + self.model = None + + if self.config is None: + self.init_config() + + if self.logger is None: + self.init_logger() + + self.init_data() + + if self.model is None: + self.init_model() + + if self.zero.isChecked(): + list_of_pcs = [self.current_pc] + self.temporal_pcs + else: + list_of_pcs = self.temporal_pcs + + try: + points = np.vstack(list_of_pcs) + except ValueError as ve: + self.logger.error(f'{ve} - There are no points to process.') + return None + + for i, batch_dict in enumerate(self.eval_loader): + input_dict = {'points': points, + 'frame_id': batch_dict['frame_id']} + + data_dict = self.eval_set.prepare_data(data_dict=input_dict) + data_dict = self.eval_set.collate_batch([data_dict]) + load_data_to_gpu(data_dict) + + with torch.no_grad(): + pred_dicts, ret_dict = self.model.forward(data_dict) + + pred_dict = pred_dicts[0] + + predictions = np.zeros((pred_dict['pred_boxes'].shape[0], 9)) + predictions[:, 0:-2] = pred_dict['pred_boxes'].cpu() + predictions[:, 7] = pred_dict['pred_labels'].cpu() + predictions[:, 8] = pred_dict['pred_scores'].cpu() + + self.predictions = predictions + + self.add_predictions() + + def init_config(self) -> None: + + exp_dir = EXPERIMENTS_ROOT / self.experiment_path_box.text() + self.current_experiment = self.experiment_path_box.text() + + cfg_path = None + + for file in os.listdir(exp_dir): + + if file.endswith(".yaml"): + cfg_path = os.path.join(exp_dir, file) + + config = cfg_from_yaml_file(cfg_path, cfg) + + sensor_flag = '_vlp32' if self.sensor == 'vlp32' else '' + + config['OPTIMIZATION']['BATCH_SIZE_PER_GPU'] = 1 + config['DATA_CONFIG']['INFO_PATH']['test'] = [f'pkl/dense_infos_all{sensor_flag}_FOV{self.signal}3000.pkl'] + + self.config = config + + def init_logger(self) -> None: + + log_file = Path(tempfile.gettempdir()) / 'inference.out' + self.logger = common_utils.create_logger(log_file, rank=self.config.LOCAL_RANK, log_level=logging.DEBUG) + + def init_data(self) -> None: + + sample_str = self.file_name_label.text().replace('.bin', '') + + # -----------------------create dataloader & network & optimizer--------------------------- + self.eval_set, self.eval_loader, self.sampler = build_dataloader( + dataset_cfg=self.config.DATA_CONFIG, + class_names=self.config.CLASS_NAMES, + batch_size=1, + dist=False, workers=0, logger=self.logger, training=False, + root_path=DENSE.parent, + search_str=sample_str + ) + + def init_model(self) -> None: + + exp_dir = EXPERIMENTS_ROOT / self.experiment_path_box.text() + + ckpt_path = None + + for file in os.listdir(exp_dir): + if file.endswith(".pth"): + ckpt_path = os.path.join(exp_dir, file) + + model = build_network(model_cfg=self.config.MODEL, + num_class=len(self.config.CLASS_NAMES), + dataset=self.eval_set) + + model.cuda() + + model.load_params_from_file(filename=ckpt_path, to_cpu=False, logger=self.logger) + + model.eval() # before wrap to DistributedDataParallel to support fixed some parameters + + self.model = model + + def show_pcdet_dict(self, filename: str) -> None: + + self.reset_viewer() + self.simulated_fog = False + self.simulated_fog_dense = False + + if self.simulated_fog: + + self.toggle_simulated_fog_btn.setText('remove our fog') + self.alpha_slider.setEnabled(True) + self.beta_slider.setEnabled(True) + self.gamma_slider.setEnabled(True) + + else: + + self.toggle_simulated_fog_btn.setText('add our fog') + self.alpha_slider.setEnabled(False) + self.beta_slider.setEnabled(False) + self.gamma_slider.setEnabled(False) + + self.file_name = filename + + self.set_pc_det('before' in filename) + + self.cb_splits.setEnabled(False) + self.cb_sensors.setEnabled(False) + self.cb_signals.setEnabled(False) + self.toggle_simulated_fog_btn.setEnabled(True) + self.toggle_simulated_fog_dense_btn.setEnabled(True) + + pcdet_dict = pkl.load(open(filename, "rb")) + + ########## + # points # + ########## + + pc = pcdet_dict['points'] + + self.log_string(pc) + + colors = self.get_colors(pc) + + mesh = gl.GLScatterPlotItem(pos=np.asarray(pc[:, 0:3]), size=self.point_size, color=colors, + glOptions='translucent') + self.current_mesh = mesh + self.current_pc = copy.deepcopy(pc) + + self.viewer.addItem(mesh) + + ######### + # boxes # + ######### + + self.boxes = {} + + self.create_boxes(pcdet_dict['gt_boxes']) + + def create_boxes(self, annotations): + + # create annotation boxes + for annotation in annotations: + + x, y, z, w, l, h, rotation, category = annotation + + rotation = np.rad2deg(rotation) + 90 + + try: + color = COLORS[int(category) - 1] + except IndexError: + color = (255, 255, 255, 255) + + try: + box = gl.GLBoxItem(QtGui.QVector3D(1, 1, 1), color=color, line_width=self.line_width) + except TypeError: + box = gl.GLBoxItem(QtGui.QVector3D(1, 1, 1), color=color) + + box.setSize(l, w, h) + box.translate(-l / 2, -w / 2, -h / 2) + box.rotate(angle=rotation, x=0, y=0, z=1) + box.translate(x, y, z) + + self.viewer.addItem(box) + + ################# + # heading lines # + ################# + + p1 = [-l / 2, -w / 2, -h / 2] + p2 = [l / 2, -w / 2, h / 2] + + pts = np.array([p1, p2]) + + l1 = gl.GLLinePlotItem(pos=pts, width=2 / 3, color=color, antialias=True, mode='lines') + l1.rotate(angle=rotation, x=0, y=0, z=1) + l1.translate(x, y, z) + + self.viewer.addItem(l1) + + p3 = [-l / 2, -w / 2, h / 2] + p4 = [l / 2, -w / 2, -h / 2] + + pts = np.array([p3, p4]) + + l2 = gl.GLLinePlotItem(pos=pts, width=2 / 3, color=color, antialias=True, mode='lines') + l2.rotate(angle=rotation, x=0, y=0, z=1) + l2.translate(x, y, z) + + self.viewer.addItem(l2) + + distance = np.linalg.norm([x, y, z], axis=0) + self.boxes[distance] = (box, l1, l2) + + def update_dror_title(self, cube_indices: np.ndarray = None, filename: str = '') -> None: + + if self.dataset == 'DENSE': + + num_snow = -1 + + if cube_indices is None: + + cube_path = f'{DROR}/alpha_0.45/all/{self.sensor}/{self.signal}/crop' + name = Path(filename).name.replace('.bin', '') + cube_file = f'{cube_path}/{name}.pkl' + + if Path(cube_file).exists(): + with open(cube_file, 'rb') as f: + cube_indices = pkl.load(f) + + num_snow = len(cube_indices) + + # because we can not create a range with stop=np.inf + intensity = f'heavy [> {int(DROR_LEVELS["light"][1])}]' + + for key, value in DROR_LEVELS.items(): + + if num_snow == -1: + intensity = 'file not found' + break + + if num_snow in range(value[0], value[1] + 1): + intensity = f'{key} [{int(value[0])} - {int(value[1])}]' + break + + self.dror_intensity = f'DROR ({num_snow} => {intensity})' + + else: + + self.dror_intensity = f'DROR' + + self.dror_headline.setText(self.dror_intensity) + + def show_pointcloud(self, filename: str, force_reload: bool = False, needs_reload: bool = True) -> None: + + if force_reload: + self.current_pc = None + + # pprint(self.viewer.opts) + + self.reset_viewer() + self.update_available_temporal_clouds(filename) + + self.cb_splits.setEnabled(False) + self.cb_sensors.setEnabled(False) + self.cb_signals.setEnabled(False) + + if self.dataset == 'DENSE': + self.cb_velocity.setEnabled(True) + self.cb_estimation.setEnabled(True) + self.flat_earth_btn.setEnabled(True) + self.toggle_wet_btn.setEnabled(True) + self.toggle_snow_btn.setEnabled(True) + self.cb_snowfall_rate.setEnabled(True) + self.noise_floor_slider.setEnabled(True) + self.power_factor_slider.setEnabled(True) + self.water_height_slider.setEnabled(True) + self.pavement_height_slider.setEnabled(True) + else: + self.cb_velocity.setEnabled(False) + self.cb_estimation.setEnabled(False) + self.flat_earth_btn.setEnabled(False) + self.toggle_wet_btn.setEnabled(False) + self.toggle_snow_btn.setEnabled(False) + self.cb_snowfall_rate.setEnabled(False) + self.noise_floor_slider.setEnabled(False) + self.power_factor_slider.setEnabled(False) + self.water_height_slider.setEnabled(False) + self.pavement_height_slider.setEnabled(False) + + self.cb_lisa.setEnabled(True) + self.r_min_slider.setEnabled(True) + self.r_max_slider.setEnabled(True) + self.toggle_seed_btn.setEnabled(True) + self.toggle_lisa_btn.setEnabled(True) + self.rain_rate_slider.setEnabled(True) + self.wavelength_slider.setEnabled(True) + self.min_diameter_slider.setEnabled(True) + self.range_accuracy_slider.setEnabled(True) + self.beam_divergence_slider.setEnabled(True) + self.toggle_simulated_fog_btn.setEnabled(True) + self.toggle_simulated_fog_dense_btn.setEnabled(True) + + if self.dataset == 'DENSE' and self.show_fov_only and CUDA_AVAILABLE: + self.inference_btn.setEnabled(True) + else: + self.inference_btn.setEnabled(False) + + if self.dataset == 'DENSE' or live_DROR_available: + + self.update_dror_title(filename=filename) + + self.toggle_dror_btn.setEnabled(True) + self.toggle_cube_btn.setEnabled(True) + + if live_DROR_available: + self.dror_alpha_slider.setEnabled(True) + self.dror_beta_slider.setEnabled(True) + self.dror_k_min_slider.setEnabled(True) + self.dror_sr_min_slider.setEnabled(True) + + else: + + self.dror_intensity = 'DROR' + self.dror_headline.setText(self.dror_intensity) + + self.toggle_cube_btn.setEnabled(False) + self.toggle_dror_btn.setEnabled(False) + self.dror_alpha_slider.setEnabled(False) + self.dror_beta_slider.setEnabled(False) + self.dror_k_min_slider.setEnabled(False) + self.dror_sr_min_slider.setEnabled(False) + + if self.file_name == filename and self.current_pc is not None: + + # reuse the current pointcloud if the filename stays the same + pc = self.current_pc + + else: + + if self.tmp_value: # it means, snow has been applied before + self.num_unchanged_label.setText('') + self.num_removed_label.setText('') + self.num_scattered_label.setText('') + self.num_attenuated_label.setText('') + self.max_value = self.tmp_value + self.tmp_value = None + + if not self.result_dict: # it means, that only live inference is available + self.predictions = None + self.prediction_boxes = [] + self.prediction_threshold_slider.setEnabled(False) + self.visualize_predictions_btn.setEnabled(False) + self.visualize_predictions_btn.setText('show predictions') + + self.file_name = filename + pc = self.load_pointcloud(filename) + + if self.apply_dror: + mask = self.get_dror_mask(filename, pc) + pc = pc[mask, :] + + self.success = False + + # filter camera FOV + if self.show_fov_only: + calib = get_calib(self.sensor) + + pts_rect = calib.lidar_to_rect(pc[:, 0:3]) + fov_flag = get_fov_flag(pts_rect, (1024, 1920), calib) + + pc = pc[fov_flag] + + min_dist_mask = np.linalg.norm(pc[:, 0:3], axis=1) > MIN_DIST + pc = pc[min_dist_mask, :] + + max_dist_mask = np.linalg.norm(pc[:, 0:3], axis=1) < self.max_distance + pc = pc[max_dist_mask, :] + + min_height_mask = pc[:, 2] > (self.min_height / 100) # in m + pc = pc[min_height_mask, :] + + if 'extraction' in filename: + intensity_mask = pc[:, 3] <= self.threshold + pc = pc[intensity_mask, :] + + colors = self.get_colors(pc) + + mesh = gl.GLScatterPlotItem(pos=np.asarray(pc[:, 0:3]), size=self.point_size, color=colors, + glOptions='translucent') + self.current_mesh = mesh + + if self.success and self.zero.isChecked(): + self.viewer.addItem(mesh) + self.zero.setEnabled(True) + + curve = self.lisa.Nd(self.lisa.D, self.rain_rate) # density of the particles (m^-3) + a = self.lisa.alpha(curve) + self.lisa_alpha_label.setText(f'\u27F9 \u03B1 = {a}') + + rain_rate = snowfall_rate_to_rainfall_rate(float(self.snowfall_rate), float(self.terminal_velocity)) + self.rainfall_rate_title.setText(f'\u27F9 rainfall rate = {round(rain_rate, 2)} mm/h') + already_augmented = False + occupancy = compute_occupancy(float(self.snowfall_rate), float(self.terminal_velocity)) + snowflake_file_prefix = f'{self.mode}_{rain_rate}_{occupancy}' + + if self.apply_wet and self.apply_snow and needs_reload: + self.reset_viewer() + + stats, pc = augment(pc=pc, only_camera_fov=self.show_fov_only, + particle_file_prefix=snowflake_file_prefix, noise_floor=self.noise_floor, + beam_divergence=float(np.degrees(self.beam_divergence)), + shuffle=True, show_progressbar=True) + + num_attenuated, num_removed, avg_intensity_diff = stats + + pc = ground_water_augmentation(pc, + water_height=self.water_height, + pavement_depth=self.pavement_height, + noise_floor=self.noise_floor, + power_factor=self.power_factor, + flat_earth=self.flat_earth, + estimation_method=self.estimation, + debug=False, delta=self.dror_alpha, replace=False) + + num_unchanged = (pc[:, 4] == 0).sum() + num_attenuated = (pc[:, 4] == 1).sum() + num_scattered = (pc[:, 4] == 2).sum() + + self.num_unchanged_label.setText(f'num_unchanged: {num_unchanged}') + self.num_removed_label.setText(f'num_removed: {num_removed}') + self.num_scattered_label.setText(f'num_scattered: {num_scattered}') + self.num_attenuated_label.setText(f'num_attenuated: {num_attenuated} (\u00B5 = -{avg_intensity_diff})') + + self.tmp_value = self.max_value + self.max_value = 3 + colors = self.get_colors(pc) + mesh = gl.GLScatterPlotItem(pos=np.asarray(pc[:, 0:3]), size=self.point_size, color=colors, + glOptions='translucent') + self.current_mesh = mesh + self.viewer.addItem(mesh) + + already_augmented = True + + if self.apply_wet and not already_augmented and needs_reload: + self.reset_viewer() + + pc = ground_water_augmentation(pc, + water_height=self.water_height, + pavement_depth=self.pavement_height, + noise_floor=self.noise_floor, + power_factor=self.power_factor, + flat_earth=self.flat_earth, + estimation_method=self.estimation, + debug=False, delta=self.dror_alpha, replace=True) + + self.tmp_value = self.max_value + self.max_value = 3 + colors = self.get_colors(pc) + mesh = gl.GLScatterPlotItem(pos=np.asarray(pc[:, 0:3]), size=self.point_size, color=colors, + glOptions='translucent') + self.current_mesh = mesh + self.viewer.addItem(mesh) + + if self.apply_snow and not already_augmented and needs_reload: + self.reset_viewer() + + stats, pc = augment(pc=pc, only_camera_fov=self.show_fov_only, + particle_file_prefix=snowflake_file_prefix, noise_floor=self.noise_floor, + beam_divergence=float(np.degrees(self.beam_divergence)), + shuffle=True, show_progressbar=True) + + num_attenuated, num_removed, avg_intensity_diff = stats + + num_unchanged = (pc[:, 4] == 0).sum() + num_scattered = (pc[:, 4] == 2).sum() + + self.num_unchanged_label.setText(f'num_unchanged: {num_unchanged}') + self.num_removed_label.setText(f'num_removed: {num_removed}') + self.num_scattered_label.setText(f'num_scattered: {num_scattered}') + self.num_attenuated_label.setText(f'num_attenuated: {num_attenuated} (\u00B5 = -{avg_intensity_diff})') + + self.tmp_value = self.max_value + self.max_value = 3 + colors = self.get_colors(pc) + mesh = gl.GLScatterPlotItem(pos=np.asarray(pc[:, 0:3]), size=self.point_size, color=colors, + glOptions='translucent') + self.current_mesh = mesh + self.viewer.addItem(mesh) + + if self.apply_lisa and needs_reload: + + self.reset_viewer() + + before_lisa = np.zeros((pc.shape[0], 4)) + before_lisa[:, :3] = copy.deepcopy(pc[:, :3]) + before_lisa[:, 3] = copy.deepcopy(pc[:, 3] / 255) + + self.lisa = LISA(wavelength=self.wavelength, mode=self.mode, r_min=self.r_min, r_max=self.r_max, + beam_divergence=self.beam_divergence, min_diameter=self.min_diameter, + range_accuracy=self.range_accuracy, signal=self.signal, show_progressbar=True) + + try: + after_lisa = self.lisa.augment(pc=before_lisa, Rr=self.rain_rate, fixed_seed=self.fixed_seed) + except TypeError: + after_lisa = self.lisa.augment(pc=before_lisa) + + num_removed = (after_lisa[:, 4] == 0).sum() + num_scattered = (after_lisa[:, 4] == 2).sum() + num_attenuated = (after_lisa[:, 4] == 1).sum() + + intensity_diff_sum = after_lisa[:, 5].sum() + + if num_attenuated > 0: + avg_intensity_diff = int(np.round(intensity_diff_sum * 255 / num_attenuated, 0)) + else: + avg_intensity_diff = 0 + + self.num_unchanged_label.setText('') + self.num_removed_label.setText(f'num_removed: {num_removed}') + self.num_scattered_label.setText(f'num_scattered: {num_scattered}') + self.num_attenuated_label.setText(f'num_attenuated: {num_attenuated} (\u00B5 = -{avg_intensity_diff})') + + after_lisa[:, 3] = np.round(after_lisa[:, 3] * 255) + + if pc.shape[1] < 5: + pc = np.zeros((pc.shape[0], pc.shape[1] + 1)) + + pc[:, :5] = after_lisa[:, :5] + + # remove points that where moved to origin + pc = pc[np.where(pc[:, 4] != 0)] + + self.tmp_value = self.max_value + self.max_value = 3 + colors = self.get_colors(pc) + mesh = gl.GLScatterPlotItem(pos=np.asarray(pc[:, 0:3]), size=self.point_size, color=colors, + glOptions='translucent') + self.current_mesh = mesh + self.viewer.addItem(mesh) + + if self.simulated_fog and self.success: + self.toggle_simulated_fog_dense_btn.setEnabled(False) + + self.reset_viewer() + + pc, simulated_fog_pc, info_dict = simulate_fog(self.p, pc, self.noise, self.gain, + self.noise_variant) + + self.simulated_fog_pc = simulated_fog_pc + + self.min_fog_response = info_dict['min_fog_response'] + self.max_fog_response = info_dict['max_fog_response'] + self.num_fog_responses = info_dict['num_fog_responses'] + + colors = self.get_colors(pc) + mesh = gl.GLScatterPlotItem(pos=np.asarray(pc[:, 0:3]), size=self.point_size, color=colors, + glOptions='translucent') + self.viewer.addItem(mesh) + + if self.simulated_fog_dense and self.success: + self.toggle_simulated_fog_btn.setEnabled(False) + + self.reset_viewer() + + B = BetaRadomization(beta=float(self.p.alpha), seed=0) + B.propagate_in_time(10) + + arguments = Namespace(sensor_type='Velodyne HDL-64E S3D', fraction_random=0.05) + n_features = pc.shape[1] + + pc = haze_point_cloud(pc, B, arguments) + pc = pc[:, :n_features] + + colors = self.get_colors(pc) + mesh = gl.GLScatterPlotItem(pos=np.asarray(pc[:, 0:3]), size=self.point_size, color=colors, + glOptions='translucent') + self.viewer.addItem(mesh) + + self.current_pc = copy.deepcopy(pc) + self.log_string(pc=pc) + + if self.dataset == 'DENSE': + + self.cb_splits.setEnabled(True) + self.cb_sensors.setEnabled(True) + self.cb_signals.setEnabled(True) + self.populate_dense_boxes(filename) + + else: + + self.cb_splits.setCurrentText('all') + self.cb_splits.setEnabled(False) + self.cb_sensors.setEnabled(False) + self.cb_signals.setEnabled(False) + + if self.show_img: + self.populate_image(filename) + + self.update_temporal_clouds() + + if self.boxes: + + try: # if heading lines are available + for box_distance, (box, l1, l2) in self.boxes.items(): + if box_distance < self.p.r_range: + self.viewer.addItem(box) + self.viewer.addItem(l1) + self.viewer.addItem(l2) + except TypeError: + for box_distance, box in self.boxes.items(): + if box_distance < self.p.r_range: + self.viewer.addItem(box) + + if self.result_dict and self.show_predictions: + self.visualize_predictions() + + if self.prediction_boxes and self.show_predictions: + self.add_predictions() + + if SAVE_IMAGES: + + pixmap = QPixmap(self.size()) + self.render(pixmap) + cropped = pixmap.toImage().copy(20, 400, 1000, 430) # 21:9 aspect ratio + + date = str(self.lastDir).split('/')[-4] + name = Path(filename).stem + '.png' + + weather = 'clear' + + w_h = self.water_height_slider.value() * 10 + p_h = self.pavement_height_slider.value() + + water_rate = int(w_h / p_h * 100) + rain_rate = str(int(snowfall_rate_to_rainfall_rate(float(self.snowfall_rate), + float(self.terminal_velocity)))) + + if self.apply_lisa: + weather = f'lisa_{rain_rate}' + + if self.apply_snow: + weather = f'snow_{rain_rate}' + + if self.apply_wet: + weather = f'wet_{water_rate}' + + if self.apply_snow and self.apply_wet: + weather = f'snow+wet_{rain_rate}_{water_rate}' + + save_path = Path.home() / 'Downloads' / date / weather / self.color_name + save_path.mkdir(parents=True, exist_ok=True) + + cropped.save(f'{save_path}/{name}', "PNG", -1) + + def populate_image(self, filename) -> None: + + if self.dataset == 'DENSE': + img_file_name = Path(filename).name.replace('.bin', '.png') + + image_path = Path(filename).parent.parent / 'cam_stereo_left_lut' / img_file_name + + width, height = self.image_window.image_label.width(), self.image_window.image_label.height() + + pixmap = QPixmap(str(image_path)) + pixmap = pixmap.scaled(width, height, Qt.KeepAspectRatio) + + self.image_window.image_label.setPixmap(pixmap) + + def populate_dense_boxes(self, filename): + + root = str(Path(__file__).parent.absolute() / 'lib/LiDAR_fog_sim/SeeingThroughFog/tools/DatasetViewer/calibs') + tf_tree = 'calib_tf_tree_full.json' + + name_camera_calib = 'calib_cam_stereo_left.json' + + sensor_names = {'hdl64': 'lidar_hdl64_s3_roof', + 'vlp32': 'lidar_vlp32_roof'} + + rgb_calib = load_calib_data(root, name_camera_calib, tf_tree, velodyne_name=sensor_names[self.sensor]) + + camera_to_velodyne_rgb = rgb_calib[1] + + label_path = Path(filename).parent.parent / 'gt_labels' / 'cam_left_labels_TMP' + + recording = Path(filename).stem # here without '.txt' as it will be added in read_label function + label_file = os.path.join(label_path, recording) + self.label = read_label(label_file, label_path, camera_to_velodyne=camera_to_velodyne_rgb) + + self.boxes = {} + + # create annotation boxes + for annotation in self.label: + + if annotation['identity'] in ['PassengerCar', 'Pedestrian', 'RidableVehicle']: + + x = annotation['posx_lidar'] + y = annotation['posy_lidar'] + z = annotation['posz_lidar'] + + if annotation['identity'] == 'PassengerCar': + color = COLORS[0] + elif annotation['identity'] == 'Pedestrian': + color = COLORS[1] + else: + color = COLORS[2] + + distance = np.sqrt(x ** 2 + y ** 2 + z ** 2) + + try: + box = gl.GLBoxItem(QtGui.QVector3D(1, 1, 1), color=color, line_width=self.line_width) + except TypeError: + box = gl.GLBoxItem(QtGui.QVector3D(1, 1, 1), color=color) + + box.setSize(annotation['length'], annotation['width'], annotation['height']) + box.translate(-annotation['length'] / 2, -annotation['width'] / 2, -annotation['height'] / 2) + box.rotate(angle=-annotation['rotz'] * 180 / np.pi, x=0, y=0, z=1) + box.rotate(angle=-annotation['roty'] * 180 / np.pi, x=0, y=1, z=0) + box.rotate(angle=-annotation['rotx'] * 180 / np.pi, x=1, y=0, z=0) + box.translate(0, 0, annotation['height'] / 2) + box.translate(x, y, z) + + self.boxes[distance] = box + + def log_string(self, pc: np.ndarray) -> None: + + try: + + distance = np.linalg.norm(pc[:, 0:3], axis=1) + + self.p.r_range = max(distance) + + log_string = f'pts ' + f'{len(pc)}'.rjust(6, ' ') + ' | ' + \ + f'max_dist ' + f'{int(self.p.r_range)}'.rjust(3, ' ') + ' m | ' + \ + f'i [ ' + f'{int(min(pc[:, 3]))}'.rjust(3, ' ') + \ + f', ' + f'{int(max(pc[:, 3]))}'.rjust(3, ' ') + ']' + ' ' + \ + f'median ' + f'{int(np.round(np.median(pc[:, 3])))}'.rjust(3, ' ') + ' ' + \ + f'\u03BC ' + f'{int(np.round(np.mean(pc[:, 3])))}'.rjust(3, ' ') + ' ' + \ + f'\u03C3 ' + f'{int(np.round(np.std(pc[:, 3])))}'.rjust(3, ' ') + + if self.num_fog_responses > 0: + range_fog_response_string = f'fog [ ' + f'{int(self.min_fog_response)}'.rjust(3, ' ') + \ + f', ' + f'{int(self.max_fog_response)}'.rjust(3, ' ') + ']' + num_fog_responses_string = f'soft ' + f'{int(self.num_fog_responses)}'.rjust(6, ' ') + num_remaining_string = f'hard ' + f'{int(len(self.current_pc) - self.num_fog_responses)}'.rjust(6, ' ') + + log_string = log_string + ' | ' + \ + range_fog_response_string + ' ' + num_fog_responses_string + ' ' + num_remaining_string + + except ValueError: + + self.p.r_range = 0 + + log_string = f'num_pts ' + f'{len(pc)}'.rjust(6, ' ') + + self.log_info.setText(log_string) + + def get_colors(self, pc: np.ndarray) -> np.ndarray: + + # create colormap + if self.color_feature == 0: + + self.success = True + feature = pc[:, 0] + min_value = np.min(feature) + max_value = np.max(feature) + + elif self.color_feature == 1: + + self.success = True + feature = pc[:, 1] + min_value = np.min(feature) + max_value = np.max(feature) + + elif self.color_feature == 2: + + self.success = True + feature = pc[:, 2] + min_value = -1.5 + max_value = 0.5 + + elif self.color_feature == 3: + + self.success = True + feature = pc[:, 3] + min_value = 0 + max_value = 255 + + elif self.color_feature == 4: + + self.success = True + feature = np.linalg.norm(pc[:, 0:3], axis=1) + + try: + min_value = np.min(feature) + max_value = np.max(feature) + except ValueError: + min_value = 0 + max_value = np.inf + + elif self.color_feature == 5: + + self.success = True + feature = np.arctan2(pc[:, 1], pc[:, 0]) + np.pi + min_value = 0 + max_value = 2 * np.pi + + else: # self.color_feature == 6: + + try: + feature = pc[:, 4] + self.success = True + + except IndexError: + feature = pc[:, 3] + + min_value = self.min_value + max_value = self.max_value + + norm = mpl.colors.Normalize(vmin=min_value, vmax=max_value) + + if self.color_feature == 5: + cmap = cm.hsv # cyclic + else: + cmap = cm.jet # sequential + + m = cm.ScalarMappable(norm=norm, cmap=cmap) + + colors = m.to_rgba(feature) + colors[:, [2, 1, 0, 3]] = colors[:, [0, 1, 2, 3]] + colors[:, 3] = 1 + + return colors + + def load_pointcloud(self, filename: str) -> np.ndarray: + + self.reset_custom_values() + + if 'KITTI' in filename: + self.set_kitti() + + if 'DENSE' in filename: + self.set_dense() + + # override sensor + filename = filename.replace('hdl64', self.sensor) + + # override signal + filename = filename.replace('strongest', self.signal) + + if 'nuScenes' in filename: + self.set_nuscenes() + + if 'Lyft' in filename: + self.set_lyft() + + if 'Waymo' in filename: + self.set_waymo() + + if 'Honda' in filename: + self.set_honda() + + if 'A2D2' in filename: + self.set_audi() + + if 'PandaSet' in filename: + self.set_panda() + + if 'Apollo' in filename: + self.set_apollo() + + if 'Argoverse' in filename: + self.set_argo() + + self.color_name = self.color_dict[self.color_feature] + self.color_label.setText(self.color_name) + + if self.extension == 'ply': + + pc = self.load_from_ply(filename) + + elif self.extension == 'npz': + + pc = self.load_from_npz(filename) + + elif 'pkl' in self.extension: + + pc = self.load_from_pkl(filename) + + else: # assume bin file + + pc = np.fromfile(filename, dtype=self.d_type) + pc = pc.reshape((-1, self.num_features)) + + pc[:, 3] = np.round(pc[:, 3] * self.intensity_multiplier) + + if self.dataset == 'Honda3D': + + self.file_name_label.setText(f'{Path(filename).parent.name}/' + f'{Path(filename).name}') + + elif self.dataset == 'PandaSet' or self.dataset == 'Apollo': + + self.file_name_label.setText(f'{Path(filename).parent.parent.name}/' + f'{Path(filename).parent.name}/' + f'{Path(filename).name}') + + else: + + self.file_name_label.setText(str(Path(filename).name)) + + self.show_fov_only_btn.setText('show full 360Β°' if self.show_fov_only else 'show camera FOV only') + + return pc + + def load_from_pkl(self, filename: str) -> np.ndarray: + + if filename.endswith('gz'): + + with gzip.open(filename, 'rb') as f: + data = pkl.load(f) + + else: + + with open(filename, 'rb') as f: + data = pkl.load(f) + + if self.dataset == 'PandaSet': + pc = data.drop(columns=['t']).values + else: + pc = data.values + + return pc + + def load_from_ply(self, filename: str) -> np.ndarray: + + with open(filename, 'rb') as f: + plydata = PlyData.read(f) + + pc = np.array(plydata.elements[0].data.tolist())[:] + + if self.dataset == 'Honda3D': + pc = np.delete(pc, [3, 4, 5, 6, 7, 8, 9, 12], 1) + elif self.dataset == 'Argoverse': + pc = pc + else: + pc = np.delete(pc, [4, 5, 6], 1) + + return pc + + def load_from_npz(self, filename: str) -> np.ndarray: + + npz = np.load(filename) + + pc_dict = {} + + for key in npz.keys(): + pc_dict[key] = npz[key] + + pc = None + + if self.dataset == 'A2D2': + pc = np.column_stack((pc_dict['points'], + pc_dict['reflectance'], + pc_dict['lidar_id'])) + + return pc + + def dragEnterEvent(self, event: QDragEnterEvent) -> None: + + logging.debug("enter") + + mimeData = event.mimeData() + mimeList = mimeData.formats() + filename = None + + if "text/uri-list" in mimeList: + filename = mimeData.data("text/uri-list") + filename = str(filename, encoding="utf-8") + filename = filename.replace("file://", "").replace("\r\n", "").replace("%20", " ") + filename = Path(filename) + + if filename.exists() and (filename.suffix == ".bin" or + filename.suffix == ".ply" or + filename.suffix == ".pickle"): + event.accept() + self.droppedFilename = filename + self.extension = filename.suffix.replace('.', '') + else: + event.ignore() + self.droppedFilename = None + + def dropEvent(self, event: QDropEvent) -> None: + + if self.droppedFilename: + 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zhHHpsI=BSL<)}Aw_SgL8g61L_M{g=F9R(13QiXOWr^c?ok{f_X~|JbVwmX*J>n-}TAs2v}=~^23hXdY8{* ztXwq$_N`bNUy#Q#{2(OsYw_>PuC*P%@y`t-`sfLeUf$n^YgWK#z^+DLcuu1Ml{D1J z8?f6dM0%WS-{#FFV$9Gg1P8*z!?7=PJ2-`?KC$5z;5K-7{E*yp;SK5;EAU(FlAd-MGOF0uc8+N4qq-+Y5g4l{NVLofihs1VV+U^RobOcywxC5(A1>iQ!hB@9%jO#{7IzP9Vf9u6 zsQhbCPle&sfy6Bm1CE}zIlkvQ#L_+^eBsv=Y1SI~H+$=`KE&Dn4e#hNq&M&mKGv*8B$(NoSv3JeV|t`!2?A7(^tu8n_5yA$+)eKYU>Tc4OQXfUvNw%QWri_Age$ zHv)@vEb==P;VUFx;+d<0&Ob&Rhz>H;q|T|`$L!Lp4?Up%8VKuM>v{O>BQ^5Q6I6=| z-3?#{`Z4z;{=QH1I+tK?!BLqP{RhREi-$2ceLOadqdT7N9%Fe7D$(6|Oy@P^X`xp) z4GUsp|5l=>mAEZ^II-a)!HFk33|l;xtoMwpLW2oIzEO9U$%lM#^Zm|~`Ww0f#cx>Z z1vc)(CA`ZGXCC>G*{y@&g6h<-Y}->^HEx0jThBfzTsJ)Mu4qP$yGO?D*`5CEVwf`z z_jV5djZMI63ing0X7E)5xw!PI4c5lpwgY&3k7txUEgv$5&)s^STw1y7zZ0AOI58Eu YM^8Td*G|(S(aFp|Ubvd#0U+T20Dot np.ndarray: + """ + :param angles: N-by-2 array containing tangent angles. + :param right_angle: Right beam angle. + :param left_angle: Left beam angle. + :param right_angle_hit: Flag if right beam angle has been exceeded. + :param left_angle_hit: Flag if left beam angle has been exceeded. + + :return: N-by-2 array of corrected tangent angles that do not exceed beam limits. + """ + + angles[right_angle_hit, 0] = right_angle + angles[left_angle_hit, 1] = left_angle + + return angles + + +def tangent_lines_to_tangent_angles(lines: Tuple[np.ndarray, np.ndarray], center_angles: np.ndarray) -> np.ndarray: + """ + :param lines: Tuple of two N-by-2 arrays holding the $a$ and $b$ coefficients of the tangents. + :param center_angles: N-by-1 array containing the angle to the particle center. + + :return: + angles: N-by-2 array of tangent angles (right angle first, left angle second). + """ + + a_s, b_s = lines + + try: + N = center_angles.shape[0] + except IndexError: + N = 1 + + angles = -np.ones((N, 2)) # (N, 2) + + ray_1_angles = np.arctan(-a_s/b_s) # in range [-PI/2, PI/2] # (N, 2) + ray_2_angles = deepcopy(ray_1_angles) + PI # in range [PI/2, 3*PI/2] # (N, 2) + + # correct value range + ray_1_angles[ray_1_angles < 0] = ray_1_angles[ray_1_angles < 0] + 2*PI # in range [0, 2*PI] # (N, 2) + ray_1_angles = np.abs(ray_1_angles) # to prevent -0 value + + # catch special case if line is vertical + ray_1_angles[b_s == 0] = PI/2 + ray_2_angles[b_s == 0] = 3*PI/2 + + tangent_1_angles = np.column_stack((ray_1_angles[:, 0], ray_2_angles[:, 0])) # (N, 2) + tangent_2_angles = np.column_stack((ray_1_angles[:, 1], ray_2_angles[:, 1])) # (N, 2) + + for i, tangent_angles in enumerate([tangent_1_angles, tangent_2_angles]): + + tangent_difference = tangent_angles - np.column_stack((center_angles, center_angles)) # (N, 2) + + correct_ray = np.logical_or.reduce((np.abs(tangent_difference) < PI/2, + np.abs(tangent_difference - 2*PI) < PI/2, + np.abs(tangent_difference + 2*PI) < PI/2)) # (N, 2) + + angles[:, i] = tangent_angles[np.where(correct_ray)] # (N, 2) + + angles.sort(axis=1) + + # swap order where discontinuity is crossed + swap = angles[:, 1] - angles[:, 0] > PI + angles[swap, 0], angles[swap, 1] = angles[swap, 1], angles[swap, 0] + + return angles + + +def angles_to_lines(angles: np.ndarray) -> Tuple[np.ndarray, np.ndarray]: + """ + :param angles: M-by-2 array of angles, where for each row, the value in the first column + is lower than the value in the second column. + :return: + a_s: N-by-2 array holding the $a$ coefficients of the tangent rays of particles passing from + the origin. Each row corresponds to a_s particle. + b_s: N-by-2 array holding the $b$ coefficients of the tangent rays of particles passing from + the origin. Each row corresponds to a_s particle. + """ + + tan_directions = np.tan(angles) # (M, 2) + + directions_vertical = np.logical_or(angles == PI/2, angles == 3 * PI/2) + directions_not_vertical = np.logical_not(directions_vertical) + + a_s = np.zeros_like(angles) + b_s = np.zeros_like(angles) + + a_s[np.where(directions_vertical)] = 1 + b_s[np.where(directions_vertical)] = 0 + + a_s[np.where(directions_not_vertical)] = -tan_directions[np.where(directions_not_vertical)] + b_s[np.where(directions_not_vertical)] = 1 + + # a_s[np.abs(a_s) < EPSILON] = 0 # to prevent -0 value + + return a_s, b_s + + +def distances_of_points_to_lines(points: np.ndarray, a: np.ndarray, b: np.ndarray, c: np.ndarray) -> np.ndarray: + """ + :param points: N-by-2 array of points, where each row contains the coordinates (abscissa, ordinate) of a point + :param a: M-by-1 array of $a$ coefficients of lines + :param b: M-by-1 array of $b$ coefficients of lines + :param c: M-by-1 array of $c$ coefficients of lines + where ax + by = c + + :return: N-by-M array containing distances of points to lines + """ + + try: + N = points.shape[0] + except IndexError: + N = 1 + + abscissa, ordinate = points[:, 0, np.newaxis], points[:, 1, np.newaxis] + + numerators = np.dot(abscissa, a.T) + np.dot(ordinate, b.T) + np.dot(np.ones((N, 1)), c.T) + + denominators = np.dot(np.ones((N, 1)), np.sqrt(a ** 2 + b ** 2).T) + + return np.abs(numerators / denominators) + + +def tangents_from_origin(samples: np.ndarray) -> Tuple[np.ndarray, np.ndarray]: + """ + :param samples: N-by-3 array of sampled particles as disks, where each row contains abscissa and + ordinate of disk center and disk radius (in meters). + :return: + a: N-by-2 array holding the $a$ coefficients of the tangent rays of particles passing from + the origin. Each row corresponds to a particle. + b: N-by-2 array holding the $b$ coefficients of the tangent rays of particles passing from + the origin. Each row corresponds to a particle. + """ + + # Solve systems of equations that encode the following information: + # 1) rays include origin, + # 2) rays are tangent to the circles corresponding to the particles, i.e., they intersect with the circles at + # exactly one point. + + x_s, y_s, r_s = samples[:, 0], samples[:, 1], samples[:, 2] + + try: + N = samples.shape[0] + except IndexError: + N = 1 + + discriminants = r_s * np.sqrt(x_s ** 2 + y_s ** 2 - r_s ** 2) + + case_1 = np.abs(x_s) - r_s == 0 # One of the two lines is vertical. + case_2 = np.logical_not(case_1) # Both lines are not vertical. + + a_1_case_1, b_1_case_1 = np.ones(N), np.zeros(N) + a_2_case_1, b_2_case_1 = (y_s ** 2 - x_s ** 2) / (2 * x_s * y_s), - np.ones(N) + + a_1_case_2 = (-x_s * y_s + discriminants) / (r_s ** 2 - x_s ** 2) + a_2_case_2 = (-x_s * y_s - discriminants) / (r_s ** 2 - x_s ** 2) + b_1_case_2 = -np.ones(N) + b_2_case_2 = -np.ones(N) + + # Compute the coefficients by distinguishing the two cases. + a_1, a_2, b_1, b_2 = np.zeros(N), np.zeros(N), np.zeros(N), np.zeros(N) + + a_1[case_1] = a_1_case_1[case_1] + a_2[case_1] = a_2_case_1[case_1] + b_1[case_1] = b_1_case_1[case_1] + b_2[case_1] = b_2_case_1[case_1] + + a_1[case_2] = a_1_case_2[case_2] + a_2[case_2] = a_2_case_2[case_2] + b_1[case_2] = b_1_case_2[case_2] + b_2[case_2] = b_2_case_2[case_2] + + a = np.column_stack((a_1, a_2)) + b = np.column_stack((b_1, b_2)) + + return a, b + + +def do_angles_intersect_particles(angles: np.ndarray, particle_centers: np.ndarray) -> np.ndarray: + """ + Assumption: either the ray that corresponds to an angle or its opposite ray intersects with all particles. + + :param angles: (M,) array of angles in the range [0, 2*PI). + :param particle_centers: (N, 2) array of particle centers (abscissa, ordinate). + + :return: + """ + try: + M = angles.shape[0] + except IndexError: + M = 1 + + try: + N = particle_centers.shape[0] + except IndexError: + N = 1 + + x, y = particle_centers[:, 0], particle_centers[:, 1] + + angle_to_centers = np.arctan2(y, x) + angle_to_centers[angle_to_centers < 0] = angle_to_centers[angle_to_centers < 0] + 2*PI # (N, 1) + + angle_differences = np.tile(angles, (1, N)) - np.tile(angle_to_centers.T, (M, 1)) # (M, N) + + answer = np.logical_or.reduce((np.abs(angle_differences) < PI/2, + np.abs(angle_differences - 2*PI) < PI/2, + np.abs(angle_differences + 2*PI) < PI/2)) # (M, N) + + return answer diff --git a/tools/snowfall/precompute.py b/tools/snowfall/precompute.py new file mode 100644 index 0000000..6e0a5aa --- /dev/null +++ b/tools/snowfall/precompute.py @@ -0,0 +1,106 @@ +__author__ = "Martin Hahner" +__contact__ = "martin.hahner@pm.me" +__license__ = "CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)" + +import copy +import numpy as np + +from tqdm import tqdm +from pathlib import Path +from simulation import augment +from lib.OpenPCDet.pcdet.utils import calibration_kitti +from sampling import compute_occupancy, snowfall_rate_to_rainfall_rate + + +SPLIT_FOLDER = Path(__file__).parent.parent.parent.resolve() / 'lib' / 'LiDAR_fog_sim' / 'SeeingThroughFog' / 'splits' +LIDAR_FOLDER = Path.home() / 'datasets' / 'DENSE' / 'SeeingThroughFog' / 'lidar_hdl64_strongest' + +SPLIT = SPLIT_FOLDER / 'train_clear.txt' + +SNOWFALL_RATES = [0.5, 1.0, 2.0, 2.5, 1.5] # mm/h +TERMINAL_VELOCITIES = [2.0, 1.6, 2.0, 1.6, 0.6] # m/s + + +def split(a, n): + k, m = divmod(len(a), n) + return (a[i*k+min(i, m):(i+1)*k+min(i+1, m)] for i in range(n)) + + +def get_calib(sensor: str = 'hdl64'): + calib_file = Path(__file__).parent.parent.parent.resolve() / \ + 'lib' / 'OpenPCDet' / 'data' / 'dense' / f'calib_{sensor}.txt' + assert calib_file.exists(), f'{calib_file} not found' + return calibration_kitti.Calibration(calib_file) + + +def get_fov_flag(pts_rect, img_shape, calib): + + pts_img, pts_rect_depth = calib.rect_to_img(pts_rect) + val_flag_1 = np.logical_and(pts_img[:, 0] >= 0, pts_img[:, 0] < img_shape[1]) + val_flag_2 = np.logical_and(pts_img[:, 1] >= 0, pts_img[:, 1] < img_shape[0]) + val_flag_merge = np.logical_and(val_flag_1, val_flag_2) + pts_valid_flag = np.logical_and(val_flag_merge, pts_rect_depth >= 0) + + return pts_valid_flag + + +if __name__ == '__main__': + + assert SPLIT.exists(), f'{SPLIT} does not exist' + + assert len(SNOWFALL_RATES) == len(TERMINAL_VELOCITIES), f'you need to provide an equal amount of ' \ + f'snowfall_rates and terminal velocities' + rainfall_rates = [] + occupancy_ratios = [] + + for j in range(len(SNOWFALL_RATES)): + rainfall_rates.append(snowfall_rate_to_rainfall_rate(SNOWFALL_RATES[j], TERMINAL_VELOCITIES[j])) + occupancy_ratios.append(compute_occupancy(SNOWFALL_RATES[j], TERMINAL_VELOCITIES[j])) + + combos = np.column_stack((rainfall_rates, occupancy_ratios)) + + sample_id_list = sorted(['_'.join(x.strip().split(',')) for x in open(SPLIT).readlines()]) + + reversed_first_half = list(split(sample_id_list, 2))[0] + reversed_first_half.reverse() + second_half = list(split(sample_id_list, 2))[1] + + new_list = second_half + reversed_first_half + + for mode in ['gunn', 'sekhon']: + + p_bar = tqdm(new_list, desc=mode) + + for sample_idx in p_bar: + + lidar_file = LIDAR_FOLDER / f'{sample_idx}.bin' + + points = np.fromfile(str(lidar_file), dtype=np.float32).reshape(-1, 5) + calibration = get_calib() + + for combo in combos: + + rainfall_rate, occupancy_ratio = combo + + save_dir = LIDAR_FOLDER.parent / 'snowfall_simulation' / mode / f'{LIDAR_FOLDER.name}_' \ + f'rainrate_{int(rainfall_rate)}' + save_dir.mkdir(parents=True, exist_ok=True) + + save_path = save_dir / f'{sample_idx}.bin' + + if save_path.is_file(): + continue + + pc = copy.deepcopy(points) + + pts_rectified = calibration.lidar_to_rect(pc[:, 0:3]) + fov_flag = get_fov_flag(pts_rectified, (1024, 1920), calibration) + + pc = pc[fov_flag] + + snowflake_file_prefix = f'{mode}_{rainfall_rate}_{occupancy_ratio}' + + stats, aug_pc = augment(pc=pc, particle_file_prefix=snowflake_file_prefix, + beam_divergence=float(np.degrees(3e-3))) + + aug_pc.astype(np.float32).tofile(save_path) \ No newline at end of file diff --git a/tools/snowfall/sampling.py b/tools/snowfall/sampling.py new file mode 100644 index 0000000..277d5e0 --- /dev/null +++ b/tools/snowfall/sampling.py @@ -0,0 +1,413 @@ +__author__ = "Martin Hahner" +__contact__ = "martin.hahner@pm.me" +__license__ = "CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)" + +import itertools +import multiprocessing + +import numpy as np + +from tqdm import tqdm +from typing import Tuple +from pathlib import Path +from datetime import datetime +from tqdm.contrib.concurrent import process_map + + +import matplotlib.pyplot as plt + +PI = np.pi +SAVE_DIR = str(Path.home() / 'Downloads' / 'snowflakes') + + +def compute_occupancy(snowfall_rate: float, terminal_velocity: float, snow_density: float=0.1) -> float: + """ + :param snowfall_rate: Typically ranges from 0 to 2.5 [mm/h] + :param terminal_velocity: Varies from 0.2 to 2 [m/s] + :param snow_density: Varies from 0.01 to 0.2 depending on snow wetness [g/cmΒ³] + :return: Occupancy ratio. + """ + water_density = 1.0 + + return (water_density * snowfall_rate) / ((3.6 * 10 ** 6) * (snow_density * terminal_velocity)) + + +def rainfall_rate_to_snowfall_rate(rainfall_rate: float, terminal_velocity: float, + snowflake_density: float = 0.1, snowflake_diameter: float = 0.003) -> float: + """ + :param rainfall_rate: Varies from 0.5 (slight rain) to 50 (violent shower) [mm/h] + :param terminal_velocity: Varies from 0.2 to 2 [m/s] + :param snowflake_density: Varies from 0.01 to 0.2 depending on snow wetness [g/cmΒ³] + :param snowflake_diameter: Varies from 1 to 10 [m] + + :return: + snowfall_rate: Typically ranges from 0 to 2.5 [mm/h] + 0 - 1 light snow + 1 - 2.5 moderate snow + > 2.5 heavy snow + """ + + snowfall_rate = 487 * snowflake_density * snowflake_diameter * terminal_velocity * (rainfall_rate ** (2/3)) + + return snowfall_rate + + +def snowfall_rate_to_rainfall_rate(snowfall_rate: float, terminal_velocity: float, + snowflake_density: float = 0.1, snowflake_diameter: float = 0.003) -> float: + """ + :param snowfall_rate: Typically ranges from 0 to 2.5 [mm/h] + :param terminal_velocity: Varies from 0.2 to 2 [m/s] + :param snowflake_density: Varies from 0.01 to 0.2 depending on snow wetness [g/cm^3] + :param snowflake_diameter: Varies from 1 to 10 [m] + + :return: + rainfall_rate: Varies from 0.5 (slight rain) to 50 (violent shower) [mm/h] + """ + + rainfall_rate = np.sqrt((snowfall_rate / (487 * snowflake_density * snowflake_diameter * terminal_velocity))**3) + + return rainfall_rate + + +def sekhon_srivastava(precipitation_rate: float) -> float: + """ + :param precipitation_rate: in mm/h + :return: in 1/cm + """ + # Determine rate parameter of distribution of snowflake diameters via formula of Sekhon and Srivastava (1970). + return 22.9 * precipitation_rate ** -0.45 + + +def gunn_marshall(precipitation_rate: float) -> float: + """ + :param precipitation_rate: in mm/h + :return: in 1/cm + """ + # Determine rate parameter of distribution of snowflake diameters via formula of Marshall and Gunn (1958). + return 25.5 * precipitation_rate ** -0.48 + + +def dart_throwing(occupancy_ratio: float, + precipitation_rate: float, + R_0: float, + rng: np.random.Generator, + distribution: str = 'sekhon_srivastava', + show_progessbar: bool = False) -> np.ndarray: + """ + :param occupancy_ratio: Ratio of the area of the medium occupied by particles. + :param precipitation_rate: Measured in millimeters of equivalent liquid water per hour. + :param R_0: Radius of circular disk that forms the domain of sampling (in meters). + :param rng: Random number generator initialized externally with a random seed. + :param distribution: Distribition model of particle diameters. + :param show_progessbar: Flag if progressbar should be displayed. + + :return: N-by-3 array of sampled particles as disks, where each row contains abscissa and + ordinate of disk center and disk radius (in meters). + """ + + if distribution == 'sekhon': + diameter_rate_parameter = sekhon_srivastava(precipitation_rate) + elif distribution == 'gunn': + diameter_rate_parameter = gunn_marshall(precipitation_rate) + else: + raise NotImplementedError('Distribution model unknown.') + + diameter_scale_parameter = 1 / diameter_rate_parameter # in cm + + # Initialize samples to empty set. + samples = np.zeros((0, 3)) + + # Initialize occupied area to 0. + area_occupied = 0.0 + + # Calculate global occupied area across entire domain. + area_occupied_global = occupancy_ratio * PI * R_0 ** 2 + + large_number = 1 / occupancy_ratio + total = area_occupied_global * large_number + 1 + + if show_progessbar: + + pbar = tqdm(total=total, desc='sampling particles', + bar_format='{desc}: {percentage:3.0f}%|{bar}|[{elapsed}<{remaining}, {rate_fmt}{postfix}]') + + else: + + pbar = None + + i = 0 + r_avg = 0 + + # Main sampling loop. + while area_occupied < area_occupied_global: + + # Sample center of particle. + length = np.sqrt(rng.uniform(0, R_0 ** 2)) + angle = rng.uniform(0, 2) * PI + + x = length * np.cos(angle) + y = length * np.sin(angle) + + particle_diameter = np.inf + # Sample diameter of particle from exponential distribution (in millimeters). + while particle_diameter > 20: # limit diameter to a maximum of 2cm + particle_diameter = rng.exponential(diameter_scale_parameter * 10) + + # Convert diameter to meters. + particle_diameter = particle_diameter / 1000 + + # Sample height of particle center relative to examined plane. + height = rng.uniform(-particle_diameter / 2, particle_diameter / 2) + + # Calculate radius of disk that constitutes the intersection of the sampled ball with the examined plane. + disk_radius = np.sqrt((particle_diameter / 2) ** 2 - height ** 2) + + # If the disk includes the origin, reject the sample and continue. + if x ** 2 + y ** 2 <= disk_radius ** 2: + continue + + # Check whether current particle overlaps with any particle that has already been sampled. + sample_has_overlap = (samples[:, 0] - x) ** 2 + (samples[:, 1] - y) ** 2 <= (samples[:, 2] + disk_radius) ** 2 + + # If yes, reject the sample and continue. + if np.any(sample_has_overlap): + continue + + else: + + r_avg = (r_avg * i + disk_radius) / (i+1) + i += 1 + + area = PI * disk_radius ** 2 + area_occupied += area + samples = np.concatenate((samples, np.array([[x, y, disk_radius]]))) + + if pbar: + pbar.update(area * large_number) + pbar.set_postfix({'n_sampled': len(samples), + 'r_avg': r_avg}) + + if pbar: + pbar.n = total + pbar.close() + + return samples + + +def incidence_range_empirical_distribution(samples, + R_0, + n_directions, + sample_regular_directions=True, + rng=None): + """ + :param samples: N-by-3 array of sampled particles as disks, where each row contains abscissa + and ordinate of disk center and disk radius (in meters). + :param R_0: Radius of circular disk that forms the domain of sampling (in meters). + :param n_directions: number of directions to sample for obtaining an empirical distribution of + incidence range. + :param sample_regular_directions: Boolean determining whether to sample directions regularly or uniformly at + random. + :param rng: NumPy random number generator object. + + :return: ranges_travelled: (n_directions,) NumPy array of ranges that rays have travelled before + intersecting a particle (in meters). + directions: directions of rays represented as angles (in the [0, 2 * pi) interval, + in radians). + """ + + n_samples = np.shape(samples)[0] + + # Create NumPy array with directions to be examined represented by angles in the [0, 2 * pi) interval. + if sample_regular_directions: + directions = np.linspace(0, 2*PI, n_directions, endpoint=False) + else: + directions = rng.uniform(0, 2*PI, n_directions) + + ranges_travelled = R_0 * np.ones(n_directions) + + for i in range(n_directions): + + direction = directions[i] + tan_direction = np.tan(direction) + + for j in range(n_samples): + sample = samples[j] + x_center, y_center, radius = sample + + if direction == PI/2 or direction == 3*PI/2: + a, b, c = 1.0, 0.0, 0.0 + else: + a, b, c = tan_direction, -1.0, 0.0 + + # Compute distance of current line from the center of the current particle. + distance_from_sample_to_line = np.abs(a * x_center + b * y_center + c) / np.sqrt(a ** 2 + b ** 2) + + # Check whether line that corresponds to current direction incides on current particle. + if distance_from_sample_to_line <= radius: + # Solve the system of (line, circle) to identify their two points of intersection. + if direction == PI/2 or direction == 3*PI/2: + x_intersection = np.array([0.0, 0.0]) + y_intersection = np.array([y_center + np.sqrt(radius ** 2 - x_center ** 2), + y_center - np.sqrt(radius ** 2 - x_center ** 2)]) + else: + discriminant = (x_center + y_center * tan_direction) ** 2\ + - (1 + tan_direction ** 2) * (x_center ** 2 + y_center ** 2 - radius ** 2) + x_intersection = np.array([(x_center + y_center * tan_direction + np.sqrt(discriminant)) / + (1 + tan_direction ** 2), + (x_center + y_center * tan_direction - np.sqrt(discriminant)) / + (1 + tan_direction ** 2)]) + y_intersection = tan_direction * x_intersection + + # Keep the solution with the smallest distance from the origin. + ind_min = np.argmin(np.sum(np.array([x_intersection, y_intersection]) ** 2, axis=0)) + x_intersection_single = x_intersection[ind_min] + y_intersection_single = y_intersection[ind_min] + + # Check whether current direction corresponds to the part of the line that intersects with the current + # particle. + phi_intersection = np.arctan2(y_intersection_single, x_intersection_single) + if np.cos(phi_intersection) * np.cos(direction) > 0 or np.sin(phi_intersection) * np.sin(direction) > 0: + # If yes, proceed with update of range for current direction if necessary. Otherwise disregard the + # current particle. + distance_from_origin_to_intersection =\ + np.sqrt(np.sum(x_intersection_single ** 2 + y_intersection_single ** 2)) + if distance_from_origin_to_intersection < ranges_travelled[i]: + ranges_travelled[i] = distance_from_origin_to_intersection + + return ranges_travelled, directions + + +def save_plot(samples: np.ndarray, R_0: float, string: str, scale_factor: int = 500, + show_progessbar: bool = False) -> None: + + fig, ax = plt.subplots(figsize=(R_0, R_0)) + + if scale_factor == 1: + filename = f'{string}.svg' + plt.title(f'sampled particles', fontsize=150) + else: + assert scale_factor > 1, 'scale_factor has to be bigger than or equal to 1' + filename = f'{string}_({scale_factor}x_increased).svg' + plt.title(f'sampled particles (radius {scale_factor}x increased)', fontsize=150) + + plt.xlim([-R_0, R_0]) + plt.ylim([-R_0, R_0]) + + if scale_factor <= 100: + plt.xticks(np.arange(-R_0, R_0+1, step=1), fontsize=10) + plt.yticks(np.arange(-R_0, R_0+1, step=1), fontsize=10) + plt.grid(color = 'red', linewidth = 0.1) + else: + plt.xticks(np.arange(-R_0, R_0+1, step=10), fontsize=100) + plt.yticks(np.arange(-R_0, R_0+1, step=10), fontsize=100) + + plt.xlabel('x (m)', fontsize=150) + plt.ylabel('y (m)', fontsize=150) + + origin = plt.Circle((0, 0), 0.1, color='red') + ax.add_patch(origin) + + if show_progessbar: + pbar = tqdm(samples, desc='creating plot') + else: + pbar = samples + + for sample in pbar: + x, y, radius = sample + circle = plt.Circle((x, y), scale_factor * radius) # original radius is too small to be visualized + ax.add_patch(circle) + + fig.savefig(f'{SAVE_DIR}/{filename}') + + +def save_array(samples: np.ndarray, string: str) -> None: + + filepath = f'{SAVE_DIR}/{string}.npy' + + np.save(filepath, samples) + + +def sampling_exists(name: str) -> bool: + + filepath = Path(f'{SAVE_DIR}/{name}.npy') + + return filepath.is_file() + + +#A function which will process a tuple of parameters +def do_in_parallel(params: Tuple) -> None: + + dist = params[0] + rate, ratio = params[1][0], params[1][1] + line = params[2] + + name = f'{dist}_{rate}_{ratio}_{line}' + + if sampling_exists(name): + print(f'\n{name} skipped') + else: + print(f'\n{name}') + + particles = dart_throwing(occupancy_ratio=ratio, precipitation_rate=rate, R_0=r, + distribution=dist, rng=random_number_generator) + + save_array(samples=particles, string=name) + + if line < 3 and rate > 5: + save_plot(samples=particles, R_0=r, string=name) + + +if __name__ == '__main__': + + date_time_obj = datetime.now() + timestamp_str = date_time_obj.strftime("%Y-%m-%d_%H-%M-%S") + + """ + Rates of rainfall: + ================== + - Drizzle Very small droplets. + - Slight (fine) drizzle Detectable as droplets only on the face, car windscreens and windows. + - Moderate drizzle Windows and other surfaces stream with water. + - Heavy (thick) drizzle Impairs visibility and is measurable in a raingauge, rates up to 1 mm per hour. + + - Rain Drops of appreciable size and may be described as small to large drops. + It is possible to have rain drops within drizzle! + - Slight rain Less than 0.5 mm per hour. + - Moderate rain Greater than 0.5 mm per hour, but less than 4.0 mm per hour. + - Heavy rain Greater than 4 mm per hour, but less than 8 mm per hour. + - Very heavy rain Greater than 8 mm per hour. + - Slight shower Less than 2 mm per hour. + - Moderate shower Greater than 2 mm, but less than 10 mm per hour. + - Heavy shower Greater than 10 mm per hour, but less than 50 mm per hour. + - Violent shower Greater than 50 mm per hour. + + https://water.usgs.gov/edu/activity-howmuchrain-metric.html + """ + + r = 80.0 # m + + random_number_generator = np.random.default_rng(42) + + # Generate values for each parameter + r_s_s = np.linspace(0.5, 2.5, 5) # mm/h + v_s_s = np.linspace(0.2, 2, 10) # m/s + + p = list(itertools.product(r_s_s, v_s_s)) + + r_r_s = [snowfall_rate_to_rainfall_rate(r_s, v_s) for r_s, v_s in p] # mm/h + ratios = [compute_occupancy(r_s, v_s) for r_s, v_s in p] + + m = ['gunn', 'sekhon'] + + runs = np.column_stack((r_r_s, ratios)) + runs = runs[runs[:, 1].argsort()] + runs = runs[::-1] + + n = range(1, 65) + + # Generate a list of tuples where each tuple is a combination of parameters. + # The list will contain all possible combinations of parameters. + paramlist = list(itertools.product(m, runs, n)) + + # Distribute the parameter sets evenly across the cores + process_map(do_in_parallel, paramlist, max_workers=multiprocessing.cpu_count(), chunksize=1) diff --git a/tools/snowfall/simulation.py b/tools/snowfall/simulation.py new file mode 100644 index 0000000..7123f32 --- /dev/null +++ b/tools/snowfall/simulation.py @@ -0,0 +1,608 @@ +__author__ = "Martin Hahner" +__contact__ = "martin.hahner@pm.me" +__license__ = "CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)" + +import copy +import yaml +import random +import itertools +import functools + +from pathlib import Path + +from lib.OpenPCDet.pcdet.utils import calibration_kitti + +from typing import Dict, List, Tuple +from tqdm.contrib.concurrent import process_map +from scipy.constants import speed_of_light as c # in m/s + +import numpy as np +import multiprocessing as mp +import tools.snowfall.geometry as g + +from tools.wet_ground.planes import calculate_plane +from tools.wet_ground.augmentation import estimate_laser_parameters + +PI = np.pi +DEBUG = False +EPSILON = np.finfo(float).eps + + + +def get_calib(sensor: str = 'hdl64'): + calib_file = Path(__file__).parent.parent.parent.absolute() / \ + 'lib' / 'OpenPCDet' / 'data' / 'dense' / f'calib_{sensor}.txt' + assert calib_file.exists(), f'{calib_file} not found' + return calibration_kitti.Calibration(calib_file) + + +def get_fov_flag(pts_rect, img_shape, calib): + + pts_img, pts_rect_depth = calib.rect_to_img(pts_rect) + val_flag_1 = np.logical_and(pts_img[:, 0] >= 0, pts_img[:, 0] < img_shape[1]) + val_flag_2 = np.logical_and(pts_img[:, 1] >= 0, pts_img[:, 1] < img_shape[0]) + val_flag_merge = np.logical_and(val_flag_1, val_flag_2) + pts_valid_flag = np.logical_and(val_flag_merge, pts_rect_depth >= 0) + + return pts_valid_flag + + +def process_single_channel(root_path: str, particle_file_prefix: str, orig_pc: np.ndarray, beam_divergence: float, + order: List[int], channel_infos: List, channel: int) -> Tuple: + """ + :param root_path: Needed for training on GPU cluster. + :param particle_file_prefix: Path to file where sampled particles are stored (x, y, r). + :param orig_pc: N-by-5 array containing original pointcloud (x, y, z, intensity, channel). + :param beam_divergence: Equivalent to the total beam opening angle (in degree). + :param order: Order of the particle disks. + :param channel_infos List of Dicts containing sensor calibration info. + + :param channel: Number of the LiDAR channel [0, 63]. + + :return: Tuple of + - intensity_diff_sum, + - idx, + - the augmented points of the current LiDAR channel. + """ + + intensity_diff_sum = 0 + + index = order[channel] + + min_intensity = channel_infos[channel].get('min_intensity', 0) # not all channels contain this info + + focal_distance = channel_infos[channel]['focal_distance'] * 100 + focal_slope = channel_infos[channel]['focal_slope'] + focal_offset = (1 - focal_distance / 13100) ** 2 # from velodyne manual + + particle_file = f'{particle_file_prefix}_{index + 1}.npy' + + channel_mask = orig_pc[:, 4] == channel + idx = np.where(channel_mask == True)[0] + + pc = orig_pc[channel_mask] + + N = pc.shape[0] + + x, y, z, intensity, label = pc[:, 0], pc[:, 1], pc[:, 2], pc[:, 3], pc[:, 4] + + distance = np.linalg.norm([x, y, z], axis=0) + + center_angles = np.arctan2(y, x) # in range [-PI, PI] + center_angles[center_angles < 0] = center_angles[center_angles < 0] + 2 * PI # in range [0, 2*PI] + + beam_angles = -np.ones((N, 2)) + + beam_angles[:, 0] = center_angles - np.radians(beam_divergence / 2) # could lead to negative values + beam_angles[:, 1] = center_angles + np.radians(beam_divergence / 2) # could lead to values above 2*PI + + # put beam_angles back in range [0, 2*PI] + beam_angles[beam_angles < 0] = beam_angles[beam_angles < 0] + 2 * PI + beam_angles[beam_angles > 2 * PI] = beam_angles[beam_angles > 2 * PI] - 2 * PI + + occlusion_list = get_occlusions(beam_angles=beam_angles, ranges_orig=distance, beam_divergence=beam_divergence, + root_path=root_path, particle_file=particle_file) + + lidar_range = 120 # in meter + intervals_per_meter = 10 # => 10cm discretization + beta_0 = 1 * 10 ** -6 / PI + tau_h = 1e-8 # value 10ns taken from HDL64-S1 specsheet + + M = lidar_range * intervals_per_meter + + M_extended = int(np.ceil(M + c * tau_h * intervals_per_meter)) + lidar_range_extended = lidar_range + c * tau_h + + R = np.round(np.linspace(0, lidar_range_extended, M_extended), len(str(intervals_per_meter))) + + for j, beam_dict in enumerate(occlusion_list): + + d_orig = distance[j] + i_orig = intensity[j] + + if channel in [53, 55, 56, 58]: + max_intensity = 230 + else: + max_intensity = 255 + + i_adjusted = i_orig - 255 * focal_slope * np.abs(focal_offset - (1 - d_orig/120)**2) + i_adjusted = np.clip(i_adjusted, 0, max_intensity) # to make sure we don't get negative values + + CA_P0 = i_adjusted * d_orig ** 2 / beta_0 + + if len(beam_dict.keys()) > 1: # otherwise there is no snowflake in the current beam + + i = np.zeros(M_extended) + + for key, tuple_value in beam_dict.items(): + + if key != -1: # if snowflake + i_orig = 0.9 * max_intensity # set i to 90% reflectivity + CA_P0 = i_orig / beta_0 # and do NOT normalize with original range + + r_j, ratio = tuple_value + + start_index = int(np.ceil(r_j * intervals_per_meter)) + end_index = int(np.floor((r_j + c * tau_h) * intervals_per_meter) + 1) + + for k in range(start_index, end_index): + i[k] += received_power(CA_P0, beta_0, ratio, R[k], r_j, tau_h) + + max_index = np.argmax(i) + i_max = i[max_index] + d_max = (max_index / intervals_per_meter) - (c * tau_h / 2) + + i_max += max_intensity * focal_slope * np.abs(focal_offset - (1 - d_max/120)**2) + i_max = np.clip(i_max, min_intensity, max_intensity) + + if abs(d_max - d_orig) < 2 * (1 / intervals_per_meter): # only alter intensity + + pc[j, 4] = 1 + + new_i = int(i_max) + + if new_i > (i_orig + 1) and DEBUG: + print(f'\nnew intensity ({new_i}) in channel {channel} bigger than before ({i_orig}) ' + f'=> clipping to {i_orig}') + new_i = np.clip(new_i, min_intensity, i_orig) + pc[j, 4] = 0 + + intensity_diff_sum += i_orig - new_i + + else: # replace point of hard target with snowflake + + pc[j, 4] = 2 + + d_scaling_factor = d_max / d_orig + + pc[j, 0] = pc[j, 0] * d_scaling_factor + pc[j, 1] = pc[j, 1] * d_scaling_factor + pc[j, 2] = pc[j, 2] * d_scaling_factor + + new_i = int(i_max) + + assert new_i >= 0, f'new intensity is negative ({new_i})' + + clipped_i = np.clip(new_i, min_intensity, max_intensity) + + pc[j, 3] = clipped_i + + else: + + pc[j, 4] = 0 + + return intensity_diff_sum, idx, pc + + +def binary_angle_search(angles: List[float], low: int, high: int, angle: float) -> int: + """ + Adapted from https://www.geeksforgeeks.org/python-program-for-binary-search + + :param angles: List of individual endpoint angles. + :param low: Start index. + :param high: End index. + :param angle: Query angle. + + :return: Index of angle if present in list of angles, else -1 + """ + + # Check base case + if high >= low: + + mid = (high + low) // 2 + + # If angle is present at the middle itself + if angles[mid] == angle: + return mid + + # If angle is smaller than mid, then it can only be present in left sublist + elif angles[mid] > angle: + return binary_angle_search(angles, low, mid - 1, angle) + + # Else the angle can only be present in right sublist + else: + return binary_angle_search(angles, mid + 1, high, angle) + + else: + # Angle is not present in the list + return -1 + + +def compute_occlusion_dict(beam_angles: Tuple[float, float], intervals: np.ndarray, + current_range: float, beam_divergence: float) -> Dict: + """ + :param beam_angles: Tuple of angles (left, right). + :param intervals: N-by-3 array of particle tangent angles and particle distance from origin. + :param current_range: Range to the original hard target. + :param beam_divergence: Equivalent to the total beam opening angle (in degree). + + :return: + occlusion_dict: Dict containing a tuple of the distance and the occluded angle by respective particle. + e.g. + 0: (distance to particle, occlusion ratio [occluded angle / total angle]) + 1: (distance to particle, occlusion ratio [occluded angle / total angle]) + 3: (distance to particle, occlusion ratio [occluded angle / total angle]) + 7: (distance to particle, occlusion ratio [occluded angle / total angle]) + ... + -1: (distance to original target, unocclusion ratio [unoccluded angle / total angle]) + + all (un)occlusion ratios always sum up to the value 1 + """ + + try: + N = intervals.shape[0] + except IndexError: + N = 1 + + right_angle, left_angle = beam_angles + + # Make everything properly sorted in the corner case of phase discontinuity. + if right_angle > left_angle: + right_angle = right_angle - 2*PI + right_left_order_violated = intervals[:, 0] > intervals[:, 1] + intervals[right_left_order_violated, 0] = intervals[right_left_order_violated, 0] - 2*PI + + endpoints = sorted(set([right_angle] + list(itertools.chain(*intervals[:, :2])) + [left_angle])) + diffs = np.diff(endpoints) + n_intervals = diffs.shape[0] + + assignment = -np.ones(n_intervals) + + occlusion_dict = {} + + for j in range(N): + + a1, a2, distance = intervals[j] + + i1 = binary_angle_search(endpoints, 0, len(endpoints), a1) + i2 = binary_angle_search(endpoints, 0, len(endpoints), a2) + + assignment_made = False + + for k in range(i1, i2): + + if assignment[k] == -1: + assignment[k] = j + assignment_made = True + + if assignment_made: + ratio = diffs[assignment == j].sum() / np.radians(beam_divergence) + occlusion_dict[j] = (distance, np.clip(ratio, 0, 1)) + + ratio = diffs[assignment == -1].sum() / np.radians(beam_divergence) + occlusion_dict[-1] = (current_range, np.clip(ratio, 0, 1)) + + return occlusion_dict + + +def get_occlusions(beam_angles: np.ndarray, ranges_orig: np.ndarray, root_path: str, particle_file: str, + beam_divergence: float) -> List: + """ + :param beam_angles: M-by-2 array of beam endpoint angles, where for each row, the value in the first column + is lower than the value in the second column. + :param ranges_orig: M-by-1 array of original ranges corresponding to beams (in m). + :param root_path: Needed for training on GPU cluster. + + :param particle_file: Path to N-by-3 array of all sampled particles as disks, + where each row contains abscissa and ordinate of the disk center and disk radius (in m). + :param beam_divergence: Equivalent to the opening angle of an individual LiDAR beam (in degree). + + :return: + occlusion_list: List of M Dicts. + Each Dict contains a Tuple of + If key == -1: + - distance to the original hard target + - angle that is not occluded by any particle + Else: + - the distance to an occluding particle + - the occluded angle by this particle + + """ + + M = np.shape(beam_angles)[0] + + if root_path: + path = Path(root_path) / 'training' / 'snowflakes' / 'npy' / particle_file + else: + path = Path(__file__).parent.parent.parent.absolute() / 'npy' / particle_file + + all_particles = np.load(str(path)) + x, y, _ = all_particles[:, 0], all_particles[:, 1], all_particles[:, 2] + + all_particle_ranges = np.linalg.norm([x, y], axis=0) # (N,) + all_beam_limits_a, all_beam_limits_b = g.angles_to_lines(beam_angles) # (M, 2) + + occlusion_list = [] + + # Main loop over beams. + for i in range(M): + + current_range = ranges_orig[i] # (K,) + + right_angle = beam_angles[i, 0] + left_angle = beam_angles[i, 1] + + in_range = np.where(all_particle_ranges < current_range) + + particles = all_particles[in_range] # (K, 3) + + x, y, particle_radii = particles[:, 0], particles[:, 1], particles[:, 2] + + particle_angles = np.arctan2(y, x) # (K,) + particle_angles[particle_angles < 0] = particle_angles[particle_angles < 0] + 2 * PI + + tangents_a, tangents_b = g.tangents_from_origin(particles) # (K, 2) + + ################################################################################################################ + # Determine whether centers of the particles lie inside the current beam, + # which is first sufficient condition for intersection. + standard_case = np.logical_and(right_angle <= particle_angles, particle_angles <= left_angle) + seldom_case = np.logical_and.reduce((right_angle - 2 * PI <= particle_angles, particle_angles <= left_angle, + np.full_like(particle_angles, right_angle > left_angle, dtype=bool))) + seldom_case_2 = np.logical_and.reduce((right_angle <= particle_angles, particle_angles <= left_angle + 2 * PI, + np.full_like(particle_angles, right_angle > left_angle, dtype=bool))) + + center_in_beam = np.logical_or.reduce((standard_case, seldom_case, seldom_case_2)) # (K,) + ################################################################################################################ + + ################################################################################################################ + # Determine whether distances from particle centers to beam rays are smaller than the radii of the particles, + # which is second sufficient condition for intersection. + beam_limits_a = all_beam_limits_a[i, np.newaxis].T # (2, 1) + beam_limits_b = all_beam_limits_b[i, np.newaxis].T # (2, 1) + beam_limits_c = np.zeros((2, 1)) # origin # (2, 1) + + # Get particle distances to right and left beam limit. + distances = g.distances_of_points_to_lines(particles[:, :2], + beam_limits_a, beam_limits_b, beam_limits_c) # (K, 2) + + radii_intersecting = distances < np.column_stack((particle_radii, particle_radii)) # (K, 2) + + intersect_right_ray = g.do_angles_intersect_particles(right_angle, particles[:, 0:2]).T # (K, 1) + intersect_left_ray = g.do_angles_intersect_particles(left_angle, particles[:, 0:2]).T # (K, 1) + + right_beam_limit_hit = np.logical_and(radii_intersecting[:, 0], intersect_right_ray[:, 0]) + left_beam_limit_hit = np.logical_and(radii_intersecting[:, 1], intersect_left_ray[:, 0]) + + ################################################################################################################ + # Determine whether particles intersect the current beam by taking the disjunction of the above conditions. + particles_intersect_beam = np.logical_or.reduce((center_in_beam, + right_beam_limit_hit, left_beam_limit_hit)) # (K,) + + ################################################################################################################ + + intersecting_beam = np.where(particles_intersect_beam) + + particles = particles[intersecting_beam] # (L, 3) + particle_angles = particle_angles[intersecting_beam] + tangents_a = tangents_a[intersecting_beam] + tangents_b = tangents_b[intersecting_beam] + tangents = (tangents_a, tangents_b) + right_beam_limit_hit = right_beam_limit_hit[intersecting_beam] + left_beam_limit_hit = left_beam_limit_hit[intersecting_beam] + + # Get the interval angles from the tangents. + tangent_angles = g.tangent_lines_to_tangent_angles(tangents, particle_angles) # (L, 2) + + # Correct tangent angles that do exceed beam limits. + interval_angles = g.tangent_angles_to_interval_angles(tangent_angles, right_angle, left_angle, + right_beam_limit_hit, left_beam_limit_hit) # (L, 2) + + ################################################################################################################ + # Sort interval angles by increasing distance from origin. + distances_to_origin = np.linalg.norm(particles[:, :2], axis=1) # (L,) + + intervals = np.column_stack((interval_angles, distances_to_origin)) # (L, 3) + ind = np.argsort(intervals[:, -1]) + intervals = intervals[ind] # (L, 3) + + occlusion_list.append(compute_occlusion_dict((right_angle, left_angle), + intervals, + current_range, + beam_divergence)) + + return occlusion_list + + +def augment(pc: np.ndarray, particle_file_prefix: str, beam_divergence: float, shuffle: bool = True, + show_progressbar: bool=False, only_camera_fov: bool=True, noise_floor: float=0.7, + root_path: str=None) -> Tuple: + """ + :param pc: N-by-5 array containing original pointcloud (x, y, z, intensity, channel). + :param particle_file_prefix: Path to file where sampled particles are stored (x, y, r). + :param beam_divergence: Beam divergence in degrees. + :param shuffle: Flag if order of sampled snowflakes should be shuffled. + :param show_progressbar: Flag if tqdm should display a progessbar. + :param only_camera_fov: Flag if the camera field of view (FOV) filter should be applied. + :param noise_floor: Noise floor threshold. + :param root_path: Optional root path, needed for training on GPU cluster. + + :return: Tuple of + - Tuple of the following statistics + - num_attenuated, + - avg_intensity_diff + - N-by-4 array of the augmented pointcloud. + """ + + pc = pc[pc[:, 4].argsort()] # sort pointcloud by channel + + w, h = calculate_plane(pc) + ground = np.logical_and(np.matmul(pc[:, :3], np.asarray(w)) + h < 0.5, + np.matmul(pc[:, :3], np.asarray(w)) + h > -0.5) + pc_ground = pc[ground] + + calculated_indicent_angle = np.arccos(np.divide(np.matmul(pc_ground[:, :3], np.asarray(w)), + np.linalg.norm(pc_ground[:, :3], axis=1) * np.linalg.norm(w))) + + relative_output_intensity, adaptive_noise_threshold, _, _ = estimate_laser_parameters(pc_ground, + calculated_indicent_angle, + noise_floor=noise_floor, + debug=False) + + adaptive_noise_threshold *= np.cos(calculated_indicent_angle) + + ground_distances = np.linalg.norm(pc_ground[:, :3], axis=1) + distances = np.linalg.norm(pc[:, :3], axis=1) + + p = np.polyfit(ground_distances, adaptive_noise_threshold, 2) + + relative_output_intensity = p[0] * distances ** 2 + p[1] * distances + p[2] + + orig_pc = copy.deepcopy(pc) + aug_pc = copy.deepcopy(pc) + + sensor_info = Path(__file__).parent.parent.parent.resolve() / 'calib' / '20171102_64E_S3.yaml' + + with open(sensor_info, 'r') as stream: + sensor_dict = yaml.safe_load(stream) + + channel_infos = sensor_dict['lasers'] + num_channels = sensor_dict['num_lasers'] + + channels = range(num_channels) + order = list(range(num_channels)) + + if shuffle: + random.shuffle(order) + + channel_list = [None] * num_channels + + if show_progressbar: + + channel_list[:] = process_map(functools.partial(process_single_channel, root_path, particle_file_prefix, + orig_pc, beam_divergence, order, channel_infos), + channels, chunksize=4) + + else: + + pool = mp.pool.ThreadPool(mp.cpu_count()) + + channel_list[:] = pool.map(functools.partial(process_single_channel, root_path, particle_file_prefix, orig_pc, + beam_divergence, order, channel_infos), channels) + + pool.close() + pool.join() + + intensity_diff_sum = 0 + + for item in channel_list: + + tmp_intensity_diff_sum, idx, pc = item + + intensity_diff_sum += tmp_intensity_diff_sum + + aug_pc[idx] = pc + + aug_pc[:, 3] = np.round(aug_pc[:, 3]) + + scattered = aug_pc[:, 4] == 2 + above_threshold = aug_pc[:, 3] > relative_output_intensity[:] + scattered_or_above_threshold = np.logical_or(scattered, above_threshold) + + num_removed = np.logical_not(scattered_or_above_threshold).sum() + aug_pc = aug_pc[np.where(scattered_or_above_threshold)] + + num_attenuated = (aug_pc[:, 4] == 1).sum() + + if num_attenuated > 0: + avg_intensity_diff = int(intensity_diff_sum / num_attenuated) + else: + avg_intensity_diff = 0 + + if only_camera_fov: + calib = get_calib() + + pts_rect = calib.lidar_to_rect(aug_pc[:, 0:3]) + fov_flag = get_fov_flag(pts_rect, (1024, 1920), calib) + + num_removed += np.logical_not(fov_flag).sum() + + aug_pc = aug_pc[fov_flag] + + stats = num_attenuated, num_removed, avg_intensity_diff + + return stats, aug_pc + + +def received_power(CA_P0: float, beta_0: float, ratio: float, r: float, r_j: float, tau_h: float) -> float: + + answer = ((CA_P0 * beta_0 * ratio * xsi(r_j)) / (r_j ** 2)) * np.sin((PI * (r - r_j)) / (c * tau_h)) ** 2 + + return answer + +def xsi(R: float, R_1: float = 0.9, R_2: float = 1.0) -> float: + + if R <= R_1: # emitted ligth beam from the tansmitter is not captured by the receiver + + return 0 + + elif R >= R_2: # emitted ligth beam from the tansmitter is fully captured by the receiver + + return 1 + + else: # emitted ligth beam from the tansmitter is partly captured by the receiver + + m = (1 - 0) / (R_2 - R_1) + b = 0 - (m * R_1) + y = m * R + b + + return y + + +if __name__ == '__main__': + + start_angle = np.radians(-22.5) + field_of_view = np.radians(360) + angular_resolution = np.radians(0.35) + + n_beams = int(np.floor(field_of_view / angular_resolution)) + + right_angles = np.linspace(start=start_angle, + stop=start_angle + field_of_view - angular_resolution, + num=n_beams, endpoint=True) + + left_angles = np.linspace(start=start_angle + angular_resolution, + stop=start_angle + field_of_view, + num=n_beams, endpoint=True) + + # Ensure angles are in [0, 2*PI] + right_angles[right_angles < 0] = right_angles[right_angles < 0] + 2*PI + left_angles[left_angles < 0] = left_angles[left_angles < 0] + 2*PI + + beam_limit_angles = np.column_stack((right_angles, left_angles)) + + ranges = 25 * np.ones(n_beams) + + test_particles = np.array([[10, 2, 1], + [15, 4, 2], + [ 3, 4, 1], + [12, 10, 3], + [17, 12, 1], + [ 0, 6, 1], + [ 7, 0, 1], + [18, 1, 1], + [ 2, 9, 2], + [ 3, -1, 1.1], + [30, 12, 3]]) + + # result = get_occlusions(beam_limit_angles, ranges, test_particles, float(np.degrees(angular_resolution))) \ No newline at end of file diff --git a/tools/wet_ground/augmentation.py b/tools/wet_ground/augmentation.py new file mode 100644 index 0000000..d68d40d --- /dev/null +++ b/tools/wet_ground/augmentation.py @@ -0,0 +1,333 @@ +__author__ = "Mario Bijelic" +__contact__ = "mario.bijelic@t-online.de" +__license__ = "CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)" + +import numpy as np + +import matplotlib.pyplot as plt + +from scipy.stats import linregress + +try: + + from utils import plot_2d_hist + from planes import calculate_plane + from phy_equations import total_transmittance_from_ground + +except ModuleNotFoundError: + + from tools.wet_ground.utils import plot_2d_hist + from tools.wet_ground.planes import calculate_plane + from tools.wet_ground.phy_equations import total_transmittance_from_ground + + + +def ground_water_augmentation(pointcloud, water_height=0.001, pavement_depth=0.0012, noise_floor=0.7, power_factor=15, + estimation_method='linear', flat_earth=False, debug=True, + delta=0.5, replace=True): + """ + :param pointcloud: 5 column DENSE pointcloud format + :param water_height: highway aquaplaning > 2.5mm in [m] + # https://www.gov.uk/aaib-reports/aar-1-2009-boeing-737-81q-g-xlac-avions-de-transport-regional-atr-72-202-g-bwda-and-embraer-emb-145eu-g-embo-29-december-2006 aquaplaning depth for airplanes + :param pavement_depth: fine graded 0.4-0.6, coarse graded 0.6-1.2, open graded friction course 1.5-3.5 mm + # https://www.researchgate.net/publication/245283455_NJTxtr_-_A_computer_program_based_on_LASER_to_monitor_asphalt_segregation?enrichId=rgreq-dc45b0cf243aa1c384fa68cc033e1a4a-XXX&enrichSource=Y292ZXJQYWdlOzI0NTI4MzQ1NTtBUzoyMTc3ODg0MTg0MDAyNTZAMTQyODkzNjIxMzYwMw%3D%3D&el=1_x_3&_esc=publicationCoverPdf + :param noise_floor Assumed minimum percentage of estimated Intensity values from minimum ground reflectance values + :param estimation_method Define how to fit estimated laser parameters choice from linear and poly + :param flat_earth Define if to use flat earth assumption for incident angle calculation + :param debug Enable debug for debugging plots + :return: augmented_pointcloud + """ + + w, h = calculate_plane(pointcloud) + + # Filter points below image plane + height_over_ground = np.matmul(pointcloud[:, :3], np.asarray(w)) + height_over_ground = height_over_ground.reshape((len(height_over_ground), 1)) + ground = np.logical_and(np.matmul(pointcloud[:, :3], np.asarray(w)) + h < delta, + np.matmul(pointcloud[:, :3], np.asarray(w)) + h > -delta) + + ground_idx = np.where(ground) + pointcloud_planes = np.hstack((pointcloud[ground, :], height_over_ground[ground])) + if pointcloud_planes.shape[0] < 1000: + return pointcloud + if not flat_earth: + # incident angle based on ground plane + # calculate incident angle based on scalar product -> resulting in the cosine of the + calculated_indicent_angle = np.arccos(np.divide(np.matmul(pointcloud_planes[:, :3], np.asarray(w)), + np.linalg.norm(pointcloud_planes[:, :3], + axis=1) * np.linalg.norm(w))) + elif flat_earth: + # incident angle based on flat earth assumption + calculated_indicent_angle = np.arccos(-np.divide(np.matmul(pointcloud_planes[:, :3], np.asarray([0, 0, 1])), + np.linalg.norm(pointcloud_planes[:, :3], + axis=1) * np.linalg.norm([0, 0, 1]))) + else: + assert False, 'flat earth tag has be bool' + + if debug: + # flat earth assumption based on internal coordinate system + plt.plot(np.linalg.norm(pointcloud_planes[:, :2], axis=1), calculated_indicent_angle * 180 / np.pi, 'x') + plt.title('Incident Angles') + plt.ylabel('Calculated incdent angle') + plt.xlabel('distance') + plt.legend(['Calculated Plane']) + plt.show() + plt.plot(np.linalg.norm(pointcloud_planes[:, :2], axis=1), pointcloud_planes[:, 3], 'x') + plt.title('Intensitites') + plt.ylabel('Intensity') + plt.xlabel('distance') + plt.show() + + relative_output_intensity, adaptive_noise_threshold, _, _ = estimate_laser_parameters(pointcloud_planes, + calculated_indicent_angle, + noise_floor=noise_floor, + estimation_method=estimation_method, + power_factor=power_factor, + debug=debug) + + # If average asphalt relfecitivity is 10% then we can use it to caclculate emmitted laserposer + + reflectivities = pointcloud_planes[:, 3] / np.cos(calculated_indicent_angle) / relative_output_intensity + + if debug: + plot_2d_hist(np.linalg.norm(pointcloud_planes[:, :2], axis=1), reflectivities) + + plt.plot(np.linalg.norm(pointcloud_planes[:, :2], axis=1), reflectivities, 'x') + plt.title('reflectivities') + plt.ylabel('Reflectivity') + plt.xlabel('distance') + plt.show() + plt.hist2d(np.linalg.norm(pointcloud_planes[:, :2], axis=1), reflectivities, bins=100) + plt.title('reflectivities distance hist') + plt.ylabel('Reflectivity') + plt.xlabel('distance') + plt.show() + + + # assumes a minimum ground reflectivity of 5% + rs, ts, rp, tp, aaout = total_transmittance_from_ground(calculated_indicent_angle, + rho=np.clip(reflectivities, 0.05, 1)) + + # estimate noiselevel from pointcloud by taking minimum reflected point value + if debug: + plt.plot(np.linalg.norm(pointcloud_planes[:, :3], axis=1), np.maximum(tp, ts) / calculated_indicent_angle, 'x') + plt.title('maximum transmittance/incident angle') + plt.ylabel('Reflectivity') + plt.xlabel('distance') + plt.show() + + t = np.maximum(tp, ts) + + # weight relflection between clear and wet -> Assumes a 45Β° thread profile + f = np.clip(water_height / pavement_depth, 0, 1) + tw = (1 - f) * reflectivities + f * t / calculated_indicent_angle + + + new_intensities = np.clip(relative_output_intensity * np.cos(calculated_indicent_angle) * tw, 0, + pointcloud_planes[:, 3]) + zero_points = new_intensities < (adaptive_noise_threshold * np.cos(calculated_indicent_angle)) + water_only_intensities = np.clip(t * relative_output_intensity, 0, 255) + + new_intensities[zero_points] = 0 + if debug: + plt.plot(np.linalg.norm(pointcloud_planes[:, :3], axis=1), pointcloud_planes[:, 3], 'bx') + plt.plot(np.linalg.norm(pointcloud_planes[:, :3], axis=1), new_intensities, 'yx') + plt.plot(np.linalg.norm(pointcloud_planes[:, :3], axis=1), water_only_intensities, 'gx') + plt.plot(np.linalg.norm(pointcloud_planes[:, :3], axis=1), + adaptive_noise_threshold * np.cos(calculated_indicent_angle), 'rx') + plt.title('Overlayed intensitites') + plt.ylabel('Intensities') + plt.xlabel('Total Distance') + plt.legend(['Original Values', 'New Intensities', 'Water Intensitites', 'Threshold']) + plt.show() + + + # Calculate which points to keep according to estimated adaptive noise threshold + keep_points = new_intensities > adaptive_noise_threshold * np.cos(calculated_indicent_angle) + keep_points_idx = np.where(keep_points) + pointcloud_planes = pointcloud_planes[:, :5] + + augmented_pointcloud = np.zeros((pointcloud.shape[0] - ground_idx[0].shape[0] + keep_points_idx[0].shape[0], 5)) + augmented_pointcloud[:pointcloud.shape[0] - ground_idx[0].shape[0], :] = pointcloud[np.logical_not(ground), :] + augmented_pointcloud[pointcloud.shape[0] - ground_idx[0].shape[0]:, :] = pointcloud_planes[keep_points_idx] + augmented_pointcloud[pointcloud.shape[0] - ground_idx[0].shape[0]:, 3] = new_intensities[keep_points_idx] + + if replace: + augmented_pointcloud[:, 4] = 0 + + # save augmented flag into laser counter + augmented_pointcloud[pointcloud.shape[0] - ground_idx[0].shape[0]:, 4] = 1 + + return augmented_pointcloud + + +def filter_below_ground(pointcloud, w, h): + above_ground = np.matmul(pointcloud[:, :3], np.asarray(w)) + h < 0.5 + pointcloud = pointcloud[above_ground, :] + + return pointcloud + + +def ransac_polyfit(x, y, order=3, n=15, k=100, t=0.1, d=15, f=0.8): + # Applied https://en.wikipedia.org/wiki/Random_sample_consensus + # Taken from https://gist.github.com/geohot/9743ad59598daf61155bf0d43a10838c + # n – minimum number of data points required to fit the model + # k – maximum number of iterations allowed in the algorithm + # t – threshold value to determine when a data point fits a model + # d – number of close data points required to assert that a model fits well to data + # f – fraction of close data points required + + bestfit = np.polyfit(x, y, order) + besterr = np.sum(np.abs(np.polyval(bestfit, x) - y)) + for kk in range(k): + maybeinliers = np.random.randint(len(x), size=n) + maybemodel = np.polyfit(x[maybeinliers], y[maybeinliers], order) + alsoinliers = np.abs(np.polyval(maybemodel, x) - y) < t + if sum(alsoinliers) > d and sum(alsoinliers) > len(x) * f: + bettermodel = np.polyfit(x[alsoinliers], y[alsoinliers], order) + thiserr = np.sum(np.abs(np.polyval(bettermodel, x[alsoinliers]) - y[alsoinliers])) + if thiserr < besterr: + bestfit = bettermodel + besterr = thiserr + return bestfit + + +def estimate_laser_parameters(pointcloud_planes, calculated_indicent_angle, power_factor=15, noise_floor=0.7, + debug=True, estimation_method='linear'): + """ + :param pointcloud_planes: Get all points which correspond to the ground + :param calculated_indicent_angle: The calculated incident angle for each individual point + :param power_factor: Determines, how much more Power is available compared to a groundplane reflection. + :param noise_floor: What are the minimum intensities that could be registered + :param debug: Show additional Method + :param estimation_method: Method to fit to outputted laser power. + :return: Fits the laser outputted power level and noiselevel for each point based on the assumed ground floor reflectivities. + """ + # normalize intensitities + normalized_intensitites = pointcloud_planes[:, 3] / np.cos(calculated_indicent_angle) + distance = np.linalg.norm(pointcloud_planes[:, :3], axis=1) + + # linear model + p = None + stat_values = None + if len(normalized_intensitites) < 3: + return None, None, None, None + if estimation_method == 'linear': + reg = linregress(distance, normalized_intensitites) + w = reg[0] + h = reg[1] + p = [w, h] + stat_values = reg[2:] + relative_output_intensity = power_factor * (p[0] * distance + p[1]) + + elif estimation_method == 'poly': + # polynomial 2degre fit + p = np.polyfit(np.linalg.norm(pointcloud_planes[:, :3], axis=1), + normalized_intensitites, 2) + relative_output_intensity = power_factor * ( + p[0] * distance ** 2 + p[1] * distance + p[2]) + + + # estimate minimum noise level therefore get minimum reflected intensitites + hist, xedges, yedges = np.histogram2d(distance, normalized_intensitites, bins=(50, 2555), + range=((10, 70), (5, np.abs(np.max(normalized_intensitites))))) + idx = np.where(hist == 0) + hist[idx] = len(pointcloud_planes) + ymins = np.argpartition(hist, 2, axis=1)[:, 0] + min_vals = yedges[ymins] + idx = np.where(min_vals > 5) + min_vals = min_vals[idx] + idx1 = [i + 1 for i in idx] + x = (xedges[idx] + xedges[idx1]) / 2 + + if estimation_method == 'poly': + pmin = ransac_polyfit(x, min_vals, order=2) + adaptive_noise_threshold = noise_floor * ( + pmin[0] * distance ** 2 + pmin[1] * distance + pmin[2]) + elif estimation_method == 'linear': + if len(min_vals) > 3: + pmin = linregress(x, min_vals) + else: + pmin = p + adaptive_noise_threshold = noise_floor * ( + pmin[0] * distance + pmin[1]) + # Guess that noise level should be half the road relfection + + if debug: + plt.plot(distance, normalized_intensitites, 'x') + plt.plot(distance, relative_output_intensity, 'x') + plt.plot(distance, adaptive_noise_threshold, 'x') + plt.title('Estimated Lidar Parameters') + plt.ylabel('Intensity') + plt.xlabel('distance') + plt.legend(['Input Intensities', 'Total Power', 'Noise Level']) + plt.show() + + return relative_output_intensity, adaptive_noise_threshold, p, stat_values + + +def get_ground_plane_intensity_stats(pointcloud, recording=None, road_wettness=0, illustreate=True): + w, h = calculate_plane(pointcloud) + # Filter points below image plane + height_over_ground = np.matmul(pointcloud[:, :3], np.asarray(w)) + height_over_ground = height_over_ground.reshape((len(height_over_ground), 1)) + ground = np.logical_and(np.matmul(pointcloud[:, :3], np.asarray(w)) + h < 0.3, + np.matmul(pointcloud[:, :3], np.asarray(w)) + h > -0.3) + + if len(ground)<1000: + return None, None, None, None, None + # Filter ground in vehicle trajectory + ground = np.logical_and(ground, (pointcloud[:, 1] > -1.5) & (pointcloud[:, 1] < 1.5) & (pointcloud[:, 3] < 200)) + + ground_idx = np.where(ground) + pointcloud_planes = np.hstack((pointcloud[ground, :], height_over_ground[ground])) + + # incident angle based on ground plane + calculated_indicent_angle = np.arccos(np.divide(np.matmul(pointcloud_planes[:, :3], np.asarray(w)), + np.linalg.norm(pointcloud_planes[:, :3], axis=1) * np.linalg.norm( + w))) + + distance = np.linalg.norm(pointcloud_planes[:, :3], axis=1) + + relative_output_intensity, adaptive_noise_threshold, p, stat_values = estimate_laser_parameters(pointcloud_planes, + calculated_indicent_angle, + debug=illustreate) + if relative_output_intensity is None: + return p, None, None, None, stat_values + + hist, xedges, yedges = np.histogram2d(distance, pointcloud_planes[:, 3], bins=(50, 255), range=((10, 70), (0, 255))) + x = (xedges[:-1] + xedges[1:]) / 2 + y = (yedges[:-1] + yedges[1:]) / 2 + + # get sums for each distance + sums = np.sum(hist, axis=1) + # filte not filled positions + filled_idx = np.where(sums > 0) + x = x[filled_idx] + + sums = sums[filled_idx] + hist = hist[filled_idx[0], :] + + # filter each distance without elements + + # normalize hist along distance to get probs + histp = hist / sums[:, None] + # calculate comulative sum + cumsum = np.cumsum(histp, axis=1) + if illustreate: + fig, ax = plt.subplots() + X, Y = np.meshgrid(x, y) + + z_min, z_max = np.abs(cumsum).min(), np.abs(cumsum).max() + + c = ax.pcolormesh(X, Y, cumsum.T, cmap='RdBu', vmin=z_min, vmax=z_max) + if recording is not None: + ax.set_title(recording + ' wettness =' + str(road_wettness)) + else: + ax.set_title('pcolormesh') + # set the limits of the plot to the limits of the data + ax.axis([x.min(), x.max(), y.min(), y.max()]) + fig.colorbar(c, ax=ax) + + plt.show() + return p, x, histp, filled_idx, stat_values diff --git a/tools/wet_ground/phy_equations.py b/tools/wet_ground/phy_equations.py new file mode 100644 index 0000000..300a6c1 --- /dev/null +++ b/tools/wet_ground/phy_equations.py @@ -0,0 +1,281 @@ +__author__ = "Mario Bijelic" +__contact__ = "mario.bijelic@t-online.de" +__license__ = "CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)" + +import os +import csv + +import numpy as np +import matplotlib.pyplot as plt + + +def frenel_equations(ain, nair=1.0003, nw=1.33): + """ + :param ain: incident angle + :param nair: refractive index air + :param nw: refractive index water + :return: rs, ts, rp, tp + """ + + # Using Snell's law to calculate the out angle + a = np.clip(np.sin(ain) * nair / nw, -1, 1) + # angles affected by total reflection due to brewster + + aout = np.arcsin(a) + + rs = (nair * np.cos(ain) - nw * np.cos(aout)) / (nair * np.cos(ain) + nw * np.cos(aout)) + ts = 2 * nair * np.cos(ain) / (nair * np.cos(ain) + nw * np.cos(aout)) + + rp = (nw * np.cos(ain) - nair * np.cos(aout)) / (nw * np.cos(ain) + nair * np.cos(aout)) + tp = 2 * nair * np.cos(ain) / (nw * np.cos(ain) + nair * np.cos(aout)) + + return rs, ts, rp, tp, aout + + +def frenel_equations_power(ain, nair=1.0003, nw=1.33): + """ + # calculate the power ratio of transmitted laser power + # Typically the Fresnel Equations are applied for the Light Wave Amplitude. For the power the transmission + the values have to be transformed. Please see: https://en.wikipedia.org/wiki/Fresnel_equations#Complex_amplitude_reflection_and_transmission_coefficients + and the section about power transmission coefficients. + :param ain: incident angle + :param nair: refractive index air + :param nw: refractive index water + :return: rs, ts, rp, tp + """ + + # Using Snell's law to calculate the out angle + a = np.clip(np.sin(ain) * nair / nw, -1, 1) + # angles affected by total reflection due to brewster + + aout = np.arcsin(a) + + power_fraction_transmittance = np.cos(ain) * nair / nw / np.cos(aout) + + rs = (nair * np.cos(ain) - nw * np.cos(aout)) / (nair * np.cos(ain) + nw * np.cos(aout)) + ts = 2 * nair * np.cos(ain) / (nair * np.cos(ain) + nw * np.cos(aout)) + + rp = (nw * np.cos(ain) - nair * np.cos(aout)) / (nw * np.cos(ain) + nair * np.cos(aout)) + tp = 2 * nair * np.cos(ain) / (nw * np.cos(ain) + nair * np.cos(aout)) + + rs = rs ** 2 + ts = ts ** 2 / power_fraction_transmittance + + rp = rp ** 2 + tp = tp ** 2 / power_fraction_transmittance + + return rs, ts, rp, tp, aout + + +def total_reflection_from_ground(ain, nair=1.0003, nw=1.33, rho=0.9): + """ + Defines a optical transition from air to water. + :param ain: incident angle + :param nair: refractive index air + :param nw: refractive index water + :param rho: ground reflectivity + :return: rs, ts, rp, tp + """ + + # First reflection parameters from air to water transition + ras, tas, rap, tap, aaout = frenel_equations_power(ain, nair=nair, nw=nw) + # Second reflection parameters from water to air transition + rws, tws, rwp, twp, awout = frenel_equations_power(aaout, nair=nw, nw=nair) + + rs = ras + ts = tas * rho * tws / (1 - rho * rws) + + rp = rap + tp = tap * rho * twp / (1 - rho * rwp) + + return rs, ts, rp, tp, aaout + + +def total_transmittance_from_ground(ain, nair=1.0003, nw=1.33, rho=0.9, water_absorbtion=0.075, water_thickness=0.0025): + """ + Defines the power transmission to lower the Laser power for a trsition from air to water and backprojection. + :param ain: incident angle + :param nair: refractive index air + :param nw: refractive index water + :param rho: ground reflectivity + :param water_absorbtion: Komplex absorption within the water slab is neglected in the current implementation + :param water_thickness: The absorption along the path lengths in water is currently neglected. As due to the low + absorption factor this can be neglected. + :return: Rs, Ts, Rp, Tp + """ + rs, ts, rp, tp, aaout = total_reflection_from_ground(ain, nair=nair, nw=nw, rho=rho) + + return rs, ts, rp, tp, aaout + + +def baryometric_sattering(ain, depth=0.002, nair=1.0003, nw=1.33, beta=0.001, height=1.55): + """ + :param ain: incident angle + :param depth: depth of the water layer + :param nair: refractive index air + :param nw: refractive index water + :param beta: Absorption + :param height: Lidar height + :return: r + """ + + aout = np.arcsin(np.sin(ain) * nair / nw) + + r = np.exp(-2 * beta * depth / np.cos(aout)) * np.cos(ain) ** 2 + + return r + + +def plot_frenel_equations(angles, x_axis, nair=1.0003, nw=1.33, equation=frenel_equations): + rs, ts, rp, tp, out_angle = equation(angles, nair=nair, nw=nw) + + plt.plot(x_axis, rs, 'r', label='rs') + plt.plot(x_axis, ts, 'b', label='ts') + plt.plot(x_axis, rp, 'r', linestyle='dashed', label='rp') + plt.plot(x_axis, tp, 'b', linestyle='dashed', label='tp') + plt.title('Single reflection on water surface') + plt.legend() + plt.show() + + +def plot_total_equations(angles, x_axis, nair=1.0003, nw=1.33, rho=0.9): + rs, ts, rp, tp, out_angle = total_reflection_from_ground(angles, nair=nair, nw=nw, rho=rho) + + power_fraction_transmittance = np.cos(angles) * nair / nw / np.cos(out_angle) + + plt.plot(x_axis, rs, 'r', label='rs') + plt.plot(x_axis, ts, 'b', label='ts') + plt.plot(x_axis, rp, 'r',linestyle='dashed', label='rp') + plt.plot(x_axis, tp, 'b',linestyle='dashed', label='tp') + plt.title('Power Intensities after laser relection on wet ground') + plt.plot(x_axis, rp ** 2, 'r', linestyle='dotted', label='lost power due to relfection p Pol.') + plt.plot(x_axis, rs ** 2, 'r', linestyle='dashed', label='lost power due to relfection s Pol.') + plt.plot(x_axis, power_fraction_transmittance * tp ** 2, 'g', linestyle='dotted', label='recieved power p Pol.') + plt.plot(x_axis, power_fraction_transmittance * ts ** 2, 'g', linestyle='dashed', label='recieved power s Pol.') + plt.plot(x_axis, power_fraction_transmittance * tp ** 2 + rp ** 2, 'b', linestyle='dashed', + label='total power p Pol.') + plt.plot(x_axis, power_fraction_transmittance * ts ** 2 + rs ** 2, 'b', linestyle='dashed', + label='total power s Pol.') + plt.xlabel('Ground Distance') + plt.ylabel('Power Intensity') + + plt.legend() + plt.show() + + +def plot_total_equations2(angles, x_axis, nair=1.0003, nw=1.33, rho=0.9, save_data=True): + Rs, Ts, Rp, Tp, out_angle = total_transmittance_from_ground(angles, nair=nair, nw=nw, rho=rho) + + plt.title('Power Intensities after laser relection on wet ground') + plt.plot(x_axis, Rp, 'r', linestyle='dotted', label='lost power due to relfection p Pol.') + plt.plot(x_axis, Rs, 'r', linestyle='dashed', label='lost power due to relfection s Pol.') + plt.plot(x_axis, Tp, 'g', linestyle='dotted', label='recieved power p Pol.') + plt.plot(x_axis, Ts, 'g', linestyle='dashed', label='recieved power s Pol.') + plt.plot(x_axis, Tp + Rp, 'b', linestyle='dashed', label='total power p Pol.') + plt.plot(x_axis, Ts + Rs, 'b', linestyle='dashed', label='total power s Pol.') + + plt.xlabel('Ground Distance') + plt.ylabel('Power Intensity') + + plt.legend() + plt.show() + if save_data: + write_csv('fresnel', x_axis, Rs.tolist(), 'relectivity_s') + write_csv('fresnel', x_axis, Ts.tolist(), 'transmission_s') + write_csv('fresnel', x_axis, Rp.tolist(), 'relectivity_p') + write_csv('fresnel', x_axis, Tp.tolist(), 'transmission_p') + + +def write_csv(name, x_data, y_data, label_file='undefined', folder='statistics_output'): + if not os.path.exists(folder): + os.makedirs(folder) + with open('%s/%s_%s.csv' % (folder, label_file, name), 'w', newline='') as csvfile: + spamwriter = csv.writer(csvfile, delimiter=',', + quotechar='|', quoting=csv.QUOTE_MINIMAL) + x_data_append = ['x_data'] + x_data_append.extend(x_data) + y_data_append = ['y_data'] + y_data + for x, y in zip(x_data_append, y_data_append): + spamwriter.writerow([x, y]) + + +def plot_total_reflection_from_ground_angles(scope=(0, np.pi / 2), num=1000, nair=1.0003, nw=1.33, rho=0.05): + angles = np.linspace(scope[0], scope[1], num=num) + plot_total_equations2(angles, angles, nair=nair, nw=nw, rho=rho) + + +def plot_total_reflection_from_ground_distance(scope=(0, 80), num=100, height=1.55, nair=1.0003, nw=1.33, rho=0.05): + distances = np.linspace(scope[0], scope[1], num=num) + angles = np.arctan(distances / height) + plot_total_equations2(angles, distances, nair=nair, nw=nw, rho=rho) + + +def plot_transmission_reflectionfactors_equations(angles, x_axis, nair=1.0003, nw=1.33): + rs, ts, rp, tp, out_angle = frenel_equations(angles, nair=nair, nw=nw) + + plt.plot(x_axis, rs ** 2, 'r', label='rs') + plt.plot(x_axis, np.tan(angles) * ts ** 2 / np.tan(out_angle), 'b', label='ts') + plt.plot(x_axis, rp ** 2, 'r', linestyle='dashed', label='rp') + plt.plot(x_axis, np.tan(angles) * tp ** 2 / np.tan(out_angle), 'b', linestyle='dashed', label='tp') + plt.plot(x_axis, np.tan(angles) * tp ** 2 / np.tan(out_angle) + rp ** 2, 'c', linestyle='dotted', label='sump') + plt.legend() + plt.show() + + +def plot_frenel_angles(scope=(0, np.pi / 2), num=100, nair=1.0003, nw=1.33): + angles = np.linspace(scope[0], scope[1], num=num) + plot_frenel_equations(angles, angles, nair=nair, nw=nw) + + +def plot_frenel_power_angles(scope=(0, np.pi / 2), num=100, nair=1.0003, nw=1.33): + angles = np.linspace(scope[0], scope[1], num=num) + plot_frenel_equations(angles, angles, nair=nair, nw=nw, equation=frenel_equations_power) + + +def plot_frenel_distance(scope=(0, 80), num=100, height=1.55, nair=1.0003, nw=1.33): + distances = np.linspace(scope[0], scope[1], num=num) + angles = np.arctan(distances / height) + plot_transmission_reflectionfactors_equations(angles, distances, nair=nair, nw=nw) + + +def plot_barymetric_lidar_equation(scope=(0, 80), num=100, height=1.55): + distances = np.linspace(scope[0], scope[1], num=num) + angles = np.arctan(distances / height) + r = baryometric_sattering(angles) + + plt.plot(distances, r, 'g', label='gs') + plt.show() + + +def lidar_measurement(distances, height=1.55, Iout=1, rho=0.12, natm=1, C=1, debug=True): + """ + Formula taken from: https://doi.org/10.1155/2019/8973248 + Simulate recieved intensities based on a flat earth assumption, ground reflectivity and scanner height. + :param distance: Distance to object + :param Iout: Sended Laser Intensity + :param height: Scanner height + :param rho: reflectivity + :param natm: athmospheric absorbtion + :param C: Sensor Paramenets + :return: Irec: Measured Intensity by scanner + """ + # asume flat earth + angles = np.arctan(distances / height) + Irec = Iout * np.cos(angles) * rho * natm * C + if debug: + plt.plot(distances, Irec) + plt.show() + + return Irec + + +if __name__ == '__main__': + # plot_frenel_angles(nair=1.0003, nw=1.5) + plot_frenel_power_angles() + # plot_frenel_distance(nair=1.0003, nw=1.33) + # for rho in range(1,5,1): + # plot_total_reflection_from_ground_angles(rho=rho/10, nw=1.003, nair=1.333) + plot_total_reflection_from_ground_distance(rho=1, nair=1.003, nw=1.333) + # plot_total_reflection_from_ground_distance(rho=1, nair=1.003, nair=1.333) + # lidar_measurement(np.linspace(1,100,1000)) + # plot_barymetric_lidar_equation() diff --git a/tools/wet_ground/planes.py b/tools/wet_ground/planes.py new file mode 100644 index 0000000..85b2326 --- /dev/null +++ b/tools/wet_ground/planes.py @@ -0,0 +1,80 @@ +__author__ = "Mario Bijelic" +__contact__ = "mario.bijelic@t-online.de" +__license__ = "CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)" + +import os + +import numpy as np + +from sklearn.linear_model import RANSACRegressor + + +def calculate_plane(pointcloud, standart_height=-1.55): + """ + caluclates plane from loaded pointcloud + returns the plane normal w and lidar height h. + :param pointcloud: binary with x,y,z, coordinates + :return: w, h + """ + + # Filter points which are close to ground based on mounting position + valid_loc = (pointcloud[:, 2] < -1.55) & \ + (pointcloud[:, 2] > -1.86 - 0.01 * pointcloud[:, 0]) & \ + (pointcloud[:, 0] > 10) & \ + (pointcloud[:, 0] < 70) & \ + (pointcloud[:, 1] > -3) & \ + (pointcloud[:, 1] < 3) + pc_rect = pointcloud[valid_loc] + + if pc_rect.shape[0] <= pc_rect.shape[1]: + w = [0, 0, 1] + # Standard height from vehicle mounting position in dense + h = standart_height + else: + try: + reg = RANSACRegressor(loss='squared_loss', max_trials=1000).fit(pc_rect[:, [0, 1]], pc_rect[:, 2]) + w = np.zeros(3) + w[0] = reg.estimator_.coef_[0] + w[1] = reg.estimator_.coef_[1] + w[2] = -1.0 + h = reg.estimator_.intercept_ + w = w / np.linalg.norm(w) + + except: + # If error occurs fall back to flat earth assumption + print('Was not able to estimate a ground plane. Using default flat earth assumption') + w = [0, 0, 1] + # Standard height from vehicle mounting position + h = standart_height + + return w, h + + +def save_plane(destination_path, file_name, w_in, h_in, projection_matrix): + + w, h_projected = transform_results_to_camera_extrinsics(projection_matrix, w_in, h_in) + + lines = ['# Plane', 'Width 4', 'Height 1'] + + plane_file = os.path.join(destination_path, file_name) + result_lines = lines[:3] + result_lines.append("{:e} {:e} {:e} {:e}".format(w[0], w[1], w[2], h_projected)) + result_str = '\n'.join(result_lines) + with open(plane_file, 'w') as f: + f.write(result_str) + + +def transform_results_to_camera_extrinsics(projection_matrix, w, h): + """ + Transform estimated groundplane values to camera coordinates + :param projection_matrix: image projection matrix + :param w: plane normal + :param h: plane height + :return: projected plane and height + """ + w = np.matmul(projection_matrix[0:3, 0:3], np.asarray(w).transpose()) + h = np.matmul(projection_matrix, np.transpose([0, 0, h, 1]))[:3] + + h_projected = np.matmul(w, h) + + return w, h_projected diff --git a/tools/wet_ground/utils.py b/tools/wet_ground/utils.py new file mode 100644 index 0000000..3dc57b7 --- /dev/null +++ b/tools/wet_ground/utils.py @@ -0,0 +1,103 @@ +__author__ = "Mario Bijelic" +__contact__ = "mario.bijelic@t-online.de" +__license__ = "CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)" + +import json + +import numpy as np +import matplotlib.pyplot as plt + + + +def plot_2d_hist(var1, var2, binsX=100, binsY=100): + """ + Taken from https://stackoverflow.com/questions/63415624/normalising-a-2d-histogram + :return: + """ + fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(12, 4)) + + norm = 10 + ax1.hist2d(var1, var2, bins=(binsX, binsY), cmap='BuPu') + ax1.set_title('regular 2d histogram') + + hist, xedges, yedges = np.histogram2d(var1, var2, bins=(binsX, binsY)) + hist = hist.T + with np.errstate(divide='ignore', invalid='ignore'): # suppress division by zero warnings + hist *= norm / hist.sum(axis=0, keepdims=True) + ax2.pcolormesh(xedges, yedges, hist, cmap='BuPu') + ax2.set_title('normalized columns') + plt.show() + +def load_velodyne_scan(file): + """Load and parse velodyne binary file""" + scan = np.fromfile(file, dtype=np.float32) + return scan.reshape((-1, 5)) # [:, :4] + +def read_meta_label(path): + with open(path) as k: + data = json.load(k) + return data + +def filter_weather(meta, weather_type): + if meta['weather'][weather_type] == True: + return True + else: + return False + +def filter_env(meta, env_type): + if meta['meta']['environment'][env_type] == True: + return True + else: + return False + +def filter_daytime(meta, day_type): + if meta['daytime'][day_type] == True: + return True + else: + return False + +def filter_infra(meta, infra_type): + if meta['meta']['infrastructure'][infra_type] == True: + return True + else: + return False + +def read_road_wetness(path): + try: + with open(path) as f: + data = json.load(f) + # print(data) + return (float(data['water_thickness']), data['surface_state_result']) + except: + pass + +def draw_image(pointcloud, map_size=(80, 15), resolution=100, color='depth'): + # Slow elementwise circle drawing through opencv + # For quick illustration purposes of example pointclouds. + + image = np.zeros((2*resolution*map_size[1],2*resolution*map_size[0],3)).astype(np.uint8) + import matplotlib as mpl + import matplotlib.cm as cm + import cv2 + norm = mpl.colors.Normalize(vmin=0, vmax=1) + cmap = cm.jet + m = cm.ScalarMappable(norm, cmap) + if color=='depth': + values = np.sqrt(np.sum(np.square(pointcloud[:,:3]), axis=1))/80 + elif color=='intensity': + values = pointcloud[:,3]/255 + + + + depth_map_color = m.to_rgba(values) + depth_map_color = (255 * depth_map_color).astype(dtype=np.uint8) + for idx in range(len(values)): + x, y = resolution*(pointcloud[idx,:2] + [map_size[0], map_size[1]]) + + if 0

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