adjust parser
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# Monoloco library
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<img src="docs/monoloco.gif" alt="gif" />
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This repository contains the code for two research projects:
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1. **Perceiving Humans: from Monocular 3D Localization to Social Distancing (MonoLoco++)**
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[README](https://github.com/vita-epfl/monstereo/blob/master/docs/MonoLoco%2B%2B.md) & [Article](https://arxiv.org/abs/2009.00984)
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2. **MonStereo: When Monocular and Stereo Meet at the Tail of 3D Human Localization**
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[README](https://github.com/vita-epfl/monstereo/blob/master/docs/MonStereo.md) & [Article](https://arxiv.org/abs/2008.10913)
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Both projects has been built upon the CVPR'19 project [Openpifpaf](https://github.com/vita-epfl/openpifpaf)
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for 2D pose estimation and the ICCV'19 project [MonoLoco](https://github.com/vita-epfl/monoloco) for monocular 3D localization.
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All projects share the AGPL Licence.
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# Setup
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Installation steps are the same for both projects.
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### Install
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The installation has been tested on OSX and Linux operating systems, with Python 3.6 or Python 3.7.
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Packages have been installed with pip and virtual environments.
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For quick installation, do not clone this repository,
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and make sure there is no folder named monstereo in your current directory.
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A GPU is not required, yet highly recommended for real-time performances.
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MonoLoco++ and MonStereo can be installed as a single package, by:
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```
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pip3 install monstereo
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```
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For development of the monstereo source code itself, you need to clone this repository and then:
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```
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pip3 install sdist
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cd monstereo
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python3 setup.py sdist bdist_wheel
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pip3 install -e .
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```
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### Interfaces
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All the commands are run through a main file called `main.py` using subparsers.
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To check all the commands for the parser and the subparsers (including openpifpaf ones) run:
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* `python3 -m monstereo.run --help`
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* `python3 -m monstereo.run predict --help`
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* `python3 -m monstereo.run train --help`
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* `python3 -m monstereo.run eval --help`
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* `python3 -m monstereo.run prep --help`
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or check the file `monstereo/run.py`
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### Data structure
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Data
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├── arrays
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├── models
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├── kitti
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├── figures
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├── logs
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Run the following to create the folders:
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```
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mkdir data
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cd data
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mkdir arrays models kitti figures logs
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```
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Further instructions for prediction, preprocessing, training and evaluation can be found here:
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* [MonoLoco++ README](https://github.com/vita-epfl/monstereo/blob/master/docs/MonoLoco%2B%2B.md)
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* [MonStereo README](https://github.com/vita-epfl/monstereo/blob/master/docs/MonStereo.md)
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@ -15,20 +15,7 @@ def cli():
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training_parser = subparsers.add_parser("train")
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eval_parser = subparsers.add_parser("eval")
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# Preprocess input data
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prep_parser.add_argument('--dir_ann', help='directory of annotations of 2d joints', required=True)
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prep_parser.add_argument('--dataset',
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help='datasets to preprocess: nuscenes, nuscenes_teaser, nuscenes_mini, kitti',
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default='kitti')
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prep_parser.add_argument('--dir_nuscenes', help='directory of nuscenes devkit', default='data/nuscenes/')
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prep_parser.add_argument('--iou_min', help='minimum iou to match ground truth', type=float, default=0.3)
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prep_parser.add_argument('--variance', help='new', action='store_true')
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prep_parser.add_argument('--activity', help='new', action='store_true')
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prep_parser.add_argument('--monocular', help='new', action='store_true')
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# Predict (2D pose and/or 3D location from images)
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# General
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predict_parser.add_argument('images', nargs='*', help='input images')
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predict_parser.add_argument('--glob', help='glob expression for input images (for many images)')
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predict_parser.add_argument('-o', '--output-directory', help='Output directory')
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@ -52,14 +39,12 @@ def cli():
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help='focal length in mm for a sensor size of 7.2x5.4 mm. Default nuScenes sensor',
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type=float, default=5.7)
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# Pifpaf parsers
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decoder.cli(parser)
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logger.cli(parser)
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network.Factory.cli(parser)
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show.cli(parser)
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visualizer.cli(parser)
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# Monoloco
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predict_parser.add_argument('--mode', help='keypoints, mono, stereo', default='mono')
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predict_parser.add_argument('--model', help='path of MonoLoco/MonStereo model to load')
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predict_parser.add_argument('--net', help='only to select older MonoLoco model, otherwise use --mode')
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@ -75,10 +60,20 @@ def cli():
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predict_parser.add_argument('--threshold_dist', type=float, help='min distance of people', default=2.5)
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predict_parser.add_argument('--radii', type=tuple, help='o-space radii', default=(0.3, 0.5, 1))
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# Preprocess input data
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prep_parser.add_argument('--dir_ann', help='directory of annotations of 2d joints', required=True)
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prep_parser.add_argument('--dataset',
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help='datasets to preprocess: nuscenes, nuscenes_teaser, nuscenes_mini, kitti',
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default='kitti')
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prep_parser.add_argument('--dir_nuscenes', help='directory of nuscenes devkit', default='data/nuscenes/')
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prep_parser.add_argument('--iou_min', help='minimum iou to match ground truth', type=float, default=0.3)
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prep_parser.add_argument('--variance', help='new', action='store_true')
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prep_parser.add_argument('--activity', help='new', action='store_true')
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prep_parser.add_argument('--monocular', help='new', action='store_true')
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# Training
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training_parser.add_argument('--joints', help='Json file with input joints',
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default='data/arrays/joints-nuscenes_teaser-190513-1846.json')
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training_parser.add_argument('--no_save', help='to not save model and log file', action='store_true')
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training_parser.add_argument('--joints', help='Json file with input joints', required=True)
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training_parser.add_argument('--mode', help='mono, stereo', default='mono')
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training_parser.add_argument('-e', '--epochs', type=int, help='number of epochs to train for', default=500)
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training_parser.add_argument('--bs', type=int, default=512, help='input batch size')
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training_parser.add_argument('--monocular', help='whether to train monoloco', action='store_true')
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@ -94,9 +89,12 @@ def cli():
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training_parser.add_argument('--print_loss', help='print training and validation losses', action='store_true')
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training_parser.add_argument('--auto_tune_mtl', help='whether to use uncertainty to autotune losses',
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action='store_true')
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training_parser.add_argument('--no_save', help='to not save model and log file', action='store_true')
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# Evaluation
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eval_parser.add_argument('--mode', help='mono, stereo', default='mono')
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eval_parser.add_argument('--dataset', help='datasets to evaluate, kitti or nuscenes', default='kitti')
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eval_parser.add_argument('--activity', help='evaluate activities', action='store_true')
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eval_parser.add_argument('--geometric', help='to evaluate geometric distance', action='store_true')
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eval_parser.add_argument('--generate', help='create txt files for KITTI evaluation', action='store_true')
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eval_parser.add_argument('--dir_ann', help='directory of annotations of 2d joints (for KITTI evaluation)')
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@ -109,10 +107,9 @@ def cli():
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eval_parser.add_argument('--show', help='whether to show statistic graphs', action='store_true')
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eval_parser.add_argument('--save', help='whether to save statistic graphs', action='store_true')
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eval_parser.add_argument('--verbose', help='verbosity of statistics', action='store_true')
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eval_parser.add_argument('--monocular', help='whether to train using the baseline', action='store_true')
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eval_parser.add_argument('--new', help='new', action='store_true')
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eval_parser.add_argument('--variance', help='evaluate keypoints variance', action='store_true')
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eval_parser.add_argument('--activity', help='evaluate activities', action='store_true')
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eval_parser.add_argument('--net', help='Choose network: monoloco, monoloco_p, monoloco_pp, monstereo')
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eval_parser.add_argument('--baselines', help='whether to evaluate stereo baselines', action='store_true')
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eval_parser.add_argument('--generate_official', help='whether to add empty txt files for official evaluation',
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@ -149,15 +146,12 @@ def main():
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multiplier=args.multiplier, r_seed=args.r_seed)
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hyp_tuning.train(args)
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else:
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from .train import Trainer
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training = Trainer(args)
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_ = training.train()
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_ = training.evaluate()
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elif args.command == 'eval':
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if args.activity:
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from .eval.eval_activity import ActivityEvaluator
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evaluator = ActivityEvaluator(args)
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