# pylint: disable=too-many-branches, too-many-statements import argparse from openpifpaf.network import nets from openpifpaf import decoder def cli(): parser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.ArgumentDefaultsHelpFormatter) # Subparser definition subparsers = parser.add_subparsers(help='Different parsers for main actions', dest='command') predict_parser = subparsers.add_parser("predict") prep_parser = subparsers.add_parser("prep") training_parser = subparsers.add_parser("train") eval_parser = subparsers.add_parser("eval") # Preprocess input data prep_parser.add_argument('--dir_ann', help='directory of annotations of 2d joints', required=True) prep_parser.add_argument('--dataset', help='datasets to preprocess: nuscenes, nuscenes_teaser, nuscenes_mini, kitti', default='kitti') prep_parser.add_argument('--dir_nuscenes', help='directory of nuscenes devkit', default='data/nuscenes/') prep_parser.add_argument('--iou_min', help='minimum iou to match ground truth', type=float, default=0.3) prep_parser.add_argument('--variance', help='new', action='store_true') prep_parser.add_argument('--activity', help='new', action='store_true') prep_parser.add_argument('--monocular', help='new', action='store_true') # Predict (2D pose and/or 3D location from images) # General predict_parser.add_argument('--mode', help='pifpaf, mono, stereo', default='stereo') predict_parser.add_argument('images', nargs='*', help='input images') predict_parser.add_argument('--glob', help='glob expression for input images (for many images)') predict_parser.add_argument('-o', '--output-directory', help='Output directory') predict_parser.add_argument('--output_types', nargs='+', default=['json'], help='what to output: json keypoints skeleton for Pifpaf' 'json bird front combined for Monoloco') predict_parser.add_argument('--show', help='to show images', action='store_true') # Pifpaf nets.cli(predict_parser) decoder.cli(predict_parser, force_complete_pose=True, instance_threshold=0.15) predict_parser.add_argument('--scale', default=1.0, type=float, help='change the scale of the image to preprocess') # Monoloco predict_parser.add_argument('--model', help='path of MonoLoco model to load', required=True) predict_parser.add_argument('--hidden_size', type=int, help='Number of hidden units in the model', default=512) predict_parser.add_argument('--path_gt', help='path of json file with gt 3d localization', default='data/arrays/names-kitti-200615-1022.json') predict_parser.add_argument('--transform', help='transformation for the pose', default='None') predict_parser.add_argument('--draw_box', help='to draw box in the images', action='store_true') predict_parser.add_argument('--z_max', type=int, help='maximum meters distance for predictions', default=22) predict_parser.add_argument('--n_dropout', type=int, help='Epistemic uncertainty evaluation', default=0) predict_parser.add_argument('--dropout', type=float, help='dropout parameter', default=0.2) predict_parser.add_argument('--show_all', help='only predict ground-truth matches or all', action='store_true') # Social distancing and social interactions predict_parser.add_argument('--social', help='social', action='store_true') predict_parser.add_argument('--activity', help='activity', action='store_true') predict_parser.add_argument('--json_dir', help='for social') predict_parser.add_argument('--threshold_prob', type=float, help='concordance for samples', default=0.25) predict_parser.add_argument('--threshold_dist', type=float, help='min distance of people', default=2) predict_parser.add_argument('--margin', type=float, help='conservative for noise in orientation', default=1.5) predict_parser.add_argument('--radii', type=tuple, help='o-space radii', default=(0.25, 1, 2)) # Training training_parser.add_argument('--joints', help='Json file with input joints', default='data/arrays/joints-nuscenes_teaser-190513-1846.json') training_parser.add_argument('--save', help='whether to not save model and log file', action='store_true') training_parser.add_argument('-e', '--epochs', type=int, help='number of epochs to train for', default=500) training_parser.add_argument('--bs', type=int, default=512, help='input batch size') training_parser.add_argument('--monocular', help='whether to train monoloco', action='store_true') training_parser.add_argument('--dropout', type=float, help='dropout. Default no dropout', default=0.2) training_parser.add_argument('--lr', type=float, help='learning rate', default=0.001) training_parser.add_argument('--sched_step', type=float, help='scheduler step time (epochs)', default=30) training_parser.add_argument('--sched_gamma', type=float, help='Scheduler multiplication every step', default=0.98) training_parser.add_argument('--hidden_size', type=int, help='Number of hidden units in the model', default=1024) training_parser.add_argument('--n_stage', type=int, help='Number of stages in the model', default=3) training_parser.add_argument('--hyp', help='run hyperparameters tuning', action='store_true') training_parser.add_argument('--multiplier', type=int, help='Size of the grid of hyp search', default=1) training_parser.add_argument('--r_seed', type=int, help='specify the seed for training and hyp tuning', default=1) training_parser.add_argument('--activity', help='new', action='store_true') # Evaluation eval_parser.add_argument('--dataset', help='datasets to evaluate, kitti or nuscenes', default='kitti') eval_parser.add_argument('--geometric', help='to evaluate geometric distance', action='store_true') eval_parser.add_argument('--generate', help='create txt files for KITTI evaluation', action='store_true') eval_parser.add_argument('--dir_ann', help='directory of annotations of 2d joints (for KITTI evaluation)') eval_parser.add_argument('--model', help='path of MonoLoco model to load') eval_parser.add_argument('--joints', help='Json file with input joints to evaluate (for nuScenes evaluation)') eval_parser.add_argument('--n_dropout', type=int, help='Epistemic uncertainty evaluation', default=0) eval_parser.add_argument('--dropout', type=float, help='dropout. Default no dropout', default=0.2) eval_parser.add_argument('--hidden_size', type=int, help='Number of hidden units in the model', default=1024) eval_parser.add_argument('--n_stage', type=int, help='Number of stages in the model', default=3) eval_parser.add_argument('--show', help='whether to show statistic graphs', action='store_true') eval_parser.add_argument('--save', help='whether to save statistic graphs', action='store_true') eval_parser.add_argument('--verbose', help='verbosity of statistics', action='store_true') eval_parser.add_argument('--monocular', help='whether to train using the baseline', action='store_true') eval_parser.add_argument('--new', help='new', action='store_true') eval_parser.add_argument('--variance', help='evaluate keypoints variance', action='store_true') eval_parser.add_argument('--activity', help='evaluate activities', action='store_true') eval_parser.add_argument('--net', help='Choose network: monoloco, monoloco_p, monoloco_pp, monstereo') args = parser.parse_args() return args def main(): args = cli() if args.command == 'predict': if args.activity: from .activity import predict else: from .predict import predict predict(args) elif args.command == 'prep': if 'nuscenes' in args.dataset: from .prep.preprocess_nu import PreprocessNuscenes prep = PreprocessNuscenes(args.dir_ann, args.dir_nuscenes, args.dataset, args.iou_min) prep.run() else: from .prep.prep_kitti import PreprocessKitti prep = PreprocessKitti(args.dir_ann, args.iou_min, args.monocular) if args.activity: prep.prep_activity() else: prep.run() elif args.command == 'train': from .train import HypTuning if args.hyp: hyp_tuning = HypTuning(joints=args.joints, epochs=args.epochs, monocular=args.monocular, dropout=args.dropout, multiplier=args.multiplier, r_seed=args.r_seed) hyp_tuning.train() else: from .train import Trainer training = Trainer(joints=args.joints, epochs=args.epochs, bs=args.bs, monocular=args.monocular, dropout=args.dropout, lr=args.lr, sched_step=args.sched_step, n_stage=args.n_stage, sched_gamma=args.sched_gamma, hidden_size=args.hidden_size, r_seed=args.r_seed, save=args.save) _ = training.train() _ = training.evaluate() elif args.command == 'eval': if args.activity: from .eval.eval_activity import ActivityEvaluator evaluator = ActivityEvaluator(args) if 'collective' in args.dataset: evaluator.eval_collective() else: evaluator.eval_kitti() elif args.geometric: assert args.joints, "joints argument not provided" from .network.geom_baseline import geometric_baseline geometric_baseline(args.joints) elif args.variance: from .eval.eval_variance import joints_variance joints_variance(args.joints, clusters=None, dic_ms=None) else: if args.generate: from .eval.generate_kitti import GenerateKitti kitti_txt = GenerateKitti(args.model, args.dir_ann, p_dropout=args.dropout, n_dropout=args.n_dropout, hidden_size=args.hidden_size) kitti_txt.run() if args.dataset == 'kitti': from .eval import EvalKitti kitti_eval = EvalKitti(verbose=args.verbose) kitti_eval.run() kitti_eval.printer(show=args.show, save=args.save) elif 'nuscenes' in args.dataset: from .train import Trainer training = Trainer(joints=args.joints, hidden_size=args.hidden_size) _ = training.evaluate(load=True, model=args.model, debug=False) else: raise ValueError("Option not recognized") else: raise ValueError("Main subparser not recognized or not provided") if __name__ == '__main__': main()