import numpy as np from lib.test.evaluation.data import Sequence, BaseDataset, SequenceList import os from lib.test.utils.load_text import load_text class TrackingNetDataset(BaseDataset): """ TrackingNet test set. Publication: TrackingNet: A Large-Scale Dataset and Benchmark for Object Tracking in the Wild. Matthias Mueller,Adel Bibi, Silvio Giancola, Salman Al-Subaihi and Bernard Ghanem ECCV, 2018 https://ivul.kaust.edu.sa/Documents/Publications/2018/TrackingNet%20A%20Large%20Scale%20Dataset%20and%20Benchmark%20for%20Object%20Tracking%20in%20the%20Wild.pdf Download the dataset using the toolkit https://github.com/SilvioGiancola/TrackingNet-devkit. """ def __init__(self): super().__init__() self.base_path = self.env_settings.trackingnet_path sets = 'TEST' if not isinstance(sets, (list, tuple)): if sets == 'TEST': sets = ['TEST'] elif sets == 'TRAIN': sets = ['TRAIN_{}'.format(i) for i in range(5)] self.sequence_list = self._list_sequences(self.base_path, sets) def get_sequence_list(self): return SequenceList([self._construct_sequence(set, seq_name) for set, seq_name in self.sequence_list]) def _construct_sequence(self, set, sequence_name): anno_path = '{}/{}/anno/{}.txt'.format(self.base_path, set, sequence_name) ground_truth_rect = load_text(str(anno_path), delimiter=',', dtype=np.float64, backend='numpy') frames_path = '{}/{}/frames/{}'.format(self.base_path, set, sequence_name) frame_list = [frame for frame in os.listdir(frames_path) if frame.endswith(".jpg")] frame_list.sort(key=lambda f: int(f[:-4])) frames_list = [os.path.join(frames_path, frame) for frame in frame_list] return Sequence(sequence_name, frames_list, 'trackingnet', ground_truth_rect.reshape(-1, 4)) def __len__(self): return len(self.sequence_list) def _list_sequences(self, root, set_ids): sequence_list = [] for s in set_ids: anno_dir = os.path.join(root, s, "anno") sequences_cur_set = [(s, os.path.splitext(f)[0]) for f in os.listdir(anno_dir) if f.endswith('.txt')] sequence_list += sequences_cur_set return sequence_list