import os import os.path import torch import numpy as np import pandas import csv import random from collections import OrderedDict from .base_video_dataset import BaseVideoDataset from lib.train.data import jpeg4py_loader from lib.train.admin import env_settings class Lasot(BaseVideoDataset): """ LaSOT dataset. Publication: LaSOT: A High-quality Benchmark for Large-scale Single Object Tracking Heng Fan, Liting Lin, Fan Yang, Peng Chu, Ge Deng, Sijia Yu, Hexin Bai, Yong Xu, Chunyuan Liao and Haibin Ling CVPR, 2019 https://arxiv.org/pdf/1809.07845.pdf Download the dataset from https://cis.temple.edu/lasot/download.html """ def __init__(self, root=None, image_loader=jpeg4py_loader, vid_ids=None, split=None, data_fraction=None): """ args: root - path to the lasot dataset. image_loader (jpeg4py_loader) - The function to read the images. jpeg4py (https://github.com/ajkxyz/jpeg4py) is used by default. vid_ids - List containing the ids of the videos (1 - 20) used for training. If vid_ids = [1, 3, 5], then the videos with subscripts -1, -3, and -5 from each class will be used for training. split - If split='train', the official train split (protocol-II) is used for training. Note: Only one of vid_ids or split option can be used at a time. data_fraction - Fraction of dataset to be used. The complete dataset is used by default """ root = env_settings().lasot_dir if root is None else root super().__init__('LaSOT', root, image_loader) # Keep a list of all classes self.class_list = [f for f in os.listdir(self.root)] self.class_to_id = {cls_name: cls_id for cls_id, cls_name in enumerate(self.class_list)} self.sequence_list = self._build_sequence_list(vid_ids, split) if data_fraction is not None: self.sequence_list = random.sample(self.sequence_list, int(len(self.sequence_list)*data_fraction)) self.seq_per_class = self._build_class_list() def _build_sequence_list(self, vid_ids=None, split=None): if split is not None: if vid_ids is not None: raise ValueError('Cannot set both split_name and vid_ids.') ltr_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), '..') if split == 'train': file_path = os.path.join(ltr_path, 'data_specs', 'lasot_train_split.txt') else: raise ValueError('Unknown split name.') # sequence_list = pandas.read_csv(file_path, header=None, squeeze=True).values.tolist() sequence_list = pandas.read_csv(file_path, header=None).squeeze("columns").values.tolist() elif vid_ids is not None: sequence_list = [c+'-'+str(v) for c in self.class_list for v in vid_ids] else: raise ValueError('Set either split_name or vid_ids.') return sequence_list def _build_class_list(self): seq_per_class = {} for seq_id, seq_name in enumerate(self.sequence_list): class_name = seq_name.split('-')[0] if class_name in seq_per_class: seq_per_class[class_name].append(seq_id) else: seq_per_class[class_name] = [seq_id] return seq_per_class def get_name(self): return 'lasot' def has_class_info(self): return True def has_occlusion_info(self): return True def get_num_sequences(self): return len(self.sequence_list) def get_num_classes(self): return len(self.class_list) def get_sequences_in_class(self, class_name): return self.seq_per_class[class_name] def _read_bb_anno(self, seq_path): bb_anno_file = os.path.join(seq_path, "groundtruth.txt") gt = pandas.read_csv(bb_anno_file, delimiter=',', header=None, dtype=np.float32, na_filter=False, low_memory=False).values return torch.tensor(gt) def _read_target_visible(self, seq_path): # Read full occlusion and out_of_view occlusion_file = os.path.join(seq_path, "full_occlusion.txt") out_of_view_file = os.path.join(seq_path, "out_of_view.txt") with open(occlusion_file, 'r', newline='') as f: occlusion = torch.ByteTensor([int(v) for v in list(csv.reader(f))[0]]) with open(out_of_view_file, 'r') as f: out_of_view = torch.ByteTensor([int(v) for v in list(csv.reader(f))[0]]) target_visible = ~occlusion & ~out_of_view return target_visible def _get_sequence_path(self, seq_id): seq_name = self.sequence_list[seq_id] class_name = seq_name.split('-')[0] vid_id = seq_name.split('-')[1] return os.path.join(self.root, class_name, class_name + '-' + vid_id) def get_sequence_info(self, seq_id): seq_path = self._get_sequence_path(seq_id) bbox = self._read_bb_anno(seq_path) valid = (bbox[:, 2] > 0) & (bbox[:, 3] > 0) visible = self._read_target_visible(seq_path) & valid.byte() return {'bbox': bbox, 'valid': valid, 'visible': visible} def _get_frame_path(self, seq_path, frame_id): return os.path.join(seq_path, 'img', '{:08}.jpg'.format(frame_id+1)) # frames start from 1 def _get_frame(self, seq_path, frame_id): return self.image_loader(self._get_frame_path(seq_path, frame_id)) def _get_class(self, seq_path): raw_class = seq_path.split('/')[-2] return raw_class def get_class_name(self, seq_id): seq_path = self._get_sequence_path(seq_id) obj_class = self._get_class(seq_path) return obj_class def get_frames(self, seq_id, frame_ids, anno=None): seq_path = self._get_sequence_path(seq_id) obj_class = self._get_class(seq_path) frame_list = [self._get_frame(seq_path, f_id) for f_id in frame_ids] if anno is None: anno = self.get_sequence_info(seq_id) anno_frames = {} for key, value in anno.items(): anno_frames[key] = [value[f_id, ...].clone() for f_id in frame_ids] object_meta = OrderedDict({'object_class_name': obj_class, 'motion_class': None, 'major_class': None, 'root_class': None, 'motion_adverb': None}) return frame_list, anno_frames, object_meta