import os import os.path import numpy as np import torch import csv import pandas 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 '''2021.1.16 Gok10k for loading lmdb dataset''' from lib.utils.lmdb_utils import * class Got10k_lmdb(BaseVideoDataset): def __init__(self, root=None, image_loader=jpeg4py_loader, split=None, seq_ids=None, data_fraction=None): """ args: root - path to the got-10k training data. Note: This should point to the 'train' folder inside GOT-10k image_loader (jpeg4py_loader) - The function to read the images. jpeg4py (https://github.com/ajkxyz/jpeg4py) is used by default. split - 'train' or 'val'. Note: The validation split here is a subset of the official got-10k train split, not NOT the official got-10k validation split. To use the official validation split, provide that as the root folder instead. seq_ids - List containing the ids of the videos to be used for training. Note: Only one of 'split' or 'seq_ids' options can be used at the same time. data_fraction - Fraction of dataset to be used. The complete dataset is used by default use_lmdb - whether the dataset is stored in lmdb format """ root = env_settings().got10k_lmdb_dir if root is None else root super().__init__('GOT10k_lmdb', root, image_loader) # all folders inside the root self.sequence_list = self._get_sequence_list() # seq_id is the index of the folder inside the got10k root path if split is not None: if seq_ids is not None: raise ValueError('Cannot set both split_name and seq_ids.') train_lib_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), '..') if split == 'train': file_path = os.path.join(train_lib_path, 'data_specs', 'got10k_train_split.txt') elif split == 'val': file_path = os.path.join(train_lib_path, 'data_specs', 'got10k_val_split.txt') elif split == 'train_full': file_path = os.path.join(train_lib_path, 'data_specs', 'got10k_train_full_split.txt') elif split == 'vottrain': file_path = os.path.join(train_lib_path, 'data_specs', 'got10k_vot_train_split.txt') elif split == 'votval': file_path = os.path.join(train_lib_path, 'data_specs', 'got10k_vot_val_split.txt') else: raise ValueError('Unknown split name.') seq_ids = pandas.read_csv(file_path, header=None, squeeze=True, dtype=np.int64).values.tolist() elif seq_ids is None: seq_ids = list(range(0, len(self.sequence_list))) self.sequence_list = [self.sequence_list[i] for i in seq_ids] if data_fraction is not None: self.sequence_list = random.sample(self.sequence_list, int(len(self.sequence_list)*data_fraction)) self.sequence_meta_info = self._load_meta_info() self.seq_per_class = self._build_seq_per_class() self.class_list = list(self.seq_per_class.keys()) self.class_list.sort() def get_name(self): return 'got10k_lmdb' def has_class_info(self): return True def has_occlusion_info(self): return True def _load_meta_info(self): def _read_meta(meta_info): object_meta = OrderedDict({'object_class_name': meta_info[5].split(': ')[-1], 'motion_class': meta_info[6].split(': ')[-1], 'major_class': meta_info[7].split(': ')[-1], 'root_class': meta_info[8].split(': ')[-1], 'motion_adverb': meta_info[9].split(': ')[-1]}) return object_meta sequence_meta_info = {} for s in self.sequence_list: try: meta_str = decode_str(self.root, "train/%s/meta_info.ini" %s) sequence_meta_info[s] = _read_meta(meta_str.split('\n')) except: sequence_meta_info[s] = OrderedDict({'object_class_name': None, 'motion_class': None, 'major_class': None, 'root_class': None, 'motion_adverb': None}) return sequence_meta_info def _build_seq_per_class(self): seq_per_class = {} for i, s in enumerate(self.sequence_list): object_class = self.sequence_meta_info[s]['object_class_name'] if object_class in seq_per_class: seq_per_class[object_class].append(i) else: seq_per_class[object_class] = [i] return seq_per_class def get_sequences_in_class(self, class_name): return self.seq_per_class[class_name] def _get_sequence_list(self): dir_str = decode_str(self.root, 'train/list.txt') dir_list = dir_str.split('\n') return dir_list def _read_bb_anno(self, seq_path): bb_anno_file = os.path.join(seq_path, "groundtruth.txt") gt_str_list = decode_str(self.root, bb_anno_file).split('\n')[:-1] # the last line in got10k is empty gt_list = [list(map(float, line.split(','))) for line in gt_str_list] gt_arr = np.array(gt_list).astype(np.float32) return torch.tensor(gt_arr) def _read_target_visible(self, seq_path): # full occlusion and out_of_view files occlusion_file = os.path.join(seq_path, "absence.label") cover_file = os.path.join(seq_path, "cover.label") # Read these files occ_list = list(map(int, decode_str(self.root, occlusion_file).split('\n')[:-1])) # the last line in got10k is empty occlusion = torch.ByteTensor(occ_list) cover_list = list(map(int, decode_str(self.root, cover_file).split('\n')[:-1])) # the last line in got10k is empty cover = torch.ByteTensor(cover_list) target_visible = ~occlusion & (cover>0).byte() visible_ratio = cover.float() / 8 return target_visible, visible_ratio def _get_sequence_path(self, seq_id): return os.path.join("train", self.sequence_list[seq_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, visible_ratio = self._read_target_visible(seq_path) visible = visible & valid.byte() return {'bbox': bbox, 'valid': valid, 'visible': visible, 'visible_ratio': visible_ratio} def _get_frame_path(self, seq_path, frame_id): return os.path.join(seq_path, '{:08}.jpg'.format(frame_id+1)) # frames start from 1 def _get_frame(self, seq_path, frame_id): return decode_img(self.root, self._get_frame_path(seq_path, frame_id)) def get_class_name(self, seq_id): obj_meta = self.sequence_meta_info[self.sequence_list[seq_id]] return obj_meta['object_class_name'] def get_frames(self, seq_id, frame_ids, anno=None): seq_path = self._get_sequence_path(seq_id) obj_meta = self.sequence_meta_info[self.sequence_list[seq_id]] 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] return frame_list, anno_frames, obj_meta