184 lines
7.8 KiB
Python
184 lines
7.8 KiB
Python
import os
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import os.path
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import numpy as np
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import torch
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import csv
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import pandas
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import random
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from collections import OrderedDict
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from .base_video_dataset import BaseVideoDataset
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from lib.train.data import jpeg4py_loader
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from lib.train.admin import env_settings
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'''2021.1.16 Gok10k for loading lmdb dataset'''
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from lib.utils.lmdb_utils import *
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class Got10k_lmdb(BaseVideoDataset):
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def __init__(self, root=None, image_loader=jpeg4py_loader, split=None, seq_ids=None, data_fraction=None):
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"""
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args:
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root - path to the got-10k training data. Note: This should point to the 'train' folder inside GOT-10k
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image_loader (jpeg4py_loader) - The function to read the images. jpeg4py (https://github.com/ajkxyz/jpeg4py)
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is used by default.
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split - 'train' or 'val'. Note: The validation split here is a subset of the official got-10k train split,
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not NOT the official got-10k validation split. To use the official validation split, provide that as
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the root folder instead.
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seq_ids - List containing the ids of the videos to be used for training. Note: Only one of 'split' or 'seq_ids'
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options can be used at the same time.
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data_fraction - Fraction of dataset to be used. The complete dataset is used by default
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use_lmdb - whether the dataset is stored in lmdb format
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"""
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root = env_settings().got10k_lmdb_dir if root is None else root
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super().__init__('GOT10k_lmdb', root, image_loader)
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# all folders inside the root
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self.sequence_list = self._get_sequence_list()
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# seq_id is the index of the folder inside the got10k root path
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if split is not None:
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if seq_ids is not None:
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raise ValueError('Cannot set both split_name and seq_ids.')
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train_lib_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), '..')
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if split == 'train':
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file_path = os.path.join(train_lib_path, 'data_specs', 'got10k_train_split.txt')
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elif split == 'val':
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file_path = os.path.join(train_lib_path, 'data_specs', 'got10k_val_split.txt')
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elif split == 'train_full':
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file_path = os.path.join(train_lib_path, 'data_specs', 'got10k_train_full_split.txt')
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elif split == 'vottrain':
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file_path = os.path.join(train_lib_path, 'data_specs', 'got10k_vot_train_split.txt')
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elif split == 'votval':
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file_path = os.path.join(train_lib_path, 'data_specs', 'got10k_vot_val_split.txt')
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else:
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raise ValueError('Unknown split name.')
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seq_ids = pandas.read_csv(file_path, header=None, squeeze=True, dtype=np.int64).values.tolist()
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elif seq_ids is None:
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seq_ids = list(range(0, len(self.sequence_list)))
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self.sequence_list = [self.sequence_list[i] for i in seq_ids]
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if data_fraction is not None:
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self.sequence_list = random.sample(self.sequence_list, int(len(self.sequence_list)*data_fraction))
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self.sequence_meta_info = self._load_meta_info()
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self.seq_per_class = self._build_seq_per_class()
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self.class_list = list(self.seq_per_class.keys())
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self.class_list.sort()
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def get_name(self):
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return 'got10k_lmdb'
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def has_class_info(self):
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return True
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def has_occlusion_info(self):
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return True
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def _load_meta_info(self):
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def _read_meta(meta_info):
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object_meta = OrderedDict({'object_class_name': meta_info[5].split(': ')[-1],
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'motion_class': meta_info[6].split(': ')[-1],
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'major_class': meta_info[7].split(': ')[-1],
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'root_class': meta_info[8].split(': ')[-1],
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'motion_adverb': meta_info[9].split(': ')[-1]})
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return object_meta
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sequence_meta_info = {}
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for s in self.sequence_list:
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try:
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meta_str = decode_str(self.root, "train/%s/meta_info.ini" %s)
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sequence_meta_info[s] = _read_meta(meta_str.split('\n'))
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except:
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sequence_meta_info[s] = OrderedDict({'object_class_name': None,
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'motion_class': None,
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'major_class': None,
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'root_class': None,
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'motion_adverb': None})
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return sequence_meta_info
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def _build_seq_per_class(self):
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seq_per_class = {}
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for i, s in enumerate(self.sequence_list):
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object_class = self.sequence_meta_info[s]['object_class_name']
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if object_class in seq_per_class:
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seq_per_class[object_class].append(i)
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else:
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seq_per_class[object_class] = [i]
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return seq_per_class
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def get_sequences_in_class(self, class_name):
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return self.seq_per_class[class_name]
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def _get_sequence_list(self):
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dir_str = decode_str(self.root, 'train/list.txt')
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dir_list = dir_str.split('\n')
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return dir_list
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def _read_bb_anno(self, seq_path):
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bb_anno_file = os.path.join(seq_path, "groundtruth.txt")
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gt_str_list = decode_str(self.root, bb_anno_file).split('\n')[:-1] # the last line in got10k is empty
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gt_list = [list(map(float, line.split(','))) for line in gt_str_list]
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gt_arr = np.array(gt_list).astype(np.float32)
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return torch.tensor(gt_arr)
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def _read_target_visible(self, seq_path):
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# full occlusion and out_of_view files
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occlusion_file = os.path.join(seq_path, "absence.label")
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cover_file = os.path.join(seq_path, "cover.label")
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# Read these files
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occ_list = list(map(int, decode_str(self.root, occlusion_file).split('\n')[:-1])) # the last line in got10k is empty
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occlusion = torch.ByteTensor(occ_list)
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cover_list = list(map(int, decode_str(self.root, cover_file).split('\n')[:-1])) # the last line in got10k is empty
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cover = torch.ByteTensor(cover_list)
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target_visible = ~occlusion & (cover>0).byte()
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visible_ratio = cover.float() / 8
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return target_visible, visible_ratio
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def _get_sequence_path(self, seq_id):
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return os.path.join("train", self.sequence_list[seq_id])
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def get_sequence_info(self, seq_id):
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seq_path = self._get_sequence_path(seq_id)
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bbox = self._read_bb_anno(seq_path)
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valid = (bbox[:, 2] > 0) & (bbox[:, 3] > 0)
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visible, visible_ratio = self._read_target_visible(seq_path)
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visible = visible & valid.byte()
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return {'bbox': bbox, 'valid': valid, 'visible': visible, 'visible_ratio': visible_ratio}
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def _get_frame_path(self, seq_path, frame_id):
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return os.path.join(seq_path, '{:08}.jpg'.format(frame_id+1)) # frames start from 1
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def _get_frame(self, seq_path, frame_id):
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return decode_img(self.root, self._get_frame_path(seq_path, frame_id))
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def get_class_name(self, seq_id):
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obj_meta = self.sequence_meta_info[self.sequence_list[seq_id]]
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return obj_meta['object_class_name']
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def get_frames(self, seq_id, frame_ids, anno=None):
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seq_path = self._get_sequence_path(seq_id)
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obj_meta = self.sequence_meta_info[self.sequence_list[seq_id]]
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frame_list = [self._get_frame(seq_path, f_id) for f_id in frame_ids]
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if anno is None:
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anno = self.get_sequence_info(seq_id)
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anno_frames = {}
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for key, value in anno.items():
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anno_frames[key] = [value[f_id, ...].clone() for f_id in frame_ids]
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return frame_list, anno_frames, obj_meta
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