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import os
import os.path
import numpy as np
import pandas
import random
from collections import OrderedDict
from lib.train.data import jpeg4py_loader
from .base_video_dataset import BaseVideoDataset
from lib.train.admin import env_settings
def list_sequences(root, set_ids):
""" Lists all the videos in the input set_ids. Returns a list of tuples (set_id, video_name)
args:
root: Root directory to TrackingNet
set_ids: Sets (0-11) which are to be used
returns:
list - list of tuples (set_id, video_name) containing the set_id and video_name for each sequence
"""
sequence_list = []
for s in set_ids:
anno_dir = os.path.join(root, "TRAIN_" + str(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
class TrackingNet(BaseVideoDataset):
""" TrackingNet dataset.
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, root=None, image_loader=jpeg4py_loader, set_ids=None, data_fraction=None,
multi_modal_vision=False, multi_modal_language=False):
"""
args:
root - The path to the TrackingNet folder, containing the training sets.
image_loader (jpeg4py_loader) - The function to read the images. jpeg4py (https://github.com/ajkxyz/jpeg4py)
is used by default.
set_ids (None) - List containing the ids of the TrackingNet sets to be used for training. If None, all the
sets (0 - 11) will be used.
data_fraction - Fraction of dataset to be used. The complete dataset is used by default
"""
root = env_settings().trackingnet_dir if root is None else root
super().__init__('TrackingNet', root, image_loader)
if set_ids is None:
set_ids = [i for i in range(12)]
self.set_ids = set_ids
# Keep a list of all videos. Sequence list is a list of tuples (set_id, video_name) containing the set_id and
# video_name for each sequence
self.sequence_list = list_sequences(self.root, self.set_ids)
if data_fraction is not None:
self.sequence_list = random.sample(self.sequence_list, int(len(self.sequence_list) * data_fraction))
self.seq_to_class_map, self.seq_per_class = self._load_class_info()
# we do not have the class_lists for the tracking net
self.class_list = list(self.seq_per_class.keys())
self.class_list.sort()
self.multi_modal_vision = multi_modal_vision
self.multi_modal_language = multi_modal_language
current_dir = os.path.dirname(os.path.abspath(__file__))
self.nlp_label_dir = os.path.join(current_dir, "../../../resource/vl_datasets_label/trackingnet/nlp")
def _load_class_info(self):
ltr_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), '..')
class_map_path = os.path.join(ltr_path, 'data_specs', 'trackingnet_classmap.txt')
with open(class_map_path, 'r') as f:
seq_to_class_map = {seq_class.split('\t')[0]: seq_class.rstrip().split('\t')[1] for seq_class in f}
seq_per_class = {}
for i, seq in enumerate(self.sequence_list):
class_name = seq_to_class_map.get(seq[1], 'Unknown')
if class_name not in seq_per_class:
seq_per_class[class_name] = [i]
else:
seq_per_class[class_name].append(i)
return seq_to_class_map, seq_per_class
def get_name(self):
return 'trackingnet'
def has_class_info(self):
return True
def get_sequences_in_class(self, class_name):
return self.seq_per_class[class_name]
def _read_bb_anno(self, seq_id):
set_id = self.sequence_list[seq_id][0]
vid_name = self.sequence_list[seq_id][1]
bb_anno_file = os.path.join(self.root, "TRAIN_" + str(set_id), "anno", vid_name + ".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 get_sequence_info(self, seq_id):
bbox = self._read_bb_anno(seq_id)
valid = (bbox[:, 2] > 0) & (bbox[:, 3] > 0)
visible = valid.clone().byte()
return {'bbox': bbox, 'valid': valid, 'visible': visible}
def _get_frame(self, seq_id, frame_id):
set_id = self.sequence_list[seq_id][0]
vid_name = self.sequence_list[seq_id][1]
frame_path = os.path.join(self.root, "TRAIN_" + str(set_id), "frames", vid_name, str(frame_id) + ".jpg")
frame = self.image_loader(frame_path)
if self.multi_modal_vision:
frame = np.concatenate((frame, frame), axis=-1)
return frame
def _get_class(self, seq_id):
seq_name = self.sequence_list[seq_id][1]
return self.seq_to_class_map[seq_name]
def get_class_name(self, seq_id):
obj_class = self._get_class(seq_id)
return obj_class
def get_frames(self, seq_id, frame_ids, anno=None):
frame_list = [self._get_frame(seq_id, f) for f 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]
obj_class = self._get_class(seq_id)
object_meta = OrderedDict({'object_class_name': obj_class,
'motion_class': None,
'major_class': None,
'root_class': None,
'motion_adverb': None})
# nlp_label_file = os.path.join(self.nlp_label_dir, self.sequence_list[seq_id][1] + ".txt")
# has_nlp_label_flag = False
# if os.path.isfile(nlp_label_file):
# with open(nlp_label_file, 'r', encoding='utf-8') as file:
# content = file.read()
# if "error" not in content:
# anno_frames["nlp"] = [content]
# has_nlp_label_flag = True
#
# if has_nlp_label_flag == False:
anno_frames["nlp"] = [obj_class]
class_len = len(obj_class.split(" "))
subject_mask_infor = []
for index in range(class_len):
subject_mask_infor.append(index)
nlp_with_mask = anno_frames["nlp"][0] + "+" + ",".join(map(str, subject_mask_infor))
anno_frames["nlp"] = [nlp_with_mask]
return frame_list, anno_frames, object_meta
def get_annos(self, seq_id, frame_ids, anno=None):
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 anno_frames |