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import os
import random
from collections import OrderedDict
import torch
from lib.train.admin import env_settings
from lib.train.data import jpeg4py_loader
from .base_video_dataset import BaseVideoDataset
from .refer import REFER
class RefCOCOSeq(BaseVideoDataset):
""" The COCO dataset. COCO is an image dataset. Thus, we treat each image as a sequence of length 1.
Publication:
Microsoft COCO: Common Objects in Context.
Tsung-Yi Lin, Michael Maire, Serge J. Belongie, Lubomir D. Bourdev, Ross B. Girshick, James Hays, Pietro Perona,
Deva Ramanan, Piotr Dollar and C. Lawrence Zitnick
ECCV, 2014
https://arxiv.org/pdf/1405.0312.pdf
Download the images along with annotations from http://cocodataset.org/#download. The root folder should be
organized as follows.
- coco_root
- annotations
- instances_train2014.json
- instances_train2017.json
- images
- train2014
- train2017
Note: You also have to install the coco pythonAPI from https://github.com/cocodataset/cocoapi.
"""
def __init__(self, root=None, image_loader=jpeg4py_loader, data_fraction=None, split="train", version="2014",
name="refcoco", splitBy="google"):
"""
args:
root - path to the coco dataset.
image_loader (default_image_loader) - The function to read the images. If installed,
jpeg4py (https://github.com/ajkxyz/jpeg4py) is used by default. Else,
opencv's imread is used.
data_fraction (None) - Fraction of images to be used. The images are selected randomly. If None, all the
images will be used
split - 'train' or 'val'.
version - version of coco dataset (2014 or 2017)
"""
root = env_settings().coco_dir if root is None else root
root = os.path.join(root,"other")
super().__init__('RefCOCOSeq', root, image_loader)
self.split = split
self.img_pth = os.path.join(root, 'images/mscoco/images/{}{}'.format("train", version))
self.anno_path = os.path.join(root, '{}/instances.json'.format(name))
self.dataset_name = name
# Load the COCO set.
self.coco_set = REFER(root, dataset=name, splitBy=splitBy)
self.cats = self.coco_set.Cats
self.class_list = self.get_class_list()
self.sequence_list = self._get_sequence_list()
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_seq_per_class()
# get_the_subject_infor
self.subject_infor_path = "./"
while "resource" not in os.listdir(self.subject_infor_path):
self.subject_infor_path = os.path.join(self.subject_infor_path, "../")
self.subject_infor_path = os.path.join(self.subject_infor_path,
"resource/text_infor/refcoco_4o_v3_extract_mask.json")
with open(self.subject_infor_path, 'r') as file:
self.subject_infor = json.load(file)
def _get_sequence_list(self):
ref_list = list(self.coco_set.getRefIds(split=self.split))
seq_list = [a for a in ref_list if self.coco_set.refToAnn[a]['iscrowd'] == 0]
return seq_list
def is_video_sequence(self):
return False
def get_num_classes(self):
return len(self.class_list)
def get_name(self):
return self.dataset_name
def has_class_info(self):
return True
def get_class_list(self):
class_list = []
for cat_id in self.cats.keys():
class_list.append(self.cats[cat_id])
return class_list
def has_segmentation_info(self):
return True
def get_num_sequences(self):
return len(self.sequence_list)
def _build_seq_per_class(self):
seq_per_class = {}
for i, seq in enumerate(self.sequence_list):
class_name = self.cats[self.coco_set.refToAnn[seq]['category_id']]
if class_name not in seq_per_class:
seq_per_class[class_name] = [i]
else:
seq_per_class[class_name].append(i)
return seq_per_class
def get_sequences_in_class(self, class_name):
return self.seq_per_class[class_name]
def get_sequence_info(self, seq_id):
anno = self._get_anno(seq_id)
bbox = torch.Tensor(anno['bbox']).view(1, 4)
# mask = torch.Tensor(self.coco_set.annToMask(anno)).unsqueeze(dim=0)
'''2021.1.3 To avoid too small bounding boxes. Here we change the threshold to 50 pixels'''
valid = (bbox[:, 2] > 50) & (bbox[:, 3] > 50)
visible = valid.clone().byte()
nlp = self._read_nlp(seq_id)
return {'bbox': bbox, 'valid': valid, 'visible': visible, 'nlp': nlp}
def _read_nlp(self, seq_id):
ref = self.coco_set.Refs[self.sequence_list[seq_id]]
sent = ref['sentences'][-1]['sent']
return sent
def _get_anno(self, seq_id):
anno = self.coco_set.refToAnn[self.sequence_list[seq_id]]
return anno
def _get_frames(self, seq_id):
path = self.coco_set.loadImgs([self.coco_set.refToAnn[self.sequence_list[seq_id]]['image_id']])[0]['file_name']
img = self.image_loader(os.path.join(self.img_pth, path))
return img
def get_meta_info(self, seq_id):
try:
cat_dict_current = self.cats[self.coco_set.refToAnn[self.sequence_list[seq_id]]['category_id']]
object_meta = OrderedDict({'object_class_name': cat_dict_current['name'],
'motion_class': None,
'major_class': cat_dict_current['supercategory'],
'root_class': None,
'motion_adverb': None})
except:
object_meta = OrderedDict({'object_class_name': None,
'motion_class': None,
'major_class': None,
'root_class': None,
'motion_adverb': None})
return object_meta
def get_class_name(self, seq_id):
cat_dict_current = self.cats[self.coco_set.refToAnn[self.sequence_list[seq_id]]['category_id']]
return cat_dict_current['name']
def get_subject_mask_infor(self,seq_id):
# seq_name = self.sequence_list[seq_id]
seq_sub_mask_infor = self.subject_infor[str(seq_id)]["subject_extrack_mask_infor"]
return seq_sub_mask_infor
def get_frames(self, seq_id=None, frame_ids=None, anno=None):
# COCO is an image dataset. Thus we replicate the image denoted by seq_id len(frame_ids) times, and return a
# list containing these replicated images.
frame = self._get_frames(seq_id)
seq_name = self.sequence_list[seq_id]
frame_list = [frame.copy() for _ in frame_ids]
if anno is None:
anno = self.get_sequence_info(seq_id)
anno_frames = {}
for key, value in anno.items():
if key == 'nlp':
anno_frames[key] = [value for _ in frame_ids]
else:
anno_frames[key] = [value[0, ...] for _ in frame_ids]
object_meta = self.get_meta_info(seq_id)
subject_mask_infor = self.get_subject_mask_infor(seq_id)
if "NoneNone" in subject_mask_infor:
subject_mask_infor = [-1]
nlp_with_mask = anno_frames["nlp"][0] + "+" + ",".join(map(str, subject_mask_infor))
anno_frames["nlp"] = [nlp_with_mask]
# anno_frames["subject_mask_infor"] = subject_mask_infor
return frame_list, anno_frames, object_meta
def get_path(self, seq_id, frame_ids):
img_name = self.coco_set.loadImgs([self.coco_set.refToAnn[self.sequence_list[seq_id]]['image_id']])[0]['file_name']
return [os.path.join(self.img_pth, img_name) for _ in range(len(frame_ids))]
def get_ref_id(self, seq_id):
return self.sequence_list[seq_id] |