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from .base_video_dataset import BaseVideoDataset
from lib.train.data import jpeg4py_loader
import torch
import random
from collections import OrderedDict
from lib.train.admin import env_settings
from lib.train.dataset.COCO_tool import COCO
from lib.utils.lmdb_utils import decode_img, decode_json
import time
import numpy as np
class MSCOCOSeq_lmdb(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",
multi_modal_vision=False, multi_modal_language=False):
"""
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
super().__init__('COCO_lmdb', root, image_loader)
self.root = root
self.img_pth = 'images/{}{}/'.format(split, version)
self.anno_path = 'annotations/instances_{}{}.json'.format(split, version)
# Load the COCO set.
print('loading annotations into memory...')
tic = time.time()
coco_json = decode_json(root, self.anno_path)
print('Done (t={:0.2f}s)'.format(time.time() - tic))
self.coco_set = COCO(coco_json)
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()
self.multi_modal_vision = multi_modal_vision
self.multi_modal_language = multi_modal_language
def _get_sequence_list(self):
ann_list = list(self.coco_set.anns.keys())
seq_list = [a for a in ann_list if self.coco_set.anns[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 'coco_lmdb'
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]['name'])
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.anns[seq]['category_id']]['name']
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()
return {'bbox': bbox, 'mask': mask, 'valid': valid, 'visible': visible}
def _get_anno(self, seq_id):
anno = self.coco_set.anns[self.sequence_list[seq_id]]
return anno
def _get_frames(self, seq_id):
path = self.coco_set.loadImgs([self.coco_set.anns[self.sequence_list[seq_id]]['image_id']])[0]['file_name']
# img = self.image_loader(os.path.join(self.img_pth, path))
frame = decode_img(self.root, os.path.join(self.img_pth, path))
if self.multi_modal_vision:
frame = np.concatenate((frame, frame), axis=-1)
return frame
def get_meta_info(self, seq_id):
try:
cat_dict_current = self.cats[self.coco_set.anns[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.anns[self.sequence_list[seq_id]]['category_id']]
return cat_dict_current['name']
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)
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():
anno_frames[key] = [value[0, ...] for _ in frame_ids]
object_meta = self.get_meta_info(seq_id)
return frame_list, anno_frames, object_meta
def get_annos(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.
if anno is None:
anno = self.get_sequence_info(seq_id)
anno_frames = {}
for key, value in anno.items():
anno_frames[key] = [value[0, ...] for _ in frame_ids]
return anno_frames |