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"""
Ref-YoutubeVOS data loader
"""
from pathlib import Path
import torch
from torch.autograd.grad_mode import F
from torch.utils.data import Dataset
import datasets.transforms_video as T
import os
from PIL import Image
import json
import numpy as np
import random
from datasets.categories import ytvos_category_dict as category_dict
class YTVOSDataset(Dataset):
"""
A dataset class for the Refer-Youtube-VOS dataset which was first introduced in the paper:
"URVOS: Unified Referring Video Object Segmentation Network with a Large-Scale Benchmark"
(see https://link.springer.com/content/pdf/10.1007/978-3-030-58555-6_13.pdf).
The original release of the dataset contained both 'first-frame' and 'full-video' expressions. However, the first
dataset is not publicly available anymore as now only the harder 'full-video' subset is available to download
through the Youtube-VOS referring video object segmentation competition page at:
https://competitions.codalab.org/competitions/29139
Furthermore, for the competition the subset's original validation set, which consists of 507 videos, was split into
two competition 'validation' & 'test' subsets, consisting of 202 and 305 videos respectively. Evaluation can
currently only be done on the competition 'validation' subset using the competition's server, as
annotations were publicly released only for the 'train' subset of the competition.
"""
def __init__(self, img_folder: Path, ann_file: Path, transforms, return_masks: bool,
num_frames: int, max_skip: int):
self.img_folder = img_folder
self.ann_file = ann_file
self._transforms = transforms
self.return_masks = return_masks # not used
self.num_frames = num_frames
self.max_skip = max_skip
# create video meta data
self.prepare_metas()
print('\n video num: ', len(self.videos), ' clip num: ', len(self.metas))
print('\n')
def prepare_metas(self):
# read object information
with open(os.path.join(str(self.img_folder), 'meta.json'), 'r') as f:
subset_metas_by_video = json.load(f)['videos']
# read expression data
with open(str(self.ann_file), 'r') as f:
subset_expressions_by_video = json.load(f)['videos']
self.videos = list(subset_expressions_by_video.keys())
self.metas = []
for vid in self.videos:
vid_meta = subset_metas_by_video[vid]
vid_data = subset_expressions_by_video[vid]
vid_frames = sorted(vid_data['frames'])
vid_len = len(vid_frames)
obj_id_to_category = {}
obj_ids = set()
for exp_id, exp_dict in vid_data['expressions'].items() :
obj_id = exp_dict['obj_id']
category = exp_dict['category']
obj_ids.add(obj_id)
obj_id_to_category[obj_id] = category
obj_ids = list(obj_ids)
obj_ids.sort()
start_idx, end_idx = 2, vid_len - 2
bin_size = (end_idx - start_idx) // 4
bins = []
for i in range(4):
bin_start = start_idx + i * bin_size
bin_end = bin_start + bin_size if i < 3 else end_idx
bins.append((bin_start, bin_end))
# Create meta data for each selected frame
vid_metas = []
for bin_start, bin_end in bins:
frame_idx = random.randint(bin_start, bin_end - 1) # Randomly sample a frame in the bin
meta = {
'video': vid,
'frame_idx': frame_idx,
'frames': vid_frames,
'bins' : bins,
'obj_ids' : obj_ids,
'obj_id_to_category': obj_id_to_category # Map of obj_id to category
}
vid_metas.append(meta)
self.metas.append(vid_metas)
@staticmethod
def bounding_box(img):
rows = np.any(img, axis=1)
cols = np.any(img, axis=0)
rmin, rmax = np.where(rows)[0][[0, -1]]
cmin, cmax = np.where(cols)[0][[0, -1]]
return rmin, rmax, cmin, cmax # y1, y2, x1, x2
def __len__(self):
return len(self.metas)
def __getitem__(self, idx):
instance_check = False
while not instance_check:
vid_metas = self.metas[idx] # List of metadata dictionaries, one per bin
video = vid_metas[0]['video']
frames = vid_metas[0]['frames']
bins = vid_metas[0]['bins']
obj_ids = vid_metas[0]['obj_ids']
obj_id_to_category = vid_metas[0]['obj_id_to_category']
sample_indx = [meta['frame_idx'] for meta in vid_metas]
annotations = {}
for frame_indx in sample_indx:
frame_name = frames[frame_indx]
img_path = os.path.join(str(self.img_folder), 'JPEGImages', video, frame_name + '.jpg')
mask_path = os.path.join(str(self.img_folder), 'Annotations', video, frame_name + '.png')
img = Image.open(img_path).convert('RGB')
mask = Image.open(mask_path).convert('P')
mask_np = np.array(mask)
frame_annotations = {}
for obj_id in obj_ids:
obj_mask = (mask_np == obj_id).astype(np.float32) # Object-specific binary mask
if obj_mask.any():
y1, y2, x1, x2 = self.bounding_box(obj_mask)
bbox = [x1, y1, x2, y2] # Bounding box in xyxy format
else:
bbox = [0, 0, 0, 0] # No valid object, default bbox
frame_annotations[obj_id] = {
'category_name': obj_id_to_category[obj_id],
'bbox': bbox,
'mask': obj_mask
}
annotations[frame_indx] = frame_annotations
# Prepare the output dictionary
video_metadata = {
'bins': bins,
'annotations': annotations, # Object annotations per frame
'frames': sample_indx, # Sampled frame indices
'video_path': os.path.join(str(self.img_folder), 'JPEGImages', video)
}
# Check if there's at least one valid instance
valid_check = any(
any(frame_annotations[obj_id]['bbox'] != [0, 0, 0, 0] for obj_id in frame_annotations)
for frame_annotations in annotations.values()
)
if valid_check:
instance_check = True
else:
idx = random.randint(0, self.__len__() - 1)
return video_metadata
def make_coco_transforms(image_set, max_size=640):
normalize = T.Compose([
T.ToTensor(),
T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
scales = [288, 320, 352, 392, 416, 448, 480, 512]
if image_set == 'train':
return T.Compose([
T.RandomHorizontalFlip(),
T.PhotometricDistort(),
T.RandomSelect(
T.Compose([
T.RandomResize(scales, max_size=max_size),
T.Check(),
]),
T.Compose([
T.RandomResize([400, 500, 600]),
T.RandomSizeCrop(384, 600),
T.RandomResize(scales, max_size=max_size),
T.Check(),
])
),
normalize,
])
# we do not use the 'val' set since the annotations are inaccessible
if image_set == 'val':
return T.Compose([
T.RandomResize([360], max_size=640),
normalize,
])
raise ValueError(f'unknown {image_set}')
def build(image_set, args):
root = Path(args.ytvos_path)
assert root.exists(), f'provided YTVOS path {root} does not exist'
PATHS = {
"train": (root / "train", root / "meta_expressions" / "train" / "meta_expressions.json"),
"val": (root / "valid", root / "meta_expressions" / "valid" / "meta_expressions.json"), # not used actually
}
img_folder, ann_file = PATHS[image_set]
# dataset = YTVOSDataset(img_folder, ann_file, transforms=make_coco_transforms(image_set, max_size=args.max_size), return_masks=args.masks,
# num_frames=args.num_frames, max_skip=args.max_skip)
dataset = YTVOSDataset(img_folder, ann_file, transforms=None, return_masks=args.masks,
num_frames=args.num_frames, max_skip=args.max_skip)
return dataset
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