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import logging
import os
from typing import List
import numpy as np
import pycocotools.mask as maskUtils
import mmcv
from mmengine import print_log, list_from_file, scandir, track_parallel_progress
from mmengine.dist import master_only, dist
from mmengine.fileio import join_path, exists, load, dump
from mmdet.datasets import BaseVideoDataset
from mmdet.registry import DATASETS
from seg.models.utils import INSTANCE_OFFSET_HB
from ext.class_names.VIPSeg import CLASSES_THING, CLASSES_STUFF, COCO_CLASSES, COCO_THINGS, COCO_STUFF, PALETTE
NO_OBJ = 0
NO_OBJ_HB = 255
NO_OBJ_BUG = (200,)
DIVISOR_PAN = 100
NUM_THING = 58
NUM_STUFF = 66
def to_coco(pan_map, divisor=INSTANCE_OFFSET_HB):
pan_new = - np.ones_like(pan_map)
vip2hb_thing = {itm['id'] + 1: idx for idx, itm in enumerate(CLASSES_THING)}
assert len(vip2hb_thing) == NUM_THING
vip2hb_stuff = {itm['id'] + 1: idx for idx, itm in enumerate(CLASSES_STUFF)}
assert len(vip2hb_stuff) == NUM_STUFF
for idx in np.unique(pan_map):
# 200 is a bug in vipseg dataset.
# Please refer to https://github.com/VIPSeg-Dataset/VIPSeg-Dataset/issues/1
if idx == NO_OBJ or idx in NO_OBJ_BUG:
pan_new[pan_map == idx] = NO_OBJ_HB * divisor
elif idx > 128:
cls_id = idx // DIVISOR_PAN
cls_new_id = vip2hb_thing[cls_id]
inst_id = idx % DIVISOR_PAN
pan_new[pan_map == idx] = cls_new_id * divisor + inst_id + 1
else:
cls_new_id = vip2hb_stuff[idx]
cls_new_id += NUM_THING
pan_new[pan_map == idx] = cls_new_id * divisor
assert -1 not in np.unique(pan_new)
return pan_new
def mask2bbox(mask):
bbox = np.zeros((4,), dtype=np.float32)
x_any = np.any(mask, axis=0)
y_any = np.any(mask, axis=1)
x = np.where(x_any)[0]
y = np.where(y_any)[0]
if len(x) > 0 and len(y) > 0:
bbox = np.array((x[0], y[0], x[-1], y[-1]), dtype=np.float32)
return bbox
def video_parser(params):
seq_id, vid_folder, ann_folder = params
images = []
assert os.path.basename(vid_folder) == os.path.basename(ann_folder)
_tmp_img_id = -1
imgs_cur = sorted(list(map(
lambda x: str(x), scandir(vid_folder, recursive=False, suffix='.jpg')
)))
pans_cur = sorted(list(map(
lambda x: str(x), scandir(ann_folder, recursive=False, suffix='.png')
)))
for img_cur, pan_cur in zip(imgs_cur, pans_cur):
assert img_cur.split('.')[0] == pan_cur.split('.')[0]
_tmp_img_id += 1
img_id = _tmp_img_id
item_full = os.path.join(vid_folder, img_cur)
inst_map = os.path.join(ann_folder, pan_cur)
img_dict = {
'img_path': item_full,
'ann_path': inst_map,
}
assert os.path.exists(img_dict['img_path'])
assert os.path.exists(img_dict['ann_path'])
instances = []
ann_map = mmcv.imread(img_dict['ann_path'], flag='unchanged').astype(np.uint32)
img_dict['height'], img_dict['width'] = ann_map.shape
pan_map = to_coco(ann_map, INSTANCE_OFFSET_HB)
for pan_seg_id in np.unique(pan_map):
label = pan_seg_id // INSTANCE_OFFSET_HB
if label == NO_OBJ_HB:
continue
instance = {}
mask = (pan_map == pan_seg_id).astype(np.uint8)
instance['instance_id'] = pan_seg_id
instance['bbox'] = mask2bbox(mask)
instance['bbox_label'] = label
instance['ignore_flag'] = 0
instance['mask'] = maskUtils.encode(np.asfortranarray(mask))
instance['mask']['counts'] = instance['mask']['counts'].decode()
instances.append(instance)
img_dict['instances'] = instances
img_dict['video_id'] = seq_id
img_dict['frame_id'] = img_id
img_dict['img_id'] = seq_id * 10000 + img_id
images.append(img_dict)
return {
'video_id': seq_id,
'images': images,
'video_length': len(images)
}
@DATASETS.register_module()
class VIPSegDataset(BaseVideoDataset):
METAINFO = {
'classes': COCO_CLASSES,
'thing_classes': COCO_THINGS,
'stuff_classes': COCO_STUFF,
'palette': PALETTE,
}
def __init__(
self,
*args,
img_map_suffix: str = '.jpg',
seg_map_suffix: str = '.png',
**kwargs
):
self.img_map_suffix = img_map_suffix
self.seg_map_suffix = seg_map_suffix
super().__init__(*args, **kwargs)
@master_only
def build_cache(self, ann_json_path, video_folders, ann_folders) -> None:
vid_ids = range(len(video_folders))
data_list = track_parallel_progress(
video_parser,
tasks=list(zip(vid_ids, video_folders, ann_folders)),
nproc=20,
keep_order=False,
)
data_list = sorted(data_list, key=lambda x: x['video_id'])
dump(data_list, ann_json_path)
def load_data_list(self) -> List[dict]:
video_folders = list_from_file(self.ann_file, prefix=self.data_prefix['img'])
ann_folders = list_from_file(self.ann_file, prefix=self.data_prefix['seg'])
assert len(video_folders) == len(ann_folders)
print_log(f"#videos : {len(video_folders)} ",
logger='current',
level=logging.INFO)
split = os.path.basename(self.ann_file).split('.')[0]
ann_json_path = f"{split}_annotations.json"
ann_json_path = join_path(self.data_root, ann_json_path)
if not exists(ann_json_path):
self.build_cache(ann_json_path, video_folders, ann_folders)
dist.barrier()
raw_data_list = load(ann_json_path)
data_list = []
for raw_data_info in raw_data_list:
data_info = self.parse_data_info(raw_data_info)
data_list.append(data_info)
vid_len_list = [itm['video_length'] for itm in data_list]
max_vid_len = max(vid_len_list)
min_vid_len = min(vid_len_list)
print_log(
f"Max video len : {max_vid_len}; "
f"Min video len : {min_vid_len}."
,
logger='current',
level=logging.INFO
)
return data_list
def parse_data_info(self, raw_data_info: dict) -> dict:
data_info = {
'video_id': raw_data_info['video_id'],
'video_length': raw_data_info['video_length']
}
images = []
for raw_img_data_info in raw_data_info['images']:
img_data_info = {
'img_path': raw_img_data_info['img_path'],
'height': raw_img_data_info['height'],
'width': raw_img_data_info['width'],
'video_id': raw_img_data_info['video_id'],
'frame_id': raw_img_data_info['frame_id'],
'img_id': raw_img_data_info['img_id']
}
instances = []
segments_info = []
for ann in raw_img_data_info['instances']:
instance = {}
category_id = ann['bbox_label']
bbox = ann['bbox']
is_thing = category_id < NUM_THING
if is_thing:
instance['bbox'] = bbox
instance['bbox_label'] = category_id
instance['ignore_flag'] = ann['ignore_flag']
instance['instance_id'] = ann['instance_id']
segment_info = {
'mask': ann['mask'],
'category': category_id,
'is_thing': is_thing
}
segments_info.append(segment_info)
if len(instance) > 0 and is_thing:
instances.append(instance)
img_data_info['instances'] = instances
img_data_info['segments_info'] = segments_info
images.append(img_data_info)
data_info['images'] = images
return data_info
def filter_data(self) -> List[dict]:
"""Filter image annotations according to filter_cfg.
Returns:
list[int]: Filtered results.
"""
if self.test_mode:
return self.data_list
num_imgs_before_filter = sum([len(info['images']) for info in self.data_list])
num_imgs_after_filter = num_imgs_before_filter
new_data_list = self.data_list
print_log(
'The number of samples before and after filtering: '
f'{num_imgs_before_filter} / {num_imgs_after_filter}', 'current')
return new_data_list
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