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# Copyright (c) Facebook, Inc. and its affiliates.
# Modified by Qihang Yu from https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/data/datasets/register_mapillary_vistas_panoptic.py
import json
import os
from detectron2.data import DatasetCatalog, MetadataCatalog
from detectron2.utils.file_io import PathManager
from . import openseg_classes
MAPILLARY_VISTAS_SEM_SEG_CATEGORIES = openseg_classes.get_mapillary_vistas_categories_with_prompt_eng()
def load_mapillary_vistas_panoptic_json(json_file, image_dir, gt_dir, semseg_dir, meta):
"""
Args:
image_dir (str): path to the raw dataset. e.g., "~/coco/train2017".
gt_dir (str): path to the raw annotations. e.g., "~/coco/panoptic_train2017".
json_file (str): path to the json file. e.g., "~/coco/annotations/panoptic_train2017.json".
Returns:
list[dict]: a list of dicts in Detectron2 standard format. (See
`Using Custom Datasets </tutorials/datasets.html>`_ )
"""
def _convert_category_id(segment_info, meta):
if segment_info["category_id"] in meta["thing_dataset_id_to_contiguous_id"]:
segment_info["category_id"] = meta["thing_dataset_id_to_contiguous_id"][
segment_info["category_id"]
]
segment_info["isthing"] = True
else:
segment_info["category_id"] = meta["stuff_dataset_id_to_contiguous_id"][
segment_info["category_id"]
]
segment_info["isthing"] = False
return segment_info
with PathManager.open(json_file) as f:
json_info = json.load(f)
ret = []
for ann in json_info["annotations"]:
image_id = ann["image_id"]
# TODO: currently we assume image and label has the same filename but
# different extension, and images have extension ".jpg" for COCO. Need
# to make image extension a user-provided argument if we extend this
# function to support other COCO-like datasets.
image_file = os.path.join(image_dir, os.path.splitext(ann["file_name"])[0] + ".jpg")
label_file = os.path.join(gt_dir, ann["file_name"])
sem_label_file = os.path.join(semseg_dir, ann["file_name"])
segments_info = [_convert_category_id(x, meta) for x in ann["segments_info"]]
ret.append(
{
"file_name": image_file,
"image_id": image_id,
"pan_seg_file_name": label_file,
"sem_seg_file_name": sem_label_file,
"segments_info": segments_info,
}
)
assert len(ret), f"No images found in {image_dir}!"
assert PathManager.isfile(ret[0]["file_name"]), ret[0]["file_name"]
assert PathManager.isfile(ret[0]["pan_seg_file_name"]), ret[0]["pan_seg_file_name"]
assert PathManager.isfile(ret[0]["sem_seg_file_name"]), ret[0]["sem_seg_file_name"]
return ret
def register_mapillary_vistas_panoptic(
name, metadata, image_root, panoptic_root, semantic_root, panoptic_json, instances_json=None
):
"""
Register a "standard" version of ADE20k panoptic segmentation dataset named `name`.
The dictionaries in this registered dataset follows detectron2's standard format.
Hence it's called "standard".
Args:
name (str): the name that identifies a dataset,
e.g. "ade20k_panoptic_train"
metadata (dict): extra metadata associated with this dataset.
image_root (str): directory which contains all the images
panoptic_root (str): directory which contains panoptic annotation images in COCO format
panoptic_json (str): path to the json panoptic annotation file in COCO format
sem_seg_root (none): not used, to be consistent with
`register_coco_panoptic_separated`.
instances_json (str): path to the json instance annotation file
"""
panoptic_name = name
DatasetCatalog.register(
panoptic_name,
lambda: load_mapillary_vistas_panoptic_json(
panoptic_json, image_root, panoptic_root, semantic_root, metadata
),
)
MetadataCatalog.get(panoptic_name).set(
panoptic_root=panoptic_root,
image_root=image_root,
panoptic_json=panoptic_json,
json_file=instances_json,
evaluator_type="mapillary_vistas_panoptic_seg",
ignore_label=65, # different from other datasets, Mapillary Vistas sets ignore_label to 65
label_divisor=1000,
**metadata,
)
_PREDEFINED_SPLITS_ADE20K_PANOPTIC = {
"openvocab_mapillary_vistas_panoptic_train": (
"mapillary_vistas/training/images",
"mapillary_vistas/training/panoptic",
"mapillary_vistas/training/panoptic/panoptic_2018.json",
"mapillary_vistas/training/labels",
),
"openvocab_mapillary_vistas_panoptic_val": (
"mapillary_vistas/validation/images",
"mapillary_vistas/validation/panoptic",
"mapillary_vistas/validation/panoptic/panoptic_2018.json",
"mapillary_vistas/validation/labels",
),
}
def get_metadata():
meta = {}
# The following metadata maps contiguous id from [0, #thing categories +
# #stuff categories) to their names and colors. We have to replica of the
# same name and color under "thing_*" and "stuff_*" because the current
# visualization function in D2 handles thing and class classes differently
# due to some heuristic used in Panoptic FPN. We keep the same naming to
# enable reusing existing visualization functions.
thing_classes = [k["name"] for k in MAPILLARY_VISTAS_SEM_SEG_CATEGORIES]
thing_colors = [k["color"] for k in MAPILLARY_VISTAS_SEM_SEG_CATEGORIES]
stuff_classes = [k["name"] for k in MAPILLARY_VISTAS_SEM_SEG_CATEGORIES]
stuff_colors = [k["color"] for k in MAPILLARY_VISTAS_SEM_SEG_CATEGORIES]
meta["thing_classes"] = thing_classes
meta["thing_colors"] = thing_colors
meta["stuff_classes"] = stuff_classes
meta["stuff_colors"] = stuff_colors
# Convert category id for training:
# category id: like semantic segmentation, it is the class id for each
# pixel. Since there are some classes not used in evaluation, the category
# id is not always contiguous and thus we have two set of category ids:
# - original category id: category id in the original dataset, mainly
# used for evaluation.
# - contiguous category id: [0, #classes), in order to train the linear
# softmax classifier.
thing_dataset_id_to_contiguous_id = {}
stuff_dataset_id_to_contiguous_id = {}
for i, cat in enumerate(MAPILLARY_VISTAS_SEM_SEG_CATEGORIES):
if cat["isthing"]:
thing_dataset_id_to_contiguous_id[cat["id"]] = i
# else:
# stuff_dataset_id_to_contiguous_id[cat["id"]] = i
# in order to use sem_seg evaluator
stuff_dataset_id_to_contiguous_id[cat["id"]] = i
meta["thing_dataset_id_to_contiguous_id"] = thing_dataset_id_to_contiguous_id
meta["stuff_dataset_id_to_contiguous_id"] = stuff_dataset_id_to_contiguous_id
return meta
def register_all_mapillary_vistas_panoptic(root):
metadata = get_metadata()
for (
prefix,
(image_root, panoptic_root, panoptic_json, semantic_root),
) in _PREDEFINED_SPLITS_ADE20K_PANOPTIC.items():
# The "standard" version of COCO panoptic segmentation dataset,
# e.g. used by Panoptic-DeepLab
register_mapillary_vistas_panoptic(
prefix,
metadata,
os.path.join(root, image_root),
os.path.join(root, panoptic_root),
os.path.join(root, semantic_root),
os.path.join(root, panoptic_json),
)
_root = os.getenv("DETECTRON2_DATASETS", "datasets")
register_all_mapillary_vistas_panoptic(_root) |