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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) |