#
# For licensing see accompanying LICENSE file.
# Copyright (C) 2023 Apple Inc. All Rights Reserved.
#
import argparse
import os
from typing import List, Mapping, Optional, Tuple, Union
import numpy as np
from pycocotools import mask
from pycocotools.coco import COCO
from torch import Tensor
from data.datasets import DATASET_REGISTRY
from data.datasets.segmentation.base_segmentation import BaseImageSegmentationDataset
[docs]@DATASET_REGISTRY.register(name="coco", type="segmentation")
class COCOSegmentationDataset(BaseImageSegmentationDataset):
"""Dataset class for the COCO dataset that maps classes to PASCAL VOC classes
Args:
opts: command-line arguments
"""
[docs] def __init__(self, opts: argparse.Namespace, *args, **kwargs) -> None:
super().__init__(opts=opts, *args, **kwargs)
year = 2017
split = "train" if self.is_training else "val"
ann_file = os.path.join(
self.root, "annotations/instances_{}{}.json".format(split, year)
)
self.img_dir = os.path.join(self.root, "images/{}{}".format(split, year))
self.split = split
self.coco = COCO(ann_file)
self.coco_mask = mask
self.ids = list(self.coco.imgs.keys())
self.ignore_label = 255
self.background_idx = 0
def __getitem__(
self, sample_size_and_index: Tuple[int, int, int], *args, **kwargs
) -> Mapping[str, Union[Tensor, Mapping[str, Tensor]]]:
"""Returns the sample corresponding to the input sample index. Returned sample is transformed
into the size specified by the input.
Args:
sample_size_and_index: Tuple of the form (crop_size_h, crop_size_w, sample_index)
Returns:
A dictionary with `samples` and `targets` as keys corresponding to input and labels of
a sample, respectively.
Shapes:
The shape of values in output dictionary, output_data, are as follows:
output_data["samples"]["image"]: Shape is [Channels, Height, Width]
output_data["targets"]["mask"]: Shape is [Height, Width]
"""
crop_size_h, crop_size_w, img_index = sample_size_and_index
_transform = self.get_augmentation_transforms(size=(crop_size_h, crop_size_w))
coco = self.coco
img_id = self.ids[img_index]
img_metadata = coco.loadImgs(img_id)[0]
path = img_metadata["file_name"]
rgb_img = self.read_image_pil(os.path.join(self.img_dir, path))
cocotarget = coco.loadAnns(coco.getAnnIds(imgIds=img_id))
mask = self._gen_seg_mask(
cocotarget, img_metadata["height"], img_metadata["width"]
)
data = {"image": rgb_img, "mask": None if self.is_evaluation else mask}
data = _transform(data)
if self.is_evaluation:
# for evaluation purposes, resize only the input and not mask
data["mask"] = mask
output_data = {"samples": data["image"], "targets": data["mask"]}
if self.is_evaluation:
im_width, im_height = rgb_img.size
img_name = path.replace("jpg", "png")
mask = output_data.pop("targets")
output_data["targets"] = {
"mask": mask,
"file_name": img_name,
"im_width": im_width,
"im_height": im_height,
}
return output_data
def _gen_seg_mask(self, target, h: int, w: int) -> np.ndarray:
"""Generates a mask in PASCAL VOC format"""
mask = np.zeros((h, w), dtype=np.uint8)
coco_mask = self.coco_mask
coco_to_pascal = self.coco_to_pascal_mapping()
for instance in target:
rle = coco_mask.frPyObjects(instance["segmentation"], h, w)
m = coco_mask.decode(rle)
cat = instance["category_id"]
if cat in coco_to_pascal:
c = coco_to_pascal.index(cat)
else:
continue
if len(m.shape) < 3:
mask[:, :] += (mask == 0) * (m * c)
else:
mask[:, :] += (mask == 0) * (((np.sum(m, axis=2)) > 0) * c).astype(
np.uint8
)
return mask
def __len__(self) -> int:
return len(self.ids)
[docs] @staticmethod
def class_names() -> List[str]:
"""PASCAL VOC classes names"""
return [
"background",
"aeroplane",
"bicycle",
"bird",
"boat",
"bottle",
"bus",
"car",
"cat",
"chair",
"cow",
"diningtable",
"dog",
"horse",
"motorbike",
"person",
"potted_plant",
"sheep",
"sofa",
"train",
"tv_monitor",
]
[docs] @staticmethod
def coco_to_pascal_mapping() -> List[int]:
"""COCO to PASCAL VOC class mapping"""
return [
0,
5,
2,
16,
9,
44,
6,
3,
17,
62,
21,
67,
18,
19,
4,
1,
64,
20,
63,
7,
72,
]