#
# For licensing see accompanying LICENSE file.
# Copyright (C) 2023 Apple Inc. All Rights Reserved.
#
import argparse
from data.datasets import BaseImageDataset
[docs]class BaseDetectionDataset(BaseImageDataset):
"""Base Dataset class for Object Dection datasets.
Args:
opts: Command-line arguments
"""
[docs] def __init__(self, opts: argparse.Namespace, *args, **kwargs) -> None:
super().__init__(opts=opts, *args, **kwargs)
[docs] @classmethod
def add_arguments(cls, parser: argparse.ArgumentParser) -> argparse.ArgumentParser:
if cls != BaseDetectionDataset:
# Don't re-register arguments in subclasses that don't override `add_arguments()`.
return parser
group = parser.add_argument_group(cls.__name__)
group.add_argument(
"--evaluation.detection.save-overlay-boxes",
action="store_true",
help="enable this flag to visualize predicted masks on top of input image",
)
group.add_argument(
"--evaluation.detection.mode",
type=str,
default="validation_set",
required=False,
choices=["single_image", "image_folder", "validation_set"],
help="Contribution of mask when overlaying on top of RGB image.",
)
group.add_argument(
"--evaluation.detection.path",
type=str,
default=None,
help="Path of the image or image folder (only required for single_image and image_folder modes).",
)
group.add_argument(
"--evaluation.detection.num-classes",
type=int,
default=None,
help="Number of segmentation classes used during training.",
)
group.add_argument(
"--evaluation.detection.resize-input-images",
action="store_true",
default=False,
help="Resize input images to fixed size during detection evaluation.",
)
return parser