dgs.models.dataset.MOT.MOTImage¶

class dgs.models.dataset.MOT.MOTImage(*args: Any, **kwargs: Any)[source]¶

Load a ground-truth- or detection-file in the MOT format.

Params¶

file_separator (str, optional):: The str or regular expression used to split the lines in the annotation file. Default DEF_VAL["dataset"]["MOT"]["file_separator"].
crop_key (str, optional):: The name of the key in the seqinfo file containing the info for the image crops. Default DEF_VAL["dataset"]["MOT"]["crop_key"].
seqinfo_path (str, optional):: The optional path to the seqinfo.ini file. Default DEF_VAL["dataset"]["MOT"]["seqinfo_path"].
seqinfo_key (str, optional):: The key to use in the seqinfo file. Default DEF_VAL["submission"]["MOT"]["seqinfo_key"].

__init__(config: dict[str, any], path: list[str])[source]¶

Methods

`arbitrary_to_ds`(a, idx)	Most of the state is available, now just load the image crops.
`configure_torch_module`(module[, train])	Set compute mode and send model to the device or multiple parallel devices if applicable.
`get_image_crops`(ds)	Add the image crops and local key-points to a given state.
`get_path_in_dataset`(path)	Given an arbitrary file- or directory-path, return its absolute path.
`terminate`()	Terminate this module and all of its submodules.
`transform_crop_resize`()	Given one single image, with its corresponding bounding boxes and key-points, obtain a cropped image for every bounding box with localized key-points.
`transform_resize_image`()	Given an image, bboxes, and key-points, resize them with custom modes.
`validate_params`(validations[, attrib_name])	Given per key validations, validate this module's parameters.

Attributes

`device`	Get the device of this module.
`is_training`	Get whether this module is set to training-mode.
`module_name`	Get the name of the module.
`module_type`
`name`	Get the name of the module.
`name_safe`	Get the escaped name of the module usable in filepaths by replacing spaces and underscores.
`precision`	Get the (floating point) precision used in multiple parts of this module.
`data`	Arbitrary data, which will be converted using `self.arbitrary_to_ds()`
`dataset_path`	The base path to the dataset.