def get_classes_and_palette()

in modified_mmseg/datasets/custom.py [0:0]

• 22 lines of code
• 10 McCabe index (conditional complexity)

    def get_classes_and_palette(self, classes=None, palette=None):
        """Get class names of current dataset.

        Args:
            classes (Sequence[str] | str | None): If classes is None, use
                default CLASSES defined by builtin dataset. If classes is a
                string, take it as a file name. The file contains the name of
                classes where each line contains one class name. If classes is
                a tuple or list, override the CLASSES defined by the dataset.
            palette (Sequence[Sequence[int]]] | np.ndarray | None):
                The palette of segmentation map. If None is given, random
                palette will be generated. Default: None
        """
        if classes is None:
            self.custom_classes = False
            return self.CLASSES, self.PALETTE

        self.custom_classes = True
        if isinstance(classes, str):
            # take it as a file path
            class_names = mmcv.list_from_file(classes)
        elif isinstance(classes, (tuple, list)):
            class_names = classes
        else:
            raise ValueError(f'Unsupported type {type(classes)} of classes.')

        if self.CLASSES:
            if not set(classes).issubset(self.CLASSES):
                raise ValueError('classes is not a subset of CLASSES.')

            # dictionary, its keys are the old label ids and its values
            # are the new label ids.
            # used for changing pixel labels in load_annotations.
            self.label_map = {}
            for i, c in enumerate(self.CLASSES):
                if c not in class_names:
                    self.label_map[i] = -1
                else:
                    self.label_map[i] = classes.index(c)

        palette = self.get_palette_for_custom_classes(class_names, palette)

        return class_names, palette