mmclassification/mmcls/core/visualization/cls_visualizer.py

# Copyright (c) OpenMMLab. All rights reserved.
from typing import Optional

import mmcv
import numpy as np
from mmengine import Visualizer
from mmengine.dist import master_only

from mmcls.core import ClsDataSample
from mmcls.registry import VISUALIZERS


@VISUALIZERS.register_module()
class ClsVisualizer(Visualizer):
    """Universal Visualizer for classification task.

    Args:
        name (str): Name of the instance. Defaults to 'visualizer'.
        image (np.ndarray, optional): the origin image to draw. The format
            should be RGB. Defaults to None.
        vis_backends (list, optional): Visual backend config list.
            Default to None.
        save_dir (str, optional): Save file dir for all storage backends.
            If it is None, the backend storage will not save any data.
        fig_save_cfg (dict): Keyword parameters of figure for saving.
            Defaults to empty dict.
        fig_show_cfg (dict): Keyword parameters of figure for showing.
            Defaults to empty dict.

    Examples:
        >>> import torch
        >>> import mmcv
        >>> from pathlib import Path
        >>> from mmcls.core import ClsDataSample, ClsVisualizer
        >>> # Example image
        >>> img = mmcv.imread("./demo/bird.JPEG", channel_order='rgb')
        >>> # Example annotation
        >>> data_sample = ClsDataSample().set_gt_label(1).set_pred_label(1).\
        ...     set_pred_score(torch.tensor([0.1, 0.8, 0.1]))
        >>> # Setup the visualizer
        >>> vis = ClsVisualizer(
        ...     save_dir="./outputs",
        ...     vis_backends=[dict(type='LocalVisBackend')])
        >>> # Set classes names
        >>> vis.dataset_meta = {'CLASSES': ['cat', 'bird', 'dog']}
        >>> # Show the example image with annotation in a figure.
        >>> # And it will ignore all preset storage backends.
        >>> vis.add_datasample('res', img, data_sample, show=True)
        >>> # Save the visualization result by the specified storage backends.
        >>> vis.add_datasample('res', img, data_sample)
        >>> assert Path('./outputs/vis_data/vis_image/res_0.png').exists()
        >>> # Save another visualization result with the same name.
        >>> vis.add_datasample('res', img, data_sample, step=1)
        >>> assert Path('./outputs/vis_data/vis_image/res_1.png').exists()
    """

    @master_only
    def add_datasample(self,
                       name: str,
                       image: np.ndarray,
                       data_sample: Optional[ClsDataSample] = None,
                       draw_gt: bool = True,
                       draw_pred: bool = True,
                       draw_score: bool = True,
                       show: bool = False,
                       text_cfg: dict = dict(),
                       wait_time: float = 0,
                       out_file: Optional[str] = None,
                       step: int = 0) -> None:
        """Draw datasample and save to all backends.

        - If ``show`` is True, all storage backends are ignored and then
        displayed in a local window.
        - If the ``out_file`` parameter is specified, the drawn image
        will be additionally saved to ``out_file``. It is usually used
        in script mode like ``image_demo.py``

        Args:
            name (str): The image identifier.
            image (np.ndarray): The image to draw.
            data_sample (:obj:`ClsDataSample`, optional): The annotation of the
                image. Default to None.
            draw_gt (bool): Whether to draw ground truth labels.
                Default to True.
            draw_pred (bool): Whether to draw prediction labels.
                Default to True.
            draw_score (bool): Whether to draw the prediction scores
                of prediction categories. Default to True.
            show (bool): Whether to display the drawn image. Default to False.
            text_cfg (dict): Extra text setting, which accepts
                arguments of :attr:`mmengine.Visualizer.draw_texts`.
                Defaults to an empty dict.
            wait_time (float): The interval of show (s). Default to 0, which
                means "forever".
            out_file (str, optional): Extra path to save the visualization
                result. Whether specified or not, the visualizer will still
                save the results by its storage backends. Default to None.
            step (int): Global step value to record. Default to 0.
        """
        classes = None
        if self.dataset_meta is not None:
            classes = self.dataset_meta.get('CLASSES', None)

        texts = []
        self.set_image(image)

        if draw_gt and 'gt_label' in data_sample:
            gt_label = data_sample.gt_label
            idx = gt_label.label.tolist()
            class_labels = [''] * len(idx)
            if classes is not None:
                class_labels = [f' ({classes[i]})' for i in idx]
            labels = [str(idx[i]) + class_labels[i] for i in range(len(idx))]
            prefix = 'Ground truth: '
            texts.append(prefix + ('\n' + ' ' * len(prefix)).join(labels))

        if draw_pred and 'pred_label' in data_sample:
            pred_label = data_sample.pred_label
            idx = pred_label.label.tolist()
            score_labels = [''] * len(idx)
            class_labels = [''] * len(idx)
            if draw_score and 'score' in pred_label:
                score_labels = [
                    f', {pred_label.score[i].item():.2f}' for i in idx
                ]

            if classes is not None:
                class_labels = [f' ({classes[i]})' for i in idx]

            labels = [
                str(idx[i]) + score_labels[i] + class_labels[i]
                for i in range(len(idx))
            ]
            prefix = 'Prediction: '
            texts.append(prefix + ('\n' + ' ' * len(prefix)).join(labels))

        text_cfg = {
            'positions': np.array([(5, 5)]),
            'font_families': 'monospace',
            'colors': 'white',
            'bboxes': dict(facecolor='black', alpha=0.5, boxstyle='Round'),
            **text_cfg
        }
        self.draw_texts('\n'.join(texts), **text_cfg)
        drawn_img = self.get_image()

        if show:
            self.show(drawn_img, win_name=name, wait_time=wait_time)
        else:
            self.add_image(name, drawn_img, step=step)

        if out_file is not None:
            mmcv.imwrite(drawn_img[..., ::-1], out_file)