python python新手教程 包含例子和说明

# Copyright (c) OpenMMLab. All rights reserved. import os.path as osp import warnings from typing import Optional, Sequence import mmcv import numpy as np from mmengine.fileio import get from mmengine.hooks import Hook from mmengine.runner import Runner from mmengine.utils import mkdir_or_exist from mmengine.visualization import Visualizer from mmdet.datasets.samplers import TrackImgSampler from mmdet.registry import HOOKS from mmdet.structures import DetDataSample, TrackDataSample from mmdet.structures.bbox import BaseBoxes from mmdet.visualization.palette import _get_adaptive_scales @HOOKS.register_module() class DetVisualizationHook(Hook): """Detection Visualization Hook. Used to visualize validation and testing process prediction results. In the testing phase: 1. If ``show`` is True, it means that only the prediction results are visualized without storing data, so ``vis_backends`` needs to be excluded. 2. If ``test_out_dir`` is specified, it means that the prediction results need to be saved to ``test_out_dir``. In order to avoid vis_backends also storing data, so ``vis_backends`` needs to be excluded. 3. ``vis_backends`` takes effect if the user does not specify ``show`` and `test_out_dir``. You can set ``vis_backends`` to WandbVisBackend or TensorboardVisBackend to store the prediction result in Wandb or Tensorboard. Args: draw (bool): whether to draw prediction results. If it is False, it means that no drawing will be done. Defaults to False. interval (int): The interval of visualization. Defaults to 50. score_thr (float): The threshold to visualize the bboxes and masks. Defaults to 0.3. show (bool): Whether to display the drawn image. Default to False. wait_time (float): The interval of show (s). Defaults to 0. test_out_dir (str, optional): directory where painted images will be saved in testing process. backend_args (dict, optional): Arguments to instantiate the corresponding backend. Defaults to None. """ def __init__(self, draw: bool = False, interval: int = 50, score_thr: float = 0.3, show: bool = False, wait_time: float = 0., test_out_dir: Optional[str] = None, backend_args: dict = None): self._visualizer: Visualizer = Visualizer.get_current_instance() self.interval = interval self.score_thr = score_thr self.show = show if self.show: # No need to think about vis backends. self._visualizer._vis_backends = {} warnings.warn('The show is True, it means that only ' 'the prediction results are visualized ' 'without storing data, so vis_backends ' 'needs to be excluded.') self.wait_time = wait_time self.backend_args = backend_args self.draw = draw self.test_out_dir = test_out_dir self._test_index = 0 def after_val_iter(self, runner: Runner, batch_idx: int, data_batch: dict, outputs: Sequence[DetDataSample]) -> None: """Run after every ``self.interval`` validation iterations. Args: runner (:obj:`Runner`): The runner of the validation process. batch_idx (int): The index of the current batch in the val loop. data_batch (dict): Data from dataloader. outputs (Sequence[:obj:`DetDataSample`]]): A batch of data samples that contain annotations and predictions. """ if self.draw is False: return # There is no guarantee that the same batch of images # is visualized for each evaluation. total_curr_iter = runner.iter + batch_idx # Visualize only the first data img_path = outputs[0].img_path img_bytes = get(img_path, backend_args=self.backend_args) img = mmcv.imfrombytes(img_bytes, channel_order='rgb') if total_curr_iter % self.interval == 0: self._visualizer.add_datasample( osp.basename(img_path) if self.show else 'val_img', img, data_sample=outputs[0], show=self.show, wait_time=self.wait_time, pred_score_thr=self.score_thr, step=total_curr_iter) def after_test_iter(self, runner: Runner, batch_idx: int, data_batch: dict, outputs: Sequence[DetDataSample]) -> None: """Run after every testing iterations. Args: runner (:obj:`Runner`): The runner of the testing process. batch_idx (int): The index of the current batch in the val loop. data_batch (dict): Data from dataloader. outputs (Sequence[:obj:`DetDataSample`]): A batch of data samples that contain annotations and predictions. """ if self.draw is False: return if self.test_out_dir is not None: self.test_out_dir = osp.join(runner.work_dir, runner.timestamp, self.test_out_dir) mkdir_or_exist(self.test_out_dir) for data_sample in outputs: self._test_index += 1 img_path = data_sample.img_path img_bytes = get(img_path, backend_args=self.backend_args) img = mmcv.imfrombytes(img_bytes, channel_order='rgb') out_file = None if self.test_out_dir is not None: out_file = osp.basename(img_path) out_file = osp.join(self.test_out_dir, out_file) self._visualizer.add_datasample( osp.basename(img_path) if self.show else 'test_img', img, data_sample=data_sample, show=self.show, wait_time=self.wait_time, pred_score_thr=self.score_thr, out_file=out_file, step=self._test_index) @HOOKS.register_module() class TrackVisualizationHook(Hook): """Tracking Visualization Hook. Used to visualize validation and testing process prediction results. In the testing phase: 1. If ``show`` is True, it means that only the prediction results are visualized without storing data, so ``vis_backends`` needs to be excluded. 2. If ``test_out_dir`` is specified, it means that the prediction results need to be saved to ``test_out_dir``. In order to avoid vis_backends also storing data, so ``vis_backends`` needs to be excluded. 3. ``vis_backends`` takes effect if the user does not specify ``show`` and `test_out_dir``. You can set ``vis_backends`` to WandbVisBackend or TensorboardVisBackend to store the prediction result in Wandb or Tensorboard. Args: draw (bool): whether to draw prediction results. If it is False, it means that no drawing will be done. Defaults to False. frame_interval (int): The interval of visualization. Defaults to 30. score_thr (float): The threshold to visualize the bboxes and masks. Defaults to 0.3. show (bool): Whether to display the drawn image. Default to False. wait_time (float): The interval of show (s). Defaults to 0. test_out_dir (str, optional): directory where painted images will be saved in testing process. backend_args (dict): Arguments to instantiate a file client. Defaults to ``None``. """ def __init__(self, draw: bool = False, frame_interval: int = 30, score_thr: float = 0.3, show: bool = False, wait_time: float = 0., test_out_dir: Op