| import os.path as osp |
| import tempfile |
|
|
| import annotator.mmpkg.mmcv as mmcv |
| import numpy as np |
| from annotator.mmpkg.mmcv.utils import print_log |
| from PIL import Image |
|
|
| from .builder import DATASETS |
| from .custom import CustomDataset |
|
|
|
|
| @DATASETS.register_module() |
| class CityscapesDataset(CustomDataset): |
| """Cityscapes dataset. |
| |
| The ``img_suffix`` is fixed to '_leftImg8bit.png' and ``seg_map_suffix`` is |
| fixed to '_gtFine_labelTrainIds.png' for Cityscapes dataset. |
| """ |
|
|
| CLASSES = ('road', 'sidewalk', 'building', 'wall', 'fence', 'pole', |
| 'traffic light', 'traffic sign', 'vegetation', 'terrain', 'sky', |
| 'person', 'rider', 'car', 'truck', 'bus', 'train', 'motorcycle', |
| 'bicycle') |
|
|
| PALETTE = [[128, 64, 128], [244, 35, 232], [70, 70, 70], [102, 102, 156], |
| [190, 153, 153], [153, 153, 153], [250, 170, 30], [220, 220, 0], |
| [107, 142, 35], [152, 251, 152], [70, 130, 180], [220, 20, 60], |
| [255, 0, 0], [0, 0, 142], [0, 0, 70], [0, 60, 100], |
| [0, 80, 100], [0, 0, 230], [119, 11, 32]] |
|
|
| def __init__(self, **kwargs): |
| super(CityscapesDataset, self).__init__( |
| img_suffix='_leftImg8bit.png', |
| seg_map_suffix='_gtFine_labelTrainIds.png', |
| **kwargs) |
|
|
| @staticmethod |
| def _convert_to_label_id(result): |
| """Convert trainId to id for cityscapes.""" |
| if isinstance(result, str): |
| result = np.load(result) |
| import cityscapesscripts.helpers.labels as CSLabels |
| result_copy = result.copy() |
| for trainId, label in CSLabels.trainId2label.items(): |
| result_copy[result == trainId] = label.id |
|
|
| return result_copy |
|
|
| def results2img(self, results, imgfile_prefix, to_label_id): |
| """Write the segmentation results to images. |
| |
| Args: |
| results (list[list | tuple | ndarray]): Testing results of the |
| dataset. |
| imgfile_prefix (str): The filename prefix of the png files. |
| If the prefix is "somepath/xxx", |
| the png files will be named "somepath/xxx.png". |
| to_label_id (bool): whether convert output to label_id for |
| submission |
| |
| Returns: |
| list[str: str]: result txt files which contains corresponding |
| semantic segmentation images. |
| """ |
| mmcv.mkdir_or_exist(imgfile_prefix) |
| result_files = [] |
| prog_bar = mmcv.ProgressBar(len(self)) |
| for idx in range(len(self)): |
| result = results[idx] |
| if to_label_id: |
| result = self._convert_to_label_id(result) |
| filename = self.img_infos[idx]['filename'] |
| basename = osp.splitext(osp.basename(filename))[0] |
|
|
| png_filename = osp.join(imgfile_prefix, f'{basename}.png') |
|
|
| output = Image.fromarray(result.astype(np.uint8)).convert('P') |
| import cityscapesscripts.helpers.labels as CSLabels |
| palette = np.zeros((len(CSLabels.id2label), 3), dtype=np.uint8) |
| for label_id, label in CSLabels.id2label.items(): |
| palette[label_id] = label.color |
|
|
| output.putpalette(palette) |
| output.save(png_filename) |
| result_files.append(png_filename) |
| prog_bar.update() |
|
|
| return result_files |
|
|
| def format_results(self, results, imgfile_prefix=None, to_label_id=True): |
| """Format the results into dir (standard format for Cityscapes |
| evaluation). |
| |
| Args: |
| results (list): Testing results of the dataset. |
| imgfile_prefix (str | None): The prefix of images files. It |
| includes the file path and the prefix of filename, e.g., |
| "a/b/prefix". If not specified, a temp file will be created. |
| Default: None. |
| to_label_id (bool): whether convert output to label_id for |
| submission. Default: False |
| |
| Returns: |
| tuple: (result_files, tmp_dir), result_files is a list containing |
| the image paths, tmp_dir is the temporal directory created |
| for saving json/png files when img_prefix is not specified. |
| """ |
|
|
| assert isinstance(results, list), 'results must be a list' |
| assert len(results) == len(self), ( |
| 'The length of results is not equal to the dataset len: ' |
| f'{len(results)} != {len(self)}') |
|
|
| if imgfile_prefix is None: |
| tmp_dir = tempfile.TemporaryDirectory() |
| imgfile_prefix = tmp_dir.name |
| else: |
| tmp_dir = None |
| result_files = self.results2img(results, imgfile_prefix, to_label_id) |
|
|
| return result_files, tmp_dir |
|
|
| def evaluate(self, |
| results, |
| metric='mIoU', |
| logger=None, |
| imgfile_prefix=None, |
| efficient_test=False): |
| """Evaluation in Cityscapes/default protocol. |
| |
| Args: |
| results (list): Testing results of the dataset. |
| metric (str | list[str]): Metrics to be evaluated. |
| logger (logging.Logger | None | str): Logger used for printing |
| related information during evaluation. Default: None. |
| imgfile_prefix (str | None): The prefix of output image file, |
| for cityscapes evaluation only. It includes the file path and |
| the prefix of filename, e.g., "a/b/prefix". |
| If results are evaluated with cityscapes protocol, it would be |
| the prefix of output png files. The output files would be |
| png images under folder "a/b/prefix/xxx.png", where "xxx" is |
| the image name of cityscapes. If not specified, a temp file |
| will be created for evaluation. |
| Default: None. |
| |
| Returns: |
| dict[str, float]: Cityscapes/default metrics. |
| """ |
|
|
| eval_results = dict() |
| metrics = metric.copy() if isinstance(metric, list) else [metric] |
| if 'cityscapes' in metrics: |
| eval_results.update( |
| self._evaluate_cityscapes(results, logger, imgfile_prefix)) |
| metrics.remove('cityscapes') |
| if len(metrics) > 0: |
| eval_results.update( |
| super(CityscapesDataset, |
| self).evaluate(results, metrics, logger, efficient_test)) |
|
|
| return eval_results |
|
|
| def _evaluate_cityscapes(self, results, logger, imgfile_prefix): |
| """Evaluation in Cityscapes protocol. |
| |
| Args: |
| results (list): Testing results of the dataset. |
| logger (logging.Logger | str | None): Logger used for printing |
| related information during evaluation. Default: None. |
| imgfile_prefix (str | None): The prefix of output image file |
| |
| Returns: |
| dict[str: float]: Cityscapes evaluation results. |
| """ |
| try: |
| import cityscapesscripts.evaluation.evalPixelLevelSemanticLabeling as CSEval |
| except ImportError: |
| raise ImportError('Please run "pip install cityscapesscripts" to ' |
| 'install cityscapesscripts first.') |
| msg = 'Evaluating in Cityscapes style' |
| if logger is None: |
| msg = '\n' + msg |
| print_log(msg, logger=logger) |
|
|
| result_files, tmp_dir = self.format_results(results, imgfile_prefix) |
|
|
| if tmp_dir is None: |
| result_dir = imgfile_prefix |
| else: |
| result_dir = tmp_dir.name |
|
|
| eval_results = dict() |
| print_log(f'Evaluating results under {result_dir} ...', logger=logger) |
|
|
| CSEval.args.evalInstLevelScore = True |
| CSEval.args.predictionPath = osp.abspath(result_dir) |
| CSEval.args.evalPixelAccuracy = True |
| CSEval.args.JSONOutput = False |
|
|
| seg_map_list = [] |
| pred_list = [] |
|
|
| |
| |
| for seg_map in mmcv.scandir( |
| self.ann_dir, 'gtFine_labelIds.png', recursive=True): |
| seg_map_list.append(osp.join(self.ann_dir, seg_map)) |
| pred_list.append(CSEval.getPrediction(CSEval.args, seg_map)) |
|
|
| eval_results.update( |
| CSEval.evaluateImgLists(pred_list, seg_map_list, CSEval.args)) |
|
|
| if tmp_dir is not None: |
| tmp_dir.cleanup() |
|
|
| return eval_results |
|
|
