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 # noqa 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 = [] # when evaluating with official cityscapesscripts, # **_gtFine_labelIds.png is used 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