| | import os.path as osp |
| | import tempfile |
| |
|
| | import annotator.uniformer.mmcv as mmcv |
| | import numpy as np |
| | from annotator.uniformer.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 |
| |
|
