[Feature] Support CityScapesDetection Metric

mmeval/metrics/__init__.py

@@ -5,6 +5,7 @@
 from .accuracy import Accuracy
 from .ava_map import AVAMeanAP
 from .bleu import BLEU
+from .cityscapes_detection import CityScapesDetection
 from .coco_detection import COCODetection
 from .connectivity_error import ConnectivityError
 from .dota_map import DOTAMeanAP
@@ -41,7 +42,7 @@
     'AveragePrecision', 'AVAMeanAP', 'BLEU', 'DOTAMeanAP',
     'SumAbsoluteDifferences', 'GradientError', 'MattingMeanSquaredError',
     'ConnectivityError', 'ROUGE', 'Perplexity', 'KeypointEndPointError',
-    'KeypointAUC', 'KeypointNME'
+    'KeypointAUC', 'KeypointNME', 'CityScapesDetection'
 ]
 
 _deprecated_msg = (

mmeval/metrics/cityscapes_detection.py

@@ -0,0 +1,324 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import logging
+import os
+import os.path as osp
+import shutil
+import warnings
+from collections import OrderedDict
+from typing import Dict, Optional, Sequence, Union
+
+from mmeval.core.base_metric import BaseMetric
+
+try:
+    import cityscapesscripts.evaluation.evalInstanceLevelSemanticLabeling as CSEval  # noqa: E501
+    import cityscapesscripts.helpers.labels as CSLabels
+    HAS_CITYSCAPESAPI = True
+except ImportError:
+    HAS_CITYSCAPESAPI = False
+
+try:
+    from mmcv import imwrite
+    HAS_MMCV = True
+except ImportError:
+    HAS_MMCV = False
+
+default_logger = logging.getLogger(__name__)
+default_logger.setLevel(logging.INFO)
+
+
+class CityScapesDetection(BaseMetric):
+    """CityScapes metric for instance segmentation.
+
+    Args:
+        outfile_prefix (str): The prefix of txt and png files. It is the
+            saving path of txt and png file, e.g. "a/b/prefix".
+        seg_prefix (str, optional): Path to the directory which contains the
+            cityscapes instance segmentation masks. It's necessary when
+            training and validation. It could be None when infer on test
+            dataset. Defaults to None.
+        format_only (bool): Format the output results without perform
+            evaluation. It is useful when you want to format the result
+            to a specific format and submit it to the test server.
+            Defaults to False.
+        keep_results (bool): Whether to keep the results. When ``format_only``
+            is True, ``keep_results`` must be True. Defaults to False.
+        keep_gt_json (bool): Whether to keep the json file during computing
+            the CityScapes metric. It will load the json file on the next
+            CSEval.evaluateImgLists without recalculating. Defaults to False.
+        classwise (bool): Whether to return the computed  results of each
+            class. Defaults to False.
+        logger (Logger, optional): logger used to record messages. When set to
+            ``None``, the default logger will be used.
+            Defaults to None.
+        **kwargs: Keyword parameters passed to :class:`BaseMetric`.
+
+    Examples:
+        >>> import numpy as np
+        >>> import os
+        >>> import os.path as osp
+        >>> import tempfile
+        >>> from PIL import Image
+        >>> from mmeval import CityScapesDetection
+        >>>
+        >>> tmp_dir = tempfile.TemporaryDirectory()
+        >>> dataset_metas = {
+        ...     'classes': ('person', 'rider', 'car', 'truck', 'bus', 'train',
+        ...                 'motorcycle', 'bicycle')
+        ... }
+        >>> seg_prefix = osp.join(tmp_dir.name, 'cityscapes', 'gtFine', 'val')
+        >>> os.makedirs(seg_prefix, exist_ok=True)
+        >>> cityscapes_det_metric = CityScapesDetection(
+        ...     dataset_meta=dataset_metas,
+        ...     outfile_prefix=osp.join(tmp_dir.name, 'test'),
+        ...     seg_prefix=seg_prefix,
+        ...     keep_results=False,
+        ...     keep_gt_json=False,
+        ...     classwise=True)
+        >>>
+        >>> def _gen_fake_datasamples(seg_prefix):
+        ...     city = 'lindau'
+        ...     os.makedirs(osp.join(seg_prefix, city), exist_ok=True)
+        ...
+        ...     sequenceNb = '000000'
+        ...     frameNb1 = '000019'
+        ...     img_name1 = f'{city}_{sequenceNb}_{frameNb1}_gtFine_instanceIds.png'
+        ...     img_path1 = osp.join(seg_prefix, city, img_name1)
+        ...     basename1 = osp.splitext(osp.basename(img_path1))[0]
+        ...     masks1 = np.zeros((20, 20), dtype=np.int32)
+        ...     masks1[:10, :10] = 24 * 1000
+        ...     Image.fromarray(masks1).save(img_path1)
+        ...
+        ...     dummy_mask1 = np.zeros((1, 20, 20), dtype=np.uint8)
+        ...     dummy_mask1[:, :10, :10] = 1
+        ...     prediction1 = {
+        ...         'basename': basename1,
+        ...         'mask_scores': np.array([1.0]),
+        ...         'labels': np.array([0]),
+        ...         'masks': dummy_mask1
+        ...     }
+        ...     groundtruth1 = {
+        ...         'file_name': img_path1
+        ...     }
+        ...
+        ...     frameNb2 = '000020'
+        ...     img_name2 = f'{city}_{sequenceNb}_{frameNb2}_gtFine_instanceIds.png'
+        ...     img_path2 = osp.join(seg_prefix, city, img_name2)
+        ...     basename2 = osp.splitext(osp.basename(img_path2))[0]
+        ...     masks2 = np.zeros((20, 20), dtype=np.int32)
+        ...     masks2[:10, :10] = 24 * 1000 + 1
+        ...     Image.fromarray(masks2).save(img_path2)
+        ...
+        ...     dummy_mask2 = np.zeros((1, 20, 20), dtype=np.uint8)
+        ...     dummy_mask2[:, :10, :10] = 1
+        ...     prediction2 = {
+        ...         'basename': basename2,
+        ...         'mask_scores': np.array([0.98]),
+        ...         'labels': np.array([1]),
+        ...         'masks': dummy_mask1
+        ...     }
+        ...     groundtruth2 = {
+        ...         'file_name': img_path2
+        ...     }
+        ...     return [prediction1, prediction2], [groundtruth1, groundtruth2]
+        >>>
+        >>> predictions, groundtruths = _gen_fake_datasamples(seg_prefix)
+        >>> cityscapes_det_metric(predictions=predictions, groundtruths=groundtruths)  # doctest: +ELLIPSIS  # noqa: E501
+        {'mAP': ..., 'AP50': ...}
+        >>> tmp_dir.cleanup()
+    """
+
+    def __init__(self,
+                 outfile_prefix: str,
+                 seg_prefix: Optional[str] = None,
+                 format_only: bool = False,
+                 keep_results: bool = False,
+                 keep_gt_json: bool = False,
+                 classwise: bool = False,
+                 logger=None,
+                 **kwargs):
+
+        if not HAS_CITYSCAPESAPI:
+            raise RuntimeError('Failed to import `cityscapesscripts`.'
+                               'Please try to install official '
+                               'cityscapesscripts by '
+                               '"pip install cityscapesscripts"')
+        if not HAS_MMCV:
+            raise RuntimeError('Failed to import `mmcv`.'
+                               'Please try to install official '
+                               'mmcv by `pip install "mmcv>=2.0.0rc4"`')
+        super().__init__(**kwargs)
+
+        assert outfile_prefix is not None, 'outfile_prefix must be not None'
+
+        if format_only:
+            assert keep_results, '`keep_results` must be True when '
+            '`format_only` is True'
+        else:
+            assert seg_prefix is not None, '`seg_prefix` is necessary when '
+            'computing the CityScapes metrics'
+
+        self.format_only = format_only
+        self.keep_results = keep_results
+        self.seg_prefix = seg_prefix
+        self.keep_gt_json = keep_gt_json
+
+        self.classwise = classwise
+        self.outfile_prefix = osp.abspath(outfile_prefix)
+        os.makedirs(outfile_prefix, exist_ok=True)
+
+        self.classes = None
+        self.logger = default_logger if logger is None else logger
+
+    def add(self, predictions: Sequence[Dict], groundtruths: Sequence[Dict]) -> None:  # type: ignore # yapf: disable # noqa: E501
+        """Add the intermediate results to `self._results`.
+
+        Args:
+            predictions (Sequence[dict]): A sequence of dict. Each dict
+                representing a detection result for an image, with the
+                following keys:
+
+                - basename (str): The image name.
+                - masks (numpy.ndarray): Shape (N, H, W), the predicted masks.
+                - labels (numpy.ndarray): Shape (N, ), the predicted labels
+                  of bounding boxes.
+                - mask_scores (np.array, optional): Shape (N, ), the predicted
+                  scores of masks.
+
+            groundtruths (Sequence[dict]): A sequence of dict. If load from
+                `ann_file`, the dict inside can be empty. Else, each dict
+                represents a groundtruths for an image, with the following
+                keys:
+
+                - file_name (str): The absolute path of groundtruth image.
+        """
+        for prediction, groundtruth in zip(predictions, groundtruths):
+            assert isinstance(prediction, dict), 'The prediciton should be ' \
+                f'a sequence of dict, but got a sequence of {type(prediction)}.'  # noqa: E501
+            assert isinstance(groundtruth, dict), 'The label should be ' \
+                f'a sequence of dict, but got a sequence of {type(groundtruth)}.'   # noqa: E501
+            prediction = self._process_prediction(prediction)
+            self._results.append((prediction, groundtruth))
+
+    def compute_metric(self, results: list) -> Dict[str, Union[float, list]]:
+        """Compute the CityScapes metrics.
+
+        Args:
+            results (List[tuple]): A list of tuple. Each tuple is the
+                prediction and ground truth of an image. This list has already
+                been synced across all ranks.
+
+        Returns:
+            dict: The computed metric. The keys are the names of
+            the metrics, and the values are corresponding results.
+        """
+        eval_results: OrderedDict = OrderedDict()
+
+        if self.format_only:
+            self.logger.info(
+                f'Results are saved in {osp.dirname(self.outfile_prefix)}')
+            return eval_results
+        gt_instances_file = osp.join(self.outfile_prefix, 'gtInstances.json')
+        # split gt and prediction list
+        preds, gts = zip(*results)
+        CSEval.args.cityscapesPath = osp.join(self.seg_prefix, '..', '..')  # type: ignore # yapf: disable # noqa: E501
+        CSEval.args.predictionPath = self.outfile_prefix
+        CSEval.args.predictionWalk = None
+        CSEval.args.JSONOutput = False
+        CSEval.args.colorized = False
+        CSEval.args.gtInstancesFile = gt_instances_file
+
+        groundTruthImgList = [gt['file_name'] for gt in gts]
+        predictionImgList = [pred['pred_txt'] for pred in preds]
+        CSEval_results = CSEval.evaluateImgLists(predictionImgList,
+                                                 groundTruthImgList,
+                                                 CSEval.args)['averages']
+
+        # If gt_instances_file exists, CHEval will load it on the next
+        # CSEval.evaluateImgLists without recalculating.
+        if not self.keep_gt_json:
+            os.remove(gt_instances_file)
+
+        if not self.keep_results:
+            shutil.rmtree(self.outfile_prefix)
+
+        # remove `matches.json` file
+        file_path = osp.join(os.getcwd(), 'matches.json')
+        if osp.exists(file_path):
+            os.remove(file_path)
+        map = float(CSEval_results['allAp'])
+        map_50 = float(CSEval_results['allAp50%'])
+
+        eval_results['mAP'] = map
+        eval_results['AP50'] = map_50
+
+        results_list = ('mAP', f'{round(map * 100, 2)}',
+                        f'{round(map_50 * 100, 2)}')
+
+        if self.classwise:
+            results_per_category = []
+            for category, aps in CSEval_results['classes'].items():
+                eval_results[f'{category}_ap'] = float(aps['ap'])
+                eval_results[f'{category}_ap50'] = float(aps['ap50%'])
+                results_per_category.append(
+                    (f'{category}', f'{round(float(aps["ap"]) * 100, 2)}',
+                     f'{round(float(aps["ap50%"]) * 100, 2)}'))
+            results_per_category.append(results_list)
+            eval_results['results_list'] = results_per_category
+        else:
+            eval_results['results_list'] = [results_list]
+
+        return eval_results
+
+    def _process_prediction(self, prediction: dict) -> dict:
+        """Process prediction.
+
+        Args:
+            prediction (Sequence[dict]): A sequence of dict. Each dict
+                representing a detection result for an image, with the
+                following keys:
+
+                - basename (str): The image name.
+                - masks (numpy.ndarray): Shape (N, H, W), the predicted masks.
+                - labels (numpy.ndarray): Shape (N, ), the predicted labels
+                  of bounding boxes.
+                - mask_scores (np.array, optional): Shape (N, ), the predicted
+                  scores of masks.
+
+        Returns:
+            dict: The processed prediction results. With key `pred_txt`.
+        """
+        if self.classes is None:
+            self._get_classes()
+        pred = dict()
+        basename = prediction['basename']
+
+        pred_txt = osp.join(self.outfile_prefix, basename + '_pred.txt')
+        pred['pred_txt'] = pred_txt
+
+        labels = prediction['labels']
+        masks = prediction['masks']
+        mask_scores = prediction['mask_scores']
+        with open(pred_txt, 'w') as f:
+            for i, (label, mask,
+                    mask_score) in enumerate(zip(labels, masks, mask_scores)):
+                class_name = self.classes[label]  # type: ignore
+                class_id = CSLabels.name2label[class_name].id
+                png_filename = osp.join(self.outfile_prefix,
+                                        basename + f'_{i}_{class_name}.png')
+                imwrite(mask, png_filename)
+                f.write(f'{osp.basename(png_filename)} '
+                        f'{class_id} {mask_score}\n')
+        return pred
+
+    def _get_classes(self) -> None:
+        """Get classes from self.dataset_meta."""
+        if self.dataset_meta and 'classes' in self.dataset_meta:
+            self.classes = self.dataset_meta['classes']
+        elif self.dataset_meta and 'CLASSES' in self.dataset_meta:
+            self.classes = self.dataset_meta['CLASSES']
+            warnings.warn(
+                'DeprecationWarning: The `CLASSES` in `dataset_meta` is '
+                'deprecated, use `classes` instead!')
+        else:
+            raise RuntimeError('Could not find `classes` in dataset_meta: '
+                               f'{self.dataset_meta}')

requirements/optional.txt

@@ -1,3 +1,4 @@
+cityscapesscripts
 opencv-python!=4.5.5.62,!=4.5.5.64
 pycocotools
 scipy