[Feature] Add DOTAMeanAP metric

docs/en/api/metrics.rst

@@ -46,3 +46,4 @@ Metrics
    GradientError
    MattingMSE
    ConnectivityError
+   DOTAMeanAP

docs/zh_cn/api/metrics.rst

@@ -46,3 +46,4 @@ Metrics
    GradientError
    MattingMSE
    ConnectivityError
+   DOTAMeanAP

mmeval/metrics/__init__.py

@@ -5,6 +5,7 @@
 from .bleu import BLEU
 from .coco_detection import COCODetectionMetric
 from .connectivity_error import ConnectivityError
+from .dota_map import DOTAMeanAP
 from .end_point_error import EndPointError
 from .f_metric import F1Metric
 from .gradient_error import GradientError
@@ -29,6 +30,6 @@
     'F1Metric', 'HmeanIoU', 'SingleLabelMetric', 'COCODetectionMetric',
     'PCKAccuracy', 'MpiiPCKAccuracy', 'JhmdbPCKAccuracy', 'ProposalRecall',
     'PSNR', 'MAE', 'MSE', 'SSIM', 'SNR', 'MultiLabelMetric',
-    'AveragePrecision', 'AVAMeanAP', 'BLEU', 'SAD', 'GradientError',
-    'MattingMSE', 'ConnectivityError'
+    'AveragePrecision', 'AVAMeanAP', 'BLEU', 'DOTAMeanAP', 'SAD',
+    'GradientError', 'MattingMSE', 'ConnectivityError'
 ]

mmeval/metrics/dota_map.py

@@ -0,0 +1,303 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import logging
+import numpy as np
+from typing import Dict, List, Optional, Sequence, Tuple, Union
+
+from .utils.bbox_overlaps_rotated import (calculate_bboxes_area_rotated,
+                                          qbox_to_rbox)
+from .voc_map import VOCMeanAP
+
+logger = logging.getLogger(__name__)
+
+try:
+    # we prefer to use `bbox_iou_rotated` in mmcv to calculate ious
+    from mmcv.ops import box_iou_rotated
+    from torch import Tensor
+    HAS_MMCV = True
+except Exception as e:  # noqa F841
+    from .utils.bbox_overlaps_rotated import calculate_overlaps_rotated
+    HAS_MMCV = False
+    logger.debug(
+        'mmcv is not installed, calculating IoU of rotated bbox with OpenCV.')
+
+
+def filter_by_bboxes_area_rotated(bboxes: np.ndarray,
+                                  min_area: Optional[float],
+                                  max_area: Optional[float]):
+    """Filter the rotated bboxes with an area range.
+
+    Args:
+        bboxes (numpy.ndarray): The bboxes with shape (n, 5) in 'xywha' format.
+        min_area (float, optional): The minimum area. If None, do not filter
+            the minimum area.
+        max_area (float, optional): The maximum area. If None, do not filter
+            the maximum area.
+
+    Returns:
+        numpy.ndarray: A mask of ``bboxes`` identify which bbox are filtered.
+    """
+    bboxes_area = calculate_bboxes_area_rotated(bboxes)
+    area_mask = np.ones_like(bboxes_area, dtype=bool)
+    if min_area is not None:
+        area_mask &= (bboxes_area >= min_area)
+    if max_area is not None:
+        area_mask &= (bboxes_area < max_area)
+    return area_mask
+
+
+class DOTAMeanAP(VOCMeanAP):
+    """DOTA evaluation metric.
+
+    DOTA is a large-scale dataset for object detection in aerial images which
+    is introduced in https://arxiv.org/abs/1711.10398. This metric computes
+    the DOTA mAP (mean Average Precision) with the given IoU thresholds and
+    scale ranges.
+
+    Args:
+        iou_thrs (float ｜ List[float]): IoU thresholds. Defaults to 0.5.
+        scale_ranges (List[tuple], optional): Scale ranges for evaluating
+            mAP. If not specified, all bounding boxes would be included in
+            evaluation. Defaults to None.
+        num_classes (int, optional): The number of classes. If None, it will be
+            obtained from the 'CLASSES' field in ``self.dataset_meta``.
+            Defaults to None.
+        eval_mode (str): 'area' or '11points', 'area' means calculating the
+            area under precision-recall curve, '11points' means calculating
+            the average precision of recalls at [0, 0.1, ..., 1].
+            The PASCAL VOC2007 defaults to use '11points', while PASCAL
+            VOC2012 defaults to use 'area'.
+            Defaults to '11points'.
+        nproc (int): Processes used for computing TP and FP. If nproc
+            is less than or equal to 1, multiprocessing will not be used.
+            Defaults to 4.
+        drop_class_ap (bool): Whether to drop the class without ground truth
+            when calculating the average precision for each class.
+        classwise (bool): Whether to return the computed results of each
+            class. Defaults to False.
+        **kwargs: Keyword parameters passed to :class:`BaseMetric`.
+
+    Examples:
+
+        >>> import numpy as np
+        >>> from mmeval import DOTAMetric
+        >>> num_classes = 15
+        >>> dota_metric = DOTAMetric(num_classes=15)
+        >>>
+        >>> def _gen_bboxes(num_bboxes, img_w=256, img_h=256):
+        ...     # random generate bounding boxes in 'xywha' formart.
+        ...     x = np.random.rand(num_bboxes, ) * img_w
+        ...     y = np.random.rand(num_bboxes, ) * img_h
+        ...     w = np.random.rand(num_bboxes, ) * (img_w - x)
+        ...     h = np.random.rand(num_bboxes, ) * (img_h - y)
+        ...     a = np.random.rand(num_bboxes, ) * np.pi / 2
+        ...     return np.stack([x, y, w, h, a], axis=1)
+        >>> prediction = {
+        ...     'bboxes': _gen_bboxes(10),
+        ...     'scores': np.random.rand(10, ),
+        ...     'labels': np.random.randint(0, num_classes, size=(10, ))
+        ... }
+        >>> groundtruth = {
+        ...     'bboxes': _gen_bboxes(10),
+        ...     'labels': np.random.randint(0, num_classes, size=(10, )),
+        ...     'bboxes_ignore': _gen_bboxes(5),
+        ...     'labels_ignore': np.random.randint(0, num_classes, size=(5, ))
+        ... }
+        >>> dota_metric(predictions=[prediction, ], groundtruths=[groundtruth, ])  # doctest: +ELLIPSIS  # noqa: E501
+        {'[email protected]': ..., 'mAP': ...}
+    """
+
+    def __init__(self,
+                 iou_thrs: Union[float, List[float]] = 0.5,
+                 scale_ranges: Optional[List[Tuple]] = None,
+                 num_classes: Optional[int] = None,
+                 eval_mode: str = '11points',
+                 nproc: int = 4,
+                 drop_class_ap: bool = True,
+                 classwise: bool = False,
+                 **kwargs) -> None:
+        super().__init__(
+            iou_thrs=iou_thrs,
+            scale_ranges=scale_ranges,
+            num_classes=num_classes,
+            eval_mode=eval_mode,
+            use_legacy_coordinate=False,
+            nproc=nproc,
+            drop_class_ap=drop_class_ap,
+            classwise=classwise,
+            **kwargs)
+
+    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:
+                - bboxes (numpy.ndarray): Shape (N, 5) or shape (N, 8).
+                  bounding bboxes of this image. The box format is depend on
+                  predict_box_type. Details in Note.
+                - scores (numpy.ndarray): Shape (N, ), the predicted scores
+                  of bounding boxes.
+                - labels (numpy.ndarray): Shape (N, ), the predicted labels
+                  of bounding boxes.
+
+            groundtruths (Sequence[Dict]):  A sequence of dict. Each dict
+                represents a groundtruths for an image, with the following
+                keys:
+
+                - bboxes (numpy.ndarray): Shape (M, 5) or shape (M, 8), the
+                  groundtruth bounding bboxes of this image, The box format
+                  is depend on predict_box_type. Details in Note.
+                - labels (numpy.ndarray): Shape (M, ), the ground truth
+                  labels of bounding boxes.
+                - bboxes_ignore (numpy.ndarray): Shape (K, 5) or shape(K, 8),
+                  the groundtruth ignored bounding bboxes of this image. The
+                  box format is depend on ``self.predict_box_type``.Details in
+                  upper note.
+                - labels_ignore (numpy.ndarray): Shape (K, ), the ground
+                  truth ignored labels of bounding boxes.
+
+        Note:
+            The box shape of ``predictions`` and ``groundtruths`` is depends
+            on the predict_box_type. If predict_box_type is 'rbox', the box
+            shape should be (N, 5) which represents the (x, y,w, h, angle),
+            otherwise the box shape should be (N, 8) which represents the
+            (x1, y1, x2, y2, x3, y3, x4, y4).
+        """
+        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
+            self._results.append((prediction, groundtruth))
+
+    @staticmethod
+    def _calculate_image_tpfp(  # type: ignore
+            pred_bboxes: np.ndarray, gt_bboxes: np.ndarray,
+            ignore_gt_bboxes: np.ndarray, iou_thrs: List[float],
+            area_ranges: List[Tuple[Optional[float], Optional[float]]], *args,
+            **kwargs) -> Tuple[np.ndarray, np.ndarray]:
+        """Calculate the true positive and false positive on an image.
+
+        Args:
+            pred_bboxes (numpy.ndarray): Predicted bboxes of this image, with
+                shape (N, 6) or shape (N,9) which depends on predict_box_type.
+                If the predict_box_type is
+                The predicted score of the bbox is concatenated behind the
+                predicted bbox.
+            gt_bboxes (numpy.ndarray): Ground truth bboxes of this image, with
+                shape (M, 5) or shape (M, 8).
+            ignore_gt_bboxes (numpy.ndarray): Ground truth ignored bboxes of
+                this image, with shape (K, 5) or shape (K, 8).
+            iou_thrs (List[float]): The IoU thresholds.
+            area_ranges (List[Tuple]): The area ranges.
+
+        Returns:
+            tuple (tp, fp):
+            - tp (numpy.ndarray): Shape (num_ious, num_scales, N),
+              the true positive flag of each predicted bbox on this image.
+            - fp (numpy.ndarray): Shape (num_ious, num_scales, N),
+              the false positive flag of each predicted bbox on this image.
+
+        Note:
+            This method should be a staticmethod to avoid resource competition
+            during multiple processes.
+        """
+        # Step 0. (optional)
+        # we need to convert qbox type box to rbox type because OpenCV only
+        # support rbox format iou calculation.
+        if gt_bboxes.shape[-1] == 8:  # qbox shape (M, 8)
+            pred_bboxes = qbox_to_rbox(pred_bboxes[:, :8])
+            gt_bboxes = qbox_to_rbox(gt_bboxes)
+            ignore_gt_bboxes = qbox_to_rbox(ignore_gt_bboxes)
+
+        # Step 1. Concatenate `gt_bboxes` and `ignore_gt_bboxes`, then set
+        # the `ignore_gt_flags`.
+        all_gt_bboxes = np.concatenate((gt_bboxes, ignore_gt_bboxes))
+        ignore_gt_flags = np.concatenate((np.zeros(
+            (gt_bboxes.shape[0], 1),
+            dtype=bool), np.ones((ignore_gt_bboxes.shape[0], 1), dtype=bool)))
+
+        # Step 2. Initialize the `tp` and `fp` arrays.
+        num_preds = pred_bboxes.shape[0]
+        tp = np.zeros((len(iou_thrs), len(area_ranges), num_preds))
+        fp = np.zeros((len(iou_thrs), len(area_ranges), num_preds))
+
+        # Step 3. If there are no gt bboxes in this image, then all pred bboxes
+        # within area range are false positives.
+        if all_gt_bboxes.shape[0] == 0:
+            for idx, (min_area, max_area) in enumerate(area_ranges):
+                area_mask = filter_by_bboxes_area_rotated(
+                    pred_bboxes[:, :5], min_area, max_area)
+                fp[:, idx, area_mask] = 1
+            return tp, fp
+
+        # Step 4. Calculate the IoUs between the predicted bboxes and the
+        # ground truth bboxes.
+        if HAS_MMCV:
+            # the input and output of box_iou_rotated are torch.Tensor
+            ious = np.array(
+                box_iou_rotated(
+                    Tensor(pred_bboxes[:, :5]), Tensor(all_gt_bboxes)))
+        else:
+            ious = calculate_overlaps_rotated((pred_bboxes[:, :5]),
+                                              all_gt_bboxes)
+        # For each pred bbox, the max iou with all gts.
+        ious_max = ious.max(axis=1)
+        # For each pred bbox, which gt overlaps most with it.
+        ious_argmax = ious.argmax(axis=1)
+        # Sort all pred bbox in descending order by scores.
+        sorted_indices = np.argsort(-pred_bboxes[:, -1])
+
+        # Step 5. Count the `tp` and `fp` of each iou threshold and area range.
+        for iou_thr_idx, iou_thr in enumerate(iou_thrs):
+            for area_idx, (min_area, max_area) in enumerate(area_ranges):
+                # The flags that gt bboxes have been matched.
+                gt_covered_flags = np.zeros(all_gt_bboxes.shape[0], dtype=bool)
+                # The flags that gt bboxes out of area range.
+                gt_area_mask = filter_by_bboxes_area_rotated(
+                    all_gt_bboxes, min_area, max_area)
+                ignore_gt_area_flags = ~gt_area_mask
+
+                # Count the prediction bboxes in order of decreasing score.
+                for pred_bbox_idx in sorted_indices:
+                    if ious_max[pred_bbox_idx] >= iou_thr:
+                        matched_gt_idx = ious_argmax[pred_bbox_idx]
+                        # Ignore the pred bbox that match an ignored gt bbox.
+                        if ignore_gt_flags[matched_gt_idx]:
+                            continue
+                        # Ignore the pred bbox that is out of area range.
+                        if ignore_gt_area_flags[matched_gt_idx]:
+                            continue
+                        if not gt_covered_flags[matched_gt_idx]:
+                            tp[iou_thr_idx, area_idx, pred_bbox_idx] = 1
+                            gt_covered_flags[matched_gt_idx] = True
+                        else:
+                            # This gt bbox has been matched and counted as fp.
+                            fp[iou_thr_idx, area_idx, pred_bbox_idx] = 1
+                    else:
+                        area_mask = filter_by_bboxes_area_rotated(
+                            pred_bboxes[pred_bbox_idx, :5], min_area, max_area)
+                        if area_mask:
+                            fp[iou_thr_idx, area_idx, pred_bbox_idx] = 1
+
+        return tp, fp
+
+    def _filter_by_bboxes_area(self, bboxes: np.ndarray,
+                               min_area: Optional[float],
+                               max_area: Optional[float]):
+        """Filter the bboxes with an area range.
+
+        Args:
+            bboxes (numpy.ndarray): The bboxes with shape (n, 5) in 'xywha'
+                format.
+            min_area (Optional[float]): The minimum area. If None, does not
+                filter the minimum area.
+            max_area (Optional[float]): The maximum area. If None, does not
+                filter the maximum area.
+
+        Returns:
+            numpy.ndarray: A mask of ``bboxes`` identify which bbox are
+            filtered.
+        """
+        return filter_by_bboxes_area_rotated(bboxes, min_area, max_area)

mmeval/metrics/ssim.py

@@ -64,6 +64,10 @@ def __init__(self,
                  **kwargs) -> None:
         super().__init__(**kwargs)
 
+        if cv2 is None:
+            raise ImportError(f'For availability of {self.__class__.__name__},'
+                              ' please install opencv-python first.')
+
         assert input_order.upper() in [
             'CHW', 'HWC'
         ], (f'Wrong input_order {input_order}. Supported input_orders are '

mmeval/metrics/utils/__init__.py

@@ -1,5 +1,7 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 from .bbox_overlaps import calculate_bboxes_area, calculate_overlaps
+from .bbox_overlaps_rotated import (calculate_bboxes_area_rotated,
+                                    calculate_overlaps_rotated)
 from .image_transforms import reorder_and_crop
 from .keypoint import calc_distances, distance_acc
 from .polygon import (poly2shapely, poly_intersection, poly_iou,
@@ -8,5 +10,6 @@
 __all__ = [
     'poly2shapely', 'polys2shapely', 'poly_union', 'poly_intersection',
     'poly_make_valid', 'poly_iou', 'calc_distances', 'distance_acc',
-    'calculate_overlaps', 'calculate_bboxes_area', 'reorder_and_crop'
+    'calculate_overlaps', 'calculate_bboxes_area', 'reorder_and_crop',
+    'calculate_bboxes_area_rotated', 'calculate_overlaps_rotated'
 ]