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import mmcv |
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import numpy as np |
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def intersect_and_union(pred_label, |
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label, |
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num_classes, |
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ignore_index, |
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label_map=dict(), |
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reduce_zero_label=False): |
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"""Calculate intersection and Union. |
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Args: |
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pred_label (ndarray): Prediction segmentation map. |
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label (ndarray): Ground truth segmentation map. |
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num_classes (int): Number of categories. |
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ignore_index (int): Index that will be ignored in evaluation. |
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label_map (dict): Mapping old labels to new labels. The parameter will |
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work only when label is str. Default: dict(). |
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reduce_zero_label (bool): Wether ignore zero label. The parameter will |
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work only when label is str. Default: False. |
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Returns: |
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ndarray: The intersection of prediction and ground truth histogram |
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on all classes. |
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ndarray: The union of prediction and ground truth histogram on all |
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classes. |
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ndarray: The prediction histogram on all classes. |
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ndarray: The ground truth histogram on all classes. |
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""" |
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if isinstance(pred_label, str): |
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pred_label = np.load(pred_label) |
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if isinstance(label, str): |
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label = mmcv.imread(label, flag='unchanged', backend='pillow') |
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if label_map is not None: |
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for old_id, new_id in label_map.items(): |
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label[label == old_id] = new_id |
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if reduce_zero_label: |
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label[label == 0] = 255 |
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label = label - 1 |
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label[label == 254] = 255 |
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mask = (label != ignore_index) |
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pred_label = pred_label[mask] |
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label = label[mask] |
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intersect = pred_label[pred_label == label] |
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area_intersect, _ = np.histogram( |
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intersect, bins=np.arange(num_classes + 1)) |
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area_pred_label, _ = np.histogram( |
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pred_label, bins=np.arange(num_classes + 1)) |
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area_label, _ = np.histogram(label, bins=np.arange(num_classes + 1)) |
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area_union = area_pred_label + area_label - area_intersect |
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return area_intersect, area_union, area_pred_label, area_label |
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def total_intersect_and_union(results, |
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gt_seg_maps, |
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num_classes, |
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ignore_index, |
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label_map=dict(), |
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reduce_zero_label=False): |
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"""Calculate Total Intersection and Union. |
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Args: |
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results (list[ndarray]): List of prediction segmentation maps. |
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gt_seg_maps (list[ndarray]): list of ground truth segmentation maps. |
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num_classes (int): Number of categories. |
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ignore_index (int): Index that will be ignored in evaluation. |
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label_map (dict): Mapping old labels to new labels. Default: dict(). |
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reduce_zero_label (bool): Wether ignore zero label. Default: False. |
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Returns: |
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ndarray: The intersection of prediction and ground truth histogram |
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on all classes. |
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ndarray: The union of prediction and ground truth histogram on all |
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classes. |
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ndarray: The prediction histogram on all classes. |
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ndarray: The ground truth histogram on all classes. |
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""" |
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num_imgs = len(results) |
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assert len(gt_seg_maps) == num_imgs |
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total_area_intersect = np.zeros((num_classes, ), dtype=np.float) |
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total_area_union = np.zeros((num_classes, ), dtype=np.float) |
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total_area_pred_label = np.zeros((num_classes, ), dtype=np.float) |
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total_area_label = np.zeros((num_classes, ), dtype=np.float) |
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for i in range(num_imgs): |
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area_intersect, area_union, area_pred_label, area_label = \ |
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intersect_and_union(results[i], gt_seg_maps[i], num_classes, |
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ignore_index, label_map, reduce_zero_label) |
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total_area_intersect += area_intersect |
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total_area_union += area_union |
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total_area_pred_label += area_pred_label |
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total_area_label += area_label |
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return total_area_intersect, total_area_union, \ |
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total_area_pred_label, total_area_label |
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def mean_iou(results, |
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gt_seg_maps, |
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num_classes, |
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ignore_index, |
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nan_to_num=None, |
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label_map=dict(), |
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reduce_zero_label=False): |
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"""Calculate Mean Intersection and Union (mIoU) |
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Args: |
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results (list[ndarray]): List of prediction segmentation maps. |
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gt_seg_maps (list[ndarray]): list of ground truth segmentation maps. |
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num_classes (int): Number of categories. |
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ignore_index (int): Index that will be ignored in evaluation. |
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nan_to_num (int, optional): If specified, NaN values will be replaced |
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by the numbers defined by the user. Default: None. |
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label_map (dict): Mapping old labels to new labels. Default: dict(). |
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reduce_zero_label (bool): Wether ignore zero label. Default: False. |
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Returns: |
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float: Overall accuracy on all images. |
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ndarray: Per category accuracy, shape (num_classes, ). |
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ndarray: Per category IoU, shape (num_classes, ). |
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""" |
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all_acc, acc, iou = eval_metrics( |
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results=results, |
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gt_seg_maps=gt_seg_maps, |
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num_classes=num_classes, |
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ignore_index=ignore_index, |
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metrics=['mIoU'], |
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nan_to_num=nan_to_num, |
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label_map=label_map, |
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reduce_zero_label=reduce_zero_label) |
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return all_acc, acc, iou |
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def mean_dice(results, |
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gt_seg_maps, |
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num_classes, |
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ignore_index, |
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nan_to_num=None, |
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label_map=dict(), |
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reduce_zero_label=False): |
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"""Calculate Mean Dice (mDice) |
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Args: |
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results (list[ndarray]): List of prediction segmentation maps. |
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gt_seg_maps (list[ndarray]): list of ground truth segmentation maps. |
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num_classes (int): Number of categories. |
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ignore_index (int): Index that will be ignored in evaluation. |
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nan_to_num (int, optional): If specified, NaN values will be replaced |
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by the numbers defined by the user. Default: None. |
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label_map (dict): Mapping old labels to new labels. Default: dict(). |
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reduce_zero_label (bool): Wether ignore zero label. Default: False. |
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Returns: |
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float: Overall accuracy on all images. |
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ndarray: Per category accuracy, shape (num_classes, ). |
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ndarray: Per category dice, shape (num_classes, ). |
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""" |
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all_acc, acc, dice = eval_metrics( |
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results=results, |
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gt_seg_maps=gt_seg_maps, |
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num_classes=num_classes, |
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ignore_index=ignore_index, |
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metrics=['mDice'], |
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nan_to_num=nan_to_num, |
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label_map=label_map, |
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reduce_zero_label=reduce_zero_label) |
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return all_acc, acc, dice |
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def eval_metrics(results, |
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gt_seg_maps, |
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num_classes, |
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ignore_index, |
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metrics=['mIoU'], |
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nan_to_num=None, |
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label_map=dict(), |
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reduce_zero_label=False): |
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"""Calculate evaluation metrics |
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Args: |
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results (list[ndarray]): List of prediction segmentation maps. |
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gt_seg_maps (list[ndarray]): list of ground truth segmentation maps. |
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num_classes (int): Number of categories. |
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ignore_index (int): Index that will be ignored in evaluation. |
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metrics (list[str] | str): Metrics to be evaluated, 'mIoU' and 'mDice'. |
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nan_to_num (int, optional): If specified, NaN values will be replaced |
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by the numbers defined by the user. Default: None. |
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label_map (dict): Mapping old labels to new labels. Default: dict(). |
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reduce_zero_label (bool): Wether ignore zero label. Default: False. |
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Returns: |
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float: Overall accuracy on all images. |
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ndarray: Per category accuracy, shape (num_classes, ). |
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ndarray: Per category evalution metrics, shape (num_classes, ). |
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""" |
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if isinstance(metrics, str): |
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metrics = [metrics] |
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allowed_metrics = ['mIoU', 'mDice'] |
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if not set(metrics).issubset(set(allowed_metrics)): |
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raise KeyError('metrics {} is not supported'.format(metrics)) |
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total_area_intersect, total_area_union, total_area_pred_label, \ |
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total_area_label = total_intersect_and_union(results, gt_seg_maps, |
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num_classes, ignore_index, |
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label_map, |
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reduce_zero_label) |
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all_acc = total_area_intersect.sum() / total_area_label.sum() |
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acc = total_area_intersect / total_area_label |
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ret_metrics = [all_acc, acc] |
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for metric in metrics: |
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if metric == 'mIoU': |
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iou = total_area_intersect / total_area_union |
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ret_metrics.append(iou) |
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elif metric == 'mDice': |
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dice = 2 * total_area_intersect / ( |
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total_area_pred_label + total_area_label) |
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ret_metrics.append(dice) |
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if nan_to_num is not None: |
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ret_metrics = [ |
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np.nan_to_num(metric, nan=nan_to_num) for metric in ret_metrics |
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] |
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return ret_metrics |
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