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