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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')
# modify if custom classes
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:
# avoid using underflow conversion
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
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