in wypr/evaluation/iou_helper.py [0:0]
def evaluate_iou(pred_ids, gt_ids, dataset='scannet', verbose=True):
if dataset == "scannet":
VALID_CLASS_IDS = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 16, 24, 28, 33, 34, 36, 39])
# Classes relabelled {-100,0,1,...,19}.
# Predictions will all be in the set {0,1,...,19}
CLASS_LABELS = ['wall', 'floor', 'cabinet', 'bed', 'chair', 'sofa', 'table', 'door', 'window',
'bookshelf', 'picture', 'counter', 'desk', 'curtain', 'refrigerator',
'shower curtain', 'toilet', 'sink', 'bathtub', 'otherfurniture']
UNKNOWN_ID = -100
N_CLASSES = len(CLASS_LABELS)
elif dataset == "scannet_dets":
VALID_CLASS_IDS = np.array([3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 16, 24, 28, 33, 34, 36, 39])
# Classes relabelled {-100,0,1,...,19}.
# Predictions will all be in the set {0,1,...,19}
CLASS_LABELS = ['cabinet', 'bed', 'chair', 'sofa', 'table', 'door', 'window',
'bookshelf', 'picture', 'counter', 'desk', 'curtain', 'refrigerator',
'shower curtain', 'toilet', 'sink', 'bathtub', 'otherfurniture']
UNKNOWN_ID = -100
N_CLASSES = len(CLASS_LABELS)
elif dataset == "s3dis":
raise NotImplementedError("To check the sem seg class for s3dis")
CLASS_LABELS = ['bed','table','sofa','chair','toilet','desk','dresser','night_stand','bookshelf','bathtub']
UNKNOWN_ID = -100
N_CLASSES = len(CLASS_LABELS)
else:
raise ValueError("Please evaluate on the supported dataset (scannet, s3dis)")
if verbose:
print('evaluating', gt_ids.size, 'points on', dataset)
confusion = confusion_matrix(pred_ids, gt_ids, N_CLASSES)
class_ious = {}
mean_iou = 0
for i in range(N_CLASSES):
label_name = CLASS_LABELS[i]
class_ious[label_name] = get_iou(i, confusion)
if not isinstance(class_ious[label_name], tuple): # for debug purpose
class_ious[label_name] = (0,0,1)
mean_iou += class_ious[label_name][0]/ N_CLASSES
if verbose:
print('classes IoU')
print('----------------------------')
for i in range(N_CLASSES):
label_name = CLASS_LABELS[i]
if isinstance(class_ious[label_name], tuple):
print('{0:<14s}: {1:>5.3f} ({2:>6d}/{3:<6d})'.format(label_name, class_ious[label_name][0], class_ious[label_name][1], class_ious[label_name][2]))
else:
print('{0:<14s}: 0.000'.format(label_name))
print('mean IOU', mean_iou)
return class_ious, mean_iou, confusion