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utils.py
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utils.py
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import numpy as np
from scipy.optimize import linear_sum_assignment
####
def get_fast_pq(true, pred, match_iou=0.5):
"""
`match_iou` is the IoU threshold level to determine the pairing between
GT instances `p` and prediction instances `g`. `p` and `g` is a pair
if IoU > `match_iou`. However, pair of `p` and `g` must be unique
(1 prediction instance to 1 GT instance mapping).
If `match_iou` < 0.5, Munkres assignment (solving minimum weight matching
in bipartite graphs) is caculated to find the maximal amount of unique pairing.
If `match_iou` >= 0.5, all IoU(p,g) > 0.5 pairing is proven to be unique and
the number of pairs is also maximal.
Fast computation requires instance IDs are in contiguous orderding
i.e [1, 2, 3, 4] not [2, 3, 6, 10]. Please call `remap_label` beforehand
and `by_size` flag has no effect on the result.
Returns:
[dq, sq, pq]: measurement statistic
[paired_true, paired_pred, unpaired_true, unpaired_pred]:
pairing information to perform measurement
"""
assert match_iou >= 0.0, "Cant' be negative"
true = np.copy(true)
pred = np.copy(pred)
true_id_list = list(np.unique(true))
pred_id_list = list(np.unique(pred))
true_masks = [None,]
for t in true_id_list[1:]:
t_mask = np.array(true == t, np.uint8)
true_masks.append(t_mask)
pred_masks = [None,]
for p in pred_id_list[1:]:
p_mask = np.array(pred == p, np.uint8)
pred_masks.append(p_mask)
# prefill with value
pairwise_iou = np.zeros([len(true_id_list) -1,
len(pred_id_list) -1], dtype=np.float64)
# caching pairwise iou
for true_id in true_id_list[1:]: # 0-th is background
t_mask = true_masks[true_id]
pred_true_overlap = pred[t_mask > 0]
pred_true_overlap_id = np.unique(pred_true_overlap)
pred_true_overlap_id = list(pred_true_overlap_id)
for pred_id in pred_true_overlap_id:
if pred_id == 0: # ignore
continue # overlaping background
p_mask = pred_masks[pred_id]
total = (t_mask + p_mask).sum()
inter = (t_mask * p_mask).sum()
iou = inter / (total - inter)
pairwise_iou[true_id-1, pred_id-1] = iou
#
if match_iou >= 0.5:
paired_iou = pairwise_iou[pairwise_iou > match_iou]
pairwise_iou[pairwise_iou <= match_iou] = 0.0
paired_true, paired_pred = np.nonzero(pairwise_iou)
paired_iou = pairwise_iou[paired_true, paired_pred]
paired_true += 1 # index is instance id - 1
paired_pred += 1 # hence return back to original
else: # * Exhaustive maximal unique pairing
#### Munkres pairing with scipy library
# the algorithm return (row indices, matched column indices)
# if there is multiple same cost in a row, index of first occurence
# is return, thus the unique pairing is ensure
# inverse pair to get high IoU as minimum
paired_true, paired_pred = linear_sum_assignment(-pairwise_iou)
### extract the paired cost and remove invalid pair
paired_iou = pairwise_iou[paired_true, paired_pred]
# now select those above threshold level
# paired with iou = 0.0 i.e no intersection => FP or FN
paired_true = list(paired_true[paired_iou > match_iou] + 1)
paired_pred = list(paired_pred[paired_iou > match_iou] + 1)
paired_iou = paired_iou[paired_iou > match_iou]
# get the actual FP and FN
unpaired_true = [idx for idx in true_id_list[1:] if idx not in paired_true]
unpaired_pred = [idx for idx in pred_id_list[1:] if idx not in paired_pred]
# print(paired_iou.shape, paired_true.shape, len(unpaired_true), len(unpaired_pred))
#
tp = len(paired_true)
fp = len(unpaired_pred)
fn = len(unpaired_true)
# get the F1-score i.e DQ
dq = tp / (tp + 0.5 * fp + 0.5 * fn)
# get the SQ, no paired has 0 iou so not impact
sq = paired_iou.sum() / (tp + 1.0e-6)
return [dq, sq, dq * sq], [paired_true, paired_pred, unpaired_true, unpaired_pred]
#####
def remap_label(pred, by_size=False):
"""
Rename all instance id so that the id is contiguous i.e [0, 1, 2, 3]
not [0, 2, 4, 6]. The ordering of instances (which one comes first)
is preserved unless by_size=True, then the instances will be reordered
so that bigger nucler has smaller ID
Args:
pred : the 2d array contain instances where each instances is marked
by non-zero integer
by_size : renaming with larger nuclei has smaller id (on-top)
"""
pred_id = list(np.unique(pred))
pred_id.remove(0)
if len(pred_id) == 0:
return pred # no label
if by_size:
pred_size = []
for inst_id in pred_id:
size = (pred == inst_id).sum()
pred_size.append(size)
# sort the id by size in descending order
pair_list = zip(pred_id, pred_size)
pair_list = sorted(pair_list, key=lambda x: x[1], reverse=True)
pred_id, pred_size = zip(*pair_list)
new_pred = np.zeros(pred.shape, np.int32)
for idx, inst_id in enumerate(pred_id):
new_pred[pred == inst_id] = idx + 1
return new_pred
####
def binarize(x):
'''
convert multichannel (multiclass) instance segmetation tensor
to binary instance segmentation (bg and nuclei),
:param x: B*B*C (for PanNuke 256*256*5 )
:return: Instance segmentation
'''
out = np.zeros([x.shape[0], x.shape[1]])
count = 1
for i in range(x.shape[2]):
x_ch = x[:,:,i]
unique_vals = np.unique(x_ch)
unique_vals = unique_vals.tolist()
unique_vals.remove(0)
for j in unique_vals:
x_tmp = x_ch == j
x_tmp_c = 1- x_tmp
out *= x_tmp_c
out += count*x_tmp
count += 1
out = out.astype('int32')
return out
####
def get_tissue_idx(tissue_indices, idx):
for i in range(len(tissue_indices)):
if tissue_indices[i].count(idx) == 1:
tiss_idx = i
return tiss_idx