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metrics.py
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metrics.py
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import torchio as tio
from pathlib import Path
import torch
import numpy as np
import copy
from torchio.transforms import (
RandomFlip,
RandomAffine,
RandomElasticDeformation,
RandomNoise,
RandomMotion,
RandomBiasField,
RescaleIntensity,
Resample,
ToCanonical,
ZNormalization,
CropOrPad,
HistogramStandardization,
OneOf,
Compose,
)
# predict_dir = 'G:/PHD/gan_medical/Results/binary'
# labels_dir = 'E:/chencheng/data/PCA 22/label/test'
predict_dir = ''
labels_dir = ''
# predict_dir = 'G:/PHD/Gabor v2/Results/TOF/resunet/Binary'
# predict_dir = 'G:/PHD/SSL/pi-trans/Results/SSL/our 55/binary'
# labels_dir = 'E:/chencheng/data/LungVessel/test/selected label'
# labels_dir = 'E:/chencheng/data/TOF MIDAS/test/label'
# labels_dir ='E:/chencheng/data/TOF SSL/35/test/label'
def do_subject(image_paths, label_paths):
for (image_path, label_path) in zip(image_paths, label_paths):
subject = tio.Subject(
pred=tio.ScalarImage(image_path),
gt=tio.LabelMap(label_path),
)
subjects.append(subject)
images_dir = Path(predict_dir)
labels_dir = Path(labels_dir)
image_paths = sorted(images_dir.glob('*.mhd'))
label_paths = sorted(labels_dir.glob('*.mhd'))\
subjects = []
do_subject(image_paths, label_paths)
training_set = tio.SubjectsDataset(subjects)
toc = ToCanonical()
acc_summary = []
pre_summary = []
rec_summary = []
dice_summary = []
for i,subj in enumerate(training_set):
gt = subj['gt'][tio.DATA]
# subj = toc(subj)
pred = subj['pred'][tio.DATA]#.permute(0,1,3,2)
# preds.append(pred)
# gts.append(gt)
preds = pred.numpy()
gts = gt.numpy()
pred = preds.astype(int) # float data does not support bit_and and bit_or
gdth = gts.astype(int) # float data does not support bit_and and bit_or
fp_array = copy.deepcopy(pred) # keep pred unchanged
fn_array = copy.deepcopy(gdth)
gdth_sum = np.sum(gdth)
pred_sum = np.sum(pred)
intersection = gdth & pred
union = gdth | pred
intersection_sum = np.count_nonzero(intersection)
union_sum = np.count_nonzero(union)
tp_array = intersection
tmp = pred - gdth
fp_array[tmp < 1] = 0
tmp2 = gdth - pred
fn_array[tmp2 < 1] = 0
tn_array = np.ones(gdth.shape) - union
tp, fp, fn, tn = np.sum(tp_array), np.sum(fp_array), np.sum(fn_array), np.sum(tn_array)
smooth = 0.001
precision = tp / (pred_sum + smooth)
recall = tp / (gdth_sum + smooth)
false_positive_rate = fp / (fp + tn + smooth)
false_negtive_rate = fn / (fn + tp + smooth)
acc = (tp+tn) / (tp+fp+fn+tn)
jaccard = intersection_sum / (union_sum + smooth)
dice = 2 * intersection_sum / (gdth_sum + pred_sum + smooth)
sen = tp/(tp+fn+smooth)
spe = tn/(tn+fp+smooth)
# print(false_positive_rate)
# print(false_negtive_rate)
# print(precision)
# print(recall)
# print(dice)
acc_summary.append(acc)
pre_summary.append(precision)
rec_summary.append(recall)
dice_summary.append(dice)
acc_mean = np.mean(acc_summary)
pre_mean = np.mean(pre_summary)
rec_mean = np.mean(rec_summary)
dice_mean = np.mean(dice_summary)
acc_sted = np.std(acc_summary)
pre_sted = np.std(pre_summary)
rec_sted = np.std(rec_summary)
dice_sted = np.std(dice_summary)
# print(acc_mean)
# print(acc_sted)
print(pre_mean)
print(pre_sted)
print(rec_mean)
print(rec_sted)
print(dice_mean)
print(dice_sted)