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criterion.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Aug 14 10:40:54 2019
@author: wujon
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import surface_distance
# import nibabel as ni
import scipy.io
import scipy.spatial
import xlwt
import os
import cv2
from skimage import morphology
from skimage.morphology import thin
from sklearn.metrics import confusion_matrix, jaccard_score, f1_score
os.chdir('./')
predictName = 'cotrain_192_pad'
predictPath = './smp/' + predictName + '/'
labelPath = "./smp/mask_ori_f1/"
name_experiment = 'exp_test'
path_experiment = './' + name_experiment + '/'
# labelPath = "./gt/"
# outpredictPath = "./gt_poor_o_thin/"
# outlabelPath = "./gt_o_thin/"
def getDSC(testImage, resultImage):
"""Compute the Dice Similarity Coefficient."""
testArray = testImage.flatten()
resultArray = resultImage.flatten()
return 1.0 - scipy.spatial.distance.dice(testArray, resultArray)
def getJaccard(testImage, resultImage):
"""Compute the Dice Similarity Coefficient."""
testArray = testImage.flatten()
resultArray = resultImage.flatten()
return 1.0 - scipy.spatial.distance.jaccard(testArray, resultArray)
def getPrecisionAndRecall(testImage, resultImage):
testArray = testImage.flatten()
resultArray = resultImage.flatten()
TP = np.sum(testArray*resultArray)
FP = np.sum((1-testArray)*resultArray)
FN = np.sum(testArray*(1-resultArray))
precision = TP/(TP+FP)
recall = TP/(TP+FN)
return precision, recall
def intersection(testImage, resultImage):
testSkel = morphology.skeletonize(testImage)
testSkel = testSkel.astype(int)
resultSkel = morphology.skeletonize(resultImage)
resultSkel = resultSkel.astype(int)
testArray = testImage.flatten()
resultArray = resultImage.flatten()
testSkel = testSkel.flatten()
resultSkel = resultSkel.flatten()
recall = np.sum(resultSkel * testArray) / (np.sum(testSkel))
precision = np.sum(resultArray * testSkel) / (np.sum(testSkel))
intersection = 2 * precision * recall / (precision + recall)
return intersection
if __name__ == "__main__":
labelList = os.listdir(labelPath)
# labelList.sort(key = lambda x: int(x[:-4]))
img_nums = len(labelList)
Q1 = []
Q2 = []
Q3 = []
Q4 = []
Q5 = []
Q6 = []
Q7 = []
Q8 = []
Q9 = []
Q10 = []
Q11 = []
Q12 = []
Q13 = []
Q14 = []
Q15 = []
Q16 = []
Q17 = []
book = xlwt.Workbook(encoding='utf-8', style_compression=0)
sheet = book.add_sheet('mysheet', cell_overwrite_ok=True)
row_num = 0
sheet.write(row_num, 0, 'CaseName')
sheet.write(row_num, 1, 'DSC')
sheet.write(row_num, 2, 'Pre')
sheet.write(row_num, 3, 'Recall')
sheet.write(row_num, 4, 'HD')
sheet.write(row_num, 5, 'ASSD')
sheet.write(row_num, 6, 'surface_dice_0')
sheet.write(row_num, 7, 'rel_overlap_gt')
sheet.write(row_num, 8, 'rel_overlap_pred')
sheet.write(row_num, 9, 'intersec')
sheet.write(row_num, 10, 'HD_thin')
sheet.write(row_num, 11, 'ASSD_thin')
sheet.write(row_num, 12, 'surface_dice_1')
sheet.write(row_num, 13, 'surface_dice_2')
sheet.write(row_num, 14, 'Jaccard')
sheet.write(row_num, 15, 'acc')
sheet.write(row_num, 16, 'spe')
sheet.write(row_num, 17, 'sen')
for idx, filename in enumerate(labelList):
label = cv2.imread(labelPath + filename, 0)
# print (label.dtype)
# label = cv2.imread(labelPath + filename)
label[label < 50] = 0
label[label >= 50] = 1
thinned_label = thin(label)
# cv2.imwrite(outlabelPath+filename,(thinned_label*255).astype(np.uint8))
# ret,label = cv2.threshold(label,127,255,cv2.THRESH_BINARY)
predict = cv2.imread(predictPath + filename.replace('_manual.png', '_expert.png'), 0)
# print(predictPath + filename)
# print (predict.dtype)
# ret,predict = cv2.threshold(predict,127,255,cv2.THRESH_BINARY)
# predict = cv2.imread(predictPath + filename)
# predict = predict / 255
predict[predict < 127] = 0
predict[predict >= 127] = 1
# ==============================================================================================================================================================================
y_scores = cv2.imread(predictPath + filename.replace('_manual.png', '_expert.png'), 0) # #####################################################################
y_scores = np.asarray(y_scores.flatten())/255.
y_scores = y_scores[:, np.newaxis]
# print(y_scores.shape)
y_true = cv2.imread(labelPath + filename, 0)
y_true = np.asarray(y_true.flatten())/255.
# fpr, tpr, thresholds = roc_curve((y_true), y_scores)
# AUC_ROC = roc_auc_score(y_true, y_scores)
# # test_integral = np.trapz(tpr,fpr) #trapz is numpy integration
# print ("\nArea under the ROC curve: " +str(AUC_ROC))
# roc_curve =plt.figure()
# plt.plot(fpr,tpr,'-',label='Area Under the Curve (AUC = %0.4f)' % AUC_ROC)
# plt.title('ROC curve')
# plt.xlabel("FPR (False Positive Rate)")
# plt.ylabel("TPR (True Positive Rate)")
# plt.legend(loc="lower right")
# plt.savefig(path_experiment+"ROC.png")
# precision, recall, thresholds = precision_recall_curve(y_true, y_scores)
# precision = np.fliplr([precision])[0] #so the array is increasing (you won't get negative AUC)
# recall = np.fliplr([recall])[0] #so the array is increasing (you won't get negative AUC)
# AUC_prec_rec = np.trapz(precision,recall)
# print ("\nArea under Precision-Recall curve: " +str(AUC_prec_rec))
# prec_rec_curve = plt.figure()
# plt.plot(recall,precision,'-',label='Area Under the Curve (AUC = %0.4f)' % AUC_prec_rec)
# plt.title('Precision - Recall curve')
# plt.xlabel("Recall")
# plt.ylabel("Precision")
# plt.legend(loc="lower right")
# plt.savefig(path_experiment+"Precision_recall.png")
# def best_f1_threshold(precision, recall, thresholds):
# best_f1=-1
# for index in range(len(precision)):
# curr_f1=2.*precision[index]*recall[index]/(precision[index]+recall[index])
# if best_f1<curr_f1:
# best_f1=curr_f1
# best_threshold=thresholds[index]
# return best_f1, best_threshold
# best_f1, best_threshold = best_f1_threshold(precision, recall, thresholds)
# print("\nthresholds: " + str(thresholds))
# print("\nbest_f1: " + str(best_f1))
# print("\nbest_threshold: " + str(best_threshold))
# Confusion matrix
threshold_confusion = 0.5
# print ("\nConfusion matrix: Custom threshold (for positive) of " +str(threshold_confusion))
y_pred = np.empty((y_scores.shape[0]))
# print(y_scores.shape[0])
# print(np.unique(y_pred))
for i in range(y_scores.shape[0]):
if y_scores[i] >= threshold_confusion:
y_pred[i] = 1
else:
y_pred[i] = 0
# print(np.unique(y_pred))
# print(np.unique(y_true))
confusion = confusion_matrix(y_true, y_pred)
# print (confusion)
accuracy = 0
if float(np.sum(confusion)) != 0:
accuracy = float(confusion[0, 0]+confusion[1, 1])/float(np.sum(confusion))
# print ("Global Accuracy: " +str(accuracy))
specificity = 0
if float(confusion[0, 0]+confusion[0, 1]) != 0: # 00 tn 11 tp 10 fn 01 fp
specificity = float(confusion[0, 0])/float(confusion[0, 0]+confusion[0, 1])
# print ("Specificity: " +str(specificity))
sensitivity = 0
if float(confusion[1, 1]+confusion[1, 0]) != 0:
sensitivity = float(confusion[1, 1])/float(confusion[1, 1]+confusion[1, 0])
# print ("Sensitivity: " +str(sensitivity))
precision = 0
if float(confusion[1, 1]+confusion[0, 1]) != 0:
precision = float(confusion[1, 1])/float(confusion[1, 1]+confusion[0, 1])
# print ("Precision: " +str(precision))
if float(confusion[1, 1]+confusion[0, 1]) != 0:
PPV = float(confusion[1, 1])/float(confusion[1, 1]+confusion[0, 1])
# print ("PPV: " +str(PPV))
# Jaccard similarity index
jaccard_index = jaccard_score(y_true, y_pred)
print("\nJaccard similarity score: " + str(jaccard_index))
# F1 score
F1_score = f1_score(y_true, y_pred, labels=None, average='binary', sample_weight=None)
# print ("\nF1 score (F-measure): " +str(F1_score))
# Save the results
# file_perf = open(path_experiment+'performances.txt', 'w')
# # file_perf.write("Area under the ROC curve: "+str(AUC_ROC)
# # + "\nArea under Precision-Recall curve: " +str(AUC_prec_rec)
# # + "\nJaccard similarity score: " +str(jaccard_index)
# # + "\nF1 score (F-measure): " +str(F1_score)
# # +"\n\nConfusion matrix:"
# # +str(confusion)
# # +"\nACCURACY: " +str(accuracy)
# # +"\nSENSITIVITY: " +str(sensitivity)
# # +"\nSPECIFICITY: " +str(specificity)
# # +"\nPRECISION: " +str(precision)
# # +"\nRECALL: " +str(sensitivity)
# # +"\nPPV: " +str(PPV)
# # +"\nbest_th: " +str(best_threshold)
# # +"\nbest_f1: " +str(best_f1)
# # )
# file_perf.write(
# "\nJaccard similarity score: " +str(jaccard_index)
# + "\nF1 score (F-measure): " +str(F1_score)
# +"\n\nConfusion matrix:"
# +str(confusion)
# +"\nACCURACY: " +str(accuracy)
# +"\nSENSITIVITY: " +str(sensitivity)
# +"\nSPECIFICITY: " +str(specificity)
# +"\nPRECISION: " +str(precision)
# +"\nRECALL: " +str(sensitivity)
# +"\nPPV: " +str(PPV)
# )
# file_perf.close()
# #==============================================================================================================================================================================
thinned_predict = thin(predict)
# cv2.imwrite(outpredictPath+filename,(thinned_predict*255).astype(np.uint8))
# predict[predict>=1] = 1
# dice = getDSC(predict, label)
# print("filename:" , filename , "dice:" , dice)
# dice_res = "the " + filename[:-4] + " image's DSC : " + str(round(dice,4)) + "\n"
DSC = getDSC(label, predict)
# surface_distances = surface_distance.compute_surface_distances(label, predict, spacing_mm=(1, 1, 1))
# HD = surface_distance.compute_robust_hausdorff(surface_distances, 95)
# distances_gt_to_pred = surface_distances["distances_gt_to_pred"]
# distances_pred_to_gt = surface_distances["distances_pred_to_gt"]
# surfel_areas_gt = surface_distances["surfel_areas_gt"]
# surfel_areas_pred = surface_distances["surfel_areas_pred"]
# ASSD = (np.sum(distances_pred_to_gt * surfel_areas_pred) +np.sum(distances_gt_to_pred * surfel_areas_gt))/(np.sum(surfel_areas_gt)+np.sum(surfel_areas_pred))
Jaccard = getJaccard(label, predict)
precision, recall = getPrecisionAndRecall(label, predict)
intersec = intersection(label, predict)
label = np.array(label, dtype=bool)
predict = np.array(predict, dtype=bool)
surface_distances = surface_distance.compute_surface_distances(label, predict, spacing_mm=(1, 1))
surface_distances_thin = surface_distance.compute_surface_distances(thinned_label, thinned_predict, spacing_mm=(1, 1))
HD = surface_distance.compute_robust_hausdorff(surface_distances, 95)
HD_thin = surface_distance.compute_robust_hausdorff(surface_distances_thin, 95)
surface_dice_2 = surface_distance.compute_surface_dice_at_tolerance(surface_distances, 2)
rel_overlap_gt, rel_overlap_pred = surface_distance.compute_surface_overlap_at_tolerance(surface_distances, 2)
surface_dice_1 = surface_distance.compute_surface_dice_at_tolerance(surface_distances, 1)
surface_dice_0 = surface_distance.compute_surface_dice_at_tolerance(surface_distances, 0)
surface_dice_3 = surface_distance.compute_surface_dice_at_tolerance(surface_distances, 3)
distances_gt_to_pred = surface_distances["distances_gt_to_pred"]
distances_pred_to_gt = surface_distances["distances_pred_to_gt"]
surfel_areas_gt = surface_distances["surfel_areas_gt"]
surfel_areas_pred = surface_distances["surfel_areas_pred"]
ASSD = (np.sum(distances_pred_to_gt * surfel_areas_pred) + np.sum(distances_gt_to_pred * surfel_areas_gt))/(np.sum(surfel_areas_gt)+np.sum(surfel_areas_pred))
distances_gt_to_pred_t = surface_distances_thin["distances_gt_to_pred"]
distances_pred_to_gt_t = surface_distances_thin["distances_pred_to_gt"]
surfel_areas_gt_t = surface_distances_thin["surfel_areas_gt"]
surfel_areas_pred_t = surface_distances_thin["surfel_areas_pred"]
ASSD_thin = (np.sum(distances_pred_to_gt_t * surfel_areas_pred_t) + np.sum(distances_gt_to_pred_t * surfel_areas_gt_t))/(np.sum(surfel_areas_gt_t)+np.sum(surfel_areas_pred_t))
# print(surface_overlap)
row_num += 1
sheet.write(row_num, 0, filename)
sheet.write(row_num, 1, DSC)
sheet.write(row_num, 2, precision)
sheet.write(row_num, 3, recall)
sheet.write(row_num, 4, HD)
sheet.write(row_num, 5, ASSD)
sheet.write(row_num, 6, surface_dice_0)
sheet.write(row_num, 7, rel_overlap_gt)
sheet.write(row_num, 8, rel_overlap_pred)
sheet.write(row_num, 9, intersec)
sheet.write(row_num, 10, HD_thin)
sheet.write(row_num, 11, ASSD_thin)
sheet.write(row_num, 12, surface_dice_1)
sheet.write(row_num, 13, surface_dice_2)
# sheet.write(row_num, 14, surface_dice_3)
sheet.write(row_num, 14, Jaccard)
sheet.write(row_num, 15, accuracy)
sheet.write(row_num, 16, specificity)
sheet.write(row_num, 17, sensitivity)
Q1.append(DSC)
Q2.append(precision)
Q3.append(recall)
Q4.append(HD)
Q5.append(ASSD)
Q6.append(surface_dice_0)
Q7.append(rel_overlap_gt)
Q8.append(rel_overlap_pred)
Q9.append(intersec)
Q10.append(HD_thin)
Q11.append(ASSD_thin)
Q12.append(surface_dice_1)
Q13.append(surface_dice_2)
# Q14.append(surface_dice_3)
Q14.append(Jaccard)
Q15.append(accuracy)
Q16.append(specificity)
Q17.append(sensitivity)
Q1 = np.array(Q1)
Q2 = np.array(Q2)
Q3 = np.array(Q3)
Q4 = np.array(Q4)
Q5 = np.array(Q5)
Q6 = np.array(Q6)
Q7 = np.array(Q7)
Q8 = np.array(Q8)
Q9 = np.array(Q9)
Q10 = np.array(Q10)
Q11 = np.array(Q11)
Q12 = np.array(Q12)
Q13 = np.array(Q13)
Q14 = np.array(Q14)
Q15 = np.array(Q15)
Q16 = np.array(Q16)
Q17 = np.array(Q17)
row_num += 2
sheet.write(row_num, 0, 'CaseName')
sheet.write(row_num, 1, 'DSC')
sheet.write(row_num, 2, 'Pre')
sheet.write(row_num, 3, 'Recall')
sheet.write(row_num, 4, 'HD')
sheet.write(row_num, 5, 'ASSD')
sheet.write(row_num, 6, 'surface_dice_0')
sheet.write(row_num, 7, 'rel_overlap_gt')
sheet.write(row_num, 8, 'rel_overlap_pred')
sheet.write(row_num, 9, 'intersec')
sheet.write(row_num, 10, 'HD_thin')
sheet.write(row_num, 11, 'ASSD_thin')
sheet.write(row_num, 12, 'surface_dice_1')
sheet.write(row_num, 13, 'surface_dice_2')
sheet.write(row_num, 14, 'Jaccard')
sheet.write(row_num, 15, 'accuracy')
sheet.write(row_num, 16, 'specificity')
sheet.write(row_num, 17, 'sensitivity')
row_num += 1
sheet.write(row_num, 0, predictName)
sheet.write(row_num, 1, Q1.mean())
sheet.write(row_num, 2, Q2.mean())
sheet.write(row_num, 3, Q3.mean())
sheet.write(row_num, 4, Q4.mean())
sheet.write(row_num, 5, Q5.mean())
sheet.write(row_num, 6, Q6.mean())
sheet.write(row_num, 7, Q7.mean())
sheet.write(row_num, 8, Q8.mean())
sheet.write(row_num, 9, Q9.mean())
sheet.write(row_num, 10, Q10.mean())
sheet.write(row_num, 11, Q11.mean())
sheet.write(row_num, 12, Q12.mean())
sheet.write(row_num, 13, Q13.mean())
sheet.write(row_num, 14, Q14.mean())
sheet.write(row_num, 15, Q15.mean())
sheet.write(row_num, 16, Q16.mean())
sheet.write(row_num, 17, Q17.mean())
book.save('./smp/' + predictName + '.xls')