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evaluation.py
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import matplotlib
matplotlib.use("Agg")
from matplotlib import pyplot as plt
# scikit learn
from sklearn.metrics import roc_curve
from sklearn.metrics import roc_auc_score
from sklearn.metrics import confusion_matrix
from sklearn.metrics import precision_recall_curve
from sklearn.metrics import jaccard_similarity_score
from sklearn.metrics import f1_score
import sys
import h5py
sys.path.insert(0, './utils/')
from help_functions import *
from extract_patches import pred_only_FOV
import numpy as np
# ========= CONFIG FILE TO READ FROM =======
import configparser
config = configparser.ConfigParser()
config.read('configuration.txt')
# ===========================================
# model name
path_data = config.get('data paths', 'path_local')
algorithm_config = config.get('experiment name', 'name')
dataset = config.get('data attributes', 'dataset')
# name_experiment_list = ["deform_v1", "unet", "deform_unet_v1"]
name_experiment_list = [algorithm_config]
algorithms = ["Deformable-ConvNet", "U-Net", "DUNet"]
test_border_masks = path_data + config.get('data paths', 'test_border_masks')
test_border_masks = load_hdf5(test_border_masks)
index = 0
for name_experiment in name_experiment_list:
algorithm = algorithms[index]
path_experiment = '/home/lvlv/lv_nian_zu/paper_3/code/my_project/DUNet-retinal-vessel-detection-master/log/experiments/'+algorithm_config + '/' + dataset + '/'
# if algorithm_config != name_experiment:
# continue
# ========== Elaborate and visualize the predicted images ====================
pred_imgs = None
orig_imgs = None
gtruth_masks = None
# apply the DRIVE masks on the repdictions #set everything outside the FOV to zero!!
# kill_border(pred_imgs, test_border_masks) #DRIVE MASK #only for visualization
## back to original dimensions
if dataset == 'HRF':
file = h5py.File(path_experiment + '0:15/' + dataset + '_predict_results.h5', 'r')
gtruth_masks = file['y_gt'][:]
pred_imgs = file['y_pred'][:]
orig_imgs = file['x_origin'][:]
file.close()
file = h5py.File(path_experiment + '15:30/' + dataset + '_predict_results.h5', 'r')
gtruth_masks = np.concatenate([gtruth_masks, file['y_gt'][:]], axis=0)
pred_imgs = np.concatenate([pred_imgs, file['y_pred'][:]], axis=0)
file.close()
gtruth_masks = np.where(gtruth_masks > 0, 1, 0)
else:
#/home/lvlv/lv_nian_zu/paper_3/code/my_project/DUNet-retinal-vessel-detection-master/log/experiments/my_unet_Trans/DRIVE/DRIVE_predict_results.h5
# h5_path = path_experiment + dataset + '_predict_results.h5'
h5_path = '/home/lvlv/lv_nian_zu/paper_3/code/my_project/DUNet-retinal-vessel-detection-master/log/experiments/Dense_Transfomer_unet/DRIVE/DRIVE001_predict_results.h5'
file = h5py.File(h5_path, 'r')
# file = h5py.File('/home/lvlv/lv_nian_zu/paper_3/code/my_project/DUNet-retinal-vessel-detection-master/log/experiments/my_unet_Trans/DRIVE/DRIVE_predict_results.h5')
gtruth_masks = file['y_gt'][:]
pred_imgs = file['y_pred'][:]
orig_imgs = file['x_origin'][:]
file.close()
# ====== Evaluate the results
print("\n\n======== Evaluate the results =======================")
print('\n', name_experiment)
print(path_experiment)
# predictions only inside the FOV
y_scores, y_true = pred_only_FOV(pred_imgs, gtruth_masks, test_border_masks,
insideFOV=True) # returns data only inside the FOV
if np.max(y_true) > 1:
y_true = y_true // np.max(y_true)
# Area under the ROC curve
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=algorithm + '_' + dataset + '(AUC = %0.4f)' % AUC_ROC)
plt.title('ROC curve', fontsize=14)
plt.xlabel("FPR (False Positive Rate)", fontsize=15)
plt.ylabel("TPR (True Positive Rate)", fontsize=15)
plt.legend(loc="lower right")
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
# plt.savefig(path_experiment + "ROC.png")
# Precision-recall curve
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.xticks(fontsize=14)
# plt.yticks(fontsize=14)
# plt.savefig(path_experiment + "Precision_recall.png")
# Confusion matrix
threshold_confusion = 0.3
print("\nConfusion matrix: Costum threshold (for positive) of " + str(threshold_confusion))
y_pred = np.empty((y_scores.shape[0]))
for i in range(y_scores.shape[0]):
if y_scores[i] >= threshold_confusion:
y_pred[i] = 1
else:
y_pred[i] = 0
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:
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))
# Jaccard similarity index
jaccard_index = jaccard_similarity_score(y_true, y_pred, normalize=True)
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('/home/lvlv/lv_nian_zu/paper_3/code/my_project/DUNet-retinal-vessel-detection-master/log/experiments/'+algorithm_config+'/DRIVE/'+ 'performances_new.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)
)
file_perf.close()
# break
index = index + 1
plt.savefig('./log/experiments/' + dataset + "_comparative_ROC.png")
# plt.savefig('./log/experiments/' + dataset + "_Precision_recall.png")
plt.show()