# skip the ground truth
if "1st_manual" not in result:
# print metrics
print "-- {} --".format(os.path.basename(result))
print "dice coefficient : {}".format(utils.dice_coefficient(gt_vessels,pred_vessels, masks))
print "f1 score : {}, accuracy : {}, specificity : {}, sensitivity : {}".format(*utils.misc_measures(gt_vessels,pred_vessels, masks))
# compute false positive rate, true positive graph
method=os.path.basename(result)
methods.append(method)
if method=='CRFs' or method=='2nd_manual':
cm=confusion_matrix(gt_vessels_in_mask, pred_vessels_in_mask)
fpr=1-1.*cm[0,0]/(cm[0,1]+cm[0,0])
tpr=1.*cm[1,1]/(cm[1,0]+cm[1,1])
prec=1.*cm[1,1]/(cm[0,1]+cm[1,1])
recall=tpr
else:
fpr, tpr, _ = roc_curve(gt_vessels_in_mask, pred_vessels_in_mask)
prec, recall, _ = precision_recall_curve(gt_vessels_in_mask, pred_vessels_in_mask)
fprs.append(fpr)
tprs.append(tpr)
precs.append(prec)
recalls.append(recall)
# save plots of ROC and PR curves
curve_dir=curves_out.format(os.path.basename(dataset))
if not os.path.isdir(curve_dir):
os.makedirs(curve_dir)
utils.plot_AUC_ROC(fprs, tprs, methods, curve_dir)
utils.plot_AUC_PR(precs, recalls, methods, curve_dir)
# compute false positive rate, true positive graph
method = os.path.basename(result)
methods.append(method)
if method == 'CRFs' or method == '2nd_manual':
cm = confusion_matrix(gt_vessels_in_mask,
pred_vessels_in_mask)
fpr = 1 - 1. * cm[0, 0] / (cm[0, 1] + cm[0, 0])
tpr = 1. * cm[1, 1] / (cm[1, 0] + cm[1, 1])
prec = 1. * cm[1, 1] / (cm[0, 1] + cm[1, 1])
recall = tpr
if method == '2nd_manual':
human_op_pts_roc, human_op_pts_pr = utils.operating_pts_human_experts(
gt_vessels, pred_vessels, masks)
else:
fpr, tpr, _ = roc_curve(gt_vessels_in_mask,
pred_vessels_in_mask)
prec, recall, _ = precision_recall_curve(
gt_vessels_in_mask, pred_vessels_in_mask)
fprs.append(fpr)
tprs.append(tpr)
precs.append(prec)
recalls.append(recall)
# save plots of ROC and PR curves
curve_dir = curves_out.format(os.path.basename(dataset))
if not os.path.isdir(curve_dir):
os.makedirs(curve_dir)
utils.plot_AUC_ROC(fprs, tprs, methods, curve_dir, human_op_pts_roc)
utils.plot_AUC_PR(precs, recalls, methods, curve_dir, human_op_pts_pr)
if method == 'CRFs' or method == '2nd_manual':
cm = confusion_matrix(gt_vessels_in_mask,
pred_vessels_in_mask)
fpr = 1 - 1. * cm[0, 0] / (cm[0, 1] + cm[0, 0])
tpr = 1. * cm[1, 1] / (cm[1, 0] + cm[1, 1])
prec = 1. * cm[1, 1] / (cm[0, 1] + cm[1, 1])
recall = tpr
else:
fpr, tpr, _ = roc_curve(gt_vessels_in_mask,
pred_vessels_in_mask)
prec, recall, _ = precision_recall_curve(
gt_vessels_in_mask, pred_vessels_in_mask)
fprs.append(fpr)
tprs.append(tpr)
precs.append(prec)
recalls.append(recall)
# save plots of ROC and PR curves
curve_dir = curves_out.format(os.path.basename(dataset))
if not os.path.isdir(curve_dir):
os.makedirs(curve_dir)
if os.path.basename(dataset) == 'STARE':
num = stare_num
elif os.path.basename(dataset) == 'DRIVE':
num = drive_num
utils.plot_AUC_ROC(fprs, tprs, methods, curve_dir,
os.path.basename(dataset), model_name, num)
utils.plot_AUC_PR(precs, recalls, methods, curve_dir,
os.path.basename(dataset), model_name, num)