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import numpy as np
from sklearn.metrics import auc, roc_auc_score, precision_recall_curve, average_precision_score
def rescale(x):
return (x - x.min()) / (x.max() - x.min())
def is_one_class(gt: np.ndarray):
gt_ravel = gt.ravel()
return gt_ravel.sum() == 0 or gt_ravel.sum() == gt_ravel.shape[0]
def calculate_px_metrics(gt_px, pr_px):
if is_one_class(gt_px): # In case there are only normal pixels or no pixel-level labels
return 0, 0, 0
auroc_px = roc_auc_score(gt_px.ravel(), pr_px.ravel())
precisions, recalls, _ = precision_recall_curve(gt_px.ravel(), pr_px.ravel())
f1_scores = (2 * precisions * recalls) / (precisions + recalls)
f1_px = np.max(f1_scores[np.isfinite(f1_scores)])
ap_px = average_precision_score(gt_px.ravel(), pr_px.ravel())
return auroc_px * 100, f1_px * 100, ap_px * 100
def calculate_im_metrics(gt_im, pr_im):
if is_one_class(gt_im): # In case there are only normal samples or no image-level labels
return 0, 0, 0
auroc_im = roc_auc_score(gt_im.ravel(), pr_im.ravel())
precisions, recalls, _ = precision_recall_curve(gt_im.ravel(), pr_im.ravel())
f1_scores = (2 * precisions * recalls) / (precisions + recalls)
f1_im = np.max(f1_scores[np.isfinite(f1_scores)])
ap_im = average_precision_score(gt_im, pr_im)
return ap_im * 100, auroc_im * 100, f1_im * 100
def calculate_average_metric(metrics: dict):
average = {}
for obj, metric in metrics.items():
for k, v in metric.items():
if k not in average:
average[k] = []
average[k].append(v)
for k, v in average.items():
average[k] = np.mean(v)
return average
def calculate_metric(results, obj):
gt_px = []
pr_px = []
gt_im = []
pr_im = []
for idx in range(len(results['cls_names'])):
if results['cls_names'][idx] == obj:
gt_px.append(results['imgs_masks'][idx])
pr_px.append(results['anomaly_maps'][idx])
gt_im.append(results['imgs_gts'][idx])
pr_im.append(results['anomaly_scores'][idx])
gt_px = np.array(gt_px)
pr_px = np.array(pr_px)
gt_im = np.array(gt_im)
pr_im = np.array(pr_im)
auroc_px, f1_px, ap_px = calculate_px_metrics(gt_px, pr_px)
ap_im, auroc_im, f1_im = calculate_im_metrics(gt_im, pr_im)
metric = {
'auroc_px': auroc_px,
'auroc_im': auroc_im,
'f1_px': f1_px,
'f1_im': f1_im,
'ap_px': ap_px,
'ap_im': ap_im,
}
return metric
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