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from sklearn.metrics import auc, roc_auc_score, average_precision_score, f1_score, precision_recall_curve, pairwise
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import numpy as np
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from skimage import measure
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def cal_pro_score(masks, amaps, max_step=200, expect_fpr=0.3):
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binary_amaps = np.zeros_like(amaps, dtype=bool)
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min_th, max_th = amaps.min(), amaps.max()
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delta = (max_th - min_th) / max_step
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pros, fprs, ths = [], [], []
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for th in np.arange(min_th, max_th, delta):
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binary_amaps[amaps <= th], binary_amaps[amaps > th] = 0, 1
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pro = []
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for binary_amap, mask in zip(binary_amaps, masks):
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for region in measure.regionprops(measure.label(mask)):
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tp_pixels = binary_amap[region.coords[:, 0], region.coords[:, 1]].sum()
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pro.append(tp_pixels / region.area)
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inverse_masks = 1 - masks
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fp_pixels = np.logical_and(inverse_masks, binary_amaps).sum()
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fpr = fp_pixels / inverse_masks.sum()
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pros.append(np.array(pro).mean())
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fprs.append(fpr)
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ths.append(th)
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pros, fprs, ths = np.array(pros), np.array(fprs), np.array(ths)
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idxes = fprs < expect_fpr
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fprs = fprs[idxes]
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fprs = (fprs - fprs.min()) / (fprs.max() - fprs.min())
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pro_auc = auc(fprs, pros[idxes])
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return pro_auc
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def image_level_metrics(results, obj, metric):
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gt = results[obj]['gt_sp']
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pr = results[obj]['pr_sp']
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gt = np.array(gt)
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pr = np.array(pr)
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if metric == 'image-auroc':
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performance = roc_auc_score(gt, pr)
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elif metric == 'image-ap':
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performance = average_precision_score(gt, pr)
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return performance
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def pixel_level_metrics(results, obj, metric):
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gt = results[obj]['imgs_masks']
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pr = results[obj]['anomaly_maps']
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gt = np.array(gt)
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pr = np.array(pr)
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if metric == 'pixel-auroc':
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performance = roc_auc_score(gt.ravel(), pr.ravel())
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elif metric == 'pixel-aupro':
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if len(gt.shape) == 4:
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gt = gt.squeeze(1)
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if len(pr.shape) == 4:
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pr = pr.squeeze(1)
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performance = cal_pro_score(gt, pr)
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return performance
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