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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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