add real predictions
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@ -85,7 +85,6 @@ class ActivityEvaluator:
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# Collect corresponding gt files (ys_gt: 1 or 0)
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boxes_gt, ys_gt = parse_gt_collective(self.dir_data, seq, path_pif)
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# Run Monoloco
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dic_out, boxes = self.run_monoloco(annotations, kk, im_size=im_size)
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@ -140,7 +139,7 @@ class ActivityEvaluator:
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stds = dic_out['stds_ale']
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xz_centers = [[xx[0], xx[2]] for xx in dic_out['xyz_pred']]
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# Count gt statistics
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# Count gt statistics. (One element each gt)
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for key in categories:
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self.cnt['gt'][key] += 1
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self.cnt['gt']['all'] += 1
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@ -220,12 +219,15 @@ def cout_results(cnt, all_gt, all_pred, categories=()):
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# Split by folders for collective activity
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for key in categories:
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acc = accuracy_score(all_gt[key], all_pred[key])
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print("Accuracy of category {}: {:.2f}% , Recall: {:.2f}%, #: {}, Predicted positive: {:.2f}%"
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print("Accuracy of category {}: {:.2f}% , Recall: {:.2f}%, #: {}, Pred/Real positive: {:.1f}% / {:.1f}%"
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.format(key,
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acc * 100,
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cnt['pred'][key] / cnt['gt'][key]*100,
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cnt['pred'][key],
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sum(all_gt[key]) / len(all_gt[key]) * 100))
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sum(all_pred[key]) / len(all_pred[key]) * 100,
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sum(all_gt[key]) / len(all_gt[key]) * 100
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)
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)
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# Final Accuracy
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acc = accuracy_score(all_gt['all'], all_pred['all'])
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