64 lines
2.7 KiB
Python
64 lines
2.7 KiB
Python
"""Universal procedure of calculating average precision defined in VOC"""
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def match_gt_with_preds(ground_truth, predictions, match_labels):
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"""Match a ground truth with every predictions and return matched index."""
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max_confidence = 0.
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matched_idx = -1
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for i, pred in enumerate(predictions):
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if match_labels(ground_truth, pred[1]) and max_confidence < pred[0]:
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max_confidence = pred[0]
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matched_idx = i
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return matched_idx
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def get_confidence_list(ground_truths_list, predictions_list, match_labels):
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"""Generate a list of confidence of true positives and false positives."""
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assert len(ground_truths_list) == len(predictions_list)
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true_positive_list = []
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false_positive_list = []
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num_samples = len(ground_truths_list)
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for i in range(num_samples):
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ground_truths = ground_truths_list[i]
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predictions = predictions_list[i]
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prediction_matched = [False] * len(predictions)
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for ground_truth in ground_truths:
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idx = match_gt_with_preds(ground_truth, predictions, match_labels)
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if idx >= 0:
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prediction_matched[idx] = True
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true_positive_list.append(predictions[idx][0])
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else:
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true_positive_list.append(.0)
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for idx, pred_matched in enumerate(prediction_matched):
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if not pred_matched:
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false_positive_list.append(predictions[idx][0])
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return true_positive_list, false_positive_list
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def calc_precision_recall(ground_truths_list, predictions_list, match_labels):
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"""Adjust threshold to get mutiple precision recall sample."""
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true_positive_list, false_positive_list = get_confidence_list(
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ground_truths_list, predictions_list, match_labels)
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recalls = [0.]
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precisions = [0.]
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thresholds = sorted(list(set(true_positive_list)))
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for thresh in reversed(thresholds):
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if thresh == 0.:
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recalls.append(1.)
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precisions.append(0.)
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true_positives = sum(i >= thresh for i in true_positive_list)
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false_positives = sum(i >= thresh for i in false_positive_list)
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false_negatives = len(true_positive_list) - true_positives
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recalls.append(true_positives / (true_positives+false_negatives))
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precisions.append(true_positives / (true_positives + false_positives))
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return precisions, recalls
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def calc_average_precision(precisions, recalls):
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"""Calculate average precision defined in VOC contest."""
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total_precision = 0.
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for i in range(11):
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index = next(conf[0] for conf in enumerate(recalls) if conf[1] >= i/10)
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total_precision += max(precisions[index:])
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return total_precision / 11
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