Np average #353
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Np average #353
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khanhlvg
requested changes
Oct 19, 2021
| @@ -63,7 +63,7 @@ def visualize(image: np.ndarray, | |||
| """ | |||
| for person in list_persons: | |||
| if person.score < instance_threshold: | |||
| continue | |||
| break | |||
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Why do you break instead of continue? We implemented this function with multiple pose model in mind, so we only skip instances that are below the instance threshold.
| @@ -108,6 +108,7 @@ def person_from_keypoints_with_scores( | |||
| # Calculate person score by averaging keypoint scores. | |||
| scores_above_threshold = list( | |||
| filter(lambda x: x > keypoint_score_threshold, scores)) | |||
| scores_above_threshold = np.append(scores_above_threshold, 0) | |||
| person_score = np.average(scores_above_threshold) | |||
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You should do this instead. If you add a zero, it will change the average value.
person_score = np.average(scores_above_threshold) if scores_above_threshold else 0
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Thanks. I have already fixed it.
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In the file https://github.com/Charlie839242/examples/blob/master/lite/examples/pose_estimation/raspberry_pi/data.py, 111 line, it is possible that the score of 17 keypoints all don't reach the threshold. In this case, the np.average receives a empty list, which will cause some warnings like:
RuntimeWarning: Mean of empty slice.
RuntimeWarning: invalid value encountered in double_scalars
By adding 'scores_above_threshold = np.append(scores_above_threshold, 0)' can solve the problem.