| import json
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|
|
|
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| import numpy as np
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|
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| API = 'http://ec2-52-11-11-89.us-west-2.compute.amazonaws.com/challenge17/api.py'
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|
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| def get_blocked_videos(api=API):
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|
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| return list()
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|
|
| def interpolated_prec_rec(prec, rec):
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| """Interpolated AP - VOCdevkit from VOC 2011.
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| """
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| mprec = np.hstack([[0], prec, [0]])
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| mrec = np.hstack([[0], rec, [1]])
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| for i in range(len(mprec) - 1)[::-1]:
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| mprec[i] = max(mprec[i], mprec[i + 1])
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| idx = np.where(mrec[1::] != mrec[0:-1])[0] + 1
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| ap = np.sum((mrec[idx] - mrec[idx - 1]) * mprec[idx])
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| return ap
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|
|
| def segment_iou(target_segment, candidate_segments):
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| """Compute the temporal intersection over union between a
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| target segment and all the test segments.
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|
|
| Parameters
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| ----------
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| target_segment : 1d array
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| Temporal target segment containing [starting, ending] times.
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| candidate_segments : 2d array
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| Temporal candidate segments containing N x [starting, ending] times.
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|
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| Outputs
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| -------
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| tiou : 1d array
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| Temporal intersection over union score of the N's candidate segments.
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| """
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| tt1 = np.maximum(target_segment[0], candidate_segments[:, 0])
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| tt2 = np.minimum(target_segment[1], candidate_segments[:, 1])
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|
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| segments_intersection = (tt2 - tt1).clip(0)
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|
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| segments_union = (candidate_segments[:, 1] - candidate_segments[:, 0]) \
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| + (target_segment[1] - target_segment[0]) - segments_intersection
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|
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| tIoU = segments_intersection.astype(float) / segments_union
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| return tIoU
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|
|
| def wrapper_segment_iou(target_segments, candidate_segments):
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| """Compute intersection over union btw segments
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| Parameters
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| ----------
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| target_segments : ndarray
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| 2-dim array in format [m x 2:=[init, end]]
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| candidate_segments : ndarray
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| 2-dim array in format [n x 2:=[init, end]]
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| Outputs
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| -------
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| tiou : ndarray
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| 2-dim array [n x m] with IOU ratio.
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| Note: It assumes that candidate-segments are more scarce that target-segments
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| """
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| if candidate_segments.ndim != 2 or target_segments.ndim != 2:
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| raise ValueError('Dimension of arguments is incorrect')
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|
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| n, m = candidate_segments.shape[0], target_segments.shape[0]
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| tiou = np.empty((n, m))
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| for i in range(m):
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| tiou[:, i] = segment_iou(target_segments[i,:], candidate_segments)
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|
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| return tiou
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|
|
