74 lines
1.9 KiB
Python
74 lines
1.9 KiB
Python
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# vim: expandtab:ts=4:sw=4
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import numpy as np
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import cv2
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def non_max_suppression(boxes, max_bbox_overlap, scores=None):
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"""Suppress overlapping detections.
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Original code from [1]_ has been adapted to include confidence score.
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.. [1] http://www.pyimagesearch.com/2015/02/16/
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faster-non-maximum-suppression-python/
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Examples
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--------
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>>> boxes = [d.roi for d in detections]
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>>> scores = [d.confidence for d in detections]
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>>> indices = non_max_suppression(boxes, max_bbox_overlap, scores)
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>>> detections = [detections[i] for i in indices]
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Parameters
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----------
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boxes : ndarray
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Array of ROIs (x, y, width, height).
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max_bbox_overlap : float
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ROIs that overlap more than this values are suppressed.
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scores : Optional[array_like]
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Detector confidence score.
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Returns
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-------
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List[int]
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Returns indices of detections that have survived non-maxima suppression.
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"""
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if len(boxes) == 0:
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return []
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boxes = boxes.astype(np.float32)
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pick = []
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x1 = boxes[:, 0]
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y1 = boxes[:, 1]
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x2 = boxes[:, 2] + boxes[:, 0]
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y2 = boxes[:, 3] + boxes[:, 1]
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area = (x2 - x1 + 1) * (y2 - y1 + 1)
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if scores is not None:
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idxs = np.argsort(scores)
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else:
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idxs = np.argsort(y2)
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while len(idxs) > 0:
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last = len(idxs) - 1
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i = idxs[last]
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pick.append(i)
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xx1 = np.maximum(x1[i], x1[idxs[:last]])
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yy1 = np.maximum(y1[i], y1[idxs[:last]])
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xx2 = np.minimum(x2[i], x2[idxs[:last]])
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yy2 = np.minimum(y2[i], y2[idxs[:last]])
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w = np.maximum(0, xx2 - xx1 + 1)
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h = np.maximum(0, yy2 - yy1 + 1)
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overlap = (w * h) / (area[idxs[:last]] + area[idxs[last]] - w * h)
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idxs = np.delete(
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idxs, np.concatenate(
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([last], np.where(overlap > max_bbox_overlap)[0])))
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return pick
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