forked from 170010011/fr
65 lines
1.9 KiB
Python
65 lines
1.9 KiB
Python
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# import the necessary packages
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import numpy as np
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def non_max_suppression(boxes, probs=None, overlapThresh=0.3):
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# if there are no boxes, return an empty list
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if len(boxes) == 0:
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return []
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# if the bounding boxes are integers, convert them to floats -- this
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# is important since we'll be doing a bunch of divisions
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if boxes.dtype.kind == "i":
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boxes = boxes.astype("float")
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# initialize the list of picked indexes
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pick = []
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# grab the coordinates of the bounding boxes
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x1 = boxes[:, 0]
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y1 = boxes[:, 1]
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x2 = boxes[:, 2]
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y2 = boxes[:, 3]
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# compute the area of the bounding boxes and grab the indexes to sort
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# (in the case that no probabilities are provided, simply sort on the
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# bottom-left y-coordinate)
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area = (x2 - x1 + 1) * (y2 - y1 + 1)
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idxs = y2
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# if probabilities are provided, sort on them instead
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if probs is not None:
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idxs = probs
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# sort the indexes
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idxs = np.argsort(idxs)
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# keep looping while some indexes still remain in the indexes list
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while len(idxs) > 0:
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# grab the last index in the indexes list and add the index value
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# to the list of picked indexes
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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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# find the largest (x, y) coordinates for the start of the bounding
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# box and the smallest (x, y) coordinates for the end of the bounding
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# box
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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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# compute the width and height of the bounding box
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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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# compute the ratio of overlap
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overlap = (w * h) / area[idxs[:last]]
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# delete all indexes from the index list that have overlap greater
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# than the provided overlap threshold
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idxs = np.delete(idxs, np.concatenate(([last],
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np.where(overlap > overlapThresh)[0])))
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# return only the bounding boxes that were picked
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return boxes[pick].astype("int")
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