Created
June 8, 2018 11:32
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# import the necessary packages | |
import numpy as np | |
# Malisiewicz et al. | |
def non_max_suppression_fast(boxes, overlapThresh): | |
# if there are no boxes, return an empty list | |
if len(boxes) == 0: | |
return [] | |
# if the bounding boxes integers, convert them to floats -- | |
# this is important since we'll be doing a bunch of divisions | |
if boxes.dtype.kind == "i": | |
boxes = boxes.astype("float") | |
# initialize the list of picked indexes | |
pick = [] | |
# grab the coordinates of the bounding boxes | |
x1 = boxes[:,0] | |
y1 = boxes[:,1] | |
x2 = boxes[:,2] | |
y2 = boxes[:,3] | |
# compute the area of the bounding boxes and sort the bounding | |
# boxes by the bottom-right y-coordinate of the bounding box | |
area = (x2 - x1 + 1) * (y2 - y1 + 1) | |
idxs = np.argsort(y2) | |
# keep looping while some indexes still remain in the indexes | |
# list | |
while len(idxs) > 0: | |
# grab the last index in the indexes list and add the | |
# index value to the list of picked indexes | |
last = len(idxs) - 1 | |
i = idxs[last] | |
pick.append(i) | |
# find the largest (x, y) coordinates for the start of | |
# the bounding box and the smallest (x, y) coordinates | |
# for the end of the bounding box | |
xx1 = np.maximum(x1[i], x1[idxs[:last]]) | |
yy1 = np.maximum(y1[i], y1[idxs[:last]]) | |
xx2 = np.minimum(x2[i], x2[idxs[:last]]) | |
yy2 = np.minimum(y2[i], y2[idxs[:last]]) | |
# compute the width and height of the bounding box | |
w = np.maximum(0, xx2 - xx1 + 1) | |
h = np.maximum(0, yy2 - yy1 + 1) | |
# compute the ratio of overlap | |
overlap = (w * h) / area[idxs[:last]] | |
# delete all indexes from the index list that have | |
idxs = np.delete(idxs, np.concatenate(([last], | |
np.where(overlap > overlapThresh)[0]))) | |
# return only the bounding boxes that were picked using the | |
# integer data type | |
return boxes[pick].astype("int") |
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