text: # 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")
  