nms.py

# -*- coding: utf-8 -*-
"""
Created on Sat Jan 13 21:47:20 2018

@author: Administrator
"""
# 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")





#%%
# import the necessary packages
import numpy as np
 
#  Felzenszwalb et al.
def non_max_suppression_slow(boxes, overlapThresh):
	# if there are no boxes, return an empty list
	if len(boxes) == 0:
		return []
 
	# 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, add the index
		# value to the list of picked indexes, then initialize
		# the suppression list (i.e. indexes that will be deleted)
		# using the last index
		last = len(idxs) - 1
		i = idxs[last]
		pick.append(i)
		suppress = [last]
        # loop over all indexes in the indexes list
		for pos in range(0, last):
			# grab the current index
			j = idxs[pos]
 
			# 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 = max(x1[i], x1[j])
			yy1 = max(y1[i], y1[j])
			xx2 = min(x2[i], x2[j])
			yy2 = min(y2[i], y2[j])
 
			# compute the width and height of the bounding box
			w = max(0, xx2 - xx1 + 1)
			h = max(0, yy2 - yy1 + 1)
 
			# compute the ratio of overlap between the computed
			# bounding box and the bounding box in the area list
			overlap = float(w * h) / area[j]
 
			# if there is sufficient overlap, suppress the
			# current bounding box
			if overlap > overlapThresh:
				suppress.append(pos)
 
		# delete all indexes from the index list that are in the
		# suppression list
		idxs = np.delete(idxs, suppress)
 
	# return only the bounding boxes that were picked
	return boxes[pick]

#%%

#def py_cpu_nms(dets, thresh):
#    """Pure Python NMS baseline."""
#    x1 = dets[:, 0]
#    y1 = dets[:, 1]
#    x2 = dets[:, 2]
#    y2 = dets[:, 3]
#    scores = dets[:, 4]
#
#    areas = (x2 - x1 + 1) * (y2 - y1 + 1)
#    order = scores.argsort()[::-1]
#
#    keep = []
#    while order.size > 0:
#        i = order[0]
#        keep.append(i)
#        xx1 = np.maximum(x1[i], x1[order[1:]])
#        yy1 = np.maximum(y1[i], y1[order[1:]])
#        xx2 = np.minimum(x2[i], x2[order[1:]])
#        yy2 = np.minimum(y2[i], y2[order[1:]])
#
#        w = np.maximum(0.0, xx2 - xx1 + 1)
#        h = np.maximum(0.0, yy2 - yy1 + 1)
#        inter = w * h
#        ovr = inter / (areas[i] + areas[order[1:]] - inter)
#
#        inds = np.where(ovr <= thresh)[0]
#        order = order[inds + 1]
#
#    return keep