Non-Maximum Suppression for Object Detection in Python

# 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)
# 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]
# 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 xrange(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]

# import the necessary packages
from pyimagesearch.nms import non_max_suppression_slow
import numpy as np
import cv2

# construct a list containing the images that will be examined
# along with their respective bounding boxes
images = [
<span style="white-space:pre">	</span>("images/audrey.jpg", np.array([
<span style="white-space:pre">	</span>(12, 84, 140, 212),
<span style="white-space:pre">	</span>(24, 84, 152, 212),
<span style="white-space:pre">	</span>(36, 84, 164, 212),
<span style="white-space:pre">	</span>(12, 96, 140, 224),
<span style="white-space:pre">	</span>(24, 96, 152, 224),
<span style="white-space:pre">	</span>(24, 108, 152, 236)])),
<span style="white-space:pre">	</span>("images/bksomels.jpg", np.array([
<span style="white-space:pre">	</span>(114, 60, 178, 124),
<span style="white-space:pre">	</span>(120, 60, 184, 124),
<span style="white-space:pre">	</span>(114, 66, 178, 130)])),
<span style="white-space:pre">	</span>("images/gpripe.jpg", np.array([
<span style="white-space:pre">	</span>(12, 30, 76, 94),
<span style="white-space:pre">	</span>(12, 36, 76, 100),
<span style="white-space:pre">	</span>(72, 36, 200, 164),
<span style="white-space:pre">	</span>(84, 48, 212, 176)]))]

# loop over the images
for (imagePath, boundingBoxes) in images:
<span style="white-space:pre">	</span># load the image and clone it
<span style="white-space:pre">	</span>print "[x] %d initial bounding boxes" % (len(boundingBoxes))
<span style="white-space:pre">	</span>image = cv2.imread(imagePath)
<span style="white-space:pre">	</span>orig = image.copy()

<span style="white-space:pre">	</span># loop over the bounding boxes for each image and draw them
<span style="white-space:pre">	</span>for (startX, startY, endX, endY) in boundingBoxes:
<span style="white-space:pre">		</span>cv2.rectangle(orig, (startX, startY), (endX, endY), (0, 0, 255), 2)

<span style="white-space:pre">	</span># perform non-maximum suppression on the bounding boxes
<span style="white-space:pre">	</span>pick = non_max_suppression_slow(boundingBoxes, 0.3)
<span style="white-space:pre">	</span>print "[x] after applying non-maximum, %d bounding boxes" % (len(pick))

<span style="white-space:pre">	</span># loop over the picked bounding boxes and draw them
<span style="white-space:pre">	</span>for (startX, startY, endX, endY) in pick:
<span style="white-space:pre">		</span>cv2.rectangle(image, (startX, startY), (endX, endY), (0, 255, 0), 2)

<span style="white-space:pre">	</span># display the images
<span style="white-space:pre">	</span>cv2.imshow("Original", orig)
<span style="white-space:pre">	</span>cv2.imshow("After NMS", image)
<span style="white-space:pre">	</span>cv2.waitKey(0)