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Hi all, the top_K parameter in the cv2.dnn.NMSBoxes correspond to the maximum number of bounding-boxes to return right ? Like if we know how many object we expect in the image.

If that's the case, I have an unexpected output when setting top_k to a value >0.
With the default value (<0), I have a sensible set of bounding-boxes (on the left below).
However with the same parameters, except setting top_k to an actual value, here 4, I get only the 2 top detections and not the top 4. Why is that ?

The bounding boxes with index 4, 100, 72 and 17 are indeed the one I want, so I can use top_k<0 and select the top_4 afterwards but I though setting top_K in the NMS could stop the NMS earlier and thus save time.

I am using
- opencv-python-headless 4.3.0.36 (installed via pip)
- python 3.7.4
on a win10 machine

Thanks !

Hi all, the top_K parameter in the cv2.dnn.NMSBoxes correspond to the maximum number of bounding-boxes to return right ? Like if we know how many object we expect in the image.

If that's the case, I have an unexpected output when setting top_k to a value >0.
With the default value (<0), I have a sensible set of bounding-boxes (on the left below).
However with the same parameters, except setting top_k to an actual value, here 4, I get only the 2 top detections and not the top 4. Why is that ?

The bounding boxes with index 4, 100, 72 and 17 are indeed the one I want, so I can use top_k<0 and select the top_4 afterwards but I though setting top_K in the NMS could stop the NMS earlier and thus save time.

I am using
- opencv-python-headless 4.3.0.36 (installed via pip)
- python 3.7.4
on a win10 machine

Thanks !

import cv2
listBoxes = [
 (946, 784, 414, 400),
 (1525, 968, 414, 400),
 (1533, 960, 414, 400),
 (947, 784, 400, 414),
 (1173, 1354, 414, 400),
 (1459, 474, 400, 414),
 (1021, 888, 414, 400),
 (1450, 492, 400, 414),
 (1398, 889, 414, 400),
 (1005, 872, 400, 414),
 (686, 1367, 414, 400),
 (697, 1371, 414, 400),
 (694, 1371, 414, 400),
 (1128, 1377, 414, 400),
 (950, 787, 414, 400),
 (1438, 481, 414, 400),
 (1064, 1238, 414, 400),
 (1455, 485, 414, 400),
 (111, 787, 414, 400),
 (109, 782, 414, 400)]

listScores = [
 1.0,
 0.5919371843338013,
 0.5894666314125061,
 0.5689446926116943,
 0.5510676503181458,
 0.5381054282188416,
 0.5311822891235352,
 0.5298448204994202,
 0.5123124122619629,
 0.511840283870697,
 0.5080571174621582,
 0.5080069303512573,
 0.5079731941223145,
 0.5000045895576477,
 0.49151238799095154,
 0.4728872776031494,
 0.4612887501716614,
 0.4540329873561859,
 0.4483684003353119,
 0.44806933403015137]

'''
# Cast boxes to float (optional)
convertBox = lambda box : list(map(float, box)) 
listBoxes = list(map(convertBox, listBoxes))
print (listBoxes)
'''

indexes1 = cv2.dnn.NMSBoxes(listBoxes, listScores, score_threshold=0.4, nms_threshold=0.3, top_k=5)
print(indexes1)

print("-------")

indexes2 = cv2.dnn.NMSBoxes(listBoxes, listScores, score_threshold=0.4, nms_threshold=0.3)
print(indexes2)