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madhawav avatar madhawav commented on August 28, 2024

Hi,
You can filter unwanted labels in results by post-processing results like below.

            exclusions = ["cat"]
            results = net.detect(dark_frame)
            results = [r for r in results if r[0] not in exclusions]

The labels in exclusions list will be removed from results.

Regarding object tracking, You can try out the trackers available with OpenCV.
This tutorial should be a good starting point - https://www.learnopencv.com/object-tracking-using-opencv-cpp-python/.

PS- You will need to have opencv installed with contrib modules in order to use trackers.

from yolo3-4-py.

sushant1727 avatar sushant1727 commented on August 28, 2024

You're awesome thanks for all the suggestions.

Is it possible to label two same cats when detected like cat[1], cat [2]?

from yolo3-4-py.

madhawav avatar madhawav commented on August 28, 2024

@sushant1727 That would require tracking of cats. You can get the idea from following tutorial.
https://www.learnopencv.com/object-tracking-using-opencv-cpp-python/

from yolo3-4-py.

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