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2025-10-14T05:53:31.5387050Z C:\GHA-OCV-1\_work\ci-gha-workflow\ci-gha-workflow\opencv\modules\imgcodecs\src\bitstrm.cpp(156,57): warning C4244: 'argument': conversion from 'int64_t' to 'ptrdiff_t', possible loss of data [C:\GHA-OCV-1\_work\ci-gha-workflow\ci-gha-workflow\build\modules\imgcodecs\opencv_imgcodecs.vcxproj] ### Pull Request Readiness Checklist Optional Known Foreground Mask for Background Subtractors #27810 See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request - [x] I agree to contribute to the project under Apache 2 License. - [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV - [x] The PR is proposed to the proper branch - [x] There is a reference to the original bug report and related work - [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable Patch to opencv_extra has the same branch name. - [x] The feature is well documented and sample code can be built with the project CMake ### Description This adds an optional foreground input mask parameter to the MOG2 and KNN background subtractors, in line with issue https://github.com/opencv/opencv/issues/26476 4 tests are added under test_bgfg2.cpp: 2 for each subtractor type (1 with shadow detection and 1 without) A demo shows the feature with only 3 parameters and with a 4th optional foreground mask for both core subtractor types. Note: To patch contrib inheritance of the background subtraction class, empty apply method which throws a not implemented error is added to contrib subclasses. This is done to keep the overloaded apply function as pure virtual. Contrib PR to be made and linked shortly. Contrib Repo Paired Pull Request: https://github.com/opencv/opencv_contrib/pull/4017
69 lines
2.1 KiB
Python
69 lines
2.1 KiB
Python
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'''
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Showcases the use of background subtraction from a live video feed,
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aswell as pass through of a known foreground parameter
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'''
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# Python 2/3 compatibility
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from __future__ import print_function
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import numpy as np
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import cv2 as cv
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def main():
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cap = cv.VideoCapture(0)
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if not cap.isOpened:
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print("Capture source avaialable.")
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exit()
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# Create background subtractor
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mog2_bg_subtractor = cv.createBackgroundSubtractorMOG2(history=300, varThreshold=50, detectShadows=False)
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knn_bg_subtractor = cv.createBackgroundSubtractorKNN(history=300, detectShadows=False)
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frame_count = 0
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# Allows for a frame buffer for the mask to learn pre known foreground
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show_count = 10
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while True:
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ret, frame = cap.read()
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if not ret:
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break
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x = 100 + (frame_count % 10) * 3
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frame = cv.resize(frame, (640, 480))
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aKnownForegroundMask = np.zeros(frame.shape[:2], dtype=np.uint8)
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# Allow for models to "settle"/learn
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if frame_count > show_count:
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cv.rectangle(aKnownForegroundMask, (x,200), (x+50,300), 255, -1)
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cv.rectangle(aKnownForegroundMask, (540,180), (640,480), 255, -1)
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#MOG2 Subtraction
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mog2_with_mask = mog2_bg_subtractor.apply(frame,knownForegroundMask=aKnownForegroundMask)
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mog2_without_mask = mog2_bg_subtractor.apply(frame)
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#KNN Subtraction
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knn_with_mask = knn_bg_subtractor.apply(frame,knownForegroundMask=aKnownForegroundMask)
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knn_without_mask = knn_bg_subtractor.apply(frame)
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# Display the 3 parameter apply and the 4 parameter apply for both subtractors
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cv.imshow("MOG2 With a Foreground Mask", mog2_with_mask)
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cv.imshow("MOG2 Without a Foreground Mask", mog2_without_mask)
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cv.imshow("KNN With a Foreground Mask", knn_with_mask)
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cv.imshow("KNN Without a Foreground Mask", knn_without_mask)
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key = cv.waitKey(30)
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if key == 27: # ESC
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break
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frame_count += 1
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cap.release()
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cv.destroyAllWindows()
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if __name__ == '__main__':
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print(__doc__)
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main()
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cv.destroyAllWindows()
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