wehub-resource-sync
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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