Python

Real time Drone object tracking using Python and OpenCV

After flying this past weekend (together with Gabriel and Leandro) with Gabriel’s drone (which is an handmade APM 2.6 based quadcopter) in our town (Porto Alegre, Brasil), I decided to implement a tracking for objects using OpenCV and Python and check how the results would be using simple and fast methods like Meanshift. The result was very impressive and I believe that there is plenty of room for optimization, but the algorithm is now able to run in real time using Python with good results and with a Full HD resolution of 1920×1080 and 30 fps.

Here is the video of the flight that was piloted by Gabriel:

See it in Full HD for more details.

The algorithm can be described as follows and it is very simple (less than 50 lines of Python) and straightforward:

  • A ROI (Region of Interest) is defined, in this case the building that I want to track
  • The normalized histogram and back-projection are calculated
  • The Meanshift algorithm is used to track the ROI

The entire code for the tracking is described below:

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import numpy as np
import cv2
def run_main():
cap = cv2.VideoCapture('upabove.mp4')
# Read the first frame of the video
ret, frame = cap.read()
# Set the ROI (Region of Interest). Actually, this is a
# rectangle of the building that we're tracking
c,r,w,h = 900,650,70,70
track_window = (c,r,w,h)
# Create mask and normalized histogram
roi = frame[r:r+h, c:c+w]
hsv_roi = cv2.cvtColor(roi, cv2.COLOR_BGR2HSV)
mask = cv2.inRange(hsv_roi, np.array((0., 30.,32.)), np.array((180.,255.,255.)))
roi_hist = cv2.calcHist([hsv_roi], [0], mask, [180], [0, 180])
cv2.normalize(roi_hist, roi_hist, 0, 255, cv2.NORM_MINMAX)
term_crit = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 80, 1)
while True:
ret, frame = cap.read()
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
dst = cv2.calcBackProject([hsv], [0], roi_hist, [0,180], 1)
ret, track_window = cv2.meanShift(dst, track_window, term_crit)
x,y,w,h = track_window
cv2.rectangle(frame, (x,y), (x+w,y+h), 255, 2)
cv2.putText(frame, 'Tracked', (x-25,y-10), cv2.FONT_HERSHEY_SIMPLEX,
1, (255,255,255), 2, cv2.CV_AA)
cv2.imshow('Tracking', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
if __name__ == "__main__":
run_main()
import numpy as np import cv2 def run_main(): cap = cv2.VideoCapture('upabove.mp4') # Read the first frame of the video ret, frame = cap.read() # Set the ROI (Region of Interest). Actually, this is a # rectangle of the building that we're tracking c,r,w,h = 900,650,70,70 track_window = (c,r,w,h) # Create mask and normalized histogram roi = frame[r:r+h, c:c+w] hsv_roi = cv2.cvtColor(roi, cv2.COLOR_BGR2HSV) mask = cv2.inRange(hsv_roi, np.array((0., 30.,32.)), np.array((180.,255.,255.))) roi_hist = cv2.calcHist([hsv_roi], [0], mask, [180], [0, 180]) cv2.normalize(roi_hist, roi_hist, 0, 255, cv2.NORM_MINMAX) term_crit = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 80, 1) while True: ret, frame = cap.read() hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV) dst = cv2.calcBackProject([hsv], [0], roi_hist, [0,180], 1) ret, track_window = cv2.meanShift(dst, track_window, term_crit) x,y,w,h = track_window cv2.rectangle(frame, (x,y), (x+w,y+h), 255, 2) cv2.putText(frame, 'Tracked', (x-25,y-10), cv2.FONT_HERSHEY_SIMPLEX, 1, (255,255,255), 2, cv2.CV_AA) cv2.imshow('Tracking', frame) if cv2.waitKey(1) & 0xFF == ord('q'): break cap.release() cv2.destroyAllWindows() if __name__ == "__main__": run_main()
import numpy as np
import cv2

def run_main():
    cap = cv2.VideoCapture('upabove.mp4')

    # Read the first frame of the video
    ret, frame = cap.read()

    # Set the ROI (Region of Interest). Actually, this is a
    # rectangle of the building that we're tracking
    c,r,w,h = 900,650,70,70
    track_window = (c,r,w,h)

    # Create mask and normalized histogram
    roi = frame[r:r+h, c:c+w]
    hsv_roi = cv2.cvtColor(roi, cv2.COLOR_BGR2HSV)
    mask = cv2.inRange(hsv_roi, np.array((0., 30.,32.)), np.array((180.,255.,255.)))
    roi_hist = cv2.calcHist([hsv_roi], [0], mask, [180], [0, 180])
    cv2.normalize(roi_hist, roi_hist, 0, 255, cv2.NORM_MINMAX)
    term_crit = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 80, 1)
    
    while True:
        ret, frame = cap.read()

        hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
        dst = cv2.calcBackProject([hsv], [0], roi_hist, [0,180], 1)

        ret, track_window = cv2.meanShift(dst, track_window, term_crit)

        x,y,w,h = track_window
        cv2.rectangle(frame, (x,y), (x+w,y+h), 255, 2)
        cv2.putText(frame, 'Tracked', (x-25,y-10), cv2.FONT_HERSHEY_SIMPLEX,
            1, (255,255,255), 2, cv2.CV_AA)
        
        cv2.imshow('Tracking', frame)

        if cv2.waitKey(1) & 0xFF == ord('q'):
            break

    cap.release()
    cv2.destroyAllWindows()

if __name__ == "__main__":
    run_main()

I hope you liked it !

by Christian S. Perone