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ct3huang/ObjectTracker.py

Forked from Thimira/ObjectTracker.py
Created February 21, 2020 07:56
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  1. @Thimira Thimira revised this gist Apr 19, 2018. No changes.
  2. @Thimira Thimira created this gist Feb 27, 2018.
    83 changes: 83 additions & 0 deletions ObjectTracker.py
    View file
    Original file line number Diff line number Diff line change
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    '''
    Using Correlation Trackers in Dlib, you can track any object in a video stream without needing to train a custom object detector.
    Check out the tutorial at: http://www.codesofinterest.com/2018/02/track-any-object-in-video-with-dlib.html
    '''
    import numpy as np
    import cv2
    import dlib

    # this variable will hold the coordinates of the mouse click events.
    mousePoints = []

    def mouseEventHandler(event, x, y, flags, param):
    # references to the global mousePoints variable
    global mousePoints

    # if the left mouse button was clicked, record the starting coordinates.
    if event == cv2.EVENT_LBUTTONDOWN:
    mousePoints = [(x, y)]

    # when the left mouse button is released, record the ending coordinates.
    elif event == cv2.EVENT_LBUTTONUP:
    mousePoints.append((x, y))

    # create the video capture.
    video_capture = cv2.VideoCapture(0)

    # create a named window in OpenCV and attach the mouse event handler to it.
    cv2.namedWindow("Webcam stream")
    cv2.setMouseCallback("Webcam stream", mouseEventHandler)

    # initialize the correlation tracker.
    tracker = dlib.correlation_tracker()

    # this is the variable indicating whether to track the object or not.
    tracked = False

    while True:
    # start capturing the video stream.
    ret, frame = video_capture.read()

    if ret:
    image = frame

    # if we have two sets of coordinates from the mouse event, draw a rectangle.
    if len(mousePoints) == 2:
    cv2.rectangle(image, mousePoints[0], mousePoints[1], (0, 255, 0), 2)
    dlib_rect = dlib.rectangle(mousePoints[0][0], mousePoints[0][1], mousePoints[1][0], mousePoints[1][1])

    # tracking in progress, update the correlation tracker and get the object position.
    if tracked == True:
    tracker.update(image)
    track_rect = tracker.get_position()
    x = int(track_rect.left())
    y = int(track_rect.top())
    x1 = int(track_rect.right())
    y1 = int(track_rect.bottom())
    cv2.rectangle(image, (x, y), (x1, y1), (0, 0, 255), 2)

    # show the current frame.
    cv2.imshow("Webcam stream", image)

    # capture the keyboard event in the OpenCV window.
    ch = 0xFF & cv2.waitKey(1)

    # press "r" to stop tracking and reset the points.
    if ch == ord("r"):
    mousePoints = []
    tracked = False

    # press "t" to start tracking the currently selected object/area.
    if ch == ord("t"):
    if len(mousePoints) == 2:
    tracker.start_track(image, dlib_rect)
    tracked = True
    mousePoints = []

    # press "q" to quit the program.
    if ch == ord('q'):
    break

    # cleanup.
    video_capture.release()
    cv2.destroyAllWindows()