import cv2

OPENCV_MAJOR_VERSION = int(cv2.__version__.split('.')[0])

BLUR_RADIUS = 21
erode_kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
dilate_kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (9, 9))

cap = cv2.VideoCapture(0)

# Capture several frames to allow the camera's autoexposure to adjust.
for i in range(10):
    success, frame = cap.read()
if not success:
    exit(1)

gray_background = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
gray_background = cv2.GaussianBlur(gray_background,
                                   (BLUR_RADIUS, BLUR_RADIUS), 0)

success, frame = cap.read()
while success:

    gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    gray_frame = cv2.GaussianBlur(gray_frame,
                                  (BLUR_RADIUS, BLUR_RADIUS), 0)

    diff = cv2.absdiff(gray_background, gray_frame)
    _, thresh = cv2.threshold(diff, 40, 255, cv2.THRESH_BINARY)
    cv2.erode(thresh, erode_kernel, thresh, iterations=2)
    cv2.dilate(thresh, dilate_kernel, thresh, iterations=2)

    if OPENCV_MAJOR_VERSION >= 4:
        # OpenCV 4 or a later version is being used.
        contours, hier = cv2.findContours(thresh, cv2.RETR_EXTERNAL,
                                          cv2.CHAIN_APPROX_SIMPLE)
    else:
        # OpenCV 3 or an earlier version is being used.
        # cv2.findContours has an extra return value.
        # The extra return value is the thresholded image, which is
        # unchanged, so we can ignore it.
        _, contours, hier = cv2.findContours(thresh, cv2.RETR_EXTERNAL,
                                             cv2.CHAIN_APPROX_SIMPLE)

    for c in contours:
        if cv2.contourArea(c) > 4000:
            x, y, w, h = cv2.boundingRect(c)
            cv2.rectangle(frame, (x, y), (x+w, y+h), (255, 255, 0), 2)

    cv2.imshow('diff', diff)
    cv2.imshow('thresh', thresh)
    cv2.imshow('detection', frame)

    k = cv2.waitKey(1)
    if k == 27:  # Escape
        break

    success, frame = cap.read()