import cv2

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)

# Define an initial tracking window in the center of the frame.
frame_h, frame_w = frame.shape[:2]
w = frame_w//8
h = frame_h//8
x = frame_w//2 - w//2
y = frame_h//2 - h//2
track_window = (x, y, w, h)

# Calculate the normalized HSV histogram of the initial window.
roi = frame[y:y+h, x:x+w]
hsv_roi =  cv2.cvtColor(roi, cv2.COLOR_BGR2HSV)
mask = None
roi_hist = cv2.calcHist([hsv_roi], [0], mask, [180], [0, 180])
cv2.normalize(roi_hist, roi_hist, 0, 255, cv2.NORM_MINMAX)

# Define the termination criteria:
# 10 iterations or convergence within 1-pixel radius.
term_crit = (cv2.TERM_CRITERIA_COUNT | cv2.TERM_CRITERIA_EPS, 10, 1)

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

    # Perform back-projection of the HSV histogram onto the frame.
    hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
    back_proj = cv2.calcBackProject([hsv], [0], roi_hist, [0, 180], 1)

    # Perform tracking with MeanShift.
    num_iters, track_window = cv2.meanShift(
        back_proj, track_window, term_crit)

    # Draw the tracking window.
    x, y, w, h = track_window
    cv2.rectangle(frame, (x, y), (x+w, y+h), (255, 0, 0), 2)

    cv2.imshow('back-projection', back_proj)
    cv2.imshow('meanshift', frame)

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

    success, frame = cap.read()