Learn how you can become a Python programmer in just 12 weeks.

    We respect your privacy. Unsubscribe at anytime.

    Average vs Weighted Average Effect in Video using OpenCV

    What will we cover in this tutorial?

    Compare the difference of using weighted average and normal average over the last frames streaming from your webcam using OpenCV in Python.

    The effect can be seen in the video below and code used to create that is provided below.

    Example output Normal Average vs Weighted Average vs One Frame

    The code

    The code is straight forward and not optimized. The average is calculated by using a deque from the collection library from Python to create a circular buffer.

    The two classes of AverageBuffer and WeightedAverageBuffer share the same code for the constructor and apply, but have each their implementation of get_frame which calculates the average and weighted average, respectively.

    Please notice, that the code is not written for efficiency and the AverageBuffer has some easy wins in performance if calculated more efficiently.

    An important point to see here, is that the frames are saved as float32 in the buffers. This is necessary when we do the actual calculations on the frames later, where we multiply them by a factor, say 4.

    Example. The frames are uint8, which are integers 0 to 255. Say we multiply the frame by 4, and the value is 128. This will give 128*4 = 512, which as an uint8 is 0. Hence, we get an undesirable effect. Therefore we convert them to float32 to avoid this.

    import cv2
    import numpy as np
    from collections import deque
    class AverageBuffer:
        def __init__(self, maxlen):
            self.buffer = deque(maxlen=maxlen)
            self.shape = None
        def apply(self, frame):
            self.shape = frame.shape
        def get_frame(self):
            mean_frame = np.zeros(self.shape, dtype='float32')
            for item in self.buffer:
                mean_frame += item
            mean_frame /= len(self.buffer)
            return mean_frame.astype('uint8')
    class WeightedAverageBuffer(AverageBuffer):
        def get_frame(self):
            mean_frame = np.zeros(self.shape, dtype='float32')
            i = 0
            for item in self.buffer:
                i += 4
                mean_frame += item*i
            mean_frame /= (i*(i + 1))/8.0
            return mean_frame.astype('uint8')
    # Setup camera
    cap = cv2.VideoCapture(0)
    # Set a smaller resolution
    cap.set(cv2.CAP_PROP_FRAME_WIDTH, 320)
    cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 240)
    average_buffer = AverageBuffer(30)
    weighted_buffer = WeightedAverageBuffer(30)
    while True:
        # Capture frame-by-frame
        _, frame = cap.read()
        frame = cv2.flip(frame, 1)
        frame = cv2.resize(frame, (320, 240))
        frame_f32 = frame.astype('float32')
        cv2.imshow('WebCam', frame)
        cv2.imshow("Average", average_buffer.get_frame())
        cv2.imshow("Weighted average", weighted_buffer.get_frame())
        if cv2.waitKey(1) == ord('q'):
    # When everything done, release the capture

    Python for Finance: Unlock Financial Freedom and Build Your Dream Life

    Discover the key to financial freedom and secure your dream life with Python for Finance!

    Say goodbye to financial anxiety and embrace a future filled with confidence and success. If you’re tired of struggling to pay bills and longing for a life of leisure, it’s time to take action.

    Imagine breaking free from that dead-end job and opening doors to endless opportunities. With Python for Finance, you can acquire the invaluable skill of financial analysis that will revolutionize your life.

    Make informed investment decisions, unlock the secrets of business financial performance, and maximize your money like never before. Gain the knowledge sought after by companies worldwide and become an indispensable asset in today’s competitive market.

    Don’t let your dreams slip away. Master Python for Finance and pave your way to a profitable and fulfilling career. Start building the future you deserve today!

    Python for Finance a 21 hours course that teaches investing with Python.

    Learn pandas, NumPy, Matplotlib for Financial Analysis & learn how to Automate Value Investing.

    “Excellent course for anyone trying to learn coding and investing.” – Lorenzo B.

    Leave a Comment