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.

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
        self.buffer.append(frame)
    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')
    average_buffer.apply(frame_f32)
    weighted_buffer.apply(frame_f32)
    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'):
        break
# When everything done, release the capture
cap.release()
cv2.destroyAllWindows()

Learn Python

Learn Python A BEGINNERS GUIDE TO PYTHON

• 70 pages to get you started on your journey to master Python.
• How to install your setup with Anaconda.
• Written description and introduction to all concepts.
• Jupyter Notebooks prepared for 17 projects.

Python 101: A CRASH COURSE

1. How to get started with this 8 hours Python 101: A CRASH COURSE.
2. Best practices for learning Python.
3. How to download the material to follow along and create projects.
4. A chapter for each lesson with a description, code snippets for easy reference, and links to a lesson video.

Expert Data Science Blueprint

Expert Data Science Blueprint

• Master the Data Science Workflow for actionable data insights.
• How to download the material to follow along and create projects.
• A chapter to each lesson with a Description, Learning Objective, and link to the lesson video.

Machine Learning

Machine Learning – The Simple Path to Mastery

• How to get started with Machine Learning.
• How to download the material to follow along and make the projects.
• One chapter for each lesson with a Description, Learning Objectives, and link to the lesson video.