opencv 15 smoothing | blurring

adaptive threshold

blur (2d convolution)

2d convolution kernel

gaussian blur removes high frequency noise

gaussian convolution kernel

median blur removes salt and pepper noise

bilateral blur preserves edges

#main.py

import numpy as np

import cv2

cap = cv2.VideoCapture("assets/Santa Barbara.mp4")

while True:

    ret, frame = cap.read()

    width = int(cap.get(3))

    height = int(cap.get(4))

    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

    #adaptive threshold (frame, max pixel value, adaptive method, threshold type, neighbour block size, c constant)

    th1 = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 11, 2)

    #th2 = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 11, 2)

    kernel = np.ones((5, 5), np.float32)/25

    #conv = cv2.filter2D(th1, -1, kernel)

    blur = cv2.blur(th1, (5,5))

    #gaussianBlur (frame, kernel, sigma)

    #designed to remove high frequency noise

    gblur = cv2.GaussianBlur(th1, (5, 5), 0)

    #median method designed to remove salt and pepper noise

    median = cv2.medianBlur(th1, 5)

    #bilateralFilter preserve edges

    bilateralFilter = cv2.bilateralFilter(th1, 9, 75, 75)

    cv2.imshow('frame', frame)

    cv2.imshow('threshold', th1)

    cv2.imshow('convolution', blur)

    cv2.imshow('gaussian', gblur)

    cv2.imshow('median', median)

    cv2.imshow('bilater', bilateralFilter)

    if cv2.waitKey(1) == ord('q'):

        break

    if cv2.waitKey(1) == ord('p'):

        # wait until any key is pressed

        cv2.waitKey(-1)

cap.release()

cv2.destroyAllWindows()

reference:

https://www.youtube.com/watch?v=u3poUhCxx4k&list=PLS1QulWo1RIa7D1O6skqDQ-JZ1GGHKK-K&index=20