Python 3 OpenCV Script to Process MP4 Video Images and Perform Smoothing, Edge Detection & Bitwise Operations

Python 3 OpenCV Script to Process MP4 Video Images and Perform Smoothing, Edge Detection & Bitwise Operations

# importing the necessary libraries
import cv2
import numpy as np

# Creating a VideoCapture object to read the video
cap = cv2.VideoCapture('sample.mp4')


# Loop until the end of the video
while (cap.isOpened()):

# Capture frame-by-frame
ret, frame = cap.read()
frame = cv2.resize(frame, (540, 380), fx = 0, fy = 0,
interpolation = cv2.INTER_CUBIC)

# Display the resulting frame
cv2.imshow('Frame', frame)

# conversion of BGR to grayscale is necessary to apply this operation
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

# adaptive thresholding to use different threshold
# values on different regions of the frame.
Thresh = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C,
cv2.THRESH_BINARY_INV, 11, 2)

cv2.imshow('Thresh', Thresh)
# define q as the exit button
if cv2.waitKey(25) & 0xFF == ord('q'):
break

# release the video capture object
cap.release()
# Closes all the windows currently opened.
cv2.destroyAllWindows()

# importing the necessary libraries
import cv2
import numpy as np

# Creating a VideoCapture object to read the video
cap = cv2.VideoCapture('sample.mp4')


# Loop until the end of the video
while (cap.isOpened()):
# Capture frame-by-frame
ret, frame = cap.read()
frame = cv2.resize(frame, (540, 380), fx = 0, fy = 0,
interpolation = cv2.INTER_CUBIC)

# Display the resulting frame
cv2.imshow('Frame', frame)

# using cv2.Gaussianblur() method to blur the video

# (5, 5) is the kernel size for blurring.
gaussianblur = cv2.GaussianBlur(frame, (5, 5), 0)
cv2.imshow('gblur', gaussianblur)

# define q as the exit button
if cv2.waitKey(25) & 0xFF == ord('q'):
break

# release the video capture object
cap.release()

# Closes all the windows currently opened.
cv2.destroyAllWindows()

# importing the necessary libraries
import cv2
import numpy as np

# Creating a VideoCapture object to read the video
cap = cv2.VideoCapture('sample.mp4')


# Loop until the end of the video
while (cap.isOpened()):
# Capture frame-by-frame
ret, frame = cap.read()

frame = cv2.resize(frame, (540, 380), fx = 0, fy = 0,
interpolation = cv2.INTER_CUBIC)

# Display the resulting frame
cv2.imshow('Frame', frame)

# using cv2.Canny() for edge detection.
edge_detect = cv2.Canny(frame, 100, 200)
cv2.imshow('Edge detect', edge_detect)

# define q as the exit button
if cv2.waitKey(25) & 0xFF == ord('q'):
break

# release the video capture object
cap.release()
# Closes all the windows currently opened.
cv2.destroyAllWindows()

# importing the necessary libraries
import cv2
import numpy as np

# Creating a VideoCapture object to read the video
cap = cv2.VideoCapture('sample.mp4')


# Loop until the end of the video
while (cap.isOpened()):
# Capture frame-by-frame
ret, frame = cap.read()
frame = cv2.resize(frame, (540, 380), fx = 0, fy = 0,
interpolation = cv2.INTER_CUBIC)

# Display the resulting frame
cv2.imshow('Frame', frame)

# conversion of BGR to grayscale is necessary to apply this operation
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

_, mask = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)

# apply NOT operation on image and mask generated by thresholding
BIT = cv2.bitwise_not(frame, frame, mask = mask)
cv2.imshow('BIT', BIT)

# define q as the exit button
if cv2.waitKey(25) & 0xFF == ord('q'):
break

# release the video capture object
cap.release()

# Closes all the windows currently opened.
cv2.destroyAllWindows()