Winter Bootcamp in ML and IoT in Jaipur
 Course content (For Bootcamp & Winter Training):- Machine Learning (ML) || Internet of Things (IoT) || Register for winter bootcamp
0 like 0 dislike
in Examples, Exercises and Projects by (18.8k points)

1 Answer

0 like 0 dislike
by (18.8k points)
 
Best answer

Scaling (Resizing) Images - Cubic, Area, Linear Interpolations
Interpolation is a method of estimating values between known data points 

# Import Computer Vision package - cv2
import cv2

# Import Numerical Python package - numpy as np
import numpy as np

# Read the image using imread built-in function
image = cv2.imread('image_2.jpg')

# Display original image using imshow built-in function
cv2.imshow("Original", image)

# Wait until any key is pressed
cv2.waitKey()

# cv2.resize(image, output image size, x scale, y scale, interpolation)

# Scaling using cubic interpolation


scaling_cubic = cv2.resize(image, None, fx=.75, fy=.75, interpolation = cv2.INTER_CUBIC)

# Display cubic interpolated image
cv2.imshow('Cubic Interpolated', scaling_cubic)

# Wait until any key is pressed
cv2.waitKey()

# Scaling using area interpolation


scaling_skewed = cv2.resize(image, (600, 300), interpolation = cv2.INTER_AREA)

# Display area interpolated image
cv2.imshow('Area Interpolated', scaling_skewed) 

# Wait until any key is pressed
cv2.waitKey()

# Scaling using linear interpolation


scaling_linear  = cv2.resize(image, None, fx=0.5, fy=0.5, interpolation = cv2.INTER_LINEAR)

# Display linear interpolated image
cv2.imshow('Linear Interpolated', scaling_linear) 

# Wait until any key is pressed
cv2.waitKey()

# Close all windows
cv2.destroyAllWindows()

Winter 10 Days bootcamp classes(7 HRS Daily) will start from 5 & 20 December 2019 in:
1) Internet of things(IoT) Using RASPBERRY-PI
2) Machine Learning (ML)

70% OFF| Fee-INR 3,000/-

Limited seats!! Hurry up!!

[[ CALL - 07976731765 ]]

Some Study Resources are compiled from original Stack Overflow Documentation, the content is developed by the different experts at Stack Overflow. Study resources are released under Creative Commons BY-SA. Images may be copyright of their respective owners. This website is for self-learning and not affiliated with Stack Overflow. All trademarks and registered trademarks are the property of their respective company owners. Please send feedback and corrections to chandwaglobal@gmail.com.

...