1. Image resizing

In OpenCV, the function that implements image scaling is: cv2.resize(InputArray src, OutputArray dst, Size, fx, fy, interpolation)

The code is running on jupyter lab

Parameter explanation:

Interpolation method used by the interpolation option:

Notice:

1. The output size format is (width, height)
2. The default interpolation method is: bilinear interpolation

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# 1 load 2 info 3 resize 4 check

import cv2

import matplotlib.pyplot as plt # Python's 2D drawing library

 

# Read in the original image

img = cv2.imread('yahboom.jpg')

#Print the image size

print(img.shape)

#Assign the height and width of the image to x and y respectively

x, y = img.shape[0:2]

 

# Show original image

#cv.imshow('OriginalPicture', img)

 

# Scale to one-half of the original size, and the output size format is (width, height)

img_test1 = cv2.resize(img, (int(y / 2), int(x / 2)))

# cv2.imshow('resize0', img_test1)

#cv2.waitKey()

 

# Nearest neighbor interpolation scaling

# Zoom to a quarter of the original size

img_test2 = cv2.resize(img, (0, 0), fx=0.25, fy=0.25, interpolation=cv2.INTER_NEAREST)

# cv.imshow('resize1', img_test2)

#cv.waitKey()

#cv.destroyAllWindows()

img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

dst1 = cv2.cvtColor(img_test1, cv2.COLOR_BGR2RGB)

dst2 = cv2.cvtColor(img_test2, cv2.COLOR_BGR2RGB)

 

# show original image

plt.imshow(img)

plt.show()

After execution, you can see that the image is 800*800

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# Display zoom 1/2

plt.imshow(dst1)

plt.show()

After execution, you can see that the image is 400*400, scaled by half.

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#Display scaling 1/4 neighbor interpolation scaling

plt.imshow(dst2)

plt.show()

After execution, you can see that the image is 200*200, scaled by a quarter.

Next let’s talk about matplotlib: Python’s 2D drawing library.

Reference tutorial: https://www.runoob.com/numpy/numpy-matplotlib.html

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import numpy as np

from matplotlib import pyplot as plt

x = np.arange(1,11)

y=2*x+5

plt.title("Matplotlib demo")

plt.xlabel("x axis caption")

plt.ylabel("y axis caption")

plt.plot(x,y)

plt.show()