Welcome to ROSMASTER M3 repository

Image zoom

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

Code path:

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opencv/opencv_basic/02_OpenCV Transform/01 pixel operation.ipynb

Parameter explanation:

The interpolation method used by the options:

Notice:

\1. The output size format is (width, height)

\2. The default interpolation method is: bilinear interpolation

The main code is as follows:

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# 1 load 2 info 3 resize 4 check
import cv2
import matplotlib.pyplot as plt # Python 2D plotting library
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# Read in the original image
img = cv2.imread('yahboom.jpg')
# Print out the image size
print(img.shape)
# Assign the image height and width to x and y respectively
x, y = img.shape[0:2]
​
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# Scale to half of the original size, output size format is (width, height)
img_test1 = cv2.resize(img, (int(y / 2), int(x / 2)))
# cv2.imshow('resize0', img_test1)
# cv2.waitKey()
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# Nearest neighbor interpolation scaling
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# Scale to one-quarter of its original size
img_test2 = cv2.resize(img, (0, 0), fx=0.25, fy=0.25, interpolation=cv2.INTER_NEAREST)
# cv.imshow('resize1', img_test2)
# cv2.waitKey()
# cv2.destroyAllWindows()
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
dst1 = cv2.cvtColor(img_test1, cv2.COLOR_BGR2RGB)
dst2 = cv2.cvtColor(img_test2, cv2.COLOR_BGR2RGB)
# Display the 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 zoom 1/4 neighbor interpolation zoom
plt.imshow(dst2)
plt.show()

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

Next, let's talk about matplotlib: Python's 2D plotting 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()