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6.Image zoom

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

Code path：/home/yahboom/Dofbot/4.opencv/02_OpenCV Transform/01 pixel operation.ipynb

Parameter explanation：

Interpolation method used by options:

Notice:

1.Output size format is (width, height)

2. The default interpolation method is: bilinear interpolation

The main code is as follows:

​x
# 1   load 2 info 3 resize 4 check
import   cv2
import   matplotlib.pyplot as plt  # Python’s 2D drawing library

# Read original image
img   = cv2.imread('yahboom.jpg')
# Print out image size
print(img.shape)
# Assign the height and width of the image to x and y respectively
x,   y = img.shape[0:2]


# 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 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 the execution is completed, you can see that the image is 800*800

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# Display zoom 1/2
plt.imshow(dst1)
plt.show()

After the execution is completed, 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 the execution is completed, you can see that the image is 200*200, zoomed 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()