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Suppose, we will get an image in Jupyter notebook with the help of the following code:

import cv2 Output
from matplotlib import pyplot as plt

import numpy as np
image = cv2.imread(‘F:\Softwares\Setups\
DataScience\Deepa\img1.jpg’)
plt.imshow(cv2.cvtColor(image,cv2.
COLOR_BGR2RGB))
plt.title(‘Summer Camp 2021’)
plt.axis(‘on’)
plt.show()

Now, we want to copy a specific part of an image i.e. Dance Callout box. To do this, we can write the following
code:

import cv2 Output
from matplotlib import pyplot as plt
import numpy as np
image = cv2.imread(‘F:\Softwares\Setups\
DataScience\Deepa\img1.jpg’)
extract_part = image[150:300,50:260]
plt.imshow(cv2.cvtColor(extract_part,
cv2.COLOR_BGR2RGB))
plt.axis(‘on’)
plt.show()
Now , we want to insert the extracted part in the image at a particular location. To do this, we can write the
following code:

import cv2 Output
from matplotlib import pyplot as plt
import numpy as np
image = cv2.imread(‘F:\Softwares\Setups\
DataScience\Deepa\img1.jpg’)
extract_part = image[150:300,50:260]
image[50:200,0:210] = extract_part
plt.imshow(cv2.cvtColor(image, cv2.COLOR_
BGR2RGB))
plt.axis(‘on’)
plt.show()

Resizing an Image

Resizing an image is an important part of image processing technique because on resizing an image, its pixel
information is changed. For example, when an image is reduced in size, any unneeded pixel information will be
discarded by the photo editor like Photoshop. When an image is enlarged, the photo editor that you use must
create and add new pixel information on the basis of its best guesses to achieve a larger size which typically
results in either a very pixelated or very soft and blurry looking image. In case of AI systems, Resizing images is
important especially when we want to train a model having the same size and aspect ratio.

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