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gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
cv2.imshow('zebra',gray)
cv2.waitKey(0)
cv2.destroyAllWindows()

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cv2.imwrite('zebra.png',img) 

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plt.figure(figsize = (30,6))
plt.imshow(img)

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cv2.imshow('zebra',img)
cv2.waitKey(0)
cv2.destroyAllWindows()

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img = cv2.imread('Zebra.jpg') 
plt.figure(figsize = (10,6))
plt.imshow(img)

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!pip install opencv-python

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dataset=[11,10,12,14,12,15,14,13,15,102,12,14,17,19,107,10,13,12,14,12,108,12,11,14,13,15,10,15,12,10,14,13,15,10]

dataset=sorted(dataset)
q1, q3= np.percentile(dataset,[25,75])
iqr = q3 - q1
lower_bound = q1 -(1.5 * iqr) 
upper_bound = q3 +(1.5 * iqr) 
print('lower_bound={},upper_bound={}'.format(lower_bound,upper_bound))

outliers_pt=[]

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import numpy as np
outliers=[]
dataset=[11,10,12,14,12,15,14,13,15,102,12,14,17,19,107,10,13,12,14,12,108,12,11,14,13,15,10,15,12,10,14,13,15,10]

def detect_outliers(data):
  threshold=3
	mean=np.mean(data)
	std=np.std(data)
	
	for i in dataset:

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import matplotlib.pyplot as plt
data =[20,25,27,75,40,67,62,75,78,71,32,82,127,140,78,67,132,82,87,66,56,52]         
plt.boxplot(data,vert=False)
plt.show()

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import matplotlib.pyplot as plt
x = [5,7,8,10,3,17,4,9,7,9,8,9,6]
y = [40,36,47,48,120,46,67,48,31,134,50,35,56]
plt.scatter(x, y)
plt.show()