The basic operations performed on the image are:

• Convert image to NumPy Array
• Convert/transform OpenCV read image to RGB from BRG channel
• Reading image as a gray image
• Resize Image
• Resize Image using Ratio
• Flip Images
• Save Image

import numpy as np
import matplotlib.pyplot as plt

import cv2

# Image Read
img = cv2.imread('signature.jpeg') # automatically converted to numpy array
type(img)  # should be: numpy.ndarray, not NoneType
img.shape

# Both opencv and matplotlib read image channels differently
# matplotlib -> RGB = Red Green Blue
# opencv -> BGR = Blue Green Red 

plt.imshow(img)

# Convert/tranform opencv read image to RGB from BRG channel
fixed_img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) 
plt.imshow(fixed_img)

# Reading image as a gray image
gray_img = cv2.imread('signature.jpeg', cv2.IMREAD_GRAYSCALE)
type(gray_img)
gray_img.shape
plt.imshow(gray_img, cmap='gray')

# Resize Image
resize_img = cv2.resize(fixed_img, (600, 250)) # width x height
plt.imshow(resize_img)

# Resize Image using Ratio
w_ratio=0.5 # 50% Width
h_ratio=0.5 # 50% height
ratio_img = cv2.resize(fixed_img, (0,0), fixed_img, w_ratio, h_ratio)
plt.imshow(ratio_img)

# Flip Images
flip_img = cv2.flip(fixed_img,1) # can be 0,1,-1
plt.imshow(flip_img)

#Save Image
cv2.imwrite('flip_signature.jpeg', fixed_img)