# importing(bringing-in) the essential libraries

# An Open-source library for Computer Vision and image processings, hence the name openCV.
import cv2

# To show the image/graphs etc.
import matplotlib.pyplot as plt

# Numpy helps us perform matrix (rows and columns of number) operation on images. Since, images are rows and columns of numbers.
import numpy as np

#The output of plotting commands is displayed inline within frontends like the Jupyter notebook.
#%matplotlib inline #uncomment %matplotlib inline while using jupyter.

# Reading in an image
img = cv2.imread('images.jpg')

# Always make a copy while operating on your image, so that your original image doesn't get affected.
img1 = np.copy(img)

#openCV reads/brings the image in BGR format (blue channel first, then green and finally red).
#If you plot/show your image using plt.imshow('img1'), it will appear weired.
# Finally plot/show the image
plt.title('weired looking image in BGR format')
plt.imshow(img1)

# Reading in an image
img = cv2.imread('images.jpg')

# Always make a copy while operating on your image, so that your original image doesn't get affected.
img1 = np.copy(img)

#openCV reads/brings the image in BGR format (blue channel first, then green and finally red).
#If you plot/show your image using plt.imshow('img1'), it will appear weired.
# Finally plot/show the image
plt.title('weired looking image in BGR format')
plt.imshow(img1)

#So you need to bring it to RGB format.By using cv2.cvtColor(img,cv2.COLOR_BGR2RGB).
img_bgr =np.copy(img)
img_rgb=cv2.cvtColor(img,cv2.COLOR_BGR2RGB)

plt.title('origional image in RGB format')
plt.imshow(img_rgb)