Import Libraries



In [1]:

    
import cv2



In [2]:

    
import matplotlib.pyplot as plt
%matplotlib inline

Read image and Check size and resolution of image



In [3]:

    
img = cv2.imread('demo1.jpg')



In [4]:

    
plt.imshow(img)









    Out[4]:





<matplotlib.image.AxesImage at 0x7f796cd4bc18>

OpenCV represents RGB images as multi-dimensional NumPy arrays…but in reverse order! This means that images are actually represented in BGR order rather than RGB!



In [5]:

    
print(img.shape)









    



(1080, 1920, 3)



In [6]:

    
print('Filesize:', img.size/1024/8, 'KB')









    



Filesize: 759.375 KB



In [7]:

    
print('Type:', img.dtype)









    



Type: uint8

To GrayScale



In [8]:

    
img_gray = cv2.imread('demo1.jpg', cv2.IMREAD_GRAYSCALE)



In [9]:

    
plt.imshow(img_gray, cmap='gray')









    Out[9]:





<matplotlib.image.AxesImage at 0x7f796cc3bdd8>



In [10]:

    
print('Shape_gray:', img_gray.shape)









    



Shape_gray: (1080, 1920)



In [11]:

    
print('File_size_gray:', img_gray.size/1024/8, 'KB')









    



File_size_gray: 253.125 KB



In [12]:

    
print('Type_gray:', img_gray.dtype)









    



Type_gray: uint8

Since in gray scale only one color is there, so no depth and therefore img_gray.shape will have only 2 paprameters in it.

To RGB color



In [13]:

    
img_RGB = cv2.imread('demo1.jpg')



In [14]:

    
plt.imshow(cv2.cvtColor(img_RGB, cv2.COLOR_BGR2RGB))









    Out[14]:





<matplotlib.image.AxesImage at 0x7f796cbe0358>



In [15]:

    
print('Shape_RGB:', img_RGB.shape)









    



Shape_RGB: (1080, 1920, 3)



In [16]:

    
print('File_size_RGB:', img_RGB.size/1024/8, 'KB')









    



File_size_RGB: 759.375 KB



In [17]:

    
print('Type_RGB:', img_RGB.dtype)









    



Type_RGB: uint8