Read Image using skimage Module

Scikit-image contains image processing algorithms and is available free of cost. It can be accessed at

http://scikit-image.org/

Let’s use skimage module for the read operation and display the image using matplotlib module.

Python Script:

from skimage import data

from skimage.color import colorconv

import matplotlib.pyplot as plt

img = data.imread('poppies.jpg');

plt.imshow(img);

plt.show();

#RGB to Grayscale Image

GrayC = colorconv.rgb2gray(img);

plt.imshow(GrayC,cmap='gray');

cbar = plt.colorbar();

plt.show();

#RGB to GrayScale Image without using the modules

GrayC1 = 0.30*img[:,:,0]+0.59*img[:,:,1]+0.11*img[:,:,2];

plt.imshow(GrayC1,cmap='gray');

cbar = plt.colorbar();

plt.show();

#Display Red, Green and Blue Channels separately

Red = 1*img;

Red[:,:,1:3]=0

#Red[:,:,1]=0;

#Red[:,:,2]=0;

plt.imshow(Red);

plt.show();

Green = 1*img;

Green[:,:,0]=0;

Green[:,:,2]=0;

plt.imshow(Green);

plt.show();

Blue = 1*img;

Blue[:,:,:-1]=0;

plt.imshow(Blue);

plt.show();

EXPLANATION:

GrayC = colorconv.rgb2gray(img);

The grayscale image values are between 0.0 and 1.0. The normalization of the image is done by dividing each pixel values by 255.  

GrayC1 = 0.30*img[:,:,0]+0.59*img[:,:,1]+0.11*img[:,:,2];

img[:,:,0] denotes the 2D array of rows and columns for the red channel

img[:,:,1] denotes the green channel of 2D array

img[:,:,2] denotes the blue channel of 2D array

Red = 1*img;

The variable ‘Red’ is assigned with the image ‘img’ which has RGB components.

Red[:,:,1:3]=0 indicates that all the rows and columns in the multidimensional array and the Green and blue Channels are assigned with zeros. ‘1:3’ indicates that 1^st and the 2^nd indices excluding the 3^rd index.

Assigning Operation in Python

Python Script:

A = [ 1,2,3,4,5]

B = A

C = 1*A

print('Values in A before modification:',A);

print('Values in B before modification:',B);

print('Values in C before modification:',C);

#Assign Value 10 to the 4^th position in the list(Remember the positioning starts from zero)

A[3] = 10

print('Values in A after modification:',A);

print('Values in B after modification:',B);

print('Values in C after modification:',C);

#Print address of the variables

print('Address of A:',hex(id(A)));

print('Address of B:',hex(id(B)));

print('Address of C:',hex(id(C)));

Output:

Values in A before modification: [1, 2, 3, 4, 5]

Values in B before modification: [1, 2, 3, 4, 5]

Values in C before modification: [1, 2, 3, 4, 5]

Values in A after modification: [1, 2, 3, 10, 5]

Values in B after modification: [1, 2, 3, 10, 5]

Values in C after modification: [1, 2, 3, 4, 5]

Address of A: 0xb454f08

Address of B: 0xb454f08

Address of C: 0xb454f88

EXPLANATION:

The memory address of A is assigned to B. So any changes undergone by B will be automatically reflected in A. In C, a small mathematical operation is performed that forces the variable to have different memory address which is unaffected. The addresses of the variables A and B are same while C has different address.