Nithilaa

import cv2

img1 = cv2.imread('image1_add.jpg', 1)
#or
#img1 = cv2.imread('C:\\Users\\Admin\\Downloads\\image1_add.jpg', 1)

cv2.imshow('Image 1', img1)
cv2.waitKey(0)
cv2.destroyAllWindows()

Nithilaa

import cv2

img = cv2.imread('flower_image.jpg', 1)
#or
#img = cv2.imread('C:\\Users\\Admin\\Downloads\\flower_image.jpg', 1)

cv2.imshow('Coloured Image', img)
cv2.waitKey(0)
cv2.destroyAllWindows()

Nithilaa

import cv2

img = cv2.imread('flower_image.jpg', cv2.IMREAD_UNCHANGED)
#or
#img = cv2.imread('C:\\Users\\Admin\\Downloads\\flower_image.jpg', cv2.IMREAD_UNCHANGED)

cv2.imshow('Unchanged Image', img)
cv2.waitKey(0)
cv2.destroyAllWindows()

Nithilaa

import cv2

img = cv2.imread('flower_image.jpg', 1)
#or
#img = cv2.imread('C:\\Users\\Admin\\Downloads\\flower_image.jpg', 1)

cv2.imshow('Colour Image', img)
cv2.waitKey(0)
cv2.destroyAllWindows()

Nithilaa

import cv2

img = cv2.imread('flower_image.jpg', 0)
#or
#img = cv2.imread('C:\\Users\\Admin\\Downloads\\flower_image.jpg', 0)

Nithilaa

import cv2

img = cv2.imread('flower_image.jpg', 1)
#or
#img = cv2.imread('C:\\Users\\Admin\\Downloads\\flower_image.jpg', 1)

Nithilaa

# get summary with classical LexRank algorithm
summary = lxr.get_summary(sentences, summary_size=2, threshold=.1)
print(summary)

# get summary with continuous LexRank
summary_cont = lxr.get_summary(sentences, threshold=None)
print(summary_cont)

Nithilaa

#remove stopwords
lxr = LexRank(documents, stopwords=STOPWORDS['en'])

sentences = [
    'One of David Cameron\'s closest friends and Conservative allies, '
    'George Osborne rose rapidly after becoming MP for Tatton in 2001.',

    'Michael Howard promoted him from shadow chief secretary to the '
    'Treasury to shadow chancellor in May 2005, at the age of 34.',

Nithilaa

! wget http://mlg.ucd.ie/files/datasets/bbc-fulltext.zip
! unzip bbc-fulltext.zip

project_path = "/text_summarization/"

documents = []
documents_dir = Path('/content/bbc/entertainment')

for file_path in documents_dir.files('*.txt'):
    with file_path.open(mode='rt', encoding='utf-8') as fp:

Nithilaa

from lexrank import LexRank
from lexrank.mappings.stopwords import STOPWORDS
import pandas as pd
import numpy as np
from path import Path