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from PIL import Image, ImageDraw, ImageFont
import cv2
import os
import textwrap
import nltk
nltk.download('punkt', quiet=True)
nltk.download('averaged_perceptron_tagger', quiet=True)
from nltk.tokenize import word_tokenize
from nltk import pos_tag
def read_image_width_height(image_path):
image = Image.open(image_path)
width, height = image.size
return width, height
def resize_long_edge(image, target_size=384):
# Calculate the aspect ratio
width, height = image.size
aspect_ratio = float(width) / float(height)
# Determine the new dimensions
if width > height:
new_width = target_size
new_height = int(target_size / aspect_ratio)
else:
new_width = int(target_size * aspect_ratio)
new_height = target_size
# Resize the image
resized_image = image.resize((new_width, new_height), Image.ANTIALIAS)
return resized_image
def display_images_and_text(source_image_path, generated_image, generated_paragraph, outfile_name):
source_image = Image.open(source_image_path)
# Create a new image that can fit the images and the text
width = source_image.width + generated_image.width
height = max(source_image.height, generated_image.height)
new_image = Image.new("RGB", (width, height + 150), "white")
# Paste the source image and the generated image onto the new image
new_image.paste(source_image, (0, 0))
new_image.paste(generated_image, (source_image.width, 0))
# Write the generated paragraph onto the new image
draw = ImageDraw.Draw(new_image)
# font_size = 12
# font = ImageFont.load_default().font_variant(size=font_size)
font_path = os.path.join(cv2.__path__[0],'qt','fonts','DejaVuSans.ttf')
font = ImageFont.truetype(font_path, size=14)
# Wrap the text for better display
wrapped_text = textwrap.wrap(generated_paragraph, width=170)
# Draw each line of wrapped text
line_spacing = 18
y_offset = 0
for line in wrapped_text:
draw.text((0, height + y_offset), line, font=font, fill="black")
y_offset += line_spacing
# Show the final image
# new_image.show()
new_image.save(outfile_name)
return 1
def extract_nouns_nltk(paragraph):
words = word_tokenize(paragraph)
pos_tags = pos_tag(words)
nouns = [word for word, tag in pos_tags if tag in ('NN', 'NNS', 'NNP', 'NNPS')]
return nouns
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