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=256): # 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