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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 resize_long_edge_cv2(image, target_size=384): | |
| height, width = image.shape[:2] | |
| aspect_ratio = float(width) / float(height) | |
| if height > width: | |
| new_height = target_size | |
| new_width = int(target_size * aspect_ratio) | |
| else: | |
| new_width = target_size | |
| new_height = int(target_size / aspect_ratio) | |
| resized_image = cv2.resize(image, (new_width, new_height), interpolation=cv2.INTER_AREA) | |
| 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 | |