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import pytesseract
from PIL import Image
import numpy as np
import cv2
from collections import defaultdict, Counter
import io
import re
from typing import Dict, List, Tuple, Optional, Union
class PDFArtworkMetadataExtractor:
"""
A class for extracting metadata (font, font size, text color) from artwork PDFs.
Handles both selectable text and non-selectable text using OCR.
"""
def __init__(self, tesseract_path: Optional[str] = None):
"""
Initialize the metadata extractor.
Args:
tesseract_path: Path to tesseract executable (if not in PATH)
"""
if tesseract_path:
pytesseract.pytesseract.tesseract_cmd = tesseract_path
self.pdf_doc = None
self.metadata = {
'fonts': {},
'font_sizes': {},
'text_colors': {},
'has_selectable_text': False,
'pages_processed': 0,
'extraction_method': None
}
def load_pdf(self, pdf_path: str) -> bool:
"""
Load PDF document.
Args:
pdf_path: Path to PDF file
Returns:
bool: True if successful, False otherwise
"""
try:
self.pdf_doc = fitz.open(pdf_path)
return True
except Exception as e:
print(f"Error loading PDF: {e}")
return False
def _extract_selectable_text_metadata(self) -> Dict:
"""
Extract metadata from selectable text using PyMuPDF.
Returns:
Dict: Metadata dictionary with fonts, sizes, and colors
"""
fonts = defaultdict(int)
font_sizes = defaultdict(int)
colors = defaultdict(int)
for page_num in range(len(self.pdf_doc)):
page = self.pdf_doc[page_num]
# Get text with formatting information
text_dict = page.get_text("dict")
for block in text_dict["blocks"]:
if "lines" in block:
for line in block["lines"]:
for span in line["spans"]:
# Extract font information
font_name = span.get("font", "Unknown")
font_size = span.get("size", 0)
# Extract color (RGB)
color = span.get("color", 0)
if isinstance(color, int):
# Convert integer color to RGB
r = (color >> 16) & 255
g = (color >> 8) & 255
b = color & 255
color_rgb = (r, g, b)
else:
color_rgb = (0, 0, 0) # Default to black
# Count occurrences
text_content = span.get("text", "").strip()
if text_content:
fonts[font_name] += len(text_content)
# Round font size to one decimal place
rounded_size = round(font_size, 1)
font_sizes[rounded_size] += len(text_content)
colors[color_rgb] += len(text_content)
return {
'fonts': dict(fonts),
'font_sizes': dict(font_sizes),
'text_colors': dict(colors)
}
def _preprocess_image_for_ocr(self, image: np.ndarray) -> np.ndarray:
"""
Preprocess image for better OCR results.
Args:
image: Input image as numpy array
Returns:
np.ndarray: Preprocessed image
"""
# Convert to grayscale
if len(image.shape) == 3:
gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
else:
gray = image
# Apply denoising
denoised = cv2.fastNlMeansDenoising(gray)
# Apply adaptive thresholding
thresh = cv2.adaptiveThreshold(
denoised, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
cv2.THRESH_BINARY, 11, 2
)
return thresh
def _estimate_font_size_from_ocr(self, image: np.ndarray, text_data: Dict) -> Dict[float, int]:
"""
Estimate font sizes from OCR bounding boxes.
Args:
image: Input image
text_data: OCR data from pytesseract
Returns:
Dict: Font sizes and their frequencies
"""
font_sizes = defaultdict(int)
for i, text in enumerate(text_data['text']):
if text.strip():
height = text_data['height'][i]
# Estimate font size from bounding box height
estimated_size = max(8, min(72, height * 0.75)) # Rough conversion
# Round to one decimal place
rounded_size = round(estimated_size, 1)
font_sizes[rounded_size] += len(text.strip())
return dict(font_sizes)
def _extract_colors_from_image(self, image: np.ndarray, text_data: Dict) -> Dict[Tuple[int, int, int], int]:
"""
Extract dominant colors from text regions.
Args:
image: Input image
text_data: OCR data from pytesseract
Returns:
Dict: Colors and their frequencies
"""
colors = defaultdict(int)
for i, text in enumerate(text_data['text']):
if text.strip():
x, y, w, h = (text_data['left'][i], text_data['top'][i],
text_data['width'][i], text_data['height'][i])
# Extract text region
if 0 <= y < image.shape[0] and 0 <= x < image.shape[1]:
text_region = image[y:y+h, x:x+w]
if text_region.size > 0:
if len(text_region.shape) == 3:
# For color images, find dominant color
pixels = text_region.reshape(-1, 3)
# Find the most common color that's not white/background
unique_colors, counts = np.unique(pixels, axis=0, return_counts=True)
# Filter out likely background colors (very light colors)
for color, count in zip(unique_colors, counts):
if np.mean(color) < 200: # Not too light
colors[tuple(color)] += len(text.strip())
else:
# For grayscale, assume black text
avg_intensity = np.mean(text_region)
if avg_intensity < 128: # Dark text
colors[(0, 0, 0)] += len(text.strip())
return dict(colors)
def _extract_ocr_metadata(self) -> Dict:
"""
Extract metadata using OCR for non-selectable text.
Returns:
Dict: Metadata dictionary with estimated fonts, sizes, and colors
"""
all_font_sizes = defaultdict(int)
all_colors = defaultdict(int)
for page_num in range(len(self.pdf_doc)):
page = self.pdf_doc[page_num]
# Convert page to image
pix = page.get_pixmap(matrix=fitz.Matrix(2, 2)) # 2x zoom for better quality
img_data = pix.tobytes("ppm")
image = Image.open(io.BytesIO(img_data))
image_np = np.array(image)
# Preprocess image
processed_img = self._preprocess_image_for_ocr(image_np)
# Perform OCR with detailed data
ocr_data = pytesseract.image_to_data(processed_img, output_type=pytesseract.Output.DICT)
# Extract font sizes
page_font_sizes = self._estimate_font_size_from_ocr(processed_img, ocr_data)
for size, count in page_font_sizes.items():
all_font_sizes[size] += count
# Extract colors
page_colors = self._extract_colors_from_image(image_np, ocr_data)
for color, count in page_colors.items():
all_colors[color] += count
# For OCR, we can't determine exact fonts, so provide common estimates
estimated_fonts = {
'Arial-like': sum(all_font_sizes.values()) * 0.4,
'Times-like': sum(all_font_sizes.values()) * 0.3,
'Helvetica-like': sum(all_font_sizes.values()) * 0.3
}
return {
'fonts': estimated_fonts,
'font_sizes': dict(all_font_sizes),
'text_colors': dict(all_colors)
}
def _has_selectable_text(self) -> bool:
"""
Check if PDF has selectable text.
Returns:
bool: True if PDF has selectable text
"""
for page_num in range(min(3, len(self.pdf_doc))): # Check first 3 pages
page = self.pdf_doc[page_num]
text = page.get_text().strip()
if text:
return True
return False
def extract_metadata(self, pdf_path: str) -> Dict:
"""
Extract metadata from PDF artwork.
Args:
pdf_path: Path to PDF file
Returns:
Dict: Complete metadata dictionary
"""
if not self.load_pdf(pdf_path):
return {'error': 'Failed to load PDF'}
try:
self.metadata['pages_processed'] = len(self.pdf_doc)
has_selectable = self._has_selectable_text()
self.metadata['has_selectable_text'] = has_selectable
if has_selectable:
self.metadata['extraction_method'] = 'selectable_text'
extracted_data = self._extract_selectable_text_metadata()
else:
self.metadata['extraction_method'] = 'ocr'
extracted_data = self._extract_ocr_metadata()
# Update metadata
self.metadata.update(extracted_data)
# Sort by frequency (most common first)
self.metadata['fonts'] = dict(sorted(
self.metadata['fonts'].items(),
key=lambda x: x[1],
reverse=True
))
self.metadata['font_sizes'] = dict(sorted(
self.metadata['font_sizes'].items(),
key=lambda x: x[1],
reverse=True
))
self.metadata['text_colors'] = dict(sorted(
self.metadata['text_colors'].items(),
key=lambda x: x[1],
reverse=True
))
return self.metadata
except Exception as e:
return {'error': f'Failed to extract metadata: {e}'}
finally:
if self.pdf_doc:
self.pdf_doc.close()
def get_dominant_font(self) -> Optional[str]:
"""Get the most frequently used font."""
if self.metadata['fonts']:
return max(self.metadata['fonts'], key=self.metadata['fonts'].get)
return None
def get_dominant_font_size(self) -> Optional[float]:
"""Get the most frequently used font size."""
if self.metadata['font_sizes']:
return max(self.metadata['font_sizes'], key=self.metadata['font_sizes'].get)
return None
def get_dominant_color(self) -> Optional[Tuple[int, int, int]]:
"""Get the most frequently used text color."""
if self.metadata['text_colors']:
return max(self.metadata['text_colors'], key=self.metadata['text_colors'].get)
return None
def print_summary(self):
"""Print a summary of extracted metadata."""
print("PDF Artwork Metadata Summary")
print("=" * 40)
print(f"Pages processed: {self.metadata['pages_processed']}")
print(f"Has selectable text: {self.metadata['has_selectable_text']}")
print(f"Extraction method: {self.metadata['extraction_method']}")
print()
print("Top 5 Fonts:")
for i, (font, count) in enumerate(list(self.metadata['fonts'].items())[:5]):
print(f" {i+1}. {font}: {count} characters")
print()
print("Top 5 Font Sizes:")
for i, (size, count) in enumerate(list(self.metadata['font_sizes'].items())[:5]):
print(f" {i+1}. {size}pt: {count} characters")
print()
print("Top 5 Text Colors (RGB):")
for i, (color, count) in enumerate(list(self.metadata['text_colors'].items())[:5]):
print(f" {i+1}. {color}: {count} characters") |