Esa Kataja
3d143ce5a2
- Add whitespace trimming functionality - Add optional PNG optimization with optipng - Add progress tracking and error handling - Improve imports organization - Add proper docstrings and return values
270 lines
8.9 KiB
Python
Executable File
270 lines
8.9 KiB
Python
Executable File
#!/usr/bin/env -S uv run
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import os
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import shutil
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import subprocess
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from pathlib import Path
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from typing import List, Tuple
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import click
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import cv2
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import img2pdf
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import numpy as np
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from pdf2image import convert_from_path
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def ensure_temp_dir():
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"""Ensure temporary directory exists and return its path."""
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temp_dir = "temp_processed_images"
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os.makedirs(temp_dir, exist_ok=True)
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return temp_dir
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def get_temp_files():
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"""Get list of temporary PNG files in order."""
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temp_dir = ensure_temp_dir()
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files = [f for f in os.listdir(temp_dir) if f.endswith('.png')]
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files.sort() # Ensure correct page order
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return [os.path.join(temp_dir, f) for f in files]
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@click.group()
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def cli():
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"""PDF cleaning toolbox for musical scores."""
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pass
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@cli.command()
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@click.argument('input_pdf', type=click.Path(exists=True))
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def extract(input_pdf):
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"""Extract pages from PDF to temporary directory."""
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temp_dir = ensure_temp_dir()
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# Convert PDF to images
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print(f"Extracting pages from {input_pdf}...")
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pages = convert_from_path(input_pdf, dpi=400)
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# Save each page
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for i, page in enumerate(pages):
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output_path = os.path.join(temp_dir, f"page_{i:03d}.png")
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page.save(output_path, "PNG", optimize=False)
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print(f"Saved page {i+1}/{len(pages)}")
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print(f"Extracted {len(pages)} pages to {temp_dir}/")
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@cli.command()
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def deskew():
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"""Deskew all pages in temporary directory."""
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temp_files = get_temp_files()
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if not temp_files:
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print("No pages found in temporary directory. Run 'extract' first.")
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return
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for file_path in temp_files:
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print(f"Deskewing {os.path.basename(file_path)}...")
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# Read image
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image = cv2.imread(file_path)
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# Convert to grayscale
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gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
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# Apply threshold to get binary image
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_, binary = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)
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# Create a rectangular kernel that's wider than it is tall
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# This helps detect horizontal lines
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kernel_length = np.array(binary).shape[1]//80
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horizontal_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (kernel_length, 1))
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# Detect horizontal lines
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horizontal_lines = cv2.erode(binary, horizontal_kernel, iterations=3)
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horizontal_lines = cv2.dilate(horizontal_lines, horizontal_kernel, iterations=3)
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# Use probabilistic Hough transform to detect line segments
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line_segments = cv2.HoughLinesP(
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cv2.bitwise_not(horizontal_lines),
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rho=1,
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theta=np.pi/180,
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threshold=100,
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minLineLength=binary.shape[1]//4, # Lines must be at least 1/4 of image width
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maxLineGap=20
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)
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if line_segments is not None and len(line_segments) > 0:
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# Calculate angles of detected line segments
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angles = []
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for line in line_segments:
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x1, y1, x2, y2 = line[0]
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if x2 - x1 == 0: # Avoid division by zero
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continue
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angle = np.degrees(np.arctan2(y2 - y1, x2 - x1))
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# Only consider angles that are close to horizontal
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if abs(angle) < 20:
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angles.append(angle)
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if angles:
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# Use median angle to avoid outliers
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median_angle = np.median(angles)
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# Only rotate if the angle is significant but not too large
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if 0.5 < abs(median_angle) < 20:
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height, width = image.shape[:2]
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center = (width/2, height/2)
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rotation_matrix = cv2.getRotationMatrix2D(center, median_angle, 1.0)
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rotated = cv2.warpAffine(image, rotation_matrix, (width, height),
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flags=cv2.INTER_CUBIC,
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borderMode=cv2.BORDER_REPLICATE)
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# Save rotated image
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cv2.imwrite(file_path, rotated)
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print(f" Rotated by {median_angle:.2f} degrees")
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else:
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print(" No significant rotation needed")
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else:
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print(" No valid horizontal lines found")
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else:
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print(" No line segments detected")
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def trim_whitespace(image_path: str) -> bool:
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"""Remove white space from around the image.
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Returns:
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bool: True if successful, False otherwise
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"""
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try:
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# Read the image
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img = cv2.imread(image_path)
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if img is None:
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print(f"Warning: Could not read image {image_path}")
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return False
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# Convert to grayscale
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gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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# Threshold the image
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_, thresh = cv2.threshold(gray, 250, 255, cv2.THRESH_BINARY_INV)
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# Find non-zero points
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coords = cv2.findNonZero(thresh)
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if coords is None:
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print(f"Warning: No content found in {image_path}")
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return False
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# Get bounding rectangle
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x, y, w, h = cv2.boundingRect(coords)
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# Add small padding (20 pixels)
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padding = 20
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height, width = img.shape[:2]
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x = max(0, x - padding)
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y = max(0, y - padding)
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w = min(width - x, w + 2 * padding)
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h = min(height - y, h + 2 * padding)
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# Crop the image
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cropped = img[y:y+h, x:x+w]
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# Save the cropped image
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cv2.imwrite(image_path, cropped)
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return True
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except Exception as e:
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print(f"Error processing {image_path}: {str(e)}")
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return False
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def check_optipng_installed() -> bool:
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"""Check if optipng is installed."""
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try:
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result = subprocess.run(['optipng', '-v'], capture_output=True, text=True)
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return result.returncode == 0
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except FileNotFoundError:
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return False
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def run_optipng(file_path: str) -> bool:
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"""Run optipng on a file with error handling.
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Returns:
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bool: True if successful, False otherwise
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"""
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try:
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result = subprocess.run(
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['optipng', '-o7', file_path],
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capture_output=True,
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text=True,
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check=True
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)
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return True
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except subprocess.CalledProcessError as e:
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print(f"Error optimizing {file_path}: {e.stderr}")
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return False
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except Exception as e:
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print(f"Unexpected error optimizing {file_path}: {str(e)}")
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return False
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@cli.command()
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def optimize():
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"""Optimize images by trimming whitespace and optionally running optipng."""
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temp_dir = ensure_temp_dir()
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temp_files = get_temp_files()
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if not temp_files:
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print("No pages found in temporary directory. Run 'extract' first.")
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return
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total_files = len(temp_files)
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successful_trims = 0
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successful_opts = 0
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print(f"Processing {total_files} images...")
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# Trim whitespace
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print("\nTrimming whitespace from images...")
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for i, file_path in enumerate(temp_files, 1):
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print(f"[{i}/{total_files}] Processing {os.path.basename(file_path)}...", end='', flush=True)
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if trim_whitespace(file_path):
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successful_trims += 1
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print(" ")
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else:
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print(" ")
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# Optimize with optipng if available
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if check_optipng_installed():
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print("\nOptimizing PNG files with optipng...")
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for i, file_path in enumerate(temp_files, 1):
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print(f"[{i}/{total_files}] Optimizing {os.path.basename(file_path)}...", end='', flush=True)
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if run_optipng(file_path):
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successful_opts += 1
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print(" ")
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else:
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print(" ")
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else:
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print("\nNote: optipng not found. Skipping PNG optimization.")
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# Print summary
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print("\nOptimization complete!")
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print(f"Successfully trimmed: {successful_trims}/{total_files} images")
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if check_optipng_installed():
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print(f"Successfully optimized: {successful_opts}/{total_files} images")
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@cli.command()
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@click.argument('output_pdf', type=click.Path())
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def finalize(output_pdf):
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"""Combine processed pages into final PDF and clean up."""
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temp_dir = ensure_temp_dir()
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temp_files = get_temp_files()
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if not temp_files:
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print("No pages found in temporary directory. Run 'extract' first.")
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return
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print(f"Combining {len(temp_files)} pages into {output_pdf}...")
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# Convert to PDF
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with open(output_pdf, "wb") as f:
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f.write(img2pdf.convert(temp_files))
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# Clean up temporary files
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print("Cleaning up temporary files...")
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for file_path in temp_files:
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os.remove(file_path)
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os.rmdir(temp_dir)
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print("PDF created successfully and temporary files removed!")
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if __name__ == '__main__':
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cli()
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