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import cv2
import numpy as np
from pathlib import Path
from PIL import Image
import pillow_avif
import logging
from config import NORMALIZE_IMAGES_TO, JPEG_QUALITY
logger = logging.getLogger(__name__)
def normalize_image(input_path: Path, output_path: Path) -> bool:
"""Convert image to normalized format (PNG or JPEG) and optionally remove black bars
Args:
input_path: Source image path
output_path: Target path
Returns:
bool: True if successful, False otherwise
"""
try:
# Open image with PIL
with Image.open(input_path) as img:
# Convert to RGB if needed
if img.mode in ('RGBA', 'LA'):
background = Image.new('RGB', img.size, (255, 255, 255))
if img.mode == 'RGBA':
background.paste(img, mask=img.split()[3])
else:
background.paste(img, mask=img.split()[1])
img = background
elif img.mode != 'RGB':
img = img.convert('RGB')
# Convert to numpy for black bar detection
img_np = np.array(img)
# Detect black bars
top, bottom, left, right = detect_black_bars(img_np)
# Crop if black bars detected
if any([top > 0, bottom < img_np.shape[0] - 1,
left > 0, right < img_np.shape[1] - 1]):
img = img.crop((left, top, right, bottom))
# Save as configured format
if NORMALIZE_IMAGES_TO == 'png':
img.save(output_path, 'PNG', optimize=True)
else: # jpg
img.save(output_path, 'JPEG', quality=JPEG_QUALITY, optimize=True)
return True
except Exception as e:
logger.error(f"Error converting image {input_path}: {str(e)}")
return False
def detect_black_bars(img: np.ndarray) -> tuple[int, int, int, int]:
"""Detect black bars in image
Args:
img: numpy array of image (HxWxC)
Returns:
Tuple of (top, bottom, left, right) crop coordinates
"""
# Convert to grayscale if needed
if len(img.shape) == 3:
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
else:
gray = img
# Threshold to detect black regions
threshold = 20
black_mask = gray < threshold
# Find black bars by analyzing row/column means
row_means = np.mean(black_mask, axis=1)
col_means = np.mean(black_mask, axis=0)
# Detect edges where black bars end (95% threshold)
black_threshold = 0.95
# Find top and bottom crops
top = 0
bottom = img.shape[0]
for i, mean in enumerate(row_means):
if mean > black_threshold:
top = i + 1
else:
break
for i, mean in enumerate(reversed(row_means)):
if mean > black_threshold:
bottom = img.shape[0] - i - 1
else:
break
# Find left and right crops
left = 0
right = img.shape[1]
for i, mean in enumerate(col_means):
if mean > black_threshold:
left = i + 1
else:
break
for i, mean in enumerate(reversed(col_means)):
if mean > black_threshold:
right = img.shape[1] - i - 1
else:
break
return top, bottom, left, right
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