feat(phase1): implement OpenCV image processing pipeline
- Create ImageProcessor service with EXIF orientation detection - Implement smart cropping via OpenCV contour detection (10% padding) - Add text orientation detection using Hough line transform - Resize and compress images to 1200px with 85% JPEG quality - Generate 200px square thumbnails with center crop - Fallback to Pillow if OpenCV fails - Comprehensive test suite: 28 tests all passing - File size validation (reject >10MB) - Graceful error handling for corrupted/invalid images - Update requirements.txt with opencv-python, piexif, python-magic
This commit is contained in:
@@ -17,3 +17,6 @@ pytest>=8.0.0
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pytest-asyncio>=0.23.0
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pytest-cov>=4.1.0
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httpx>=0.27.0
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opencv-python>=4.8.0
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piexif>=1.1.3
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python-magic>=0.4.27
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447
backend/services/image_processing.py
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447
backend/services/image_processing.py
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@@ -0,0 +1,447 @@
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"""
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OpenCV-based image processing pipeline for smart photo handling.
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Handles:
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- EXIF orientation detection and auto-rotation
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- Smart cropping using OpenCV contour detection
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- Text orientation detection using Hough lines
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- Resize and compression to 1200px
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- Thumbnail generation (200px square)
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- Fallback to Pillow for basic processing if OpenCV fails
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"""
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import io
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import logging
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from typing import Dict, Optional, Tuple
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from PIL import Image
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from PIL.ExifTags import TAGS
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import piexif
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import cv2
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import numpy as np
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logger = logging.getLogger(__name__)
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class ImageProcessor:
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"""Service for processing uploaded images with smart features."""
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MAX_FILE_SIZE = 10 * 1024 * 1024 # 10MB
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LONG_SIDE = 1200
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THUMBNAIL_SIZE = 200
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JPEG_QUALITY = 85
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def __init__(self):
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"""Initialize the image processor."""
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self.logger = logger
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def process_photo(
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self, file_bytes: bytes, crop_bounds: Optional[Dict] = None
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) -> Dict:
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"""
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Process a photo with EXIF rotation, smart cropping, and compression.
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Args:
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file_bytes: Raw image file bytes
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crop_bounds: Optional manual crop bounds {x, y, width, height}
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Returns:
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{
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'status': 'success' | 'error',
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'cropped_image_bytes': bytes or None,
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'thumbnail_bytes': bytes or None,
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'original_size': (width, height),
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'crop_size': (width, height) or None,
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'text_angle': float or None,
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'metadata': {
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'exif_orientation': int,
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'crop_method': 'manual' | 'opencv' | 'pillow' | 'none',
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'file_size_bytes': int
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}
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}
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"""
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try:
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# Validate file size
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if len(file_bytes) > self.MAX_FILE_SIZE:
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return {
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'status': 'error',
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'error': f'File too large: {len(file_bytes)} > {self.MAX_FILE_SIZE}',
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'cropped_image_bytes': None,
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'thumbnail_bytes': None,
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}
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# Open image with PIL
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image = Image.open(io.BytesIO(file_bytes))
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original_size = image.size
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# Extract and apply EXIF orientation
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exif_orientation = self._extract_exif_orientation(image)
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if exif_orientation and exif_orientation > 1:
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image = self._rotate_by_orientation(image, exif_orientation)
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self.logger.info(f"Applied EXIF rotation: {exif_orientation}")
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# Smart cropping
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cropped_image = image
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crop_size = None
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text_angle = None
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crop_method = 'none'
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if crop_bounds:
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# Manual crop bounds provided
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cropped_image = image.crop(
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(
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crop_bounds['x'],
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crop_bounds['y'],
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crop_bounds['x'] + crop_bounds['width'],
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crop_bounds['y'] + crop_bounds['height'],
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)
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)
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crop_size = cropped_image.size
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crop_method = 'manual'
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self.logger.info(f"Applied manual crop: {crop_size}")
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else:
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# Try OpenCV smart crop
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try:
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crop_result = self._smart_crop_opencv(image)
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if crop_result is not None:
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cropped_image, crop_size = crop_result
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crop_method = 'opencv'
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self.logger.info(f"Applied OpenCV crop: {crop_size}")
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# Detect text orientation within the cropped region
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text_angle, angle_status = self._detect_text_orientation(
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cropped_image
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)
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if text_angle is not None:
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self.logger.info(
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f"Detected text angle: {text_angle}° ({angle_status})"
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)
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if angle_status in ['upside_down', 'sideways']:
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cropped_image = self._rotate_image(
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cropped_image, text_angle
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)
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else:
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crop_method = 'pillow'
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except Exception as e:
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# Fallback to Pillow if OpenCV fails
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self.logger.warning(
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f"OpenCV crop failed, falling back to Pillow: {e}"
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)
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crop_method = 'pillow'
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# Resize and compress
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compressed_bytes = self._resize_and_compress(cropped_image)
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# Generate thumbnail
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thumbnail_bytes = self._generate_thumbnail(image)
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return {
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'status': 'success',
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'cropped_image_bytes': compressed_bytes,
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'thumbnail_bytes': thumbnail_bytes,
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'original_size': original_size,
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'crop_size': crop_size,
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'text_angle': text_angle,
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'metadata': {
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'exif_orientation': exif_orientation or 1,
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'crop_method': crop_method,
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'file_size_bytes': len(file_bytes),
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},
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}
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except Exception as e:
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self.logger.error(f"Image processing failed: {e}")
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return {
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'status': 'error',
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'error': str(e),
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'cropped_image_bytes': None,
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'thumbnail_bytes': None,
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}
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def _extract_exif_orientation(self, image: Image.Image) -> Optional[int]:
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"""
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Extract EXIF orientation tag from image.
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Returns:
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Orientation value (1-8) or None if not present
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"""
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try:
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# Try piexif first
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if hasattr(image, 'info') and 'exif' in image.info:
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exif_dict = piexif.load(image.info['exif'])
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orientation = exif_dict['0th'].get(piexif.ImageIFD.Orientation)
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if orientation:
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return orientation
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# Fallback to PIL EXIF
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exif_data = image._getexif()
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if exif_data:
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for tag_id, value in exif_data.items():
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tag = TAGS.get(tag_id, tag_id)
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if tag == 'Orientation':
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return value
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return None
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except Exception as e:
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self.logger.debug(f"Could not extract EXIF orientation: {e}")
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return None
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def _rotate_by_orientation(
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self, image: Image.Image, orientation: int
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) -> Image.Image:
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"""
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Rotate image based on EXIF orientation tag.
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Args:
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image: PIL Image
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orientation: EXIF orientation value (1-8)
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Returns:
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Rotated PIL Image
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"""
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if orientation == 1:
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return image
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elif orientation == 2:
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return image.transpose(Image.FLIP_LEFT_RIGHT)
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elif orientation == 3:
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return image.transpose(Image.ROTATE_180)
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elif orientation == 4:
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return image.transpose(Image.FLIP_TOP_BOTTOM)
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elif orientation == 5:
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return image.transpose(Image.TRANSPOSE)
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elif orientation == 6:
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return image.transpose(Image.ROTATE_270)
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elif orientation == 7:
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return image.transpose(Image.TRANSVERSE)
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elif orientation == 8:
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return image.transpose(Image.ROTATE_90)
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return image
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def _smart_crop_opencv(
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self, image: Image.Image
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) -> Optional[Tuple[Image.Image, Tuple[int, int]]]:
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"""
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Use OpenCV to detect and crop the main object in the image.
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Args:
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image: PIL Image
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Returns:
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Tuple of (cropped PIL Image, crop size) or None if no contours found
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"""
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try:
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# Convert PIL image to OpenCV format
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cv_image = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
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gray = cv2.cvtColor(cv_image, cv2.COLOR_BGR2GRAY)
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# Edge detection
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edges = cv2.Canny(gray, 100, 200)
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# Find contours
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contours, _ = cv2.findContours(
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edges, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE
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)
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if not contours:
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self.logger.debug("No contours found in image")
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return None
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# Get bounding box of largest contour
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largest_contour = max(contours, key=cv2.contourArea)
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x, y, w, h = cv2.boundingRect(largest_contour)
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# Apply 10% padding around bounds
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padding = 0.1
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pad_x = int(w * padding)
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pad_y = int(h * padding)
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x1 = max(0, x - pad_x)
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y1 = max(0, y - pad_y)
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x2 = min(cv_image.shape[1], x + w + pad_x)
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y2 = min(cv_image.shape[0], y + h + pad_y)
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# Crop image
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cropped = image.crop((x1, y1, x2, y2))
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crop_size = cropped.size
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self.logger.debug(
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f"OpenCV crop bounds: ({x1}, {y1}, {x2}, {y2}), size: {crop_size}"
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)
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return cropped, crop_size
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except Exception as e:
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self.logger.warning(f"OpenCV smart crop failed: {e}")
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return None
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def _detect_text_orientation(
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self, image: Image.Image
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) -> Tuple[Optional[float], str]:
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"""
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Detect text orientation using Hough line transform.
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Args:
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image: PIL Image
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Returns:
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Tuple of (angle in degrees, status string)
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status: 'normal', 'upside_down', 'sideways', 'not_detected'
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"""
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try:
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# Convert to OpenCV format
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cv_image = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
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gray = cv2.cvtColor(cv_image, cv2.COLOR_BGR2GRAY)
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# Edge detection
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edges = cv2.Canny(gray, 50, 150)
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# Hough line detection
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lines = cv2.HoughLines(edges, 1, np.pi / 180, 100)
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if lines is None or len(lines) == 0:
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self.logger.debug("No lines detected for text orientation")
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return None, 'not_detected'
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# Extract angles from lines
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angles = []
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for line in lines:
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rho, theta = line[0]
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angle = np.degrees(theta)
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angles.append(angle)
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# Normalize angles to 0-180 range
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angles = np.array(angles)
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angles = np.where(angles > 90, angles - 180, angles)
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# Find dominant angle
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mean_angle = np.mean(angles)
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# Determine orientation status
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status = 'normal'
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corrected_angle = mean_angle
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# Check for upside-down text (~180°)
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if abs(mean_angle) > 80:
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status = 'upside_down'
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corrected_angle = mean_angle + 180 if mean_angle > 0 else mean_angle - 180
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# Check for sideways text (~90°)
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elif abs(mean_angle) > 45:
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status = 'sideways'
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self.logger.debug(
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f"Text orientation: angle={mean_angle:.1f}°, status={status}"
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)
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return corrected_angle, status
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except Exception as e:
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self.logger.warning(f"Text orientation detection failed: {e}")
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return None, 'not_detected'
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def _rotate_image(self, image: Image.Image, angle: float) -> Image.Image:
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"""
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Rotate image by specified angle.
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Args:
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image: PIL Image
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angle: Rotation angle in degrees
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Returns:
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Rotated PIL Image
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"""
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return image.rotate(angle, expand=False, fillcolor='white')
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def _resize_and_compress(self, image: Image.Image) -> bytes:
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"""
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Resize image to 1200px on long side and compress to JPEG.
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Args:
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image: PIL Image
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Returns:
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Compressed JPEG bytes
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"""
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# Get current size
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width, height = image.size
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max_dim = max(width, height)
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# Only resize if necessary
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if max_dim > self.LONG_SIDE:
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scale = self.LONG_SIDE / max_dim
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new_width = int(width * scale)
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new_height = int(height * scale)
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image = image.resize((new_width, new_height), Image.Resampling.LANCZOS)
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self.logger.debug(
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f"Resized from {(width, height)} to {(new_width, new_height)}"
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)
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# Convert to RGB if necessary (for JPEG)
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if image.mode in ('RGBA', 'LA', 'P'):
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rgb_image = Image.new('RGB', image.size, (255, 255, 255))
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rgb_image.paste(image, mask=image.split()[-1] if image.mode == 'RGBA' else None)
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image = rgb_image
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# Compress to JPEG
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output = io.BytesIO()
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image.save(output, format='JPEG', quality=self.JPEG_QUALITY, optimize=True)
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compressed_bytes = output.getvalue()
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self.logger.debug(
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f"Compressed to JPEG: {len(compressed_bytes)} bytes, "
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f"quality={self.JPEG_QUALITY}"
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)
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return compressed_bytes
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def _generate_thumbnail(self, image: Image.Image) -> bytes:
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"""
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Generate 200px square thumbnail with center crop.
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Args:
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image: PIL Image
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Returns:
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Thumbnail JPEG bytes
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"""
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try:
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# Get current size
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width, height = image.size
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# Center crop to square
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min_dim = min(width, height)
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left = (width - min_dim) // 2
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top = (height - min_dim) // 2
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right = left + min_dim
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bottom = top + min_dim
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square = image.crop((left, top, right, bottom))
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# Resize to thumbnail size
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thumbnail = square.resize(
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(self.THUMBNAIL_SIZE, self.THUMBNAIL_SIZE),
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Image.Resampling.LANCZOS,
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)
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# Convert to RGB if necessary
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if thumbnail.mode in ('RGBA', 'LA', 'P'):
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rgb_thumbnail = Image.new('RGB', thumbnail.size, (255, 255, 255))
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rgb_thumbnail.paste(
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thumbnail,
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mask=thumbnail.split()[-1] if thumbnail.mode == 'RGBA' else None,
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)
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thumbnail = rgb_thumbnail
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# Compress
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output = io.BytesIO()
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thumbnail.save(output, format='JPEG', quality=self.JPEG_QUALITY, optimize=True)
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thumbnail_bytes = output.getvalue()
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self.logger.debug(
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f"Generated thumbnail: {self.THUMBNAIL_SIZE}x{self.THUMBNAIL_SIZE}, "
|
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f"{len(thumbnail_bytes)} bytes"
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)
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||||
|
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return thumbnail_bytes
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||||
except Exception as e:
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self.logger.error(f"Thumbnail generation failed: {e}")
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return b''
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393
backend/tests/test_image_processing.py
Normal file
393
backend/tests/test_image_processing.py
Normal file
@@ -0,0 +1,393 @@
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"""
|
||||
Test suite for OpenCV-based image processing pipeline.
|
||||
|
||||
Tests cover:
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- EXIF orientation detection and rotation
|
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- OpenCV smart cropping
|
||||
- Text orientation detection
|
||||
- Resize and compression
|
||||
- Thumbnail generation
|
||||
- Fallback to Pillow
|
||||
- File size validation
|
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- Edge cases (corrupted images, no contours, etc.)
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"""
|
||||
|
||||
import io
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import pytest
|
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from PIL import Image, PngImagePlugin
|
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import piexif
|
||||
import numpy as np
|
||||
from backend.services.image_processing import ImageProcessor
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def processor():
|
||||
"""Create ImageProcessor instance."""
|
||||
return ImageProcessor()
|
||||
|
||||
|
||||
@pytest.fixture
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||||
def sample_image_rgb():
|
||||
"""Create a simple RGB test image (100x100 red square)."""
|
||||
img = Image.new('RGB', (100, 100), color='red')
|
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output = io.BytesIO()
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||||
img.save(output, format='JPEG', quality=85)
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return output.getvalue()
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||||
|
||||
|
||||
@pytest.fixture
|
||||
def sample_image_with_object():
|
||||
"""Create test image with a distinct object (100x100, white bg, black square)."""
|
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img = Image.new('RGB', (100, 100), color='white')
|
||||
# Draw a black square in center
|
||||
pixels = img.load()
|
||||
for x in range(30, 70):
|
||||
for y in range(30, 70):
|
||||
pixels[x, y] = (0, 0, 0)
|
||||
output = io.BytesIO()
|
||||
img.save(output, format='JPEG', quality=85)
|
||||
return output.getvalue()
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def sample_image_with_exif():
|
||||
"""Create a test image with EXIF orientation tag."""
|
||||
img = Image.new('RGB', (100, 50), color='blue') # Wide image
|
||||
|
||||
# Create EXIF data with orientation = 6 (rotate 270 CW)
|
||||
exif_dict = {
|
||||
"0th": {piexif.ImageIFD.Orientation: 6}
|
||||
}
|
||||
exif_bytes = piexif.dump(exif_dict)
|
||||
|
||||
output = io.BytesIO()
|
||||
img.save(output, format='JPEG', quality=85, exif=exif_bytes)
|
||||
return output.getvalue()
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def large_file(sample_image_rgb):
|
||||
"""Create a file larger than 10MB limit."""
|
||||
# Repeat image bytes to create large file
|
||||
return sample_image_rgb * 2_000_000 # ~12MB
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def corrupted_image():
|
||||
"""Create corrupted image data."""
|
||||
return b'NOT_VALID_IMAGE_DATA_' * 100
|
||||
|
||||
|
||||
class TestExifRotation:
|
||||
"""Test EXIF orientation detection and rotation."""
|
||||
|
||||
def test_extract_exif_orientation_with_exif(self, processor, sample_image_with_exif):
|
||||
"""Test extracting EXIF orientation from image."""
|
||||
image = Image.open(io.BytesIO(sample_image_with_exif))
|
||||
orientation = processor._extract_exif_orientation(image)
|
||||
|
||||
# Should detect orientation tag (value 6)
|
||||
assert orientation is not None
|
||||
assert orientation == 6
|
||||
|
||||
def test_extract_exif_orientation_without_exif(self, processor, sample_image_rgb):
|
||||
"""Test handling image without EXIF data."""
|
||||
image = Image.open(io.BytesIO(sample_image_rgb))
|
||||
orientation = processor._extract_exif_orientation(image)
|
||||
|
||||
# Should gracefully return None
|
||||
assert orientation is None
|
||||
|
||||
def test_rotate_by_orientation_identity(self, processor):
|
||||
"""Test rotation with orientation=1 (no rotation needed)."""
|
||||
img = Image.new('RGB', (100, 50), color='red')
|
||||
rotated = processor._rotate_by_orientation(img, 1)
|
||||
|
||||
assert rotated.size == (100, 50)
|
||||
|
||||
def test_rotate_by_orientation_180(self, processor):
|
||||
"""Test rotation with orientation=3 (180°)."""
|
||||
img = Image.new('RGB', (100, 50), color='red')
|
||||
rotated = processor._rotate_by_orientation(img, 3)
|
||||
|
||||
assert rotated.size == (100, 50)
|
||||
|
||||
def test_rotate_by_orientation_90(self, processor):
|
||||
"""Test rotation with orientation=6 (270° CW = 90° CCW)."""
|
||||
img = Image.new('RGB', (100, 50), color='red')
|
||||
rotated = processor._rotate_by_orientation(img, 6)
|
||||
|
||||
# After 270° CW, dimensions swap: (100, 50) -> (50, 100)
|
||||
assert rotated.size == (50, 100)
|
||||
|
||||
|
||||
class TestSmartCrop:
|
||||
"""Test OpenCV-based smart cropping."""
|
||||
|
||||
def test_smart_crop_detects_object(self, processor, sample_image_with_object):
|
||||
"""Test that smart crop detects and bounds main object."""
|
||||
image = Image.open(io.BytesIO(sample_image_with_object))
|
||||
result = processor._smart_crop_opencv(image)
|
||||
|
||||
assert result is not None
|
||||
cropped, crop_size = result
|
||||
|
||||
# Should crop to a bounding box around the black square
|
||||
assert cropped is not None
|
||||
assert crop_size is not None
|
||||
# Crop should be smaller than original (with 10% padding)
|
||||
assert crop_size[0] < 100 or crop_size[1] < 100
|
||||
|
||||
def test_smart_crop_handles_no_contours(self, processor):
|
||||
"""Test smart crop gracefully returns None when no contours found."""
|
||||
# Create a plain image with no edges
|
||||
img = Image.new('RGB', (100, 100), color='gray')
|
||||
result = processor._smart_crop_opencv(img)
|
||||
|
||||
# Should return None if no significant contours
|
||||
assert result is None
|
||||
|
||||
def test_smart_crop_respects_padding(self, processor, sample_image_with_object):
|
||||
"""Test that smart crop applies 10% padding around bounds."""
|
||||
image = Image.open(io.BytesIO(sample_image_with_object))
|
||||
result = processor._smart_crop_opencv(image)
|
||||
|
||||
# Should include padding without exceeding image bounds
|
||||
assert result is not None
|
||||
cropped, _ = result
|
||||
assert cropped.size[0] > 0
|
||||
assert cropped.size[1] > 0
|
||||
|
||||
|
||||
class TestTextOrientation:
|
||||
"""Test text orientation detection using Hough lines."""
|
||||
|
||||
def test_text_orientation_normal(self, processor, sample_image_rgb):
|
||||
"""Test text orientation detection on normal image."""
|
||||
image = Image.open(io.BytesIO(sample_image_rgb))
|
||||
angle, status = processor._detect_text_orientation(image)
|
||||
|
||||
# May detect angle or not (depends on image content)
|
||||
# Status should be one of expected values
|
||||
assert status in ['normal', 'upside_down', 'sideways', 'not_detected']
|
||||
|
||||
def test_text_orientation_handles_plain_image(self, processor):
|
||||
"""Test text orientation on plain image with no lines."""
|
||||
img = Image.new('RGB', (100, 100), color='white')
|
||||
|
||||
angle, status = processor._detect_text_orientation(img)
|
||||
|
||||
# Should handle gracefully
|
||||
assert status == 'not_detected'
|
||||
assert angle is None
|
||||
|
||||
|
||||
class TestResizeAndCompress:
|
||||
"""Test image resizing and JPEG compression."""
|
||||
|
||||
def test_resize_and_compress_large_image(self, processor):
|
||||
"""Test resizing image larger than 1200px."""
|
||||
# Create a 2400x2400 image
|
||||
img = Image.new('RGB', (2400, 2400), color='red')
|
||||
|
||||
compressed = processor._resize_and_compress(img)
|
||||
|
||||
# Should return JPEG bytes
|
||||
assert isinstance(compressed, bytes)
|
||||
assert len(compressed) > 0
|
||||
|
||||
# Decompress and check size
|
||||
decompressed = Image.open(io.BytesIO(compressed))
|
||||
assert decompressed.size[0] <= 1200
|
||||
assert decompressed.size[1] <= 1200
|
||||
|
||||
def test_resize_and_compress_small_image(self, processor):
|
||||
"""Test resizing image smaller than 1200px (should not upscale)."""
|
||||
img = Image.new('RGB', (500, 500), color='red')
|
||||
|
||||
compressed = processor._resize_and_compress(img)
|
||||
|
||||
# Should still return valid JPEG
|
||||
assert isinstance(compressed, bytes)
|
||||
assert len(compressed) > 0
|
||||
|
||||
# Should not upscale
|
||||
decompressed = Image.open(io.BytesIO(compressed))
|
||||
assert decompressed.size[0] <= 500
|
||||
assert decompressed.size[1] <= 500
|
||||
|
||||
def test_resize_and_compress_jpeg_quality(self, processor):
|
||||
"""Test that compression uses 85% quality."""
|
||||
img = Image.new('RGB', (800, 600), color='red')
|
||||
|
||||
compressed = processor._resize_and_compress(img)
|
||||
|
||||
# File should be compressed (not raw uncompressed image)
|
||||
# 800x600x3 = 1.44MB uncompressed, should be much smaller at 85% quality
|
||||
assert len(compressed) < 500_000 # Should be < 500KB
|
||||
|
||||
def test_resize_and_compress_rgba_to_rgb(self, processor):
|
||||
"""Test that RGBA images are converted to RGB."""
|
||||
# Create RGBA image
|
||||
img = Image.new('RGBA', (500, 500), color=(255, 0, 0, 255))
|
||||
|
||||
compressed = processor._resize_and_compress(img)
|
||||
|
||||
# Should succeed and return valid JPEG
|
||||
assert isinstance(compressed, bytes)
|
||||
decompressed = Image.open(io.BytesIO(compressed))
|
||||
assert decompressed.mode == 'RGB'
|
||||
|
||||
|
||||
class TestThumbnailGeneration:
|
||||
"""Test thumbnail generation."""
|
||||
|
||||
def test_generate_thumbnail_200px_square(self, processor):
|
||||
"""Test thumbnail is exactly 200x200 pixels."""
|
||||
img = Image.new('RGB', (500, 500), color='red')
|
||||
|
||||
thumbnail = processor._generate_thumbnail(img)
|
||||
|
||||
# Should return JPEG bytes
|
||||
assert isinstance(thumbnail, bytes)
|
||||
assert len(thumbnail) > 0
|
||||
|
||||
# Should be exactly 200x200
|
||||
thumb_img = Image.open(io.BytesIO(thumbnail))
|
||||
assert thumb_img.size == (200, 200)
|
||||
|
||||
def test_generate_thumbnail_center_crop(self, processor):
|
||||
"""Test thumbnail uses center crop for non-square images."""
|
||||
# Create wide image (400x200)
|
||||
img = Image.new('RGB', (400, 200), color='red')
|
||||
|
||||
thumbnail = processor._generate_thumbnail(img)
|
||||
|
||||
# Should be 200x200 (center cropped then resized)
|
||||
thumb_img = Image.open(io.BytesIO(thumbnail))
|
||||
assert thumb_img.size == (200, 200)
|
||||
|
||||
def test_generate_thumbnail_small_image(self, processor):
|
||||
"""Test thumbnail from very small image."""
|
||||
# Create small image (50x50)
|
||||
img = Image.new('RGB', (50, 50), color='red')
|
||||
|
||||
thumbnail = processor._generate_thumbnail(img)
|
||||
|
||||
# Should still generate 200x200 thumbnail
|
||||
thumb_img = Image.open(io.BytesIO(thumbnail))
|
||||
assert thumb_img.size == (200, 200)
|
||||
|
||||
def test_generate_thumbnail_rgba_to_rgb(self, processor):
|
||||
"""Test thumbnail converts RGBA to RGB."""
|
||||
img = Image.new('RGBA', (500, 500), color=(255, 0, 0, 255))
|
||||
|
||||
thumbnail = processor._generate_thumbnail(img)
|
||||
|
||||
# Should convert to RGB
|
||||
thumb_img = Image.open(io.BytesIO(thumbnail))
|
||||
assert thumb_img.mode == 'RGB'
|
||||
|
||||
|
||||
class TestProcessPhoto:
|
||||
"""Test main process_photo method."""
|
||||
|
||||
def test_process_photo_success(self, processor, sample_image_rgb):
|
||||
"""Test successful photo processing."""
|
||||
result = processor.process_photo(sample_image_rgb)
|
||||
|
||||
assert result['status'] == 'success'
|
||||
assert result['cropped_image_bytes'] is not None
|
||||
assert result['thumbnail_bytes'] is not None
|
||||
assert result['original_size'] is not None
|
||||
assert result['metadata'] is not None
|
||||
|
||||
def test_process_photo_file_size_validation(self, processor, large_file):
|
||||
"""Test that files > 10MB are rejected."""
|
||||
result = processor.process_photo(large_file)
|
||||
|
||||
assert result['status'] == 'error'
|
||||
assert 'File too large' in result['error']
|
||||
assert result['cropped_image_bytes'] is None
|
||||
|
||||
def test_process_photo_with_exif(self, processor, sample_image_with_exif):
|
||||
"""Test photo processing with EXIF orientation."""
|
||||
result = processor.process_photo(sample_image_with_exif)
|
||||
|
||||
assert result['status'] == 'success'
|
||||
assert result['metadata']['exif_orientation'] == 6
|
||||
|
||||
def test_process_photo_with_manual_crop(self, processor, sample_image_rgb):
|
||||
"""Test photo processing with manual crop bounds."""
|
||||
crop_bounds = {'x': 10, 'y': 10, 'width': 50, 'height': 50}
|
||||
result = processor.process_photo(sample_image_rgb, crop_bounds=crop_bounds)
|
||||
|
||||
assert result['status'] == 'success'
|
||||
assert result['metadata']['crop_method'] == 'manual'
|
||||
assert result['crop_size'] == (50, 50)
|
||||
|
||||
def test_process_photo_fallback_to_pillow(self, processor, sample_image_rgb):
|
||||
"""Test fallback to Pillow if OpenCV fails."""
|
||||
result = processor.process_photo(sample_image_rgb)
|
||||
|
||||
assert result['status'] == 'success'
|
||||
# Crop method should be one of: opencv, pillow, manual, or none
|
||||
assert result['metadata']['crop_method'] in [
|
||||
'opencv',
|
||||
'pillow',
|
||||
'manual',
|
||||
'none',
|
||||
]
|
||||
|
||||
def test_process_photo_corrupted_image(self, processor, corrupted_image):
|
||||
"""Test handling of corrupted image data."""
|
||||
result = processor.process_photo(corrupted_image)
|
||||
|
||||
assert result['status'] == 'error'
|
||||
assert result['cropped_image_bytes'] is None
|
||||
assert result['thumbnail_bytes'] is None
|
||||
|
||||
def test_process_photo_empty_file(self, processor):
|
||||
"""Test handling of empty file."""
|
||||
result = processor.process_photo(b'')
|
||||
|
||||
assert result['status'] == 'error'
|
||||
|
||||
|
||||
class TestIntegration:
|
||||
"""Integration tests for full image processing pipeline."""
|
||||
|
||||
def test_end_to_end_processing(self, processor, sample_image_with_exif):
|
||||
"""Test complete image processing pipeline."""
|
||||
result = processor.process_photo(sample_image_with_exif)
|
||||
|
||||
# All required fields should be present
|
||||
assert 'status' in result
|
||||
assert 'cropped_image_bytes' in result
|
||||
assert 'thumbnail_bytes' in result
|
||||
assert 'original_size' in result
|
||||
assert 'crop_size' in result
|
||||
assert 'text_angle' in result
|
||||
assert 'metadata' in result
|
||||
|
||||
# All metadata fields should be present
|
||||
metadata = result['metadata']
|
||||
assert 'exif_orientation' in metadata
|
||||
assert 'crop_method' in metadata
|
||||
assert 'file_size_bytes' in metadata
|
||||
|
||||
def test_multiple_images_processing(self, processor, sample_image_rgb):
|
||||
"""Test processing multiple images."""
|
||||
for _ in range(3):
|
||||
result = processor.process_photo(sample_image_rgb)
|
||||
assert result['status'] == 'success'
|
||||
|
||||
def test_processing_with_all_options(self, processor, sample_image_with_exif):
|
||||
"""Test processing with manual crop bounds."""
|
||||
crop_bounds = {'x': 5, 'y': 5, 'width': 40, 'height': 40}
|
||||
result = processor.process_photo(sample_image_with_exif, crop_bounds=crop_bounds)
|
||||
|
||||
assert result['status'] == 'success'
|
||||
assert result['crop_size'] == (40, 40)
|
||||
assert result['metadata']['exif_orientation'] == 6
|
||||
assert result['metadata']['crop_method'] == 'manual'
|
||||
Reference in New Issue
Block a user