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

venv/lib/python3.12/site-packages/cv2/utils/logging/__init__.pyi

@@ -0,0 +1,22 @@
+__all__: list[str] = []
+
+# Enumerations
+LOG_LEVEL_SILENT: int
+LOG_LEVEL_FATAL: int
+LOG_LEVEL_ERROR: int
+LOG_LEVEL_WARNING: int
+LOG_LEVEL_INFO: int
+LOG_LEVEL_DEBUG: int
+LOG_LEVEL_VERBOSE: int
+ENUM_LOG_LEVEL_FORCE_INT: int
+LogLevel = int
+"""One of [LOG_LEVEL_SILENT, LOG_LEVEL_FATAL, LOG_LEVEL_ERROR, LOG_LEVEL_WARNING, LOG_LEVEL_INFO, LOG_LEVEL_DEBUG, LOG_LEVEL_VERBOSE, ENUM_LOG_LEVEL_FORCE_INT]"""
+
+
+
+# Functions
+def getLogLevel() -> LogLevel: ...
+
+def setLogLevel(logLevel: LogLevel) -> LogLevel: ...
+
+